sgl-project--sglang/python/sglang/multimodal_gen/runtime/utils/distributed.py

# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo

import pickle
from typing import Any, List, Optional

import numpy as np
import torch
import torch.distributed as dist

from sglang.multimodal_gen.runtime.platforms import current_platform


def broadcast_pyobj(
    data: List[Any],
    rank: int,
    dist_group: Optional[torch.distributed.ProcessGroup] = None,
    src: int = 0,
    force_cpu_device: bool = True,
):
    """Broadcast inputs from src rank to all other ranks with torch.dist backend.
    The `rank` here refer to the source rank on global process group (regardless
    of dist_group argument).
    """

    device = torch.device(
        current_platform.device_type if not force_cpu_device else "cpu"
    )

    if rank == src:
        if data is None or len(data) == 0:
            tensor_size = torch.tensor([0], dtype=torch.long, device=device)
            dist.broadcast(tensor_size, src=src, group=dist_group)
        else:
            serialized_data = pickle.dumps(data)
            size = len(serialized_data)

            tensor_data = torch.ByteTensor(
                np.frombuffer(serialized_data, dtype=np.uint8).copy()
            ).to(device)
            tensor_size = torch.tensor([size], dtype=torch.long, device=device)

            dist.broadcast(tensor_size, src=src, group=dist_group)
            dist.broadcast(tensor_data, src=src, group=dist_group)
        return data
    else:
        tensor_size = torch.tensor([0], dtype=torch.long, device=device)
        dist.broadcast(tensor_size, src=src, group=dist_group)
        size = tensor_size.item()

        if size == 0:
            return []

        tensor_data = torch.empty(size, dtype=torch.uint8, device=device)
        dist.broadcast(tensor_data, src=src, group=dist_group)

        serialized_data = bytes(tensor_data.cpu().numpy())
        data = pickle.loads(serialized_data)
        return data


def generate_masked_orthogonal_rank_groups(
    world_size: int, parallel_size: list[int], mask: list[bool]
) -> list[list[int]]:
    """Generate orthogonal parallel groups based on the parallel size and mask.

    Arguments:
        world_size (int): world size

        parallel_size (List[int]):
            The parallel size of each orthogonal parallel type. For example, if
            tensor_parallel_size = 2, pipeline_model_parallel_group = 3, data_parallel_size = 4,
            and the parallel mapping order is tp-pp-dp, then the parallel_size = [2, 3, 4].

        mask (List[bool]):
            The mask controls which parallel methods the generated groups represent. If mask[i] is
            True, it means the generated group contains the i-th parallelism method. For example,
            if parallel_size = [tp_size, pp_size, dp_size], and mask = [True, False , True], then
            the generated group is the `tp-dp` group, if the mask = [False, True, False], then the
            generated group is the `pp` group.

    Algorithm:
        For orthogonal parallelism, such as tp/dp/pp/cp, the global_rank and

        If we want to get the `dp_group` (tp_size * pp_size groups of dp_size ranks each.
        For example,  if the gpu size is 8 and order is 'tp-pp-dp', size is '2-2-2', and the
        dp_group here is [[0, 4], [1, 5], [2, 6], [3, 7]].)
        The tp_rank and pp_rank will be combined to form the `dp_group_index`.
            dp_group_index = tp_rank + pp_rank * tp_size (2)

        So, Given that tp_rank and pp_rank satisfy equation (2), and dp_rank in
        range(0, dp_size), the ranks in dp_group[dp_group_index] satisfies the
        equation (1).

        This function solve this math problem.

