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[rocprofiler-compute] Add database output format to analyze mode (#748)

Forráskód böngészése 
Analysis data dump

* Add `--output-format` and `--output-name` option to analyze mode

* Remove `--output` and `-save-dfs` option to analyze mode

* Add documentation on `rocpd` output format and analysis database file

* Create sqlite3 database using object relation mapping (ORM) provided
  by sqlalchemy library

* Fix metrics config to remove metrics marked as `null`, fix `Unit` header, add
  missing `title`

* Add test cases to ensure analysis data dump work
This commit is contained in:
vedithal-amd
2025-08-26 14:15:05 -04:00
committed by GitHub
szülő 09cfa97156
commit 323d06c79c
41 fájl változott, egészen pontosan 1130 új sor hozzáadva és 117 régi sor törölve
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projects/rocprofiler-compute/CHANGELOG.md
+8
Fájl megtekintése
@@ -21,6 +21,8 @@ Full documentation for ROCm Compute Profiler is available at [https://rocm.docs.
* Added interactive metric descriptions in TUI analyze mode
* users can now left click on any metric cell to view detailed descriptions in the dedicated `METRIC DESCRIPTION` tab
* Add support for analysis report output as a sqlite database using ``--output-format db`` analysis mode option
### Changed
* Add notice for change in default output format to `rocpd` in a future release
@@ -100,6 +102,12 @@ Full documentation for ROCm Compute Profiler is available at [https://rocm.docs.
* L1I-L2 Bandwidth
* sL1D-L2 BW
* Analysis output:
* Replace `-o / --output` analyze mode option with `--output-format` and `--output-name`
* Add ``--output-format`` analysis mode option to select the output format of the analysis report.
* Add ``--output-name`` analysis mode option to override the default file/folder name.
* Replace `--save-dfs` analyze mode option with `--output-format csv`
### Resolved issues
* Fixed not detecting memory clock issue when using amd-smi
projects/rocprofiler-compute/docker/Dockerfile.doctest
+2 -1
Fájl megtekintése
@@ -22,6 +22,7 @@ RUN git config --global --add safe.directory /app
# Install any dependencies specified in requirements.txt
# Run interactive bash shell
CMD ["/bin/bash", "-c", "\
python3 -m pip install -r docs/sphinx/requirements.txt \
cd /app/projects/rocprofiler-compute \
&& python3 -m pip install -r docs/sphinx/requirements.txt \
&& exec /bin/bash \
"]
projects/rocprofiler-compute/docker/docker-compose.doctest.yml
+3 -3
Fájl megtekintése
@@ -1,9 +1,9 @@
services:
doctest: # service name
build:
context: ../
dockerfile: docker/Dockerfile.doctest
context: ../../../
dockerfile: projects/rocprofiler-compute/docker/Dockerfile.doctest
volumes:
- ../:/app
- ../../../:/app
tty: true
stdin_open: true
projects/rocprofiler-compute/docs/conf.py
+1 -1
Fájl megtekintése
@@ -213,4 +213,4 @@ extlinks = {
}
# Uncomment if facing rate limit exceed issue with local build
external_projects_remote_repository = ""
# external_projects_remote_repository = ""
projects/rocprofiler-compute/docs/data/analyze/analysis_data_dump_schema.png
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projects/rocprofiler-compute/docs/data/analyze/analysis_data_dump_views.png
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projects/rocprofiler-compute/docs/how-to/analyze/cli.rst
+102
Fájl megtekintése
@@ -318,10 +318,12 @@ Per-kernel roofline analysis
When analyzing specific kernels, the roofline analysis provides detailed metrics for each filtered kernel:
.. code-block:: shell-session
$ rocprof-compute analyze -p workloads/vcopy/MI200/ -k 0 -b 4
This generates enhanced roofline output showing per-kernel performance rates and arithmetic intensity calculations:
.. code-block:: text
================================================================================
4. Roofline
================================================================================
@@ -372,6 +374,7 @@ Per-kernel roofline analysis
Analyze multiple kernels for comparison:
.. code-block:: shell-session
$ rocprof-compute analyze -p workloads/vcopy/MI200/ -k 0 1 2 -b 4
Baseline comparison
@@ -384,3 +387,102 @@ Baseline comparison
.. code-block:: shell
rocprof-compute analyze -p workload1/path/ -k 0 -p workload2/path/ -k 1
Analysis output format
======================
Use the ``--output-format <format>`` analyze mode option to specify the output format of the
analysis report. Supported formats are ``stdout``, ``txt``, ``csv``, and ``db``. The default output
format is ``stdout``.
* ``stdout`` format:
* Print analysis report to the terminal.
* NOTE: This option will not generate any file or folder.
* ``txt`` format:
* Generate a file named ``rocprof_compute_<uuid>.txt`` in the current working directory.
* This file contains the entire analysis report as printed on the terminal.
* This is useful in case of searching across long analysis reports.
* NOTE: This option will disable output of analysis report to terminal.
* ``csv`` format:
* Generate a folder named ``rocprof_compute_<uuid>`` in the current working directory.
* This folder contains multiple csv files representing the data in each metric table in the analysis report.
* This is useful for further programmatic analysis of analysis reports.
* NOTE: This will print the analysis report to the terminal as well.
* ``db`` format:
* NOTE: This only works when provided workload paths are created using ``--format-rocprof-output rocpd`` profile mode option.
* Generate a file named ``rocprof_compute_<uuid>.db`` in the current working directory.
* This is a SQLite database file containing all the data in the analysis report structured according to :ref:`analysis database schema <analysis-database>`.
* This is useful for further programmatic analysis of analysis reports.
* NOTE: This option will disable output of analysis report to terminal.
Default file/folder name ``rocprofiler_compute_<uuid>`` can be overriden using ``--output-name <name>`` analyze mode option.
.. _analysis-database:
Analysis database schema
========================
Analysis database tables
.. image:: ../../data/analyze/analysis_data_dump_schema.png
:align: center
:alt: Analysis database tables
Analysis database views
.. image:: ../../data/analyze/analysis_data_dump_views.png
:align: center
:alt: Analysis database views
Analysis database example
.. note::
Some metrics cannot be calculated when corresponding counters are missing as shown in the warnings below
.. note::
It is possible to merge the analysis data dump for multiple workload folders (resulting from multiple profiles) by repeating ``-p`` option for each workload
.. code-block:: shell-session
$ rocprof-compute analyze --verbose --db test -p workloads/vmem/MI300X_A1 -p workloads/vmem1/MI300X_A1
DEBUG Execution mode = analyze
__ _
_ __ ___ ___ _ __ _ __ ___ / _| ___ ___ _ __ ___ _ __ _ _| |_ ___
| '__/ _ \ / __| '_ \| '__/ _ \| |_ _____ / __/ _ \| '_ ` _ \| '_ \| | | | __/ _ \
| | | (_) | (__| |_) | | | (_) | _|_____| (_| (_) | | | | | | |_) | |_| | || __/
|_| \___/ \___| .__/|_| \___/|_| \___\___/|_| |_| |_| .__/ \__,_|\__\___|
|_| |_|
INFO Analysis mode = db
DEBUG [omnisoc init]
DEBUG [omnisoc init]
DEBUG [analysis] prepping to do some analysis
INFO [analysis] deriving rocprofiler-compute metrics...
WARNING Roofline ceilings not found for /app/projects/rocprofiler-compute/workloads/vmem/MI300X_A1.
WARNING Roofline ceilings not found for /app/projects/rocprofiler-compute/workloads/vmem1/MI300X_A1.
WARNING PC sampling data not found for /app/projects/rocprofiler-compute/workloads/vmem/MI300X_A1.
WARNING PC sampling data not found for /app/projects/rocprofiler-compute/workloads/vmem1/MI300X_A1.
DEBUG Collected dispatch data
DEBUG Applied analysis mode filters
DEBUG Calculated dispatch data
DEBUG Collected metrics data
WARNING Failed to evaluate expression for 3.1.25 - Value: to_round(to_avg(
(pmc_df.get("TCP_TCP_LATENCY_sum") / pmc_df.get("TCP_TA_TCP_STATE_READ_sum")).where((pmc_df.get("TCP_TA_TCP_STATE_READ_sum") != 0), None)), 0) - unsupported operand type(s) for /: 'NoneType' and 'float'
WARNING Failed to evaluate expression for 3.1.39 - Value: to_round((to_avg(
(pmc_df.get("pmc_perf_ACCUM") / pmc_df.get("SQC_ICACHE_REQ")).where((pmc_df.get("SQC_ICACHE_REQ") != 0), None)) * 100), 0) - unsupported operand type(s) for /: 'NoneType' and 'float'
WARNING Failed to evaluate expression for 3.1.25 - Value: to_round(to_avg(
(pmc_df.get("TCP_TCP_LATENCY_sum") / pmc_df.get("TCP_TA_TCP_STATE_READ_sum")).where((pmc_df.get("TCP_TA_TCP_STATE_READ_sum") != 0), None)), 0) - unsupported operand type(s) for /: 'NoneType' and 'float'
WARNING Failed to evaluate expression for 3.1.39 - Value: to_round((to_avg(
(pmc_df.get("pmc_perf_ACCUM") / pmc_df.get("SQC_ICACHE_REQ")).where((pmc_df.get("SQC_ICACHE_REQ") != 0), None)) * 100), 0) - unsupported operand type(s) for /: 'NoneType' and 'float'
DEBUG Calculated metric values
DEBUG Calculated roofline data points
DEBUG [analysis] generating analysis
DEBUG SQLite database initialized with name: test.db
DEBUG Initialized database: test.db
DEBUG Completed writing database
projects/rocprofiler-compute/docs/how-to/analyze/mode.rst
+1 -1
Fájl megtekintése
@@ -22,7 +22,7 @@ options.
