# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import torch
from vllm_test_utils.monitor import monitor

from vllm.utils.mem_utils import MemorySnapshot, memory_profiling

from ..utils import create_new_process_for_each_test


@create_new_process_for_each_test()
def test_memory_profiling():
    # Fake out some model loading + inference memory usage to test profiling
    # Memory used by other processes will show up as cuda usage outside of torch
    from vllm.distributed.device_communicators.cuda_wrapper import CudaRTLibrary

    lib = CudaRTLibrary()
    # 512 MiB allocation outside of this instance
    handle1 = lib.cudaMalloc(512 * 1024 * 1024)

    baseline_snapshot = MemorySnapshot()

    # load weights

    weights = torch.randn(128, 1024, 1024, device="cuda", dtype=torch.float32)

    weights_memory = 128 * 1024 * 1024 * 4  # 512 MiB

    def measure_current_non_torch():
        free, total = torch.cuda.mem_get_info()
        current_used = total - free
        current_torch = torch.accelerator.memory_reserved()
        current_non_torch = current_used - current_torch
        return current_non_torch

    with (
        memory_profiling(
            baseline_snapshot=baseline_snapshot, weights_memory=weights_memory
        ) as result,
        monitor(measure_current_non_torch) as monitored_values,
    ):
        # make a memory spike, 1 GiB
        spike = torch.randn(256, 1024, 1024, device="cuda", dtype=torch.float32)
        del spike

        # Add some extra non-torch memory 256 MiB (simulate NCCL)
        handle2 = lib.cudaMalloc(256 * 1024 * 1024)

    # this is an analytic value, it is exact,
    # we only have 256 MiB non-torch memory increase
    measured_diff = monitored_values.values[-1] - monitored_values.values[0]
    assert measured_diff == 256 * 1024 * 1024

    # Check that the memory usage is within 5% of the expected values
    # 5% tolerance is caused by cuda runtime.
    # we cannot control cuda runtime in the granularity of bytes,
    # which causes a small error (<10 MiB in practice)
    non_torch_ratio = result.non_torch_increase / (256 * 1024 * 1024)  # noqa
    assert abs(non_torch_ratio - 1) <= 0.05
    assert result.torch_peak_increase == 1024 * 1024 * 1024
    del weights
    lib.cudaFree(handle1)
    lib.cudaFree(handle2)
