rocm-systems/src/utils/file_io.py

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import collections
import glob
import os
import re
import sys
from collections import OrderedDict
from pathlib import Path

import pandas as pd
import yaml

import config
from utils import schema
from utils.kernel_name_shortener import kernel_name_shortener
from utils.utils import console_debug, console_error, console_log, demarcate

# TODO: use pandas chunksize or dask to read really large csv file
# from dask import dataframe as dd

# the build-in config to list kernel names purpose only
top_stats_build_in_config = {
    0: {
        "id": 0,
        "title": "Top Kernels",
        "data source": [{"raw_csv_table": {"id": 1, "source": "pmc_kernel_top.csv"}}],
    },
    1: {
        "id": 1,
        "title": "Dispatch List",
        "data source": [{"raw_csv_table": {"id": 2, "source": "pmc_dispatch_info.csv"}}],
    },
}

time_units = {"s": 10**9, "ms": 10**6, "us": 10**3, "ns": 1}


def load_sys_info(f):
    """
    Load sys running info from csv file to a df.
    """
    return pd.read_csv(f)


def load_panel_configs(dir):
    """
    Load all panel configs from yaml file.
    """
    d = {}
    for root, dirs, files in os.walk(dir):
        for f in files:
            if f.endswith(".yaml"):
                with open(str(Path(root).joinpath(f))) as file:
                    config = yaml.safe_load(file)
                    d[config["Panel Config"]["id"]] = config["Panel Config"]

    # TODO: sort metrics as the header order in case they are not defined in the same order

    od = OrderedDict(sorted(d.items()))
    # for key, value in od.items():
    #     print(key, value)
    return od


def load_profiling_config(config_dir):
    """
    Load profiling config from yaml file.
    """
    try:
        with open(Path(config_dir).joinpath("profiling_config.yaml")) as file:
            prof_config = yaml.safe_load(file)
            return prof_config
    except FileNotFoundError:
        console_log(
            f"Could not find profiling_config.yaml in {config_dir} for filtering analysis report"
        )
    return dict()


@demarcate
def create_df_kernel_top_stats(
    df_in,
    raw_data_dir,
    filter_gpu_ids,
    filter_dispatch_ids,
    filter_nodes,
    time_unit,
    max_stat_num,
    kernel_verbose,
    sortby="sum",
):
    """
    Create top stats info by grouping kernels with user's filters.
    """

    df = df_in["pmc_perf"]
    # Demangle original KernelNames
    kernel_name_shortener(df, kernel_verbose)

    # The logic below for filters are the same as in parser.apply_filters(),
    # which can be merged together if need it.

    if filter_nodes:
        df = df.loc[df["Node"].astype(str).isin([filter_nodes])]

    if filter_gpu_ids:
        df = df.loc[df["GPU_ID"].astype(str).isin([filter_gpu_ids])]

    if filter_dispatch_ids:
        # NB: support ignoring the 1st n dispatched execution by '> n'
        #     The better way may be parsing python slice string
        if ">" in filter_dispatch_ids[0]:
            m = re.match(r"\> (\d+)", filter_dispatch_ids[0])
            df = df[df["Dispatch_ID"] > int(m.group(1))]
        else:
            df = df.loc[df["Dispatch_ID"].astype(str).isin(filter_dispatch_ids)]

    # First, create a dispatches file used to populate global vars
    dispatch_info = (
        df.loc[:, ["Node", "Dispatch_ID", "Kernel_Name", "GPU_ID"]]
        if "Node" in df.columns
        else df.loc[:, ["Dispatch_ID", "Kernel_Name", "GPU_ID"]]
    )
    dispatch_info.to_csv(
        str(Path(raw_data_dir).joinpath("pmc_dispatch_info.csv")), index=False
    )

    time_stats = pd.concat(
        [df["Kernel_Name"], (df["End_Timestamp"] - df["Start_Timestamp"])],
        keys=["Kernel_Name", "ExeTime"],
        axis=1,
    )

    grouped = time_stats.groupby(by=["Kernel_Name"]).agg(
        {"ExeTime": ["count", "sum", "mean", "median"]}
    )

    time_unit_str = "(" + time_unit + ")"
    grouped.columns = [
        x.capitalize() + time_unit_str if x != "count" else x.capitalize()
        for x in grouped.columns.get_level_values(1)
    ]

    key = "Sum" + time_unit_str
    grouped[key] = grouped[key].div(time_units[time_unit])
    key = "Mean" + time_unit_str
    grouped[key] = grouped[key].div(time_units[time_unit])
    key = "Median" + time_unit_str
    grouped[key] = grouped[key].div(time_units[time_unit])

    grouped = grouped.reset_index()  # Remove special group indexing

    key = "Sum" + time_unit_str
    grouped["Pct"] = grouped[key] / grouped[key].sum() * 100

