ywang103-amd
1286 lines
41 KiB
Python
1286 lines
41 KiB
Python
##############################################################################bl
|
|
# MIT License
|
|
#
|
|
# Copyright (c) 2021 - 2025 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
|
|
# in the Software without restriction, including without limitation the rights
|
|
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
|
# copies of the Software, and to permit persons to whom the Software is
|
|
# furnished to do so, subject to the following conditions:
|
|
#
|
|
# The above copyright notice and this permission notice shall be included in all
|
|
# copies or substantial portions of the Software.
|
|
#
|
|
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
|
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
|
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
|
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
|
# 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 glob
|
|
import io
|
|
import json
|
|
import locale
|
|
import logging
|
|
import os
|
|
import pathlib
|
|
import re
|
|
import selectors
|
|
import shutil
|
|
import subprocess
|
|
import sys
|
|
import time
|
|
from collections import OrderedDict
|
|
from itertools import product
|
|
from pathlib import Path as path
|
|
|
|
import pandas as pd
|
|
|
|
import config
|
|
from utils.logger import console_debug, console_error, console_log, console_warning
|
|
from utils.mi_gpu_spec import get_mi300_num_xcds
|
|
|
|
rocprof_cmd = ""
|
|
rocprof_args = ""
|
|
|
|
|
|
def is_tcc_channel_counter(counter):
|
|
return counter.startswith("TCC") and counter.endswith("]")
|
|
|
|
|
|
def using_v1():
|
|
return "ROCPROF" in os.environ.keys() and os.environ["ROCPROF"].endswith("rocprof")
|
|
|
|
|
|
def using_v3():
|
|
return "ROCPROF" in os.environ.keys() and os.environ["ROCPROF"].endswith("rocprofv3")
|
|
|
|
|
|
def get_version(rocprof_compute_home) -> dict:
|
|
"""Return ROCm Compute Profiler versioning info"""
|
|
|
|
# symantic version info - note that version file(s) can reside in
|
|
# two locations depending on development vs formal install
|
|
searchDirs = [rocprof_compute_home, rocprof_compute_home.parent]
|
|
found = False
|
|
versionDir = None
|
|
|
|
for dir in searchDirs:
|
|
version = str(path(dir).joinpath("VERSION"))
|
|
try:
|
|
with open(version, "r") as file:
|
|
VER = file.read().replace("\n", "")
|
|
found = True
|
|
versionDir = dir
|
|
break
|
|
except:
|
|
pass
|
|
if not found:
|
|
console_error("Cannot find VERSION file at {}".format(searchDirs))
|
|
|
|
# git version info
|
|
try:
|
|
success, output = capture_subprocess_output(
|
|
["git", "-C", versionDir, "log", "--pretty=format:%h", "-n", "1"],
|
|
)
|
|
if success:
|
|
SHA = output
|
|
MODE = "dev"
|
|
else:
|
|
raise Exception(output)
|
|
except:
|
|
try:
|
|
shaFile = path(versionDir).joinpath("VERSION.sha").absolute().resolve()
|
|
with open(shaFile, "r") as file:
|
|
SHA = file.read().replace("\n", "")
|
|
MODE = "release"
|
|
except Exception:
|
|
SHA = "unknown"
|
|
MODE = "unknown"
|
|
|
|
versionData = {"version": VER, "sha": SHA, "mode": MODE}
|
|
return versionData
|
|
|
|
|
|
def get_version_display(version, sha, mode):
|
|
"""Pretty print versioning info"""
|
|
buf = io.StringIO()
|
|
print("-" * 40, file=buf)
|
|
print("rocprofiler-compute version: %s (%s)" % (version, mode), file=buf)
|
|
print("Git revision: %s" % sha, file=buf)
|
|
print("-" * 40, file=buf)
|
|
return buf.getvalue()
|
|
|
|
|
|
def detect_rocprof():
|
|
"""Detect loaded rocprof version. Resolve path and set cmd globally."""
|
|
global rocprof_cmd
|
|
# detect rocprof
|
|
if not "ROCPROF" in os.environ.keys():
|
|
rocprof_cmd = "rocprof"
|
|
else:
|
|
rocprof_cmd = os.environ["ROCPROF"]
|
|
|
|
# resolve rocprof path
|
|
rocprof_path = shutil.which(rocprof_cmd)
|
|
|
|
if not rocprof_path:
|
|
rocprof_cmd = "rocprof"
|
|
console_warning(
|
|
"Unable to resolve path to %s binary. Reverting to default." % rocprof_cmd
|
|
)
|
|
rocprof_path = shutil.which(rocprof_cmd)
|
|
if not rocprof_path:
|
|
console_error(
|
|
"Please verify installation or set ROCPROF environment variable with full path."
