diff --git a/.ai_context.md b/.ai_context.md new file mode 100644 index 0000000000000000000000000000000000000000..8e0b96caf62e247a177cf12abcc0c8d6b516b2f7 --- /dev/null +++ b/.ai_context.md @@ -0,0 +1,29 @@ +# ๐Ÿค– AI AGENT OPERATING INSTRUCTIONS +## Portable DevStation Pro (God-Tier Environment) + +This environment is designed for high-performance AI, Game, and Software Engineering. Any AI agent (like Gemini, Cursor, or GPT) operating here MUST adhere to these professional standards. + +### ๐Ÿ“ Environment Context +- **Root Directory:** The current folder where this file resides (Dynamic Portable Root). +- **Activation:** Always assume `activate_env.bat` has been run or use full paths from the root. +- **Portability:** NEVER write to `C:\` or system registries. All data must stay within the root. + +### ๐Ÿ› ๏ธ Mandatory Engineering Tools +When writing code, you MUST use these installed tools for quality control: +1. **Linting/Analysis:** Use `ruff` (Python) and `cppcheck` (C++/CUDA). +2. **Type Checking:** Use `mypy` for all Python code. +3. **Formatting:** Use `black` for Python to maintain a professional look. +4. **Testing:** All new features MUST have tests written in `pytest`. +5. **Documentation:** Use `mkdocs` for project manuals and `doxygen` for code-level docs. + +### ๐Ÿš€ High-Performance Libraries +- **Inference:** Prioritize `TensorRT 10.x` for NVIDIA GPU acceleration. +- **Deep Learning:** Use `cuDNN 9.22` with `CUDA 12.4`. +- **Compression:** Use `nvCOMP` for GPU-accelerated data handling. + +### ๐ŸŽจ Coding Style +- **Aesthetics:** Code must be clean, modular, and well-commented. +- **Master Level:** Use advanced CUDA kernels and modern C++20/Python 3.13 features. + +--- +*Note: This file ensures that any AI working in this environment maintains the "God-Tier" standard set during creation.* diff --git a/Easy_Setup.bat b/Easy_Setup.bat new file mode 100644 index 0000000000000000000000000000000000000000..2dd69ec86751f6757d9ff96890d42aa209e70d34 --- /dev/null +++ b/Easy_Setup.bat @@ -0,0 +1,94 @@ +@echo off +setlocal enabledelayedexpansion +title DEVSTATION PRO - ONE-CLICK SETUP +color 0B + +:: Get Admin Rights +>nul 2>&1 "%SYSTEMROOT%\system32\cacls.exe" "%SYSTEMROOT%\system32\config\system" +if '%errorlevel%' NEQ '0' ( + echo [INFO] Requesting Admin Rights for System Integration... + goto UACPrompt +) else ( goto gotAdmin ) +:UACPrompt + echo Set UAC = CreateObject^("Shell.Application"^) > "%temp%\getadmin.vbs" + echo UAC.ShellExecute "%~s0", "", "", "runas", 1 >> "%temp%\getadmin.vbs" + "%temp%\getadmin.vbs" + exit /B +:gotAdmin + if exist "%temp%\getadmin.vbs" ( del "%temp%\getadmin.vbs" ) + +set "ROOT=%~dp0" +set "ROOT=%ROOT:~0,-1%" + +:menu +cls +echo. +echo ####################################################### +echo # # +echo # GOD-TIER PORTABLE DEVSTATION SETUP # +echo # # +echo ####################################################### +echo. +echo [1] PERMANENT INSTALL (Add to System PATH) +echo - Tools will work in ANY terminal (CMD/PowerShell) +echo. +echo [2] TEMPORARY SESSION (No System Changes) +echo - Just open a temporary session with tools active +echo. +echo [3] PORTABLE SYNC (Fix Paths for New Drive) +echo - Use this if you moved the folder to a new drive +echo. +echo [4] UNINSTALL (Remove from System PATH) +echo - Clean all paths from your computer +echo. +echo [5] EXIT +echo. +set /p choice="ENTER CHOICE (1-5): " + +if "%choice%"=="1" goto install +if "%choice%"=="2" goto temporary +if "%choice%"=="3" goto sync +if "%choice%"=="4" goto uninstall +if "%choice%"=="5" exit +goto menu + +:temporary +echo. +echo [INFO] Launching Temporary Session... +start cmd /k "scripts\activate_env.bat" +goto menu + +:install +echo. +echo [STEP 1] Syncing local paths... +set "MAMBA_ROOT_PREFIX=%ROOT%\micromamba_root" +"%ROOT%\micromamba\micromamba.exe" shell init -s cmd -p "%MAMBA_ROOT_PREFIX%" >nul 2>&1 + +echo [STEP 2] Building Global Path List... +:: Comprehensive paths for ALL tools (Dotnet at the front) +set "NP=%ROOT%\dotnet;%ROOT%\bin;%ROOT%\git\cmd;%ROOT%\git\bin;%ROOT%\node;%ROOT%\node24;%ROOT%\python;%ROOT%\python\Scripts;%ROOT%\cmake\bin;%ROOT%\rust\.cargo\bin;%ROOT%\go\bin;%ROOT%\cuda_toolkit\bin;%ROOT%\tensorrt\bin;%ROOT%\cudnn\bin\x64;%ROOT%\micromamba_root\Scripts;%ROOT%\micromamba_root\Library\bin;%ROOT%\micromamba_root\Library\usr\bin;%ROOT%\llvm\bin;%ROOT%\java\bin;%ROOT%\java21\bin;%ROOT%\php;%ROOT%\ffmpeg\bin;%ROOT%\svn\bin;%ROOT%\gh;%ROOT%\msbuild;%ROOT%\vulkan\Bin;%ROOT%\pgsql\bin;%ROOT%\sqlite;%ROOT%\directstorage\native\bin\x64" + +echo [STEP 3] Injecting into User Environment... +:: Set Global Environment Variables permanently +powershell -NoProfile -Command "[Environment]::SetEnvironmentVariable('DEVTOOLS_ROOT', '%ROOT%', 'User'); [Environment]::SetEnvironmentVariable('JAVA_HOME', '%ROOT%\java', 'User'); [Environment]::SetEnvironmentVariable('DOTNET_ROOT', '%ROOT%\dotnet', 'User'); [Environment]::SetEnvironmentVariable('DOTNET_ROOT(x86)', '%ROOT%\dotnet', 'User'); [Environment]::SetEnvironmentVariable('DOTNET_MULTILEVEL_LOOKUP', '0', 'User'); [Environment]::SetEnvironmentVariable('DOTNET_NOLOGO', '1', 'User'); [Environment]::SetEnvironmentVariable('DOTNET_MSBUILD_SDK_RESOLVER_CLI_DIR', '%ROOT%\dotnet', 'User'); [Environment]::SetEnvironmentVariable('GOROOT', '%ROOT%\go', 'User'); [Environment]::SetEnvironmentVariable('RUSTUP_HOME', '%ROOT%\rust\.rustup', 'User'); [Environment]::SetEnvironmentVariable('CARGO_HOME', '%ROOT%\rust\.cargo', 'User'); [Environment]::SetEnvironmentVariable('CUDA_PATH', '%ROOT%\cuda_toolkit', 'User'); [Environment]::SetEnvironmentVariable('CUDA_HOME', '%ROOT%\cuda_toolkit', 'User'); [Environment]::SetEnvironmentVariable('TENSORRT_HOME', '%ROOT%\tensorrt', 'User'); [Environment]::SetEnvironmentVariable('CUDNN_HOME', '%ROOT%\cudnn', 'User'); $oldPath = [Environment]::GetEnvironmentVariable('Path', 'User'); $newPath = '%NP%;' + $oldPath; $uniquePath = ($newPath.Split(';') | Select-Object -Unique | Where-Object { $_ -ne '' -and $_ -notlike '*C:\Program Files\dotnet*' }) -join ';'; [Environment]::SetEnvironmentVariable('Path', $uniquePath, 'User')" + +echo. +echo ======================================================= +echo [SUCCESS] ALL TOOLS ARE NOW INTEGRATED SYSTEM-WIDE! +echo Please RESTART your terminal (CMD/PowerShell). +echo Try running 'nvcc --version' or 'python --version'. +echo ======================================================= +pause +goto menu + +:sync +echo. +echo [INFO] Synchronizing paths to %ROOT%... +call "scripts\Portable_Fixer.bat" +goto menu + +:uninstall +echo. +echo [INFO] Launching Uninstaller... +call "scripts\Uninstall_Global_PATH.bat" +goto menu diff --git a/php/README.md b/php/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c5bc2fce4a7941e29af4f6884aac709e2bc22be0 --- /dev/null +++ b/php/README.md @@ -0,0 +1,172 @@ +
+ + PHP + +
+ +# The PHP Interpreter + +PHP is a popular general-purpose scripting language that is especially suited to +web development. Fast, flexible and pragmatic, PHP powers everything from your +blog to the most popular websites in the world. PHP is distributed under the +[PHP License v3.01](LICENSE). + +[![Push](https://github.com/php/php-src/actions/workflows/push.yml/badge.svg)](https://github.com/php/php-src/actions/workflows/push.yml) +[![Fuzzing Status](https://oss-fuzz-build-logs.storage.googleapis.com/badges/php.svg)](https://issues.oss-fuzz.com/issues?q=project:php) + +## Documentation + +The PHP manual is available at [php.net/docs](https://www.php.net/docs). + +## Installation + +### Prebuilt packages and binaries + +Prebuilt packages and binaries can be used to get up and running fast with PHP. + +For Windows, the PHP binaries can be obtained from +[windows.php.net](https://windows.php.net). After extracting the archive the +`*.exe` files are ready to use. + +For other systems, see the [installation chapter](https://www.php.net/install). + +### Building PHP source code + +*For Windows, see [Build your own PHP on Windows](https://wiki.php.net/internals/windows/stepbystepbuild_sdk_2).* + +For a minimal PHP build from Git, you will need autoconf, bison, and re2c. For +a default build, you will additionally need libxml2 and libsqlite3. + +On Ubuntu, you can install these using: + +```shell +sudo apt install -y pkg-config build-essential autoconf bison re2c libxml2-dev libsqlite3-dev +``` + +On Fedora, you can install these using: + +```shell +sudo dnf install re2c bison autoconf make libtool ccache libxml2-devel sqlite-devel +``` + +On MacOS, you can install these using `brew`: + +```shell +brew install autoconf bison re2c libiconv libxml2 sqlite +``` + +or with `MacPorts`: + +```shell +sudo port install autoconf bison re2c libiconv libxml2 sqlite3 +``` + +Generate configure: + +```shell +./buildconf +``` + +Configure your build. `--enable-debug` is recommended for development, see +`./configure --help` for a full list of options. + +```shell +# For development +./configure --enable-debug +# For production +./configure +``` + +Build PHP. To speed up the build, specify the maximum number of jobs using the +`-j` argument: + +```shell +make -j4 +``` + +The number of jobs should usually match the number of available cores, which +can be determined using `nproc`. + +## Testing PHP source code + +PHP ships with an extensive test suite, the command `make test` is used after +successful compilation of the sources to run this test suite. + +It is possible to run tests using multiple cores by setting `-jN` in +`TEST_PHP_ARGS` or `TESTS`: + +```shell +make TEST_PHP_ARGS=-j4 test +``` + +Shall run `make test` with a maximum of 4 concurrent jobs: Generally the maximum +number of jobs should not exceed the number of cores available. + +Use the `TEST_PHP_ARGS` or `TESTS` variable to test only specific directories: + +```shell +make TESTS=tests/lang/ test +``` + +The [qa.php.net](https://qa.php.net) site provides more detailed info about +testing and quality assurance. + +## Installing PHP built from source + +After a successful build (and test), PHP may be installed with: + +```shell +make install +``` + +Depending on your permissions and prefix, `make install` may need superuser +permissions. + +## PHP extensions + +Extensions provide additional functionality on top of PHP. PHP consists of many +essential bundled extensions. Additional extensions can be found in the PHP +Extension Community Library - [PECL](https://pecl.php.net). + +## Contributing + +The PHP source code is located in the Git repository at +[github.com/php/php-src](https://github.com/php/php-src). Contributions are most +welcome by forking the repository and sending a pull request. + +Discussions are done on GitHub, but depending on the topic can also be relayed +to the official PHP developer mailing list internals@lists.php.net. + +New features require an RFC and must be accepted by the developers. See +[Request for comments - RFC](https://wiki.php.net/rfc) and +[Voting on PHP features](https://wiki.php.net/rfc/voting) for more information +on the process. + +Bug fixes don't require an RFC. If the bug has a GitHub issue, reference it in +the commit message using `GH-NNNNNN`. Use `#NNNNNN` for tickets in the old +[bugs.php.net](https://bugs.php.net) bug tracker. + + Fix GH-7815: php_uname doesn't recognise latest Windows versions + Fix #55371: get_magic_quotes_gpc() throws deprecation warning + +See [Git workflow](https://wiki.php.net/vcs/gitworkflow) for details on how pull +requests are merged. + +### Guidelines for contributors + +See further documents in the repository for more information on how to +contribute: + +- [Contributing to PHP](/CONTRIBUTING.md) +- [PHP coding standards](/CODING_STANDARDS.md) +- [Internal documentation](https://php.github.io/php-src/) +- [Mailing list rules](/docs/mailinglist-rules.md) +- [PHP release process](/docs/release-process.md) + +## Credits + +For the list of people who've put work into PHP, please see the +[PHP credits page](https://www.php.net/credits.php). diff --git a/php/readme-redist-bins.txt b/php/readme-redist-bins.txt new file mode 100644 index 0000000000000000000000000000000000000000..41c86c3982a1da0e532e10b7368687fe2848ae95 --- /dev/null +++ b/php/readme-redist-bins.txt @@ -0,0 +1,682 @@ + 1. libmagic (ext/fileinfo) see ext/fileinfo/libmagic/LICENSE + 2. libmbfl (ext/mbstring) see ext/mbstring/libmbfl/LICENSE + 3. pcre2lib (ext/pcre) + 4. ext/standard crypt + 5. ext/standard crypt's blowfish implementation + 6. ext/standard/rand + 7. ext/standard/scanf + 8. ext/standard/strnatcmp.c + 9. ext/standard/uuencode +10. main/snprintf.c +11. main/strlcat +12. main/strlcpy +13. libgd (ext/gd) +14. ext/phar portions of tar implementations +15. ext/phar/zip.c portion extracted from libzip +16. libbcmath (ext/bcmath) see ext/bcmath/libbcmath/LICENSE +17. ext/mbstring/ucgendat portions based on the ucgendat.c from the OpenLDAP +18. avifinfo (ext/standard/libavifinfo) see ext/standard/libavifinfo/LICENSE +19. xxHash (ext/hash/xxhash) +20. Lexbor (ext/lexbor/lexbor) see ext/lexbor/LICENSE +21. Portions of libcperciva (ext/hash/hash_sha_{ni,sse2}.c) see the header in the source file +22. uriparser (ext/uri/uriparser) see ext/uri/uriparser/COPYING + +3. pcre2lib (ext/pcre) + +PCRE2 LICENCE +------------- + +PCRE2 is a library of functions to support regular expressions whose syntax +and semantics are as close as possible to those of the Perl 5 language. + +Releases 10.00 and above of PCRE2 are distributed under the terms of the "BSD" +licence, as specified below, with one exemption for certain binary +redistributions. The documentation for PCRE2, supplied in the "doc" directory, +is distributed under the same terms as the software itself. The data in the +testdata directory is not copyrighted and is in the public domain. + +The basic library functions are written in C and are freestanding. Also +included in the distribution is a just-in-time compiler that can be used to +optimize pattern matching. This is an optional feature that can be omitted when +the library is built. + + +THE BASIC LIBRARY FUNCTIONS +--------------------------- + +Written by: Philip Hazel +Email local part: ph10 +Email domain: cam.ac.uk + +University of Cambridge Computing Service, +Cambridge, England. + +Copyright (c) 1997-2019 University of Cambridge +All rights reserved. + + +PCRE2 JUST-IN-TIME COMPILATION SUPPORT +-------------------------------------- + +Written by: Zoltan Herczeg +Email local part: hzmester +Email domain: freemail.hu + +Copyright(c) 2010-2019 Zoltan Herczeg +All rights reserved. + + +STACK-LESS JUST-IN-TIME COMPILER +-------------------------------- + +Written by: Zoltan Herczeg +Email local part: hzmester +Email domain: freemail.hu + +Copyright(c) 2009-2019 Zoltan Herczeg +All rights reserved. + + +THE "BSD" LICENCE +----------------- + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notices, + this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright + notices, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + * Neither the name of the University of Cambridge nor the names of any + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. + + +EXEMPTION FOR BINARY LIBRARY-LIKE PACKAGES +------------------------------------------ + +The second condition in the BSD licence (covering binary redistributions) does +not apply all the way down a chain of software. If binary package A includes +PCRE2, it must respect the condition, but if package B is software that +includes package A, the condition is not imposed on package B unless it uses +PCRE2 independently. + +End + + +4. ext/standard crypt + +FreeSec: libcrypt for NetBSD + +Copyright (c) 1994 David Burren +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. +3. Neither the name of the author nor the names of other contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS +OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) +HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY +OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF +SUCH DAMAGE. + + +5. ext/standard crypt's blowfish implementation + +The crypt_blowfish homepage is: + +http://www.openwall.com/crypt/ + +This code comes from John the Ripper password cracker, with reentrant +and crypt(3) interfaces added, but optimizations specific to password +cracking removed. + +Written by Solar Designer in 1998-2011. +No copyright is claimed, and the software is hereby placed in the public +domain. In case this attempt to disclaim copyright and place the software +in the public domain is deemed null and void, then the software is +Copyright (c) 1998-2011 Solar Designer and it is hereby released to the +general public under the following terms: + +Redistribution and use in source and binary forms, with or without +modification, are permitted. + +There's ABSOLUTELY NO WARRANTY, express or implied. + +It is my intent that you should be able to use this on your system, +as part of a software package, or anywhere else to improve security, +ensure compatibility, or for any other purpose. I would appreciate +it if you give credit where it is due and keep your modifications in +the public domain as well, but I don't require that in order to let +you place this code and any modifications you make under a license +of your choice. + +This implementation is mostly compatible with OpenBSD's bcrypt.c (prefix +"$2a$") by Niels Provos , and uses some of his +ideas. The password hashing algorithm was designed by David Mazieres +. For more information on the level of compatibility, +please refer to the comments in BF_set_key() and to the crypt(3) man page +included in the crypt_blowfish tarball. + +There's a paper on the algorithm that explains its design decisions: + +http://www.usenix.org/events/usenix99/provos.html + +Some of the tricks in BF_ROUND might be inspired by Eric Young's +Blowfish library (I can't be sure if I would think of something if I +hadn't seen his code). + + +6. ext/standard/rand + +The following php_mt_...() functions are based on a C++ class MTRand by +Richard J. Wagner. For more information see the web page at +http://www-personal.engin.umich.edu/~wagnerr/MersenneTwister.html + +Mersenne Twister random number generator -- a C++ class MTRand +Based on code by Makoto Matsumoto, Takuji Nishimura, and Shawn Cokus +Richard J. Wagner v1.0 15 May 2003 rjwagner@writeme.com + +The Mersenne Twister is an algorithm for generating random numbers. It +was designed with consideration of the flaws in various other generators. +The period, 2^19937-1, and the order of equidistribution, 623 dimensions, +are far greater. The generator is also fast; it avoids multiplication and +division, and it benefits from caches and pipelines. For more information +see the inventors' web page at http://www.math.keio.ac.jp/~matumoto/emt.html + +Reference +M. Matsumoto and T. Nishimura, "Mersenne Twister: A 623-Dimensionally +Equidistributed Uniform Pseudo-Random Number Generator", ACM Transactions on +Modeling and Computer Simulation, Vol. 8, No. 1, January 1998, pp 3-30. + +Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura, +Copyright (C) 2000 - 2003, Richard J. Wagner +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + +3. The names of its contributors may not be used to endorse or promote + products derived from this software without specific prior written + permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR +CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + +7. ext/standard/scanf + +scanf.c -- + +This file contains the base code which implements sscanf and by extension +fscanf. Original code is from TCL8.3.0 and bears the following copyright: + +This software is copyrighted by the Regents of the University of +California, Sun Microsystems, Inc., Scriptics Corporation, +and other parties. The following terms apply to all files associated +with the software unless explicitly disclaimed in individual files. + +The authors hereby grant permission to use, copy, modify, distribute, +and license this software and its documentation for any purpose, provided +that existing copyright notices are retained in all copies and that this +notice is included verbatim in any distributions. No written agreement, +license, or royalty fee is required for any of the authorized uses. +Modifications to this software may be copyrighted by their authors +and need not follow the licensing terms described here, provided that +the new terms are clearly indicated on the first page of each file where +they apply. + +IN NO EVENT SHALL THE AUTHORS OR DISTRIBUTORS BE LIABLE TO ANY PARTY +FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES +ARISING OUT OF THE USE OF THIS SOFTWARE, ITS DOCUMENTATION, OR ANY +DERIVATIVES THEREOF, EVEN IF THE AUTHORS HAVE BEEN ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. + +THE AUTHORS AND DISTRIBUTORS SPECIFICALLY DISCLAIM ANY WARRANTIES, +INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. THIS SOFTWARE +IS PROVIDED ON AN "AS IS" BASIS, AND THE AUTHORS AND DISTRIBUTORS HAVE +NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR +MODIFICATIONS. + +GOVERNMENT USE: If you are acquiring this software on behalf of the +U.S. government, the Government shall have only "Restricted Rights" +in the software and related documentation as defined in the Federal +Acquisition Regulations (FARs) in Clause 52.227.19 (c) (2). If you +are acquiring the software on behalf of the Department of Defense, the +software shall be classified as "Commercial Computer Software" and the +Government shall have only "Restricted Rights" as defined in Clause +252.227-7013 (c) (1) of DFARs. Notwithstanding the foregoing, the +authors grant the U.S. Government and others acting in its behalf +permission to use and distribute the software in accordance with the +terms specified in this license. + + +8. ext/standard/strnatcmp.c + +strnatcmp.c -- Perform 'natural order' comparisons of strings in C. +Copyright (C) 2000 by Martin Pool + +This software is provided 'as-is', without any express or implied +warranty. In no event will the authors be held liable for any damages +arising from the use of this software. + +Permission is granted to anyone to use this software for any purpose, +including commercial applications, and to alter it and redistribute it +freely, subject to the following restrictions: + +1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. +2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. +3. This notice may not be removed or altered from any source distribution. + + +9. ext/standard/uuencode + +Portions of this code are based on Berkeley's uuencode/uudecode +implementation. + +Copyright (c) 1983, 1993 +The Regents of the University of California. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. +3. All advertising materials mentioning features or use of this software + must display the following acknowledgement: +This product includes software developed by the University of +California, Berkeley and its contributors. +4. Neither the name of the University nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS +OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) +HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY +OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF +SUCH DAMAGE. + + +10. main/snprintf.c + +Copyright (c) 2002, 2006 Todd C. Miller + +Permission to use, copy, modify, and distribute this software for any +purpose with or without fee is hereby granted, provided that the above +copyright notice and this permission notice appear in all copies. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES +WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR +ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF +OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. + +Sponsored in part by the Defense Advanced Research Projects +Agency (DARPA) and Air Force Research Laboratory, Air Force +Materiel Command, USAF, under agreement number F39502-99-1-0512. + +main/spprintf +Copyright (c) 1995-1998 The Apache Group. All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + +3. All advertising materials mentioning features or use of this + software must display the following acknowledgment: + "This product includes software developed by the Apache Group + for use in the Apache HTTP server project (http://www.apache.org/)." + +4. The names "Apache Server" and "Apache Group" must not be used to + endorse or promote products derived from this software without + prior written permission. + +5. Redistributions of any form whatsoever must retain the following + acknowledgment: + "This product includes software developed by the Apache Group + for use in the Apache HTTP server project (http://www.apache.org/)." + +THIS SOFTWARE IS PROVIDED BY THE APACHE GROUP ``AS IS'' AND ANY +EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE APACHE GROUP OR +ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT +NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) +HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, +STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. +==================================================================== + +This software consists of voluntary contributions made by many +individuals on behalf of the Apache Group and was originally based +on public domain software written at the National Center for +Supercomputing Applications, University of Illinois, Urbana-Champaign. +For more information on the Apache Group and the Apache HTTP server +project, please see . + +This code is based on, and used with the permission of, the +SIO stdio-replacement strx_* functions by Panos Tsirigotis + for xinetd. + + +11. main/strlcat +12. main/strlcpy + +Copyright (c) 1998 Todd C. Miller +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. +3. The name of the author may not be used to endorse or promote products + derived from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, +INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY +AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. 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Funded under + Grant P41-RR02188 by the National Institutes of Health. + +* Portions copyright 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, + 2004 by Boutell.Com, Inc. + +* Portions relating to GD2 format copyright 1999, 2000, 2001, 2002, + 2003, 2004 Philip Warner. + +* Portions relating to PNG copyright 1999, 2000, 2001, 2002, 2003, + 2004 Greg Roelofs. + +* Portions relating to gdttf.c copyright 1999, 2000, 2001, 2002, + 2003, 2004 John Ellson (ellson@graphviz.org). + +* Portions relating to gdft.c copyright 2001, 2002, 2003, 2004 John + Ellson (ellson@graphviz.org). + +* Portions copyright 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007 + Pierre-Alain Joye (pierre@libgd.org). + +* Portions relating to JPEG and to color quantization copyright + 2000, 2001, 2002, 2003, 2004, Doug Becker and copyright (C) 1994, + 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004 Thomas + G. Lane. This software is based in part on the work of the + Independent JPEG Group. See the file README-JPEG.TXT for more + information. + +* Portions relating to GIF compression copyright 1989 by Jef + Poskanzer and David Rowley, with modifications for thread safety + by Thomas Boutell. + +* Portions relating to GIF decompression copyright 1990, 1991, 1993 + by David Koblas, with modifications for thread safety by Thomas + Boutell. + +* Portions relating to WBMP copyright 2000, 2001, 2002, 2003, 2004 + Maurice Szmurlo and Johan Van den Brande. + +* Portions relating to GIF animations copyright 2004 Jaakko Hyvรคtti + (jaakko.hyvatti@iki.fi) + +Permission has been granted to copy, distribute and modify gd in +any context without fee, including a commercial application, +provided that this notice is present in user-accessible supporting +documentation. + +This does not affect your ownership of the derived work itself, +and the intent is to assure proper credit for the authors of gd, +not to interfere with your productive use of gd. If you have +questions, ask. "Derived works" includes all programs that utilize +the library. Credit must be given in user-accessible +documentation. + +This software is provided "AS IS." The copyright holders disclaim +all warranties, either express or implied, including but not +limited to implied warranties of merchantability and fitness for a +particular purpose, with respect to this code and accompanying +documentation. + +Although their code does not appear in the current release, the +authors wish to thank David Koblas, David Rowley, and Hutchison +Avenue Software Corporation for their prior contributions. + +END OF COPYRIGHT STATEMENT + + +14. ext/phar portions of tar implementations + +portions of tar implementations in ext/phar - phar_tar_octal() are based on an +implementation by Tim Kientzle from libarchive, licensed with this license: + + Copyright (c) 2003-2007 Tim Kientzle + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + 2. 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Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. +3. The names of the authors may not be used to endorse or promote + products derived from this software without specific prior + written permission. + +THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS +OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. 