v0.10.1rc1

tests/e2e/singlecard/test_chunked.py

@@ -0,0 +1,81 @@
+#
+# 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.
+#
+"""
+Compare the outputs of vLLM with and without aclgraph.
+
+Run `pytest tests/compile/test_aclgraph.py`.
+"""
+import gc
+
+import pytest
+import torch
+from vllm import SamplingParams
+
+from tests.e2e.conftest import VllmRunner
+
+MODELS = ["Qwen/Qwen2.5-0.5B-Instruct"]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [1])
+def test_models(
+    model: str,
+    max_tokens: int,
+) -> None:
+    prompts = ["The president of the United States is"]
+
+    sampling_params = SamplingParams(
+        max_tokens=max_tokens,
+        temperature=0.0,
+    )
+
+    with VllmRunner(model, long_prefill_token_threshold=20,
+                    enforce_eager=True) as vllm_model:
+        output1 = vllm_model.generate(prompts, sampling_params)
+
+    with VllmRunner(model,
+                    enforce_eager=True,
+                    additional_config={
+                        'ascend_scheduler_config': {
+                            'enabled': True
+                        },
+                    }) as vllm_model:
+        output2 = vllm_model.generate(prompts, sampling_params)
+
+    # Extract the generated token IDs for comparison
+    token_ids1 = output1[0][0][0]
+    token_ids2 = output2[0][0][0]
+
+    print(f"Token IDs 1: {token_ids1}")
+    print(f"Token IDs 2: {token_ids2}")
+
+    # Convert token IDs to tensors and calculate cosine similarity
+    # Take the length of a shorter sequence to ensure consistent dimensions
+    min_len = min(len(token_ids1), len(token_ids2))
+
+    tensor1 = torch.tensor(token_ids1[:min_len], dtype=torch.float32)
+    tensor2 = torch.tensor(token_ids2[:min_len], dtype=torch.float32)
+
+    # Calculate similarity using torch.cosine_similarity
+    similarity = torch.cosine_similarity(tensor1, tensor2, dim=0)
+    print(f"Token IDs cosine similarity: {similarity.item()}")
+
+    assert similarity > 0.95
+
+    gc.collect()
+    torch.npu.empty_cache()
+    torch.npu.reset_peak_memory_stats()