|
|
)
|
|
else:
|
|
# Resolve any sym links in file path
|
|
rocprof_path = str(path(rocprof_path.rstrip("\n")).resolve())
|
|
console_debug("ROC Profiler: " + str(rocprof_path))
|
|
|
|
console_debug("rocprof_cmd is {}".format(str(rocprof_cmd)))
|
|
return rocprof_cmd # TODO: Do we still need to return this? It's not being used in the function call
|
|
|
|
|
|
def store_app_cmd(args):
|
|
global rocprof_args
|
|
rocprof_args = args
|
|
|
|
|
|
def capture_subprocess_output(
|
|
subprocess_args, new_env=None, profileMode=False, enable_logging=True
|
|
):
|
|
console_debug("subprocess", "Running: " + " ".join(subprocess_args))
|
|
# Start subprocess
|
|
# bufsize = 1 means output is line buffered
|
|
# universal_newlines = True is required for line buffering
|
|
process = (
|
|
subprocess.Popen(
|
|
subprocess_args,
|
|
bufsize=1,
|
|
stdout=subprocess.PIPE,
|
|
stderr=subprocess.STDOUT,
|
|
universal_newlines=True,
|
|
)
|
|
if new_env == None
|
|
else subprocess.Popen(
|
|
subprocess_args,
|
|
bufsize=1,
|
|
stdout=subprocess.PIPE,
|
|
stderr=subprocess.STDOUT,
|
|
universal_newlines=True,
|
|
env=new_env,
|
|
)
|
|
)
|
|
|
|
# Create callback function for process output
|
|
buf = io.StringIO()
|
|
|
|
def handle_output(stream, mask):
|
|
try:
|
|
# Because the process' output is line buffered, there's only ever one
|
|
# line to read when this function is called
|
|
line = stream.readline()
|
|
buf.write(line)
|
|
if enable_logging:
|
|
if profileMode:
|
|
console_log(rocprof_cmd, line.strip(), indent_level=1)
|
|
else:
|
|
console_log(line.strip())
|
|
except UnicodeDecodeError:
|
|
# Skip this line
|
|
pass
|
|
|
|
# Register callback for an "available for read" event from subprocess' stdout stream
|
|
selector = selectors.DefaultSelector()
|
|
selector.register(process.stdout, selectors.EVENT_READ, handle_output)
|
|
|
|
# Loop until subprocess is terminated
|
|
while process.poll() is None:
|
|
# Wait for events and handle them with their registered callbacks
|
|
events = selector.select()
|
|
for key, mask in events:
|
|
callback = key.data
|
|
callback(key.fileobj, mask)
|
|
|
|
# Get process return code
|
|
return_code = process.wait()
|
|
selector.close()
|
|
|
|
success = return_code == 0
|
|
|
|
# Store buffered output
|
|
output = buf.getvalue()
|
|
buf.close()
|
|
|
|
return (success, output)
|
|
|
|
|
|
# Create a dictionary that maps agent ID to agent objects
|
|
def get_agent_dict(data):
|
|
agents = data["rocprofiler-sdk-tool"][0]["agents"]
|
|
|
|
agent_map = {}
|
|
|
|
for agent in agents:
|
|
agent_id = agent["id"]["handle"]
|
|
agent_map[agent_id] = agent
|
|
|
|
return agent_map
|
|
|
|
|
|
# Returns a dictionary that maps agent ID to GPU ID
|
|
# starting at 0.
|
|
def get_gpuid_dict(data):
|
|
|
|
agents = data["rocprofiler-sdk-tool"][0]["agents"]
|
|
|
|
agent_list = []
|
|
|
|
# Get agent ID and node_id for GPU agents only
|
|
for agent in agents:
|
|
|
|
if agent["type"] == 2:
|
|
agent_id = agent["id"]["handle"]
|
|
node_id = agent["node_id"]
|
|
agent_list.append((agent_id, node_id))
|
|
|
|
# Sort by node ID
|
|
agent_list.sort(key=lambda x: x[1])
|
|
|
|
# Map agent ID to node id
|
|
map = {}
|
|
gpu_id = 0
|
|
for agent in agent_list:
|
|
map[agent[0]] = gpu_id
|
|
gpu_id = gpu_id + 1
|
|
|
|
return map
|
|
|
|
|
|
# Create a dictionary that maps counter ID to counter objects
|
|
def v3_json_get_counters(data):
|
|
counters = data["rocprofiler-sdk-tool"][0]["counters"]
|
|
|
|
counter_map = {}
|
|
|
|
for counter in counters:
|
|
counter_id = counter["id"]["handle"]
|
|
agent_id = counter["agent_id"]["handle"]
|
|
|
|
counter_map[(agent_id, counter_id)] = counter
|
|
|
|
return counter_map
|
|
|
|
|
|
def v3_json_get_dispatches(data):
|
|
records = data["rocprofiler-sdk-tool"][0]["buffer_records"]
|
|
|
|
records_map = {}
|
|
|
|
for rec in records["kernel_dispatch"]:
|
|
id = rec["correlation_id"]["internal"]
|
|
|
|
records_map[id] = rec
|
|
|
|
return records_map
|
|
|
|
|
|
def v3_json_to_csv(json_file_path, csv_file_path):
|
|
|
|
f = open(json_file_path, "rt")
|
|
data = json.load(f)
|
|
|
|
dispatch_records = v3_json_get_dispatches(data)
|
|
dispatches = data["rocprofiler-sdk-tool"][0]["callback_records"]["counter_collection"]
|
|
kernel_symbols = data["rocprofiler-sdk-tool"][0]["kernel_symbols"]
|
|
agents = get_agent_dict(data)
|
|
pid = data["rocprofiler-sdk-tool"][0]["metadata"]["pid"]
|
|
|
|
gpuid_map = get_gpuid_dict(data)
|
|
|
|
counter_info = v3_json_get_counters(data)
|
|
|
|
# CSV headers. If there are no dispatches we still end up with a valid CSV file.
|
|
csv_data = dict.fromkeys(
|
|
[
|
|
"Dispatch_ID",
|
|
"GPU_ID",
|
|
"Queue_ID",
|
|
"PID",
|
|
"TID",
|
|
"Grid_Size",
|
|
"Workgroup_Size",
|
|
"LDS_Per_Workgroup",
|
|
"Scratch_Per_Workitem",
|
|
"Arch_VGPR",
|
|
"Accum_VGPR",
|
|
"SGPR",
|
|
"Wave_Size",
|
|
"Kernel_Name",
|
|
"Start_Timestamp",
|
|
"End_Timestamp",
|
|
"Correlation_ID",
|
|
]
|
|
)
|
|
|
|
for key in csv_data:
|
|
csv_data[key] = []
|
|
|
|
for d in dispatches:
|
|
|
|
dispatch_info = d["dispatch_data"]["dispatch_info"]
|
|
|
|
agent_id = dispatch_info["agent_id"]["handle"]
|
|
|
|
kernel_id = dispatch_info["kernel_id"]
|
|
|
|
row = {}
|
|
|
|
row["Dispatch_ID"] = dispatch_info["dispatch_id"]
|
|
|
|
row["GPU_ID"] = gpuid_map[agent_id]
|
|
|
|
row["Queue_ID"] = dispatch_info["queue_id"]["handle"]
|
|
row["PID"] = pid
|
|
row["TID"] = d["thread_id"]
|
|
|
|
grid_size = dispatch_info["grid_size"]
|
|
row["Grid_Size"] = grid_size["x"] * grid_size["y"] * grid_size["z"]
|
|
|
|
wg = dispatch_info["workgroup_size"]
|
|
row["Workgroup_Size"] = wg["x"] * wg["y"] * wg["z"]
|
|
|
|
row["LDS_Per_Workgroup"] = d["lds_block_size_v"]
|
|
|
|
row["Scratch_Per_Workitem"] = kernel_symbols[kernel_id]["private_segment_size"]
|
|
row["Arch_VGPR"] = d["arch_vgpr_count"]
