Add sleep mode feature for Ascend NPU (#513)

### What this PR does / why we need it?
This PR adds sleep mode feature for vllm-ascend, when sleeps, we do
mainly two things:

- offload model weights
- discard kv cache

RLHF tools(such as https://github.com/volcengine/verl and
https://github.com/OpenRLHF/OpenRLHF) have a strong need of sleep mode
to accelerate the training process.

This PR may solve #375 and #320 .

### Does this PR introduce _any_ user-facing change?
No existing user interfaces changed.
Users will have two new methods(`sleep()` and `wake_up()`) to use.

### How was this patch tested?
This PR is tested with Qwen/Qwen2.5-0.5B-Instruct.

At first, we have free NPU memory M1.

After `llm = LLM("Qwen/Qwen2.5-0.5B-Instruct", enable_sleep_mode=True)`
executed, we have free NPU memory M2. M2 < M1.

Then we call `llm.sleep(level=1)`, we have free NPU memory M3.

We have M3 > M2, M3 is very close to M1.

Plus, we have the same output tokens before sleep and after wake up,
with the config of `SamplingParams(temperature=0, max_tokens=10)` and
with the same input tokens of course.


This PR is utilizing the CMake procedure of #371 , thanks a lot.

Signed-off-by: Shuqiao Li <celestialli@outlook.com>

tests/singlecard/test_camem.py

@@ -0,0 +1,92 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
+# 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.
+#
+import os
+
+import torch
+from vllm import LLM, SamplingParams
+from vllm.utils import GiB_bytes
+
+from tests.utils import fork_new_process_for_each_test
+from vllm_ascend.device_allocator.camem import CaMemAllocator
+
+try:
+    import torch_npu  # noqa: F401
+except ImportError:
+    print("Failed to import torch_npu.")
+
+
+@fork_new_process_for_each_test
+def test_basic_camem():
+    # some tensors from default memory pool
+    shape = (1024, 1024)
+    x = torch.empty(shape, device='npu:0')
+    x.zero_()
+
+    # some tensors from custom memory pool
+    allocator = CaMemAllocator.get_instance()
+    with allocator.use_memory_pool():
+        # custom memory pool
+        y = torch.empty(shape, device='npu:0')
+        y.zero_()
+        y += 1
+        z = torch.empty(shape, device='npu:0')
+        z.zero_()
+        z += 2
+
+    # they can be used together
+    output = x + y + z
+    assert torch.allclose(output, torch.ones_like(output) * 3)
+
+    free_bytes = torch_npu.npu.mem_get_info()[0]
+    allocator.sleep()
+    free_bytes_after_sleep = torch_npu.npu.mem_get_info()[0]
+    assert free_bytes_after_sleep > free_bytes
+    allocator.wake_up()
+
+    # they can be used together
+    output = x + y + z
+    assert torch.allclose(output, torch.ones_like(output) * 3)
+
+
+@fork_new_process_for_each_test
+def test_end_to_end():
+    os.environ["VLLM_USE_V1"] = "0"
+    free, total = torch_npu.npu.mem_get_info()
+    used_bytes_baseline = total - free  # in case other process is running
+    llm = LLM("Qwen/Qwen2.5-0.5B-Instruct", enable_sleep_mode=True)
+    prompt = "How are you?"
+    sampling_params = SamplingParams(temperature=0, max_tokens=10)
+    output = llm.generate(prompt, sampling_params)
+
+    # the benefit of `llm.sleep(level=2)` is mainly CPU memory usage,
+    # which is difficult to measure in the test. therefore, we only
+    # test sleep level 1 here.
+    llm.sleep(level=1)
+
+    free_gpu_bytes_after_sleep, total = torch_npu.npu.mem_get_info()
+    used_bytes = total - free_gpu_bytes_after_sleep - used_bytes_baseline
+    # now the memory usage should be less than the model weights
+    # (0.5B model, 1GiB weights)
+    assert used_bytes < 1 * GiB_bytes
+
+    llm.wake_up()
+    output2 = llm.generate(prompt, sampling_params)
+
+    # cmp output
+    assert output[0].outputs[0].text == output2[0].outputs[0].text