[V1][Core] Add support for V1 Engine (#295)

### What this PR does / why we need it?
Add support for V1 Engine.

Please note that this is just the initial version, and there may be some
places need to be fixed or optimized in the future, feel free to leave
some comments to us.

### Does this PR introduce _any_ user-facing change?

To use V1 Engine on NPU device, you need to set the env variable shown
below:

```bash
export VLLM_USE_V1=1
export VLLM_WORKER_MULTIPROC_METHOD=spawn
```

If you are using vllm for offline inferencing, you must add a `__main__`
guard like:

```bash
if __name__ == '__main__':

    llm = vllm.LLM(...)
```

Find more details
[here](https://docs.vllm.ai/en/latest/getting_started/troubleshooting.html#python-multiprocessing).

### How was this patch tested?
I have tested the online serving with `Qwen2.5-7B-Instruct` using this
command:

```bash
vllm serve Qwen/Qwen2.5-7B-Instruct --max_model_len 26240
```

Query the model with input prompts:

```bash
curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "Qwen/Qwen2.5-7B-Instruct",
        "prompt": "The future of AI is",
        "max_tokens": 7,
        "temperature": 0
    }'
```

---------

Signed-off-by: shen-shanshan <467638484@qq.com>
Co-authored-by: didongli182 <didongli@huawei.com>

vllm_ascend/platform.py

@@ -19,13 +19,10 @@ import os
 from typing import TYPE_CHECKING, Optional, Tuple
 
 import torch
-
-try:
-    import torch_npu  # noqa: F401
-except ImportError:
-    print("Failed to import torch_npu.")
-
-from vllm.config import VllmConfig
+import torch_npu  # noqa: F401
+import vllm.envs as envs
+from vllm.config import CompilationLevel, VllmConfig
+from vllm.logger import init_logger
 from vllm.platforms import Platform, PlatformEnum
 
 if TYPE_CHECKING:
@@ -35,18 +32,7 @@ else:
 
 os.environ["RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES"] = "1"
 
-
-def _device_id_to_physical_device_id(device_id: int) -> int:
-    if "ASCEND_RT_VISIBLE_DEVICES" in os.environ:
-        device_ids = os.environ["ASCEND_RT_VISIBLE_DEVICES"].split(",")
-        if device_ids == [""]:
-            raise RuntimeError("ASCEND_RT_VISIBLE_DEVICES is set to empty"
-                               "string, which means Ascend NPU support is"
-                               "disabled.")
-        physical_device_id = device_ids[device_id]
-        return int(physical_device_id)
-    else:
-        return device_id
+logger = init_logger(__name__)
 
 
 class NPUPlatform(Platform):
@@ -74,8 +60,7 @@ class NPUPlatform(Platform):
 
     @classmethod
     def get_device_name(cls, device_id: int = 0) -> str:
-        physical_device_id = _device_id_to_physical_device_id(device_id)
-        return torch.npu.get_device_name(physical_device_id)
+        return torch.npu.get_device_name(device_id)
 
     @classmethod
     def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
@@ -103,23 +88,41 @@ class NPUPlatform(Platform):
 
     @classmethod
     def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
+        compilation_config = vllm_config.compilation_config
+        if compilation_config.level != CompilationLevel.NO_COMPILATION:
+            logger.warning(
+                "Compilation level %s is not supported on NPU now, forcing compilation level to NO_COMPILATION",
+                compilation_config.level)
+            compilation_config.level = CompilationLevel.NO_COMPILATION
+
         parallel_config = vllm_config.parallel_config
         if parallel_config.worker_cls == "auto":
-            if vllm_config.scheduler_config.is_multi_step:
-                parallel_config.worker_cls = "vllm_ascend.worker.multi_step_worker.MultiStepWorker"
+            if envs.VLLM_USE_V1:
+                parallel_config.worker_cls = "vllm_ascend.worker.worker_v1.NPUWorker"
             else:
-                parallel_config.worker_cls = "vllm_ascend.worker.worker.NPUWorker"
+                if vllm_config.scheduler_config.is_multi_step:
+                    parallel_config.worker_cls = "vllm_ascend.worker.multi_step_worker.MultiStepWorker"
+                else:
+                    parallel_config.worker_cls = "vllm_ascend.worker.worker.NPUWorker"
 
         cache_config = vllm_config.cache_config
         if cache_config and cache_config.block_size is None:
             cache_config.block_size = 128
 
+        if envs.VLLM_USE_V1 and cache_config.enable_prefix_caching:
+            logger.warning(
+                "Prefix caching is not supported for V1 now, disable prefix caching"
+            )
+            cache_config.enable_prefix_caching = False
+
     @classmethod
     def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
                              kv_cache_dtype, block_size, use_v1, use_mla):
+        if use_v1:
+            return "vllm_ascend.attention.attention_v1.AscendAttentionBackend"
         if use_mla:
-            return "vllm_ascend.attention.AscendMLAAttentionBackend"
-        return "vllm_ascend.attention.AscendAttentionBackend"
+            return "vllm_ascend.attention.attention.AscendMLAAttentionBackend"
+        return "vllm_ascend.attention.attention.AscendAttentionBackend"
 
     @classmethod
     def get_current_memory_usage(cls,
@@ -131,3 +134,7 @@ class NPUPlatform(Platform):
     @classmethod
     def get_device_communicator_cls(cls) -> str:
         return "vllm_ascend.communicator.NPUCommunicator"
+
+    @classmethod
+    def is_pin_memory_available(cls):
+        return True