| Name | tt-perf-report JSON |
| Version |
1.1.0
JSON |
| download |
| home_page | None |
| Summary | This tool analyzes performance traces from TT-Metal operations, providing insights into throughput, bottlenecks, and optimization opportunities. |
| upload_time | 2025-08-19 13:48:53 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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-------------------------------------------------------------------------------
Third-Party Dependencies:
The following separate and independent dependencies are utilized by this
project and are included in a distributed build of a Python Wheel and
are subject to their own license terms listed as follows:
- sfpi-gcc - [License available here](https://github.com/tenstorrent-metal/sfpi-rel-temp/blob/master/LICENSE) [and here](https://github.com/tenstorrent-metal/sfpi-rel-temp/blob/master/compiler/LICENSE)
The following dependencies are utilized by this project but are not explicitly
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- yaml-cpp - [License available here](https://github.com/jbeder/yaml-cpp/blob/master/LICENSE)
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- boost - [License available here](https://www.boost.org/LICENSE_1_0.txt)
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|
| keywords |
tenstorrent
tt-metal
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
pandas
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Performance Report Analysis Tool
This tool analyzes performance traces from Metal operations, providing insights into throughput, bottlenecks, and optimization opportunities.
## Installation
This tool can be installed from PyPI:
```bash
pipx install tt-perf-report
```
Installing with pipx will automatically create a virtual environment and make the `tt-perf-report` command available.
## Generating Performance Traces
1. Build Metal with performance tracing enabled:
```bash
./build_metal -p
```
2. Run your test in TT-Metal with the tracy module to capture traces:
```bash
python -m tracy -r -p -v -m pytest path/to/test.py
```
This generates a CSV file containing operation timing data.
## Using Tracy Signposts
Tracy signposts mark specific sections of code for analysis. Add signposts to your Python code:
```python
import tracy
# Mark different sections of your code
tracy.signpost("Compilation pass")
model(input_data)
tracy.signpost("Performance pass")
for _ in range(10):
model(input_data)
```
The tool uses the last signpost by default, which is typically the most relevant section for a performance test(e.g., the final iteration after compilation / warmup).
Common signpost usage:
- `--signpost name`: Analyze ops after the specified signpost
- `--ignore-signposts`: Analyze the entire trace
## Filtering Operations
The output of the performance report is a table of operations. Each operation is assigned a unique ID starting from 1. You can re-run the tool with different IDs to focus on specific sections of the trace.
Use `--id-range` to analyze specific sections:
```bash
# Analyze ops 5 through 10
tt-perf-report trace.csv --id-range 5-10
# Analyze from op 31 onwards
tt-perf-report trace.csv --id-range 31-
# Analyze up to op 12
tt-perf-report trace.csv --id-range -12
```
This is particularly useful for:
- Isolating decode pass in prefill+decode LLM inference
- Analyzing single transformer layers without embeddings/projections
- Focusing on specific model components
## Output Options
- `--min-percentage value`: Hide ops below specified % of total time (default: 0.5)
- `--color/--no-color`: Force colored/plain output
- `--csv FILENAME`: Output the table to CSV format for further analysis or inclusion into automated reporting pipelines
- `--no-advice`: Show only performance table, skip optimization advice
## Understanding the Performance Report
The performance report provides several key metrics for analyzing operation performance:
### Core Metrics
- **Device Time**: Time spent executing the operation on device (in microseconds)
- **Op-to-op Gap**: Time between operations, including host overhead and kernel dispatch (in microseconds)
- **Total %**: Percentage of total execution time spent on this operation
- **Cores**: Number of cores used by the operation (max 64 on Wormhole)
### Performance Metrics
- **DRAM**: Memory bandwidth achieved (in GB/s)
- **DRAM %**: Percentage of theoretical peak DRAM bandwidth (288 GB/s on Wormhole)
- **FLOPs**: Compute throughput achieved (in TFLOPs)
- **FLOPs %**: Percentage of theoretical peak compute for the given math fidelity
