colramos-amd
149f7ca530
Co-authored-by: Nick Curtis <nicholas.curtis@amd.com>
Signed-off-by: colramos-amd <colramos@amd.com>
[ROCm/rocprofiler-compute commit: 66fcfd3def]
1033 regels
40 KiB
Python
1033 regels
40 KiB
Python
##############################################################################bl
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# MIT License
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#
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# Copyright (c) 2021 - 2024 Advanced Micro Devices, Inc. All Rights Reserved.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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##############################################################################el
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import ast
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import sys
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import astunparse
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import re
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import os
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import pandas as pd
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import numpy as np
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from utils import schema
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from utils.utils import error, get_hbm_stack_num
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from pathlib import Path
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import logging
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# ------------------------------------------------------------------------------
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# Internal global definitions
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# NB:
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# Ammolite is unique gemstone from the Rocky Mountains.
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# "ammolite__" is a special internal prefix to mark build-in global variables
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# calculated or parsed from raw data sources. Its range is only in this file.
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# Any other general prefixes string, like "buildin__", might be used by the
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# editor. Whenever change it to a new one, replace all appearances in this file.
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# 001 is ID of pmc_kernel_top.csv table
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pmc_kernel_top_table_id = 1
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# Build-in $denom defined in mongodb query:
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# "denom": {
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# "$switch" : {
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# "branches": [
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# {
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# "case": { "$eq": [ $normUnit, "per Wave"]} ,
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# "then": "&SQ_WAVES"
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# },
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# {
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# "case": { "$eq": [ $normUnit, "per Cycle"]} ,
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# "then": "&GRBM_GUI_ACTIVE"
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# },
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# {
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# "case": { "$eq": [ $normUnit, "per Sec"]} ,
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# "then": {"$divide":[{"$subtract": ["&End_Timestamp", "&Start_Timestamp" ]}, 1000000000]}
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# }
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# ],
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# "default": 1
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# }
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# }
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supported_denom = {
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"per_wave": "SQ_WAVES",
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"per_cycle": "GRBM_GUI_ACTIVE",
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"per_second": "((End_Timestamp - Start_Timestamp) / 1000000000)",
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"per_kernel": "1",
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}
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# Build-in defined in mongodb variables:
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build_in_vars = {
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"numActiveCUs": "TO_INT(MIN((((ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) / GRBM_GUI_ACTIVE)), \
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0) / $maxWavesPerCU) * 8) + MIN(MOD(ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) \
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/ GRBM_GUI_ACTIVE)), 0), $maxWavesPerCU), 8)), $numCU))",
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"kernelBusyCycles": "ROUND(AVG((((End_Timestamp - Start_Timestamp) / 1000) * $sclk)), 0)",
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}
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supported_call = {
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# If the below has single arg, like(expr), it is a aggr, in which turn to a pd function.
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# If it has args like list [], in which turn to a python function.
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"MIN": "to_min",
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"MAX": "to_max",
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# simple aggr
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"AVG": "to_avg",
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"MEDIAN": "to_median",
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"STD": "to_std",
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# functions apply to whole column of df or a single value
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"TO_INT": "to_int",
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# Support the below with 2 inputs
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"ROUND": "to_round",
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"QUANTILE": "to_quantile",
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"MOD": "to_mod",
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# Concat operation from the memory chart "active cus"
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"CONCAT": "to_concat",
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}
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# ------------------------------------------------------------------------------
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def to_min(*args):
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if len(args) == 1 and isinstance(args[0], pd.core.series.Series):
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return args[0].min()
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elif min(args) == None:
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return np.nan
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else:
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return min(args)
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def to_max(*args):
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if len(args) == 1 and isinstance(args[0], pd.core.series.Series):
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return args[0].max()
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elif len(args) == 2 and (
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isinstance(args[0], pd.core.series.Series)
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or isinstance(args[1], pd.core.series.Series)
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):
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return np.maximum(args[0], args[1])
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elif max(args) == None:
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return np.nan
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else:
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return max(args)
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def to_avg(a):
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if str(type(a)) == "<class 'NoneType'>":
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return np.nan
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elif a.empty:
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return np.nan
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elif isinstance(a, pd.core.series.Series):
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return a.mean()
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else:
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raise Exception("to_avg: unsupported type.")
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def to_median(a):
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if a is None:
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return None
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elif isinstance(a, pd.core.series.Series):
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return a.median()
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else:
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raise Exception("to_median: unsupported type.")
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def to_std(a):
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if isinstance(a, pd.core.series.Series):
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return a.std()
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else:
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raise Exception("to_std: unsupported type.")
