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/two_card/test_flashcomm_distributed.py
+++ b/tests/e2e/pull_request/two_card/test_flashcomm_distributed.py
@@ -0,0 +1,117 @@
+#
+# 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.
+# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
+#
+"""Compare the short outputs of HF and vLLM when using greedy sampling.
+
+Run `pytest tests/e2e/pull_request/two_card/test_flashcomm_distributed.py`.
+"""
+
+import os
+from unittest.mock import patch
+
+import pytest
+from vllm import SamplingParams
+from vllm.config import KVTransferConfig
+
+from tests.e2e.conftest import VllmRunner
+
+QWEN_DENSE_MODELS = [
+    "vllm-ascend/Qwen3-0.6B-W8A8",
+]
+
+
+@pytest.mark.skip(reason="test is broken, fix me")
+@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
+@patch.dict(os.environ, {"VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE": "1"})
+def test_qwen3_moe_fc2_oshard_tp2() -> None:
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    sampling_params = SamplingParams(max_tokens=5, temperature=0.0, top_k=50, top_p=0.9)
+
+    with VllmRunner(
+        "Qwen/Qwen3-30B-A3B",
+        dtype="auto",
+        tensor_parallel_size=2,
+        distributed_executor_backend="mp",
+        enable_expert_parallel=True,
+        enforce_eager=True,
+        additional_config={"layer_sharding": ["o_proj"]},
+        kv_transfer_config=KVTransferConfig(kv_role="kv_producer"),
+    ) as vllm_model:
+        vllm_model.generate(example_prompts, sampling_params)
+
+
+@pytest.mark.skip(reason="test is broken, fix me")
+@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
+def test_deepseek_v2_lite_fc1_tp2() -> None:
+    example_prompts = [
+        "test" * 1001,
+    ]
+    sampling_params = SamplingParams(max_tokens=5, temperature=0.0, top_k=50, top_p=0.9)
+    with VllmRunner(
+        "vllm-ascend/DeepSeek-V2-Lite-W8A8",
+        dtype="auto",
+        tensor_parallel_size=2,
+        distributed_executor_backend="mp",
+        enable_expert_parallel=True,
+        enforce_eager=True,
+        quantization="ascend",
+    ) as vllm_model:
+        vllm_model.generate(example_prompts, sampling_params)
+
+
+@pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
+@pytest.mark.skip(reason="test is broken, fix me")
+@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
+def test_qwen3_dense_fc1_tp2(model):
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+
+    with VllmRunner(
+        model,
+        max_model_len=8192,
+        dtype="auto",
+        tensor_parallel_size=2,
+        cudagraph_capture_sizes=[1, 2, 4, 8],
+        quantization="ascend",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+@pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
+@pytest.mark.skip(reason="test is broken, fix me")
+@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
+def test_qwen3_dense_prefetch_mlp_weight_tp2(model):
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+
+    with VllmRunner(
+        model,
+        max_model_len=8192,
+        dtype="auto",
+        tensor_parallel_size=2,
+        cudagraph_capture_sizes=[1, 2, 4, 8],
+        quantization="ascend",
+        additional_config={"weight_prefetch_config": {"enabled": True}},
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)