* :doc:`cli`
* :doc:`grafana-gui`
* :doc:`standalone-gui`
* :doc:`text-based user interface (TUI)`
* :doc:`tui`
.. note::
projects/rocprofiler-compute/docs/how-to/profile/mode.rst
+35
Fájl megtekintése
@@ -24,6 +24,15 @@ Profiling with ROCm Compute Profiler yields the following benefits.
* :ref:`Automate counter collection <profiling-routine>`: ROCm Compute Profiler handles all
of your profiling via pre-configured input files.
* :ref:`Profiling output format <profiling-output-format>`: ROCm Compute Profile can adjust the
output format of underlying rocprof tool which changes the output format of raw performance
counter data in the workload folder created during profiling. Supported output formats are
``json``, ``csv``, and ``rocpd``. The default output format is ``csv``.
.. note::
The default output format will be changed to ``rocpd`` in a future release of ROCm Compute Profiler.
* :ref:`Filtering <filtering>`: Apply runtime filters to speed up the profiling
process.
@@ -217,6 +226,32 @@ an Instinct MI210 vs an Instinct MI250.
-rw-r--r-- 1 auser agroup 650 Mar 1 15:15 sysinfo.csv
-rw-r--r-- 1 auser agroup 399 Mar 1 15:15 timestamps.csv
.. _profiling-output-format:
Profiling output format
-----------------------
Use the ``--format-rocprof-output <format>`` profile mode option to specify the output format
of the underlying ``rocprof`` tool. The following formats are supported:
* ``csv`` format:
* Ask underlying rocprof tool to dump raw performance counter data in csv format.
* The generated csv files across multiple runs of rocprof are processed and dumped into the workload directory as csv files.
* Multiple csv files are merged into single pmc_perf.csv file in workload directory.
* ``json`` format:
* Ask underlying rocprof tool to dump raw performance counter data in json format.
* The generated json files across multiple runs of rocprof are processed and dumped into the workload directory as csv files.
* Multiple csv files are merged into single pmc_perf.csv file in workload directory.
* ``rocpd`` format:
* Ask underlying rocprof tool to dump raw performance counter data in rocpd format.
* Multiple ``rocpd`` database files containding counter collection data are merged into a single csv under the workload folder.
The database files are then removed.
* Use ``--retain-rocpd-output`` profile mode option to preserve the ``rocpd`` database(s) in the workload folder.
This is useful for custom analysis of profiling data.
.. _filtering:
Filtering
projects/rocprofiler-compute/requirements.txt
+1
Fájl megtekintése
@@ -12,6 +12,7 @@ plotille
pymongo
pyyaml
setuptools
sqlalchemy>=2.0.42
tabulate
textual
textual_plotext
projects/rocprofiler-compute/src/argparser.py
+22 -10
Fájl megtekintése
@@ -633,11 +633,29 @@ Examples:
help="\t\tMode of spatial multiplexing.",
)
analyze_group.add_argument(
"-o",
"--output",
"--output-format",
metavar="",
dest="output_file",
help="\t\tSpecify an output file to save analysis results.",
dest="output_format",
choices=["stdout", "txt", "csv", "db"],
default="stdout",
help=(
"\t\tSet the format of output file or folder containing analysis data.\n"
"\t\tBy default, file or folder created will "
"have the name rocprof_compute_<uuid>.\n"
"\t\tFile or folder name can be overriden using --output-name.\n"
"\t\tDefault output format is stdout which will not "
"generate any file/folder.\n"
),
)
analyze_group.add_argument(
"--output-name",
metavar="",
dest="output_name",
help=(
"\t\tOverride the default output file name rocprof_compue_<uuid> "
"with the specified name.\n"
"\t\tThis is only applicable when --output-format txt/csv/db is used.\n"
),
)
analyze_group.add_argument(
"--gui",
@@ -756,12 +774,6 @@ Examples:
help="\t\tSpecify the directory of customized configs.",
default=rocprof_compute_home.joinpath("rocprof_compute_soc/analysis_configs/"),
)
analyze_advanced_group.add_argument(
"--save-dfs",
dest="df_file_dir",
metavar="",
help="\t\tSpecify the dirctory to save analysis dataframe csv files.",
)
analyze_advanced_group.add_argument(
"--cols",
type=int,
projects/rocprofiler-compute/src/rocprof_compute_analyze/analysis_base.py
+26 -6
Fájl megtekintése
@@ -24,6 +24,7 @@
##############################################################################
import copy
import re
import sys
import textwrap
from abc import abstractmethod
@@ -41,7 +42,7 @@ from utils.logger import (
console_warning,
demarcate,
)
from utils.utils import is_workload_empty, merge_counters_spatial_multiplex
from utils.utils import get_uuid, is_workload_empty, merge_counters_spatial_multiplex
class OmniAnalyze_Base:
@@ -284,6 +285,23 @@ class OmniAnalyze_Base:
print("Node list:", " ".join(nodes))
sys.exit(0)
# Ensure analysis output does not overwrite existing files
if self.__args.output_name:
if not re.match(r"^[A-Za-z0-9_-]+$", self.__args.output_name):
console_error(
"Analysis output file/folder name must "
"contain only alphanumeric characters "
"or underscores (_), hyphens (-)."
)
path_to_check = self.__args.output_name
if self.__args.output_format in ("txt", "db"):
path_to_check += f".{self.__args.output_format}"
if Path(path_to_check).exists():
console_error(
f"Analysis output file/folder {path_to_check} already exists. "
"Please choose a different name."
)
# ----------------------------------------------------
# Required methods to be implemented by child classes
# ----------------------------------------------------
@@ -293,11 +311,13 @@ class OmniAnalyze_Base:
console_debug("analysis", "prepping to do some analysis")
console_log("analysis", "deriving rocprofiler-compute metrics...")
# initalize output file
self._output = (
open(self.__args.output_file, "w+")
if self.__args.output_file
else sys.stdout
)
if self.__args.output_format == "txt":
output_filename = self.__args.output_name or f"rocprof_compute_{get_uuid()}"
output_filename += ".txt"
self._output = open(output_filename, "w+")
console_warning(f"Created file: {output_filename}")
elif self.__args.output_format == "stdout":
self._output = sys.stdout
# Read profiling config
self._profiling_config = file_io.load_profiling_config(self.__args.path[0][0])
projects/rocprofiler-compute/src/rocprof_compute_analyze/analysis_db.py
+601
Fájl megtekintése
@@ -0,0 +1,601 @@
##############################################################################bl
# MIT License
#
# Copyright (c) 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 ast
import json
import re
from pathlib import Path
import astunparse
import pandas as pd
import utils.analysis_orm as orm
from config import rocprof_compute_home
from rocprof_compute_analyze.analysis_base import OmniAnalyze_Base
from utils import rocpd_data
from utils.analysis_orm import Database, get_views
from utils.logger import console_debug, console_error, console_warning, demarcate
from utils.parser import (
PC_SAMPLING_NOT_ISSUE_PREFIX,
CodeTransformer,
build_in_vars,
to_avg,
to_concat,
to_int,
to_max,
to_median,
to_min,
to_mod,
to_quantile,
to_round,
to_std,
to_sum,
)
from utils.roofline_calc import (
CACHE_HIERARCHY,
MFMA_DATATYPES,
PEAK_OPS_DATATYPES,
SUPPORTED_DATATYPES,
)
from utils.utils import get_uuid, get_version
class db_analysis(OmniAnalyze_Base):
# -----------------------
# Required child methods
# -----------------------
@demarcate
def pre_processing(self):
"""Perform any pre-processing steps prior to analysis."""
super().pre_processing()
if self._profiling_config.get("format_rocprof_output") != "rocpd":
console_error(
"Creation of analysis database is only supported "
"for profiling data with rocpd output format."