    For example, if the parallel_size = [tp_size, dp_size, pp_size] = [2, 3, 4],
    and the mask = [False, True, False]. Then,
        dp_group_index(0) = tp_rank(0) + pp_rank(0) * 2
        dp_group_index(1) = tp_rank(1) + pp_rank(0) * 2
        ...
        dp_group_index(7) = tp_rank(1) + pp_rank(3) * 2

        dp_group[0] = 0 + range(0, 3) * 2 + 0 = [0, 2, 4]
        dp_group[1] = 1 + range(0, 3) * 2 + 0 = [1, 3, 5]
        ...
        dp_group[7] = 1 + range(0, 3) * 2 + 3 * 2 * 3 = [19, 21, 23]
    """

    def prefix_product(a: List[int], init=1) -> List[int]:
        r = [init]
        for v in a:
            init = init * v
            r.append(init)
        return r

    def inner_product(a: List[int], b: List[int]) -> int:
        return sum([x * y for x, y in zip(a, b)])

    def decompose(index, shape, stride=None):
        """
        This function solve the math problem below:
            There is an equation:
                index = sum(idx[i] * stride[i])
            And given the value of index, stride.
            Return the idx.
        This function will used to get the pp/dp/pp_rank
        from group_index and rank_in_group.
        """
        if stride is None:
            stride = prefix_product(shape)
        idx = [(index // d) % s for s, d in zip(shape, stride)]
        # stride is a prefix_product result. And the value of stride[-1]
        # is not used.
        assert (
            sum([x * y for x, y in zip(idx, stride[:-1])]) == index
        ), "idx {} with shape {} mismatch the return idx {}".format(index, shape, idx)
        return idx

    masked_shape = [s for s, m in zip(parallel_size, mask) if m]
    unmasked_shape = [s for s, m in zip(parallel_size, mask) if not m]

    global_stride = prefix_product(parallel_size)
    masked_stride = [d for d, m in zip(global_stride, mask) if m]
    unmasked_stride = [d for d, m in zip(global_stride, mask) if not m]

    group_size = prefix_product(masked_shape)[-1]
    num_of_group = world_size // group_size

    ranks = []
    for group_index in range(num_of_group):
        # get indices from unmaksed for group_index.
        decomposed_group_idx = decompose(group_index, unmasked_shape)
        rank = []
        for rank_in_group in range(group_size):
            # get indices from masked for rank_in_group.
            decomposed_rank_idx = decompose(rank_in_group, masked_shape)
            rank.append(
                inner_product(decomposed_rank_idx, masked_stride)
                + inner_product(decomposed_group_idx, unmasked_stride)
            )
        ranks.append(rank)
    return ranks


class RankGenerator(object):
    def __init__(
        self,
        tp: int,
        sp: int,
        pp: int,
        cfg: int,
        dp: int,
        order: str,
        rank_offset: int = 0,
    ) -> None:
        self.tp = tp
        self.sp = sp
        self.pp = pp
        self.cfg = cfg
        self.dp = dp
        self.rank_offset = rank_offset
        self.world_size = tp * sp * pp * cfg * dp

        self.name_to_size = {
            "tp": self.tp,
            "sp": self.sp,
            "pp": self.pp,
            "cfg": self.cfg,
            "dp": self.dp,
        }
        order = order.lower()

        for name in self.name_to_size.keys():
            if name not in order and self.name_to_size[name] != 1:
                raise RuntimeError(
                    f"The size of ({name}) is ({self.name_to_size[name]}), but you haven't specified the order ({self.order})."
                )
            elif name not in order:
                order = order + "-" + name

        self.order = order
        self.ordered_size = []

        for token in order.split("-"):
            self.ordered_size.append(self.name_to_size[token])

    def get_mask(self, order: str, token: str):
        ordered_token = order.split("-")
        token = token.split("-")
        mask = [False] * len(ordered_token)
        for t in token:
            mask[ordered_token.index(t)] = True
        return mask

    def get_ranks(self, token):
        """Get rank group by input token.

        Arguments:
            token (str):
                Specify the ranks type that want to get. If we want
                to obtain multiple parallel types, we can use a hyphen
                '-' to separate them. For example, if we want to obtain
                the TP_DP group, the token should be 'tp-dp'.

        """
        mask = self.get_mask(self.order, token)
        ranks = generate_masked_orthogonal_rank_groups(
            self.world_size, self.ordered_size, mask
        )
        if self.rank_offset > 0:
            for rank_group in ranks:
                for i in range(len(rank_group)):
                    rank_group[i] += self.rank_offset
        return ranks