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Permission to copy and +distribute verbatim copies of this document is granted. + + +19. xxHash + +xxHash - Extremely Fast Hash algorithm +Header File +Copyright (C) 2012-2020 Yann Collet + +BSD 2-Clause License (https://www.opensource.org/licenses/bsd-license.php) + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are +met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following disclaimer + in the documentation and/or other materials provided with the + distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +You can contact the author at: + - xxHash homepage: https://www.xxhash.com + - xxHash source repository: https://github.com/Cyan4973/xxHash diff --git a/php/snapshot.txt b/php/snapshot.txt new file mode 100644 index 0000000000000000000000000000000000000000..62d0b15ac188184fa2adf7bc6f6ede8c493ab194 --- /dev/null +++ b/php/snapshot.txt @@ -0,0 +1,154 @@ +This snapshot was automatically generated on +Wed, 06 May 2026 09:36:45 +0000 + +Version: 8.5.6 +Branch: HEAD +Build: C:\Users\runneradmin\AppData\Local\Temp\php-e122e17e-74ec-4483-853b-88ed221b3b3a\config\vs17\x64\obj\Release_TS + +Built-in Extensions +=========================== +Core +bcmath +calendar +ctype +date +filter +hash +iconv +json +SPL +pcre +random +readline +Reflection +session +standard +mysqlnd +tokenizer +zlib +libxml +dom +PDO +openssl +SimpleXML +xml +xmlreader +xmlwriter +curl +ftp +sqlite3 +Phar +mbstring +mysqli +zip + + +Dependency information: +Module: php_curl.dll +=========================== + libcrypto-3-x64.dll + libssl-3-x64.dll + libssh2.dll + nghttp2.dll + brotlidec.dll + libzstd.dll + +Module: libssl-3-x64.dll +=========================== + libcrypto-3-x64.dll + +Module: libssh2.dll +=========================== + libcrypto-3-x64.dll + +Module: brotlidec.dll +=========================== + brotlicommon.dll + +Module: php_enchant.dll +=========================== + libenchant2.dll + +Module: libenchant2.dll +=========================== + glib-2.dll + gobject-2.dll + gmodule-2.dll + +Module: gobject-2.dll +=========================== + glib-2.dll + +Module: gmodule-2.dll +=========================== + glib-2.dll + +Module: php_ftp.dll +=========================== + libcrypto-3-x64.dll + libssl-3-x64.dll + +Module: php_ldap.dll +=========================== + libsasl.dll + libcrypto-3-x64.dll + libssl-3-x64.dll + +Module: libsasl.dll +=========================== + libcrypto-3-x64.dll + +Module: php_openssl.dll +=========================== + libcrypto-3-x64.dll + libssl-3-x64.dll + +Module: php_sodium.dll +=========================== + libsodium.dll + +Module: php_sqlite3.dll +=========================== + libsqlite3.dll + +Module: php_intl.dll +=========================== + icuin77.dll + icuio77.dll + icuuc77.dll + +Module: icuin77.dll +=========================== + icuuc77.dll + +Module: icuuc77.dll +=========================== + icudt77.dll + +Module: icuio77.dll +=========================== + icuuc77.dll + icuin77.dll + +Module: php_pdo_pgsql.dll +=========================== + libpq.dll + +Module: libpq.dll +=========================== + libssl-3-x64.dll + libcrypto-3-x64.dll + +Module: php_pdo_sqlite.dll +=========================== + libsqlite3.dll + +Module: php_pgsql.dll +=========================== + libpq.dll + +Module: php_snmp.dll +=========================== + libcrypto-3-x64.dll + libssl-3-x64.dll + diff --git a/php/vcruntime140_1.dll b/php/vcruntime140_1.dll new file mode 100644 index 0000000000000000000000000000000000000000..2071f2814bbc6dac9a18b32c77ca86da09d579c3 Binary files /dev/null and b/php/vcruntime140_1.dll differ diff --git a/python/LICENSE.txt b/python/LICENSE.txt new file mode 100644 index 0000000000000000000000000000000000000000..89bc34808ca6b85055824b4c276778be7a376113 --- /dev/null +++ b/python/LICENSE.txt @@ -0,0 +1,645 @@ +A. HISTORY OF THE SOFTWARE +========================== + +Python was created in the early 1990s by Guido van Rossum at Stichting +Mathematisch Centrum (CWI, see https://www.cwi.nl) in the Netherlands +as a successor of a language called ABC. Guido remains Python's +principal author, although it includes many contributions from others. + +In 1995, Guido continued his work on Python at the Corporation for +National Research Initiatives (CNRI, see https://www.cnri.reston.va.us) +in Reston, Virginia where he released several versions of the +software. + +In May 2000, Guido and the Python core development team moved to +BeOpen.com to form the BeOpen PythonLabs team. In October of the same +year, the PythonLabs team moved to Digital Creations, which became +Zope Corporation. In 2001, the Python Software Foundation (PSF, see +https://www.python.org/psf/) was formed, a non-profit organization +created specifically to own Python-related Intellectual Property. +Zope Corporation was a sponsoring member of the PSF. + +All Python releases are Open Source (see https://opensource.org for +the Open Source Definition). Historically, most, but not all, Python +releases have also been GPL-compatible; the table below summarizes +the various releases. + + Release Derived Year Owner GPL- + from compatible? (1) + + 0.9.0 thru 1.2 1991-1995 CWI yes + 1.3 thru 1.5.2 1.2 1995-1999 CNRI yes + 1.6 1.5.2 2000 CNRI no + 2.0 1.6 2000 BeOpen.com no + 1.6.1 1.6 2001 CNRI yes (2) + 2.1 2.0+1.6.1 2001 PSF no + 2.0.1 2.0+1.6.1 2001 PSF yes + 2.1.1 2.1+2.0.1 2001 PSF yes + 2.1.2 2.1.1 2002 PSF yes + 2.1.3 2.1.2 2002 PSF yes + 2.2 and above 2.1.1 2001-now PSF yes + +Footnotes: + +(1) GPL-compatible doesn't mean that we're distributing Python under + the GPL. All Python licenses, unlike the GPL, let you distribute + a modified version without making your changes open source. The + GPL-compatible licenses make it possible to combine Python with + other software that is released under the GPL; the others don't. + +(2) According to Richard Stallman, 1.6.1 is not GPL-compatible, + because its license has a choice of law clause. According to + CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1 + is "not incompatible" with the GPL. + +Thanks to the many outside volunteers who have worked under Guido's +direction to make these releases possible. + + +B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON +=============================================================== + +Python software and documentation are licensed under the +Python Software Foundation License Version 2. + +Starting with Python 3.8.6, examples, recipes, and other code in +the documentation are dual licensed under the PSF License Version 2 +and the Zero-Clause BSD license. + +Some software incorporated into Python is under different licenses. +The licenses are listed with code falling under that license. + + +PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 +-------------------------------------------- + +1. This LICENSE AGREEMENT is between the Python Software Foundation +("PSF"), and the Individual or Organization ("Licensee") accessing and +otherwise using this software ("Python") in source or binary form and +its associated documentation. + +2. Subject to the terms and conditions of this License Agreement, PSF hereby +grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, +analyze, test, perform and/or display publicly, prepare derivative works, +distribute, and otherwise use Python alone or in any derivative version, +provided, however, that PSF's License Agreement and PSF's notice of copyright, +i.e., "Copyright (c) 2001-2024 Python Software Foundation; All Rights Reserved" +are retained in Python alone or in any derivative version prepared by Licensee. + +3. In the event Licensee prepares a derivative work that is based on +or incorporates Python or any part thereof, and wants to make +the derivative work available to others as provided herein, then +Licensee hereby agrees to include in any such work a brief summary of +the changes made to Python. + +4. PSF is making Python available to Licensee on an "AS IS" +basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR +IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND +DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS +FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT +INFRINGE ANY THIRD PARTY RIGHTS. + +5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON +FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS +A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON, +OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. + +6. This License Agreement will automatically terminate upon a material +breach of its terms and conditions. + +7. Nothing in this License Agreement shall be deemed to create any +relationship of agency, partnership, or joint venture between PSF and +Licensee. This License Agreement does not grant permission to use PSF +trademarks or trade name in a trademark sense to endorse or promote +products or services of Licensee, or any third party. + +8. By copying, installing or otherwise using Python, Licensee +agrees to be bound by the terms and conditions of this License +Agreement. + + +BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0 +------------------------------------------- + +BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1 + +1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an +office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the +Individual or Organization ("Licensee") accessing and otherwise using +this software in source or binary form and its associated +documentation ("the Software"). + +2. Subject to the terms and conditions of this BeOpen Python License +Agreement, BeOpen hereby grants Licensee a non-exclusive, +royalty-free, world-wide license to reproduce, analyze, test, perform +and/or display publicly, prepare derivative works, distribute, and +otherwise use the Software alone or in any derivative version, +provided, however, that the BeOpen Python License is retained in the +Software, alone or in any derivative version prepared by Licensee. + +3. BeOpen is making the Software available to Licensee on an "AS IS" +basis. BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR +IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND +DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS +FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT +INFRINGE ANY THIRD PARTY RIGHTS. + +4. 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As an exception, the "BeOpen Python" logos available at +http://www.pythonlabs.com/logos.html may be used according to the +permissions granted on that web page. + +7. By copying, installing or otherwise using the software, Licensee +agrees to be bound by the terms and conditions of this License +Agreement. + + +CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1 +--------------------------------------- + +1. This LICENSE AGREEMENT is between the Corporation for National +Research Initiatives, having an office at 1895 Preston White Drive, +Reston, VA 20191 ("CNRI"), and the Individual or Organization +("Licensee") accessing and otherwise using Python 1.6.1 software in +source or binary form and its associated documentation. + +2. Subject to the terms and conditions of this License Agreement, CNRI +hereby grants Licensee a nonexclusive, royalty-free, world-wide +license to reproduce, analyze, test, perform and/or display publicly, +prepare derivative works, distribute, and otherwise use Python 1.6.1 +alone or in any derivative version, provided, however, that CNRI's +License Agreement and CNRI's notice of copyright, i.e., "Copyright (c) +1995-2001 Corporation for National Research Initiatives; All Rights +Reserved" are retained in Python 1.6.1 alone or in any derivative +version prepared by Licensee. Alternately, in lieu of CNRI's License +Agreement, Licensee may substitute the following text (omitting the +quotes): "Python 1.6.1 is made available subject to the terms and +conditions in CNRI's License Agreement. This Agreement together with +Python 1.6.1 may be located on the internet using the following +unique, persistent identifier (known as a handle): 1895.22/1013. This +Agreement may also be obtained from a proxy server on the internet +using the following URL: http://hdl.handle.net/1895.22/1013". + +3. In the event Licensee prepares a derivative work that is based on +or incorporates Python 1.6.1 or any part thereof, and wants to make +the derivative work available to others as provided herein, then +Licensee hereby agrees to include in any such work a brief summary of +the changes made to Python 1.6.1. + +4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS" +basis. CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR +IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND +DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS +FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT +INFRINGE ANY THIRD PARTY RIGHTS. + +5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON +1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS +A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1, +OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF. + +6. This License Agreement will automatically terminate upon a material +breach of its terms and conditions. + +7. This License Agreement shall be governed by the federal +intellectual property law of the United States, including without +limitation the federal copyright law, and, to the extent such +U.S. federal law does not apply, by the law of the Commonwealth of +Virginia, excluding Virginia's conflict of law provisions. +Notwithstanding the foregoing, with regard to derivative works based +on Python 1.6.1 that incorporate non-separable material that was +previously distributed under the GNU General Public License (GPL), the +law of the Commonwealth of Virginia shall govern this License +Agreement only as to issues arising under or with respect to +Paragraphs 4, 5, and 7 of this License Agreement. Nothing in this +License Agreement shall be deemed to create any relationship of +agency, partnership, or joint venture between CNRI and Licensee. This +License Agreement does not grant permission to use CNRI trademarks or +trade name in a trademark sense to endorse or promote products or +services of Licensee, or any third party. + +8. By clicking on the "ACCEPT" button where indicated, or by copying, +installing or otherwise using Python 1.6.1, Licensee agrees to be +bound by the terms and conditions of this License Agreement. + + ACCEPT + + +CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2 +-------------------------------------------------- + +Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam, +The Netherlands. All rights reserved. + +Permission to use, copy, modify, and distribute this software and its +documentation for any purpose and without fee is hereby granted, +provided that the above copyright notice appear in all copies and that +both that copyright notice and this permission notice appear in +supporting documentation, and that the name of Stichting Mathematisch +Centrum or CWI not be used in advertising or publicity pertaining to +distribution of the software without specific, written prior +permission. + +STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO +THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND +FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE +FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT +OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. + +ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION +---------------------------------------------------------------------- + +Permission to use, copy, modify, and/or distribute this software for any +purpose with or without fee is hereby granted. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH +REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY +AND FITNESS. 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See Microsoft's documentation +(included in its developer tools and on its website at microsoft.com) +for specific details. + +Redistribution of the Windows binary build of the Python interpreter +complies with this agreement, provided that you do not: + +- alter any copyright, trademark or patent notice in Microsoft's +Distributable Code; + +- use Microsoft's trademarks in your programs' names or in a way that +suggests your programs come from or are endorsed by Microsoft; + +- distribute Microsoft's Distributable Code to run on a platform other +than Microsoft operating systems, run-time technologies or application +platforms; or + +- include Microsoft Distributable Code in malicious, deceptive or +unlawful programs. + +These restrictions apply only to the Microsoft Distributable Code as +defined above, not to Python itself or any programs running on the +Python interpreter. The redistribution of the Python interpreter and +libraries is governed by the Python Software License included with this +file, or by other licenses as marked. + + + +-------------------------------------------------------------------------- + +This program, "bzip2", the associated library "libbzip2", and all +documentation, are copyright (C) 1996-2019 Julian R Seward. All +rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. The origin of this software must not be misrepresented; you must + not claim that you wrote the original software. If you use this + software in a product, an acknowledgment in the product + documentation would be appreciated but is not required. + +3. 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Notwithstanding the foregoing, the +authors grant the U.S. Government and others acting in its behalf +permission to use and distribute the software in accordance with the +terms specified in this license. + diff --git a/python/get-pip.py b/python/get-pip.py new file mode 100644 index 0000000000000000000000000000000000000000..ac3bf23c2c6f7a95e32f7b08aefc646c7e71dbb2 --- /dev/null +++ b/python/get-pip.py @@ -0,0 +1,27918 @@ +#!/usr/bin/env python +# +# Hi There! +# +# You may be wondering what this giant blob of binary data here is, you might +# even be worried that we're up to something nefarious (good for you for being +# paranoid!). This is a base85 encoding of a zip file, this zip file contains +# an entire copy of pip (version 26.1.1). +# +# Pip is a thing that installs packages, pip itself is a package that someone +# might want to install, especially if they're looking to run this get-pip.py +# script. Pip has a lot of code to deal with the security of installing +# packages, various edge cases on various platforms, and other such sort of +# "tribal knowledge" that has been encoded in its code base. Because of this +# we basically include an entire copy of pip inside this blob. We do this +# because the alternatives are attempt to implement a "minipip" that probably +# doesn't do things correctly and has weird edge cases, or compress pip itself +# down into a single file. +# +# If you're wondering how this is created, it is generated using +# `scripts/generate.py` in https://github.com/pypa/get-pip. + +import sys + +this_python = sys.version_info[:2] +min_version = (3, 10) +if this_python < min_version: + message_parts = [ + "This script does not work on Python {}.{}.".format(*this_python), + "The minimum supported Python version is {}.{}.".format(*min_version), + "Please use https://bootstrap.pypa.io/pip/{}.{}/get-pip.py instead.".format(*this_python), + ] + print("ERROR: " + " ".join(message_parts)) + sys.exit(1) + + +import os.path +import pkgutil +import shutil +import tempfile +import argparse +import importlib +from base64 import b85decode + + +def include_setuptools(args): + """ + Install setuptools only if absent, not excluded and when using Python <3.12. + """ + cli = not args.no_setuptools + env = not os.environ.get("PIP_NO_SETUPTOOLS") + absent = not importlib.util.find_spec("setuptools") + python_lt_3_12 = this_python < (3, 12) + return cli and env and absent and python_lt_3_12 + + +def include_wheel(args): + """ + Install wheel only if absent, not excluded and when using Python <3.12. + """ + cli = not args.no_wheel + env = not os.environ.get("PIP_NO_WHEEL") + absent = not importlib.util.find_spec("wheel") + python_lt_3_12 = this_python < (3, 12) + return cli and env and absent and python_lt_3_12 + + +def determine_pip_install_arguments(): + pre_parser = argparse.ArgumentParser() + pre_parser.add_argument("--no-setuptools", action="store_true") + pre_parser.add_argument("--no-wheel", action="store_true") + pre, args = pre_parser.parse_known_args() + + args.append("pip") + + if include_setuptools(pre): + args.append("setuptools") + + if include_wheel(pre): + args.append("wheel") + + return ["install", "--upgrade", "--force-reinstall"] + args + + +def monkeypatch_for_cert(tmpdir): + """Patches `pip install` to provide default certificate with the lowest priority. + + This ensures that the bundled certificates are used unless the user specifies a + custom cert via any of pip's option passing mechanisms (config, env-var, CLI). + + A monkeypatch is the easiest way to achieve this, without messing too much with + the rest of pip's internals. + """ + from pip._internal.commands.install import InstallCommand + + # We want to be using the internal certificates. + cert_path = os.path.join(tmpdir, "cacert.pem") + with open(cert_path, "wb") as cert: + cert.write(pkgutil.get_data("pip._vendor.certifi", "cacert.pem")) + + install_parse_args = InstallCommand.parse_args + + def cert_parse_args(self, args): + if not self.parser.get_default_values().cert: + # There are no user provided cert -- force use of bundled cert + self.parser.defaults["cert"] = cert_path # calculated above + return install_parse_args(self, args) + + InstallCommand.parse_args = cert_parse_args + + +def bootstrap(tmpdir): + monkeypatch_for_cert(tmpdir) + + # Execute the included pip and use it to install the latest pip and + # any user-requested packages from PyPI. + from pip._internal.cli.main import main as pip_entry_point + args = determine_pip_install_arguments() + sys.exit(pip_entry_point(args)) + + +def main(): + tmpdir = None + try: + # Create a temporary working directory + tmpdir = tempfile.mkdtemp() + + # Unpack the zipfile into the temporary directory + pip_zip = os.path.join(tmpdir, "pip.zip") + with open(pip_zip, "wb") as fp: + fp.write(b85decode(DATA.replace(b"\n", b""))) + + # Add the zipfile to sys.path so that we can import it + sys.path.insert(0, pip_zip) + + # Run the bootstrap + bootstrap(tmpdir=tmpdir) + finally: + # Clean up our temporary working directory + if tmpdir: + shutil.rmtree(tmpdir, ignore_errors=True) + + +DATA = b""" 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http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + + + +APACHE PORTABLE RUNTIME SUBCOMPONENTS: + +The Apache Portable Runtime includes a number of subcomponents with +separate copyright notices and license terms. Your use of the source +code for the these subcomponents is subject to the terms and +conditions of the following licenses. + +From strings/apr_fnmatch.c, include/apr_fnmatch.h, misc/unix/getopt.c, +file_io/unix/mktemp.c, strings/apr_strings.c: + +/* + * Copyright (c) 1987, 1993, 1994 + * The Regents of the University of California. All rights reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * 3. All advertising materials mentioning features or use of this software + * must display the following acknowledgement: + * This product includes software developed by the University of + * California, Berkeley and its contributors. + * 4. Neither the name of the University nor the names of its contributors + * may be used to endorse or promote products derived from this software + * without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + * ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE + * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS + * OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) + * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT + * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY + * OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF + * SUCH DAMAGE. + +From network_io/unix/inet_ntop.c, network_io/unix/inet_pton.c: + +/* Copyright (c) 1996 by Internet Software Consortium. + * + * Permission to use, copy, modify, and distribute this software for any + * purpose with or without fee is hereby granted, provided that the above + * copyright notice and this permission notice appear in all copies. + * + * THE SOFTWARE IS PROVIDED "AS IS" AND INTERNET SOFTWARE CONSORTIUM DISCLAIMS + * ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES + * OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL INTERNET SOFTWARE + * CONSORTIUM BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL + * DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR + * PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS + * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS + * SOFTWARE. + +From dso/aix/dso.c: + + * Based on libdl (dlfcn.c/dlfcn.h) which is + * Copyright (c) 1992,1993,1995,1996,1997,1988 + * Jens-Uwe Mager, Helios Software GmbH, Hannover, Germany. + * + * Not derived from licensed software. + * + * Permission is granted to freely use, copy, modify, and redistribute + * this software, provided that the author is not construed to be liable + * for any results of using the software, alterations are clearly marked + * as such, and this notice is not modified. + +From strings/apr_strnatcmp.c, include/apr_strings.h: + + strnatcmp.c -- Perform 'natural order' comparisons of strings in C. + Copyright (C) 2000 by Martin Pool + + This software is provided 'as-is', without any express or implied + warranty. In no event will the authors be held liable for any damages + arising from the use of this software. + + Permission is granted to anyone to use this software for any purpose, + including commercial applications, and to alter it and redistribute it + freely, subject to the following restrictions: + + 1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software + in a product, an acknowledgment in the product documentation would be + appreciated but is not required. + 2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. + 3. This notice may not be removed or altered from any source distribution. + diff --git a/svn/Licenses/APR Util License.txt b/svn/Licenses/APR Util License.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ec7e560022060feb25a4b78c95f94528dca18d8 --- /dev/null +++ b/svn/Licenses/APR Util License.txt @@ -0,0 +1,404 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + + + +APACHE PORTABLE RUNTIME SUBCOMPONENTS: + +The Apache Portable Runtime includes a number of subcomponents with +separate copyright notices and license terms. Your use of the source +code for the these subcomponents is subject to the terms and +conditions of the following licenses. + +For the include\apr_md5.h component: +/* + * This is work is derived from material Copyright RSA Data Security, Inc. + * + * The RSA copyright statement and Licence for that original material is + * included below. This is followed by the Apache copyright statement and + * licence for the modifications made to that material. + */ + +/* Copyright (C) 1991-2, RSA Data Security, Inc. Created 1991. All + rights reserved. + + License to copy and use this software is granted provided that it + is identified as the "RSA Data Security, Inc. MD5 Message-Digest + Algorithm" in all material mentioning or referencing this software + or this function. + + License is also granted to make and use derivative works provided + that such works are identified as "derived from the RSA Data + Security, Inc. MD5 Message-Digest Algorithm" in all material + mentioning or referencing the derived work. + + RSA Data Security, Inc. makes no representations concerning either + the merchantability of this software or the suitability of this + software for any particular purpose. It is provided "as is" + without express or implied warranty of any kind. + + These notices must be retained in any copies of any part of this + documentation and/or software. + */ + +For the passwd\apr_md5.c component: + +/* + * This is work is derived from material Copyright RSA Data Security, Inc. + * + * The RSA copyright statement and Licence for that original material is + * included below. This is followed by the Apache copyright statement and + * licence for the modifications made to that material. + */ + +/* MD5C.C - RSA Data Security, Inc., MD5 message-digest algorithm + */ + +/* Copyright (C) 1991-2, RSA Data Security, Inc. Created 1991. All + rights reserved. + + License to copy and use this software is granted provided that it + is identified as the "RSA Data Security, Inc. MD5 Message-Digest + Algorithm" in all material mentioning or referencing this software + or this function. + + License is also granted to make and use derivative works provided + that such works are identified as "derived from the RSA Data + Security, Inc. MD5 Message-Digest Algorithm" in all material + mentioning or referencing the derived work. + + RSA Data Security, Inc. makes no representations concerning either + the merchantability of this software or the suitability of this + software for any particular purpose. It is provided "as is" + without express or implied warranty of any kind. + + These notices must be retained in any copies of any part of this + documentation and/or software. + */ +/* + * The apr_md5_encode() routine uses much code obtained from the FreeBSD 3.0 + * MD5 crypt() function, which is licenced as follows: + * ---------------------------------------------------------------------------- + * "THE BEER-WARE LICENSE" (Revision 42): + * wrote this file. As long as you retain this notice you + * can do whatever you want with this stuff. If we meet some day, and you think + * this stuff is worth it, you can buy me a beer in return. Poul-Henning Kamp + * ---------------------------------------------------------------------------- + */ + +For the crypto\apr_md4.c component: + + * This is derived from material copyright RSA Data Security, Inc. + * Their notice is reproduced below in its entirety. + * + * Copyright (C) 1991-2, RSA Data Security, Inc. Created 1991. All + * rights reserved. + * + * License to copy and use this software is granted provided that it + * is identified as the "RSA Data Security, Inc. MD4 Message-Digest + * Algorithm" in all material mentioning or referencing this software + * or this function. + * + * License is also granted to make and use derivative works provided + * that such works are identified as "derived from the RSA Data + * Security, Inc. MD4 Message-Digest Algorithm" in all material + * mentioning or referencing the derived work. + * + * RSA Data Security, Inc. makes no representations concerning either + * the merchantability of this software or the suitability of this + * software for any particular purpose. It is provided "as is" + * without express or implied warranty of any kind. + * + * These notices must be retained in any copies of any part of this + * documentation and/or software. + */ + +For the include\apr_md4.h component: + + * + * This is derived from material copyright RSA Data Security, Inc. + * Their notice is reproduced below in its entirety. + * + * Copyright (C) 1991-2, RSA Data Security, Inc. Created 1991. All + * rights reserved. + * + * License to copy and use this software is granted provided that it + * is identified as the "RSA Data Security, Inc. MD4 Message-Digest + * Algorithm" in all material mentioning or referencing this software + * or this function. + * + * License is also granted to make and use derivative works provided + * that such works are identified as "derived from the RSA Data + * Security, Inc. MD4 Message-Digest Algorithm" in all material + * mentioning or referencing the derived work. + * + * RSA Data Security, Inc. makes no representations concerning either + * the merchantability of this software or the suitability of this + * software for any particular purpose. It is provided "as is" + * without express or implied warranty of any kind. + * + * These notices must be retained in any copies of any part of this + * documentation and/or software. + */ + +For the test\testmd4.c component: + + * + * This is derived from material copyright RSA Data Security, Inc. + * Their notice is reproduced below in its entirety. + * + * Copyright (C) 1990-2, RSA Data Security, Inc. Created 1990. All + * rights reserved. + * + * RSA Data Security, Inc. makes no representations concerning either + * the merchantability of this software or the suitability of this + * software for any particular purpose. It is provided "as is" + * without express or implied warranty of any kind. + * + * These notices must be retained in any copies of any part of this + * documentation and/or software. + */ + +For the xml\expat\conftools\install-sh component: + +# +# install - install a program, script, or datafile +# This comes from X11R5 (mit/util/scripts/install.sh). +# +# Copyright 1991 by the Massachusetts Institute of Technology +# +# Permission to use, copy, modify, distribute, and sell this software and its +# documentation for any purpose is hereby granted without fee, provided that +# the above copyright notice appear in all copies and that both that +# copyright notice and this permission notice appear in supporting +# documentation, and that the name of M.I.T. not be used in advertising or +# publicity pertaining to distribution of the software without specific, +# written prior permission. M.I.T. makes no representations about the +# suitability of this software for any purpose. It is provided "as is" +# without express or implied warranty. +# + +For the expat xml parser component: + +Copyright (c) 1998, 1999, 2000 Thai Open Source Software Center Ltd + and Clark Cooper + +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. + +==================================================================== diff --git a/svn/Licenses/OpenSSL License.txt b/svn/Licenses/OpenSSL License.txt new file mode 100644 index 0000000000000000000000000000000000000000..49cc83d2ee29d13453188217f0e4edd70c7f842f --- /dev/null +++ b/svn/Licenses/OpenSSL License.txt @@ -0,0 +1,177 @@ + + Apache License + Version 2.0, January 2004 + https://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. 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All rights reserved. +SPDX-License-Identifier: LicenseRef-NvidiaProprietary +NVIDIA CORPORATION, its affiliates and licensors retain all intellectual +property and proprietary rights in and to this material, related +documentation and any modifications thereto. Any use, reproduction, +disclosure or distribution of this material and related documentation +without an express license agreement from NVIDIA CORPORATION or +its affiliates is strictly prohibited. + +This project will download and install additional third-party open source software projects. +Review the license terms of these open source projects before use. + +TensorRT uses elements from the following software, whose licenses are reproduced below. + +Abseil C++ Common Libraries +--------------------------- + + Apache License + Version 2.0, January 2004 + https://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + +3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + +4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + +5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + +6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + +7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + +8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + +9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + +END OF TERMS AND CONDITIONS + +Boost Beast +----------- +Copyright (c) 2016-2017 Vinnie Falco (vinnie dot falco at gmail dot com) + +Boost Software License - Version 1.0 - August 17th, 2003 + +Permission is hereby granted, free of charge, to any person or organization +obtaining a copy of the software and accompanying documentation covered by +this license (the "Software") to use, reproduce, display, distribute, +execute, and transmit the Software, and to prepare derivative works of the +Software, and to permit third-parties to whom the Software is furnished to +do so, all subject to the following: + +The copyright notices in the Software and this entire statement, including +the above license grant, this restriction and the following disclaimer, +must be included in all copies of the Software, in whole or in part, and +all derivative works of the Software, unless such copies or derivative +works are solely in the form of machine-executable object code generated by +a source language processor. + +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, TITLE AND NON-INFRINGEMENT. IN NO EVENT +SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE +FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, +ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +DEALINGS IN THE SOFTWARE. + +thread-pool +----------- +MIT License + +Copyright (c) 2023 Barak Shoshany + +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. + +docs +---- + +https://pypi.org/pypi/docs +Unknown License + +Notices Text Not Available + +efficientnet +---- +Apache License Version 2.0 + +https://github.com/qubvel/efficientnet + +Copyright 2019 The TensorFlow Authors, Pavel Yakubovskiy. All rights reserved. + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2017, The TensorFlow Authors. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + +giza +---- +Apache License Version 2.0 + +https://github.com/mongodb/docs-tools + + + Copyright 2014 MongoDB, Inc. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + +onnx +---- +Apache License 2.0 + +https://github.com/onnx/onnx + + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + +packaging +--------- +Apache License Version 2.0 + +https://github.com/pypa/packaging + +This software is made available under the terms of *either* of the licenses +found in LICENSE.APACHE or LICENSE.BSD. Contributions to this software is made +under the terms of *both* these licenses. + + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. 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Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND +ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/tensorrt/doc/README.txt b/tensorrt/doc/README.txt new file mode 100644 index 0000000000000000000000000000000000000000..b4c413cfe6e032e659a71300c79e71108ee767fa --- /dev/null +++ b/tensorrt/doc/README.txt @@ -0,0 +1,31 @@ +=== NVIDIA TensorRT === + +NVIDIAยฎ TensorRTโ„ข is a C++ library that facilitates high-performance inference +on NVIDIA GPUs. TensorRT takes a trained network, which consists of a network +definition and a set of trained parameters, and produces a highly optimized +runtime engine that performs inference for that network. TensorRT provides APIs +using C++ and Python that help to express deep learning models using the Network +Definition API or load a pre-defined model using the parsers that allow TensorRT +to optimize and run them on an NVIDIA GPU. TensorRT applies graph optimizations, +layer fusion, among other optimizations, while also finding the fastest +implementation of that model leveraging a diverse collection of highly optimized +kernels. TensorRT also supplies a runtime that you can use to execute this +network on NVIDIAโ€™s GPUs. + +For more information about TensorRT, visit https://developer.nvidia.com/tensorrt. + +In previous TensorRT releases, PDF documentation was included inside the TensorRT +package. The PDF documentation has been removed from the package in favor of +online documentation, which is updated regularly. Online documentation can be +found at https://docs.nvidia.com/deeplearning/tensorrt/latest/index.html. + +For details on TensorRT's license agreement, visit https://docs.nvidia.com/deeplearning/tensorrt/latest/reference/sla.html. + +=== References === + +Quick Start Guide: https://docs.nvidia.com/deeplearning/tensorrt/latest/getting-started/quick-start-guide.html +Release Notes: https://docs.nvidia.com/deeplearning/tensorrt/latest/getting-started/release-notes.html +Support Matrix: https://docs.nvidia.com/deeplearning/tensorrt/latest/getting-started/support-matrix.html +Installation Guide: https://docs.nvidia.com/deeplearning/tensorrt/latest/installing-tensorrt/overview.html +C++ API: https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/c-api-docs.html +Python API: https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/python-api-docs.html diff --git a/tensorrt/include/NvInfer.h b/tensorrt/include/NvInfer.h new file mode 100644 index 0000000000000000000000000000000000000000..da2928ad70705c41cfbbdabf7df2a89e128f003f --- /dev/null +++ b/tensorrt/include/NvInfer.h @@ -0,0 +1,12559 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NV_INFER_H +#define NV_INFER_H + +#include "NvInferLegacyDims.h" +#include "NvInferRuntime.h" // IWYU pragma: export + +//! +//! \mainpage +//! +//! This is the API documentation for the NVIDIA TensorRT library. It provides information on individual +//! functions, classes and methods. Use the index on the left to navigate the documentation. +//! +//! Please see the accompanying user guide and samples for higher-level information and general advice on +//! using TensorRT. +//! +//! TensorRT Versioning follows Semantic Versioning Guidelines specified here: https://semver.org/ +//! + +//! +//! \file NvInfer.h +//! +//! This is the top-level API file for TensorRT. +//! + +//! +//! \namespace nvinfer1 +//! +//! \brief The TensorRT API version 1 namespace. +//! +namespace nvinfer1 +{ + +//! +//! \enum LayerType +//! +//! \brief The type values of layer classes. +//! +//! \see ILayer::getType() +//! +enum class LayerType : int32_t +{ + kCONVOLUTION = 0, //!< Convolution layer. + kCAST = 1, //!< Cast layer + kACTIVATION = 2, //!< Activation layer. + kPOOLING = 3, //!< Pooling layer. + kLRN = 4, //!< LRN layer. + kSCALE = 5, //!< Scale layer. + kSOFTMAX = 6, //!< SoftMax layer. + kDECONVOLUTION = 7, //!< Deconvolution layer. + kCONCATENATION = 8, //!< Concatenation layer. + kELEMENTWISE = 9, //!< Elementwise layer. + kPLUGIN = 10, //!< Plugin layer. + kUNARY = 11, //!< UnaryOp operation Layer. + kPADDING = 12, //!< Padding layer. + kSHUFFLE = 13, //!< Shuffle layer. + kREDUCE = 14, //!< Reduce layer. + kTOPK = 15, //!< TopK layer. + kGATHER = 16, //!< Gather layer. + kMATRIX_MULTIPLY = 17, //!< Matrix multiply layer. + kRAGGED_SOFTMAX = 18, //!< Ragged softmax layer. + kCONSTANT = 19, //!< Constant layer. + kIDENTITY = 20, //!< Identity layer. + kPLUGIN_V2 = 21, //!< PluginV2 layer. + kSLICE = 22, //!< Slice layer. + kSHAPE = 23, //!< Shape layer. + kPARAMETRIC_RELU = 24, //!< Parametric ReLU layer. + kRESIZE = 25, //!< Resize Layer. + kTRIP_LIMIT = 26, //!< Loop Trip limit layer + kRECURRENCE = 27, //!< Loop Recurrence layer + kITERATOR = 28, //!< Loop Iterator layer + kLOOP_OUTPUT = 29, //!< Loop output layer + kSELECT = 30, //!< Select layer. + kFILL = 31, //!< Fill layer + kQUANTIZE = 32, //!< Quantize layer + kDEQUANTIZE = 33, //!< Dequantize layer + kCONDITION = 34, //!< Condition layer + kCONDITIONAL_INPUT = 35, //!< Conditional Input layer + kCONDITIONAL_OUTPUT = 36, //!< Conditional Output layer + kSCATTER = 37, //!< Scatter layer + kEINSUM = 38, //!< Einsum layer + kASSERTION = 39, //!< Assertion layer + kONE_HOT = 40, //!< OneHot layer + kNON_ZERO = 41, //!< NonZero layer + kGRID_SAMPLE = 42, //!< Grid sample layer + kNMS = 43, //!< NMS layer + kREVERSE_SEQUENCE = 44, //!< Reverse sequence layer + kNORMALIZATION = 45, //!< Normalization layer + kPLUGIN_V3 = 46, //!< PluginV3 layer. + kSQUEEZE = 47, //!< Squeeze Layer. + kUNSQUEEZE = 48, //!< Unsqueeze Layer. + kCUMULATIVE = 49, //!< Cumulative layer. + kDYNAMIC_QUANTIZE = 50, //!< Dynamic Quantize layer. + kATTENTION_INPUT = 51, //!< Attention Input. + kATTENTION_OUTPUT = 52, //!< Attention Output. + kROTARY_EMBEDDING = 53, //!< Rotary Embedding layer. + kKVCACHE_UPDATE = 54, //!< KV Cache Update layer. + kMOE = 55, //!< MoE layer. + kDIST_COLLECTIVE = 56, //!< DistCollective layer. +}; + +//! +//! Maximum number of elements in LayerType enum. +//! +//! \see LayerType +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 57; +} + +//! +//! \brief It is capable of representing one or more TensorFormat by binary OR +//! operations, e.g., 1U << TensorFormat::kCHW4 | 1U << TensorFormat::kCHW32. +//! +//! \see ITensor::getAllowedFormats(), ITensor::setAllowedFormats(), +//! +using TensorFormats = uint32_t; + +//! +//! \enum ActivationType +//! +//! \brief Enumerates the types of activation to perform in an activation layer. +//! +enum class ActivationType : int32_t +{ + kRELU = 0, //!< Rectified linear activation. + kSIGMOID = 1, //!< Sigmoid activation. + kTANH = 2, //!< TanH activation. + kLEAKY_RELU = 3, //!< LeakyRelu activation: x>=0 ? x : alpha * x. + kELU = 4, //!< Elu activation: x>=0 ? x : alpha * (exp(x) - 1). + kSELU = 5, //!< Selu activation: x>0 ? beta * x : beta * (alpha*exp(x) - alpha) + kSOFTSIGN = 6, //!< Softsign activation: x / (1+|x|) + kSOFTPLUS = 7, //!< Parametric softplus activation: alpha*log(exp(beta*x)+1) + kCLIP = 8, //!< Clip activation: max(alpha, min(beta, x)) + kHARD_SIGMOID = 9, //!< Hard sigmoid activation: max(0, min(1, alpha*x+beta)) + kSCALED_TANH = 10, //!< Scaled tanh activation: alpha*tanh(beta*x) + kTHRESHOLDED_RELU = 11, //!< Thresholded ReLU activation: x>alpha ? x : 0 + kGELU_ERF = 12, //!< GELU erf activation: 0.5 * x * (1 + erf(sqrt(0.5) * x)) + kGELU_TANH = 13 //!< GELU tanh activation: 0.5 * x * (1 + tanh(sqrt(2/pi) * (0.044715F * pow(x, 3) + x))) +}; + +namespace impl +{ +//! +//! Maximum number of elements in ActivationType enum. +//! +//! \see ActivationType +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 14; +}; +} // namespace impl + +//! +//! \class ITensor +//! +//! \brief A tensor in a network definition. +//! +//! To remove a tensor from a network definition, use INetworkDefinition::removeTensor(). +//! +//! When using the DLA, the cumulative size of all Tensors that are not marked as Network Input or Output tensors, +//! must be less than 1GB in size to fit into a single subgraph. If the build option kGPU_FALLBACK is specified, then +//! multiple subgraphs can be created, with each subgraph limited to less than 1GB of internal tensors data. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and +//! ABI. +//! +class ITensor : public INoCopy +{ +public: + //! + //! \brief Set the tensor name. + //! + //! For a network input, the name is assigned by the application. For tensors which are layer outputs, + //! a default name is assigned consisting of the layer name followed by the index of the output in brackets. + //! Each input and output tensor must have a unique name. + //! + //! This method copies the name string. + //! + //! \param name The name. + //! + //! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see getName() + //! + void setName(char const* name) noexcept + { + mImpl->setName(name); + } + + //! + //! \brief Get the tensor name. + //! + //! \return The name as a null-terminated C-style string. + //! + //! \see setName() + //! + char const* getName() const noexcept + { + return mImpl->getName(); + } + + //! + //! \brief Set the dimensions of a tensor. + //! + //! For a network input, the dimensions are assigned by the application. For a network output, the dimensions are + //! computed based on the layer parameters and the inputs to the layer. If a tensor size or a parameter is modified + //! in the network, the dimensions of all dependent tensors will be recomputed. + //! + //! This call is only legal for network input tensors, since the dimensions of layer output tensors are inferred + //! based on layer inputs and parameters. + //! + //! \param dimensions The dimensions of the tensor. + //! + //! \see getDimensions() + //! + void setDimensions(Dims const& dimensions) noexcept + { + mImpl->setDimensions(dimensions); + } + + //! + //! \brief Get the dimensions of a tensor. + //! + //! \return The dimensions of the tensor. + //! + //! \warning getDimensions() returns a -1 for dimensions that are derived from a wildcard dimension. + //! + //! \see setDimensions() + //! + Dims getDimensions() const noexcept + { + return mImpl->getDimensions(); + } + + //! + //! \brief Set the data type of a tensor. + //! + //! \param type The data type of the tensor when the type is not inferred. + //! + //! For strongly typed networks, this method should be used only for network inputs, + //! since the types of all other tensors are inferred. Setting the type of a network + //! output is tolerated if the type equals the inferred type, otherwise an error occurs + //! and the type is not updated. + //! + //! For weakly typed networks, this method can be used for network outputs too, but + //! the type merely has to be implicitly convertible from the inferred type to the + //! specified type. In this case it does not matter whether the type is set first + //! or the tensor is marked as an output first (via `INetworkDefinition::markOutput` + //! or `INetworkDefinition::markOutputForShapes`). + //! + //! However, marking it first has two advantages: + //! + //! * It avoids warnings that the tensor is not yet a network I/O tensor. + //! * It causes method `getType()` to return the type that was set instead of the inferred type. + //! + //! \see getType() + //! + //! \note This function does more than just set the type, so `t.setType(t.getType())` is not necessarily a no-op, + //! particularly for input and output tensors! + //! + //! \note Repeated consecutive applications of `t.setType(t.getType())` + //! would be idempotent, provided the state of the `ITensor` isn't changed between calls. + //! + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + //! + TRT_DEPRECATED void setType(DataType type) noexcept + { + mImpl->setType(type); + } + + //! + //! \brief Get the data type of a tensor. + //! + //! \return The data type of the tensor. + //! + //! The type is the type set by `setType` if the tensor is a network input or output. + //! Otherwise the type is the inferred type. + //! + //! \see setType() + //! + DataType getType() const noexcept + { + return mImpl->getType(); + } + + //! + //! \brief Set dynamic range for the tensor + //! + //! Currently, only symmetric ranges are supported. + //! Therefore, the larger of the absolute values of the provided bounds is used. + //! + //! \return Whether the dynamic range was set successfully. + //! + //! Requires that min and max be finite, and min <= max. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. + //! + TRT_DEPRECATED bool setDynamicRange(float min, float max) noexcept + { + return mImpl->setDynamicRange(min, max); + } + + //! + //! \brief Whether the tensor is a network input. + //! + bool isNetworkInput() const noexcept + { + return mImpl->isNetworkInput(); + } + + //! + //! \brief Whether the tensor is a network output. + //! + bool isNetworkOutput() const noexcept + { + return mImpl->isNetworkOutput(); + } + + //! + //! \brief Set whether to enable broadcast of tensor across the implicit batch dimension. + //! + //! \warning This method has no effect other than issuing a warning. + //! + //! \param broadcastAcrossBatch Whether to broadcast the tensor across the implicit + //! batch dimension that was a feature of TensorRT 9.x and prior. + //! + //! \see getBroadcastAcrossBatch() + //! + //! \deprecated Deprecated in TensorRT 10.0. Implicit batch is not supported since TensorRT 10.0. + //! + TRT_DEPRECATED void setBroadcastAcrossBatch(bool broadcastAcrossBatch) noexcept + { + mImpl->setBroadcastAcrossBatch(broadcastAcrossBatch); + } + + //! + //! \brief Check if tensor is broadcast across the implicit batch dimension. + //! + //! \return Always false since TensorRT 10.0 does not support an implicit batch dimension. + //! + //! \see setBroadcastAcrossBatch() + //! + //! \deprecated Deprecated in TensorRT 10.0. Implicit batch is not supported since TensorRT 10.0. + //! + TRT_DEPRECATED bool getBroadcastAcrossBatch() const noexcept + { + return mImpl->getBroadcastAcrossBatch(); + } + + //! + //! \brief Get the storage location of a tensor. + //! + //! \return The location of tensor data. + //! + //! \see setLocation() + //! + TensorLocation getLocation() const noexcept + { + return mImpl->getLocation(); + } + + //! + //! \brief Set the storage location of a tensor + //! + //! \param location the location of tensor data + //! + //! Only network input tensors for storing sequence lengths for RNNv2 are supported. + //! Using host storage for layers that do not support it will generate + //! errors at build time. + //! + //! \see getLocation() + //! + //! \deprecated Deprecated in TensorRT 10.0. RNNv2 is not supported and the location must + //! always be TensorLocation::kDEVICE since TensorRT 10.0. + //! + TRT_DEPRECATED void setLocation(TensorLocation location) noexcept + { + mImpl->setLocation(location); + } + + //! + //! \brief Query whether dynamic range is set. + //! + //! \return True if dynamic range is set, false otherwise. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. + //! + TRT_DEPRECATED bool dynamicRangeIsSet() const noexcept + { + return mImpl->dynamicRangeIsSet(); + } + + //! + //! \brief Undo effect of setDynamicRange. + //! + void resetDynamicRange() noexcept + { + mImpl->resetDynamicRange(); + } + + //! + //! \brief Get minimum of dynamic range. + //! + //! \return Minimum of dynamic range, or quiet NaN if range was not set. + //! + float getDynamicRangeMin() const noexcept + { + return mImpl->getDynamicRangeMin(); + } + + //! + //! \brief Get maximum of dynamic range. + //! + //! \return Maximum of dynamic range, or quiet NaN if range was not set. + //! + float getDynamicRangeMax() const noexcept + { + return mImpl->getDynamicRangeMax(); + } + + //! + //! \brief Set allowed formats for an input or output tensor. By default all formats are allowed. + //! Shape tensors (for which isShapeTensor() returns true) may only have row-major linear format. + //! + //! When running network on DLA and the build option kGPU_FALLBACK is not specified, if DLA format(kCHW4 with Int8, + //! kCHW4 with FP16, kCHW16 with FP16, kCHW32 with Int8) is set, the input format is treated as native DLA format + //! with line stride requirement. Input/output binding with these format should have correct layout during + //! inference. + //! + //! Tensor formats are determined at build time by TensorRT for tensors not marked as input or output. + //! + //! \param formats A bitmask of TensorFormat values that are supported for this tensor. + //! + //! \see ITensor::getAllowedFormats() + //! + //! \see TensorFormats + //! + void setAllowedFormats(TensorFormats formats) noexcept + { + mImpl->setAllowedFormats(formats); + } + + //! + //! \brief Get a bitmask of TensorFormat values that the tensor supports. + //! For a shape tensor, only row-major linear format is allowed. + //! + //! \return The value specified by setAllowedFormats or all possible formats. + //! + //! \see ITensor::setAllowedFormats() + //! + TensorFormats getAllowedFormats() const noexcept + { + return mImpl->getAllowedFormats(); + } + + //! + //! \brief Whether the tensor is a shape tensor. + //! + //! A shape tensor is a tensor that is related to shape calculations. + //! It must have type Int32, Int64, Bool, or Float, and its shape must be determinable at build time. + //! Furthermore, it must be needed as a shape tensor, either marked as a network shape + //! output via markOutputForShapes(), or as a layer input that is required to be a shape + //! tensor, such as the second input to IShuffleLayer. Some layers are "polymorphic" in + //! this respect. For example, the inputs to IElementWiseLayer must be shape tensors + //! if the output is a shape tensor. + //! + //! The TensorRT Developer Guide gives the formal rules for what tensors are shape tensors. + //! + //! The result of isShapeTensor() is reliable only when network construction is complete. + //! For example, if a partially built network sums two tensors T1 and T2 to create + //! tensor T3, and none are yet needed as shape tensors, isShapeTensor() returns false + //! for all three tensors. Setting the second input of IShuffleLayer to be T3 would + //! cause all three tensors to be shape tensors, because IShuffleLayer requires that its + //! second optional input be a shape tensor, and IElementWiseLayer is "polymorphic". + //! + //! It is possible for a tensor to be both a shape tensor and an execution tensor. + //! + //! \return True if tensor is a shape tensor, false otherwise. + //! + //! \see INetworkDefinition::markOutputForShapes() + //! + bool isShapeTensor() const noexcept + { + return mImpl->isShapeTensor(); + } + + //! + //! \brief Whether the tensor is an execution tensor. + //! + //! Tensors are usually execution tensors. The exceptions are tensors used + //! solely for shape calculations or whose contents are not needed to compute the outputs. + //! + //! The result of isExecutionTensor() is reliable only when network construction is complete. + //! For example, if a partially built network has no path from a tensor to a network output, + //! isExecutionTensor() returns false. Completing the path would cause it to become true. + //! + //! + //! A tensor with isShapeTensor() == false and isExecutionTensor() == false + //! can still show up as an input to the engine if its dimensions are required. + //! In that case, only its dimensions need to be set at runtime and a nullptr + //! can be passed instead of a pointer to its contents. + //! + bool isExecutionTensor() const noexcept + { + return mImpl->isExecutionTensor(); + } + + //! + //! \brief Name a dimension of an input tensor. + //! + //! Associate a runtime dimension of an input tensor with a symbolic name. + //! Dimensions with the same non-empty name must be equal at runtime. + //! Knowing this equality for runtime dimensions may help the TensorRT optimizer. + //! Both runtime and build-time dimensions can be named. + //! + //! For example, setDimensionName(0, "n") associates the symbolic name "n" with the leading dimension. + //! + //! This method copies the name string. + //! If the function is called again, with the same index, it will overwrite the previous name. + //! If nullptr is passed as name, it will clear the name of the dimension. + //! + //! \param index index of the dimension + //! \param name of the dimension, as a pointer to a null-terminated character sequence. + //! + //! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see getDimensionName() + //! + void setDimensionName(int32_t index, char const* name) noexcept + { + mImpl->setDimensionName(index, name); + } + + //! + //! \brief Get the name of an input dimension. + //! + //! \param index index of the dimension + //! + //! \return The name of the input dimension, or nullptr if the dimension has no name. + //! The name is a pointer to a null-terminated character sequence. + //! + //! \see setDimensionName() + //! + char const* getDimensionName(int32_t index) const noexcept + { + return mImpl->getDimensionName(index); + } + +protected: + apiv::VTensor* mImpl; + virtual ~ITensor() noexcept = default; +}; + +//! +//! \class ILayer +//! +//! \brief Base class for all layer classes in a network definition. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class ILayer : public INoCopy +{ +public: + //! + //! \brief Return the type of a layer. + //! + //! \see LayerType + //! + LayerType getType() const noexcept + { + return mLayer->getType(); + } + + //! + //! \brief Set the name of a layer. + //! + //! This method copies the name string. + //! + //! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see getName() + //! + void setName(char const* name) noexcept + { + mLayer->setName(name); + } + + //! + //! \brief Return the name of a layer. + //! + //! \see setName() + //! + char const* getName() const noexcept + { + return mLayer->getName(); + } + + //! + //! \brief Get the number of inputs of a layer. + //! + int32_t getNbInputs() const noexcept + { + return mLayer->getNbInputs(); + } + + //! + //! \brief Get the layer input corresponding to the given index. + //! + //! \param index The index of the input tensor. + //! + //! \return The input tensor, or nullptr if the index is out of range or the tensor is optional + //! (\ref ISliceLayer). + //! + ITensor* getInput(int32_t index) const noexcept + { + return mLayer->getInput(index); + } + + //! + //! \brief Get the number of outputs of a layer. + //! + int32_t getNbOutputs() const noexcept + { + return mLayer->getNbOutputs(); + } + + //! + //! \brief Get the layer output corresponding to the given index. + //! + //! \return The indexed output tensor, or nullptr if the index is out of range or the tensor is optional. + //! + ITensor* getOutput(int32_t index) const noexcept + { + return mLayer->getOutput(index); + } + + //! + //! \brief Replace an input of this layer with a specific tensor. + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor + //! + //! Except for IFillLayer, ILoopOutputLayer, INMSLayer, IResizeLayer, IShuffleLayer, and ISliceLayer, + //! this method cannot change the number of inputs to a layer. The index argument must be + //! less than the value of getNbInputs(). + //! + //! See comments for overloads of setInput() for layers with special behavior. + //! + void setInput(int32_t index, ITensor& tensor) noexcept + { + return mLayer->setInput(index, tensor); + } + + //! + //! \brief Set the preferred or required computational precision of this layer in a weakly-typed network. + //! + //! Setting the precision directs TensorRT to choose an implementation that runs at this computational precision. + //! TensorRT could still choose a non-conforming fastest implementation that ignores the requested precision. + //! To force choosing an implementation with the requested precision, set exactly one of the following flags, + //! which differ in what happens if no such implementation exists: + //! + //! * BuilderFlag::kOBEY_PRECISION_CONSTRAINTS - build fails with an error message. + //! + //! * BuilderFlag::kPREFER_PRECISION_CONSTRAINTS - TensorRT falls back to an + //! implementation without the requested precision. + //! + //! If precision is not set, or falling back, TensorRT will select the layer computational precision + //! and layer input type based on global performance considerations and the flags specified to the builder. + //! + //! For a IIdentityLayer: If it casts to/from float/half/int8/uint8, the precision must be one of those types, + //! otherwise it must be either the input or output type. + //! + //! Strongly-typed networks reject calls to method setPrecision. In strongly-typed networks, the computation + //! precision is typically controlled by casting the input tensors to the desired type. + //! + //! \param dataType the computational precision. + //! + //! \see getPrecision() precisionIsSet() resetPrecision() + //! + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + //! + TRT_DEPRECATED void setPrecision(DataType dataType) noexcept + { + mLayer->setPrecision(dataType); + } + + //! + //! \brief get the computational precision of this layer + //! + //! \return the computational precision + //! + //! \see setPrecision() precisionIsSet() resetPrecision() + //! + DataType getPrecision() const noexcept + { + return mLayer->getPrecision(); + } + + //! + //! \brief whether the computational precision has been set for this layer + //! + //! \return whether the computational precision has been explicitly set + //! + //! \see setPrecision() getPrecision() resetPrecision() + //! + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + //! + TRT_DEPRECATED bool precisionIsSet() const noexcept + { + return mLayer->precisionIsSet(); + } + + //! + //! \brief reset the computational precision for this layer + //! + //! \see setPrecision() getPrecision() precisionIsSet() + //! + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + //! + TRT_DEPRECATED void resetPrecision() noexcept + { + mLayer->resetPrecision(); + } + + //! + //! \brief Set the output type of this layer in a weakly-typed network. + //! + //! Setting the output type constrains TensorRT to choose implementations which generate output data with the + //! given type. If it is not set, TensorRT will select output type based on layer computational precision. TensorRT + //! could still choose non-conforming output type based on fastest implementation. To force choosing the requested + //! output type, set exactly one of the following flags, which differ in what happens if no such implementation + //! exists: + //! + //! * BuilderFlag::kOBEY_PRECISION_CONSTRAINTS - build fails with an error message. + //! + //! * BuilderFlag::kPREFER_PRECISION_CONSTRAINTS - TensorRT falls back to an + //! implementation with a non-conforming output type. + //! + //! In case layer precision is not specified, or falling back, the output type depends on the + //! chosen implementation, based on performance considerations and the flags specified to the builder. + //! + //! This method cannot be used to set the data type of the second output tensor of the TopK layer. The data type of + //! the second output tensor of the topK layer is always Int32. Also the output type of all layers that are shape + //! operations must be DataType::kINT32, and all attempts to set the output type to some other data type will be + //! ignored except for issuing an error message. + //! + //! Note that the layer output type is generally not identical to the data type of the output tensor, as TensorRT + //! may insert implicit reformatting operations to convert the former to the latter. Calling layer->setOutputType(i, + //! type) has no effect on the data type of the i-th output tensor of layer, and users need to call + //! layer->getOutput(i)->setType(type) to change the tensor data type. This is particularly relevant if the tensor + //! is marked as a network output, since only setType() [but not setOutputType()] will affect the data + //! representation in the corresponding output binding. + //! + //! Strongly-typed networks reject calls to method setOutputType. Instead, the output type can be set + //! only for layers that define method setToType(). Those layers are: + //! + //! * ICastLayer + //! * IDequantizeLayer + //! * IDynamicQuantizeLayer + //! * IFillLayer + //! * IQuantizeLayer + //! + //! \param index the index of the output to set + //! \param dataType the type of the output + //! + //! \see getOutputType() outputTypeIsSet() resetOutputType() + //! + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + //! + TRT_DEPRECATED void setOutputType(int32_t index, DataType dataType) noexcept + { + mLayer->setOutputType(index, dataType); + } + + //! + //! \brief get the output type of this layer + //! + //! \param index the index of the output + //! + //! \return the output precision. If no precision has been set, DataType::kFLOAT will be returned, + //! unless the output type is inherently DataType::kINT32. + //! + //! \see getOutputType() outputTypeIsSet() resetOutputType() + //! + DataType getOutputType(int32_t index) const noexcept + { + return mLayer->getOutputType(index); + } + + //! + //! \brief whether the output type has been set for this layer + //! + //! \param index the index of the output + //! + //! \return whether the output type has been explicitly set + //! + //! \see setOutputType() getOutputType() resetOutputType() + //! + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + //! + TRT_DEPRECATED bool outputTypeIsSet(int32_t index) const noexcept + { + return mLayer->outputTypeIsSet(index); + } + + //! + //! \brief reset the output type for this layer + //! + //! \param index the index of the output + //! + //! \see setOutputType() getOutputType() outputTypeIsSet() + //! + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + //! + TRT_DEPRECATED void resetOutputType(int32_t index) noexcept + { + return mLayer->resetOutputType(index); + } + + //! + //! \brief Set the metadata for this layer. + //! + //! The metadata is emitted in the JSON returned by IEngineInspector with + //! ProfilingVerbosity set to kDETAILED. + //! + //! \param metadata The per-layer metadata. + //! + //! \warning The string name must be null-terminated and be at most 4096 bytes including the terminator. + //! + //! \see getMetadata() + //! \see getLayerInformation() + //! + void setMetadata(char const* metadata) noexcept + { + mLayer->setMetadata(metadata); + } + + //! + //! \brief Get the metadata of the layer. + //! + //! \return The metadata as a null-terminated C-style string. If setMetadata() has not been called, + //! an empty string "" will be returned as a default value. + //! + //! \see setMetadata() + //! + char const* getMetadata() const noexcept + { + return mLayer->getMetadata(); + } + + //! + //! \brief Set the number of ranks for multi-device execution. + //! + //! Setting nbRanks > 1 is only allowed for specific layer types that support multi-device execution: + //! - IDistCollectiveLayer: Determines output shape for kALL_GATHER and kREDUCE_SCATTER operations. + //! + //! For attention layers, use IAttention::setNbRanks() instead. + //! For all other layer types, nbRanks must be 1. + //! + //! \param nbRanks The number of ranks for multi-device execution. Must be >= 1. + //! + //! \return true if successful, false if the layer type does not support the specified nbRanks value. + //! + //! \see getNbRanks() + //! \see IAttention::setNbRanks() + //! + bool setNbRanks(int32_t nbRanks) noexcept + { + return mLayer->setNbRanks(nbRanks); + } + + //! + //! \brief Get the number of ranks for multi-device execution. + //! + //! \return The number of ranks configured for multi-device execution. Default is 1. + //! + //! \see setNbRanks() + //! + int32_t getNbRanks() const noexcept + { + return mLayer->getNbRanks(); + } + +protected: + virtual ~ILayer() noexcept = default; + apiv::VLayer* mLayer; +}; + +//! +//! \enum PaddingMode +//! +//! \brief Enumerates the modes of padding to perform in convolution, deconvolution and pooling layer, +//! padding mode takes precedence if setPaddingMode() and setPrePadding() are also used. +//! +//! There are two padding styles, EXPLICIT and SAME with each style having two variants. +//! The EXPLICIT style determine if the final sampling location is used or not. +//! The SAME style determine if the asymmetry in the padding is on the pre or post padding. +//! +//! \code +//! Shorthand: +//! I = dimensions of input image. +//! B = prePadding, before the image data. +//! A = postPadding, after the image data. +//! P = delta between input and output +//! S = stride +//! F = filter +//! O = output +//! D = dilation +//! M = I + B + A ; The image data plus any padding +//! DK = 1 + D * (F - 1) +//! \endcode +//! +//! Formulas for Convolution: +//! - EXPLICIT_ROUND_DOWN: +//! \code +//! O = floor((M - DK) / S) + 1 +//! \endcode +//! - EXPLICIT_ROUND_UP: +//! \code +//! O = ceil((M - DK) / S) + 1 +//! \endcode +//! - SAME_UPPER: +//! \code +//! O = ceil(I / S) +//! P = floor((I - 1) / S) * S + DK - I; +//! B = floor(P / 2) +//! A = P - B +//! \endcode +//! - SAME_LOWER: +//! \code +//! O = ceil(I / S) +//! P = floor((I - 1) / S) * S + DK - I; +//! A = floor(P / 2) +//! B = P - A +//! \endcode +//! +//! Formulas for Deconvolution: +//! - EXPLICIT_ROUND_DOWN: +//! - EXPLICIT_ROUND_UP: +//! \code +//! O = (I - 1) * S + DK - (B + A) +//! \endcode +//! - SAME_UPPER: +//! \code +//! O = min(I * S, (I - 1) * S + DK) +//! P = max(DK - S, 0) +//! B = floor(P / 2) +//! A = P - B +//! \endcode +//! - SAME_LOWER: +//! \code +//! O = min(I * S, (I - 1) * S + DK) +//! P = max(DK - S, 0) +//! A = floor(P / 2) +//! B = P - A +//! \endcode +//! +//! Formulas for Pooling: +//! - EXPLICIT_ROUND_DOWN: +//! \code +//! O = floor((M - F) / S) + 1 +//! \endcode +//! - EXPLICIT_ROUND_UP: +//! \code +//! O = ceil((M - F) / S) + 1 +//! \endcode +//! - SAME_UPPER: +//! \code +//! O = ceil(I / S) +//! P = floor((I - 1) / S) * S + F - I; +//! B = floor(P / 2) +//! A = P - B +//! \endcode +//! - SAME_LOWER: +//! \code +//! O = ceil(I / S) +//! P = floor((I - 1) / S) * S + F - I; +//! A = floor(P / 2) +//! B = P - A +//! \endcode +//! +//! Pooling Example 1: +//! \code +//! Given I = {6, 6}, B = {3, 3}, A = {2, 2}, S = {2, 2}, F = {3, 3}. What is O? +//! (B, A can be calculated for SAME_UPPER and SAME_LOWER mode) +//! \endcode +//! +//! - EXPLICIT_ROUND_DOWN: +//! \code +//! Computation: +//! M = {6, 6} + {3, 3} + {2, 2} ==> {11, 11} +//! O ==> floor((M - F) / S) + 1 +//! ==> floor(({11, 11} - {3, 3}) / {2, 2}) + {1, 1} +//! ==> floor({8, 8} / {2, 2}) + {1, 1} +//! ==> {5, 5} +//! \endcode +//! - EXPLICIT_ROUND_UP: +//! \code +//! Computation: +//! M = {6, 6} + {3, 3} + {2, 2} ==> {11, 11} +//! O ==> ceil((M - F) / S) + 1 +//! ==> ceil(({11, 11} - {3, 3}) / {2, 2}) + {1, 1} +//! ==> ceil({8, 8} / {2, 2}) + {1, 1} +//! ==> {5, 5} +//! \endcode +//! The sample points are {0, 2, 4, 6, 8} in each dimension. +//! +//! - SAME_UPPER: +//! \code +//! Computation: +//! I = {6, 6} +//! S = {2, 2} +//! O = ceil(I / S) = {3, 3} +//! P = floor((I - 1) / S) * S + F - I +//! ==> floor(({6, 6} - {1, 1}) / {2, 2}) * {2, 2} + {3, 3} - {6, 6} +//! ==> {4, 4} + {3, 3} - {6, 6} +//! ==> {1, 1} +//! B = floor({1, 1} / {2, 2}) +//! ==> {0, 0} +//! A = {1, 1} - {0, 0} +//! ==> {1, 1} +//! \endcode +//! - SAME_LOWER: +//! \code +//! Computation: +//! I = {6, 6} +//! S = {2, 2} +//! O = ceil(I / S) = {3, 3} +//! P = floor((I - 1) / S) * S + F - I +//! ==> {1, 1} +//! A = floor({1, 1} / {2, 2}) +//! ==> {0, 0} +//! B = {1, 1} - {0, 0} +//! ==> {1, 1} +//! \endcode +//! The sample pointers are {0, 2, 4} in each dimension. +//! SAMPLE_UPPER has {O0, O1, O2, pad} in output in each dimension. +//! SAMPLE_LOWER has {pad, O0, O1, O2} in output in each dimension. +//! +//! Pooling Example 2: +//! \code +//! Given I = {6, 6}, B = {3, 3}, A = {3, 3}, S = {2, 2}, F = {3, 3}. What is O? +//! \endcode +//! +enum class PaddingMode : int32_t +{ + kEXPLICIT_ROUND_DOWN = 0, //!