init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>
2026-08-27 15:11:51 +08:00
parent b582a8e7d1
commit 7f8a1b1f7a
2849 changed files with 712887 additions and 22001 deletions
--- a/tests/e2e/pull_request/one_card/model_runner_v2/init.py
+++ b/tests/e2e/pull_request/one_card/model_runner_v2/init.py
--- a/tests/e2e/pull_request/one_card/model_runner_v2/test_basic.py
+++ b/tests/e2e/pull_request/one_card/model_runner_v2/test_basic.py
@@ -0,0 +1,171 @@
+#
+# 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.
+#
+
+import os
+from unittest.mock import patch
+
+import pytest
+from vllm import SamplingParams
+
+from tests.e2e.conftest import VllmRunner
+from vllm_ascend.utils import vllm_version_is
+
+MODELS = ["Qwen/Qwen3-0.6B", "vllm-ascend/DeepSeek-V2-Lite-W8A8"]
+
+MAIN_MODELS = ["LLM-Research/Meta-Llama-3.1-8B-Instruct"]
+EGALE_MODELS = ["vllm-ascend/EAGLE-LLaMA3.1-Instruct-8B"]
+
+pytestmark = pytest.mark.skipif(
+    vllm_version_is("0.23.0"),
+    reason="v2 model runner patches not supported on v0.23.0",
+)
+
+
+@pytest.mark.skipif(True, reason="Fix me, it's broken after CANN and trition-ascend are upgraded.")
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("enforce_eager", [True])
+@patch.dict(os.environ, {"VLLM_USE_V2_MODEL_RUNNER": "1"})
+def test_qwen3_dense_eager_mode(
+    model: str,
+    max_tokens: int,
+    enforce_eager: bool,
+) -> None:
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    sampling_params = SamplingParams(
+        max_tokens=max_tokens,
+        temperature=0.5,
+        logprobs=2,
+        prompt_logprobs=2,
+        logit_bias={0: -1.0, 1: 0.5},
+        min_p=0.01,
+        bad_words=["the", " the"],
+    )
+    with VllmRunner(
+        model,
+        max_model_len=1024,
+        enforce_eager=enforce_eager,
+        async_scheduling=True,
+    ) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.parametrize("model", MAIN_MODELS)
+@pytest.mark.parametrize("eagle_model", EGALE_MODELS)
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("enforce_eager", [False])
+@pytest.mark.parametrize(
+    "compilation_config",
+    [
+        pytest.param(
+            {"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes": [4, 8]},
+            id="full_decode_only",
+        ),
+        pytest.param({}, id="default_full_and_piecewise"),
+    ],
+)
+@patch.dict(os.environ, {"VLLM_USE_V2_MODEL_RUNNER": "1"})
+def test_egale_spec_decoding(
+    model: str,
+    eagle_model: str,
+    max_tokens: int,
+    enforce_eager: bool,
+    compilation_config: dict,
+) -> None:
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
+    with VllmRunner(
+        model,
+        max_model_len=1024,
+        enforce_eager=enforce_eager,
+        async_scheduling=True,
+        speculative_config={
+            "model": eagle_model,
+            "method": "eagle",
+            "num_speculative_tokens": 3,
+        },
+        compilation_config=compilation_config,
+    ) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("enforce_eager", [False])
+@pytest.mark.parametrize(
+    "compilation_config",
+    [
+        pytest.param({"cudagraph_mode": "FULL_DECODE_ONLY"}, id="full_decode_only"),
+        pytest.param({}, id="default_full_and_piecewise"),
+    ],
+)
+@patch.dict(os.environ, {"VLLM_USE_V2_MODEL_RUNNER": "1"})
+def test_qwen3_dense_graph_mode(
+    model: str,
+    max_tokens: int,
+    enforce_eager: bool,
+    compilation_config: dict,
+) -> None:
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
+    with VllmRunner(
+        model,
+        max_model_len=1024,
+        enforce_eager=enforce_eager,
+        compilation_config=compilation_config,
+    ) as runner:
+        outputs = runner.model.generate(prompts, sampling_params)
+
+    if model != "Qwen/Qwen3-0.6B":
+        return
+
+    expected_outputs = [
+        " Lina. I'm a 22-year-old student from China.",
+        " the same as the president of the United Nations. This is because the president",
+        " Paris. The capital of France is also the capital of the Republic of France",
+        " not just about the technology itself but also about the human aspect-how we",
+    ]
+
+    matches = 0
+    misses = 0
+    for output, expected_output in zip(outputs, expected_outputs):
+        if output.outputs[0].text[:10] == expected_output[:10]:
+            matches += 1
+        else:
+            misses += 1
+            print(f"output: {output.outputs[0].text}")
+            print(f"expected_output: {expected_output}")
+
+    assert misses == 0
--- a/tests/e2e/pull_request/one_card/model_runner_v2/test_uva.py
+++ b/tests/e2e/pull_request/one_card/model_runner_v2/test_uva.py
@@ -0,0 +1,171 @@
+#
+# 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.