|
|
|
|
# TODO: Accum VGPR is missing from rocprofv3 output.
|
|
row["Accum_VGPR"] = 0
|
|
|
|
row["SGPR"] = d["sgpr_count"]
|
|
row["Wave_Size"] = agents[agent_id]["wave_front_size"]
|
|
|
|
row["Kernel_Name"] = kernel_symbols[kernel_id]["formatted_kernel_name"]
|
|
|
|
id = d["dispatch_data"]["correlation_id"]["internal"]
|
|
rec = dispatch_records[id]
|
|
|
|
row["Start_Timestamp"] = rec["start_timestamp"]
|
|
row["End_Timestamp"] = rec["end_timestamp"]
|
|
row["Correlation_ID"] = d["dispatch_data"]["correlation_id"]["external"]
|
|
|
|
# Get counters
|
|
ctrs = {}
|
|
|
|
records = d["records"]
|
|
for r in records:
|
|
ctr_id = r["counter_id"]["handle"]
|
|
value = r["value"]
|
|
|
|
name = counter_info[(agent_id, ctr_id)]["name"]
|
|
|
|
if name.endswith("_ACCUM"):
|
|
# It's an accumulate counter. Omniperf expects the accumulated value
|
|
# to be in SQ_ACCUM_PREV_HIRES.
|
|
name = "SQ_ACCUM_PREV_HIRES"
|
|
|
|
# Some counters appear multiple times and need to be summed
|
|
if name in ctrs:
|
|
ctrs[name] += value
|
|
else:
|
|
ctrs[name] = value
|
|
|
|
# Append counter values
|
|
for ctr, value in ctrs.items():
|
|
row[ctr] = value
|
|
|
|
# Add row to CSV data
|
|
for col_name, value in row.items():
|
|
if col_name not in csv_data:
|
|
csv_data[col_name] = []
|
|
|
|
csv_data[col_name].append(value)
|
|
|
|
df = pd.DataFrame(csv_data)
|
|
|
|
df.to_csv(csv_file_path, index=False)
|
|
|
|
|
|
def v3_counter_csv_to_v2_csv(counter_file, agent_info_filepath, converted_csv_file):
|
|
"""
|
|
Convert the counter file of csv output for a certain csv from rocprofv3 format to rocprfv2 format.
|
|
This function is not for use of other csv out file such as kernel trace file.
|
|
"""
|
|
pd_counter_collections = pd.read_csv(counter_file)
|
|
pd_agent_info = pd.read_csv(agent_info_filepath)
|
|
result = pd_counter_collections.pivot_table(
|
|
index=[
|
|
"Correlation_Id",
|
|
"Dispatch_Id",
|
|
"Agent_Id",
|
|
"Queue_Id",
|
|
"Process_Id",
|
|
"Thread_Id",
|
|
"Grid_Size",
|
|
"Kernel_Id",
|
|
"Kernel_Name",
|
|
"Workgroup_Size",
|
|
"LDS_Block_Size",
|
|
"Scratch_Size",
|
|
"VGPR_Count",
|
|
"SGPR_Count",
|
|
"Start_Timestamp",
|
|
"End_Timestamp",
|
|
],
|
|
columns="Counter_Name",
|
|
values="Counter_Value",
|
|
).reset_index()
|
|
|
|
# NB: Agent_Id is int in older rocporfv3, now switched to string with prefix "Agent ". We need to make sure handle both cases.
|
|
console_debug(
|
|
"The type of Agent ID from counter csv file is {}".format(
|
|
result["Agent_Id"].dtype
|
|
)
|
|
)
|
|
if result["Agent_Id"].dtype == "object":
|
|
try:
|
|
result["Agent_Id"] = (
|
|
result["Agent_Id"]
|
|
.apply(lambda x: int(re.search(r"Agent (\d+)", x).group(1)))
|
|
.astype("int64")
|
|
)
|
|
except Exception as e:
|
|
console_error(
|
|
'Parsing rocprofv3 csv output: Error of getting "Agent_Id", the error message "{}"'.format(
|
|
e
|
|
)
|
|
)
|
|
|
|
# Grab the Wave_Front_Size column from agent info
|
|
result = result.merge(
|
|
pd_agent_info[["Node_Id", "Wave_Front_Size"]],
|
|
left_on="Agent_Id",
|
|
right_on="Node_Id",
|
|
how="left",
|
|
)
|
|
|
|
# Map agent ID (Node_Id) to GPU_ID
|
|
gpu_id_map = {}
|
|
gpu_id = 0
|
|
for idx, row in pd_agent_info.iterrows():
|
|
if row["Agent_Type"] == "GPU":
|
|
agent_id = row["Node_Id"]
|
|
gpu_id_map[agent_id] = gpu_id
|
|
gpu_id = gpu_id + 1
|
|
|
|
# Update Agent_Id for each record to match GPU ID
|
|
for idx, row in result["Agent_Id"].items():
|
|
agent_id = result.at[idx, "Agent_Id"]
|
|
result.at[idx, "Agent_Id"] = gpu_id_map[agent_id]
|
|
|
|
# Accum_VGPR is currently missing in rocprofv3 output
|
|
result["Accum_VGPR"] = 0
|
|
|
|
# Drop the 'Node_Id' column if you don't need it in the final DataFrame
|
|
result.drop(columns="Node_Id", inplace=True)
|
|
result["Accum_VGPR"] = 0
|
|
|
|
name_mapping = {
|
|
"Dispatch_Id": "Dispatch_ID",
|
|
"Agent_Id": "GPU_ID",
|
|
"Queue_Id": "Queue_ID",
|
|
"Process_Id": "PID",
|
|
"Thread_Id": "TID",
|
|
"Grid_Size": "Grid_Size",
|
|
"Workgroup_Size": "Workgroup_Size",
|
|
"LDS_Block_Size": "LDS_Per_Workgroup",
|
|
"Scratch_Size": "Scratch_Per_Workitem",
|
|
"VGPR_Count": "Arch_VGPR",
|
|
# "":"Accum_VGPR",
|
|
"SGPR_Count": "SGPR",
|
|
"Wave_Front_Size": "Wave_Size",
|
|
"Kernel_Name": "Kernel_Name",
|
|
"Start_Timestamp": "Start_Timestamp",
|
|
"End_Timestamp": "End_Timestamp",
|
|
"Correlation_Id": "Correlation_ID",
|
|
"Kernel_Id": "Kernel_ID",
|
|
}
|
|
result.rename(columns=name_mapping, inplace=True)
|
|
|
|
index = [
|
|
"Dispatch_ID",
|
|
"GPU_ID",
|
|
"Queue_ID",
|
|
"PID",
|
|
"TID",
|
|
"Grid_Size",
|
|
"Workgroup_Size",
|
|
"LDS_Per_Workgroup",
|
|
"Scratch_Per_Workitem",
|
|
"Arch_VGPR",
|
|
"Accum_VGPR",
|
|
"SGPR",
|
|
"Wave_Size",
|
|
"Kernel_Name",
|
|
"Start_Timestamp",
|
|
"End_Timestamp",
|
|
"Correlation_ID",
|
|
"Kernel_ID",
|
|
]
|
|
|
|
remaining_column_names = [col for col in result.columns if col not in index]
|
|
index = index + remaining_column_names
|
|
result = result.reindex(columns=index)