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def to_int(a):
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if str(type(a)) == "<class 'NoneType'>":
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return np.nan
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elif isinstance(a, (int, float, np.int64)):
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return int(a)
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elif isinstance(a, pd.core.series.Series):
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return a.astype("Int64")
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# Do we need it?
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# elif isinstance(a, str):
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# return int(a)
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else:
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raise Exception("to_int: unsupported type.")
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def to_round(a, b):
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if isinstance(a, pd.core.series.Series):
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return a.round(b)
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else:
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return round(a, b)
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def to_quantile(a, b):
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if a is None:
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return None
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elif isinstance(a, pd.core.series.Series):
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return a.quantile(b)
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else:
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raise Exception("to_quantile: unsupported type.")
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def to_mod(a, b):
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if isinstance(a, pd.core.series.Series):
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return a.mod(b)
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else:
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return a % b
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def to_concat(a, b):
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return str(a) + str(b)
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class CodeTransformer(ast.NodeTransformer):
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"""
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Python AST visitor to transform user defined equation string to df format
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"""
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def visit_Call(self, node):
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self.generic_visit(node)
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# print("--- debug visit_Call --- ", node.args, node.func)
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# print(astunparse.dump(node))
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# print(astunparse.unparse(node))
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if isinstance(node.func, ast.Name):
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if node.func.id in supported_call:
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node.func.id = supported_call[node.func.id]
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else:
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raise Exception(
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"Unknown call:", node.func.id
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) # Could be removed if too strict
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return node
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def visit_IfExp(self, node):
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self.generic_visit(node)
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# print("visit_IfExp", type(node.test), type(node.body), type(node.orelse), dir(node))
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if isinstance(node.body, ast.Num):
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raise Exception(
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"Don't support body of IF with number only! Has to be expr with df['column']."
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)
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new_node = ast.Expr(
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value=ast.Call(
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func=ast.Attribute(value=node.body, attr="where", ctx=ast.Load()),
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args=[node.test, node.orelse],
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keywords=[],
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)
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)
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# print("-------------")
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# print(astunparse.dump(new_node))
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# print("-------------")
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return new_node
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# NB:
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# visit_Name is for replacing HW counter to its df expr. In this way, we
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# could support any HW counter names, which is easier than regex.
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#
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# There are 2 limitations:
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# - It is not straightforward to support types other than simple column
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# in df, such as [], (). If we need to support those, have to implement
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# in correct way or work around.
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# - The 'raw_pmc_df' is hack code. For other data sources, like wavefront
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# data,We need to think about template or pass it as a parameter.
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def visit_Name(self, node):
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self.generic_visit(node)
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# print("-------------", node.id)
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if (not node.id.startswith("ammolite__")) and (not node.id in supported_call):
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new_node = ast.Subscript(
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value=ast.Name(id="raw_pmc_df", ctx=ast.Load()),
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slice=ast.Index(value=ast.Str(s=node.id)),
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ctx=ast.Load(),
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)
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node = new_node
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return node
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def build_eval_string(equation, coll_level):
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"""
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Convert user defined equation string to eval executable string
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For example,
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input: AVG(100 * SQ_ACTIVE_INST_SCA / ( GRBM_GUI_ACTIVE * $numCU ))
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output: to_avg(100 * raw_pmc_df["pmc_perf"]["SQ_ACTIVE_INST_SCA"] / \
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(raw_pmc_df["pmc_perf"]["GRBM_GUI_ACTIVE"] * numCU))
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input: AVG(((TCC_EA_RDREQ_LEVEL_31 / TCC_EA_RDREQ_31) if (TCC_EA_RDREQ_31 != 0) else (0)))
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output: to_avg((raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_LEVEL_31"] / raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"]).where(raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"] != 0, 0))
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We can not handle the below for now,
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input: AVG((0 if (TCC_EA_RDREQ_31 == 0) else (TCC_EA_RDREQ_LEVEL_31 / TCC_EA_RDREQ_31)))
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But potential workaound is,
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output: to_avg(raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"].where(raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"] == 0, raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_LEVEL_31"] / raw_pmc_df["pmc_perf"]["TCC_EA_RDREQ_31"]))
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"""
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if coll_level is None:
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raise Exception("Error: coll_level can not be None.")
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if not equation:
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return ""
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s = str(equation)
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# print("input:", s)
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# build-in variable starts with '$', python can not handle it.
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# replace '$' with 'ammolite__'.
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# TODO: pre-check there is no "ammolite__" in all config files.
