rocm-systems/src/utils/parser.py

##############################################################################bl
# MIT License
#
# Copyright (c) 2021 - 2023 Advanced Micro Devices, Inc. All Rights Reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
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# The above copyright notice and this permission notice shall be included in all
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
##############################################################################el

import ast
import sys
import astunparse
import re
import os
import pandas as pd
import numpy as np
from utils import schema
from utils.utils import error
from pathlib import Path
import logging

# ------------------------------------------------------------------------------
# Internal global definitions

# NB:
# Ammolite is unique gemstone from the Rocky Mountains.
# "ammolite__" is a special internal prefix to mark build-in global variables
# calculated or parsed from raw data sources. Its range is only in this file.
# Any other general prefixes string, like "buildin__", might be used by the
# editor. Whenever change it to a new one, replace all appearances in this file.

# 001 is ID of pmc_kernel_top.csv table
pmc_kernel_top_table_id = 1

# Build-in $denom defined in mongodb query:
#       "denom": {
#              "$switch" : {
#                 "branches": [
#                    {
#                         "case":  { "$eq": [ $normUnit, "per Wave"]} ,
#                         "then":  "&SQ_WAVES"
#                    },
#                    {
#                         "case":  { "$eq": [ $normUnit, "per Cycle"]} ,
#                         "then":  "&GRBM_GUI_ACTIVE"
#                    },
#                    {
#                         "case":  { "$eq": [ $normUnit, "per Sec"]} ,
#                         "then":  {"$divide":[{"$subtract": ["&EndNs", "&BeginNs" ]}, 1000000000]}
#                    }
#                 ],
#                "default": 1
#              }
#       }
supported_denom = {
    "per_wave": "SQ_WAVES",
    "per_cycle": "GRBM_GUI_ACTIVE",
    "per_second": "((EndNs - BeginNs) / 1000000000)",
    "per_kernel": "1",
}

# Build-in defined in mongodb variables:
build_in_vars = {
    "numActiveCUs": "TO_INT(MIN((((ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) / GRBM_GUI_ACTIVE)), \
              0) / $maxWavesPerCU) * 8) + MIN(MOD(ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) \
              / GRBM_GUI_ACTIVE)), 0), $maxWavesPerCU), 8)), $numCU))",
    "kernelBusyCycles": "ROUND(AVG((((EndNs - BeginNs) / 1000) * $sclk)), 0)",
}

supported_call = {
    # If the below has single arg, like(expr), it is a aggr, in which turn to a pd function.
    # If it has args like list [], in which turn to a python function.
    "MIN": "to_min",
    "MAX": "to_max",
    # simple aggr
    "AVG": "to_avg",
    "MEDIAN": "to_median",
    "STD": "to_std",
    # functions apply to whole column of df or a single value
    "TO_INT": "to_int",
    # Support the below with 2 inputs
    "ROUND": "to_round",
    "QUANTILE": "to_quantile",
    "MOD": "to_mod",
    # Concat operation from the memory chart "active cus"
    "CONCAT": "to_concat",
}

# ------------------------------------------------------------------------------


def to_min(*args):
    if len(args) == 1 and isinstance(args[0], pd.core.series.Series):
        return args[0].min()
    elif min(args) == None:
        return np.nan
    else:
        return min(args)


def to_max(*args):
    if len(args) == 1 and isinstance(args[0], pd.core.series.Series):
        return args[0].max()
    elif max(args) == None:
        return np.nan
    else:
        return max(args)


def to_avg(a):
    if str(type(a)) == "<class 'NoneType'>":
        return np.nan
    elif a.empty:
        return np.nan
    elif isinstance(a, pd.core.series.Series):
        return a.mean()
    else:
        raise Exception("to_avg: unsupported type.")


def to_median(a):
    if a is None:
        return None
    elif isinstance(a, pd.core.series.Series):
        return a.median()
    else:
        raise Exception("to_median: unsupported type.")


def to_std(a):
    if isinstance(a, pd.core.series.Series):
        return a.std()
    else:
        raise Exception("to_std: unsupported type.")