< Use explicit padding, rounding output size down. + kEXPLICIT_ROUND_UP = 1, //!< Use explicit padding, rounding output size up. + kSAME_UPPER = 2, //!< Use SAME padding, with prePadding <= postPadding. + kSAME_LOWER = 3, //!< Use SAME padding, with prePadding >= postPadding. +}; + +namespace impl +{ +//! +//! Maximum number of elements in PaddingMode enum. +//! +//! \see PaddingMode +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 4; +}; +} // namespace impl + +//! +//! \class IConvolutionLayer +//! +//! \brief A convolution layer in a network definition. +//! +//! This layer performs a correlation operation between 3 or 4 dimensional filter with a 4 or 5 dimensional tensor to +//! produce another 4 or 5 dimensional tensor. +//! +//! An optional bias argument is supported, which adds a per-channel constant to each value in the output. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IConvolutionLayer : public ILayer +{ +public: + //! + //! \brief Set the number of output maps for the convolution. + //! + //! If executing this layer on DLA, the number of output maps must be in the range [1,8192]. + //! + //! \see getNbOutputMaps() + //! + void setNbOutputMaps(int64_t nbOutputMaps) noexcept + { + mImpl->setNbOutputMaps(nbOutputMaps); + } + + //! + //! \brief Get the number of output maps for the convolution. + //! + //! \see setNbOutputMaps() + //! + int64_t getNbOutputMaps() const noexcept + { + return mImpl->getNbOutputMaps(); + } + + //! + //! \brief Set the number of groups for a convolution. + //! + //! The input tensor channels are divided into \p nbGroups groups, and a convolution is executed for each group, + //! using a filter per group. The results of the group convolutions are concatenated to form the output. + //! + //! \note When using groups in int8 mode, the size of the groups (i.e. the channel count divided by the group + //! count) must be a multiple of 4 for both input and output. + //! + //! Default: 1 + //! + //! If executing this layer on DLA, the max number of groups is 8192. + //! + //! \see getNbGroups() + //! + void setNbGroups(int64_t nbGroups) noexcept + { + mImpl->setNbGroups(nbGroups); + } + + //! + //! \brief Get the number of groups of the convolution. + //! + //! \see setNbGroups() + //! + int64_t getNbGroups() const noexcept + { + return mImpl->getNbGroups(); + } + + //! + //! \brief Set the kernel weights for the convolution. + //! + //! The weights are specified as a contiguous array in \p GKCRS order, where \p G is the number of groups, \p K + //! the number of output feature maps, \p C the number of input channels, and \p R and \p S are the height and + //! width of the filter. + //! + //! \see getKernelWeights() + //! + void setKernelWeights(Weights weights) noexcept + { + mImpl->setKernelWeights(weights); + } + + //! + //! \brief Get the kernel weights of the convolution. + //! + //! \see setKernelWeights() + //! + Weights getKernelWeights() const noexcept + { + return mImpl->getKernelWeights(); + } + + //! + //! \brief Set the bias weights for the convolution. + //! + //! Bias is optional. To omit bias, set the count value of the weights structure to zero. + //! + //! The bias is applied per-channel, so the number of weights (if non-zero) must be equal to the number of output + //! feature maps. + //! + //! \see getBiasWeights() + //! + void setBiasWeights(Weights weights) noexcept + { + mImpl->setBiasWeights(weights); + } + + //! + //! \brief Get the bias weights for the convolution. + //! + //! \see setBiasWeights() + //! + Weights getBiasWeights() const noexcept + { + return mImpl->getBiasWeights(); + } + + //! + //! \brief Set the multi-dimension pre-padding of the convolution. + //! + //! The start of the input will be zero-padded by this number of elements in each dimension. + //! + //! Default: (0, 0, ..., 0) + //! + //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range + //! [0,31], and the padding must be less than the kernel size. + //! + //! \see getPrePadding() + //! + void setPrePadding(Dims const& padding) noexcept + { + mImpl->setPrePadding(padding); + } + + //! + //! \brief Get the pre-padding. + //! + //! \see setPrePadding() + //! + Dims getPrePadding() const noexcept + { + return mImpl->getPrePadding(); + } + + //! + //! \brief Set the multi-dimension post-padding of the convolution. + //! + //! The end of the input will be zero-padded by this number of elements in each dimension. + //! + //! Default: (0, 0, ..., 0) + //! + //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range + //! [0,31], and the padding must be less than the kernel size. + //! + //! \see getPostPadding() + //! + void setPostPadding(Dims const& padding) noexcept + { + mImpl->setPostPadding(padding); + } + + //! + //! \brief Get the post-padding. + //! + //! \see setPostPadding() + //! + Dims getPostPadding() const noexcept + { + return mImpl->getPostPadding(); + } + + //! + //! \brief Set the padding mode. + //! + //! Padding mode takes precedence if both setPaddingMode and setPre/PostPadding are used. + //! + //! Default: kEXPLICIT_ROUND_DOWN + //! + //! \see getPaddingMode() + //! + void setPaddingMode(PaddingMode paddingMode) noexcept + { + mImpl->setPaddingMode(paddingMode); + } + + //! + //! \brief Get the padding mode. + //! + //! Default: kEXPLICIT_ROUND_DOWN + //! + //! \see setPaddingMode() + //! + PaddingMode getPaddingMode() const noexcept + { + return mImpl->getPaddingMode(); + } + + //! + //! \brief Set the multi-dimension kernel size of the convolution. + //! + //! If executing this layer on DLA, only support 2D kernel size, both height and width of kernel size must be in the + //! range [1,32]. + //! + //! \see getKernelSizeNd() + //! + void setKernelSizeNd(Dims const& kernelSize) noexcept + { + mImpl->setKernelSizeNd(kernelSize); + } + + //! + //! \brief Get the multi-dimension kernel size of the convolution. + //! + //! \see setKernelSizeNd() + //! + Dims getKernelSizeNd() const noexcept + { + return mImpl->getKernelSizeNd(); + } + + //! + //! \brief Set the multi-dimension stride of the convolution. + //! + //! Default: (1, 1, ..., 1) + //! + //! If executing this layer on DLA, only support 2D stride, both height and width of stride must be in the range + //! [1,8]. + //! + //! \see getStrideNd() + //! + void setStrideNd(Dims const& stride) noexcept + { + mImpl->setStrideNd(stride); + } + + //! + //! \brief Get the multi-dimension stride of the convolution. + //! + //! \see setStrideNd() + //! + Dims getStrideNd() const noexcept + { + return mImpl->getStrideNd(); + } + + //! + //! \brief Set the multi-dimension padding of the convolution. + //! + //! The input will be zero-padded by this number of elements in each dimension. + //! Padding is symmetric. + //! + //! Default: (0, 0, ..., 0) + //! + //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range + //! [0,31], and the padding must be less than the kernel size. + //! + //! \see getPaddingNd() setPadding() getPadding() + //! + void setPaddingNd(Dims const& padding) noexcept + { + mImpl->setPaddingNd(padding); + } + + //! + //! \brief Get the multi-dimension padding of the convolution. + //! + //! If the padding is asymmetric, the pre-padding is returned. + //! + //! \see setPaddingNd() + //! + Dims getPaddingNd() const noexcept + { + return mImpl->getPaddingNd(); + } + + //! + //! \brief Set the multi-dimension dilation of the convolution. + //! + //! Default: (1, 1, ..., 1) + //! + //! If executing this layer on DLA, only support 2D padding, both height and width must be in the range [1,32]. + //! + //! \see getDilationNd() + //! + void setDilationNd(Dims const& dilation) noexcept + { + mImpl->setDilationNd(dilation); + } + + //! + //! \brief Get the multi-dimension dilation of the convolution. + //! + //! \see setDilationNd() + //! + Dims getDilationNd() const noexcept + { + return mImpl->getDilationNd(); + } + + //! + //! \brief Append or replace an input of this layer with a specific tensor + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor + //! + //! The indices are as follows: + //! + //! Input 0 is the input activation tensor. + //! Input 1 is the kernel tensor. If used, the kernel weights parameter must be set to empty weights. + //! Input 2 is the bias tensor. If used, the bias parameter must be set to empty weights. + //! + //! \see getKernelWeights(), setKernelWeights(), getBiasWeights(), setBiasWeights() + //! + using ILayer::setInput; + +protected: + virtual ~IConvolutionLayer() noexcept = default; + apiv::VConvolutionLayer* mImpl; +}; + +//! +//! \class IActivationLayer +//! +//! \brief An Activation layer in a network definition. +//! +//! This layer applies a per-element activation function to its input. +//! +//! The output has the same shape as the input. +//! +//! The input is a shape tensor if the output is a shape tensor. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IActivationLayer : public ILayer +{ +public: + //! + //! \brief Set the type of activation to be performed. + //! + //! On the DLA, the valid activation types are kRELU, kSIGMOID, kTANH, and kCLIP. + //! + //! \see getActivationType(), ActivationType + //! + void setActivationType(ActivationType type) noexcept + { + mImpl->setActivationType(type); + } + + //! + //! \brief Get the type of activation to be performed. + //! + //! \see setActivationType(), ActivationType + //! + ActivationType getActivationType() const noexcept + { + return mImpl->getActivationType(); + } + + //! + //! \brief Set the alpha parameter (must be finite). + //! + //! This parameter is used by the following activations: + //! LeakyRelu, Elu, Selu, Softplus, Clip, HardSigmoid, ScaledTanh, + //! ThresholdedRelu. + //! + //! It is ignored by the other activations. + //! + //! \see getAlpha(), setBeta() + void setAlpha(float alpha) noexcept + { + mImpl->setAlpha(alpha); + } + + //! + //! \brief Set the beta parameter (must be finite). + //! + //! This parameter is used by the following activations: + //! Selu, Softplus, Clip, HardSigmoid, ScaledTanh. + //! + //! It is ignored by the other activations. + //! + //! \see getBeta(), setAlpha() + void setBeta(float beta) noexcept + { + mImpl->setBeta(beta); + } + + //! + //! \brief Get the alpha parameter. + //! + //! \see getBeta(), setAlpha() + float getAlpha() const noexcept + { + return mImpl->getAlpha(); + } + + //! + //! \brief Get the beta parameter. + //! + //! \see getAlpha(), setBeta() + float getBeta() const noexcept + { + return mImpl->getBeta(); + } + +protected: + virtual ~IActivationLayer() noexcept = default; + apiv::VActivationLayer* mImpl; +}; + +//! +//! \enum PoolingType +//! +//! \brief The type of pooling to perform in a pooling layer. +//! +enum class PoolingType : int32_t +{ + kMAX = 0, //!< Maximum over elements + kAVERAGE = 1, //!< Average over elements. If the tensor is padded, the count includes the padding + kMAX_AVERAGE_BLEND = 2 //!< Blending between max and average pooling: (1-blendFactor)*maxPool + blendFactor*avgPool +}; + +namespace impl +{ +//! +//! Maximum number of elements in PoolingType enum. +//! +//! \see PoolingType +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 3; +}; +} // namespace impl + +//! \class IPoolingLayer +//! +//! \brief A Pooling layer in a network definition. +//! +//! The layer applies a reduction operation within a window over the input. +//! +//! \warning When running pooling layer with DeviceType::kDLA in Int8 mode, the dynamic ranges +//! for input and output tensors must be equal. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IPoolingLayer : public ILayer +{ +public: + //! + //! \brief Set the type of activation to be performed. + //! + //! DLA only supports kMAX and kAVERAGE pooling types. + //! + //! \see getPoolingType(), PoolingType + //! + void setPoolingType(PoolingType type) noexcept + { + mImpl->setPoolingType(type); + } + + //! + //! \brief Get the type of activation to be performed. + //! + //! \see setPoolingType(), PoolingType + //! + PoolingType getPoolingType() const noexcept + { + return mImpl->getPoolingType(); + } + + //! + //! \brief Set the blending factor for the max_average_blend mode: + //! max_average_blendPool = (1-blendFactor)*maxPool + blendFactor*avgPool + //! blendFactor is a user value in [0,1] with the default value of 0.0 + //! This value only applies for the kMAX_AVERAGE_BLEND mode. + //! + //! Since DLA does not support kMAX_AVERAGE_BLEND, blendFactor is ignored on the DLA. + //! + //! \see getBlendFactor() + //! + void setBlendFactor(float blendFactor) noexcept + { + mImpl->setBlendFactor(blendFactor); + } + + //! + //! \brief Get the blending factor for the max_average_blend mode: + //! max_average_blendPool = (1-blendFactor)*maxPool + blendFactor*avgPool + //! blendFactor is a user value in [0,1] with the default value of 0.0 + //! In modes other than kMAX_AVERAGE_BLEND, blendFactor is ignored. + //! + //! \see setBlendFactor() + //! + float getBlendFactor() const noexcept + { + return mImpl->getBlendFactor(); + } + + //! + //! \brief Set whether average pooling uses as a denominator the overlap area between the window + //! and the unpadded input. + //! If this is not set, the denominator is the overlap between the pooling window and the padded input. + //! + //! Default: true + //! + //! \see getAverageCountExcludesPadding() + //! + void setAverageCountExcludesPadding(bool exclusive) noexcept + { + mImpl->setAverageCountExcludesPadding(exclusive); + } + + //! + //! \brief Get whether average pooling uses as a denominator the overlap area between the window + //! and the unpadded input. + //! + //! \see setAverageCountExcludesPadding() + //! + bool getAverageCountExcludesPadding() const noexcept + { + return mImpl->getAverageCountExcludesPadding(); + } + + //! + //! \brief Set the multi-dimension pre-padding for pooling. + //! + //! The start of the input will be padded by this number of elements in each dimension. + //! Padding value depends on pooling type, -inf is used for max pooling and zero padding for average pooling. + //! + //! Default: (0, 0, ..., 0) + //! + //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range + //! [0,7]. + //! + //! \see getPrePadding() + //! + void setPrePadding(Dims const& padding) noexcept + { + mImpl->setPrePadding(padding); + } + + //! + //! \brief Get the pre-padding. + //! + //! \see setPrePadding() + //! + Dims getPrePadding() const noexcept + { + return mImpl->getPrePadding(); + } + + //! + //! \brief Set the multi-dimension post-padding for pooling. + //! + //! The end of the input will be padded by this number of elements in each dimension. + //! Padding value depends on pooling type, -inf is used for max pooling and zero padding for average pooling. + //! + //! Default: (0, 0, ..., 0) + //! + //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range + //! [0,7]. + //! + //! \see getPostPadding() + //! + void setPostPadding(Dims const& padding) noexcept + { + mImpl->setPostPadding(padding); + } + + //! + //! \brief Get the padding. + //! + //! \see setPostPadding() + //! + Dims getPostPadding() const noexcept + { + return mImpl->getPostPadding(); + } + + //! + //! \brief Set the padding mode. + //! + //! Padding mode takes precedence if both setPaddingMode and setPre/PostPadding are used. + //! + //! Default: kEXPLICIT_ROUND_DOWN + //! + //! \see getPaddingMode() + void setPaddingMode(PaddingMode paddingMode) noexcept + { + mImpl->setPaddingMode(paddingMode); + } + + //! + //! \brief Get the padding mode. + //! + //! Default: kEXPLICIT_ROUND_DOWN + //! + //! \see setPaddingMode() + PaddingMode getPaddingMode() const noexcept + { + return mImpl->getPaddingMode(); + } + + //! + //! \brief Set the multi-dimension window size for pooling. + //! + //! If executing this layer on DLA, only support 2D window size, both height and width of window size must be in the + //! range [1,8]. + //! + //! \see getWindowSizeNd() setWindowSize() getWindowSize() + //! + void setWindowSizeNd(Dims const& windowSize) noexcept + { + mImpl->setWindowSizeNd(windowSize); + } + + //! + //! \brief Get the multi-dimension window size for pooling. + //! + //! \see setWindowSizeNd() + //! + Dims getWindowSizeNd() const noexcept + { + return mImpl->getWindowSizeNd(); + } + + //! + //! \brief Set the multi-dimension stride for pooling. + //! + //! Default: (1, 1, ..., 1) + //! + //! If executing this layer on DLA, only support 2D stride, both height and width of stride must be in the range + //! [1,16]. + //! + //! \see getStrideNd() + //! + void setStrideNd(Dims const& stride) noexcept + { + mImpl->setStrideNd(stride); + } + + //! + //! \brief Get the multi-dimension stride for pooling. + //! + //! \see setStrideNd() + //! + Dims getStrideNd() const noexcept + { + return mImpl->getStrideNd(); + } + + //! + //! \brief Set the multi-dimension padding for pooling. + //! + //! The input will be padded by this number of elements in each dimension. + //! Padding is symmetric. + //! Padding value depends on pooling type, -inf is used for max pooling and zero padding for average pooling. + //! + //! Default: (0, 0, ..., 0) + //! + //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range + //! [0,7]. + //! + //! \see getPaddingNd() setPadding() getPadding() + //! + void setPaddingNd(Dims const& padding) noexcept + { + mImpl->setPaddingNd(padding); + } + + //! + //! \brief Get the multi-dimension padding for pooling. + //! + //! If the padding is asymmetric, the pre-padding is returned. + //! + //! \see setPaddingNd() + //! + Dims getPaddingNd() const noexcept + { + return mImpl->getPaddingNd(); + } + +protected: + virtual ~IPoolingLayer() noexcept = default; + apiv::VPoolingLayer* mImpl; +}; + +//! +//! \class ILRNLayer +//! +//! \brief A LRN layer in a network definition. +//! +//! The output size is the same as the input size. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class ILRNLayer : public ILayer +{ +public: + //! + //! \brief Set the LRN window size. + //! + //! The window size must be odd and in the range of [1, 15]. + //! + //! If executing this layer on the DLA, only values in the set, [3, 5, 7, 9], are valid. + //! + //! \see setWindowStride() + //! + void setWindowSize(int64_t windowSize) noexcept + { + mImpl->setWindowSize(windowSize); + } + + //! + //! \brief Get the LRN window size. + //! + //! \see getWindowStride() + //! + int64_t getWindowSize() const noexcept + { + return mImpl->getWindowSize(); + } + + //! + //! \brief Set the LRN alpha value. + //! + //! The valid range is [-1e20, 1e20]. + //! + //! \see getAlpha() + //! + void setAlpha(float alpha) noexcept + { + mImpl->setAlpha(alpha); + } + + //! + //! \brief Get the LRN alpha value. + //! + //! \see setAlpha() + //! + float getAlpha() const noexcept + { + return mImpl->getAlpha(); + } + + //! + //! \brief Set the LRN beta value. + //! + //! The valid range is [0.01, 1e5f]. + //! + //! \see getBeta() + //! + void setBeta(float beta) noexcept + { + mImpl->setBeta(beta); + } + + //! + //! \brief Get the LRN beta value. + //! + //! \see setBeta() + //! + float getBeta() const noexcept + { + return mImpl->getBeta(); + } + + //! + //! \brief Set the LRN K value. + //! + //! The valid range is [1e-5, 1e10]. + //! + //! \see getK() + //! + void setK(float k) noexcept + { + mImpl->setK(k); + } + + //! + //! \brief Get the LRN K value. + //! + //! \see setK() + //! + float getK() const noexcept + { + return mImpl->getK(); + } + +protected: + virtual ~ILRNLayer() noexcept = default; + apiv::VLRNLayer* mImpl; +}; + +//! +//! \brief Controls how shift, scale and power are applied in a Scale layer. +//! +//! \see IScaleLayer +//! +enum class ScaleMode : int32_t +{ + kUNIFORM = 0, //!< Identical coefficients across all elements of the tensor. + kCHANNEL = 1, //!< Per-channel coefficients. + kELEMENTWISE = 2 //!< Elementwise coefficients. +}; + +//! +//! Maximum number of elements in ScaleMode enum. +//! +//! \see ScaleMode +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 3; +} + +//! +//! \class IScaleLayer +//! +//! \brief A Scale layer in a network definition. +//! +//! This layer applies a per-element computation to its input: +//! +//! \p output = (\p input* \p scale + \p shift)^ \p power +//! +//! The coefficients can be applied on a per-tensor, per-channel, or per-element basis. +//! +//! \note If the number of weights is 0, then a default value is used for shift, power, and scale. +//! The default shift is 0, the default power is 1, and the default scale is 1. +//! +//! The output size is the same as the input size. +//! +//! \note The input tensor is required to have at least 4 dimensions. +//! +//! A scale layer may be used as an INT8 quantization node in a graph, if the output is constrained to INT8 and +//! the input to FP32. Quantization rounds ties to even, and clamps to [-128, 127]. +//! +//! \see ScaleMode +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IScaleLayer : public ILayer +{ +public: + //! + //! \brief Set the scale mode. + //! + //! \see getMode() + //! + void setMode(ScaleMode mode) noexcept + { + mImpl->setMode(mode); + } + + //! + //! \brief Get the scale mode. + //! + //! \see setMode() + //! + ScaleMode getMode() const noexcept + { + return mImpl->getMode(); + } + + //! + //! \brief Set the shift value. + //! + //! \see getShift() + //! + void setShift(Weights shift) noexcept + { + mImpl->setShift(shift); + } + + //! + //! \brief Get the shift value. + //! + //! \see setShift() + //! + Weights getShift() const noexcept + { + return mImpl->getShift(); + } + + //! + //! \brief Set the scale value. + //! + //! \see getScale() + //! + void setScale(Weights scale) noexcept + { + mImpl->setScale(scale); + } + + //! + //! \brief Get the scale value. + //! + //! \see setScale() + //! + Weights getScale() const noexcept + { + return mImpl->getScale(); + } + + //! + //! \brief Set the power value. + //! + //! \see getPower() + //! + void setPower(Weights power) noexcept + { + mImpl->setPower(power); + } + + //! + //! \brief Get the power value. + //! + //! \see setPower() + //! + Weights getPower() const noexcept + { + return mImpl->getPower(); + } + + //! + //! \brief Get the channel axis. + //! + //! \return channelAxis parameter passed to addScaleNd() or set by setChannelAxis() + //! + //! The value is the index of the channel axis in the input tensor's dimensions. + //! Scaling happens along the channel axis when ScaleMode::kCHANNEL is enabled. + //! + //! \see addScaleNd() + //! + int32_t getChannelAxis() const noexcept + { + return mImpl->getChannelAxis(); + } + + //! + //! \brief Set the channel axis. + //! + //! The value is the index of the channel axis in the input tensor's dimensions. + //! + //! For ScaleMode::kCHANNEL, there can be distinct scale, shift, and power weights for each channel coordinate. + //! For ScaleMode::kELEMENTWISE, there can be distinct scale, shift, and power weights for each combination of + //! coordinates from the channel axis and axes after it. + //! + //! For example, suppose the input tensor has dimensions [10,20,30,40] and the channel axis is 1. + //! Let [n,c,h,w] denote an input coordinate. + //! For ScaleMode::kCHANNEL, the scale, shift, and power weights are indexed by c. + //! For ScaleMode::kELEMENTWISE, the scale, shift, and power weights are indexed by [c,h,w]. + //! + //! \see addScaleNd() + //! + void setChannelAxis(int32_t channelAxis) noexcept + { + mImpl->setChannelAxis(channelAxis); + } + +protected: + virtual ~IScaleLayer() noexcept = default; + apiv::VScaleLayer* mImpl; +}; + +//! +//! \class ISoftMaxLayer +//! +//! \brief A Softmax layer in a network definition. +//! +//! This layer applies a per-channel softmax to its input. +//! +//! The output size is the same as the input size. +//! +//! The following constraints must be satisfied to execute this layer on DLA: +//! * Axis must be one of the channel or spatial dimensions. +//! * There are two classes of supported input sizes: +//! 1. Non-axis, non-batch dimensions are all 1 and the axis dimension is at most 8192. +//! This is the recommended case for using softmax since it is the most accurate. +//! 2. At least one non-axis, non-batch dimension greater than 1 and the axis dimension is at most 1024. +//! Note that in this case, there may be some approximation error as the axis dimension size approaches +//! the upper bound. See the TensorRT Developer Guide for more details on the approximation error. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class ISoftMaxLayer : public ILayer +{ +public: + //! + //! \brief Set the axis along which softmax is computed. Currently, only one axis can be set. + //! + //! The axis is specified by setting the bit corresponding to the axis to 1. + //! For example, consider an NCHW tensor as input. + //! + //! Bit 0 corresponds to the N dimension boolean. + //! Bit 1 corresponds to the C dimension boolean. + //! Bit 2 corresponds to the H dimension boolean. + //! Bit 3 corresponds to the W dimension boolean. + //! By default, softmax is performed on the axis which is the number of axes minus three. It is 0 if + //! there are fewer than 3 axes. For example, if the input is NCHW, the default axis is C. If the input + //! is NHW, then the default axis is N. + //! + //! For example, to perform softmax on axis R of a NPQRCHW input, set bit 3. + //! + //! \param axes The axis along which softmax is computed. + //! Here axes is a bitmap. For example, when doing softmax along axis 0, bit 0 is set to 1, axes = 1 << axis + //! = 1. + //! + void setAxes(uint32_t axes) noexcept + { + mImpl->setAxes(axes); + } + + //! + //! \brief Get the axis along which softmax occurs. + //! + //! \see setAxes() + //! + uint32_t getAxes() const noexcept + { + return mImpl->getAxes(); + } + +protected: + virtual ~ISoftMaxLayer() noexcept = default; + apiv::VSoftMaxLayer* mImpl; +}; + +//! +//! \class IConcatenationLayer +//! +//! \brief A concatenation layer in a network definition. +//! +//! The output dimension along the concatenation axis is the sum of the corresponding input dimensions. +//! Every other output dimension is the same as the corresponding dimension of the inputs. +//! +//! \warning All tensors must have the same dimensions except along the concatenation axis. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IConcatenationLayer : public ILayer +{ +public: + //! + //! \brief Set the axis along which concatenation occurs. + //! + //! The default axis is the number of tensor dimensions minus three, or zero if the tensor has fewer than three + //! dimensions. For example, for a tensor with dimensions NCHW, it is C. + //! + //! When running this layer on the DLA, the concatenation axis must be the third to last axis, e.g. C if tensor + //! dimensions are NCHW. + //! + //! \param axis The axis along which concatenation occurs. + //! + void setAxis(int32_t axis) noexcept + { + mImpl->setAxis(axis); + } + + //! + //! \brief Get the axis along which concatenation occurs. + //! + //! \see setAxis() + //! + int32_t getAxis() const noexcept + { + return mImpl->getAxis(); + } + +protected: + virtual ~IConcatenationLayer() noexcept = default; + apiv::VConcatenationLayer* mImpl; +}; + +//! +//! \class IDeconvolutionLayer +//! +//! \brief A deconvolution layer in a network definition. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IDeconvolutionLayer : public ILayer +{ +public: + //! + //! \brief Set the number of output feature maps for the deconvolution. + //! + //! If executing this layer on DLA, the number of output maps must be in the range [1,8192]. + //! + //! \see getNbOutputMaps() + //! + void setNbOutputMaps(int64_t nbOutputMaps) noexcept + { + mImpl->setNbOutputMaps(nbOutputMaps); + } + + //! + //! \brief Get the number of output feature maps for the deconvolution. + //! + //! \see setNbOutputMaps() + //! + int64_t getNbOutputMaps() const noexcept + { + return mImpl->getNbOutputMaps(); + } + + //! + //! \brief Set the number of groups for a deconvolution. + //! + //! The input tensor channels are divided into \p nbGroups groups, and a deconvolution is executed for each group, + //! using a filter per group. The results of the group convolutions are concatenated to form the output. + //! + //! If executing this layer on DLA, nbGroups must be one + //! + //! \note When using groups in int8 mode, the size of the groups (i.e. the channel count divided by the group count) + //! must be a multiple of 4 for both input and output. + //! + //! Default: 1 + //! + //! \see getNbGroups() + //! + void setNbGroups(int64_t nbGroups) noexcept + { + mImpl->setNbGroups(nbGroups); + } + + //! + //! \brief Get the number of groups for a deconvolution. + //! + //! \see setNbGroups() + //! + int64_t getNbGroups() const noexcept + { + return mImpl->getNbGroups(); + } + + //! + //! \brief Set the kernel weights for the deconvolution. + //! + //! The weights are specified as a contiguous array in \p CKRS order, where \p C the number of + //! input channels, \p K the number of output feature maps, and \p R and \p S are the height and width + //! of the filter. + //! + //! \see getWeights() + //! + void setKernelWeights(Weights weights) noexcept + { + mImpl->setKernelWeights(weights); + } + + //! + //! \brief Get the kernel weights for the deconvolution. + //! + //! \see setNbGroups() + //! + Weights getKernelWeights() const noexcept + { + return mImpl->getKernelWeights(); + } + + //! + //! \brief Set the bias weights for the deconvolution. + //! + //! Bias is optional. To omit bias, set the count value of the weights structure to zero. + //! + //! The bias is applied per-feature-map, so the number of weights (if non-zero) must be equal to the number of + //! output feature maps. + //! + //! \see getBiasWeights() + //! + void setBiasWeights(Weights weights) noexcept + { + mImpl->setBiasWeights(weights); + } + + //! + //! \brief Get the bias weights for the deconvolution. + //! + //! \see getBiasWeights() + //! + Weights getBiasWeights() const noexcept + { + return mImpl->getBiasWeights(); + } + + //! + //! \brief Set the multi-dimension pre-padding of the deconvolution. + //! + //! The output will be trimmed by this number of elements on the start of every dimension. + //! In other words, it resembles the inverse of a convolution layer with this padding size. + //! Negative padding is not supported. + //! + //! Default: (0, 0, ..., 0) + //! + //! + //! \see getPrePadding() + //! + void setPrePadding(Dims const& padding) noexcept + { + mImpl->setPrePadding(padding); + } + + //! + //! \brief Get the pre-padding. + //! + //! \see setPrePadding() + //! + Dims getPrePadding() const noexcept + { + return mImpl->getPrePadding(); + } + + //! + //! \brief Set the multi-dimension post-padding of the deconvolution. + //! + //! The output will be trimmed by this number of elements on the end of every dimension. + //! In other words, it resembles the inverse of a convolution layer with this padding size. + //! Negative padding is not supported. + //! + //! Default: (0, 0, ..., 0) + //! + //! + //! \see getPostPadding() + //! + void setPostPadding(Dims const& padding) noexcept + { + mImpl->setPostPadding(padding); + } + + //! + //! \brief Get the padding. + //! + //! \see setPostPadding() + //! + Dims getPostPadding() const noexcept + { + return mImpl->getPostPadding(); + } + + //! + //! \brief Set the padding mode. + //! + //! Padding mode takes precedence if both setPaddingMode and setPre/PostPadding are used. + //! + //! Default: kEXPLICIT_ROUND_DOWN + //! + //! \see getPaddingMode() + //! + void setPaddingMode(PaddingMode paddingMode) noexcept + { + mImpl->setPaddingMode(paddingMode); + } + + //! + //! \brief Get the padding mode. + //! + //! Default: kEXPLICIT_ROUND_DOWN + //! + //! \see setPaddingMode() + //! + PaddingMode getPaddingMode() const noexcept + { + return mImpl->getPaddingMode(); + } + + //! + //! \brief Set the multi-dimension kernel size of the deconvolution. + //! + //! If executing this layer on DLA, there are two restrictions: + //! 1) Only 2D Kernel is supported. + //! 2) Kernel height and width must be in the range [1,32] or the combinations of [64, 96, 128] in one + //! dimension and 1 in the other dimensions, i.e. [1x64] or [64x1] are valid, but not [64x64]. + //! + //! \see getKernelSizeNd() + //! + void setKernelSizeNd(Dims const& kernelSize) noexcept + { + mImpl->setKernelSizeNd(kernelSize); + } + + //! + //! \brief Get the multi-dimension kernel size of the deconvolution. + //! + //! \see setKernelSizeNd() + //! + Dims getKernelSizeNd() const noexcept + { + return mImpl->getKernelSizeNd(); + } + + //! + //! \brief Set the multi-dimension stride of the deconvolution. + //! + //! Default: (1, 1, ..., 1) + //! + //! If executing this layer on DLA, there are two restrictions: + //! 1) Only 2D Stride is supported. + //! 2) Stride height and width must be in the range [1,32] or the combinations of [64, 96, 128] in one + //! dimension and 1 in the other dimensions, i.e. [1x64] or [64x1] are valid, but not [64x64]. + //! + //! \see getStrideNd() + //! + void setStrideNd(Dims const& stride) noexcept + { + mImpl->setStrideNd(stride); + } + + //! + //! \brief Get the multi-dimension stride of the deconvolution. + //! + //! \see setStrideNd() + //! + Dims getStrideNd() const noexcept + { + return mImpl->getStrideNd(); + } + + //! + //! \brief Set the multi-dimension padding of the deconvolution. + //! + //! The output will be trimmed by this number of elements on both sides of every dimension. + //! In other words, it resembles the inverse of a convolution layer with this padding size. + //! Padding is symmetric, and negative padding is not supported. + //! + //! Default: (0, 0, ..., 0) + //! + //! If executing this layer on DLA, padding must be 0. + //! + //! \see getPaddingNd() setPadding() getPadding() + //! + void setPaddingNd(Dims const& padding) noexcept + { + mImpl->setPaddingNd(padding); + } + + //! + //! \brief Get the multi-dimension padding of the deconvolution. + //! + //! If the padding is asymmetric, the pre-padding is returned. + //! + //! \see setPaddingNd() + //! + Dims getPaddingNd() const noexcept + { + return mImpl->getPaddingNd(); + } + + //! + //! \brief Append or replace an input of this layer with a specific tensor + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor + //! + //! Input 0 is the input activation tensor. + //! Input 1 is the kernel tensor. If used, the kernel weights parameter must be set to empty weights. + //! Input 2 is the bias tensor. If used, the bias parameter must be set to empty weights. + //! + //! \see getKernelWeights(), setKernelWeights(), getBiasWeights(), setBiasWeights() + //! + using ILayer::setInput; + + //! + //! \brief Set the multi-dimension dilation of the deconvolution. + //! + //! Default: (1, 1, ..., 1) + //! + //! \see getDilationNd() + //! + void setDilationNd(Dims const& dilation) noexcept + { + mImpl->setDilationNd(dilation); + } + + //! + //! \brief Get the multi-dimension dilation of the deconvolution. + //! + //! \see setDilationNd() + //! + Dims getDilationNd() const noexcept + { + return mImpl->getDilationNd(); + } + +protected: + virtual ~IDeconvolutionLayer() noexcept = default; + apiv::VDeconvolutionLayer* mImpl; +}; + +//! +//! \enum ElementWiseOperation +//! +//! \brief Enumerates the binary operations that may be performed by an ElementWise layer. +//! +//! Operations kAND, kOR, and kXOR must have inputs of DataType::kBOOL. +//! +//! All other operations must have inputs of floating-point type, DataType::kINT8, DataType::kINT32, or +//! DataType::kINT64. +//! +//! \see IElementWiseLayer +//! +enum class ElementWiseOperation : int32_t +{ + kSUM = 0, //!< Sum of the two elements. + kPROD = 1, //!< Product of the two elements. + kMAX = 2, //!< Maximum of the two elements. + kMIN = 3, //!< Minimum of the two elements. + kSUB = 4, //!< Subtract the second element from the first. + kDIV = 5, //!< Divide the first element by the second. + kPOW = 6, //!< The first element to the power of the second element. + kFLOOR_DIV = 7, //!< Floor division of the first element by the second. + kAND = 8, //!< Logical AND of two elements. + kOR = 9, //!< Logical OR of two elements. + kXOR = 10, //!< Logical XOR of two elements. + kEQUAL = 11, //!< Check if two elements are equal. + kGREATER = 12, //!< Check if element in first tensor is greater than corresponding element in second tensor. + kLESS = 13 //!< Check if element in first tensor is less than corresponding element in second tensor. +}; + +namespace impl +{ +//! +//! Maximum number of elements in ElementWiseOperation enum. +//! +//! \see ElementWiseOperation +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 14; +}; +} // namespace impl + +//! +//! \class IElementWiseLayer +//! +//! \brief A elementwise layer in a network definition. +//! +//! This layer applies a per-element binary operation between corresponding elements of two tensors. +//! +//! The input tensors must have the same rank. For each dimension, their lengths must +//! match, or one of them must be one. In the latter case, the tensor is broadcast along that axis. +//! +//! The output tensor has the same rank as the inputs. For each output dimension, +//! its length is equal to the lengths of the corresponding input dimensions if they match, +//! otherwise it is equal to the length that is not one. +//! +//! \warning When running this layer on the DLA with Int8 data type, the dynamic ranges of two input tensors shall be +//! equal. If the dynamic ranges are generated using calibrator, the largest value shall be used. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IElementWiseLayer : public ILayer +{ +public: + //! + //! \brief Set the binary operation for the layer. + //! + //! DLA supports only kSUM, kPROD, kMAX, kMIN, and kSUB. + //! + //! \see getOperation(), ElementWiseOperation + //! + //! \see getBiasWeights() + //! + void setOperation(ElementWiseOperation op) noexcept + { + return mImpl->setOperation(op); + } + + //! + //! \brief Get the binary operation for the layer. + //! + //! \see setOperation(), ElementWiseOperation + //! + //! \see setBiasWeights() + //! + ElementWiseOperation getOperation() const noexcept + { + return mImpl->getOperation(); + } + +protected: + apiv::VElementWiseLayer* mImpl; + virtual ~IElementWiseLayer() noexcept = default; +}; + +//! +//! \brief Control form of IGatherLayer +//! +//! \see IGatherLayer +//! +enum class GatherMode : int32_t +{ + kDEFAULT = 0, //!< Similar to ONNX Gather + kELEMENT = 1, //!< Similar to ONNX GatherElements + kND = 2 //!< Similar to ONNX GatherND +}; + +//! +//! Maximum number of elements in GatherMode enum. +//! +//! \see GatherMode +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 3; +} + +//! +//! \class IGatherLayer +//! +//! \brief A Gather layer in a network definition. Supports several kinds of gathering. +//! +//! The Gather layer has two input tensors, Data and Indices, and an output tensor Output. +//! Additionally, there are three parameters: mode, nbElementwiseDims, and axis that control +//! how the indices are interpreted. +//! +//! * Data is a tensor of rank r >= 1 that stores the values to be gathered in Output. +//! * Indices is a tensor of rank q that determines which locations in Data to gather. +//! * GatherMode::kDEFAULT: q >= 0 +//! * GatherMode::kND: q >= 1 and the last dimension of Indices must be a build time constant. +//! * GatherMode::kELEMENT: q = r +//! * Output stores the gathered results. Its rank s depends on the mode: +//! * GatherMode::kDEFAULT: s = q + r - 1 - nbElementwiseDims +//! * GatherMode::kND: s = q + r - indices.d[q-1] - 1 - nbElementwiseDims +//! * GatherMode::kELEMENT: s = q = r. +//! +//! The dimensions of the output likewise depends on the mode: +//! +//! GatherMode::kDEFAULT: +//! +//! First nbElementwiseDims of output are computed by applying broadcast rules to +//! first nbElementwiseDims of indices and data. Note that nbElementwiseDims <= 1. +//! Rest of dimensions are computed by copying dimensions of Data, and replacing +//! the dimension for axis gatherAxis with the dimensions of indices. +//! +//! GatherMode::kND: +//! If indices.d[q-1] = r - nbElementwiseDims +//! output.d = [indices.d[0], ... , indices.d[q-2]] +//! Else if indices.d[q-1] < r - nbElementwiseDims +//! output.d = [indices.d[0], ... , indices.d[q-1], data.d[nbElementwiseDims + indices.d[q-1] + q], +//! data.d[r-1]] +//! Else +//! This is build time error +//! +//! GatherMode::kELEMENT: +//! The output dimensions match the dimensions of the indices tensor. +//! +//! The types of Data and Output must be the same, and Indices shall be DataType::kINT32 or DataType::kINT64. +//! +//! How the elements of Data are gathered depends on the mode: +//! +//! GatherMode::kDEFAULT: +//! Each index in indices is used to index Data along axis gatherAxis. +//! +//! GatherMode::kND: +//! Indices is a rank q integer tensor, best thought of as a rank (q-1) tensor of +//! indices into data, where each element defines a slice of data +//! The operation can be formulated as output[i_1, ..., i_{q-1}] = data[indices[i_1, ..., i_{q-1}]] +//! +//! GatherMode::kELEMENT: +//! +//! Here "axis" denotes the result of getGatherAxis(). +//! For each element X of indices: +//! Let J denote a sequence for the subscripts of X +//! Let K = sequence J with element [axis] replaced by X +//! output[J] = data[K] +//! +//! The handling of nbElementWiseDims depends on the mode: +//! * GatherMode::kDEFAULT: nbElementWiseDims <= 1. Broadcast is supported across the elementwise dimension if +//! present. +//! * GatherMode::kND: 0 <= nbElementWiseDims < rank(Data)-1. Broadcast is not supported across the elementwise +//! dimensions. +//! * GatherMode::kELEMENT: nbElementWiseDims = 0 +//! +//! Notes: +//! * For modes GatherMode::kND and GatherMode::kELEMENT, the first nbElementWiseDims dimensions of data and index must +//! be equal. If not, an error will be reported at build time or run time. +//! * If an axis of Data has dynamic length, using a negative index for it has undefined behavior. +//! * No DLA support +//! * Zero will be stored for OOB access +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IGatherLayer : public ILayer +{ +public: + //! + //! \brief Set the axis used by GatherMode::kELEMENTS and GatherMode::kDEFAULT + //! The axis must be less than the number of dimensions in the data input. + //! The axis defaults to 0. + //! + //! \warning Undefined behavior when used with GatherMode::kND. + //! + //! \see getGatherAxis() + //! + void setGatherAxis(int32_t axis) noexcept + { + mImpl->setGatherAxis(axis); + } + + //! + //! \brief Get the axis to gather on. + //! + //! \warning Undefined behavior when used with GatherMode::kND. + //! + //! \see setGatherAxis() + //! + int32_t getGatherAxis() const noexcept + { + return mImpl->getGatherAxis(); + } + + //! + //! \brief Set the number of leading dimensions of indices tensor to be handled elementwise. + //! + //! The gathering of indexing starts from the dimension of data[NbElementWiseDims:]. + //! The NbElementWiseDims must be less than the Rank of the data input. + //! + //! \param elementWiseDims number of dims to be handled as elementwise. + //! + //! Default: 0 + //! + //! The value of nbElementWiseDims and GatherMode are checked during network validation: + //! + //! GatherMode::kDEFAULT: nbElementWiseDims can be 0 or 1. + //! GatherMode::kND: nbElementWiseDims can be between 0 and one less than rank(data). + //! GatherMode::kELEMENT: nbElementWiseDims must be 0 + //! + //! \see getNbElementWiseDims() + //! + void setNbElementWiseDims(int32_t elementWiseDims) noexcept + { + mImpl->setNbElementWiseDims(elementWiseDims); + } + + //! + //! \brief Get the number of leading dimensions of indices tensor to be handled elementwise. + //! + //! \see setNbElementWiseDims() + //! + int32_t getNbElementWiseDims() const noexcept + { + return mImpl->getNbElementWiseDims(); + } + + //! + //! \brief Set the gather mode. + //! + //! \see getMode() + //! + void setMode(GatherMode mode) noexcept + { + mImpl->setMode(mode); + } + + //! + //! \brief Get the gather mode. + //! + //! \see setMode() + //! + GatherMode getMode() const noexcept + { + return mImpl->getMode(); + } + +protected: + apiv::VGatherLayer* mImpl; + virtual ~IGatherLayer() noexcept = default; +}; + +//! +//! \class IPluginV2Layer +//! +//! \brief Layer type for pluginV2 +//! +//! \see IPluginV2 +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +//! \deprecated Deprecated in TensorRT 10.8. Superseded by IPluginV3Layer. +//! +class TRT_DEPRECATED IPluginV2Layer : public ILayer +{ +public: + //! + //! \brief Get the plugin for the layer. + //! + //! \see IPluginV2 + //! + IPluginV2& getPlugin() noexcept + { + return mImpl->getPlugin(); + } + +protected: + apiv::VPluginV2Layer* mImpl; + virtual ~IPluginV2Layer() noexcept = default; +}; + +//! +//! \class IPluginV3Layer +//! +//! \brief Layer type for V3 plugins +//! +//! \see IPluginV3 +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IPluginV3Layer : public ILayer +{ +public: + //! + //! \brief Get the plugin for the layer. + //! + //! \see IPluginV3 + //! + IPluginV3& getPlugin() noexcept + { + return mImpl->getPlugin(); + } + +protected: + apiv::VPluginV3Layer* mImpl; + virtual ~IPluginV3Layer() noexcept = default; +}; + +//! +//! \enum UnaryOperation +//! +//! \brief Enumerates the unary operations that may be performed by a Unary layer. +//! +//! Operations kNOT must have inputs of DataType::kBOOL. +//! +//! Operation kSIGN and kABS must have inputs of floating-point type, DataType::kINT8, DataType::kINT32 or +//! DataType::kINT64. +//! +//! Operation kISINF must have inputs of floating-point type. +//! +//! All other operations must have inputs of floating-point type. +//! +//! \see IUnaryLayer +//! +enum class UnaryOperation : int32_t +{ + kEXP = 0, //!< Exponentiation. + kLOG = 1, //!< Log (base e). + kSQRT = 2, //!< Square root. + kRECIP = 3, //!< Reciprocal. + kABS = 4, //!< Absolute value. + kNEG = 5, //!< Negation. + kSIN = 6, //!< Sine. + kCOS = 7, //!< Cosine. + kTAN = 8, //!< Tangent. + kSINH = 9, //!< Hyperbolic sine. + kCOSH = 10, //!< Hyperbolic cosine. + kASIN = 11, //!< Inverse sine. + kACOS = 12, //!< Inverse cosine. + kATAN = 13, //!< Inverse tangent. + kASINH = 14, //!< Inverse hyperbolic sine. + kACOSH = 15, //!< Inverse hyperbolic cosine. + kATANH = 16, //!< Inverse hyperbolic tangent. + kCEIL = 17, //!< Ceiling. + kFLOOR = 18, //!< Floor. + kERF = 19, //!< Gauss error function. + kNOT = 20, //!< Logical NOT. + kSIGN = 21, //!< Sign, If input > 0, output 1; if input < 0, output -1; if input == 0, output 0. + kROUND = 22, //!< Round to nearest even for floating-point data type. + kISINF = 23, //!< Return true if input value equals +/- infinity for floating-point data type. + kISNAN = 24, //!< Return true if input value is a NaN for floating-point data type. +}; + +//! +//! Maximum number of elements in UnaryOperation enum. +//! +//! \see UnaryOperation +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 25; +} + +//! +//! \class IUnaryLayer +//! +//! \brief Layer that represents an unary operation. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IUnaryLayer : public ILayer +{ +public: + //! + //! \brief Set the unary operation for the layer. + //! + //! When running this layer on DLA, only UnaryOperation::kABS is supported. + //! + //! \see getOperation(), UnaryOperation + //! + void setOperation(UnaryOperation op) noexcept + { + mImpl->setOperation(op); + } + + //! + //! \brief Get the unary operation for the layer. + //! + //! \see setOperation(), UnaryOperation + //! + UnaryOperation getOperation() const noexcept + { + return mImpl->getOperation(); + } + +protected: + apiv::VUnaryLayer* mImpl; + virtual ~IUnaryLayer() noexcept = default; +}; + +//! +//! \enum ReduceOperation +//! +//! \brief Enumerates the reduce operations that may be performed by a Reduce layer. +//! +//! The table shows the result of reducing across an empty volume of a given type. +//! +//! Operation | kFLOAT and kHALF | kINT32 | kINT8 +//! --------- | ----------------- | ------- | ----- +//! kSUM | 0 | 0 | 0 +//! kPROD | 1 | 1 | 1 +//! kMAX | negative infinity | INT_MIN | -128 +//! kMIN | positive infinity | INT_MAX | 127 +//! kAVG | NaN | 0 | -128 +//! kNONE | Undefined | Undefined | Undefined +//! +//! The current version of TensorRT usually performs reduction for kINT8 via kFLOAT or kHALF. +//! The kINT8 values show the quantized representations of the floating-point values. +//! \note kNONE is a reduce operation which does not modify the input tensor. +//! This is applicable to Multi-Device mode only, +//! as a reduce operation is not mandatory for certain collective operations. +//! See \ref INetworkDefinition::addDistCollective for more details. +//! +enum class ReduceOperation : int32_t +{ + kSUM = 0, //!< Sum of the elements. + kPROD = 1, //!< Product of the elements. + kMAX = 2, //!< Maximum of the elements. + kMIN = 3, //!< Minimum of the elements. + kAVG = 4, //!< Average of the elements. + kNONE = 5, //!< No reduction. +}; + +//! +//! Maximum number of elements in ReduceOperation enum. +//! +//! \see ReduceOperation +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 6; +} + +//! +//! \enum CollectiveOperation +//! +//! \brief Enumerates the collective operations that may be performed by a DistCollective layer. +//! +//! \see IDistCollectiveLayer +//! +enum class CollectiveOperation : int32_t +{ + kALL_REDUCE = 0, //!< All reduce. + kALL_GATHER = 1, //!< All gather. + kBROADCAST = 2, //!< Broadcast. + kREDUCE = 3, //!< Reduce. + kREDUCE_SCATTER = 4, //!< Reduce scatter. +}; + +//! +//! Maximum number of elements in CollectiveOperation enum. +//! +//! \see CollectiveOperation +//! +template <> +struct impl::EnumMaxImpl +{ + static constexpr int32_t kVALUE = 5; +}; + +//! +//! \class IReduceLayer +//! +//! \brief Layer that represents a reduction across a non-bool tensor. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IReduceLayer : public ILayer +{ +public: + //! + //! \brief Set the reduce operation for the layer. + //! + //! \see getOperation(), ReduceOperation + //! + void setOperation(ReduceOperation op) noexcept + { + mImpl->setOperation(op); + } + + //! + //! \brief Get the reduce operation for the layer. + //! + //! \see setOperation(), ReduceOperation + //! + ReduceOperation getOperation() const noexcept + { + return mImpl->getOperation(); + } + + //! + //! \brief Set the axes over which to reduce. + //! + //! \see getReduceAxes + //! + void setReduceAxes(uint32_t reduceAxes) noexcept + { + mImpl->setReduceAxes(reduceAxes); + } + + //! + //! \brief Get the axes over which to reduce for the layer. + //! + //! \see setReduceAxes + //! + uint32_t getReduceAxes() const noexcept + { + return mImpl->getReduceAxes(); + } + + //! + //! \brief Set the boolean that specifies whether or not to keep the reduced dimensions for the layer. + //! + //! \see getKeepDimensions + //! + void setKeepDimensions(bool keepDimensions) noexcept + { + mImpl->setKeepDimensions(keepDimensions); + } + + //! + //! \brief Get the boolean that specifies whether or not to keep the reduced dimensions for the layer. + //! + //! \see setKeepDimensions + //! + bool getKeepDimensions() const noexcept + { + return mImpl->getKeepDimensions(); + } + +protected: + apiv::VReduceLayer* mImpl; + virtual ~IReduceLayer() noexcept = default; +}; + +//! +//! \class IPaddingLayer +//! +//! \brief Layer that represents a padding operation. +//! +//! The padding layer adds zero-padding at the start and end of the input tensor. It supports padding +//! only the last two dimensions. Applying negative padding results in cropping of the input. +//! +//! To pad across any subset of dimensions, use ISliceLayer with SampleMode::kFILL. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IPaddingLayer : public ILayer +{ +public: + //! + //! \brief Set the padding that is applied at the start of the tensor. + //! + //! Negative padding results in trimming the edge by the specified amount. + //! + //! \warning Only 2 dimensional padding is currently supported. + //! + //! \see getPrePaddingNd + //! + void setPrePaddingNd(Dims const& padding) noexcept + { + mImpl->setPrePaddingNd(padding); + } + + //! + //! \brief Get the padding that is applied at the start of the tensor. + //! + //! \warning Only 2 dimensional padding is currently supported. + //! + //! \see setPrePaddingNd + //! + Dims getPrePaddingNd() const noexcept + { + return mImpl->getPrePaddingNd(); + } + + //! + //! \brief Set the padding that is applied at the end of the tensor. + //! + //! Negative padding results in trimming the edge by the specified amount + //! + //! \warning Only 2 dimensional padding is currently supported. + //! + //! \see getPostPaddingNd + //! + void setPostPaddingNd(Dims const& padding) noexcept + { + mImpl->setPostPaddingNd(padding); + } + + //! + //! \brief Get the padding that is applied at the end of the tensor. + //! + //! \warning Only 2 dimensional padding is currently supported. + //! + //! \see setPostPaddingNd + //! + Dims getPostPaddingNd() const noexcept + { + return mImpl->getPostPaddingNd(); + } + +protected: + apiv::VPaddingLayer* mImpl; + virtual ~IPaddingLayer() noexcept = default; +}; + +//! +//! \struct Permutation +//! +//! \brief Represents a permutation of dimensions. +//! +struct Permutation +{ + //! + //! The elements of the permutation. + //! The permutation is applied as outputDimensionIndex = permutation.order[inputDimensionIndex], so to + //! permute from CHW order to HWC order, the required permutation is [1, 2, 0], and to permute + //! from HWC to CHW, the required permutation is [2, 0, 1]. + //! + int32_t order[Dims::MAX_DIMS]; +}; + +//! \class IShuffleLayer +//! +//! \brief Layer type for shuffling data. +//! +//! This layer shuffles data by applying in sequence: a transpose operation, a reshape operation +//! and a second transpose operation. The dimension types of the output are those of the reshape dimension. +//! +//! The layer has an optional second input. If present, it must be a 1D tensor of type Int32 or Int64, +//! and the reshape dimensions are taken from it. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IShuffleLayer : public ILayer +{ +public: + //! + //! \brief Set the permutation applied by the first transpose operation. + //! + //! \param permutation The dimension permutation applied before the reshape. + //! + //! The default is the identity permutation. + //! + //! \see getFirstTranspose + //! + void setFirstTranspose(Permutation permutation) noexcept + { + mImpl->setFirstTranspose(permutation); + } + + //! + //! \brief Get the permutation applied by the first transpose operation. + //! + //! \return The dimension permutation applied before the reshape. + //! + //! \see setFirstTranspose + //! + Permutation getFirstTranspose() const noexcept + { + return mImpl->getFirstTranspose(); + } + + //! + //! \brief Set the reshaped dimensions. + //! + //! \param dimensions The reshaped dimensions. + //! + //! Two special values can be used as dimensions. + //! + //! Value 0 copies the corresponding dimension from input. This special value + //! can be used more than once in the dimensions. If number of reshape + //! dimensions is less than input, 0s are resolved by aligning the most + //! significant dimensions of input. + //! + //! Value -1 infers that particular dimension by looking at input and rest + //! of the reshape dimensions. Note that only a maximum of one dimension is + //! permitted to be specified as -1. + //! Avoid using -1 if the input can have zero volume and any of the other + //! reshape dimensions can be zero (after resolving special treatment of 0), + //! because the solution for the -1 becomes indeterminate and TensorRT will report an error. + //! + //! The product of the new dimensions must be equal to the product of the old. + //! + //! If a second input had been used to create this layer, that input is reset to null by this method. + //! + void setReshapeDimensions(Dims const& dimensions) noexcept + { + mImpl->setReshapeDimensions(dimensions); + } + + //! + //! \brief Get the reshaped dimensions. + //! + //! \return The reshaped dimensions. + //! + //! If a second input is present and non-null, or setReshapeDimensions has + //! not yet been called, this function returns Dims with nbDims == -1. + //! + Dims getReshapeDimensions() const noexcept + { + return mImpl->getReshapeDimensions(); + } + + //! + //! \brief Append or replace an input of this layer with a specific tensor + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor + // + //! Sets the input tensor for the given index. The index must be 0 for a static shuffle layer. + //! A static shuffle layer is converted to a dynamic shuffle layer by calling setInput with an index 1. + //! A dynamic shuffle layer cannot be converted back to a static shuffle layer. + //! + //! For a dynamic shuffle layer, the values 0 and 1 are valid. + //! The indices in the dynamic case are as follows: + //! + //! - 0: Data or Shape tensor to be shuffled. + //! - 1: The dimensions for the reshape operation, as a 1D tensor of type Int32 or Int64. + //! + //! If this function is called with the value 1, then the function getNbInputs() changes + //! from returning 1 to 2. + //! + //! The reshape dimensions are treated identically to how they are treated if set statically + //! via setReshapeDimensions. In particular, a -1 is treated as a wildcard even if dynamically + //! supplied at runtime, and a 0 is treated as a placeholder if getZeroIsPlaceholder() = true, + //! which is the default. If the placeholder interpretation of 0 is unwanted because the + //! runtime dimension should be 0 when the reshape dimension is 0, be sure to call + //! setZeroIsPlacholder(false) on the IShuffleLayer. + //! + //! \see setReshapeDimensions. + //! + using ILayer::setInput; + + //! + //! \brief Set the permutation applied by the second transpose operation. + //! + //! \param permutation The dimension permutation applied after the reshape. + //! + //! The default is the identity permutation. + //! + //! The permutation is applied as outputDimensionIndex = permutation.order[inputDimensionIndex], so to + //! permute from CHW order to HWC order, the required permutation is [1, 2, 0]. + //! + //! \see getSecondTranspose + //! + void setSecondTranspose(Permutation permutation) noexcept + { + mImpl->setSecondTranspose(permutation); + } + + //! + //! \brief Get the permutation applied by the second transpose operation. + //! + //! \return The dimension permutation applied after the reshape. + //! + //! \see setSecondTranspose + //! + Permutation getSecondTranspose() const noexcept + { + return mImpl->getSecondTranspose(); + } + + //! + //! \brief Set meaning of 0 in reshape dimensions. + //! + //! If true, then a 0 in the reshape dimensions denotes copying the corresponding + //! dimension from the first input tensor. If false, then a 0 in the reshape + //! dimensions denotes a zero-length dimension. + //! + //! Default: true + //! + //! \see getZeroIsPlaceholder(); + //! + void setZeroIsPlaceholder(bool zeroIsPlaceholder) noexcept + { + return mImpl->setZeroIsPlaceholder(zeroIsPlaceholder); + } + + //! + //! \brief Get meaning of 0 in reshape dimensions. + //! + //! \return true if 0 is placeholder for corresponding input dimension, + //! false if 0 denotes a zero-length dimension. + //! + //! \see setZeroIsPlaceholder + //! + bool getZeroIsPlaceholder() const noexcept + { + return mImpl->getZeroIsPlaceholder(); + } + +protected: + apiv::VShuffleLayer* mImpl; + virtual ~IShuffleLayer() noexcept = default; +}; + +//! +//! \brief Controls how ISliceLayer and IGridSample handle out-of-bounds coordinates. +//! +//! \see ISliceLayer and IGridSample +//! +enum class SampleMode : int32_t +{ + kSTRICT_BOUNDS = 0, //!< Fail with error when the coordinates are out of bounds. + kWRAP = 1, //!< Coordinates wrap around periodically. + kCLAMP = 2, //!< Out of bounds indices are clamped to bounds. + kFILL = 3, //!< Use fill input value when coordinates are out of bounds. + kREFLECT = 4, //!< Coordinates reflect. The axis of reflection is the middle of the perimeter pixel and the + //!< reflections are repeated indefinitely within the padded regions. Repeats values for a single + //!