)
self._roofline_ceilings_per_workload = self.calc_roofline_ceilings()
self._pc_sampling_data_per_workload = self.calc_pc_sampling_data()
self._pmc_df_per_workload = {
workload_path: rocpd_data.process_rocpd_csv(
pd.read_csv(Path(workload_path) / "pmc_perf.csv")
)
for workload_path in self._runs.keys()
}
self._top_kernels_per_workload = {
workload_path: pmc_df.assign(
duration=pmc_df["End_Timestamp"] - pmc_df["Start_Timestamp"]
)
.sort_values(by="duration", ascending=False)
.drop_duplicates("Kernel_Name")["Kernel_Name"]
.to_list()
for workload_path, pmc_df in self._pmc_df_per_workload.items()
}
console_debug("Collected dispatch data")
self._pmc_df_per_workload = self.apply_pmc_filters()
self._dispatch_data_per_workload = self.calc_dispatch_data()
self._metrics_info_data_per_workload, self._values_data_per_workload = (
self.calc_metrics_data()
)
self._values_data_per_workload = self.calc_expressions()
self._roofline_data_per_workload = self.calc_roofline_data()
@demarcate
def run_analysis(self):
"""Run CLI analysis."""
super().run_analysis()
# Initialize analysis database
# Create db uuid
if self.get_args().output_name:
db_name = f"{self.get_args().output_name}.db"
else:
db_name = f"rocprof_compute_{get_uuid()}.db"
Database.init(db_name)
console_debug(f"Initialized database: {db_name}")
for workload_path in self._runs.keys():
workload_obj = orm.Workload(
name=workload_path.split("/")[-2],
sub_name=workload_path.split("/")[-1],
sys_info_extdata=self._runs[workload_path].sys_info.iloc[0].to_dict(),
roofline_bench_extdata=self._roofline_ceilings_per_workload.get(
workload_path
),
profiling_config_extdata=self._profiling_config,
)
Database.get_session().add(workload_obj)
for pc_sample in self._pc_sampling_data_per_workload.get(
workload_path, pd.DataFrame()
).itertuples():
Database.get_session().add(
orm.PCsampling(
source=pc_sample.source_line,
instruction=pc_sample.instruction,
count=pc_sample.count,
kernel_name=pc_sample.kernel_name,
offset=pc_sample.offset,
count_issue=pc_sample.count_issued,
count_stall=pc_sample.count_stalled,
stall_reason=pc_sample.stall_reason,
workload=workload_obj,
)
)
for dispatch in self._dispatch_data_per_workload.get(
workload_path, pd.DataFrame()
).itertuples():
Database.get_session().add(
orm.Dispatch(
dispatch_id=dispatch.dispatch_id,
kernel_name=dispatch.kernel_name,
gpu_id=dispatch.gpu_id,
duration=dispatch.duration,
workload=workload_obj,
)
)
for metric in self._metrics_info_data_per_workload.get(
workload_path, pd.DataFrame()
).itertuples():
metric_obj = orm.Metric(
name=metric.name,
metric_id=metric.metric_id,
description=metric.description,
unit=metric.unit,
table_name=metric.table_name,
sub_table_name=metric.sub_table_name,
workload=workload_obj,
)
Database.get_session().add(metric_obj)
for value in self._values_data_per_workload.get(
workload_path, pd.DataFrame()
).itertuples():
if value.metric_id == metric.metric_id:
Database.get_session().add(
orm.Value(
metric=metric_obj,
value_name=value.value_name,
value=value.value,
)
)
for roofline_data in self._roofline_data_per_workload.get(
workload_path, pd.DataFrame()
).itertuples():
Database.get_session().add(
orm.RooflineData(
kernel_name=roofline_data.kernel_name,
total_flops=roofline_data.total_flops,
l1_cache_data=roofline_data.l1_cache_data,
l2_cache_data=roofline_data.l2_cache_data,
hbm_cache_data=roofline_data.hbm_cache_data,
workload=workload_obj,
)
)
version = get_version(rocprof_compute_home)
Database.get_session().add(
orm.Metadata(
compute_version=version["version"],
git_version=version["sha"],
schema_version=orm.SCHEMA_VERSION,
)
)
# Create views
for view_stmt in get_views():
Database.get_session().execute(view_stmt)
# Write database
Database.write()
console_debug("Completed writing database")
console_warning(f"Created file: {db_name}")
def calc_roofline_ceilings(self):
roofline_ceilings_per_workload = dict()
for workload_path in self._runs.keys():
if not (Path(workload_path) / "roofline.csv").exists():
console_warning(f"Roofline ceilings not found for {workload_path}.")
continue
roofline_dict = (
pd.read_csv(f"{workload_path}/roofline.csv").iloc[0].to_dict()
)
keys = list()
for mem_level in CACHE_HIERARCHY:
keys.append(f"{mem_level}Bw")
for dtype in SUPPORTED_DATATYPES[
self._runs[workload_path].sys_info.iloc[0]["gpu_arch"]
]:
if dtype in PEAK_OPS_DATATYPES:
if dtype.startswith("F") or dtype.startswith("B"):
keys.append(f"{dtype}Flops")
elif dtype.startswith("I"):
keys.append(f"{dtype}Ops")
if dtype in MFMA_DATATYPES:
if dtype.startswith("F") or dtype.startswith("B"):
# FP16 -> F16
dtype = dtype.replace("FP", "F")
keys.append(f"MFMA{dtype}Flops")
elif dtype.startswith("I"):
keys.append(f"MFMA{dtype}Ops")
roofline_ceilings_per_workload[workload_path] = {
key: roofline_dict[key] for key in keys if key in roofline_dict
}
if roofline_ceilings_per_workload:
console_debug("Collected roofline ceilings")
return roofline_ceilings_per_workload
def calc_pc_sampling_data(self):
pc_sampling_data_per_workload = dict()
for workload_path in self._runs.keys():
if not (Path(workload_path) / "ps_file_results.json").exists():
console_warning(f"PC sampling data not found for {workload_path}.")
continue
pc_sampling_data = json.loads(
(Path(workload_path) / "ps_file_results.json").read_text()
)
pc_sampling_data = pc_sampling_data["rocprofiler-sdk-tool"][0]
pc_sampling_stochastic = pc_sampling_data["buffer_records"][
"pc_sample_stochastic"
]
pc_sampling_host_trap = pc_sampling_data["buffer_records"][
"pc_sample_host_trap"
]
pc_sampling_instruction = pc_sampling_data["strings"][
"pc_sample_instructions"
]
pc_sampling_comments = pc_sampling_data["strings"]["pc_sample_comments"]
pc_sampling_kernel_name_dict = {
symbol["code_object_id"]: symbol["formatted_kernel_name"]
for symbol in pc_sampling_data["kernel_symbols"]
}
pc_df = pd.DataFrame([
{
"inst_index": pc_sample["inst_index"],
"code_object_id": pc_sample["record"]["pc"]["code_object_id"],
"code_object_offset": pc_sample["record"]["pc"][
"code_object_offset"
],
"stall_reason": pc_sample["record"]
.get("snapshot", {})
.get("stall_reason"),
"wave_issued": pc_sample["record"].get("wave_issued"),
}
for pc_sample in pc_sampling_stochastic + pc_sampling_host_trap
])
def custom_aggregator(column_name):
if column_name == "count_issued":
def aggregator(series):
return None if series.isnull().all() else series.sum()
return aggregator
if column_name == "count_stalled":
def aggregator(series):
if series.isnull().all():
return None
return series.count() - series.sum()
return aggregator
if column_name == "stall_reason":
def aggregator(series):
if series.isnull().all():
return None
cleaned_series = series.dropna().str[
len(PC_SAMPLING_NOT_ISSUE_PREFIX) :
]
return cleaned_series.value_counts().to_dict()
return aggregator
raise ValueError(f"Unknown column name: {column_name}")
grouped_df = (
pc_df.groupby(["code_object_id", "code_object_offset"])
.agg(
count=("code_object_id", "size"),
inst_index=("inst_index", "last"),
count_issued=("wave_issued", custom_aggregator("count_issued")),
count_stalled=("wave_issued", custom_aggregator("count_stalled")),
stall_reason=("stall_reason", custom_aggregator("stall_reason")),
)
.reset_index()
)
grouped_df["instruction"] = grouped_df["inst_index"].apply(
lambda x: pc_sampling_instruction[x]
if x < len(pc_sampling_instruction)
else None
)
grouped_df["source_line"] = grouped_df["inst_index"].apply(