def to_int(a):
    if str(type(a)) == "<class 'NoneType'>":
        return np.nan
    elif isinstance(a, (int, float, np.int64)):
        return int(a)
    elif isinstance(a, pd.core.series.Series):
        return a.astype("Int64")
    # Do we need it?
    # elif isinstance(a, str):
    #     return int(a)
    else:
        raise Exception("to_int: unsupported type.")


def to_round(a, b):
    if isinstance(a, pd.core.series.Series):
        return a.round(b)
    else:
        return round(a, b)

def to_quantile(a, b):
    if a is None:
        return None
    elif isinstance(a, pd.core.series.Series):
        return a.quantile(b)
    else:
        raise Exception("to_quantile: unsupported type.")

def to_mod(a, b):
    if isinstance(a, pd.core.series.Series):
        return a.mod(b)
    else:
        return a % b


def to_concat(a, b):
    return str(a) + str(b)


class CodeTransformer(ast.NodeTransformer):
    """
    Python AST visitor to transform user defined equation string to df format
    """

    def visit_Call(self, node):
        self.generic_visit(node)
        # print("--- debug visit_Call --- ", node.args, node.func)
        # print(astunparse.dump(node))
        # print(astunparse.unparse(node))
        if isinstance(node.func, ast.Name):
            if node.func.id in supported_call:
                node.func.id = supported_call[node.func.id]
            else:
                raise Exception(
                    "Unknown call:", node.func.id
                )  # Could be removed if too strict
        return node

    def visit_IfExp(self, node):
        self.generic_visit(node)
        # print("visit_IfExp", type(node.test), type(node.body), type(node.orelse), dir(node))

        if isinstance(node.body, ast.Num):
            raise Exception(
                "Don't support body of IF with number only! Has to be expr with df['column']."
            )

        new_node = ast.Expr(
            value=ast.Call(
                func=ast.Attribute(value=node.body, attr="where", ctx=ast.Load()),
                args=[node.test, node.orelse],
                keywords=[],
            )
        )
        # print("-------------")
        # print(astunparse.dump(new_node))
        # print("-------------")

        return new_node

    # NB:
    # visit_Name is for replacing HW counter to its df expr. In this way, we
    # could support any HW counter names, which is easier than regex.
    #
    # There are 2 limitations:
    #   - It is not straightforward to support types other than simple column
    #     in df, such as [], (). If we need to support those, have to implement
    #     in correct way or work around.
    #   - The 'raw_pmc_df' is hack code. For other data sources, like wavefront
    #     data,We need to think about template or pass it as a parameter.
    def visit_Name(self, node):
        self.generic_visit(node)
        # print("-------------", node.id)
        if (not node.id.startswith("ammolite__")) and (not node.id in supported_call):
            new_node = ast.Subscript(
                value=ast.Name(id="raw_pmc_df", ctx=ast.Load()),
                slice=ast.Index(value=ast.Str(s=node.id)),
                ctx=ast.Load(),
            )

            node = new_node
        return node


def build_eval_string(equation, coll_level):
    """
    Convert user defined equation string to eval executable string
    For example,
        input: AVG(100  * SQ_ACTIVE_INST_SCA / ( GRBM_GUI_ACTIVE * $numCU ))
        output: to_avg(100 * raw_pmc_df["pmc_perf"]["SQ_ACTIVE_INST_SCA"] / \
                 (raw_pmc_df["pmc_perf"]["GRBM_GUI_ACTIVE"] * numCU))
        input: AVG(((TCC_EA_RDREQ_LEVEL_31 / TCC_EA_RDREQ_31) if (TCC_EA_RDREQ_31 != 0) else (0)))
        output: to_avg((raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_LEVEL_31"] / raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"]).where(raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"] != 0, 0))
        We can not handle the below for now,
        input: AVG((0 if (TCC_EA_RDREQ_31 == 0) else (TCC_EA_RDREQ_LEVEL_31 / TCC_EA_RDREQ_31)))
        But potential workaound is,
        output: to_avg(raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"].where(raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"] == 0, raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_LEVEL_31"] / raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"]))
    """

    if coll_level is None:
        raise Exception("Error: coll_level can not be None.")

    if not equation:
        return ""

    s = str(equation)
    # print("input:", s)

    # build-in variable starts with '$', python can not handle it.
    # replace '$' with 'ammolite__'.
    # TODO: pre-check there is no "ammolite__" in all config files.
    s = re.sub("\$", "ammolite__", s)

