aphrodite-engine/aphrodite/distributed/utils.py

# Copyright 2023 The PygmalionAI team.
# Copyright 2023 The vLLM team.
# Adapted from
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/tensor_parallel/utils.py
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
import json
import os
from typing import Dict, Optional, Sequence

import torch
import torch.distributed as dist
from loguru import logger

from .parallel_state import get_cpu_world_group, get_local_rank


def ensure_divisibility(numerator, denominator):
    """Ensure that numerator is divisible by the denominator."""
    assert numerator % denominator == 0, "{} is not divisible by {}".format(
        numerator, denominator)


def divide(numerator, denominator):
    """Ensure that numerator is divisible by the denominator and return
    the division value."""
    ensure_divisibility(numerator, denominator)
    return numerator // denominator


def split_tensor_along_last_dim(
    tensor: torch.Tensor,
    num_partitions: int,
    contiguous_split_chunks: bool = False,
) -> Sequence[torch.Tensor]:
    """ Split a tensor along its last dimension.

        Arguments:
            tensor: input tensor.
            num_partitions: number of partitions to split the tensor
            contiguous_split_chunks: If True, make each chunk contiguous
                                     in memory.

        Returns:
            A list of Tensors
    """
    # Get the size and dimension.
    last_dim = tensor.dim() - 1
    last_dim_size = divide(tensor.size()[last_dim], num_partitions)
    # Split.
    tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
    # NOTE: torch.split does not create contiguous tensors by default.
    if contiguous_split_chunks:
        return tuple(chunk.contiguous() for chunk in tensor_list)

    return tensor_list


# code partly borrowed from
# https://github.com/turboderp/exllamav2/blob/1c67f97f3d2a968605a9c31ab791a05c85bb7879/exllamav2/compat.py#L10
# License: MIT
def _can_actually_p2p(idx_a, idx_b):
    dev_i = f"cuda:{idx_a}"
    dev_j = f"cuda:{idx_b}"
    a = torch.randn(5, device=dev_i) + 123.0
    b = a.to(dev_j)
    c = b.to(dev_i)
    return torch.all(a == c).cpu().item()


# why do we need this cache?
# 1. we can have runtime checks for P2P access, where every process checks
#  P2P access to all other GPUs. Unfortunately, the test might cost many
#  (world_size * world_size) cuda context, and reduce the memory available
#  for the model.
# 2. alternatively, we can have a p2p map that is generated by the master
#  process and broadcasted to all other processes. This still requires
#  #world_size of cuda context, belonging to the master process, on each GPU.
# 3. we can have a cache file, that records the p2p access status. The first
#  time the master process checks the p2p access, it will generate the cache
#  file, at the cost of #world_size of cuda context. Later on, all processes
#  can read the cache file to check the p2p access status without any cost of
#  additional cuda context.
# Note that the cache file is suffixed by the CUDA_VISIBLE_DEVICES, so that we
#  can have different cache files for different CUDA_VISIBLE_DEVICES settings,
#  e.g. used by different aphrodite engines. The device id in the cache file is
#  a **local** device id, i.e. from 0 to num_dev-1, where num_dev is the number
#  of visible devices in the aphrodite engine.
_gpu_p2p_access_cache: Optional[Dict[str, bool]] = None


def gpu_p2p_access_check(i: int, j: int) -> bool:
    """Check if GPU i can access GPU j."""

    # if the cache variable is already calculated,
    # read from the cache instead of checking it again
    global _gpu_p2p_access_cache
    if _gpu_p2p_access_cache is not None:
        return _gpu_p2p_access_cache[f"{i}->{j}"]

    is_distributed = dist.is_initialized()

    num_dev = torch.cuda.device_count()
    cuda_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES", None)
    if cuda_visible_devices is None:
        cuda_visible_devices = ",".join(str(i) for i in range(num_dev))
    path = os.path.expanduser(
        f"~/.config/aphrodite/gpu_p2p_access_cache_for_{cuda_visible_devices}.json"
    )
    os.makedirs(os.path.dirname(path), exist_ok=True)
    if (not is_distributed or get_local_rank() == 0) \
        and (not os.path.exists(path)):
        # only the local master process (with local_rank == 0) can
        #  enter this block to calculate the cache
        logger.info(f"generating GPU P2P access cache for in {path}")
        cache = {}
        for _i in range(num_dev):
            for _j in range(num_dev):
                # on some platforms, P2P support might be buggy and we need
                # additional checks.
                cache[f"{_i}->{_j}"] = torch.cuda.can_device_access_peer(
                    _i, _j) and _can_actually_p2p(_i, _j)
        with open(path, "w") as f:
            json.dump(cache, f, indent=4)
    if is_distributed:
        cpu_world_group = get_cpu_world_group()
        dist.barrier(cpu_world_group)
    logger.info(f"reading GPU P2P access cache from {path}")
    with open(path, "r") as f:
        cache = json.load(f)
    _gpu_p2p_access_cache = cache
    return _gpu_p2p_access_cache[f"{i}->{j}"]