|
|
|
|
# Rename the accumulate counter to SQ_ACCUM_PREV_HIRES.
|
|
for col in result.columns:
|
|
if col.endswith("_ACCUM"):
|
|
result.rename(columns={col: "SQ_ACCUM_PREV_HIRES"}, inplace=True)
|
|
|
|
result.to_csv(converted_csv_file, index=False)
|
|
|
|
|
|
def run_prof(
|
|
fname, profiler_options, workload_dir, mspec, loglevel, format_rocprof_output
|
|
):
|
|
time_0 = time.time()
|
|
fbase = path(fname).stem
|
|
|
|
console_debug("pmc file: %s" % path(fname).name)
|
|
|
|
path_counter_config_yaml = path(fname).with_suffix(".yaml")
|
|
# standard rocprof options
|
|
default_options = ["-i", fname]
|
|
options = default_options + profiler_options
|
|
if path_counter_config_yaml.exists():
|
|
options = ["-E", str(path_counter_config_yaml)] + options
|
|
|
|
# set required env var for mi300
|
|
new_env = None
|
|
if (
|
|
mspec.gpu_model.lower() == "mi300x_a0"
|
|
or mspec.gpu_model.lower() == "mi300x_a1"
|
|
or mspec.gpu_model.lower() == "mi300a_a0"
|
|
or mspec.gpu_model.lower() == "mi300a_a1"
|
|
):
|
|
new_env = os.environ.copy()
|
|
new_env["ROCPROFILER_INDIVIDUAL_XCC_MODE"] = "1"
|
|
|
|
is_timestamps = False
|
|
if path(fname).name == "timestamps.txt":
|
|
is_timestamps = True
|
|
time_1 = time.time()
|
|
|
|
console_debug("rocprof command: {}".format([rocprof_cmd] + options))
|
|
# profile the app
|
|
if new_env:
|
|
success, output = capture_subprocess_output(
|
|
[rocprof_cmd] + options, new_env=new_env, profileMode=True
|
|
)
|
|
else:
|
|
success, output = capture_subprocess_output(
|
|
[rocprof_cmd] + options, profileMode=True
|
|
)
|
|
|
|
time_2 = time.time()
|
|
console_debug(
|
|
"Finishing subprocess of fname {}, the time it takes was {} m {} sec ".format(
|
|
fname, int((time_2 - time_1) / 60), str((time_2 - time_1) % 60)
|
|
)
|
|
)
|
|
|
|
if not success:
|
|
if loglevel > logging.INFO:
|
|
for line in output.splitlines():
|
|
console_error(output, exit=False)
|
|
console_error("Profiling execution failed.")
|
|
|
|
results_files = []
|
|
|
|
if rocprof_cmd.endswith("v2"):
|
|
# rocprofv2 has separate csv files for each process
|
|
results_files = glob.glob(workload_dir + "/out/pmc_1/results_*.csv")
|
|
|
|
# Combine results into single CSV file
|
|
combined_results = pd.concat(
|
|
[pd.read_csv(f) for f in results_files], ignore_index=True
|
|
)
|
|
|
|
# Overwrite column to ensure unique IDs.
|
|
combined_results["Dispatch_ID"] = range(0, len(combined_results))
|
|
|
|
combined_results.to_csv(
|
|
workload_dir + "/out/pmc_1/results_" + fbase + ".csv", index=False
|
|
)
|
|
elif rocprof_cmd.endswith("v3"):
|
|
# rocprofv3 requires additional processing for each process
|
|
results_files = process_rocprofv3_output(
|
|
format_rocprof_output, workload_dir, is_timestamps
|
|
)
|
|
# kokkos trace output processing for --kokkos-trace
|
|
# TODO: as rocprofv3 --kokkos-trace feature improves, rocprof-compute should make updates accordingly
|
|
if "--kokkos-trace" in options:
|
|
console_debug(
|
|
"[run_prof] --kokkos-trace detected, handling *_marker_api_trace.csv outputs."
|
|
)
|
|
process_kokkos_trace_output(workload_dir, fbase)
|
|
# TODO: add hip trace output processing
|
|
|
|
# Combine results into single CSV file
|
|
if results_files:
|
|
combined_results = pd.concat(
|
|
[pd.read_csv(f) for f in results_files], ignore_index=True
|
|
)
|
|
else:
|
|
console_warning(
|
|
f"Cannot write results for {fbase}.csv due to no counter csv files generated."
|
|
)