+#
+
+import os
+from unittest.mock import patch
+
+import pytest
+from vllm import SamplingParams
+
+from tests.e2e.conftest import VllmRunner
+from vllm_ascend.utils import vllm_version_is
+
+MODELS = ["Qwen/Qwen3-0.6B", "vllm-ascend/DeepSeek-V2-Lite-W8A8"]
+
+MAIN_MODELS = ["LLM-Research/Meta-Llama-3.1-8B-Instruct"]
+EGALE_MODELS = ["vllm-ascend/EAGLE-LLaMA3.1-Instruct-8B"]
+
+pytestmark = pytest.mark.skipif(
+    vllm_version_is("0.23.0"),
+    reason="v2 model runner patches not supported on v0.23.0",
+)
+
+
+@pytest.mark.skipif(True, reason="Fix me, it's broken after CANN and trition-ascend are upgraded.")
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("enforce_eager", [True])
+@patch.dict(os.environ, {"VLLM_USE_V2_MODEL_RUNNER": "1", "PYTORCH_NPU_ALLOC_CONF": "pinned_mem_register:True"})
+def test_qwen3_dense_eager_mode(
+    model: str,
+    max_tokens: int,
+    enforce_eager: bool,
+) -> None:
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    sampling_params = SamplingParams(
+        max_tokens=max_tokens,
+        temperature=0.5,
+        logprobs=2,
+        prompt_logprobs=2,
+        logit_bias={0: -1.0, 1: 0.5},
+        min_p=0.01,
+        bad_words=["the", " the"],
+    )
+    with VllmRunner(
+        model,
+        max_model_len=1024,
+        enforce_eager=enforce_eager,
+        async_scheduling=True,
+    ) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.parametrize("model", MAIN_MODELS)
+@pytest.mark.parametrize("eagle_model", EGALE_MODELS)
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("enforce_eager", [False])
+@pytest.mark.parametrize(
+    "compilation_config",
+    [
+        pytest.param(
+            {"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes": [4, 8]},
+            id="full_decode_only",
+        ),
+        pytest.param({}, id="default_full_and_piecewise"),
+    ],
+)
+@patch.dict(os.environ, {"VLLM_USE_V2_MODEL_RUNNER": "1", "PYTORCH_NPU_ALLOC_CONF": "pinned_mem_register:True"})
+def test_egale_spec_decoding(
+    model: str,
+    eagle_model: str,
+    max_tokens: int,
+    enforce_eager: bool,
+    compilation_config: dict,
+) -> None:
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
+    with VllmRunner(
+        model,
+        max_model_len=1024,
+        enforce_eager=enforce_eager,
+        async_scheduling=True,
+        speculative_config={
+            "model": eagle_model,
+            "method": "eagle",
+            "num_speculative_tokens": 3,
+        },
+        compilation_config=compilation_config,
+    ) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("enforce_eager", [False])
+@pytest.mark.parametrize(
+    "compilation_config",
+    [
+        pytest.param({"cudagraph_mode": "FULL_DECODE_ONLY"}, id="full_decode_only"),
+        pytest.param({}, id="default_full_and_piecewise"),
+    ],
+)
+@patch.dict(os.environ, {"VLLM_USE_V2_MODEL_RUNNER": "1", "PYTORCH_NPU_ALLOC_CONF": "pinned_mem_register:True"})
+def test_qwen3_dense_graph_mode(
+    model: str,
+    max_tokens: int,
+    enforce_eager: bool,
+    compilation_config: dict,
+) -> None:
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
+    with VllmRunner(
+        model,
+        max_model_len=1024,
+        enforce_eager=enforce_eager,
+        compilation_config=compilation_config,
+    ) as runner:
+        outputs = runner.model.generate(prompts, sampling_params)
+
+    if model != "Qwen/Qwen3-0.6B":
+        return
+
+    expected_outputs = [
+        " Lina. I'm a 22-year-old student from China.",
+        " the same as the president of the United Nations. This is because the president",
+        " Paris. The capital of France is also the capital of the Republic of France",
+        " not just about the technology itself but also about the human aspect-how we",
+    ]
+
+    matches = 0
+    misses = 0
+    for output, expected_output in zip(outputs, expected_outputs):
+        if output.outputs[0].text[:10] == expected_output[:10]:
+            matches += 1
+        else:
+            misses += 1
+            print(f"output: {output.outputs[0].text}")
+            print(f"expected_output: {expected_output}")
+
+    assert misses == 0