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Enable specs correction, detection of cli_style property, and hbm stack num

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Co-authored-by: fei.zheng <fei.zheng@amd.com>
Signed-off-by: coleramos425 <colramos@amd.com>
This commit is contained in:
coleramos425
2024-01-16 10:07:46 -06:00
committed by Cole Ramos
parent e3f76599b3
commit 92f429f8b2
3 changed files with 215 additions and 37 deletions
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src/omniperf_analyze/analysis_base.py
+11 -4
View File
@@ -51,7 +51,7 @@ class OmniAnalyze_Base():
return self.__socs
@demarcate
def generate_configs(self, arch, config_dir, list_kernels, filter_metrics):
def generate_configs(self, arch, config_dir, list_kernels, filter_metrics, sys_info):
single_panel_config = file_io.is_single_panel_config(Path(config_dir), self.__supported_archs)
ac = schema.ArchConfig()
@@ -66,7 +66,11 @@ class OmniAnalyze_Base():
# TODO: filter_metrics should/might be one per arch
# print(ac)
parser.build_dfs(ac, filter_metrics)
parser.build_dfs(
archConfigs=ac,
filter_metrics=filter_metrics,
sys_info=sys_info
)
self._arch_configs[arch] = ac
return self._arch_configs
@@ -76,7 +80,8 @@ class OmniAnalyze_Base():
if args.list_metrics in file_io.supported_arch.keys():
arch = args.list_metrics
if arch not in self._arch_configs.keys():
self.generate_configs(arch, args.config_dir, args.list_kernels, args.filter_metrics)
sys_info = file_io.load_sys_info(Path(self.__args.path[0][0], "sysinfo.csv"))
self.generate_configs(arch, args.config_dir, args.list_kernels, args.filter_metrics, sys_info)
print(
tabulate(
pd.DataFrame.from_dict(
@@ -121,13 +126,15 @@ class OmniAnalyze_Base():
sys_info = file_io.load_sys_info(Path(d[0], "sysinfo.csv"))
arch = sys_info.iloc[0]["gpu_soc"]
args = self.__args
self.generate_configs(arch, args.config_dir, args.list_kernels, args.filter_metrics)
self.generate_configs(arch, args.config_dir, args.list_kernels, args.filter_metrics, sys_info)
self.load_options(normalization_filter)
for d in self.__args.path:
w = schema.Workload()
w.sys_info = file_io.load_sys_info(Path(d[0], "sysinfo.csv"))
if self.__args.specs_correction:
w.sys_info = parser.correct_sys_info(w.sys_info, self.__args.specs_correction)
w.avail_ips = w.sys_info["ip_blocks"].item().split("|")
arch = w.sys_info.iloc[0]["gpu_soc"]
w.dfs = copy.deepcopy(self._arch_configs[arch].dfs)
src/utils/parser.py
+203 -32
View File
@@ -29,8 +29,10 @@ import re
import os
import pandas as pd
import numpy as np
from tabulate import tabulate
from utils import schema
from utils.utils import error
from pathlib import Path
import logging
# ------------------------------------------------------------------------------
# Internal global definitions
@@ -93,6 +95,7 @@ supported_call = {
"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",
@@ -131,7 +134,9 @@ def to_avg(a):
def to_median(a):
if isinstance(a, pd.core.series.Series):
if a is None:
return None
elif isinstance(a, pd.core.series.Series):
return a.median()
else:
raise Exception("to_median: unsupported type.")
@@ -164,6 +169,13 @@ def to_round(a, 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):
@@ -385,8 +397,36 @@ def gen_counter_list(formula):
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):
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.
@@ -400,33 +440,91 @@ def build_dfs(archConfigs, 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():
panel_idx = str(panel_id // 100)
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":
metric_list[panel_idx] = panel["title"]
table_idx = panel_idx + "." + str(data_config["id"] % 100)
metric_list[table_idx] = data_config["title"]
headers = ["Index"]
for key, tile in data_config["header"].items():
if key != "tips":
headers.append(tile)
headers.append("coll_level")
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():
metric_idx = table_idx + "." + str(i)
data_source_idx = (
str(data_config["id"] // 100)
+ "."
+ str(data_config["id"] % 100)
)
metric_idx = data_source_idx + "." + str(i)
values = []
eqn_content = []
@@ -435,17 +533,38 @@ def build_dfs(archConfigs, filter_metrics):
or (metric_idx in filter_metrics) # no filter
or # metric in filter
# the whole table in filter
(table_idx in filter_metrics)
(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)
for k, v in entries.items():
if k != "tips" and k != "coll_level" and k != "alias":
values.append(v)
eqn_content.append(v)
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"])
@@ -458,6 +577,7 @@ def build_dfs(archConfigs, filter_metrics):
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])
@@ -580,13 +700,14 @@ def eval_metric(dfs, dfs_type, sys_info, soc_spec, raw_pmc_df, debug):
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 build in vars from pmc_perf.csv for now
# 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"))
@@ -726,6 +847,15 @@ def apply_filters(workload, is_gui, debug):
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
@@ -745,9 +875,9 @@ def apply_filters(workload, is_gui, debug):
if debug:
print("GUI kernel filtering")
ret_df = ret_df.loc[
ret_df[schema.pmc_perf_file_prefix]["KernelName"].isin(
workload.filter_kernel_ids
)
ret_df[schema.pmc_perf_file_prefix]["Index"]
.astype(str)
.isin(workload.filter_dispatch_ids)
]
if workload.filter_dispatch_ids:
@@ -781,18 +911,24 @@ def load_kernel_top(workload, dir):
tmp = {}
for id, df in workload.dfs.items():
if "from_csv" in df.columns:
tmp[id] = pd.read_csv(os.path.join(dir, df.loc[0, "from_csv"]))
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.
tmp[id] = pd.read_csv(
os.path.join(dir, df.loc[0, "from_csv_columnwise"])
).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"]
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)
@@ -879,3 +1015,38 @@ def correct_sys_info(df, specs_correction):
df[name_map[k]] = v
return df
def get_hbm_stack_num(gpu_name, memory_partition):
"""
Get total HBM stack numbers based on memory partition for MI300.
"""
# TODO:
# - move this function to the proper file
# - better err log
if gpu_name.lower() == "mi300a_a0" or gpu_name.lower() == "mi300a_a1":
if memory_partition.lower() == "nps1":
return 6
elif memory_partition.lower() == "nps4":
return 2
elif memory_partition.lower() == "nps8":
return 1
else:
print("Invalid MI300A memory partition mode!")
sys.exit()
elif gpu_name.lower() == "mi300x_a0" or gpu_name.lower() == "mi300x_a1":
if memory_partition.lower() == "nps1":
return 8
elif memory_partition.lower() == "nps2":
return 4
elif memory_partition.lower() == "nps4":
return 2
elif memory_partition.lower() == "nps8":
return 1
else:
print("Invalid MI300X memory partition mode!")
sys.exit()
else:
# Fixme: add proper numbers for other archs
return -1
src/utils/tty.py
+1 -1
View File
@@ -82,7 +82,7 @@ def show_all(args, runs, archConfigs, output):
if (
type == "raw_csv_table"
and table_config["source"] == "pmc_kernel_top.csv"
and header == "KernelName"
and (header == "KernelName" or header == "Kernel_Name")
):
# NB: the width of kernel name might depend on the header of the table.
adjusted_name = base_df["KernelName"].apply(