- **Bound**: Performance classification of the operation:
- `DRAM`: Memory bandwidth bound (>65% of peak DRAM)
- `FLOP`: Compute bound (>65% of peak FLOPs)
- `BOTH`: Both memory and compute bound
- `SLOW`: Neither memory nor compute bound
- `HOST`: Operation running on host CPU
### Additional Fields
- **Math Fidelity**: Precision configuration used for matrix operations:
- `HiFi4`: Highest precision (74 TFLOPs/core)
- `HiFi2`: Medium precision (148 TFLOPs/core)
- `LoFi`: Lowest precision (262 TFLOPs/core)
The tool automatically highlights potential optimization opportunities:
- Red op-to-op times indicate high host or kernel launch overhead (>6.5μs)
- Red core counts indicate underutilization (<10 cores)
- Green metrics indicate good utilization of available resources
- Yellow metrics indicate room for optimization
## Examples
Typical use:
```bash
tt-perf-report trace.csv
```
Build a table of all ops with no advice:
```bash
tt-perf-report trace.csv --no-advice
```
View ops 100-200 with advice:
```bash
tt-perf-report trace.csv --id-range 100-200
```
Export the table of ops and columns as a CSV file:
```bash
tt-perf-report trace.csv --csv my_report.csv
```
Raw data
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"platform": null,
"description": "# Performance Report Analysis Tool\n\n\n\nThis tool analyzes performance traces from Metal operations, providing insights into throughput, bottlenecks, and optimization opportunities.\n\n## Installation\n\nThis tool can be installed from PyPI:\n\n```bash\npipx install tt-perf-report\n```\n\nInstalling with pipx will automatically create a virtual environment and make the `tt-perf-report` command available.\n\n## Generating Performance Traces\n\n1. Build Metal with performance tracing enabled:\n```bash\n./build_metal -p\n```\n\n2. Run your test in TT-Metal with the tracy module to capture traces:\n```bash\npython -m tracy -r -p -v -m pytest path/to/test.py\n```\nThis generates a CSV file containing operation timing data.\n\n## Using Tracy Signposts\n\nTracy signposts mark specific sections of code for analysis. Add signposts to your Python code:\n\n```python\nimport tracy\n\n# Mark different sections of your code\ntracy.signpost(\"Compilation pass\")\nmodel(input_data)\n\ntracy.signpost(\"Performance pass\")\nfor _ in range(10):\n model(input_data)\n```\n\nThe tool uses the last signpost by default, which is typically the most relevant section for a performance test(e.g., the final iteration after compilation / warmup).\n\nCommon signpost usage:\n- `--signpost name`: Analyze ops after the specified signpost\n- `--ignore-signposts`: Analyze the entire trace\n\n## Filtering Operations\n\nThe output of the performance report is a table of operations. Each operation is assigned a unique ID starting from 1. You can re-run the tool with different IDs to focus on specific sections of the trace.\n\nUse `--id-range` to analyze specific sections:\n```bash\n# Analyze ops 5 through 10\ntt-perf-report trace.csv --id-range 5-10\n\n# Analyze from op 31 onwards\ntt-perf-report trace.csv --id-range 31-\n\n# Analyze up to op 12\ntt-perf-report trace.csv --id-range -12\n```\n\nThis is particularly useful for:\n- Isolating decode pass in prefill+decode LLM inference\n- Analyzing single transformer layers without embeddings/projections\n- Focusing on specific model components\n\n## Output Options\n\n- `--min-percentage value`: Hide ops below specified % of total time (default: 0.5)\n- `--color/--no-color`: Force colored/plain output\n- `--csv FILENAME`: Output the table to CSV format for further analysis or inclusion into automated reporting pipelines\n- `--no-advice`: Show only performance table, skip optimization advice\n\n## Understanding the Performance Report\n\nThe performance report provides several key metrics for analyzing operation performance:\n\n### Core Metrics\n\n- **Device Time**: Time spent executing the operation on device (in microseconds)\n- **Op-to-op Gap**: Time between operations, including host overhead and kernel dispatch (in microseconds)\n- **Total %**: Percentage of total execution time spent on this operation\n- **Cores**: Number of cores used by the operation (max 64 on Wormhole)\n\n### Performance Metrics\n\n- **DRAM**: Memory bandwidth achieved (in GB/s)\n- **DRAM %**: Percentage of theoretical peak DRAM bandwidth (288 GB/s on Wormhole)\n- **FLOPs**: Compute throughput achieved (in TFLOPs)\n- **FLOPs %**: Percentage of theoretical peak compute for the given math fidelity\n- **Bound**: Performance classification of the operation:\n - `DRAM`: Memory bandwidth bound (>65% of peak DRAM)\n - `FLOP`: Compute bound (>65% of peak FLOPs)\n - `BOTH`: Both memory and compute bound\n - `SLOW`: Neither memory nor compute bound\n - `HOST`: Operation running on host CPU\n\n### Additional Fields\n\n- **Math Fidelity**: Precision configuration used for matrix operations:\n - `HiFi4`: Highest precision (74 TFLOPs/core)\n - `HiFi2`: Medium precision (148 TFLOPs/core)\n - `LoFi`: Lowest precision (262 TFLOPs/core)\n\nThe tool automatically highlights potential optimization opportunities:\n- Red op-to-op times indicate high host or kernel launch overhead (>6.5\u03bcs)\n- Red core counts indicate underutilization (<10 cores)\n- Green metrics indicate good utilization of available resources\n- Yellow metrics indicate room for optimization\n\n## Examples\n\nTypical use:\n\n```bash\ntt-perf-report trace.csv\n```\n\nBuild a table of all ops with no advice:\n\n```bash\ntt-perf-report trace.csv --no-advice\n```\n\nView ops 100-200 with advice:\n\n```bash\ntt-perf-report trace.csv --id-range 100-200\n```\n\nExport the table of ops and columns as a CSV file:\n\n```bash\ntt-perf-report trace.csv --csv my_report.csv\n```\n",
"bugtrack_url": null,
"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n \n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. For the purposes of this definition, \"submitted\"\n means any form of electronic, verbal, or written communication sent\n to the Licensor or its representatives, including but not limited to\n communication on electronic mailing lists, source code control systems,\n and issue tracking systems that are managed by, or on behalf of, the\n Licensor for the purpose of discussing and improving the Work, but\n excluding communication that is conspicuously marked or otherwise\n designated in writing by the copyright owner as \"Not a Contribution.\"\n \n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n \n 2. Grant of Copyright License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n copyright license to reproduce, prepare Derivative Works of,\n publicly display, publicly perform, sublicense, and distribute the\n Work and such Derivative Works in Source or Object form.\n \n 3. Grant of Patent License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n (except as stated in this section) patent license to make, have made,\n use, offer to sell, sell, import, and otherwise transfer the Work,\n where such license applies only to those patent claims licensable\n by such Contributor that are necessarily infringed by their\n Contribution(s) alone or by combination of their Contribution(s)\n with the Work to which such Contribution(s) was submitted. If You\n institute patent litigation against any entity (including a\n cross-claim or counterclaim in a lawsuit) alleging that the Work\n or a Contribution incorporated within the Work constitutes direct\n or contributory patent infringement, then any patent licenses\n granted to You under this License for that Work shall terminate\n as of the date such litigation is filed.\n \n 4. Redistribution. You may reproduce and distribute copies of the\n Work or Derivative Works thereof in any medium, with or without\n modifications, and in Source or Object form, provided that You\n meet the following conditions:\n \n (a) You must give any other recipients of the Work or\n Derivative Works a copy of this License; and\n \n (b) You must cause any modified files to carry prominent notices\n stating that You changed the files; and\n \n (c) You must retain, in the Source form of any Derivative Works\n that You distribute, all copyright, patent, trademark, and\n attribution notices from the Source form of the Work,\n excluding those notices that do not pertain to any part of\n the Derivative Works; and\n \n (d) If the Work includes a \"NOTICE\" text file as part of its\n distribution, then any Derivative Works that You distribute must\n include a readable copy of the attribution notices contained\n within such NOTICE file, excluding those notices that do not\n pertain to any part of the Derivative Works, in at least one\n of the following places: within a NOTICE text file distributed\n as part of the Derivative Works; within the Source form or\n documentation, if provided along with the Derivative Works; or,\n within a display generated by the Derivative Works, if and\n wherever such third-party notices normally appear. The contents\n of the NOTICE file are for informational purposes only and\n do not modify the License. You may add Your own attribution\n notices within Derivative Works that You distribute, alongside\n or as an addendum to the NOTICE text from the Work, provided\n that such additional attribution notices cannot be construed\n as modifying the License.