    # convert equation string to intermediate expression in df array format
    ast_node = ast.parse(s)
    # print(astunparse.dump(ast_node))
    transformer = CodeTransformer()
    transformer.visit(ast_node)

    s = astunparse.unparse(ast_node)

    # correct column name/label in df with [], such as TCC_HIT[0],
    # the target is df['TCC_HIT[0]']
    s = re.sub(r"\'\]\[(\d+)\]", r"[\g<1>]']", s)
    # use .get() to catch any potential KeyErrors
    s = re.sub("raw_pmc_df\['(.*?)']", r'raw_pmc_df.get("\1")', s)
    # apply coll_level
    s = re.sub(r"raw_pmc_df", "raw_pmc_df.get('" + coll_level + "')", s)
    # print("--- build_eval_string, return: ", s)
    return s


def update_denom_string(equation, unit):
    """
    Update $denom in equation with runtime nomorlization unit.
    """
    if not equation:
        return ""

    s = str(equation)

    if unit in supported_denom.keys():
        s = re.sub(r"\$denom", supported_denom[unit], s)

    return s


def update_normUnit_string(equation, unit):
    """
    Update $normUnit in equation with runtime nomorlization unit.
    It is string replacement for display only.
    """

    # TODO: We might want to do it for subtitle contains $normUnit
    if not equation:
        return ""

    return re.sub(
        "\((?P<PREFIX>\w*)\s+\+\s+(\$normUnit\))",
        "\g<PREFIX> " + re.sub("_", " ", unit),
        str(equation),
    ).capitalize()


def gen_counter_list(formula):
    function_filter = {
        "MIN": None,
        "MAX": None,
        "AVG": None,
        "ROUND": None,
        "TO_INT": None,
        "GB": None,
        "STD": None,
        "GFLOP": None,
        "GOP": None,
        "OP": None,
        "CU": None,
        "NC": None,
        "UC": None,
        "CC": None,
        "RW": None,
        "GIOP": None,
        "GFLOPs": None,
        "CONCAT": None,
        "MOD": None,
    }

    built_in_counter = [
        "lds",
        "grd",
        "wgr",
        "arch_vgpr",
        "accum_vgpr",
        "sgpr",
        "scr",
        "BeginNs",
        "EndNs",
    ]

    visited = False
    counters = []
    if not isinstance(formula, str):
        return visited, counters
    try:
        tree = ast.parse(
            formula.replace("$normUnit", "SQ_WAVES")
            .replace("$denom", "SQ_WAVES")
            .replace(
                "$numActiveCUs",
                "TO_INT(MIN((((ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) / GRBM_GUI_ACTIVE)), \
              0) / $maxWavesPerCU) * 8) + MIN(MOD(ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) \
              / GRBM_GUI_ACTIVE)), 0), $maxWavesPerCU), 8)), $numCU))",
            )
            .replace("$", "")
        )
        for node in ast.walk(tree):
            if isinstance(node, ast.Name):
                val = str(node.id)[:-4] if str(node.id).endswith("_sum") else str(node.id)
                if val.isupper() and val not in function_filter:
                    counters.append(val)
                    visited = True
                if val in built_in_counter:
                    visited = True
    except:
        pass

    return visited, counters

def calc_builtin_var(var, sys_info):
    """
    Calculate build-in variable based on sys_info:
    """
    if isinstance(var, int):
        return var
    elif isinstance(var, str) and var.startswith("$totalL2Banks"):
        # Fixme: support all supported partitioning mode
        # Fixme: "name" is a bad name!
        totalL2Banks = sys_info.L2Banks
        if (
            sys_info["name"].lower() == "mi300a_a0"
            or sys_info["name"].lower() == "mi300a_a1"
        ):
            totalL2Banks = sys_info.L2Banks * get_hbm_stack_num(
                sys_info["name"], sys_info["memory_partition"]
            )
        elif (
            sys_info["name"].lower() == "mi300x_a0"
            or sys_info["name"].lower() == "mi300x_a1"
        ):
            totalL2Banks = sys_info.L2Banks * get_hbm_stack_num(
                sys_info["name"], sys_info["memory_partition"]
            )
        return totalL2Banks
    else:
        print("Don't support", var)
        sys.exit(1)

def build_dfs(archConfigs, filter_metrics, sys_info):
    """
    - Build dataframe for each type of data source within each panel.
      Each dataframe will be used as a template to load data with each run later.
      For now, support "metric_table" and "raw_csv_table". Otherwise, put an empty df.
    - Collect/build metric_list to suport customrized metrics profiling.
    """