lambda x: pc_sampling_comments[x]
if x < len(pc_sampling_comments)
else None
)
grouped_df["kernel_name"] = grouped_df["code_object_id"].apply(
lambda x: pc_sampling_kernel_name_dict.get(x)
)
grouped_df = grouped_df.rename(columns={"code_object_offset": "offset"})
grouped_df = grouped_df.drop(columns=["code_object_id", "inst_index"])
pc_sampling_data_per_workload[workload_path] = grouped_df
if pc_sampling_data_per_workload:
console_debug("Collected PC sampling data")
return pc_sampling_data_per_workload
@staticmethod
def evaluate(name, value, pmc_df, sys_info, parse=False):
if parse:
value = re.sub(
r"\$([0-9A-Za-z_]+)",
lambda m: f'sys_info["{m.group(1)}"]',
value,
)
ast_node = ast.parse(value)
transformer = CodeTransformer()
transformer.visit(ast_node)
value = astunparse.unparse(ast_node)
value = value.replace("raw_pmc_df", "pmc_df")
value = value.replace("pmc_df['sys_info']", "sys_info")
else:
value = value.replace("raw_pmc_df['pmc_perf']", "pmc_df")
value = re.sub(
"ammolite__([0-9A-Za-z_]+)",
lambda m: f'sys_info["{m.group(1)}"]',
value,
)
try:
return eval(
compile(value, "<string>", "eval"),
{}, # no globals
{
# only locals
"pmc_df": pmc_df,
"sys_info": sys_info,
"to_avg": to_avg,
"to_concat": to_concat,
"to_int": to_int,
"to_max": to_max,
"to_median": to_median,
"to_min": to_min,
"to_mod": to_mod,
"to_quantile": to_quantile,
"to_round": to_round,
"to_std": to_std,
"to_sum": to_sum,
},
)
except Exception as e:
console_warning(f"Failed to evaluate expression for {name}: {value} - {e}")
return None
def calc_expressions(self):
values_data_per_workload = self._values_data_per_workload.copy()
for workload_path in self._runs.keys():
pmc_df = self._pmc_df_per_workload[workload_path].copy()
sys_info = self._runs[workload_path].sys_info.iloc[0].to_dict()
for key, value in self._roofline_ceilings_per_workload[
workload_path
].items():
sys_info[f"{key}_empirical_peak"] = value
# Calculate PER_XCD variables first
for key, value in build_in_vars.items():
if "PER_XCD" in key:
sys_info[key] = db_analysis.evaluate(
key, value, pmc_df, sys_info, parse=True
)
# variable dependent on PER_XCD variables
for key, value in build_in_vars.items():
if "PER_XCD" not in key:
sys_info[key] = db_analysis.evaluate(
key, value, pmc_df, sys_info, parse=True
)
# Get name and print warning
values_data_per_workload[workload_path]["value"] = values_data_per_workload[
workload_path
].apply(
lambda row: db_analysis.evaluate(
f"{row['metric_id']} - {row['value_name']}",
row["value"],
pmc_df,
sys_info,
),
axis=1,
)
console_debug("Calculated metric values")
return values_data_per_workload
def calc_metrics_data(self):
metrics_info_data_per_workload = dict()
values_data_per_workload = dict()
for workload_path in self._runs.keys():
gfx_arch = self._runs[workload_path].sys_info.iloc[0]["gpu_arch"]
# for example 201 -> Wavefront
table_names_map = dict()
for panel_config in self._arch_configs[gfx_arch].panel_configs.values():
table_names_map[panel_config["id"]] = panel_config["title"]
for source in panel_config["data source"]:
table_names_map[list(source.values())[0]["id"]] = list(
source.values()
)[0]["title"]
# Build metric data
non_expression_columns = [
"Metric",
"Channel",
"Unit",
"Description",
"coll_level",
"Type",
"Xfer",
"Coherency",
"Transaction",
]
metrics_info_df = pd.DataFrame([
{
"name": row.get("Metric") or row["Channel"].strip(),
"metric_id": metric_id,
"description": row.get("Description"),
"unit": row.get("Unit"),
"table_name": table_names_map[int(metric_id.split(".")[0]) * 100],
"sub_table_name": table_names_map[
int(metric_id.split(".")[0]) * 100
+ int(metric_id.split(".")[1])
],
}
for metric_df_id, metric_df in self._arch_configs[gfx_arch].dfs.items()
if metric_df_id
!= 402 # Skip roofline data points handled in calc_roofline_data
if set(metric_df.columns).intersection({"Metric", "Channel"})
for metric_id, row in metric_df.iterrows()
])
values_df = pd.DataFrame([
{
"metric_id": metric_id,
"value_name": value_name,
"value": row[value_name].strip(),
}
for metric_df_id, metric_df in self._arch_configs[gfx_arch].dfs.items()
if metric_df_id
!= 402 # Skip roofline data points handled in calc_roofline_data
if set(metric_df.columns).intersection({"Metric", "Channel"})
for metric_id, row in metric_df.iterrows()
for value_name in metric_df.drop(
columns=non_expression_columns, errors="ignore"
).columns
])
metrics_info_data_per_workload[workload_path] = metrics_info_df
values_data_per_workload[workload_path] = values_df
console_debug("Collected metrics data")
return metrics_info_data_per_workload, values_data_per_workload
def calc_dispatch_data(self):
dispatch_data_per_workload = dict()
for workload_path in self._runs.keys():
dispatch_df = pd.DataFrame([
{
"dispatch_id": row.Dispatch_ID,
"kernel_name": row.Kernel_Name,
"gpu_id": row.GPU_ID,
"duration": row.End_Timestamp - row.Start_Timestamp,
}
for row in self._pmc_df_per_workload[workload_path].itertuples()
])
dispatch_data_per_workload[workload_path] = dispatch_df
console_debug("Calculated dispatch data")
return dispatch_data_per_workload
def apply_pmc_filters(self):
pmc_df_per_workload = self._pmc_df_per_workload.copy()
for workload_path, pmc_df in pmc_df_per_workload.items():
# Filter gpu_ids
if self._runs[workload_path].filter_gpu_ids:
pmc_df = pmc_df.loc[
pmc_df["GPU_ID"]
.astype(str)
.isin([self._runs[workload_path].filter_gpu_ids])
]
# Filter kernel_ids
if self._runs[workload_path].filter_kernel_ids:
pmc_df = pmc_df.loc[
pmc_df["Kernel_Name"].isin([
self._top_kernels_per_workload[workload_path][id]
for id in self._runs[workload_path].filter_kernel_ids
])
]
# Filter dispatch_ids
if self._runs[workload_path].filter_dispatch_ids:
if ">" in self._runs[workload_path].filter_dispatch_ids[0]:
m = re.match(
r"\> (\d+)", self._runs[workload_path].filter_dispatch_ids[0]
)
pmc_df = pmc_df[pmc_df["Dispatch_ID"] > int(m.group(1))]
else:
pmc_df = pmc_df.loc[
pmc_df["Dispatch_ID"]
.astype(str)
.isin(self._runs[workload_path].filter_dispatch_ids)
]
pmc_df_per_workload[workload_path] = pmc_df
console_debug("Applied analysis mode filters")
return pmc_df_per_workload
def calc_roofline_data(self):
roofline_data_per_workload = dict()
for workload_path in self._runs.keys():
pmc_df = self._pmc_df_per_workload[workload_path].copy()
sys_info = self._runs[workload_path].sys_info.iloc[0].to_dict()
gfx_arch = sys_info["gpu_arch"]
roofline_data_df = self._arch_configs[gfx_arch].dfs[402]
roofline_data_expressions = dict(
zip(roofline_data_df["Metric"], roofline_data_df["Value"])
)
roofline_data_expressions = {
"total_flops": roofline_data_expressions["Performance (GFLOPs)"],
"l1_cache_data": roofline_data_expressions["AI L1"],
"l2_cache_data": roofline_data_expressions["AI L2"],
"hbm_cache_data": roofline_data_expressions["AI HBM"],
}
roofline_df = pd.DataFrame([
{
"kernel_name": kernel_name,
**{
metric_name: db_analysis.evaluate(
metric_name,
roofline_data_expressions[metric_name],
pmc_df[pmc_df["Kernel_Name"] == kernel_name],
sys_info,
)
for metric_name in roofline_data_expressions
},
}
for kernel_name in self._top_kernels_per_workload[workload_path][
: self.get_args().max_stat_num
]
])
roofline_data_per_workload[workload_path] = roofline_df
console_debug("Calculated roofline data points")
return roofline_data_per_workload
projects/rocprofiler-compute/src/rocprof_compute_base.py
+6
Fájl megtekintése
@@ -137,6 +137,8 @@ class RocProfCompute:
self.__analyze_mode = "web_ui"
elif self.__args.tui:
self.__analyze_mode = "tui"
elif self.__args.output_format == "db":
self.__analyze_mode = "db"
else:
self.__analyze_mode = "cli"
return
@@ -447,6 +449,10 @@ class RocProfCompute:
run_tui(self.__args, self.__supported_archs)
return
elif self.__analyze_mode == "db":
from rocprof_compute_analyze.analysis_db import db_analysis
analyzer = db_analysis(self.__args, self.__supported_archs)
else:
console_error("Unsupported analysis mode -> %s" % self.__analyze_mode)
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx908/0100_system_info.yaml
+1
Fájl megtekintése
@@ -6,5 +6,6 @@ Panel Config:
data source:
- raw_csv_table:
id: 101
title: System Info
source: sysinfo.csv
columnwise: true
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx908/1500_address_processing_unit_and_data_return_path_ta_td.yaml
-5
Fájl megtekintése
@@ -206,11 +206,6 @@ Panel Config:
min: MIN(((100 * TD_TC_STALL_sum) / ($GRBM_GUI_ACTIVE_PER_XCD * $cu_per_gpu)))
max: MAX(((100 * TD_TC_STALL_sum) / ($GRBM_GUI_ACTIVE_PER_XCD * $cu_per_gpu)))
unit: pct
"Workgroup manager \u2192 Data-Return Stall":
avg: null
min: null
max: null
unit: pct
Coalescable Instructions:
avg: AVG((TD_COALESCABLE_WAVEFRONT_sum / $denom))
min: MIN((TD_COALESCABLE_WAVEFRONT_sum / $denom))
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx908/1600_vector_l1_data_cache.yaml
+2 -2
Fájl megtekintése
@@ -400,7 +400,7 @@ Panel Config:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric:
Req:
avg: AVG((TCP_UTCL1_REQUEST_sum / $denom))
@@ -438,5 +438,5 @@ Panel Config:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric: {}
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx90a/0100_system_info.yaml
+1
Fájl megtekintése
@@ -6,5 +6,6 @@ Panel Config:
data source:
- raw_csv_table:
id: 101
title: System Info
source: sysinfo.csv
columnwise: true
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx90a/1600_vector_l1_data_cache.yaml
+2 -2
Fájl megtekintése
@@ -400,7 +400,7 @@ Panel Config:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric:
Req:
avg: AVG((TCP_UTCL1_REQUEST_sum / $denom))
@@ -438,5 +438,5 @@ Panel Config:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric: {}
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx940/0100_system_info.yaml
+1
Fájl megtekintése
@@ -6,5 +6,6 @@ Panel Config:
data source:
- raw_csv_table:
id: 101
title: System Info
source: sysinfo.csv
columnwise: true
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx940/0300_memory_chart.yaml
-4
Fájl megtekintése
@@ -236,10 +236,6 @@ Panel Config:
L2 Hit:
value: ROUND(AVG((((100 * TCC_HIT_sum) / (TCC_HIT_sum + TCC_MISS_sum)) if
((TCC_HIT_sum + TCC_MISS_sum) != 0) else 0)), 0)
L2 Rd Lat:
value: null
L2 Wr Lat:
value: null
Fabric_L2 Rd:
value: ROUND(AVG((TCC_EA0_RDREQ_sum / $denom)), 0)
Fabric_L2 Wr:
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx940/1600_vector_l1_data_cache.yaml
+2 -2
Fájl megtekintése
@@ -370,7 +370,7 @@ Panel Config:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric:
Req:
avg: AVG((TCP_UTCL1_REQUEST_sum / $denom))
@@ -408,5 +408,5 @@ Panel Config:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric: {}
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx941/0100_system_info.yaml
+1
Fájl megtekintése
@@ -6,5 +6,6 @@ Panel Config:
data source:
- raw_csv_table:
id: 101
title: System Info
source: sysinfo.csv
columnwise: true
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx941/0300_memory_chart.yaml
-4
Fájl megtekintése
@@ -236,10 +236,6 @@ Panel Config:
L2 Hit:
value: ROUND(AVG((((100 * TCC_HIT_sum) / (TCC_HIT_sum + TCC_MISS_sum)) if
((TCC_HIT_sum + TCC_MISS_sum) != 0) else 0)), 0)
L2 Rd Lat:
value: null
L2 Wr Lat:
value: null
Fabric_L2 Rd:
value: ROUND(AVG((TCC_EA0_RDREQ_sum / $denom)), 0)
Fabric_L2 Wr:
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx941/1600_vector_l1_data_cache.yaml
+2 -2
Fájl megtekintése
@@ -370,7 +370,7 @@ Panel Config:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric:
Req:
avg: AVG((TCP_UTCL1_REQUEST_sum / $denom))
@@ -408,5 +408,5 @@ Panel Config:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric: {}
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx942/0100_system_info.yaml
+1
Fájl megtekintése
@@ -6,5 +6,6 @@ Panel Config:
data source:
- raw_csv_table:
id: 101
title: System Info
source: sysinfo.csv
columnwise: true
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx942/0300_memory_chart.yaml
-4
Fájl megtekintése
@@ -236,10 +236,6 @@ Panel Config:
L2 Hit:
value: ROUND(AVG((((100 * TCC_HIT_sum) / (TCC_HIT_sum + TCC_MISS_sum)) if
((TCC_HIT_sum + TCC_MISS_sum) != 0) else 0)), 0)
L2 Rd Lat:
value: null
L2 Wr Lat:
value: null
Fabric_L2 Rd:
value: ROUND(AVG((TCC_EA0_RDREQ_sum / $denom)), 0)
Fabric_L2 Wr:
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx942/1600_vector_l1_data_cache.yaml
+2 -2
Fájl megtekintése
@@ -370,7 +370,7 @@ Panel Config:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric:
Req:
avg: AVG((TCP_UTCL1_REQUEST_sum / $denom))
@@ -408,5 +408,5 @@ Panel Config:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric: {}
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx950/0100_system_info.yaml
+1
Fájl megtekintése
@@ -6,5 +6,6 @@ Panel Config:
data source:
- raw_csv_table:
id: 101
title: System Info
source: sysinfo.csv
columnwise: true
projects/rocprofiler-compute/src/rocprof_compute_soc/analysis_configs/gfx950/1600_vector_l1_data_cache.yaml
+2 -2
Fájl megtekintése
@@ -420,7 +420,7 @@ Panel Config:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric:
Req:
avg: AVG((TCP_UTCL1_REQUEST_sum / $denom))
@@ -468,7 +468,7 @@ Panel Config:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric:
Cache Full Stall:
avg: AVG((TCP_UTCL1_STALL_INFLIGHT_MAX_sum / $denom))
projects/rocprofiler-compute/src/rocprof_compute_tui/utils/tui_utils.py
+1 -1
Fájl megtekintése
@@ -109,7 +109,7 @@ def process_panels_to_dataframes(args, kernel_df, archConfigs, roof_plot=None):
# args.filter_metrics
# args.cols
# args.max_stat_num
# args.df_file_dir
# dfs file dir
result_structure = {}
decimal_precision = getattr(args, "decimal", 2) if args else 2
projects/rocprofiler-compute/src/utils/analysis_orm.py
+216
Fájl megtekintése
@@ -0,0 +1,216 @@
##############################################################################bl
# MIT License
#
# Copyright (c) 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
from sqlalchemy import (
JSON,
Column,
Float,
ForeignKey,
Integer,
String,
Text,
create_engine,
func,
select,
text,
)
from sqlalchemy.orm import declarative_base, relationship, sessionmaker
from utils.logger import console_debug, console_error
Base = declarative_base()
PREFIX = "compute_"
SCHEMA_VERSION = "1.0.0"
class Workload(Base):
__tablename__ = f"{PREFIX}workload"
workload_id = Column(Integer, primary_key=True)
name = Column(String)
sub_name = Column(String)
sys_info_extdata = Column(JSON)
roofline_bench_extdata = Column(JSON)
profiling_config_extdata = Column(JSON)
# Workload can have multiple dispatches
dispatches = relationship("Dispatch", back_populates="workload")
# Workload can have multiple metrics
metrics = relationship("Metric", back_populates="workload")
# Workload can have multiple roofline data points
roofline_data_points = relationship("RooflineData", back_populates="workload")
# Workload can have multiple pc_sampling values
pc_sampling_values = relationship("PCsampling", back_populates="workload")