\n \n You may add Your own copyright statement to Your modifications and\n may provide additional or different license terms and conditions\n for use, reproduction, or distribution of Your modifications, or\n for any such Derivative Works as a whole, provided Your use,\n reproduction, and distribution of the Work otherwise complies with\n the conditions stated in this License.\n \n 5. Submission of Contributions. Unless You explicitly state otherwise,\n any Contribution intentionally submitted for inclusion in the Work\n by You to the Licensor shall be under the terms and conditions of\n this License, without any additional terms or conditions.\n Notwithstanding the above, nothing herein shall supersede or modify\n the terms of any separate license agreement you may have executed\n with Licensor regarding such Contributions.\n \n 6. Trademarks. This License does not grant permission to use the trade\n names, trademarks, service marks, or product names of the Licensor,\n except as required for reasonable and customary use in describing the\n origin of the Work and reproducing the content of the NOTICE file.\n \n 7. Disclaimer of Warranty. Unless required by applicable law or\n agreed to in writing, Licensor provides the Work (and each\n Contributor provides its Contributions) on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n implied, including, without limitation, any warranties or conditions\n of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n PARTICULAR PURPOSE. You are solely responsible for determining the\n appropriateness of using or redistributing the Work and assume any\n risks associated with Your exercise of permissions under this License.\n \n 8. Limitation of Liability. In no event and under no legal theory,\n whether in tort (including negligence), contract, or otherwise,\n unless required by applicable law (such as deliberate and grossly\n negligent acts) or agreed to in writing, shall any Contributor be\n liable to You for damages, including any direct, indirect, special,\n incidental, or consequential damages of any character arising as a\n result of this License or out of the use or inability to use the\n Work (including but not limited to damages for loss of goodwill,\n work stoppage, computer failure or malfunction, or any and all\n other commercial damages or losses), even if such Contributor\n has been advised of the possibility of such damages.\n \n 9. Accepting Warranty or Additional Liability. While redistributing\n the Work or Derivative Works thereof, You may choose to offer,\n and charge a fee for, acceptance of support, warranty, indemnity,\n or other liability obligations and/or rights consistent with this\n License. However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n \n END OF TERMS AND CONDITIONS\n \n Copyright (c) 2025 Tenstorrent AI ULC\n \n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n \n http://www.apache.org/licenses/LICENSE-2.0\n \n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n \n -------------------------------------------------------------------------------\n \n Third-Party Dependencies:\n \n The following separate and independent dependencies are utilized by this\n project and are included in a distributed build of a Python Wheel and\n are subject to their own license terms listed as follows:\n \n - sfpi-gcc - [License available here](https://github.com/tenstorrent-metal/sfpi-rel-temp/blob/master/LICENSE) [and here](https://github.com/tenstorrent-metal/sfpi-rel-temp/blob/master/compiler/LICENSE)\n \n The following dependencies are utilized by this project but are not explicitly\n distributed as part of the software:\n \n - yaml-cpp - [License available here](https://github.com/jbeder/yaml-cpp/blob/master/LICENSE)\n - Doxygen - [License available here](https://github.com/jbeder/yaml-cpp/blob/master/LICENSE)\n - boost - [License available here](https://www.boost.org/LICENSE_1_0.txt)\n - glog - [License available here](https://github.com/google/glog/blob/v0.4.0/COPYING)\n - icu - [License available here](http://www.unicode.org/copyright.html#license)\n ",
"summary": "This tool analyzes performance traces from TT-Metal operations, providing insights into throughput, bottlenecks, and optimization opportunities.",
"version": "1.1.0",
"project_urls": {
"Repository": "https://github.com/tenstorrent/tt-perf-report"
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