    # NB:
    #   Sort by total time as default.
    if sortby == "sum":
        grouped = grouped.sort_values(by=("Sum" + time_unit_str), ascending=False)
        grouped.to_csv(
            str(Path(raw_data_dir).joinpath("pmc_kernel_top.csv")), index=False
        )
    elif sortby == "kernel":
        grouped = grouped.sort_values("Kernel_Name")
        grouped.to_csv(
            str(Path(raw_data_dir).joinpath("pmc_kernel_top.csv")), index=False
        )


@demarcate
def create_df_pmc(
    raw_data_root_dir, nodes, spatial_multiplexing, kernel_verbose, verbose
):
    """
    Load all raw pmc counters and join into one df.
    """

    def create_single_df_pmc(raw_data_dir, node_name, kernel_verbose, verbose):
        dfs = []
        coll_levels = []

        df = pd.DataFrame()
        new_df = pd.DataFrame()
        for root, dirs, files in os.walk(raw_data_dir):
            for f in files:
                # print("file ", f)
                if (f.endswith(".csv") and f.startswith("SQ")) or (
                    f == schema.pmc_perf_file_prefix + ".csv"
                ):
                    tmp_df = pd.read_csv(str(Path(root).joinpath(f)))
                    # Demangle original KernelNames
                    kernel_name_shortener(tmp_df, kernel_verbose)

                    # NB:
                    #   Idealy, the Node column should be added out of
                    #   multiindexing level. Here, we add it into pmc_perf
                    #   as it is the main sub-df which can be handled easily
                    #   later.
                    if f == "pmc_perf.csv" and node_name != None:
                        tmp_df.insert(0, "Node", node_name)
                    dfs.append(tmp_df)
                    coll_levels.append(f[:-4])

        final_df = pd.concat(dfs, keys=coll_levels, axis=1, copy=False)
        if verbose >= 2:
            console_debug("pmc_raw_data final_single_df %s" % final_df.info)
        return final_df

    if spatial_multiplexing:
        df = pd.DataFrame()
        # todo: more err check
        for subdir in Path(raw_data_root_dir).iterdir():
            if subdir.is_dir():
                new_df = create_single_df_pmc(
                    subdir, str(subdir.name), kernel_verbose, verbose
                )
                df = pd.concat([df, new_df])
        return df

    # specified node list
    else:
        # regular single node case
        if nodes is None:
            return create_single_df_pmc(raw_data_root_dir, None, kernel_verbose, verbose)

        # "empty list" means all nodes
        elif not nodes:
            df = pd.DataFrame()
            # todo: more err check
            for subdir in Path(raw_data_root_dir).iterdir():
                if subdir.is_dir():
                    new_df = create_single_df_pmc(
                        subdir, str(subdir.name), kernel_verbose, verbose
                    )
                    df = pd.concat([df, new_df])
            return df

        # specified node list
        else:
            df = pd.DataFrame()
            # todo: more err check
            for subdir in nodes:
                p = Path(raw_data_root_dir)
                new_df = create_single_df_pmc(
                    p.joinpath(subdir), subdir, kernel_verbose, verbose
                )
                df = pd.concat([df, new_df])
            return df


def collect_wave_occu_per_cu(in_dir, out_dir, numSE):
    """
    Collect wave occupancy info from in_dir csv files
    and consolidate into out_dir/wave_occu_per_cu.csv.
    It depends highly on wave_occu_se*.csv format.
    """

    all = pd.DataFrame()

    for i in range(numSE):
        p = Path(in_dir, "wave_occu_se" + str(i) + ".csv")
        if p.exists():
            tmp_df = pd.read_csv(p)
            SE_idx = "SE" + str(tmp_df.loc[0, "SE"])
            tmp_df.rename(
                columns={
                    "Dispatch": "Dispatch",
                    "SE": "SE",
                    "CU": "CU",
                    "Occupancy": SE_idx,
                },
                inplace=True,
            )

            # TODO: join instead of concat!
            if i == 0:
                all = tmp_df[{"CU", SE_idx}]
                all.sort_index(axis=1, inplace=True)
            else:
                all = pd.concat([all, tmp_df[SE_idx]], axis=1, copy=False)

    if not all.empty:
        # print(all.transpose())
        all.to_csv(Path(out_dir, "wave_occu_per_cu.csv"), index=False)


def is_single_panel_config(root_dir, supported_archs):
    """
    Check the root configs dir structure to decide using one config set for all
    archs, or one for each arch.
    """
    # If not single config, verify all supported archs have defined configs
    supported_archs = supported_archs.keys()
    counter = 0
    for arch in supported_archs:
        if root_dir.joinpath(arch).exists():
            counter += 1
    if counter == 0:
        return True
    elif counter == len(supported_archs):
        return False
    else:
        console_error("Found multiple panel config sets but incomplete for all archs.")


def find_1st_sub_dir(directory):
    """
    Find the first sub dir in a directory
    """
    dir_path = Path(directory)
    try:
        # Iterate over entries in the directory
        for entry in dir_path.iterdir():
            if entry.is_dir():  # Check if it's a directory
                return entry
    except FileNotFoundError:
        print(f"The directory '{directory}' does not exist.")
    return None