Sync from v0.13

tests/basic_correctness/test_cumem.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import asyncio
+
+import pytest
+import torch
+
+from vllm import LLM, AsyncEngineArgs, AsyncLLMEngine, SamplingParams
+from vllm.device_allocator.cumem import CuMemAllocator
+from vllm.platforms import current_platform
+from vllm.utils.mem_constants import GiB_bytes
+
+from ..utils import create_new_process_for_each_test, requires_fp8
+
+
+@create_new_process_for_each_test("fork" if not current_platform.is_rocm() else "spawn")
+def test_python_error():
+    """
+    Test if Python error occurs when there's low-level
+    error happening from the C++ side.
+    """
+    allocator = CuMemAllocator.get_instance()
+    total_bytes = torch.cuda.mem_get_info()[1]
+    alloc_bytes = int(total_bytes * 0.7)
+    tensors = []
+    with allocator.use_memory_pool():
+        # allocate 70% of the total memory
+        x = torch.empty(alloc_bytes, dtype=torch.uint8, device="cuda")
+        tensors.append(x)
+    # release the memory
+    allocator.sleep()
+
+    # allocate more memory than the total memory
+    y = torch.empty(alloc_bytes, dtype=torch.uint8, device="cuda")
+    tensors.append(y)
+    with pytest.raises(RuntimeError):
+        # when the allocator is woken up, it should raise an error
+        # because we don't have enough memory
+        allocator.wake_up()
+
+
+@create_new_process_for_each_test("fork" if not current_platform.is_rocm() else "spawn")
+def test_basic_cumem():
+    # some tensors from default memory pool
+    shape = (1024, 1024)
+    x = torch.empty(shape, device="cuda")
+    x.zero_()
+
+    # some tensors from custom memory pool
+    allocator = CuMemAllocator.get_instance()
+    with allocator.use_memory_pool():
+        # custom memory pool
+        y = torch.empty(shape, device="cuda")
+        y.zero_()
+        y += 1
+        z = torch.empty(shape, device="cuda")
+        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.cuda.mem_get_info()[0]
+    allocator.sleep()
+    free_bytes_after_sleep = torch.cuda.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)
+
+
+@create_new_process_for_each_test("fork" if not current_platform.is_rocm() else "spawn")
+def test_cumem_with_cudagraph():
+    allocator = CuMemAllocator.get_instance()
+    with allocator.use_memory_pool():
+        weight = torch.eye(1024, device="cuda")
+    with allocator.use_memory_pool(tag="discard"):
+        cache = torch.empty(1024, 1024, device="cuda")
+
+    def model(x):
+        out = x @ weight
+        cache[: out.size(0)].copy_(out)
+        return out + 1
+
+    x = torch.empty(128, 1024, device="cuda")
+
+    # warmup
+    model(x)
+
+    # capture cudagraph
+    model_graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(model_graph):
+        y = model(x)
+
+    free_bytes = torch.cuda.mem_get_info()[0]
+    allocator.sleep()
+    free_bytes_after_sleep = torch.cuda.mem_get_info()[0]
+    assert free_bytes_after_sleep > free_bytes
+    allocator.wake_up()
+
+    # after waking up, the content in the weight tensor
+    # should be restored, but the content in the cache tensor
+    # should be discarded
+
+    # this operation is also compatible with cudagraph
+
+    x.random_()
+    model_graph.replay()
+
+    # cache content is as expected
+    assert torch.allclose(x, cache[: x.size(0)])
+
+    # output content is as expected
+    assert torch.allclose(y, x + 1)
+
+
+@create_new_process_for_each_test("fork" if not current_platform.is_rocm() else "spawn")
+@pytest.mark.parametrize(
+    "model",
+    [
+        # sleep mode with safetensors
+        "hmellor/tiny-random-LlamaForCausalLM",
+        # sleep mode with pytorch checkpoint
+        "facebook/opt-125m",
+    ],
+)
+def test_end_to_end(model: str):
+    free, total = torch.cuda.mem_get_info()
+    used_bytes_baseline = total - free  # in case other process is running
+    llm = LLM(model, 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.cuda.mem_get_info()
+    used_bytes = total - free_gpu_bytes_after_sleep - used_bytes_baseline
+    # now the memory usage is mostly cudagraph memory pool,
+    # and it should be less than the model weights (1B model, 2GiB weights)
+
+    # NOTE: In V1, the memory buffer for logits (max_num_reqs x vocab_size)
+    # is captured but cannot be releasesd from PyTorch due to a known bug,
+    # therefore high memory usage after `llm.sleep` is called is expected.
+    # FIXME(youkaichao & ywang96): Fix memory buffer issue with sleep mode
+    # in V1.