class Metric(Base):
__tablename__ = f"{PREFIX}metric"
metric_uuid = Column(Integer, primary_key=True)
workload_id = Column(
Integer, ForeignKey(f"{PREFIX}workload.workload_id"), nullable=False
)
name = Column(String) # e.g. Wavefronts Num
metric_id = Column(String) # e.g. 4.1.3
description = Column(Text) # e.g. Number of wavefronts
table_name = Column(String) # e.g. Wavefront
sub_table_name = Column(String) # e.g. Wavefront stats
unit = Column(String) # e.g. Gbps
# Metric can have one workload
workload = relationship("Workload", back_populates="metrics")
# Metric can have multiple values
values = relationship("Value", back_populates="metric")
class RooflineData(Base):
__tablename__ = f"{PREFIX}roofline_data"
roofline_uuid = Column(Integer, primary_key=True)
workload_id = Column(
Integer, ForeignKey(f"{PREFIX}workload.workload_id"), nullable=False
)
kernel_name = Column(String)
total_flops = Column(Float)
l1_cache_data = Column(Float)
l2_cache_data = Column(Float)
hbm_cache_data = Column(Float)
# Roofline data point can have one workload
workload = relationship("Workload", back_populates="roofline_data_points")
class Dispatch(Base):
__tablename__ = f"{PREFIX}dispatch"
dispatch_uuid = Column(Integer, primary_key=True)
workload_id = Column(
Integer, ForeignKey(f"{PREFIX}workload.workload_id"), nullable=False
)
dispatch_id = Column(Integer)
kernel_name = Column(String)
gpu_id = Column(Integer)
duration = Column(Integer)
# Dispatch can have one workload
workload = relationship("Workload", back_populates="dispatches")
class PCsampling(Base):
__tablename__ = f"{PREFIX}pcsampling"
pc_sampling_uuid = Column(Integer, primary_key=True)
workload_id = Column(
Integer, ForeignKey(f"{PREFIX}workload.workload_id"), nullable=False
)
source = Column(String)
instruction = Column(String)
count = Column(Integer)
kernel_name = Column(String)
offset = Column(Integer)
count_issue = Column(Integer)
count_stall = Column(Integer)
stall_reason = Column(JSON)
# PCsampling can have one workload
workload = relationship("Workload", back_populates="pc_sampling_values")
class Value(Base):
__tablename__ = f"{PREFIX}value"
value_uuid = Column(Integer, primary_key=True)
metric_uuid = Column(
Integer, ForeignKey(f"{PREFIX}metric.metric_uuid"), nullable=False
)
value_name = Column(String) # e.g. min, max, avg
value = Column(Float) # e.g. 123.45
# Value can have one metric
metric = relationship("Metric", back_populates="values")
class Metadata(Base):
__tablename__ = f"{PREFIX}metadata"
id = Column(Integer, primary_key=True)
compute_version = Column(String)
git_version = Column(String)
schema_version = Column(String)
class Database:
_session = None
@classmethod
def init(cls, db_name):
engine = create_engine(f"sqlite:///{db_name}")
Base.metadata.create_all(engine)
cls._session = sessionmaker(bind=engine)()
console_debug(f"SQLite database initialized with name: {db_name}")
return db_name
@classmethod
def get_session(cls):
return cls._session
@classmethod
def write(self):
try:
self._session.commit()
except Exception as e:
self._session.rollback()
console_error(f"Error writing analysis database: {e}")
finally:
self._session.close()
def get_views():
views = {
"kernel_view": select(
Dispatch.kernel_name,
func.count(Dispatch.dispatch_id).label("dispatch_count"),
func.sum(Dispatch.duration).label("duration_sum"),
func.avg(Dispatch.duration).label("duration_mean"),
).group_by(Dispatch.kernel_name),
"metric_view": select(
Metric.workload_id,
Metric.name,
Metric.metric_id,
Metric.description,
Metric.table_name,
Metric.sub_table_name,
Metric.unit,
Value.value_name,
Value.value,
).join(Value, Metric.metric_uuid == Value.metric_uuid),
}
return [
text(
f"CREATE VIEW {PREFIX}{view_name} AS "
f"{stmt.compile(compile_kwargs={'literal_binds': True})}"
)
for view_name, stmt in views.items()
]
projects/rocprofiler-compute/src/utils/parser.py
+3 -3
Fájl megtekintése
@@ -114,6 +114,8 @@ supported_call = {
"CONCAT": "to_concat",
}
PC_SAMPLING_NOT_ISSUE_PREFIX = "ROCPROFILER_PC_SAMPLING_INSTRUCTION_NOT_ISSUED_REASON_"
# ------------------------------------------------------------------------------
@@ -1283,9 +1285,7 @@ def search_pc_sampling_record(records):
)
)
rocp_inst_not_issued_prefix_len = len(
"ROCPROFILER_PC_SAMPLING_INSTRUCTION_NOT_ISSUED_REASON_"
)
rocp_inst_not_issued_prefix_len = len(PC_SAMPLING_NOT_ISSUE_PREFIX)
# Populate grouped_data
for i, item in enumerate(records):
projects/rocprofiler-compute/src/utils/roofline_calc.py
+2 -1
Fájl megtekintése
@@ -104,6 +104,7 @@ SUPPORTED_DATATYPES = {
PEAK_OPS_DATATYPES = ["FP8", "FP16", "BF16", "FP32", "FP64", "I8", "I32", "I64"]
MFMA_DATATYPES = ["FP4", "FP6", "FP8", "FP16", "BF16", "FP32", "FP64", "I8"]
CACHE_HIERARCHY = ["HBM", "L2", "L1", "LDS"]
TOP_N = 10
@@ -164,7 +165,7 @@ def calc_ceilings(roofline_parameters, dtype, benchmark_data):
graphPoints = {"hbm": [], "l2": [], "l1": [], "lds": [], "valu": [], "mfma": []}
if roofline_parameters["mem_level"] == "ALL":
cacheHierarchy = ["HBM", "L2", "L1", "LDS"]
cacheHierarchy = CACHE_HIERARCHY
else:
cacheHierarchy = roofline_parameters["mem_level"]
projects/rocprofiler-compute/src/utils/tty.py
+18 -12
Fájl megtekintése
@@ -34,7 +34,7 @@ import config
from utils import mem_chart, parser
from utils.kernel_name_shortener import kernel_name_shortener
from utils.logger import console_error, console_log, console_warning
from utils.utils import convert_metric_id_to_panel_info
from utils.utils import convert_metric_id_to_panel_info, get_uuid
def string_multiple_lines(source, width, max_rows):
@@ -141,6 +141,14 @@ def show_all(args, runs, archConfigs, output, profiling_config, roof_plot=None):
else:
hidden_cols = config.HIDDEN_COLUMNS_CLI
if args.output_format == "csv":
if args.output_name:
csv_dir = Path(f"{args.output_name}")
else:
csv_dir = Path(f"rocprof_compute_{get_uuid()}")
if not csv_dir.exists():
csv_dir.mkdir()
for panel_id, panel in archConfigs.panel_configs.items():
# Skip panels that don't support baseline comparison
if len(args.path) > 1 and panel_id in config.HIDDEN_SECTIONS:
@@ -484,17 +492,15 @@ def show_all(args, runs, archConfigs, output, profiling_config, roof_plot=None):
):
ss += table_id_str + " " + table_config["title"] + "\n"
if args.df_file_dir:
p = Path(args.df_file_dir)
if not p.exists():
p.mkdir()
if p.is_dir():
if "title" in table_config and table_config["title"]:
table_id_str += "_" + table_config["title"]
df.to_csv(
p.joinpath(table_id_str.replace(" ", "_") + ".csv"),
index=False,
)
if args.output_format == "csv" and csv_dir.is_dir():
if "title" in table_config and table_config["title"]:
table_id_str += "_" + table_config["title"]
csv_filename = str(
csv_dir.joinpath(table_id_str.replace(" ", "_") + ".csv"),
)
df.to_csv(csv_filename, index=False)
console_warning(f"Created file: {csv_filename}")
# Only show top N kernels (as specified in --max-kernel-num)
# in "Top Stats" section
if type == "raw_csv_table" and (
projects/rocprofiler-compute/src/utils/utils.py
+5
Fájl megtekintése
@@ -36,6 +36,7 @@ import shutil
import subprocess
import tempfile
import time
import uuid
from pathlib import Path as path
from typing import Optional
@@ -1640,3 +1641,7 @@ def parse_sets_yaml(arch):
if set_option:
sets_info[set_option] = set_item
return sets_info
def get_uuid(length=8):
return uuid.uuid4().hex[:length]
projects/rocprofiler-compute/tests/test_TCP_counters.py