|
|
return
|
|
|
|
# Overwrite column to ensure unique IDs.
|
|
combined_results["Dispatch_ID"] = range(0, len(combined_results))
|
|
|
|
combined_results.to_csv(
|
|
workload_dir + "/out/pmc_1/results_" + fbase + ".csv", index=False
|
|
)
|
|
|
|
if new_env and not using_v3() and not using_v1():
|
|
# flatten tcc for applicable mi300 input
|
|
f = path(workload_dir + "/out/pmc_1/results_" + fbase + ".csv")
|
|
xcds = total_xcds(mspec.gpu_model, mspec.compute_partition)
|
|
df = flatten_tcc_info_across_xcds(f, xcds, int(mspec._l2_banks))
|
|
df.to_csv(f, index=False)
|
|
|
|
if path(workload_dir + "/out").exists():
|
|
# copy and remove out directory if needed
|
|
shutil.copyfile(
|
|
workload_dir + "/out/pmc_1/results_" + fbase + ".csv",
|
|
workload_dir + "/" + fbase + ".csv",
|
|
)
|
|
# Remove temp directory
|
|
shutil.rmtree(workload_dir + "/" + "out")
|
|
|
|
# Standardize rocprof headers via overwrite
|
|
# {<key to remove>: <key to replace>}
|
|
output_headers = {
|
|
# ROCm-6.1.0 specific csv headers
|
|
"KernelName": "Kernel_Name",
|
|
"Index": "Dispatch_ID",
|
|
"grd": "Grid_Size",
|
|
"gpu-id": "GPU_ID",
|
|
"wgr": "Workgroup_Size",
|
|
"lds": "LDS_Per_Workgroup",
|
|
"scr": "Scratch_Per_Workitem",
|
|
"sgpr": "SGPR",
|
|
"arch_vgpr": "Arch_VGPR",
|
|
"accum_vgpr": "Accum_VGPR",
|
|
"BeginNs": "Start_Timestamp",
|
|
"EndNs": "End_Timestamp",
|
|
# ROCm-6.0.0 specific csv headers
|
|
"GRD": "Grid_Size",
|
|
"WGR": "Workgroup_Size",
|
|
"LDS": "LDS_Per_Workgroup",
|
|
"SCR": "Scratch_Per_Workitem",
|
|
"ACCUM_VGPR": "Accum_VGPR",
|
|
}
|
|
df = pd.read_csv(workload_dir + "/" + fbase + ".csv")
|
|
df.rename(columns=output_headers, inplace=True)
|
|
df.to_csv(workload_dir + "/" + fbase + ".csv", index=False)
|
|
|
|
|
|
def process_rocprofv3_output(rocprof_output, workload_dir, is_timestamps):
|
|
"""
|
|
rocprofv3 specific output processing.
|
|
takes care of json or csv formats, for csv format, additional processing is performed.
|
|
"""
|
|
results_files_csv = {}
|
|
|
|
if rocprof_output == "json":
|
|
results_files_json = glob.glob(workload_dir + "/out/pmc_1/*/*.json")
|
|
|
|
for json_file in results_files_json:
|
|
csv_file = pathlib.Path(json_file).with_suffix(".csv")
|
|
v3_json_to_csv(json_file, csv_file)
|
|
results_files_csv = glob.glob(workload_dir + "/out/pmc_1/*/*.csv")
|
|
|
|
elif rocprof_output == "csv":
|
|
counter_info_csvs = glob.glob(
|
|
workload_dir + "/out/pmc_1/*/*_counter_collection.csv"
|
|
)
|
|
existing_counter_files_csv = [d for d in counter_info_csvs if path(d).is_file()]
|
|
|
|
if existing_counter_files_csv:
|
|
for counter_file in existing_counter_files_csv:
|
|
counter_path = path(counter_file)
|
|
current_dir = counter_path.parent
|
|
|
|
agent_info_filepath = current_dir / counter_path.name.replace(
|
|
"_counter_collection", "_agent_info"
|
|
)
|
|
|
|
if not agent_info_filepath.is_file():
|
|
raise ValueError(
|
|
'{} has no coresponding "agent info" file'.format(counter_file)
|
|
)
|
|
|
|
converted_csv_file = current_dir / counter_path.name.replace(
|
|
"_counter_collection", "_converted"
|
|
)
|
|
|
|
try:
|
|
v3_counter_csv_to_v2_csv(
|
|
counter_file, str(agent_info_filepath), str(converted_csv_file)
|
|
)
|
|
except Exception as e:
|
|
console_warning(
|
|
f"Error converting {counter_file} from v3 to v2 csv: {e}"
|
|
)
|
|
return []
|
|
|
|
results_files_csv = glob.glob(workload_dir + "/out/pmc_1/*/*_converted.csv")
|
|
elif is_timestamps:
|
|
# when the input is timestamps, we know counter csv file is not generated and will instead parse kernel trace file
|
|
results_files_csv = glob.glob(
|
|
workload_dir + "/out/pmc_1/*/*_kernel_trace.csv"
|
|
)
|
|
else:
|
|
# when the input is not for timestamps, and counter csv file is not generated, we assume failed rocprof run and will completely bypass the file generation and merging for current pmc
|
|
results_files_csv = []
|
|
console_warning("No counter csv files generated, rocprofv3 run failed!!!")
|
|
|
|
else:
|
|
console_error("The output file of rocprofv3 can only support json or csv!!!")
|
|
|
|
return results_files_csv
|
|
|
|
|
|
def process_kokkos_trace_output(workload_dir, fbase):
|
|
# marker api trace csv files are generated for each process
|
|
marker_api_trace_csvs = glob.glob(
|
|
workload_dir + "/out/pmc_1/*/*_marker_api_trace.csv"
|
|
)
|
|
existing_marker_files_csv = [d for d in marker_api_trace_csvs if path(d).is_file()]
|
|
|
|
# concate and output marker api trace info
|
|
combined_results = pd.concat(
|
|
[pd.read_csv(f) for f in existing_marker_files_csv], ignore_index=True
|
|
)
|
|
|
|
combined_results.to_csv(
|
|
workload_dir + "/out/pmc_1/results_" + fbase + "_marker_api_trace.csv",
|
|
index=False,
|
|
)
|
|
|
|
if path(workload_dir + "/out").exists():
|
|
shutil.copyfile(
|
|
workload_dir + "/out/pmc_1/results_" + fbase + "_marker_api_trace.csv",
|
|
workload_dir + "/" + fbase + "_marker_api_trace.csv",
|
|
)
|
|
|
|
|
|
def replace_timestamps(workload_dir):
|
|
df_stamps = pd.read_csv(workload_dir + "/timestamps.csv")
|
|
if "Start_Timestamp" in df_stamps.columns and "End_Timestamp" in df_stamps.columns:
|
|
# Update timestamps for all *.csv output files
|
|
for fname in glob.glob(workload_dir + "/" + "*.csv"):
|
|
if path(fname).name != "sysinfo.csv":
|
|
df_pmc_perf = pd.read_csv(fname)
|
|
|
|
df_pmc_perf["Start_Timestamp"] = df_stamps["Start_Timestamp"]
|
|
df_pmc_perf["End_Timestamp"] = df_stamps["End_Timestamp"]
|
|
df_pmc_perf.to_csv(fname, index=False)
|
|
else:
|
|
console_warning(
|
|
"Incomplete profiling data detected. Unable to update timestamps.\n"
|
|
)
|
|
|
|
|
|
def gen_sysinfo(
|
|
workload_name, workload_dir, ip_blocks, app_cmd, skip_roof, roof_only, mspec, soc
|
|
):
|
|
console_debug("[gen_sysinfo]")
|
|
df = mspec.get_class_members()
|
|
|
|
# Append workload information to machine specs
|
|
df["command"] = app_cmd
|
|
df["workload_name"] = workload_name
|
|
|
|
blocks = []
|
|
if not ip_blocks:
|
|
t = ["SQ", "LDS", "SQC", "TA", "TD", "TCP", "TCC", "SPI", "CPC", "CPF"]
|
|
blocks += t
|
|
else:
|
|
blocks += ip_blocks
|
|
if hasattr(soc, "roofline_obj") and (not skip_roof):
|
|
blocks.append("roofline")
|
|
df["ip_blocks"] = "|".join(blocks)
|
|
|
|
# Save csv
|
|
df.to_csv(workload_dir + "/" + "sysinfo.csv", index=False)
|
|
|
|
|
|
def detect_roofline(mspec):
|
|
from utils import specs
|
|
|
|
rocm_ver = mspec.rocm_version[:1]
|
|
|
|
os_release = path("/etc/os-release").read_text()
|
|
ubuntu_distro = specs.search(r'VERSION_ID="(.*?)"', os_release)
|
|
rhel_distro = specs.search(r'PLATFORM_ID="(.*?)"', os_release)
|
|
sles_distro = specs.search(r'VERSION_ID="(.*?)"', os_release)
|
|
|
|
if "ROOFLINE_BIN" in os.environ.keys():
|
|
rooflineBinary = os.environ["ROOFLINE_BIN"]
|
|
if path(rooflineBinary).exists():
|
|
console_warning("roofline", "Detected user-supplied binary")
|
|
return {
|
|
"rocm_ver": "override",
|
|
"distro": "override",
|
|
"path": rooflineBinary,
|
|
}
|
|
else:
|
|
msg = "user-supplied path to binary not accessible"
|
|
msg += "--> ROOFLINE_BIN = %s\n" % target_binary
|
|
console_error("roofline", msg)
|
|
elif (
|
|
rhel_distro == "platform:el8"
|
|
or rhel_distro == "platform:el9"
|
|
or rhel_distro == "platform:al8"
|
|
):
|
|
# Must be a valid RHEL machine
|
|
distro = "platform:el8"
|
|
elif (
|
|
(type(sles_distro) == str and len(sles_distro) >= 3)
|
|
and sles_distro[:2] == "15" # confirm string and len
|
|
and int(sles_distro[3]) >= 3 # SLES15 and SP >= 3
|
|
):
|
|
# Must be a valid SLES machine
|
|
# Use SP3 binary for all forward compatible service pack versions
|
|
distro = "15.3"
|
|
elif ubuntu_distro == "20.04" or ubuntu_distro == "22.04" or ubuntu_distro == "24.04":
|
|
# Must be a valid Ubuntu machine
|
|
distro = ubuntu_distro
|
|
else:
|
|
console_error("roofline", "Cannot find a valid binary for your operating system")
|
|
|
|
target_binary = {"rocm_ver": rocm_ver, "distro": distro}
|
|
return target_binary
|
|
|
|
|
|
def run_rocscope(args, fname):
|
|
# profile the app
|
|
if args.use_rocscope == True:
|
|
result = shutil.which("rocscope")
|
|
if result:
|
|
rs_cmd = [
|
|
result.stdout.decode("ascii").strip(),
|
|
"metrics",
|
|
"-p",
|
|
args.path,
|
|
"-n",
|
|
args.name,
|
|
"-t",
|
|
fname,
|
|
"--",
|
|
]
|
|
for i in args.remaining.split():
|
|
rs_cmd.append(i)
|
|
console_log(rs_cmd)
|
|
success, output = capture_subprocess_output(rs_cmd)
|
|
if not success:
|
|
console_error(result.stderr.decode("ascii"))
|
|
|
|
|
|
def mibench(args, mspec):
|
|
"""Run roofline microbenchmark to generate peek BW and FLOP measurements."""
|
|
console_log("roofline", "No roofline data found. Generating...")
|
|
|
|
distro_map = {
|
|
"platform:el8": "rhel8",
|
|
"15.3": "sles15sp5",
|
|
"20.04": "ubuntu20_04",
|
|
"22.04": "ubuntu20_04",
|
|
"24.04": "ubuntu20_04",
|
|
}
|
|
|
|
binary_paths = []
|
|
|
|
target_binary = detect_roofline(mspec)
|
|
if target_binary["rocm_ver"] == "override":
|
|
binary_paths.append(target_binary["path"])
|
|
else:
|
|
# check two potential locations for roofline binaries due to differences in
|
|
# development usage vs formal install
|
|
potential_paths = [
|
|
"%s/utils/rooflines/roofline" % config.rocprof_compute_home,
|
|
"%s/bin/roofline" % config.rocprof_compute_home.parent.parent,
|
|
]
|
|
|
|
for dir in potential_paths:
|
|
path_to_binary = (
|
|
dir
|
|
+ "-"
|
|
+ distro_map[target_binary["distro"]]
|
|
+ "-"
|
|
+ mspec.gpu_series.lower()
|
|
+ "-rocm"
|
|
+ target_binary["rocm_ver"]
|
|
)
|
|
binary_paths.append(path_to_binary)
|
|
|
|
# Distro is valid but cant find rocm ver
|
|
found = False
|
|
for path in binary_paths:
|
|
if pathlib.Path(path).exists():
|
|
found = True
|
|
path_to_binary = path
|
|
break
|
|
|
|
if not found:
|
|
console_error("roofline", "Unable to locate expected binary (%s)." % binary_paths)
|
|
|
|
my_args = [
|
|
path_to_binary,
|
|
"-o",
|
|
args.path + "/" + "roofline.csv",
|
|
"-d",
|
|
str(args.device),
|
|
]
|
|
if args.quiet:
|
|
my_args += "--quiet"
|
|
subprocess.run(
|
|
my_args,
|
|
check=True,
|
|
)
|
|
|
|
|
|
def flatten_tcc_info_across_xcds(file, xcds, tcc_channel_per_xcd):
|
|
"""
|
|
Flatten TCC per channel counters across all XCDs in partition.