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s = re.sub("\$", "ammolite__", s)
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# convert equation string to intermediate expression in df array format
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ast_node = ast.parse(s)
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# print(astunparse.dump(ast_node))
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transformer = CodeTransformer()
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transformer.visit(ast_node)
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s = astunparse.unparse(ast_node)
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# correct column name/label in df with [], such as TCC_HIT[0],
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# the target is df['TCC_HIT[0]']
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s = re.sub(r"\'\]\[(\d+)\]", r"[\g<1>]']", s)
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# use .get() to catch any potential KeyErrors
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s = re.sub("raw_pmc_df\['(.*?)']", r'raw_pmc_df.get("\1")', s)
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# apply coll_level
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s = re.sub(r"raw_pmc_df", "raw_pmc_df.get('" + coll_level + "')", s)
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# print("--- build_eval_string, return: ", s)
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return s
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def update_denom_string(equation, unit):
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"""
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Update $denom in equation with runtime normalization unit.
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"""
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if not equation:
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return ""
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s = str(equation)
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if unit in supported_denom.keys():
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s = re.sub(r"\$denom", supported_denom[unit], s)
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return s
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def update_normUnit_string(equation, unit):
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"""
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Update $normUnit in equation with runtime normalization unit.
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It is string replacement for display only.
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"""
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# TODO: We might want to do it for subtitle contains $normUnit
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if not equation:
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return ""
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return re.sub(
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r"\((?P<PREFIX>\w*)\s+\+\s+(\$normUnit\))",
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r"\g<PREFIX> " + re.sub("_", " ", unit),
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str(equation),
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).capitalize()
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def gen_counter_list(formula):
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function_filter = {
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"MIN": None,
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"MAX": None,
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"AVG": None,
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"ROUND": None,
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"TO_INT": None,
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"GB": None,
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"STD": None,
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"GFLOP": None,
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"GOP": None,
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"OP": None,
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"CU": None,
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"NC": None,
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"UC": None,
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"CC": None,
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"RW": None,
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"GIOP": None,
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"GFLOPs": None,
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"CONCAT": None,
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"MOD": None,
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}
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built_in_counter = [
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"LDS_Per_Workgroup",
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"Grid_Size",
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"Workgroup_Size",
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"Arch_VGPR",
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"Accum_VGPR",
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"SGPR",
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"Scratch_Per_Workitem",
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"Start_Timestamp",
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"End_Timestamp",
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]
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visited = False
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counters = []
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if not isinstance(formula, str):
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return visited, counters
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try:
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tree = ast.parse(
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formula.replace("$normUnit", "SQ_WAVES")
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.replace("$denom", "SQ_WAVES")
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.replace(
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"$numActiveCUs",
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"TO_INT(MIN((((ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) / GRBM_GUI_ACTIVE)), \
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0) / $maxWavesPerCU) * 8) + MIN(MOD(ROUND(AVG(((4 * SQ_BUSY_CU_CYCLES) \
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/ GRBM_GUI_ACTIVE)), 0), $maxWavesPerCU), 8)), $numCU))",
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)
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.replace("$", "")
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)
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for node in ast.walk(tree):
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if isinstance(node, ast.Name):
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val = str(node.id)[:-4] if str(node.id).endswith("_sum") else str(node.id)
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if val.isupper() and val not in function_filter:
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counters.append(val)
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visited = True
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if val in built_in_counter:
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visited = True
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except:
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pass
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return visited, counters
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def calc_builtin_var(var, sys_info):
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"""
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Calculate build-in variable based on sys_info:
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"""
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if isinstance(var, int):
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return var
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elif isinstance(var, str) and var.startswith("$totalL2Banks"):
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# Fixme: support all supported partitioning mode
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# Fixme: "name" is a bad name!
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totalL2Banks = sys_info.L2Banks
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if (
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sys_info["name"].lower() == "mi300a_a0"
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or sys_info["name"].lower() == "mi300a_a1"
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):
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totalL2Banks = sys_info.L2Banks * get_hbm_stack_num(
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sys_info["name"], sys_info["memory_partition"]
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)
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elif (
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sys_info["name"].lower() == "mi300x_a0"
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or sys_info["name"].lower() == "mi300x_a1"
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):
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totalL2Banks = sys_info.L2Banks * get_hbm_stack_num(
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sys_info["name"], sys_info["memory_partition"]
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)
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return totalL2Banks
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else:
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print("Don't support", var)
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sys.exit(1)
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def build_dfs(archConfigs, filter_metrics, sys_info):
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"""
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- Build dataframe for each type of data source within each panel.