+8 -4
Fájl megtekintése
@@ -147,7 +147,8 @@ def test_L1_cache_counters(
base = Path(test_utils.get_output_dir())
for app_name in app_names:
workload_dir = str(base / app_name)
workload_dir = f"{base}/{app_name}"
workload_dir_output = f"{base}_{app_name}"
# 1. profile the app
return_code = binary_handler_profile_rocprof_compute(
@@ -167,15 +168,17 @@ def test_L1_cache_counters(
workload_dir,
"-b",
"16.3",
"--save-dfs",
workload_dir,
"--output-format",
"csv",
"--output-name",
workload_dir_output,
])
assert return_code == 0
# 3. save results in local
# FIXME: customize file name to avoid hardcode
csv_path = workload_dir + "/16.3_vL1D_cache_access_metrics.csv"
csv_path = workload_dir_output + "/16.3_vL1D_cache_access_metrics.csv"
data = load_metrics(csv_path)
for metric in metrics:
@@ -185,6 +188,7 @@ def test_L1_cache_counters(
# 4. clean local output
test_utils.clean_output_dir(config["cleanup"], workload_dir)
test_utils.clean_output_dir(config["cleanup"], workload_dir_output)
test_utils.clean_output_dir(config["cleanup"], base)
# 5. check results are expected
projects/rocprofiler-compute/tests/test_analyze_commands.py
+6 -10
Fájl megtekintése
@@ -25,6 +25,7 @@
import os
import shutil
from pathlib import Path
from unittest.mock import Mock
import pandas as pd
@@ -608,14 +609,16 @@ def test_decimal_3(binary_handler_analyze_rocprof_compute):
@pytest.mark.misc
def test_save_dfs(binary_handler_analyze_rocprof_compute):
output_path = "tests/workloads/vcopy/saved_analysis"
output_path = test_utils.get_output_dir()
for dir in indirs:
workload_dir = test_utils.setup_workload_dir(dir)
code = binary_handler_analyze_rocprof_compute([
"analyze",
"--path",
workload_dir,
"--save-dfs",
"--output-format",
"csv",
"--output-name",
output_path,
])
assert code == 0
@@ -627,6 +630,7 @@ def test_save_dfs(binary_handler_analyze_rocprof_compute):
shutil.rmtree(output_path)
test_utils.clean_output_dir(config["cleanup"], workload_dir)
test_utils.clean_output_dir(config["cleanup"], output_path)
@pytest.mark.col
@@ -860,7 +864,6 @@ def test_dependency_MI100(binary_handler_analyze_rocprof_compute):
def test_parser_utility_functions():
"""Test parser utility functions edge cases"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
@@ -969,7 +972,6 @@ def test_parser_utility_functions():
def test_parser_error_handling():
"""Test parser error handling paths"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
@@ -1009,7 +1011,6 @@ def test_missing_file_handling(binary_handler_analyze_rocprof_compute):
def test_ast_transformer_edge_cases():
"""Simplified test focusing on the actual code paths"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
@@ -1051,7 +1052,6 @@ def test_ast_transformer_edge_cases():
def test_analyze_with_debug_mode(binary_handler_analyze_rocprof_compute):
"""Test analyze to cover debug paths in eval_metric - using direct function call"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
@@ -1138,7 +1138,6 @@ def test_filter_combinations_coverage(binary_handler_analyze_rocprof_compute):
def test_apply_filters_direct():
"""Test apply_filters function directly to cover filter branches"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
@@ -1213,7 +1212,6 @@ def test_missing_files_scenarios(binary_handler_analyze_rocprof_compute):
def test_pc_sampling_basic_coverage():
"""Test PC sampling functions with minimal data"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
@@ -1245,7 +1243,6 @@ def test_pc_sampling_basic_coverage():
def test_build_dfs_edge_cases():
"""Test build_dfs and gen_counter_list with various configurations"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
@@ -1275,7 +1272,6 @@ def test_build_dfs_edge_cases():
def test_update_functions_coverage():
"""Test update_denom_string and update_normUnit_string branches"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
projects/rocprofiler-compute/tests/test_profile_general.py
+26
Fájl megtekintése
@@ -766,6 +766,32 @@ def test_roof_rocpd(binary_handler_profile_rocprof_compute):
test_utils.clean_output_dir(config["cleanup"], workload_dir)
@pytest.mark.misc
def test_analyze_rocpd(
binary_handler_profile_rocprof_compute, binary_handler_analyze_rocprof_compute
):
workload_dir = test_utils.get_output_dir()
options = ["--device", "0", "--format-rocprof-output", "rocpd"]
binary_handler_profile_rocprof_compute(config, workload_dir, options, roof=True)
db_name = "test"
code = binary_handler_analyze_rocprof_compute([
"analyze",
"--output-format",
"db",
"--output-name",
f"{db_name}",
"--path",
workload_dir,
])
assert code == 0
assert os.path.isfile(f"{db_name}.db")
# Remove test.db
os.remove(f"{db_name}.db")
test_utils.clean_output_dir(config["cleanup"], workload_dir)
@pytest.mark.misc
def test_roofline_workload_dir_not_set_error():
"""
projects/rocprofiler-compute/utils/autogen_hash.yaml
+16 -16
Fájl megtekintése
@@ -5,12 +5,12 @@ src/rocprof_compute_soc/analysis_configs/gfx940/0000_top_stats.yaml: 401770cff80
src/rocprof_compute_soc/analysis_configs/gfx941/0000_top_stats.yaml: 401770cff804c6e51b78dff61390d8b5977598a2b09c6601ac593653e912535b
src/rocprof_compute_soc/analysis_configs/gfx942/0000_top_stats.yaml: 401770cff804c6e51b78dff61390d8b5977598a2b09c6601ac593653e912535b
src/rocprof_compute_soc/analysis_configs/gfx950/0000_top_stats.yaml: 401770cff804c6e51b78dff61390d8b5977598a2b09c6601ac593653e912535b
src/rocprof_compute_soc/analysis_configs/gfx908/0100_system_info.yaml: 739e39e69056984c277a69c17a6866effa860f56e8b1d3ea5d625582f16228ef
src/rocprof_compute_soc/analysis_configs/gfx90a/0100_system_info.yaml: 739e39e69056984c277a69c17a6866effa860f56e8b1d3ea5d625582f16228ef
src/rocprof_compute_soc/analysis_configs/gfx940/0100_system_info.yaml: 739e39e69056984c277a69c17a6866effa860f56e8b1d3ea5d625582f16228ef
src/rocprof_compute_soc/analysis_configs/gfx941/0100_system_info.yaml: 739e39e69056984c277a69c17a6866effa860f56e8b1d3ea5d625582f16228ef
src/rocprof_compute_soc/analysis_configs/gfx942/0100_system_info.yaml: 739e39e69056984c277a69c17a6866effa860f56e8b1d3ea5d625582f16228ef
src/rocprof_compute_soc/analysis_configs/gfx950/0100_system_info.yaml: 739e39e69056984c277a69c17a6866effa860f56e8b1d3ea5d625582f16228ef
src/rocprof_compute_soc/analysis_configs/gfx908/0100_system_info.yaml: b883dc360890c8d4fae49542b3362fa341598b86198cc7f2b9b9a3cf987f9576
src/rocprof_compute_soc/analysis_configs/gfx90a/0100_system_info.yaml: b883dc360890c8d4fae49542b3362fa341598b86198cc7f2b9b9a3cf987f9576
src/rocprof_compute_soc/analysis_configs/gfx940/0100_system_info.yaml: b883dc360890c8d4fae49542b3362fa341598b86198cc7f2b9b9a3cf987f9576
src/rocprof_compute_soc/analysis_configs/gfx941/0100_system_info.yaml: b883dc360890c8d4fae49542b3362fa341598b86198cc7f2b9b9a3cf987f9576
src/rocprof_compute_soc/analysis_configs/gfx942/0100_system_info.yaml: b883dc360890c8d4fae49542b3362fa341598b86198cc7f2b9b9a3cf987f9576
src/rocprof_compute_soc/analysis_configs/gfx950/0100_system_info.yaml: b883dc360890c8d4fae49542b3362fa341598b86198cc7f2b9b9a3cf987f9576
src/rocprof_compute_soc/analysis_configs/gfx908/0200_system_speed_of_light.yaml: 2103e9d6123f473f1cb18b71c046f197b5d1d873563c4aad4933d7361255f0c1
src/rocprof_compute_soc/analysis_configs/gfx90a/0200_system_speed_of_light.yaml: e9f552ee72849dc9c4ab14fee77ecc2681f4bcf610a8649c55365ab7eea7aafc
src/rocprof_compute_soc/analysis_configs/gfx940/0200_system_speed_of_light.yaml: 70716745e727d3a7e6fa706d34c346f796c241c485516da52e0c694386b3cf57