    # TODO: more error checking for filter_metrics!!
    # if filter_metrics:
    #     for metric in filter_metrics:
    #         if not metric in avail_ip_blocks:
    #             print("{} is not a valid metric to filter".format(metric))
    #             exit(1)
    simple_box = {
        "Min": ["MIN(", ")"],
        "Q1": ["QUANTILE(", ", 0.25)"],
        "Median": ["MEDIAN(", ")"],
        "Q3": ["QUANTILE(", ", 0.75)"],
        "Max": ["MAX(", ")"],
    }

    d = {}
    metric_list = {}
    dfs_type = {}
    metric_counters = {}
    for panel_id, panel in archConfigs.panel_configs.items():
        for data_source in panel["data source"]:
            for type, data_config in data_source.items():
                if (
                    type == "metric_table"
                    and "metric" in data_config
                    and "placeholder_range" in data_config["metric"]
                    
                ):
                    # print(data_config["metric"])
                    new_metrics = {}
                    # NB: support single placeholder for now!!
                    p_range = data_config["metric"].pop("placeholder_range")
                    metric, metric_expr = data_config["metric"].popitem()
                    # print(len(data_config["metric"]))
                    # data_config['metric'].clear()
                    for p, r in p_range.items():
                        # NB: We have to resolve placeholder range first if it
                        #   is a build-in var. It will be too late to do it in
                        #   eval_metric(). This is the only reason we need
                        #   sys_info at this stage.
                        var = calc_builtin_var(r, sys_info)
                        for i in range(var):
                            new_key = metric.replace(p, str(i))
                            new_val = {}
                            for k, v in metric_expr.items():
                                new_val[k] = metric_expr[k].replace(p, str(i))
                            # print(new_val)
                            new_metrics[new_key] = new_val

                    # print(p_range)
                    # print(new_metrics)
                    data_config["metric"] = new_metrics
                    # print(data_config)
                    # print(data_config["metric"])
                             
    for panel_id, panel in archConfigs.panel_configs.items():
        for data_source in panel["data source"]:
            for type, data_config in data_source.items():
                if type == "metric_table":
                    headers = ["Index"]

                    if (
                        "cli_style" in data_config
                        and data_config["cli_style"] == "simple_box"
                    ):
                        headers.append("Metric")
                        for k in simple_box.keys():
                            headers.append(k)

                        for key, tile in data_config["header"].items():
                            if key != "metric" and key != "tips" and key != "expr":
                                headers.append(tile)
                    else:
                        for key, tile in data_config["header"].items():
                            if key != "tips":
                                headers.append(tile)

                    # do we always need one?
                    headers.append("coll_level")
                    if "tips" in data_config["header"].keys():
                        headers.append(data_config["header"]["tips"])
                    
                    df = pd.DataFrame(columns=headers)
                
                    i = 0
                    for key, entries in data_config["metric"].items():
                        data_source_idx = (
                            str(data_config["id"] // 100)
                            + "."
                            + str(data_config["id"] % 100)
                        )
                        metric_idx = data_source_idx + "." + str(i)
                        values = []
                        eqn_content = []

                        if (
                            (not filter_metrics)
                            or (metric_idx in filter_metrics)  # no filter
                            or  # metric in filter
                            # the whole table in filter
                            (data_source_idx in filter_metrics)
                            or
                            # the whole IP block in filter
                            (str(panel_id // 100) in filter_metrics)
                        ):
                            values.append(metric_idx)
                            values.append(key)
                            
                            if (
                                "cli_style" in data_config
                                and data_config["cli_style"] == "simple_box"
                            ):
                                # print("~~~~~~~~~~~~~~~~~")
                                # print(entries)
                                # print("~~~~~~~~~~~~~~~~~")
                                for k, v in entries.items():
                                    if k == "expr":
                                        for bk, bv in simple_box.items():
                                            values.append(bv[0] + v + bv[1])
                                    else:
                                        if (
                                            k != "tips"
                                            and k != "coll_level"
                                            and k != "alias"
                                        ):
                                            values.append(v)