|
|
NB: This func highly depends on the default behavior of rocprofv2 on MI300,
|
|
which might be broken anytime in the future!
|
|
"""
|
|
df_orig = pd.read_csv(file)
|
|
# display(df_orig.info)
|
|
|
|
### prepare column headers
|
|
tcc_cols_orig = []
|
|
non_tcc_cols_orig = []
|
|
for c in df_orig.columns.to_list():
|
|
if "TCC" in c:
|
|
tcc_cols_orig.append(c)
|
|
else:
|
|
non_tcc_cols_orig.append(c)
|
|
# print(tcc_cols_orig)
|
|
|
|
cols = non_tcc_cols_orig
|
|
tcc_cols_in_group = {}
|
|
for i in range(0, xcds):
|
|
tcc_cols_in_group[i] = []
|
|
|
|
for col in tcc_cols_orig:
|
|
for i in range(0, xcds):
|
|
# filter the channel index only
|
|
p = re.compile(r"\[(\d+)\]")
|
|
# pick up the 1st element only
|
|
r = (
|
|
lambda match: "["
|
|
+ str(int(match.group(1)) + i * tcc_channel_per_xcd)
|
|
+ "]"
|
|
)
|
|
tcc_cols_in_group[i].append(re.sub(pattern=p, repl=r, string=col))
|
|
|
|
for i in range(0, xcds):
|
|
# print(tcc_cols_in_group[i])
|
|
cols += tcc_cols_in_group[i]
|
|
# print(cols)
|
|
df = pd.DataFrame(columns=cols)
|
|
|
|
### Rearrange data with extended column names
|
|
|
|
# print(len(df_orig.index))
|
|
for idx in range(0, len(df_orig.index), xcds):
|
|
# assume the front none TCC columns are the same for all XCCs
|
|
df_non_tcc = df_orig.iloc[idx].filter(regex=r"^(?!.*TCC).*$")
|
|
# display(df_non_tcc)
|
|
flatten_list = df_non_tcc.tolist()
|
|
|
|
# extract all tcc from one dispatch
|
|
# NB: assuming default contiguous order might not be safe!
|
|
df_tcc_all = df_orig.iloc[idx : (idx + xcds)].filter(regex="TCC")
|
|
# display(df_tcc_all)
|
|
|
|
for idx, row in df_tcc_all.iterrows():
|
|
flatten_list += row.tolist()
|
|
# print(len(df.index), len(flatten_list), len(df.columns), flatten_list)
|
|
# NB: It is not the best perf to append a row once a time
|
|
df.loc[len(df.index)] = flatten_list
|
|
|
|
return df
|
|
|
|
|
|
def total_xcds(gpu_model, compute_partition):
|
|
"""
|
|
Returns the number of xcds for a gpu model and compute_partition pair.
|
|
"""
|
|
|
|
# For mi300 chips, return result from mi_gpu_spec
|
|
result = get_mi300_num_xcds(gpu_model, compute_partition)
|
|
if result:
|
|
return result
|
|
|
|
# For other systems, use manual check
|
|
# check MI300 has a valid compute partition
|
|
mi300a_model = ["mi300a_a0", "mi300a_a1"]
|
|
mi300x_model = ["mi300x_a0", "mi300x_a1"]
|
|
mi308x_model = ["mi308x"]
|
|
if (
|
|
gpu_model.lower() in mi300a_model + mi300x_model + mi308x_model
|
|
and compute_partition == "NA"
|
|
):
|
|
console_error("Invalid compute partition found for {}".format(gpu_model))
|
|
|
|
if gpu_model.lower() not in mi300a_model + mi300x_model + mi308x_model:
|
|
return 1
|
|
# from the whitepaper
|
|
# https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/white-papers/amd-cdna-3-white-paper.pdf
|
|
if compute_partition.lower() == "spx":
|
|
if gpu_model.lower() in mi300a_model:
|
|
return 6
|
|
if gpu_model.lower() in mi300x_model:
|
|
return 8
|
|
if gpu_model.lower() in mi308x_model:
|
|
return 4
|
|
if compute_partition.lower() == "tpx":
|
|
if gpu_model.lower() in mi300a_model:
|
|
return 2
|
|
if compute_partition.lower() == "dpx":
|
|
if gpu_model.lower() in mi300x_model:
|
|
return 4
|
|
if gpu_model.lower() in mi308x_model:
|
|
return 2
|
|
if compute_partition.lower() == "qpx":
|
|
if gpu_model.lower() in mi300x_model:
|
|
return 2
|
|
if compute_partition.lower() == "cpx":
|
|
if gpu_model.lower() in mi300x_model:
|
|
return 1
|
|
if gpu_model.lower() in mi308x_model:
|
|
return 1
|
|
# TODO implement other archs here as needed
|
|
console_error(
|
|
"Unknown compute partition / arch found for {} / {}".format(
|
|
compute_partition, gpu_model
|
|
)
|
|
)
|
|
|
|
|
|
def get_submodules(package_name):
|
|
"""List all submodules for a target package"""
|
|
import importlib
|
|
import pkgutil
|
|
|
|
submodules = []
|
|
|
|
# walk all submodules in target package
|
|
package = importlib.import_module(package_name)
|
|
for _, name, _ in pkgutil.walk_packages(package.__path__):
|
|
pretty_name = name.split("_", 1)[1].replace("_", "")
|
|
# ignore base submodule, add all other
|
|
if pretty_name != "base":
|
|
submodules.append(pretty_name)
|
|
|
|
return submodules
|
|
|
|
|
|
def is_workload_empty(path):
|
|
"""Peek workload directory to verify valid profiling output"""
|
|
pmc_perf_path = path + "/pmc_perf.csv"
|
|
if pathlib.Path(pmc_perf_path).is_file():
|
|
temp_df = pd.read_csv(pmc_perf_path)
|
|
if temp_df.dropna().empty:
|
|
console_error(
|
|
"profiling"
|
|
"Found empty cells in %s.\nProfiling data could be corrupt."