@@ -19,9 +19,9 @@ src/rocprof_compute_soc/analysis_configs/gfx942/0200_system_speed_of_light.yaml:
src/rocprof_compute_soc/analysis_configs/gfx950/0200_system_speed_of_light.yaml: a2cb003c74c0a75b9fe690da4e21b46e78fdb2f3233fc4753bca9276e93d60b0
src/rocprof_compute_soc/analysis_configs/gfx908/0300_memory_chart.yaml: 190c31ddc0bc713dba8b508faf13f0630b268ed15a0d9206f30998a0a071136f
src/rocprof_compute_soc/analysis_configs/gfx90a/0300_memory_chart.yaml: 8eeb4bb544eebd59aa10b51c1149ee4d015c76073c9a35e673210d9740fbf808
src/rocprof_compute_soc/analysis_configs/gfx940/0300_memory_chart.yaml: cff5509ac8502bad6dbd75e3058159fe429aece5d93279c66b2a6a8c887b43b6
src/rocprof_compute_soc/analysis_configs/gfx941/0300_memory_chart.yaml: cff5509ac8502bad6dbd75e3058159fe429aece5d93279c66b2a6a8c887b43b6
src/rocprof_compute_soc/analysis_configs/gfx942/0300_memory_chart.yaml: cff5509ac8502bad6dbd75e3058159fe429aece5d93279c66b2a6a8c887b43b6
src/rocprof_compute_soc/analysis_configs/gfx940/0300_memory_chart.yaml: 249e9ae0445de0927827ec14d20f946a07d50d92fd56e1993bbe0c17eb65bd51
src/rocprof_compute_soc/analysis_configs/gfx941/0300_memory_chart.yaml: 249e9ae0445de0927827ec14d20f946a07d50d92fd56e1993bbe0c17eb65bd51
src/rocprof_compute_soc/analysis_configs/gfx942/0300_memory_chart.yaml: 249e9ae0445de0927827ec14d20f946a07d50d92fd56e1993bbe0c17eb65bd51
src/rocprof_compute_soc/analysis_configs/gfx950/0300_memory_chart.yaml: 643b31ffa43bc3613d6f90b0c23d95093d0d0aa5bc8e72d9a0fbc1b739a08b67
src/rocprof_compute_soc/analysis_configs/gfx908/0400_roofline.yaml: 6406ce67cd55064f0d2db2a3511c6536cc1625314ddb31366900fbf3c60ed523
src/rocprof_compute_soc/analysis_configs/gfx90a/0400_roofline.yaml: 100d555cf9e70b892e22f92ddd9c0a5d1f914d07077c4a8d35941e8ad62b5b30
@@ -77,18 +77,18 @@ src/rocprof_compute_soc/analysis_configs/gfx940/1400_scalar_l1_data_cache.yaml:
src/rocprof_compute_soc/analysis_configs/gfx941/1400_scalar_l1_data_cache.yaml: 29fac4ea38e4a018baffc4a27a720b47078fd890c10da307655d40f693e6f0e7
src/rocprof_compute_soc/analysis_configs/gfx942/1400_scalar_l1_data_cache.yaml: 29fac4ea38e4a018baffc4a27a720b47078fd890c10da307655d40f693e6f0e7
src/rocprof_compute_soc/analysis_configs/gfx950/1400_scalar_l1_data_cache.yaml: 29fac4ea38e4a018baffc4a27a720b47078fd890c10da307655d40f693e6f0e7
src/rocprof_compute_soc/analysis_configs/gfx908/1500_address_processing_unit_and_data_return_path_ta_td.yaml: 633d59aba82b3a495b7ba33fa4b2ae4da638b58632bcc37ff18be87af68ce4d4
src/rocprof_compute_soc/analysis_configs/gfx908/1500_address_processing_unit_and_data_return_path_ta_td.yaml: 1e7717fcbd3c8cdf87d593a33f350ca240c1db8f8065a778cca926da1f517088
src/rocprof_compute_soc/analysis_configs/gfx90a/1500_address_processing_unit_and_data_return_path_ta_td.yaml: 2bdb9d7b3bea1057b3baee29ba3b428b211808261063a97bc4b6b319f4a19fb3
src/rocprof_compute_soc/analysis_configs/gfx940/1500_address_processing_unit_and_data_return_path_ta_td.yaml: 3180c2f3266be0ff44e01d73d247ca43ae2ee18ecaf61765f58849e36c701b19
src/rocprof_compute_soc/analysis_configs/gfx941/1500_address_processing_unit_and_data_return_path_ta_td.yaml: 3180c2f3266be0ff44e01d73d247ca43ae2ee18ecaf61765f58849e36c701b19
src/rocprof_compute_soc/analysis_configs/gfx942/1500_address_processing_unit_and_data_return_path_ta_td.yaml: 3180c2f3266be0ff44e01d73d247ca43ae2ee18ecaf61765f58849e36c701b19
src/rocprof_compute_soc/analysis_configs/gfx950/1500_address_processing_unit_and_data_return_path_ta_td.yaml: 9e56cef5b066fb575a5c530bcf9400f1291dd8636b12c8a2244cdba1defafc9f
src/rocprof_compute_soc/analysis_configs/gfx908/1600_vector_l1_data_cache.yaml: 438d0f4a972dd341eb2485f51a47d6860fbb30a6169054cd8550b4b7226e199f
src/rocprof_compute_soc/analysis_configs/gfx90a/1600_vector_l1_data_cache.yaml: 438d0f4a972dd341eb2485f51a47d6860fbb30a6169054cd8550b4b7226e199f
src/rocprof_compute_soc/analysis_configs/gfx940/1600_vector_l1_data_cache.yaml: 6100b218f24de9f1433b39a093ed04b9bb9dfe656c5df77583c9db332c447230
src/rocprof_compute_soc/analysis_configs/gfx941/1600_vector_l1_data_cache.yaml: 6100b218f24de9f1433b39a093ed04b9bb9dfe656c5df77583c9db332c447230
src/rocprof_compute_soc/analysis_configs/gfx942/1600_vector_l1_data_cache.yaml: 6100b218f24de9f1433b39a093ed04b9bb9dfe656c5df77583c9db332c447230
src/rocprof_compute_soc/analysis_configs/gfx950/1600_vector_l1_data_cache.yaml: 67054ec0a4c6ca147a5dd40cc91f0e8e81378e1affe7d479274747579ecc524a
src/rocprof_compute_soc/analysis_configs/gfx908/1600_vector_l1_data_cache.yaml: 360a9cd6df4e345a45f0660bc8df2003d5eb5dba2359d7e59c89933dc9fba94e
src/rocprof_compute_soc/analysis_configs/gfx90a/1600_vector_l1_data_cache.yaml: 360a9cd6df4e345a45f0660bc8df2003d5eb5dba2359d7e59c89933dc9fba94e
src/rocprof_compute_soc/analysis_configs/gfx940/1600_vector_l1_data_cache.yaml: 37c061bc9751828621a72aa6576596262b684fca7b764adbb991cd7eef58987d
src/rocprof_compute_soc/analysis_configs/gfx941/1600_vector_l1_data_cache.yaml: 37c061bc9751828621a72aa6576596262b684fca7b764adbb991cd7eef58987d
src/rocprof_compute_soc/analysis_configs/gfx942/1600_vector_l1_data_cache.yaml: 37c061bc9751828621a72aa6576596262b684fca7b764adbb991cd7eef58987d
src/rocprof_compute_soc/analysis_configs/gfx950/1600_vector_l1_data_cache.yaml: ae0388f43813302969f51a80ac58678614b993f5163083a69e1c99811d730064
src/rocprof_compute_soc/analysis_configs/gfx908/1700_l2_cache.yaml: 54ff1df4ee08206d0aa4ff9cd9f0b20cbaa3866aecb9b40a0ac5969e9e25ed20
src/rocprof_compute_soc/analysis_configs/gfx90a/1700_l2_cache.yaml: ee87b5b6cdaca98de6e5cb0d06e2e092470e0e25aac1498f8abcfc8421932ae6
src/rocprof_compute_soc/analysis_configs/gfx940/1700_l2_cache.yaml: 78f9fee5dafc83d311da1c801200c1820e16a0678dd0548fafa8a966ec6a94d5
projects/rocprofiler-compute/utils/unified_config.yaml
+3 -19
Fájl megtekintése
@@ -16,6 +16,7 @@ panels:
data source:
- raw_csv_table:
id: 101
title: System Info
source: sysinfo.csv
columnwise: true
- id: 200
@@ -1878,10 +1879,6 @@ panels:
L2 Hit:
value: ROUND(AVG((((100 * TCC_HIT_sum) / (TCC_HIT_sum + TCC_MISS_sum))
if ((TCC_HIT_sum + TCC_MISS_sum) != 0) else 0)), 0)
L2 Rd Lat:
value: null
L2 Wr Lat:
value: null
Fabric_L2 Rd:
value: ROUND(AVG((TCC_EA0_RDREQ_sum / $denom)), 0)
Fabric_L2 Wr:
@@ -2012,10 +2009,6 @@ panels:
L2 Hit:
value: ROUND(AVG((((100 * TCC_HIT_sum) / (TCC_HIT_sum + TCC_MISS_sum))
if ((TCC_HIT_sum + TCC_MISS_sum) != 0) else 0)), 0)
L2 Rd Lat:
value: null
L2 Wr Lat:
value: null
Fabric_L2 Rd:
value: ROUND(AVG((TCC_EA0_RDREQ_sum / $denom)), 0)
Fabric_L2 Wr:
@@ -2146,10 +2139,6 @@ panels:
L2 Hit:
value: ROUND(AVG((((100 * TCC_HIT_sum) / (TCC_HIT_sum + TCC_MISS_sum))
if ((TCC_HIT_sum + TCC_MISS_sum) != 0) else 0)), 0)
L2 Rd Lat:
value: null
L2 Wr Lat:
value: null
Fabric_L2 Rd:
value: ROUND(AVG((TCC_EA0_RDREQ_sum / $denom)), 0)
Fabric_L2 Wr:
@@ -11704,11 +11693,6 @@ panels:
min: MIN(((100 * TD_TC_STALL_sum) / ($GRBM_GUI_ACTIVE_PER_XCD * $cu_per_gpu)))
max: MAX(((100 * TD_TC_STALL_sum) / ($GRBM_GUI_ACTIVE_PER_XCD * $cu_per_gpu)))
unit: pct
"Workgroup manager \u2192 Data-Return Stall":
avg: null
min: null
max: null
unit: pct
Coalescable Instructions:
avg: AVG((TD_COALESCABLE_WAVEFRONT_sum / $denom))
min: MIN((TD_COALESCABLE_WAVEFRONT_sum / $denom))
@@ -13338,7 +13322,7 @@ panels:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric:
gfx90a:
Req:
@@ -13532,7 +13516,7 @@ panels:
avg: Avg
min: Min
max: Max
units: Units
units: Unit
metric:
gfx90a: {}
gfx941: {}