                            else:
                                for k, v in entries.items():
                                    if k != "tips" and k != "coll_level" and k != "alias":
                                        values.append(v)
                                        eqn_content.append(v)

                            if "alias" in entries.keys():
                                values.append(entries["alias"])

                            if "coll_level" in entries.keys():
                                values.append(entries["coll_level"])
                            else:
                                values.append(schema.pmc_perf_file_prefix)

                            if "tips" in entries.keys():
                                values.append(entries["tips"])

                            # print(headers, values)
                            # print(key, entries)
                            df_new_row = pd.DataFrame([values], columns=headers)
                            df = pd.concat([df, df_new_row])

                        # collect metric_list
                        metric_list[metric_idx] = key
                        # generate mapping of counters and metrics
                        filter = {}
                        _visited = False
                        for formula in eqn_content:
                            if formula is not None and formula != "None":
                                visited, counters = gen_counter_list(formula)
                                if visited:
                                    _visited = True
                                for k in counters:
                                    filter[k] = None

                        if len(filter) > 0 or _visited:
                            metric_counters[key] = list(filter)

                        i += 1

                    df.set_index("Index", inplace=True)
                    # df.set_index('Metric', inplace=True)
                    # print(tabulate(df, headers='keys', tablefmt='fancy_grid'))
                elif type == "raw_csv_table":
                    data_source_idx = str(data_config["id"] // 100)
                    if (
                        (not filter_metrics)
                        or (data_source_idx == "0")  # no filter
                        or (data_source_idx in filter_metrics)
                    ):
                        if (
                            "columnwise" in data_config
                            and data_config["columnwise"] == True
                        ):
                            df = pd.DataFrame(
                                [data_config["source"]], columns=["from_csv_columnwise"]
                            )
                        else:
                            df = pd.DataFrame(
                                [data_config["source"]], columns=["from_csv"]
                            )
                        metric_list[data_source_idx] = panel["title"]
                    else:
                        df = pd.DataFrame()
                else:
                    df = pd.DataFrame()

                d[data_config["id"]] = df
                dfs_type[data_config["id"]] = type

    setattr(archConfigs, "dfs", d)
    setattr(archConfigs, "metric_list", metric_list)
    setattr(archConfigs, "dfs_type", dfs_type)
    setattr(archConfigs, "metric_counters", metric_counters)


def build_metric_value_string(dfs, dfs_type, normal_unit):
    """
    Apply the real eval string to its field in the metric_table df.
    """

    for id, df in dfs.items():
        if dfs_type[id] == "metric_table":
            for expr in df.columns:
                if expr in schema.supported_field:
                    # NB: apply all build-in before building the whole string
                    df[expr] = df[expr].apply(update_denom_string, unit=normal_unit)

                    # NB: there should be a faster way to do with single apply
                    if not df.empty:
                        for i in range(df.shape[0]):
                            row_idx_label = df.index.to_list()[i]
                            # print(i, "row_idx_label", row_idx_label, expr)
                            if expr.lower() != "alias":
                                df.at[row_idx_label, expr] = build_eval_string(
                                    df.at[row_idx_label, expr],
                                    df.at[row_idx_label, "coll_level"],
                                )

                elif expr.lower() == "unit" or expr.lower() == "units":
                    df[expr] = df[expr].apply(update_normUnit_string, unit=normal_unit)

        # print(tabulate(df, headers='keys', tablefmt='fancy_grid'))


def eval_metric(dfs, dfs_type, sys_info, soc_spec, raw_pmc_df, debug):
    """
    Execute the expr string for each metric in the df.
    """

    # confirm no illogical counter values (only consider non-roofline runs)
    roof_only_run = sys_info.ip_blocks == "roofline"
    rocscope_run = sys_info.ip_blocks == "rocscope"
    if (
        not rocscope_run
        and not roof_only_run
        and (raw_pmc_df["pmc_perf"]["GRBM_GUI_ACTIVE"] == 0).any()
    ):
        print("WARNING: Dectected GRBM_GUI_ACTIVE == 0\nHaulting execution.")
        sys.exit(1)