+    assert used_bytes < 7 * GiB_bytes
+
+    llm.wake_up()
+    output2 = llm.generate(prompt, sampling_params)
+    # cmp output
+    assert output[0].outputs[0].text == output2[0].outputs[0].text
+
+    llm.sleep(level=1)
+    llm.wake_up(tags=["weights"])
+
+    free_gpu_bytes_wake_up_w, total = torch.cuda.mem_get_info()
+    used_bytes = total - free_gpu_bytes_wake_up_w - used_bytes_baseline
+
+    # should just reallocate memory for weights (1B model, ~2GiB weights)
+    assert used_bytes < 10 * GiB_bytes
+
+    # now allocate kv cache memory
+    llm.wake_up(tags=["kv_cache"])
+    output3 = llm.generate(prompt, sampling_params)
+
+    # cmp output
+    assert output[0].outputs[0].text == output3[0].outputs[0].text
+
+
+@create_new_process_for_each_test()
+def test_deep_sleep():
+    model = "hmellor/tiny-random-LlamaForCausalLM"
+    free, total = torch.cuda.mem_get_info()
+    used_bytes_baseline = total - free  # in case other process is running
+    llm = LLM(model, enable_sleep_mode=True)
+    prompt = "How are you?"
+    sampling_params = SamplingParams(temperature=0, max_tokens=10)
+    output = llm.generate(prompt, sampling_params)
+
+    # Put the engine to deep sleep
+    llm.sleep(level=2)
+
+    free_gpu_bytes_after_sleep, total = torch.cuda.mem_get_info()
+    used_bytes = total - free_gpu_bytes_after_sleep - used_bytes_baseline
+    assert used_bytes < 3 * GiB_bytes
+
+    llm.wake_up(tags=["weights"])
+    llm.collective_rpc("reload_weights")
+    free_gpu_bytes_wake_up_w, total = torch.cuda.mem_get_info()
+    used_bytes = total - free_gpu_bytes_wake_up_w - used_bytes_baseline
+    assert used_bytes < 4 * GiB_bytes
+
+    # now allocate kv cache and cuda graph memory
+    llm.wake_up(tags=["kv_cache"])
+    output2 = llm.generate(prompt, sampling_params)
+
+    # cmp output
+    assert output[0].outputs[0].text == output2[0].outputs[0].text
+
+
+@create_new_process_for_each_test()
+def test_deep_sleep_async():
+    async def test():
+        model = "hmellor/tiny-random-LlamaForCausalLM"
+        free, total = torch.cuda.mem_get_info()
+        used_bytes_baseline = total - free  # in case other process is running
+        engine_args = AsyncEngineArgs(
+            model=model,
+            enable_sleep_mode=True,
+        )
+
+        llm = AsyncLLMEngine.from_engine_args(engine_args)
+        prompt = "How are you?"
+        sampling_params = SamplingParams(temperature=0, max_tokens=10)
+        outputs = llm.generate(prompt, sampling_params, request_id="test_request_id1")
+        async for output in outputs:
+            pass
+
+        # Put the engine to deep sleep
+        await llm.sleep(level=2)
+
+        await llm.wake_up(tags=["weights"])
+        await llm.collective_rpc("reload_weights")
+        free_gpu_bytes_wake_up_w, total = torch.cuda.mem_get_info()
+        used_bytes = total - free_gpu_bytes_wake_up_w - used_bytes_baseline
+        assert used_bytes < 4 * GiB_bytes
+
+        # now allocate kv cache and cuda graph memory
+        await llm.wake_up(tags=["kv_cache"])
+        outputs2 = llm.generate(prompt, sampling_params, request_id="test_request_id2")
+        async for output2 in outputs2:
+            pass
+
+        # cmp output
+        assert output.outputs[0].text == output2.outputs[0].text
+
+    asyncio.run(test())
+
+
+@requires_fp8
+def test_deep_sleep_fp8_kvcache():
+    GiB_bytes = 1 << 30
+    model = "Qwen/Qwen2-0.5B"
+    used_bytes_baseline = current_platform.get_current_memory_usage()
+
+    llm = LLM(model, enable_sleep_mode=True, kv_cache_dtype="fp8")
+    prompt = "How are you?"
+    sampling_params = SamplingParams(temperature=0, max_tokens=10)
+    output = llm.generate(prompt, sampling_params)
+
+    # Put the engine to deep sleep
+    llm.sleep(level=2)
+
+    used_bytes = current_platform.get_current_memory_usage() - used_bytes_baseline
+
+    # Rocm uses more memory for CudaGraphs, so we add 2 GiB more for the threshold
+    rocm_extra_mem_bytes = 2 * GiB_bytes if current_platform.is_rocm() else 0
+    mem_threshold_after_sleep = 3 * GiB_bytes + rocm_extra_mem_bytes
+    assert used_bytes < mem_threshold_after_sleep
+
+    llm.wake_up(tags=["weights"])
+    llm.collective_rpc("reload_weights")
+
+    used_bytes = current_platform.get_current_memory_usage() - used_bytes_baseline
+    mem_threshold_after_wake_up = 4 * GiB_bytes + rocm_extra_mem_bytes
+    assert used_bytes < mem_threshold_after_wake_up
+
+    # now allocate kv cache and cuda graph memory
+    llm.wake_up(tags=["kv_cache"])
+    output2 = llm.generate(prompt, sampling_params)
+
+    # cmp output
+    assert output[0].outputs[0].text == output2[0].outputs[0].text