test ge graph for torchair

tests/e2e/multicard/test_torchair_ge_graph_mode.py

@@ -0,0 +1,235 @@
+#
+# 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.
+#
+"""Compare the short outputs of HF and vLLM when using greedy sampling.
+
+Run `pytest tests/multicard/test_torchair_ge_graph_mode.py`.
+"""
+import os
+from typing import Dict
+
+import pytest
+
+from tests.e2e.conftest import VllmRunner
+
+os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
+
+
+def _deepseek_torchair_test_fixture(
+    additional_config: Dict,
+    *,
+    tensor_parallel_size=2,
+    use_v1_schduler=False,
+):
+    example_prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+    kwargs = {}
+    if not use_v1_schduler:
+        kwargs = {
+            "ascend_scheduler_config": {
+                "enabled": True,
+                "mode": "max-autotune",
+            },
+            "refresh": True,
+        }
+    additional_config.update(**kwargs)
+
+    with VllmRunner(
+            "vllm-ascend/DeepSeek-V3-Pruning",
+            dtype="half",
+            tensor_parallel_size=tensor_parallel_size,
+            distributed_executor_backend="mp",
+            additional_config=additional_config,
+    ) as vllm_model:
+        # use greedy sampler to make sure the generated results are fix
+        vllm_output = vllm_model.generate_greedy(example_prompts, 5)
+
+    # NOTE: vllm-ascend/DeepSeek-V3-Pruning is a random weight of
+    # DeepSeek-V3 with 2 hidden layers, thus the golden results seems
+    # inaccurate. This will only change if accuracy improves with the
+    # official weights of DeepSeek-V3.
+    golden_results = [
+        'Hello, my name is下载早点向前很有่อง',
+        'The president of the United States isSender)## physiological Albany',
+        'The capital of France is Rocky转角 hospitalizedinterval sparked',
+        'The future of AI is её asegο BIOS一扫',
+    ]
+
+    assert len(golden_results) == len(vllm_output)
+    for i in range(len(vllm_output)):
+        assert golden_results[i] == vllm_output[i][1]
+        print(f"Generated text: {vllm_output[i][1]!r}")
+
+
+def test_e2e_deepseekv3_with_torchair():
+    additional_config = {
+        "torchair_graph_config": {
+            "enabled": True,
+            "mode": "max-autotune",
+        },
+    }
+    _deepseek_torchair_test_fixture(additional_config)
+
+
+def test_e2e_deepseekv3_with_torchair_ms_mla():
+    additional_config = {
+        "torchair_graph_config": {
+            "enabled": True,
+            "enable_multistream_mla": True,
+            "mode": "max-autotune",
+        },
+    }
+    _deepseek_torchair_test_fixture(additional_config)
+
+
+def test_e2e_deepseekv3_with_torchair_v1scheduler():
+    additional_config = {
+        "torchair_graph_config": {
+            "enabled": True,
+            "mode": "max-autotune",
+        },
+    }
+    _deepseek_torchair_test_fixture(additional_config, use_v1_schduler=True)
+
+
+def _pangu_torchair_test_fixture(
+    additional_config: Dict,
+    *,
+    tensor_parallel_size=2,
+):
+    example_prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    # torchair is only work without chunked-prefill now
+    kwargs = {
+        "ascend_scheduler_config": {
+            "enabled": True,
+            "mode": "max-autotune",
+        },
+        "refresh": True,
+    }
+    additional_config.update(**kwargs)
+
+    with VllmRunner(
+            "vllm-ascend/pangu-pro-moe-pruing",
+            dtype="half",
+            tensor_parallel_size=tensor_parallel_size,
+            distributed_executor_backend="mp",
+            additional_config=additional_config,
+            enable_expert_parallel=True,
+    ) as vllm_model:
+        # use greedy sampler to make sure the generated results are fix
+        vllm_output = vllm_model.generate_greedy(example_prompts, 5)
+
+    # NOTE: vllm-ascend/pangu-pro-moe-pruing is only part of PanguProMoE
+    # with 2 hidden layers, thus the golden results seems inaccurate.
+    # This will only change if accuracy changes with the official weights
+    # of PanguProMoE.
+    golden_results = [
+        'Hello, my name is Remempondeprecatedmiot忱',
+        'The president of the United States is Remem下的一个 rever ceremoni Segnali',
+        'The capital of France is Rememvoud administrativ Remem投',
+        'The future of AI isotope Segnali Zoeken精细化 supus',
+    ]
+
+    assert len(golden_results) == len(vllm_output)
+    for i in range(len(vllm_output)):
+        assert golden_results[i] == vllm_output[i][1]
+        print(f"Generated text: {vllm_output[i][1]!r}")
+
+
+@pytest.mark.skip("skipping test_e2e_pangu_with_torchair")
+def test_e2e_pangu_with_torchair():
+    additional_config = {
+        "torchair_graph_config": {
+            "enabled": True,
+            "mode": "max-autotune",
+        },
+    }
+    _pangu_torchair_test_fixture(additional_config)
+
+
+def _qwen_torchair_test_fixture(
+    model,
+    tp,
+    enable_expert_parallel,
+):
+    # The current access control does not support 16 cards,
+    # so the MC2 operator in Qwen's graph mode cannot run.
+    # Once 16-card support is available,
+    # this e2e can be switched to graph mode.
+    example_prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    additional_config = {
+        "torchair_graph_config": {
+            "enabled": False,
+            "mode": "max-autotune",
+        },
+        "ascend_scheduler_config": {
+            "enabled": True,
+            "mode": "max-autotune",
+        },
+        "refresh": True,
+    }
+
+    with VllmRunner(
+            model,
+            dtype="half",
+            tensor_parallel_size=tp,
+            distributed_executor_backend="mp",
+            enforce_eager=True,
+            additional_config=additional_config,
+            enable_expert_parallel=enable_expert_parallel,
+    ) as vllm_model:
+        # use greedy sampler to make sure the generated results are fix
+        vllm_output = vllm_model.generate_greedy(example_prompts, 5)
+
+    # NOTE: vllm-ascend/pangu-pro-moe-pruing is only part of PanguProMoE
+    # with 2 hidden layers, thus the golden results seems inaccurate.
+    # This will only change if accuracy changes with the official weights
+    # of PanguProMoE.
+    golden_results = [
+        'Hello, my name is Remempondeprecatedmiot忱',
+        'The president of the United States is Remem下的一个 rever ceremoni Segnali',
+        'The capital of France is Rememvoud administrativ Remem投',
+        'The future of AI isotope Segnali Zoeken精细化 supus',
+    ]
+
+    assert len(golden_results) == len(vllm_output)
+    for i in range(len(vllm_output)):
+        print(f"Generated text: {vllm_output[i][1]!r}")
+
+
+def test_e2e_qwen2_with_torchair():
+    _qwen_torchair_test_fixture("Qwen/Qwen2.5-0.5B-Instruct", 2, False)
+
+
+def test_e2e_qwen3_moe_with_torchair():
+    _qwen_torchair_test_fixture("Qwen/Qwen3-30B-A3B", 2, True)