    # NB:
    #  Following with Omniperf 0.2.0, we are using HW spec from sys_info instead.
    #  The soc_spec is not in using right now, but can be used to do verification
    #  aganist sys_info, forced theoretical evaluation, or supporting tool-chains
    #  broken.
    ammolite__numSE = sys_info.numSE
    ammolite__numCU = sys_info.numCU
    ammolite__numSIMD = sys_info.numSIMD
    ammolite__numWavesPerCU = sys_info.maxWavesPerCU  # todo: check do we still need it
    ammolite__numSQC = sys_info.numSQC
    ammolite__L2Banks = sys_info.L2Banks
    ammolite__LDSBanks = (
        soc_spec['LDSBanks']
    )  # todo: eventually switch this over to sys_info. its a new spec so trying not to break compatibility
    ammolite__freq = sys_info.cur_sclk  # todo: check do we still need it
    ammolite__mclk = sys_info.cur_mclk
    ammolite__sclk = sys_info.sclk
    ammolite__maxWavesPerCU = sys_info.maxWavesPerCU
    ammolite__hbmBW = sys_info.hbmBW
    ammolite__totalL2Banks = calc_builtin_var("$totalL2Banks", sys_info)

    # TODO: fix all $normUnit in Unit column or title

    # build and eval all derived build-in global variables
    ammolite__build_in = {}
    for key, value in build_in_vars.items():
        # NB: assume all built-in vars from pmc_perf.csv for now
        s = build_eval_string(value, schema.pmc_perf_file_prefix)
        try:
            ammolite__build_in[key] = eval(compile(s, "<string>", "eval"))
        except TypeError:
            ammolite__build_in[key] = None
        except AttributeError as ae:
            if ae == "'NoneType' object has no attribute 'get'":
                ammolite__build_in[key] = None

    ammolite__numActiveCUs = ammolite__build_in["numActiveCUs"]
    ammolite__kernelBusyCycles = ammolite__build_in["kernelBusyCycles"]

    # Hmmm... apply + lambda should just work
    # df['Value'] = df['Value'].apply(lambda s: eval(compile(str(s), '<string>', 'eval')))
    for id, df in dfs.items():
        if dfs_type[id] == "metric_table":
            for idx, row in df.iterrows():
                for expr in df.columns:
                    if expr in schema.supported_field:
                        if expr.lower() != "alias":
                            if row[expr]:
                                if debug:  # debug won't impact the regular calc
                                    print("~" * 40 + "\nExpression:")
                                    print(expr, "=", row[expr])
                                    print("Inputs:")
                                    matched_vars = re.findall("ammolite__\w+", row[expr])
                                    if matched_vars:
                                        for v in matched_vars:
                                            print(
                                                "Var ",
                                                v,
                                                ":",
                                                eval(compile(v, "<string>", "eval")),
                                            )
                                    matched_cols = re.findall(
                                        "raw_pmc_df\['\w+'\]\['\w+'\]", row[expr]
                                    )
                                    if matched_cols:
                                        for c in matched_cols:
                                            m = re.match(
                                                "raw_pmc_df\['(\w+)'\]\['(\w+)'\]", c
                                            )
                                            t = raw_pmc_df[m.group(1)][
                                                m.group(2)
                                            ].to_list()
                                            print(c)
                                            print(
                                                raw_pmc_df[m.group(1)][
                                                    m.group(2)
                                                ].to_list()
                                            )
                                            # print(
                                            #     tabulate(raw_pmc_df[m.group(1)][
                                            #         m.group(2)],
                                            #              headers='keys',
                                            #              tablefmt='fancy_grid'))
                                    print("\nOutput:")
                                    try:
                                        print(
                                            eval(compile(row[expr], "<string>", "eval"))
                                        )
                                        print("~" * 40)
                                    except TypeError:
                                        print(
                                            "skiping entry. Encounterd a missing counter"
                                        )
                                        print(expr, " has been assigned to None")
                                        print(np.nan)
                                    except AttributeError as ae:
                                        if (
                                            str(ae)
                                            == "'NoneType' object has no attribute 'get'"
                                        ):
                                            print(
                                                "skiping entry. Encounterd a missing csv"
                                            )
                                            print(np.nan)
                                        else:
                                            print(ae)
                                            sys.exit(1)