|
|
% pmc_perf_path
|
|
)
|
|
|
|
else:
|
|
console_error("profiling", "Cannot find pmc_perf.csv in %s" % path)
|
|
|
|
|
|
def print_status(msg):
|
|
msg_length = len(msg)
|
|
|
|
console_log("")
|
|
console_log("~" * (msg_length + 1))
|
|
console_log(msg)
|
|
console_log("~" * (msg_length + 1))
|
|
console_log("")
|
|
|
|
|
|
def set_locale_encoding():
|
|
try:
|
|
# Attempt to set the locale to 'C.UTF-8'
|
|
locale.setlocale(locale.LC_ALL, "C.UTF-8")
|
|
except locale.Error:
|
|
# If 'C.UTF-8' is not available, check if the current locale is UTF-8 based
|
|
current_locale = locale.getdefaultlocale()
|
|
if current_locale and "UTF-8" in current_locale[1]:
|
|
try:
|
|
locale.setlocale(locale.LC_ALL, current_locale[0])
|
|
except locale.Error as error:
|
|
console_error(
|
|
"Failed to set locale to the current UTF-8-based locale.",
|
|
exit=False,
|
|
)
|
|
console_error(error)
|
|
else:
|
|
console_error(
|
|
"Please ensure that a UTF-8-based locale is available on your system.",
|
|
exit=False,
|
|
)
|
|
|
|
|
|
def reverse_multi_index_df_pmc(final_df):
|
|
"""
|
|
Util function to decompose multi-index dataframe.
|
|
"""
|
|
# Check if the columns have more than one level
|
|
if len(final_df.columns.levels) < 2:
|
|
raise ValueError("Input DataFrame does not have a multi-index column.")
|
|
|
|
# Extract the first level of the MultiIndex columns (the file names)
|
|
coll_levels = final_df.columns.get_level_values(0).unique().tolist()
|
|
|
|
# Initialize the list of DataFrames
|
|
dfs = []
|
|
|
|
# Loop through each 'coll_level' and rebuild the DataFrames
|
|
for level in coll_levels:
|
|
# Select columns that belong to the current 'coll_level'
|
|
columns_for_level = final_df.xs(level, axis=1, level=0)
|
|
|
|
# Append the DataFrame for this level
|
|
dfs.append(columns_for_level)
|
|
|
|
# Return the list of DataFrames and the column levels
|
|
return dfs, coll_levels
|
|
|
|
|
|
def merge_counters_spatial_multiplex(df_multi_index):
|
|
"""
|
|
For spatial multiplexing, this merges counter values for the same kernel that runs on different devices. For time stamp, start time stamp will use median while for end time stamp, it will be equal to the summation between median start stamp and median delta time.
|
|
"""
|
|
non_counter_column_index = [
|
|
"Dispatch_ID",
|
|
"GPU_ID",
|
|
"Queue_ID",
|
|
"PID",
|
|
"TID",
|
|
"Grid_Size",
|
|
"Workgroup_Size",
|
|
"LDS_Per_Workgroup",
|
|
"Scratch_Per_Workitem",
|
|
"Arch_VGPR",
|
|
"Accum_VGPR",
|
|
"SGPR",
|
|
"Wave_Size",
|
|
"Kernel_Name",
|
|
"Start_Timestamp",
|
|
"End_Timestamp",
|
|
"Correlation_ID",
|
|
"Kernel_ID",
|
|
"Node",
|
|
]
|
|
|
|
expired_column_index = [
|
|
"Node",
|
|
"PID",
|
|
"TID",
|
|
"Queue_ID",
|
|
]
|
|
|
|
result_dfs = []
|
|
|
|
# TODO: will need optimize to avoid this convertion to single index format and do merge directly on multi-index dataframe
|
|
dfs, coll_levels = reverse_multi_index_df_pmc(df_multi_index)
|
|
|
|
for df in dfs:
|
|
kernel_name_column_name = "Kernel_Name"
|
|
if not "Kernel_Name" in df and "Name" in df:
|
|
kernel_name_column_name = "Name"
|
|
|
|
# Find the values in Kernel_Name that occur more than once
|
|
kernel_single_occurances = df[kernel_name_column_name].value_counts().index
|
|
|
|
# Define a list to store the merged rows
|
|
result_data = []
|
|
|
|
for kernel_name in kernel_single_occurances:
|
|
# Get all rows for the current kernel_name
|
|
group = df[df[kernel_name_column_name] == kernel_name]
|
|
|
|
# Create a dictionary to store the merged row for the current group
|
|
merged_row = {}
|
|
|
|
# Process non-counter columns
|
|
for col in [
|
|
col for col in non_counter_column_index if col not in expired_column_index
|
|
]:
|
|
if col == "Start_Timestamp":
|
|
# For Start_Timestamp, take the median
|
|
merged_row[col] = group["Start_Timestamp"].median()
|
|
elif col == "End_Timestamp":
|
|
# For End_Timestamp, calculate the median delta time
|
|
delta_time = group["End_Timestamp"] - group["Start_Timestamp"]
|
|
median_delta_time = delta_time.median()
|
|
merged_row[col] = merged_row["Start_Timestamp"] + median_delta_time
|
|
else:
|
|
# For other non-counter columns, take the first occurrence (0th row)
|
|
merged_row[col] = group.iloc[0][col]
|
|
|
|
# Process counter columns (assumed to be all columns not in non_counter_column_index)
|
|
counter_columns = [
|
|
col for col in group.columns if col not in non_counter_column_index
|
|
]
|
|
for counter_col in counter_columns:
|
|
# for counter columns, take the first non-none (or non-nan) value
|
|
current_valid_counter_group = group[group[counter_col].notna()]
|
|
first_valid_value = (
|
|
current_valid_counter_group.iloc[0][counter_col]
|
|
if len(current_valid_counter_group) > 0
|
|
else None
|
|
)
|
|
merged_row[counter_col] = first_valid_value
|
|
|
|
# Append the merged row to the result list
|
|
result_data.append(merged_row)
|
|
|
|
# Create a new DataFrame from the merged rows
|
|
result_dfs.append(pd.DataFrame(result_data))
|
|
|
|
final_df = pd.concat(result_dfs, keys=coll_levels, axis=1, copy=False)
|
|
return final_df
|
|
|
|
|
|
def convert_metric_id_to_panel_idx(metric_id):
|
|
# "4.02" -> 402
|
|
# "4.23" -> 423
|
|
# "4" -> 400
|
|
tokens = metric_id.split(".")
|
|
if len(tokens) == 1:
|
|
return int(tokens[0]) * 100
|
|
elif len(tokens) == 2:
|
|
return int(tokens[0]) * 100 + int(tokens[1])
|
|
else:
|
|
raise Exception(f"Invalid metric id: {metric_id}")
|