< pixel and throws error for zero pixels. +}; + +//! +//! Maximum number of elements in SampleMode enum. +//! +//! \see SampleMode +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 5; +} + +//! +//! \brief Slices an input tensor into an output tensor based on the offset and strides. +//! +//! The slice layer has two variants, static and dynamic. Static slice specifies the start, size, and stride +//! dimensions at layer creation time via Dims and can use the get/set accessor functions of the ISliceLayer. +//! Static slice layers can also optionally specify axes through the get/set accessor functions of the ISliceLayer. +//! Dynamic slice specifies one or more of start, size, stride, or axes as ITensors, by using ILayer::setInput to add +//! a second, third, fourth, or sixth input respectively. The corresponding Dims are used if an input +//! is missing or null. +//! +//! An application can determine if the ISliceLayer has a dynamic output shape based on whether +//! the size or axes input is present and non-null. +//! +//! The slice layer selects for each dimension a start location from within the input tensor, and +//! copies elements to the output tensor using the specified stride across the input tensor. +//! Start, size, and stride tensors must be 1D tensors of type Int32 or Int64 if not specified via Dims. +//! +//! An example of using slice on a tensor: +//! input = {{0, 2, 4}, {1, 3, 5}} +//! start = {1, 0} +//! size = {1, 2} +//! stride = {1, 2} +//! output = {{1, 5}} +//! +//! If axes are provided then starts, ends, and strides must have the same length as axes +//! and specifies a subset of dimensions to slice. If axes are not provided, starts, ends, and strides +//! must be of the same length as the rank of the input tensor. +//! +//! An example of using slice on a tensor with axes specified: +//! input = {{0, 2, 4}, {1, 3, 5}} +//! start = {1} +//! size = {2} +//! stride = {1} +//! axes = {1} +//! output = {{2, 4}, {3, 5}} +//! +//! When the sampleMode is kCLAMP or kREFLECT, for each input dimension, if its size is 0 then the corresponding output +//! dimension must be 0 too. +//! +//! When the sampleMode is kFILL, the fifth input to the slice layer is used to determine the value to fill in out-of-bound +//! indices. It is an error to specify the fifth input in any other sampleMode. +//! +//! A slice layer can produce a shape tensor if the following conditions are met: +//! +//! * start, size, and stride are build time constants, either as static Dims or as constant input tensors. +//! * axes, if provided, are build time constants, either as static Dims or as a constant input tensor. +//! * The number of elements in the output tensor does not exceed 2 * Dims::MAX_DIMS. +//! +//! The input tensor is a shape tensor if the output is a shape tensor. +//! +//! The following constraints must be satisfied to execute this layer on DLA: +//! * start, size, and stride are build time constants, either as static Dims or as constant input tensors. +//! * axes, if provided, are build time constants, either as static Dims or as a constant input tensor. +//! * sampleMode is kDEFAULT, kWRAP, or kFILL. +//! * Strides are 1 for all dimensions. +//! * Slicing is not performed on the first dimension. +//! * The input tensor has four dimensions. +//! * For kFILL sliceMode, the fill value input is a scalar output of an IConstantLayer with value 0 that is not +//! consumed by any other layer. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class ISliceLayer : public ILayer +{ +public: + //! + //! \brief Set the start offset that the slice layer uses to create the output slice. + //! + //! \param start The start offset to read data from the input tensor. + //! + //! If a second input had been used to create this layer, that input is reset to null by this method. + //! + //! \see getStart + //! + void setStart(Dims const& start) noexcept + { + mImpl->setStart(start); + } + + //! + //! \brief Get the start offset for the slice layer. + //! + //! \return The start offset, or an invalid Dims structure. + //! + //! If the second input is present and non-null, + //! this function returns a Dims with nbDims = -1. + //! + //! \see setStart + //! + Dims getStart() const noexcept + { + return mImpl->getStart(); + } + + //! + //! \brief Set the dimensions of the output slice. + //! + //! \param size The dimensions of the output slice. + //! + //! If a third input had been used to create this layer, that input is reset to null by this method. + //! + //! \see getSize + //! + void setSize(Dims const& size) noexcept + { + return mImpl->setSize(size); + } + + //! + //! \brief Get dimensions of the output slice. + //! + //! \return The output dimension, or an invalid Dims structure. + //! + //! If the third input is present and non-null, + //! this function returns a Dims with nbDims = -1. + //! + //! \see setSize + //! + Dims getSize() const noexcept + { + return mImpl->getSize(); + } + + //! + //! \brief Set the stride for computing the output slice data. + //! + //! \param stride The dimensions of the stride to compute the values to store in the output slice. + //! + //! If a fourth input had been used to create this layer, that input is reset to null by this method. + //! + //! \see getStride + //! + void setStride(Dims const& stride) noexcept + { + mImpl->setStride(stride); + } + + //! + //! \brief Get the stride for the output slice. + //! + //! \return The slicing stride, or an invalid Dims structure. + //! + //! If the fourth input is present and non-null, + //! this function returns a Dims with nbDims = -1. + //! + //! \see setStride + //! + Dims getStride() const noexcept + { + return mImpl->getStride(); + } + + //! + //! \brief Set the slice mode. + //! + //! \see getMode() + //! + void setMode(SampleMode mode) noexcept + { + mImpl->setMode(mode); + } + + //! + //! \brief Get the slice mode. + //! + //! \see setMode() + //! + SampleMode getMode() const noexcept + { + return mImpl->getMode(); + } + + //! + //! \brief Append or replace an input of this layer with a specific tensor + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor + //! + //! For a slice layer, the values 0-5 are valid. + //! The indices are as follows: + //! + //! - 0: Tensor to be sliced. + //! - 1: The start tensor to begin slicing, as a 1D tensor of type Int32 or Int64. + //! - 2: The size tensor of the resulting slice, as a 1D tensor of type Int32 or Int64. + //! - 3: The stride of the slicing operation, as a 1D tensor of type Int32 or Int64. + //! - 4: Value for the kFILL slice mode. The fill value data type should either be the same + //! or be implicitly convertible to the input data type. + //! Implicit data type conversion is supported among kFLOAT, kHALF, kINT8, and kFP8 data types. + //! This input is disallowed for other modes. + //! - 5: The axes tensor indicating the corresponding axes that start, size, and stride + //! should apply to, as a 1D tensor or type Int32 or Int64. Negative values for axes + //! indicate indexing from the back of the input tensor. Values must be unique and be + //! within the interval of [-rank(input), rank(input)-1]. + //! + //! Using the corresponding setter resets the input to null. + //! + //! If this function is called with a value greater than 0, then the function getNbInputs() changes + //! from returning 1 to index + 1. + //! + using ILayer::setInput; + + //! + //! \brief Set the axes for this ISliceLayer. + //! + //! \param axes The axes on which the starts, ends, and strides parameters of the slice apply to. + //! + //! If a sixth input had been used to create this layer, that input is reset to null by this method. + //! + //! \see getAxes + //! + void setAxes(Dims const& axes) noexcept + { + mImpl->setAxes(axes); + } + + //! + //! \brief Get the axes for this ISliceLayer. + //! + //! \return The axes on which the starts, ends, and strides parameters of this slice apply to. + //! + //! If the sixth input is present and non-null, + //! this function returns a Dims with nbDims = -1. + //! + //! \see setAxes + //! + Dims getAxes() const noexcept + { + return mImpl->getAxes(); + } + +protected: + apiv::VSliceLayer* mImpl; + virtual ~ISliceLayer() noexcept = default; +}; + +//! \class IShapeLayer +//! +//! \brief Layer type for getting shape of a tensor. +//! +//! This layer sets the output to a 1D tensor of type Int64 with the dimensions of the input tensor. +//! +//! For example, if the input is a four-dimensional tensor (of any type) with +//! dimensions [2,3,5,7], the output tensor is a one-dimensional Int64 tensor +//! of length 4 containing the sequence 2, 3, 5, 7. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IShapeLayer : public ILayer +{ +protected: + apiv::VShapeLayer* mImpl; + virtual ~IShapeLayer() noexcept = default; +}; + +//! +//! \enum TopKOperation +//! +//! \brief Enumerates the operations that may be performed by a TopK layer. +//! +enum class TopKOperation : int32_t +{ + kMAX = 0, //!< Maximum of the elements. + kMIN = 1, //!< Minimum of the elements. +}; + +//! +//! Maximum number of elements in TopKOperation enum. +//! +//! \see TopKOperation +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 2; +} + +//! +//! \class ITopKLayer +//! +//! \brief Layer that represents a TopK reduction. +//! +//! This layer can accept both static and dynamic k. Static k can be set through the addTopK() API function, +//! or accessed using the getK() and setK() functions after layer creation. For dynamic k, use the setInput() +//! method to pass in k as a tensor with index 1, which overrides the static k value in calculations. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class ITopKLayer : public ILayer +{ +public: + //! + //! \brief Set the operation for the layer. + //! + //! \see getOperation(), TopKOperation + //! + void setOperation(TopKOperation op) noexcept + { + mImpl->setOperation(op); + } + + //! + //! \brief Get the operation for the layer. + //! + //! \see setOperation(), TopKOperation + //! + TopKOperation getOperation() const noexcept + { + return mImpl->getOperation(); + } + + //! + //! \brief Set the static k value for the layer. + //! + //! Currently only values up to 3840 are supported. + //! + //! If a second input to this layer has been set, it will be reset to null by this method. + //! + //! \see getK() + //! + void setK(int32_t k) noexcept + { + mImpl->setK(k); + } + + //! + //! \brief Get the k value for the layer. + //! + //! This function will return the static k value passed into addTopK(), or the value passed into setK(). + //! + //! If a second layer input is present and non-null, this function returns -1. + //! + //! \see setK() + //! + int32_t getK() const noexcept + { + return mImpl->getK(); + } + + //! + //! \brief Set which axes to reduce for the layer. + //! + //! \see getReduceAxes() + //! + void setReduceAxes(uint32_t reduceAxes) noexcept + { + mImpl->setReduceAxes(reduceAxes); + } + + //! + //! \brief Get the axes to reduce for the layer. + //! + //! \see setReduceAxes() + //! + uint32_t getReduceAxes() const noexcept + { + return mImpl->getReduceAxes(); + } + + //! + //! \brief Append or replace an input of this layer with a specific tensor + //! + //! \param index The index of the input to modify. + //! \param tensor The new input tensor. + //! + //! For a TopK layer, the values 0-1 are valid. + //! The indices are as follows: + //! + //! - 0: Input data tensor. + //! - 1: A scalar Int32 tensor containing a positive value corresponding to the number of top + //! elements to retrieve. Values larger than 3840 will result in a runtime error. If provided, + //! this will override the static k value in calculations. + //! + using ILayer::setInput; + + //! + //! \brief Set the indices type for the layer. + //! + //! \param type The DataType of the indices tensor. + //! + //! \return true if set successfully, false otherwise. + //! + //! Set the indices (the second output) type of the TopK layer. Valid values are DataType::kINT32 and + //! DataType::kINT64, otherwise an error occurs and the type is not updated. + //! + bool setIndicesType(DataType type) noexcept + { + return mImpl->setIndicesType(type); + } + + //! + //! \brief Return the TopK layer indices type. + //! + //! \return indices type set during layer creation or by setIndicesType(). + //! The return value is the indices type of the TopK layer. + //! The default value is DataType::kINT32. + //! + DataType getIndicesType() const noexcept + { + return mImpl->getIndicesType(); + } + +protected: + apiv::VTopKLayer* mImpl; + virtual ~ITopKLayer() noexcept = default; +}; + +//! +//! \enum MatrixOperation +//! +//! \brief Enumerates the operations that may be performed on a tensor +//! by IMatrixMultiplyLayer before multiplication. +//! +enum class MatrixOperation : int32_t +{ + //! Treat x as a matrix if it has two dimensions, or as a collection of + //! matrices if x has more than two dimensions, where the last two dimensions + //! are the matrix dimensions. x must have at least two dimensions. + kNONE = 0, + + //! Like kNONE, but transpose the matrix dimensions. + kTRANSPOSE = 1, + + //! Treat x as a vector if it has one dimension, or as a collection of + //! vectors if x has more than one dimension. x must have at least one dimension. + //! + //! The first input tensor with dimensions [M,K] used with MatrixOperation::kVECTOR is equivalent to a tensor + //! with dimensions [M, 1, K] with MatrixOperation::kNONE, i.e. is treated as M row vectors of length K, + //! or dimensions [M, K, 1] with MatrixOperation::kTRANSPOSE. + //! + //! The second input tensor with dimensions [M,K] used with MatrixOperation::kVECTOR is equivalent to a tensor + //! with dimensions [M, K, 1] with MatrixOperation::kNONE, i.e. is treated as M column vectors of length K, + //! or dimensions [M, 1, K] with MatrixOperation::kTRANSPOSE. + kVECTOR = 2, +}; + +//! +//! Maximum number of elements in MatrixOperation enum. +//! +//! \see DataType +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 3; +} + +//! +//! \class IMatrixMultiplyLayer +//! +//! \brief Layer that represents a Matrix Multiplication. +//! +//! Let A be op(getInput(0)) and B be op(getInput(1)) where +//! op(x) denotes the corresponding MatrixOperation. +//! +//! When A and B are matrices or vectors, computes the inner product A * B: +//! +//! matrix * matrix -> matrix +//! matrix * vector -> vector +//! vector * matrix -> vector +//! vector * vector -> scalar +//! +//! Inputs of higher rank are treated as collections of matrices or vectors. +//! The output will be a corresponding collection of matrices, vectors, or scalars. +//! +//! For a dimension that is not one of the matrix or vector dimensions: +//! If the dimension is 1 for one of the tensors but not the other tensor, +//! the former tensor is broadcast along that dimension to match the dimension of the latter tensor. +//! The number of these extra dimensions for A and B must match. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IMatrixMultiplyLayer : public ILayer +{ +public: + //! + //! \brief Set the operation for an input tensor. + //! + //! \param index Input tensor number (0 or 1). + //! \param op New operation. + //! + //! \see getOperation() + //! + void setOperation(int32_t index, MatrixOperation op) noexcept + { + mImpl->setOperation(index, op); + } + + //! + //! \brief Get the operation for an input tensor. + //! + //! \param index Input tensor number (0 or 1). + //! + //! \see setOperation() + //! + MatrixOperation getOperation(int32_t index) const noexcept + { + return mImpl->getOperation(index); + } + +protected: + apiv::VMatrixMultiplyLayer* mImpl; + virtual ~IMatrixMultiplyLayer() noexcept = default; +}; + +//! \class INonZero +//! +//! \brief A NonZero layer in a network. +//! +//! This layer gets the positions of elements that are non-zero in the input. +//! For boolean input, "non-zero" means "true". Semantics are similar to ONNX NonZero. +//! +//! The input may have type kFLOAT, kHALF, kINT32, or kBOOL. +//! +//! The output is a matrix of type kINT32 or kINT64. +//! For an input with dimensions [L1, L2, ..., Lm], the output has dimensions [m,n], +//! where n is the number of non-zero elements. I.e., each column denotes a m-D position. +//! +//! The columns are lexically ordered. +//! E.g., a column with [3,2,4,7] precedes a column with [3,2,5,6]. +//! +//! Tip: "compress" can be implemented with INonZero+IShuffle+Gather. +//! For example, to compress a tensor x over axis k using mask vector v, +//! use nonzero(v) to compute the subscripts, shuffle with reshape dimensions = [-1] +//! to make the subscripts 1D, and then gather with the subscripts. +//! +class INonZeroLayer : public ILayer +{ +public: + //! + //! \brief Set the indices type for the layer. + //! + //! \param type The DataType of the indices tensor. + //! + //! \return true if set successfully, false otherwise. + //! + //! Set the indices (the first output) type of the NonZero layer. Valid values are DataType::kINT32 and + //! DataType::kINT64, otherwise an error occurs and the type is not updated. + //! + bool setIndicesType(DataType type) noexcept + { + return mImpl->setIndicesType(type); + } + + //! + //! \brief Return the NonZero layer indices type. + //! + //! \return indices type set during layer creation or by setIndicesType(). + //! The return value is the indices type of the NonZero layer. + //! The default value is DataType::kINT32. + //! + DataType getIndicesType() const noexcept + { + return mImpl->getIndicesType(); + } + +protected: + virtual ~INonZeroLayer() noexcept = default; + apiv::VNonZeroLayer* mImpl; +}; + +//! +//! \class IRaggedSoftMaxLayer +//! +//! \brief A RaggedSoftmax layer in a network definition. +//! +//! This layer takes a ZxS input tensor and an additional Zx1 bounds tensor +//! holding the lengths of the Z sequences. +//! +//! This layer computes a softmax across each of the Z sequences. +//! +//! The output tensor is of the same size as the input tensor. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IRaggedSoftMaxLayer : public ILayer +{ +protected: + apiv::VRaggedSoftMaxLayer* mImpl; + virtual ~IRaggedSoftMaxLayer() noexcept = default; +}; + +//! \class IIdentityLayer +//! +//! \brief A layer that represents the identity function. +//! +//! For a strongly typed network, the layer is an identity function, i.e. the output +//! tensor elements are identical to the input tensor elements, possibly with a change +//! in layout. For example, if a network consists of a single IIdentityLayer, the network +//! input and output must have the same type, but the input can have NCHW layout and +//! the output can have NHWC layout. +//! +//! If the network is weakly typed, the layer is additionally permitted some type conversions +//! as described below. +//! +//! If the output type is explicitly specified via setOutputType, IIdentityLayer can be +//! used to convert from one type to another. Other than conversions between the same +//! type (kFLOAT -> kFLOAT for example), the only valid conversions are: +//! +//! (kFLOAT | kHALF | kINT32 | kBOOL) -> (kFLOAT | kHALF | kINT32 | kBOOL) +//! +//! (kFLOAT | kHALF) -> kUINT8 +//! +//! kUINT8 -> (kFLOAT | kHALF) +//! +//! Conversion also happens implicitly, without calling setOutputType, if the output +//! tensor is a network output. +//! +//! Two types are compatible if they are identical, or are both in {kFLOAT, kHALF}. +//! Implicit conversion between incompatible types, i.e. without using setOutputType, +//! was recognized as incorrect as of TensorRT 8.4, but was retained for API compatibility +//! within TensorRT 8.x releases. In TensorRT 10.0 onwards it is an error if the network +//! output tensor type is incompatible with the layer output type. E.g., implicit conversion +//! from kFLOAT to kINT32 is not allowed. +//! +//! To explicitly convert kFLOAT to kINT32: +//! +//! * Preferred: use ICastLayer. +//! +//! * Legacy alternative: use IIdentityLayer and setOutputType(DataType::kINT32). +//! +//! Similar advice applies for explicit conversion in the other direction. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IIdentityLayer : public ILayer +{ +protected: + apiv::VIdentityLayer* mImpl; + virtual ~IIdentityLayer() noexcept = default; +}; + +//! \class ICastLayer +//! +//! \brief A cast layer in a network. +//! +//! This layer casts a given tensor to the datatype specified by \p toType. +//! +class ICastLayer : public ILayer +{ +public: + //! + //! \brief Set cast layer output type. + //! + //! \param toType The DataType of the output tensor. + //! + //! Set the output type of the cast layer. + //! + void setToType(DataType toType) noexcept + { + mImpl->setToType(toType); + } + + //! + //! \brief Return cast layer output type. + //! + //! \return toType parameter set during layer creation or by setToType(). + //! The return value is the output type of the cast layer. + //! + DataType getToType() const noexcept + { + return mImpl->getToType(); + } + +protected: + apiv::VCastLayer* mImpl; + virtual ~ICastLayer() noexcept = default; +}; + +//! \class IConstantLayer +//! +//! \brief Layer that represents a constant value. +//! +//! \note This layer does not support boolean types. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IConstantLayer : public ILayer +{ +public: + //! + //! \brief Set the weights for the layer. + //! + //! The output type is weights.type. If the network is weakly typed and the weights have a real type, + //! the output type might be different per TensorRT's type conversion rules. + //! + //! \see getWeights() + //! + void setWeights(Weights weights) noexcept + { + mImpl->setWeights(weights); + } + + //! + //! \brief Get the weights for the layer. + //! + //! \see setWeights + //! + Weights getWeights() const noexcept + { + return mImpl->getWeights(); + } + + //! + //! \brief Set the dimensions for the layer. + //! + //! \param dimensions The dimensions of the layer + //! + //! \see setDimensions + //! + void setDimensions(Dims const& dimensions) noexcept + { + mImpl->setDimensions(dimensions); + } + + //! + //! \brief Get the dimensions for the layer. + //! + //! \return the dimensions for the layer + //! + //! \see getDimensions + //! + Dims getDimensions() const noexcept + { + return mImpl->getDimensions(); + } + +protected: + apiv::VConstantLayer* mImpl; + virtual ~IConstantLayer() noexcept = default; +}; + +//! +//! \class IParametricReLULayer +//! +//! \brief Layer that represents a parametric ReLU operation. +//! +//! When running this layer on DLA, the slopes input must be a build-time constant. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IParametricReLULayer : public ILayer +{ +protected: + apiv::VParametricReLULayer* mImpl; + virtual ~IParametricReLULayer() noexcept = default; +}; + +//! \enum InterpolationMode +//! +//! \brief Enumerates various modes of interpolation +//! +//! +enum class InterpolationMode : int32_t +{ + kNEAREST = 0, //!< ND (0 < N <= 8) nearest neighbor resizing. + kLINEAR = 1, //!< Supports linear (1D), bilinear (2D), and trilinear (3D) interpolation + kCUBIC = 2 //!< Supports bicubic (2D) interpolation +}; + +namespace impl +{ +//! +//! Maximum number of elements in InterpolationMode enum. +//! +//! \see InterpolationMode +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 3; +}; +} // namespace impl + +//! +//! \enum ResizeCoordinateTransformation +//! +//! \brief The resize coordinate transformation function. +//! +//! \see IResizeLayer::setCoordinateTransformation() +//! +enum class ResizeCoordinateTransformation : int32_t +{ + //! Think of each value in the tensor as a unit volume, and the coordinate is a point inside this volume. + //! The coordinate point is drawn as a star `(*)` in the below diagram, and multiple values range has a length. + //! Define `x_origin` as the coordinate of axis x in the input tensor, `x_resized` as the coordinate of axis x in + //! the output tensor, `length_origin` as length of the input tensor in axis x, and `length_resize` as length of the + //! output tensor in axis x. + //! + //! |<--------------length---------->| + //! | 0 | 1 | 2 | 3 | + //! * * * * + //! + //! x_origin = x_resized * (length_origin - 1) / (length_resize - 1) + //! + kALIGN_CORNERS = 0, + + //! |<--------------length--------------------->| + //! | 0 | 1 | 2 | 3 | + //! * * * * + //! + //! x_origin = x_resized * (length_origin / length_resize) + //! + kASYMMETRIC = 1, + + //! |<--------------length--------------------->| + //! | 0 | 1 | 2 | 3 | + //! * * * * + //! + //! x_origin = (x_resized + 0.5) * (length_origin / length_resize) - 0.5 + //! + kHALF_PIXEL = 2, +}; + +namespace impl +{ +//! +//! Maximum number of elements in ResizeCoordinateTransformation enum. +//! +//! \see ResizeCoordinateTransformation +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 3; +}; +} // namespace impl + +//! +//! \enum ResizeSelector +//! +//! \brief The coordinate selector when resize to single pixel output. +//! +//! \see IResizeLayer::setSelectorForSinglePixel() +//! +enum class ResizeSelector : int32_t +{ + //! Use formula to map the original index. + kFORMULA = 0, + + //! Select the upper left pixel. + kUPPER = 1, +}; + +namespace impl +{ +//! +//! Maximum number of elements in ResizeSelector enum. +//! +//! \see ResizeSelector +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 2; +}; +} // namespace impl + +//! +//! \enum ResizeRoundMode +//! +//! \brief The rounding mode for nearest neighbor resize. +//! +//! \see IResizeLayer::setNearestRounding() +//! +enum class ResizeRoundMode : int32_t +{ + //! Round half up. + kHALF_UP = 0, + + //! Round half down. + kHALF_DOWN = 1, + + //! Round to floor. + kFLOOR = 2, + + //! Round to ceil. + kCEIL = 3, +}; + +namespace impl +{ +//! +//! Maximum number of elements in ResizeRoundMode enum. +//! +//! \see ResizeRoundMode +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 4; +}; +} // namespace impl + +//! \class IResizeLayer +//! +//! \brief A resize layer in a network definition. +//! +//! Resize layer can be used for resizing a N-D tensor. +//! +//! Resize layer currently supports the following configurations: +//! - InterpolationMode::kNEAREST - resizes last `m` dimensions of N-D, where 0 < m <= min(8, N) and N > 0 +//! - InterpolationMode::kLINEAR - resizes last `m` dimensions of N-D, where 0 < m <= min(3, N) and N > 0 +//! +//! Default resize mode is InterpolationMode::kNEAREST. +//! +//! The coordinates in the output tensor are mapped to coordinates in the input tensor using a function set by calling +//! setCoordinateTransformation(). The default for all InterpolationMode settings (nearest, linear, bilinear, etc.) is +//! ResizeCoordinateTransformation::kASYMMETRIC. +//! +//! The resize layer provides two ways to resize tensor dimensions. +//! - Set output dimensions directly. It can be done for static as well as dynamic resize layer. +//! Static resize layer requires output dimensions to be known at build-time. +//! Dynamic resize layer requires output dimensions to be set as one of the input tensors. +//! - Set scales for resize. Each output dimension is calculated as floor(input dimension * scale). +//! Only static resize layer allows setting scales where the scales are known at build-time. +//! +//! If executing this layer on DLA, the following combinations of parameters are supported: +//! +//! - In kNEAREST mode: +//! * (ResizeCoordinateTransformation::kASYMMETRIC, ResizeSelector::kFORMULA, ResizeRoundMode::kFLOOR) +//! * (ResizeCoordinateTransformation::kHALF_PIXEL, ResizeSelector::kFORMULA, ResizeRoundMode::kHALF_DOWN) +//! * (ResizeCoordinateTransformation::kHALF_PIXEL, ResizeSelector::kFORMULA, ResizeRoundMode::kHALF_UP) +//! +//! - In kLINEAR mode: +//! * (ResizeCoordinateTransformation::kHALF_PIXEL, ResizeSelector::kFORMULA) +//! * (ResizeCoordinateTransformation::kHALF_PIXEL, ResizeSelector::kUPPER) +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IResizeLayer : public ILayer +{ +public: + //! + //! \brief Set the output dimensions. + //! + //! \param dimensions The output dimensions. Number of output dimensions must be the same as the number of input + //! dimensions. + //! + //! If executing this layer on DLA, setOutputDimensions() is not supported. + //! + //! If there is a second input, i.e. resize layer is dynamic, + //! calling setOutputDimensions() is an error and does not update the + //! dimensions. + //! + //! Output dimensions can be specified directly, or via scale factors relative to input dimensions. + //! Scales for resize can be provided using setScales(). + //! + //! \see setScales + //! \see getOutputDimensions + //! + void setOutputDimensions(Dims const& dimensions) noexcept + { + return mImpl->setOutputDimensions(dimensions); + } + + //! + //! \brief Get the output dimensions. + //! + //! \return The output dimensions. + //! + Dims getOutputDimensions() const noexcept + { + return mImpl->getOutputDimensions(); + } + + //! + //! \brief Set the resize scales. + //! + //! \param scales An array of resize scales. + //! \param nbScales Number of scales. Number of scales must be equal to the number of input dimensions. + //! + //! If executing this layer on DLA, there are three restrictions: + //! 1) nbScales has to be exactly 4. + //! 2) the first two elements in scales need to be exactly 1 (for unchanged batch and channel dimensions). + //! 3) The last two elements in scales, representing the scale values along height and width dimensions, + //! respectively, need to be integer values in the range of [1, 32] for kNEAREST mode and [1, 4] for kLINEAR. + //! Example of DLA-supported scales: {1, 1, 2, 2}. + //! + //! If there is a second input, i.e. resize layer is dynamic, + //! calling setScales() is an error and does not update the scales. + //! + //! Output dimensions are calculated as follows: + //! outputDims[i] = floor(inputDims[i] * scales[i]) + //! + //! Output dimensions can be specified directly, or via scale factors relative to input dimensions. + //! Output dimensions can be provided directly using setOutputDimensions(). + //! + //! \see setOutputDimensions + //! \see getScales + //! + void setScales(float const* scales, int32_t nbScales) noexcept + { + mImpl->setScales(scales, nbScales); + } + + //! + //! \brief Copies resize scales to scales[0, ..., nbScales-1], where nbScales is the number of scales that were set. + //! + //! \param size The number of scales to get. If size != nbScales, no scales will be copied. + //! + //! \param scales Pointer to where to copy the scales. Scales will be copied only if + //! size == nbScales and scales != nullptr. + //! + //! In case the size is not known consider using size = 0 and scales = nullptr. This method will return + //! the number of resize scales. + //! + //! \return The number of resize scales i.e. nbScales if scales were set. + //! Return -1 in case no scales were set or resize layer is used in dynamic mode. + //! + int32_t getScales(int32_t size, float* scales) const noexcept + { + return mImpl->getScales(size, scales); + } + + //! + //! \brief Set resize mode for an input tensor. + //! + //! Supported resize modes are Nearest Neighbor and Linear. + //! + //! \see InterpolationMode + //! + void setResizeMode(InterpolationMode interpolationMode) noexcept + { + mImpl->setResizeMode(interpolationMode); + } + + //! + //! \brief Get resize mode for an input tensor. + //! + //! \return The resize mode. + //! + InterpolationMode getResizeMode() const noexcept + { + return mImpl->getResizeMode(); + } + + //! + //! \brief Append or replace an input of this layer with a specific tensor + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor. + //! + //! Sets the input tensor for the given index. The index must be 0 for a static resize layer. + //! A static resize layer is converted to a dynamic resize layer by calling setInput with an index 1. + //! A dynamic resize layer cannot be converted back to a static resize layer. + //! + //! For a dynamic resize layer, the values 0 and 1 are valid. + //! The indices in the dynamic case are as follows: + //! + //! - 0: Execution tensor to be resized. + //! - 1: The output dimensions, as a 1D tensor of type Int32 or Int64. + //! + //! If this function is called with the value 1, then the function getNbInputs() changes + //! from returning 1 to 2. + //! + using ILayer::setInput; + + //! + //! \brief Set coordinate transformation function. + //! + //! The function maps a coordinate in the output tensor to a coordinate in the input tensor. + //! + //! Default function is ResizeCoordinateTransformation::kASYMMETRIC. + //! + //! \see ResizeCoordinateTransformation + //! + void setCoordinateTransformation(ResizeCoordinateTransformation coordTransform) noexcept + { + mImpl->setCoordinateTransformation(coordTransform); + } + + //! + //! \brief Get coordinate transformation function. + //! + //! \return The coordinate transformation function. + //! + ResizeCoordinateTransformation getCoordinateTransformation() const noexcept + { + return mImpl->getCoordinateTransformation(); + } + + //! + //! \brief Set coordinate selector function when resized to single pixel. + //! + //! When resize to single pixel image, use this function to decide how to map the coordinate in the original + //! image. + //! + //! Default is ResizeSelector::kFORMULA. + //! + //! \see ResizeSelector + //! + void setSelectorForSinglePixel(ResizeSelector selector) noexcept + { + mImpl->setSelectorForSinglePixel(selector); + } + + //! + //! \brief Get the coordinate selector function when resized to single pixel. + //! + //! \return The selector function. + //! + ResizeSelector getSelectorForSinglePixel() const noexcept + { + return mImpl->getSelectorForSinglePixel(); + } + + //! + //! \brief Set rounding mode for nearest neighbor resize. + //! + //! This value is used for nearest neighbor interpolation rounding. It is applied after coordinate transformation. + //! + //! Default is kFLOOR. + //! + //! \see ResizeRoundMode + //! + void setNearestRounding(ResizeRoundMode value) noexcept + { + mImpl->setNearestRounding(value); + } + + //! + //! \brief Get rounding mode for nearest neighbor resize. + //! + //! \return The rounding mode. + //! + ResizeRoundMode getNearestRounding() const noexcept + { + return mImpl->getNearestRounding(); + } + + //! + //! \brief Set the coefficient 'A' used in cubic interpolation. + //! + //! Cubic uses the coefficient 'A' to calculate the weight of input pixels: + //! + //! 