                                # print("eval_metric", id, expr)
                                try:
                                    out = eval(compile(row[expr], "<string>", "eval"))
                                    if row.name != "19.1.1" and np.isnan(
                                        out
                                    ):  # Special exception for unique format of Active CUs in mem chart
                                        row[expr] = ""
                                    else:
                                        row[expr] = out
                                except TypeError:
                                    row[expr] = ""
                                except AttributeError as ae:
                                    if (
                                        str(ae)
                                        == "'NoneType' object has no attribute 'get'"
                                    ):
                                        row[expr] = ""
                                    else:
                                        print(ae)
                                        sys.exit(1)

                            else:
                                # If not insert nan, the whole col might be treated
                                # as string but not nubmer if there is NONE
                                row[expr] = ""

            # print(tabulate(df, headers='keys', tablefmt='fancy_grid'))


def apply_filters(workload, dir, is_gui, debug):
    """
    Apply user's filters to the raw_pmc df.
    """

    # TODO: error out properly if filters out of bound
    ret_df = workload.raw_pmc

    if workload.filter_gpu_ids:
        ret_df = ret_df.loc[
            ret_df[schema.pmc_perf_file_prefix]["gpu-id"]
            .astype(str)
            .isin([workload.filter_gpu_ids])
        ]
        if ret_df.empty:
            print("{} is an invalid gpu-id".format(workload.filter_gpu_ids))
            sys.exit(1)

    # NB:
    # Kernel id is unique!
    # We pick up kernel names from kerne ids first.
    # Then filter valid entries with kernel names.
    if workload.filter_kernel_ids:
        # There are two ways Kernel filtering is done:
        # 1) CLI accepts an array of ints, representing indexes of kernels from the pmc_kernel_top.csv
        # 2) GUI will be passing an array of strs. The full names of kernels as selected from dropdown
        if not is_gui:
            if debug:
                print("CLI kernel filtering")
            # Verify valid kernel filter
            kernels_df = pd.read_csv(os.path.join(dir, "pmc_kernel_top.csv"))
            for kernel_id in workload.filter_kernel_ids:
                if kernel_id > len(kernels_df["KernelName"]):
                    error(
                        "{} is an invalid kernel id. Please enter an id between 0-{}".format(
                            kernel_id, len(kernels_df["KernelName"])
                        )
                    )
            kernels = []
            # NB: mark selected kernels with "*"
            #    Todo: fix it for unaligned comparison
            kernel_top_df = workload.dfs[pmc_kernel_top_table_id]
            kernel_top_df["S"] = ""
            for kernel_id in workload.filter_kernel_ids:
                # print("------- ", kernel_id)
                kernels.append(kernel_top_df.loc[kernel_id, "KernelName"])
                kernel_top_df.loc[kernel_id, "S"] = "*"

            if kernels:
                # print("fitlered df:", len(df.index))
                ret_df = ret_df.loc[
                    ret_df[schema.pmc_perf_file_prefix]["KernelName"].isin(kernels)
                ]
        else:
            if debug:
                print("GUI kernel filtering")
            ret_df = ret_df.loc[
                ret_df[schema.pmc_perf_file_prefix]["KernelName"].isin(
                    workload.filter_kernel_ids
                )
            ]

    if workload.filter_dispatch_ids:
        # NB: support ignoring the 1st n dispatched execution by '> n'
        #     The better way may be parsing python slice string
        for d in workload.filter_dispatch_ids:
            if int(d) >= len(ret_df):  # subtract 2 bc of the two header rows
                print("{} is an invalid dispatch id.".format(d))
                sys.exit(1)
        if ">" in workload.filter_dispatch_ids[0]:
            m = re.match("\> (\d+)", workload.filter_dispatch_ids[0])
            ret_df = ret_df[
                ret_df[schema.pmc_perf_file_prefix]["Index"] > int(m.group(1))
            ]
        else:
            dispatches = [int(x) for x in workload.filter_dispatch_ids]
            ret_df = ret_df.loc[dispatches]
    if debug:
        print("~" * 40, "\nraw pmc df info:\n")
        print(workload.raw_pmc.info())
        print("~" * 40, "\nfiltered pmc df info:")
        print(ret_df.info())

