srt/eplb/eplb_simulator/reader.py

from collections import defaultdict
from pathlib import Path

import torch
from tqdm import tqdm

from sglang.srt.eplb.expert_distribution import (
    _convert_global_physical_count_to_logical_count,
)

convert_global_physical_count_to_logical_count = (
    _convert_global_physical_count_to_logical_count
)


def read_mode_per_pass(dir_data: Path):
    """Read data from ExpertDistributionRecorder when recorded with mode `per_pass`"""

    # gpc := global_physical_count
    gpc_of_forward_pass_and_rank = defaultdict(lambda: defaultdict())
    for path in tqdm(list(dir_data.glob("*.pt"))):
        data_pack = torch.load(path, weights_only=True)
        last_physical_to_logical_map = data_pack["last_physical_to_logical_map"]
        for record in data_pack["records"]:
            forward_pass_id = record["forward_pass_id"]
            rank = record["rank"]
            assert (
                gpc_of_forward_pass_and_rank[forward_pass_id].get(rank) is None
            ), f"Duplicated {forward_pass_id=} {rank=}"
            gpc_of_forward_pass_and_rank[forward_pass_id][rank] = record[
                "global_physical_count"
            ]

    forward_pass_ids = sorted(gpc_of_forward_pass_and_rank.keys())
    print(f"Make {forward_pass_ids=} into array")

    items = []
    for forward_pass_id, gpc_of_rank in sorted(gpc_of_forward_pass_and_rank.items()):
        gpc_of_rank_tensor = torch.stack(
            [gpc for rank, gpc in sorted(gpc_of_rank.items())]
        ).sum(dim=0)
        items.append(gpc_of_rank_tensor)

    gpc_of_forward_pass = torch.stack(items)
    print(f"{gpc_of_forward_pass.shape=}")

    return dict(
        global_physical_count_of_forward_pass=gpc_of_forward_pass,
        last_physical_to_logical_map=last_physical_to_logical_map,
        forward_pass_ids=forward_pass_ids,
    )