+    //! x := The relative distance between the sampled pixels and the input coordinates.
+    //!
+    //! weight(x) := for |x| <= 1, ((A + 2) * x - (A + 3)) * x * x + 1,
+    //!              for 1 < |x| < 2, ((A * x - 5 * A) * x + 8 * A) * x - 4 * A,
+    //!              others 0;
+    //!
+ //! + //! This attribute is valid only if "resize mode" is "cubic". + //! + //! The default value is -0.75. + //! + void setCubicCoeff(float A) noexcept + { + mImpl->setCubicCoeff(A); + } + + //! + //! \brief Get the coefficient 'A' used in cubic interpolation. + //! + //! \see setCubicCoeff() + //! + float getCubicCoeff() const noexcept + { + return mImpl->getCubicCoeff(); + } + + //! + //! \brief Set the state for excluding outside pixels. + //! + //! If set to true, the weight of sampling locations outside the input tensor will be set to false, and the weight + //! will be renormalized so that their sum is 1.0. + //! + //! The default value is false. + //! + void setExcludeOutside(bool excludeFlag) noexcept + { + mImpl->setExcludeOutside(excludeFlag); + } + + //! + //! \brief Get the state for excluding outside pixels. + //! + //! \see setExcludeOutside() + //! + bool getExcludeOutside() const noexcept + { + return mImpl->getExcludeOutside(); + } + +protected: + virtual ~IResizeLayer() noexcept = default; + apiv::VResizeLayer* mImpl; +}; + +//! +//! \enum LoopOutput +//! +//! \brief Enum that describes kinds of loop outputs. +//! +enum class LoopOutput : int32_t +{ + //! Output value is value of tensor for last iteration. + kLAST_VALUE = 0, + + //! Output value is concatenation of values of tensor for each iteration, in forward order. + kCONCATENATE = 1, + + //! Output value is concatenation of values of tensor for each iteration, in reverse order. + kREVERSE = 2 +}; + +//! +//! Maximum number of elements in LoopOutput enum. +//! +//! \see DataType +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 3; +} + +//! +//! \enum TripLimit +//! +//! \brief Enum that describes kinds of trip limits. +//! +enum class TripLimit : int32_t +{ + + kCOUNT = 0, //!< Tensor is a scalar of type kINT32 or kINT64 that contains the trip count. + kWHILE = 1 //!< Tensor is a scalar of type kBOOL. Loop terminates when value is false. +}; + +//! +//! Maximum number of elements in TripLimit enum. +//! +//! \see DataType +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 2; +} + +class ILoop; + +//! +//! \class ILoopBoundaryLayer +//! +//! \brief This is a base class for Loop boundary layers. +//! +//! The loop boundary layers are used to define loops within a network, enabling the implementation +//! of recurrences. The boundary layers for a loop are created by class ILoop. +//! +//! There are four kinds of boundary layers. +//! * ITripLimitLayer: controls the number of loop iterations. +//! * IIterationLayer: iterates over an input tensor. +//! * IRecurrenceLayer: returns an initial value or value from the previous loop iteration. +//! * ILoopOutputLayer: generates an output tensor from the loop iterations. +class ILoopBoundaryLayer : public ILayer +{ +public: + //! + //! \brief Get a pointer to ILoop associated with this boundary layer. + //! + ILoop* getLoop() const noexcept + { + return mBoundary->getLoop(); + } + +protected: + virtual ~ILoopBoundaryLayer() noexcept = default; + apiv::VLoopBoundaryLayer* mBoundary; +}; + +//! +//! \class IIfConditionalBoundaryLayer +//! +//! \brief This is a base class for Conditional boundary layers. +//! +//! Boundary layers are used to demarcate the boundaries of Conditionals. +//! +class IIfConditionalBoundaryLayer : public ILayer +{ +public: + //! + //! \brief Get a pointer to the IIfConditional associated with this boundary layer. + //! + IIfConditional* getConditional() const noexcept + { + return mBoundary->getConditional(); + } + +protected: + virtual ~IIfConditionalBoundaryLayer() noexcept = default; + apiv::VConditionalBoundaryLayer* mBoundary; +}; + +//! +//! \class IConditionLayer +//! +//! \brief This layer represents a condition input to an IIfConditional. +//! +class IConditionLayer : public IIfConditionalBoundaryLayer +{ +public: +protected: + virtual ~IConditionLayer() noexcept = default; + apiv::VConditionLayer* mImpl; +}; + +//! +//! \class IIfConditionalOutputLayer +//! +//! \brief This layer represents an output of an IIfConditional. +//! +//! An IIfConditionalOutputLayer has two inputs and one output. +//! +//! \see IIfConditional::addOutput +//! +class IIfConditionalOutputLayer : public IIfConditionalBoundaryLayer +{ +public: +protected: + virtual ~IIfConditionalOutputLayer() noexcept = default; + apiv::VConditionalOutputLayer* mImpl; +}; + +//! +//! \class IIfConditionalInputLayer +//! +//! \brief This layer represents an input to an IIfConditional. +//! +class IIfConditionalInputLayer : public IIfConditionalBoundaryLayer +{ +public: +protected: + virtual ~IIfConditionalInputLayer() noexcept = default; + apiv::VConditionalInputLayer* mImpl; +}; + +//! +//! \class IIfConditional +//! +//! \brief Helper for constructing conditionally-executed subgraphs. +//! +//! An If-conditional conditionally executes part of the network according +//! to the following pseudo-code: +//! +//! If condition is true then: +//! output = trueSubgraph(trueInputs); +//! Else +//! output = falseSubgraph(falseInputs); +//! Emit output +//! +//! Condition is a 0D boolean tensor (representing a scalar). +//! trueSubgraph represents a network subgraph that is executed when condition evaluates to True. +//! falseSubgraph represents a network subgraph that is executed when condition evaluates to False. +//! +//! The following constraints apply to If-conditionals: +//! - Both the trueSubgraph and falseSubgraph must be defined. +//! - The number of output tensors in both subgraphs is the same. +//! - Corresponding output tensors from the true/false subgraphs have the same type and rank. +//! +//! The subgraphs may directly use tensors defined outside of the IIfConditional. +class IIfConditional : public INoCopy +{ +public: + //! + //! \brief Set the condition tensor for this If-Conditional construct. + //! + //! \param condition The condition tensor that will determine which subgraph to execute. + //! + //! \p condition tensor must be a 0D execution tensor (scalar) with type DataType::kBOOL. + //! + //! \see IConditionLayer + //! + IConditionLayer* setCondition(ITensor& condition) noexcept + { + return mImpl->setCondition(condition); + } + + //! + //! \brief Add an If-conditional output. + //! + //! \param trueSubgraphOutput The output of the subgraph executed when the conditional evaluates to true. + //! \param falseSubgraphOutput The output of the subgraph executed when the conditional evaluates to false. + //! + //! Each output layer of an IIfConditional represents a single output of either the true-subgraph or the + //! false-subgraph of an IIfConditional, depending on which subgraph was executed. + //! + //! The ranks of the two tensors must be equal unless the condition is a build-time constant. + //! + //! \see IIfConditionalOutputLayer + //! + IIfConditionalOutputLayer* addOutput(ITensor& trueSubgraphOutput, ITensor& falseSubgraphOutput) noexcept + { + return mImpl->addOutput(trueSubgraphOutput, falseSubgraphOutput); + } + + //! + //! \brief Add an If-conditional input. + //! + //! \param input An input to the conditional that can be used by either or both of the conditional's subgraphs. + //! + //! \see IIfConditionalInputLayer + //! + IIfConditionalInputLayer* addInput(ITensor& input) noexcept + { + return mImpl->addInput(input); + } + + //! + //! \brief Set the name of the conditional. + //! + //! The name is used in error diagnostics. + //! This method copies the name string. + //! + //! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see getName() + //! + void setName(char const* name) noexcept + { + mImpl->setName(name); + } + + //! + //! \brief Return the name of the conditional. + //! + //! \see setName() + //! + char const* getName() const noexcept + { + return mImpl->getName(); + } + +protected: + virtual ~IIfConditional() noexcept = default; + apiv::VIfConditional* mImpl; +}; + +//! +//! \class IRecurrenceLayer +//! +//! \brief A recurrence layer in a network definition. +//! +//! The recurrence layer allows a loop iteration to compute a result from a value computed in the previous iteration. +//! +class IRecurrenceLayer : public ILoopBoundaryLayer +{ +public: + //! + //! \brief Append or replace an input of this layer with a specific tensor + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor + // + //! Sets the input tensor for the given index. + //! + //! For a recurrence layer, the values 0 and 1 are valid. + //! The indices are as follows: + //! + //! - 0: The initial value of the output tensor. The value must come from outside the loop. + //! - 1: The next value of the output tensor. The value usually comes from inside the loop, and must have the same + //! dimensions as input 0. + //! + //! If this function is called with the value 1, then the function getNbInputs() changes + //! from returning 1 to 2. + //! + using ILayer::setInput; + +protected: + virtual ~IRecurrenceLayer() noexcept = default; + apiv::VRecurrenceLayer* mImpl; +}; + +//! +//! \class ILoopOutputLayer +//! +//! \brief An ILoopOutputLayer is the sole way to get output from a loop. +//! +//! The first input tensor must be defined inside the loop; the output tensor is outside the loop. +//! The second input tensor, if present, must be defined outside the loop. +//! +//! If getLoopOutput() is kLAST_VALUE, a single input must be provided, +//! and that input must be from an IRecurrenceLayer in the same loop. +//! +//! If getLoopOutput() is kCONCATENATE or kREVERSE, a second input must be provided. +//! The second input must be a 0D shape tensor, defined before the loop commences, +//! that specifies the concatenation length of the output. +//! +//! The output tensor has j more dimensions than the input tensor, where +//! j == 0 if getLoopOutput() is kLAST_VALUE +//! j == 1 if getLoopOutput() is kCONCATENATE or kREVERSE. +//! +class ILoopOutputLayer : public ILoopBoundaryLayer +{ +public: + //! + //! \brief Get which kind a loop output has. + //! + LoopOutput getLoopOutput() const noexcept + { + return mImpl->getLoopOutput(); + } + + //! + //! \brief Set where to insert the contenation axis. Ignored if getLoopOutput() is kLAST_VALUE. + //! + //! For example, if the input tensor has dimensions [b,c,d], + //! and getLoopOutput() is kCONCATENATE, the output has four dimensions. + //! Let a be the value of the second input. + //! setAxis(0) causes the output to have dimensions [a,b,c,d]. + //! setAxis(1) causes the output to have dimensions [b,a,c,d]. + //! setAxis(2) causes the output to have dimensions [b,c,a,d]. + //! setAxis(3) causes the output to have dimensions [b,c,d,a]. + //! Default is axis is 0. + //! + void setAxis(int32_t axis) noexcept + { + mImpl->setAxis(axis); + } + + //! + //! \brief Get axis being concatenated over. + //! + int32_t getAxis() const noexcept + { + return mImpl->getAxis(); + } + + //! + //! \brief Append or replace an input of this layer with a specific tensor + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor + // + //! Sets the input tensor for the given index. The index must be 0 for a kLAST_VALUE loop output layer. + //! Loop output layer is converted to a kCONCATENATE or kREVERSE loop output layer by calling setInput with an + //! index 1. A kCONCATENATE or kREVERSE loop output layer cannot be converted back to a kLAST_VALUE loop output + //! layer. + //! + //! For a kCONCATENATE or kREVERSE loop output layer, the values 0 and 1 are valid. + //! The indices in the kCONCATENATE or kREVERSE cases are as follows: + //! + //! - 0: Contribution to the output tensor. The contribution must come from inside the loop. + //! - 1: The concatenation length scalar value, must come from outside the loop, as a 0D shape tensor of type Int32 or Int64. + //! + //! If this function is called with the value 1, then the function getNbInputs() changes + //! from returning 1 to 2. + //! + using ILayer::setInput; + +protected: + virtual ~ILoopOutputLayer() noexcept = default; + apiv::VLoopOutputLayer* mImpl; +}; + +//! +//! \class ITripLimitLayer +//! +//! \brief A layer that represents a trip-count limiter. +//! +//! The trip limit layer sets the execution condition for loops, using kCOUNT to define the number of iterations or +//! kWHILE for a conditional loop. A loop can have one of each kind of limit, in which case the loop exits when +//! the trip count is reached or the condition becomes false. +//! +//! See INetworkDefinition::addTripLimit(). +//! +class ITripLimitLayer : public ILoopBoundaryLayer +{ +public: + //! + //! \brief Get a trip limiter type. + //! + TripLimit getTripLimit() const noexcept + { + return mImpl->getTripLimit(); + } + +protected: + virtual ~ITripLimitLayer() noexcept = default; + apiv::VTripLimitLayer* mImpl; +}; + +//! +//! \class IIteratorLayer +//! +//! \brief A layer to do iterations. +//! +//! The iterator layer iterates over a tensor along the given axis and in the given direction. +//! It enables each loop iteration to inspect a different slice of the tensor. +//! +//! \see ILoop::addIterator() +//! +class IIteratorLayer : public ILoopBoundaryLayer +{ +public: + //! + //! \brief Set axis to iterate over. + //! + void setAxis(int32_t axis) noexcept + { + mImpl->setAxis(axis); + } + + //! + //! \brief Get axis being iterated over. + //! + int32_t getAxis() const noexcept + { + return mImpl->getAxis(); + } + + //! + //! \brief Set iteration order to be reverse. + //! + //! For reverse=false, the layer is equivalent to addGather(tensor, I, 0) where I is a + //! scalar tensor containing the loop iteration number. + //! For reverse=true, the layer is equivalent to addGather(tensor, M-1-I, 0) where M is the trip count + //! computed from TripLimits of kind kCOUNT. + //! The default is reverse=false. + //! + void setReverse(bool reverse) noexcept + { + mImpl->setReverse(reverse); + } + + //! + //! \brief Check if the iteration order is reverse. + //! + //! \return True if and only if reversing input. + //! + bool getReverse() const noexcept + { + return mImpl->getReverse(); + } + +protected: + virtual ~IIteratorLayer() noexcept = default; + apiv::VIteratorLayer* mImpl; +}; + +//! +//! \class ILoop +//! +//! \brief Helper for creating a recurrent subgraph. +//! +//! An ILoop defines a loop within a network. It supports the implementation of recurrences, +//! which are crucial for iterative computations, such as RNNs for natural language processing and +//! time-series analysis. +//! +//! The subgraph may directly use tensors defined outside of the ILoop. +class ILoop : public INoCopy +{ +public: + //! + //! \brief Create a recurrence layer for this loop with initialValue as its first input. + //! + //! IRecurrenceLayer requires exactly two inputs. The 2nd input must be added, via method + //! IRecurrenceLayer::setInput(1,...) before an Engine can be built. + //! + IRecurrenceLayer* addRecurrence(ITensor& initialValue) noexcept + { + return mImpl->addRecurrence(initialValue); + } + + //! + //! \brief Add a trip-count limiter, based on the given tensor. + //! + //! There may be at most one kCOUNT and one kWHILE limiter for a loop. + //! When both trip limits exist, the loop exits when the + //! count is reached or condition is falsified. + //! It is an error to not add at least one trip limiter. + //! + //! For kCOUNT, the input tensor must be available before the loop starts. + //! + //! For kWHILE, the input tensor must be the output of a subgraph that contains + //! only layers that are not ITripLimitLayer, IIteratorLayer or ILoopOutputLayer. + //! Any IRecurrenceLayers in the subgraph must belong to the same loop as the + //! ITripLimitLayer. A trivial example of this rule is that the input to the kWHILE + //! is the output of an IRecurrenceLayer for the same loop. + //! + ITripLimitLayer* addTripLimit(ITensor& tensor, TripLimit limit) noexcept + { + return mImpl->addTripLimit(tensor, limit); + } + + //! + //! \brief Return layer that subscripts tensor by loop iteration. + //! + //! For reverse=false, this is equivalent to addGather(tensor, I, 0) where I is a + //! scalar tensor containing the loop iteration number. + //! For reverse=true, this is equivalent to addGather(tensor, M-1-I, 0) where M is the trip count + //! computed from TripLimits of kind kCOUNT. + //! + IIteratorLayer* addIterator(ITensor& tensor, int32_t axis = 0, bool reverse = false) noexcept + { + return mImpl->addIterator(tensor, axis, reverse); + } + + //! + //! \brief Make an output for this loop, based on the given tensor. + //! + //! axis is the axis for concatenation (if using outputKind of kCONCATENATE or kREVERSE). + //! + //! If outputKind is kCONCATENATE or kREVERSE, a second input specifying the + //! concatenation dimension must be added via method ILoopOutputLayer::setInput. + //! + ILoopOutputLayer* addLoopOutput(ITensor& tensor, LoopOutput outputKind, int32_t axis = 0) noexcept + { + return mImpl->addLoopOutput(tensor, outputKind, axis); + } + + //! + //! \brief Set the name of the loop. + //! + //! The name is used in error diagnostics. + //! This method copies the name string. + //! + //! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see getName() + //! + void setName(char const* name) noexcept + { + mImpl->setName(name); + } + + //! + //! \brief Return the name of the loop. + //! + //! \see setName() + //! + char const* getName() const noexcept + { + return mImpl->getName(); + } + +protected: + virtual ~ILoop() noexcept = default; + apiv::VLoop* mImpl; +}; + +//! +//! \class ISelectLayer +//! +//! \brief Select elements from two data tensors based on a condition tensor. +//! +//! The select layer makes elementwise selections from two data tensors based on a condition tensor, +//! behaving similarly to the `numpy.where` function with three parameters. +//! The three input tensors must share the same rank. Multidirectional broadcasting is supported. +//! The output tensor has the dimensions of the inputs AFTER applying the broadcast rule. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class ISelectLayer : public ILayer +{ +protected: + virtual ~ISelectLayer() noexcept = default; + apiv::VSelectLayer* mImpl; +}; + +//! +//! \class IAssertionLayer +//! +//! \brief An assertion layer in a network +//! +//! The layer has a single input and no output. The input must be a boolean shape tensor. +//! If any element of the input is provably false at build time, the network is rejected. +//! If any element of the input is false at runtime for the supplied runtime dimensions, +//! an error occurs, much the same as if any other runtime error (e.g. using IShuffleLayer +//! to change the volume of a tensor) is handled. +//! +//! Asserting equality of input dimensions may help the optimizer. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IAssertionLayer : public ILayer +{ +public: + //! + //! \brief Set the message to print if the assertion fails. + //! + //! The name is used in error diagnostics. + //! This method copies the message string. + //! + //! \see getMessage() + //! + void setMessage(char const* message) noexcept + { + mImpl->setMessage(message); + } + + //! + //! \brief Return the assertion message. + //! + //! \see setMessage() + //! + char const* getMessage() const noexcept + { + return mImpl->getMessage(); + } + +protected: + virtual ~IAssertionLayer() noexcept = default; + + apiv::VAssertionLayer* mImpl; +}; + +//! +//! \enum FillOperation +//! +//! \brief Enumerates the tensor fill operations that may performed by a fill layer. +//! +//! \see IFillLayer +//! +enum class FillOperation : int32_t +{ + //! Compute each value via an affine function of its indices. + //! For example, suppose the parameters for the IFillLayer are: + //! + //! * Dimensions = [3,4] + //! * Alpha = 1 + //! * Beta = [100,10] + //! + //! Element [i,j] of the output is Alpha + Beta[0]*i + Beta[1]*j. + //! Thus the output matrix is: + //! + //! 1 11 21 31 + //! 101 111 121 131 + //! 201 211 221 231 + //! + //! A static beta b is implicitly a 1D tensor, i.e. Beta = [b]. + //! Output type must be INT32, INT64, or FLOAT. + kLINSPACE = 0, + + //! Randomly draw values from a uniform distribution. + //! Output type must be FLOAT or HALF. + kRANDOM_UNIFORM = 1, + + //! Randomly draw values from a normal distribution. + //! Output type must be FLOAT or HALF. + kRANDOM_NORMAL = 2 +}; + +//! +//! Maximum number of elements in FillOperation enum. +//! +//! \see FillOperation +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 3; +} + +//! +//! \class IFillLayer +//! +//! \brief Generate a tensor according to a specified mode. +//! +//! The fill layer generates a tensor with values that are drawn from a random distribution +//! or an affine function of their indices, as specified by the FillMode. +//! +//! When an IFillLayer is initially added to a network, all of its parameters are static. +//! Each parameter may be changed to dynamic by setting a corresponding input. +//! A parameter is considered dynamic even if that input is the output of an IConstantLayer. +//! The inputs for each parameter are: +//! +//! - 0: Dimensions +//! - 1: Alpha +//! - 2: Beta +//! +//! The parameter Dimensions describes the shape of the output. If the Dimensions input is provided, +//! it must be a 1D tensor of type Int32 or Int64 whose length is computable by constant folding. +//! +//! The meanings of Alpha and Beta depend on the mode, as described in IFillLayer::setAlpha(), +//! IFillLayer::setBeta(), and IFillLayer::setInput(). Parameters Alpha and Beta must both be static +//! or both be dynamic. +//! +//! An IFillLayer can produce a shape tensor if the following restrictions are met: +//! +//! * The FillOperation is kLINSPACE. +//! * The output has type Int32, Int64, or Float. +//! * The volume of the output is within the volume limit imposed on shape tensors. +//! * If input 0 exists, the values of input 0 must be computable by constant folding. +//! +//! \see FillOperation +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IFillLayer : public ILayer +{ +public: + //! + //! \brief Set the output tensor's dimensions. + //! + //! \param dimensions The output tensor's dimensions. + //! + //! If the first input had been used to create this layer, that input is reset to null by this method. + //! + //! \see getDimensions + // + void setDimensions(Dims const& dimensions) noexcept + { + mImpl->setDimensions(dimensions); + } + + //! + //! \brief Get the output tensor's dimensions. + //! + //! \return The output tensor's dimensions, or an invalid Dims structure. + //! + //! If the first input is present and non-null, + //! this function returns a Dims with nbDims = -1. + //! + //! \see setDimensions + //! + Dims getDimensions() const noexcept + { + return mImpl->getDimensions(); + } + + //! + //! \brief Set the fill operation for the layer. + //! + //! \see getOperation(), FillOperation + //! + void setOperation(FillOperation op) noexcept + { + mImpl->setOperation(op); + } + + //! + //! \brief Get the fill operation for the layer. + //! + //! \see setOperation(), FillOperation + //! + FillOperation getOperation() const noexcept + { + return mImpl->getOperation(); + } + + //! + //! \brief Set the alpha parameter. + //! + //! \param alpha has different meanings for each operator: + //! + //! Operation | Usage + //! kLINSPACE | the start value, defaults to 0.0; + //! kRANDOM_UNIFORM | the minimum value, defaults to 0.0; + //! kRANDOM_NORMAL | the mean of the normal distribution, default is 0.0; + //! + //! If input 1 exists, it is reset to null by this method. + //! + //! \see getAlpha, setAlphaInt64 + // + void setAlpha(double alpha) noexcept + { + mImpl->setAlpha(alpha); + } + + //! + //! \brief Get the value of alpha parameter. + //! + //! \return A double value of alpha. + //! + //! If the second input is present and non-null, + //! this function returns -1.0. + //! + //! \see setAlpha + //! + double getAlpha() const noexcept + { + return mImpl->getAlpha(); + } + + //! + //! \brief Set the beta parameter. + //! + //! \param beta has different meanings for each operator: + //! + //! Operation | Usage + //! kLINSPACE | the delta value, defaults to 1.0; + //! kRANDOM_UNIFORM | the maximal value, defaults to 1.0; + //! kRANDOM_NORMAL | the standard deviation of the normal distribution, default is 1.0; + //! + //! If input 2 exists, it is reset to null by this method. + //! + //! \see getBeta + //! + void setBeta(double beta) noexcept + { + mImpl->setBeta(beta); + } + + //! + //! \brief Get the value of beta parameter. + //! + //! \return A double value of beta. + //! + //! If the third input is present and non-null, + //! this function returns -1.0. + //! + //! \see setBeta, setBetaInt64 + //! + double getBeta() const noexcept + { + return mImpl->getBeta(); + } + + //! + //! \brief Replace an input of this layer with a specific tensor. + //! + //! \param index the index of the input to set. + //! \param tensor the new input tensor + //! + //! The three inputs correspond to these setters of IFillLayer: + //! + //! - 0: setDimensions + //! - 1: setAlpha + //! - 2: setBeta + //! + //! The following descriptions give more intuitive names for the inputs. + //! + //! Indices for kLINSPACE are: + //! + //! - 0: Shape, a 1D shape tensor, specifies the output tensor's dimensions. + //! - 1: Start, a scalar, specifies the start value. + //! - 2: Delta, a 1D tensor, specifies the delta value for each dimension. + //! + //! Indices for kRANDOM_UNIFORM are: + //! + //! - 0: Shape, a 1D shape tensor, specifies the output tensor's dimensions. + //! - 1: Minimum, a scalar, specifies the minimum random value. + //! - 2: Maximum, a scalar, specifies the maximal random value. + //! + //! Indices for kRANDOM_NORMAL are: + //! + //! - 0: Shape, a 1D shape tensor, specifies the output tensor's dimensions. + //! - 1: Mean, a scalar, specifies the mean of the normal distribution,. + //! - 2: Scale, a scalar, specifies the standard deviation of the normal distribution. + //! + //! Using the corresponding setter resets the input to null. + //! + //! If either inputs 1 or 2 is non-null, then both must be non-null and have the same data type. + //! + //! If this function is called for an index greater or equal to getNbInputs(), + //! then afterwards getNbInputs() returns index + 1, and any missing intervening + //! inputs are set to null. + //! + using ILayer::setInput; + + //! + //! \brief Set the alpha parameter with int64 datatype. + //! + //! \param alpha has different meanings for each operator: + //! + //! Operation | Usage + //! kLINSPACE | the start value, defaults to 0; + //! kRANDOM_UNIFORM | the minimum value, defaults to 0; + //! kRANDOM_NORMAL | the mean of the normal distribution, default is 0; + //! + //! If a third input had been used to create this layer, that input is reset to null by this method. + //! + //! \see getAlphaInt64 + // + void setAlphaInt64(int64_t alpha) noexcept + { + mImpl->setAlphaInt64(alpha); + } + + //! + //! \brief Get the value of alpha parameter with int64 datatype. + //! + //! \return A int64 value of alpha. + //! + //! If the second input is present and non-null, + //! this function returns -1. + //! + //! \see setAlphaInt64 + //! + int64_t getAlphaInt64() const noexcept + { + return mImpl->getAlphaInt64(); + } + + //! + //! \brief Set the beta parameter with int64 datatype. + //! + //! \param beta has different meanings for each operator: + //! + //! Operation | Usage + //! kLINSPACE | the delta value, defaults to 1; + //! kRANDOM_UNIFORM | the maximal value, defaults to 1; + //! kRANDOM_NORMAL | the standard deviation of the normal distribution, default is 1; + //! + //! If a third input had been used to create this layer, that input is reset to null by this method. + //! + //! \see getBetaInt64 + //! + void setBetaInt64(int64_t beta) noexcept + { + mImpl->setBetaInt64(beta); + } + + //! + //! \brief Get the value of beta parameter with int64 datatype. + //! + //! \return A int64 value of beta. + //! + //! If the third input is present and non-null, + //! this function returns -1.0. + //! + //! \see setBetaInt64 + //! + int64_t getBetaInt64() const noexcept + { + return mImpl->getBetaInt64(); + } + + //! + //! \brief Return true if alpha/beta have type int64, false if they have type double. + //! + bool isAlphaBetaInt64() const noexcept + { + return mImpl->isAlphaBetaInt64(); + } + + //! + //! \brief Set the fill layer output type. + //! + //! \param toType The DataType of the output tensor. + //! + //! Set the output type of the fill layer. Valid values are DataType::kFLOAT, DataType::kHALF, DataType::kINT32, + //! and DataType::kINT64. + //! If the network is strongly typed, setToType must be used to set the output type, and use of setOutputType + //! is an error. Otherwise, types passed to setOutputType and setToType must be the same. + //! + //! \see