init v0.11.0rc0

tests/e2e/multicard/test_offline_inference_distributed.py

@@ -23,6 +23,7 @@ Run `pytest tests/test_offline_inference.py`.
 import os
 from unittest.mock import patch
 
+import pytest
 from modelscope import snapshot_download  # type: ignore
 from vllm import SamplingParams
 
@@ -30,6 +31,15 @@ from tests.e2e.conftest import VllmRunner
 
 os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
 
+QWEN_DENSE_MODELS = [
+    "vllm-ascend/Qwen3-8B-W8A8", "vllm-ascend/Qwen2.5-0.5B-Instruct-W8A8"
+]
+
+DEEPSEEK_W4A8_MODELS = [
+    "vllm-ascend/DeepSeek-V3-W4A8-Pruing",
+    "vllm-ascend/DeepSeek-V3.1-W4A8-puring"
+]
+
 
 def test_models_distributed_QwQ():
     example_prompts = [
@@ -61,8 +71,8 @@ def test_models_distributed_DeepSeek_multistream_moe():
             additional_config={
                 "torchair_graph_config": {
                     "enabled": True,
-                    "enable_multistream_moe": True,
                 },
+                "enable_multistream_moe": True,
                 "ascend_scheduler_config": {
                     "enabled": True,
                 },
@@ -104,14 +114,15 @@ def test_models_distributed_Qwen3_W4A8DYNAMIC():
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
+@pytest.mark.parametrize("model", DEEPSEEK_W4A8_MODELS)
 @patch.dict(os.environ, {"VLLM_ASCEND_MLA_PA": "1"})
-def test_models_distributed_DeepSeek_W4A8DYNAMIC():
+def test_models_distributed_DeepSeek_W4A8DYNAMIC(model):
     prompts = [
         "Hello, my name is",
     ]
     max_tokens = 5
     with VllmRunner(
-            snapshot_download("vllm-ascend/DeepSeek-V3-W4A8-Pruing"),
+            snapshot_download(model),
             dtype="auto",
             tensor_parallel_size=2,
             quantization="ascend",
@@ -150,3 +161,46 @@ def test_sp_for_qwen3_moe() -> None:
                     enable_expert_parallel=True,
                     enforce_eager=True) as vllm_model:
         vllm_model.generate(example_prompts, sampling_params)
+
+
+@pytest.mark.parametrize("enforce_eager", [True, False])
+@pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
+@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE": "1"})
+@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM": "1"})
+def test_models_distributed_Qwen_Dense_with_flashcomm_v1(model, enforce_eager):
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+
+    with VllmRunner(
+            snapshot_download(model),
+            max_model_len=8192,
+            enforce_eager=enforce_eager,
+            dtype="auto",
+            tensor_parallel_size=2,
+            quantization="ascend",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+@pytest.mark.parametrize("enforce_eager", [True, False])
+@pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
+@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE": "1"})
+@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_PREFETCH_MLP": "1"})
+def test_models_distributed_Qwen_Dense_with_prefetch_mlp_weight(
+        model, enforce_eager):
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+
+    with VllmRunner(
+            snapshot_download(model),
+            max_model_len=8192,
+            enforce_eager=enforce_eager,
+            dtype="auto",
+            tensor_parallel_size=2,
+            quantization="ascend",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)