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Each dataframe will be used as a template to load data with each run later.
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For now, support "metric_table" and "raw_csv_table". Otherwise, put an empty df.
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- Collect/build metric_list to suport customrized metrics profiling.
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"""
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# TODO: more error checking for filter_metrics!!
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# if filter_metrics:
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# for metric in filter_metrics:
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# if not metric in avail_ip_blocks:
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# print("{} is not a valid metric to filter".format(metric))
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# exit(1)
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simple_box = {
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"Min": ["MIN(", ")"],
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"Q1": ["QUANTILE(", ", 0.25)"],
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"Median": ["MEDIAN(", ")"],
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"Q3": ["QUANTILE(", ", 0.75)"],
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"Max": ["MAX(", ")"],
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}
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d = {}
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metric_list = {}
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dfs_type = {}
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metric_counters = {}
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for panel_id, panel in archConfigs.panel_configs.items():
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for data_source in panel["data source"]:
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for type, data_config in data_source.items():
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if (
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type == "metric_table"
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and "metric" in data_config
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and "placeholder_range" in data_config["metric"]
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):
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# print(data_config["metric"])
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new_metrics = {}
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# NB: support single placeholder for now!!
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p_range = data_config["metric"].pop("placeholder_range")
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metric, metric_expr = data_config["metric"].popitem()
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# print(len(data_config["metric"]))
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# data_config['metric'].clear()
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for p, r in p_range.items():
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# NB: We have to resolve placeholder range first if it
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# is a build-in var. It will be too late to do it in
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# eval_metric(). This is the only reason we need
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# sys_info at this stage.
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var = calc_builtin_var(r, sys_info)
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for i in range(var):
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new_key = metric.replace(p, str(i))
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new_val = {}
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for k, v in metric_expr.items():
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new_val[k] = metric_expr[k].replace(p, str(i))
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# print(new_val)
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new_metrics[new_key] = new_val
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# 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 = ["Metric_ID"]
|
|
data_source_idx = str(data_config["id"] // 100)
|
|
if (data_source_idx != 0 or
|
|
data_source_idx in filter_metrics
|
|
):
|
|
metric_list[data_source_idx] = panel["title"]
|
|
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)
|
|
|
|
metric_list[data_source_idx] = data_config["title"]
|
|
|
|
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("Metric_ID", 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
|
|
# against sys_info, forced theoretical evaluation, or supporting tool-chains
|
|
# broken.
|
|
ammolite__numSE = sys_info.numSE
|
|
ammolite__numPipes = sys_info.numPipes
|
|
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(r"ammolite__\w+", row[expr])
|
|
if matched_vars:
|
|
for v in matched_vars:
|
|
print(
|
|
"Var ",
|
|
v,
|
|
":",
|
|
eval(compile(v, "<string>", "eval")),
|
|
)
|
|
matched_cols = re.findall(
|
|
r"raw_pmc_df\['\w+'\]\['\w+'\]", row[expr]
|
|
)
|
|
if matched_cols:
|
|
for c in matched_cols:
|
|
m = re.match(
|
|
r"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(
|
|
"skipping entry. Encountered 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(
|
|
"skipping entry. Encountered 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["Kernel_Name"]):
|
|
error(
|
|
"{} is an invalid kernel id. Please enter an id between 0-{}".format(
|
|
kernel_id, len(kernels_df["Kernel_Name"])
|
|
)
|
|
)
|
|
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, "Kernel_Name"])
|
|
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]["Kernel_Name"].isin(kernels)
|
|
]
|
|
else:
|
|
if debug:
|
|
print("GUI kernel filtering")
|
|
ret_df = ret_df.loc[
|
|
ret_df[schema.pmc_perf_file_prefix]["Kernel_Name"].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(r"\> (\d+)", workload.filter_dispatch_ids[0])
|
|
ret_df = ret_df[
|
|
ret_df[schema.pmc_perf_file_prefix]["Dispatch_ID"] > 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,"
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# header += "gpu_soc,numSE,numCU,numSIMD,waveSize,maxWavesPerCU,maxWorkgroupSize,"
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# header += "L1,L2,sclk,mclk,cur_sclk,cur_mclk,L2Banks,LDSBanks,name,numSQC,numPipes,hbmBW,compute_partition,memory_partition,"
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# header += "ip_blocks\n"
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name_map = {
|
|
"host_name": "hostname",
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"CPU": "host_cpu",
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"kernel_version": "host_kernel",
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"host_distro": "distro",
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|
# "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",
|
|
"numPipes": "numPipes",
|
|
"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
|
|
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