    return ret_df


def load_kernel_top(workload, dir):
    # NB:
    #   - Do pmc_kernel_top.csv loading before eval_metric because we need the kernel names.
    #   - There might be a better way/timing to load raw_csv_table.
    tmp = {}
    for id, df in workload.dfs.items():
        if "from_csv" in df.columns:
            file = Path.joinpath(Path(dir), df.loc[0, "from_csv"])
            if file.exists():
                tmp[id] = pd.read_csv(file)
            else:
                logging.info("Warning: Issue loading top kernels. Check pmc_kernel_top.csv")
        elif "from_csv_columnwise" in df.columns:
            # NB:
            #   Another way might be doing transpose in tty like metric_table.
            #   But we need to figure out headers and comparison properly.
            file = Path.joinpath(Path(dir), df.loc[0, "from_csv_columnwise"])
            if file.exists():
                tmp[id] = pd.read_csv(file).transpose()
                # NB:
                #   All transposed columns should be marked with a general header,
                #   so tty could detect them and show them correctly in comparison.
                tmp[id].columns = ["Info"]
            else:
                logging.info("Warning: Issue loading top kernels. Check pmc_kernel_top.csv")
    workload.dfs.update(tmp)


def load_table_data(workload, dir, is_gui, debug, verbose, skipKernelTop=False):
    """
    Load data for all "raw_csv_table".
    Calculate mertric value for all "metric_table".
    """
    if not skipKernelTop:
        load_kernel_top(workload, dir)

    eval_metric(
        workload.dfs,
        workload.dfs_type,
        workload.sys_info.iloc[0],
        workload.soc_spec,
        apply_filters(workload, dir, is_gui, debug),
        debug,
    )


def build_comparable_columns(time_unit):
    """
    Build comparable columns/headers for display
    """
    comparable_columns = schema.supported_field
    top_stat_base = ["Count", "Sum", "Mean", "Median", "Standard Deviation"]

    for h in top_stat_base:
        comparable_columns.append(h + "(" + time_unit + ")")

    return comparable_columns

def correct_sys_info(df, specs_correction):
    """
    Correct system spec items manually
    """

    # NB: to keep the backwards compatibility, we don't touch the current
    #   naming convention. Ideally, the header of sysinfo should use/include
    #   the members of MachineSpecs directly.

    # Sync up with the header defined in omniperf gen_sysinfo() !!
    # header = "workload_name,"
    # header += "command,"
    # header += "host_name,host_cpu,host_distro,host_kernel,host_rocmver,date,"
    # header += "gpu_soc,numSE,numCU,numSIMD,waveSize,maxWavesPerCU,maxWorkgroupSize,"
    # header += "L1,L2,sclk,mclk,cur_sclk,cur_mclk,L2Banks,LDSBanks,name,numSQC,hbmBW,compute_partition,memory_partition,"
    # header += "ip_blocks\n"

    name_map = {
        "host_name": "hostname",
        "CPU": "host_cpu",
        "kernel_version": "host_kernel",
        "host_distro": "distro",
        # "ram": "",
        "distro": "host_distro",
        "rocm_version": "host_rocmver",
        "GPU": "name",
        "arch": "gpu_soc",
        "L1": "L1",
        "L2": "L2",
        "CU": "numCU",
        "SIMD": "numSIMD",
        "SE": "numSE",
        "wave_size": "waveSize",
        "max_waves_per_cu": "maxWavesPerCU",
        "max_waves_per_cu": "maxWorkgroupSize",
        "max_sclk": "sclk",
        "mclk": "mclk",
        "cur_sclk": "cur_sclk",
        "cur_mclk": "cur_mclk",
        "L2Banks": "L2Banks",
        "LDSBanks": "LDSBanks",
        "numSQC": "numSQC",
        "hbmBW": "hbmBW",
        "compute_partition": "compute_partition",
        "memory_partition": "memory_partition",
    }

    # todo: more err checking for string specs_correction
    pairs = dict(re.findall(r"(\w+):\s*(\d+)", specs_correction))
    for k, v in pairs.items():
        df[name_map[k]] = v

    return df