NetworkDefinitionCreationFlag::kSTRONGLY_TYPED + //! + //! \see FillOperation for more information on which types are supported for each mode. + void setToType(DataType toType) noexcept + { + mImpl->setToType(toType); + } + + //! + //! \brief Get the fill layer output type. + //! + //! \return toType parameter set during layer creation or by setToType(). + //! The return value is the output type of the fill layer. + //! The default value is DataType::kFLOAT. + //! + DataType getToType() const noexcept + { + return mImpl->getToType(); + } + +protected: + virtual ~IFillLayer() noexcept = default; + apiv::VFillLayer* mImpl; +}; + +//! +//! \class IQuantizeLayer +//! +//! \brief A Quantize layer in a network definition. +//! +//! This layer accepts a floating-point data input tensor, and uses the scale and zeroPt inputs to +//! quantize the data according to: +//! \p output = clamp(round(\p input / \p scale) + \p zeroPt) +//! +//! Rounding type is rounding-to-nearest ties-to-even (https://en.wikipedia.org/wiki/Rounding#Round_half_to_even). +//! Clamping range according to data type: +//! - FP8: [-448, 448] +//! - INT4: [-8, 7] +//! - INT8: [-128, 127] +//! +//! The first input (index 0) is the tensor to be quantized. +//! The second (index 1) and third (index 2) are the scale and zero point respectively. +//! \p scale and \p zeroPt should have identical dimensions, and rank lower or equal to 2. +//! +//! The \p zeroPt tensor is optional, and if not set, will be assumed to be zero. Its data type must match the +//! output data type. \p zeroPt must only contain zero-valued coefficients, because only symmetric quantization is +//! supported. +//! The \p scale value must be a scalar for per-tensor quantization, a 1D tensor for per-channel quantization, or the +//! same rank as the input tensor for block quantization. All \p scale coefficients must have strictly positive values. +//! The size of the 1D \p scale tensor must match the size of the quantization axis. For block quantization, the shape +//! of \p scale tensor must match the shape of the input, except for the blocking dimension (the last or second to last +//! dimension). The size of \p zeroPt must match the size of \p scale. +//! +//! The subgraph which terminates with the \p zeroPt tensor must be a build-time constant containing only zeros. +//! The output type, if constrained, must be constrained to DataType::kINT8, DataType::kFP8, DataType::kINT4 or +//! DataType::kFP4. The input type, if constrained, must be constrained to DataType::kFLOAT, DataType::kHALF, or +//! DataType::kBF16. The output size is the same as the input size. The quantization axis is in reference to the input +//! tensor's dimensions. +//! +//! IQuantizeLayer supports DataType::kFLOAT, DataType::kHALF, or DataType::kBF16 precision and will default to +//! DataType::kFLOAT precision during instantiation. For strongly typed networks, if the scale data type is +//! DataType::kHALF or DataType::kBF16, it must match the input data type. For MXFP8 quantization, the \p scale +//! data type must be DataType::kE8M0. +//! +//! IQuantizeLayer supports DataType::kINT8, DataType::kFP8, DataType::kINT4 or DataType::kFP4 output. +//! +//! As an example of the operation of this layer, imagine a 4D NCHW activation input which can be quantized using a +//! single scale coefficient (referred to as per-tensor quantization): +//! For each n in N: +//! For each c in C: +//! For each h in H: +//! For each w in W: +//! output[n,c,h,w] = clamp(round(\p input[n,c,h,w] / \p scale) + \p zeroPt) +//! +//! Per-channel quantization is supported only for weight inputs. Thus, Activations cannot be quantized per-channel. +//! As an example of per-channel operation, imagine a 4D KCRS weights input and K (dimension 0) as the quantization +//! axis. The scale is an array of coefficients, and must have the same size as the quantization axis. +//! For each k in K: +//! For each c in C: +//! For each r in R: +//! For each s in S: +//! output[k,c,r,s] = clamp(round(\p input[k,c,r,s] / \p scale[k]) + \p zeroPt[k]) +//! +//! Block quantization is supported for input types DataType::kFP4, DataType::kFP8 and DataType::kINT4. +//! As an example of blocked operation, imagine a 2D RS input with R (dimension 0) as the blocking axis and B as the +//! block size. The scale is a 2D array of coefficients, with dimensions (R//B, S). +//! For each r in R: +//! For each s in S: +//! output[r,s] = clamp(round(\p input[r,s] / \p scale[r//B, s]) + \p zeroPt[r//B, s]) +//! +//! \note Only symmetric quantization is supported. +//! \note Currently the only allowed build-time constant \p zeroPt subgraphs are: +//! 1. Constant -> Quantize +//! 2. Constant -> Cast -> Quantize +//! +//! \note The input tensor for this layer must not be a scalar. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IQuantizeLayer : public ILayer +{ +public: + //! + //! \brief Get the quantization axis. + //! + //! \return axis parameter set by setAxis(). + //! The return value is the index of the quantization axis in the input tensor's dimensions. + //! A value of -1 indicates per-tensor quantization. + //! The default value is -1. + //! + int32_t getAxis() const noexcept + { + return mImpl->getAxis(); + } + //! + //! \brief Set the quantization axis. + //! + //! Set the index of the quantization axis (with reference to the input tensor's dimensions). + //! The axis must be a valid axis if the scale tensor has more than one coefficient. + //! The axis value is used only for per-axis (per-channel) quantization. + //! + void setAxis(int32_t axis) noexcept + { + mImpl->setAxis(axis); + } + + //! + //! \brief Set the shape of the quantization block. + //! + //! \see getBlockShape() + //! Allowed values are positive values and -1 which denotes a fully blocked dimension. + //! Returns true if the block shape was set successfully, false if the block shape is invalid. + //! The default value is empty Dims. + //! + bool setBlockShape(Dims const& blockShape) noexcept + { + return mImpl->setBlockShape(blockShape); + } + + //! + //! \brief Get the shape of the quantization block. + //! + //! The default value is empty Dims. + //! \see setBlockShape() + //! + TRT_NODISCARD Dims getBlockShape() const noexcept + { + return mImpl->getBlockShape(); + } + + //! + //! \brief Set the Quantize layer output type. + //! + //! \param toType The DataType of the output tensor. + //! + //! Set the output type of the quantize layer. Valid values are DataType::kINT8, DataType::kFP8, DataType::kINT4 and + //! DataType::kFP4. If the network is strongly typed, setToType must be used to set the output type, and use of + //! setOutputType is an error. Otherwise, types passed to setOutputType and setToType must be the same. + //! + //! \see NetworkDefinitionCreationFlag::kSTRONGLY_TYPED + //! + void setToType(DataType toType) noexcept + { + mImpl->setToType(toType); + } + + //! + //! \brief Return the Quantize layer output type. + //! + //! \return toType parameter set during layer creation or by setToType(). + //! The return value is the output type of the quantize layer. + //! The default value is DataType::kINT8. + //! + DataType getToType() const noexcept + { + return mImpl->getToType(); + } + +protected: + virtual ~IQuantizeLayer() noexcept = default; + apiv::VQuantizeLayer* mImpl; +}; + +//! +//! \class IDequantizeLayer +//! +//! \brief A Dequantize layer in a network definition. +//! +//! This layer accepts a quantized type input tensor, and uses the configured scale and zeroPt inputs to +//! dequantize the input according to: +//! \p output = (\p input - \p zeroPt) * \p scale +//! +//! The first input (index 0) is the tensor to be dequantized. +//! The second (index 1) and third (index 2) are the scale and zero point respectively. +//! \p scale and \p zeroPt should have identical dimensions, and a rank that is lower or equal to 2. +//! +//! The \p zeroPt tensor is optional, and if not set, will be assumed to be zero. Its data type must be identical to +//! the input's data type. \p zeroPt must only contain zero-valued coefficients, because only symmetric quantization is +//! supported. +//! The \p scale value must be a scalar for per-tensor quantization, a 1D tensor for per-channel quantization, or the +//! same rank as the input tensor for block quantization. All \p scale coefficients must have strictly positive values. +//! The size of the 1D \p scale tensor must match the size of the quantization axis. For block quantization, the shape +//! of \p scale tensor must match the shape of the input, except for one dimension (the last or second to last +//! dimension) in which blocking occurs. The size of \p zeroPt must match the size of \p scale. +//! +//! The subgraph which terminates with the \p zeroPt tensor must be a build-time constant containing only zeros. +//! The output type, if constrained, must be constrained to DataType::kFLOAT, DataType::kHALF, or DataType::kBF16. The +//! input type, if constrained, must be constrained to DataType::kINT8, DataType::kFP8, DataType::kINT4 or +//! DataType::kFP4. The output size is the same as the input size. The quantization axis is in reference to the input +//! tensor's dimensions. +//! +//! IDequantizeLayer supports DataType::kINT8 (default), DataType::kFP8, DataType::kINT4 or DataType::kFP4. For strongly +//! typed networks, \p input data type must be the same as \p zeroPt data type. +//! +//! IDequantizeLayer supports DataType::kFLOAT, DataType::kHALF, or DataType::kBF16 output. The output data type must +//! be configured explicitly using \p setToType. +//! +//! As an example of the operation of this layer, imagine a 4D NCHW activation input which can be quantized using a +//! single scale coefficient (referred to as per-tensor quantization): +//! For each n in N: +//! For each c in C: +//! For each h in H: +//! For each w in W: +//! output[n,c,h,w] = (\p input[n,c,h,w] - \p zeroPt) * \p scale +//! +//! Per-channel dequantization is supported only for input that is rooted at an IConstantLayer (i.e. weights). +//! Activations cannot be quantized per-channel. As an example of per-channel operation, imagine a 4D KCRS weights input +//! and K (dimension 0) as the quantization axis. The scale is an array of coefficients, which is the same size as the +//! quantization axis. +//! For each k in K: +//! For each c in C: +//! For each r in R: +//! For each s in S: +//! output[k,c,r,s] = (\p input[k,c,r,s] - \p zeroPt[k]) * \p scale[k] +//! +//! Block dequantization is supported for input types DataType::kFP4, DataType::kFP8 and DataType::kINT4. +//! As an example of blocked operation, imagine a 2D RS input with R (dimension 0) as the blocking axis and B as the +//! block size. The scale is a 2D array of coefficients, with dimensions (R//B, S). +//! For each r in R: +//! For each s in S: +//! output[r,s] = (\p input[r,s] - \p zeroPt[r//B, s]) * \p scale[r//B, s] +//! +//! \note Only symmetric quantization is supported. +//! \note Currently the only allowed build-time constant \p zeroPt subgraphs are: +//! 1. Constant -> Quantize +//! 2. Constant -> Cast -> Quantize +//! +//! \note The input tensor for this layer must not be a scalar. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IDequantizeLayer : public ILayer +{ +public: + //! + //! \brief Get the quantization axis. + //! + //! \return axis parameter set by setAxis(). + //! The return value is the index of the quantization axis in the input tensor's dimensions. + //! A value of -1 indicates per-tensor quantization. + //! The default value is -1. + //! + int32_t getAxis() const noexcept + { + return mImpl->getAxis(); + } + //! + //! \brief Set the quantization axis. + //! + //! Set the index of the quantization axis (with reference to the input tensor's dimensions). + //! The axis must be a valid axis if the scale tensor has more than one coefficient. + //! The axis value will be ignored if the scale tensor has exactly one coefficient (per-tensor quantization). + //! + void setAxis(int32_t axis) noexcept + { + mImpl->setAxis(axis); + } + + //! + //! \brief Set the shape of the quantization block. + //! + //! \param blockShape The shape of the quantization block. + //! + //! Set the shape of the quantization block. + //! Allowed values are positive values and -1 which denotes a fully blocked dimension. + //! Returns true if the block shape was set successfully, false if the block shape is invalid. + //! The default value is empty Dims. + //! + //! \see getBlockShape() + //! + bool setBlockShape(Dims const& blockShape) noexcept + { + return mImpl->setBlockShape(blockShape); + } + + //! + //! \brief Get the shape of the quantization block. + //! + //! The default value is empty Dims. + //! \see setBlockShape() + //! + TRT_NODISCARD Dims getBlockShape() const noexcept + { + return mImpl->getBlockShape(); + } + + //! + //! \brief Set the Dequantize layer output type. + //! + //! \param toType The DataType of the output tensor. + //! + //! Set the output type of the dequantize layer. Valid values are DataType::kFLOAT, DataType::kHALF and + //! DataType::kBF16. If the network is strongly typed, setToType must be used to set the output type, and use of + //! setOutputType is an error. Otherwise, types passed to setOutputType and setToType must be the same. + //! + //! \see NetworkDefinitionCreationFlag::kSTRONGLY_TYPED + //! + void setToType(DataType toType) noexcept + { + mImpl->setToType(toType); + } + + //! + //! \brief Return the Dequantize layer output type. + //! + //! \return toType parameter set during layer creation or by setToType(). + //! The return value is the output type of the quantize layer. + //! The default value is DataType::kFLOAT. + //! + DataType getToType() const noexcept + { + return mImpl->getToType(); + } + +protected: + virtual ~IDequantizeLayer() noexcept = default; + apiv::VDequantizeLayer* mImpl; +}; + +//! +//! \class IDynamicQuantizeLayer +//! +//! \brief A network layer to perform dynamic quantization. +//! +//! This layer accepts a floating-point input tensor and computes the block scale factors needed to +//! quantize the input's data. It outputs the quantized tensor as its first output and +//! the scale factors as its second output. +//! +//! Use ILayer::setInput to add an input for the double-quantization scale factor. +//! +//! \note Only symmetric quantization is supported. +//! \note The input tensor for this layer must not be a scalar. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the +//! API and ABI. +//! +class IDynamicQuantizeLayer : public ILayer +{ +public: + //! + //! \brief Append or replace an input of this layer with a specific tensor + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor + //! + //! Input 0 is the input activation tensor. + //! Input 1 is the double-quantization scale factor. This scale is used to quantize the + //! dynamically computed high-precision scale factors that are used to quantize the + //! activation data. Currently this input must be a positive scalar (a 0D tensor). + //! + using ILayer::setInput; + + //! + //! \brief Set DynamicQuantizeLayer's quantized output type. + //! + //! \param toType The data type of the quantized output tensor. + //! + //! Set the type of the dynamic quantization layer's quantized output.If the network is strongly typed, setToType + //! must be used to set the output type, and use of setOutputType is an error. Otherwise, types passed to + //! setOutputType and setToType must be the same. + //! Valid values for \p toType are DataType::kFP4 (NVFP4 quantization) and DataType::kFP8 (MXFP8 quantization). + //! + //! \see NetworkDefinitionCreationFlag::kSTRONGLY_TYPED + //! + void setToType(DataType toType) noexcept + { + mImpl->setToType(toType); + } + + //! + //! \brief Return DynamicQuantizeLayer's quantized output type. + //! + //! \return toType parameter set during layer creation or by setToType(). + //! + //! The return value is the type of the quantized output tensor. + //! The default value is DataType::kFP4. + //! + DataType getToType() const noexcept + { + return mImpl->getToType(); + } + + //! + //! \brief Set the data type of the scale factors used to quantize the data. + //! + //! \param scaleType The scale factors data type. + //! + //! Set the scale-factors type. + //! Valid values are DataType::kFP8, DataType::kE8M0 or DataType::kFLOAT. + //! + void setScaleType(DataType scaleType) noexcept + { + mImpl->setScaleType(scaleType); + } + + //! + //! \brief Return the scale factors data type. + //! + //! \return scaleType parameter set during layer creation or by setScaleType(). + //! + //! The return value is the type of the scale factors used to quantize the dynamic data. + //! The default value is DataType::kFP8. + //! + DataType getScaleType() const noexcept + { + return mImpl->getScaleType(); + } + + //! + //! \brief Set the axis along which block quantization occurs. + //! + //! The axis must be the last dimension or second to last dimension. + //! The input's shape along the axis must be constant. + //! + //! \see getAxis() + //! + TRT_DEPRECATED void setAxis(int32_t axis) noexcept + { + mImpl->setAxis(axis); + } + + //! + //! \brief Get the axis along which blocking occurs. + //! + //! \see setAxis() + //! + TRT_DEPRECATED int32_t getAxis() const noexcept + { + return mImpl->getAxis(); + } + + //! + //! \brief Set the size of the quantization block. + //! + //! Note: The block size must divide the input in the blocked axis without remainder. + //! Valid values are 16 (NVFP4 quantization) and 32 (MXFP8 quantization). + //! + //! \see getBlockSize() + //! + TRT_DEPRECATED void setBlockSize(int32_t size) noexcept + { + mImpl->setBlockSize(size); + } + + //! + //! \brief Get the size of the quantization block. + //! + //! \see setBlockSize() + //! + TRT_DEPRECATED int32_t getBlockSize() const noexcept + { + return mImpl->getBlockSize(); + } + + //! + //! \brief Set the shape of the quantization block. + //! + //! Note: The block shape rank must match the input rank. + //! The default value is empty Dims. + //! + //! \see getBlockShape() + //! + void setBlockShape(Dims const& blockShape) noexcept + { + mImpl->setBlockShape(blockShape); + } + + //! + //! \brief Get the shape of the quantization block. + //! + //! The default value is empty Dims. + //! + //! \see setBlockShape() + //! + Dims getBlockShape() const noexcept + { + return mImpl->getBlockShape(); + } + +protected: + virtual ~IDynamicQuantizeLayer() noexcept = default; + apiv::VDynamicQuantizeLayer* mImpl; +}; + +//! +//! \class IEinsumLayer +//! +//! \brief An Einsum layer in a network +//! +//! This layer implements a summation over the elements of the inputs along dimensions specified by the equation +//! parameter, based on the Einstein summation convention. +//! The layer can have one or more inputs of rank >= 0. All the inputs must have type DataType::kFLOAT +//! or DataType::kHALF, not necessarily the same. There is one output of type DataType::kFLOAT. +//! The shape of the output tensor is determined by the equation. +//! +//! The equation specifies ASCII lower-case letters for each dimension in the inputs in the same order as the +//! dimensions, separated by comma for each input. The dimensions labeled with the same subscript must match or be +//! broadcastable. Repeated subscript labels in one input take the diagonal. Repeating a label across multiple inputs +//! means that those axes will be multiplied. Omitting a label from the output means values along those axes will be +//! summed. In implicit mode, the indices which appear once in the expression will be part of the output in increasing +//! alphabetical order. In explicit mode, the output can be controlled by specifying output subscript labels by adding +//! an arrow ('->') followed by subscripts for the output. +//! For example, "ij,jk->ik" is equivalent to "ij,jk". +//! Ellipsis ('...') can be used in place of subscripts to broadcast the dimensions. +//! See the TensorRT Developer Guide for more details on equation syntax. +//! +//! Many common operations can be expressed using the Einsum equation. +//! For example: +//! Matrix Transpose: ij->ji +//! Sum: ij-> +//! Matrix-Matrix Multiplication: ik,kj->ij +//! Dot Product: i,i-> +//! Matrix-Vector Multiplication: ik,k->i +//! Batch Matrix Multiplication: ijk,ikl->ijl +//! Batch Diagonal: ...ii->...i +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IEinsumLayer : public ILayer +{ +public: + //! + //! \brief Set the equation. + //! The equation is a comma-separated list of subscript labels, where each label refers to a + //! dimension of the corresponding tensor. + //! + //! \return true if the equation was syntactically valid and set successfully, false otherwise. + //! + //! \see setEquation() + //! + bool setEquation(char const* equation) noexcept + { + return mImpl->setEquation(equation); + } + + //! + //! \brief Return the equation. + //! + //! \see setEquation() + //! + char const* getEquation() const noexcept + { + return mImpl->getEquation(); + } + +protected: + virtual ~IEinsumLayer() noexcept = default; + apiv::VEinsumLayer* mImpl; +}; + +//! +//! \enum ScatterMode +//! +//! \brief Control form of IScatterLayer +//! +//! \see IScatterLayer +//! +enum class ScatterMode : int32_t +{ + kELEMENT = 0, //!< Similar to ONNX ScatterElements + kND = 1, //!< Similar to ONNX ScatterND +}; + +//! +//! Maximum number of elements in ScatterMode enum. +//! +//! \see ScatterMode +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 2; +} + +//! +//! \class IScatterLayer +//! +//! \brief A scatter layer in a network definition. Supports several kinds of scattering. +//! +//! The Scatter layer has three input tensors: Data, Indices, and Updates, one output tensor +//! Output, and a scatter mode. When kELEMENT mode is used an optional axis parameter is available. +//! * Data is a tensor of rank r >= 1 that stores the values to be duplicated in Output. +//! * Indices is a tensor of rank q that determines which locations in Output to write new +//! values to. Constraints on the rank q depend on the mode: +//! ScatterMode::kND: q >= 1 +//! ScatterMode::kELEMENT: q must be the same as r +//! * Updates is a tensor of rank s >= 1 that provides the data +//! to write to Output specified by its corresponding location in Indices. +//! Constraints on the rank of Updates depend on the mode: +//! ScatterMode::kND: s = r + q - shape(Indices)[-1] - 1 +//! Scattermode::kELEMENT: s = q = r +//! * Output is a tensor with the same dimensions as Data that stores the resulting values of the +//! transformation. It must not be a shape tensor. +//! The types of Data, Update, and Output shall be the same, and Indices shall be of type DataType::kINT32 or +//! DataType::kINT64. +//! +//! The output is computed by copying the data, and then updating elements of it based on indices. +//! How Indices are interpreted depends upon the ScatterMode. +//! +//! ScatterMode::kND +//! +//! The indices are interpreted as a tensor of rank q-1 of indexing tuples. +//! The axis parameter is ignored. +//! +//! Given that data dims are {d_0,...,d_{r-1}} and indices dims are {i_0,...,i_{q-1}}, +//! define k = indices[q-1], it follows that updates dims are {i_0,...,i_{q-2},d_k,...,d_{r-1}} +//! The updating can be computed by: +//! foreach slice in indices[i_0,...,i_{q-2}] +//! output[indices[slice]] = updates[slice] +//! +//! ScatterMode::kELEMENT +//! +//! Here "axis" denotes the result of getAxis(). +//! +//! For each element X of indices: +//! Let J denote a sequence for the subscripts of X +//! Let K = sequence J with element [axis] replaced by X +//! output[K] = updates[J] +//! +//! For example, if indices has dimensions [N,C,H,W] and axis is 2, then the updates happen as: +//! +//! for n in [0,n) +//! for c in [0,n) +//! for h in [0,n) +//! for w in [0,n) +//! output[n,c,indices[n,c,h,w],w] = updates[n,c,h,w] +//! +//! Writes to the same output element cause undefined behavior. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IScatterLayer : public ILayer +{ +public: + //! + //! \brief Set the scatter mode. + //! + //! \see getMode() + //! + void setMode(ScatterMode mode) noexcept + { + mImpl->setMode(mode); + } + + //! + //! \brief Get the scatter mode. + //! + //! \see setMode() + //! + ScatterMode getMode() const noexcept + { + return mImpl->getMode(); + } + + //! + //! \brief Set the axis used by ScatterMode::kELEMENTS. + //! + //! The axis defaults to 0. + //! + void setAxis(int32_t axis) noexcept + { + mImpl->setAxis(axis); + } + + //! + //! \brief Get the axis. + //! + int32_t getAxis() const noexcept + { + return mImpl->getAxis(); + } + +protected: + apiv::VScatterLayer* mImpl; + virtual ~IScatterLayer() noexcept = default; +}; // class IScatterLayer + +//! +//! \class IOneHotLayer +//! +//! \brief A OneHot layer in a network definition. +//! +//! The OneHot layer has three input tensors: Indices, Values, and Depth, one output tensor: +//! Output, and an axis attribute. +//! * Indices is an Int32 tensor that determines which locations in Output to set as on_value. +//! * Values is a two-element (rank=1) tensor that consists of [off_value, on_value] +//! * Depth is an 0D tensor of type Int32 or Int64, which contains the depth (number of classes) of the one-hot encoding. +//! The depth tensor must be a positive build-time constant. +//! * Output is a tensor with rank = rank(indices)+1, where the added dimension contains the one-hot encoding. +//! The data types of Output is equal to the Values data type. +//! * Axis is a scalar specifying to which dimension of the output one-hot encoding is added. +//! Valid range for axis is -rank(indices)-1 <= axis <= rank(indices). +//! +//! The output is computed by copying off_values to all output elements, then setting on_value on the indices +//! specified by the indices tensor. +//! when axis = 0: +//! output[indices[i, j, k], i, j, k] = on_value for all i, j, k and off_value otherwise. +//! +//! when axis = -1: +//! output[i, j, k, indices[i, j, k]] = on_value for all i, j, k and off_value otherwise. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IOneHotLayer : public ILayer +{ +public: + //! + //! \brief Set the axis parameter. + //! + //! \see IOneHotLayer + //! + void setAxis(int32_t axis) noexcept + { + mImpl->setAxis(axis); + } + + //! + //! \brief Get the value of the axis parameter. + //! + int32_t getAxis() const noexcept + { + return mImpl->getAxis(); + } + +protected: + apiv::VOneHotLayer* mImpl; + virtual ~IOneHotLayer() noexcept = default; +}; + +//! +//! \class IGridSampleLayer +//! +//! \brief A GridSample layer in a network definition. +//! +//! This layer uses an input tensor and a grid tensor to produce an interpolated output tensor. +//! The input and grid tensors must be shape tensors of rank 4. The only supported SampleMode +//! values are SampleMode::kCLAMP, SampleMode::kFILL, and SampleMode::kREFLECT. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IGridSampleLayer : public ILayer +{ +public: + //! + //! \brief Set the grid sample interpolation mode. + //! + //! \see getInterpolationMode() + //! + void setInterpolationMode(InterpolationMode mode) noexcept + { + mImpl->setInterpolationMode(mode); + } + + //! + //! \brief Get the grid sample interpolation mode. + //! + //! \see setInterpolationMode() + //! + //! \return The value specified by setInterpolationMode, or InterpolationMode::kLINEAR otherwise. + //! + InterpolationMode getInterpolationMode() const noexcept + { + return mImpl->getInterpolationMode(); + } + + //! + //! \brief Set the align corners mode. + //! + //! \see getAlignCorners() + //! + void setAlignCorners(bool alignCorners) noexcept + { + mImpl->setAlignCorners(alignCorners); + } + + //! + //! \brief Get the align corners mode. + //! + //! \see setAlignCorners() + //! + //! \return The value specified by setAlignCorners(), or false otherwise. + //! + bool getAlignCorners() const noexcept + { + return mImpl->getAlignCorners(); + } + + //! + //! \brief Set the sample mode. + //! + //! \see getSampleMode() + //! + //! \return true if layer's sample mode was set to mode, false otherwise. + //! + bool setSampleMode(SampleMode mode) noexcept + { + return mImpl->setSampleMode(mode); + } + + //! + //! \brief Get the sample mode. + //! + //! \see setSampleMode() + //! + //! \returns the value specified by a successful call to setSampleMode(), or SampleMode::kFILL otherwise. + //! + SampleMode getSampleMode() const noexcept + { + return mImpl->getSampleMode(); + } + +protected: + apiv::VGridSampleLayer* mImpl; + virtual ~IGridSampleLayer() noexcept = default; +}; // class IGridSampleLayer + +//! +//! \enum BoundingBoxFormat +//! +//! \brief Representation of bounding box data used for the Boxes input tensor in INMSLayer +//! +//! \see INMSLayer +//! +enum class BoundingBoxFormat : int32_t +{ + //! (x1, y1, x2, y2) where (x1, y1) and (x2, y2) are any pair of diagonal corners + kCORNER_PAIRS = 0, + //! (x_center, y_center, width, height) where (x_center, y_center) is the center point of the box + kCENTER_SIZES = 1 +}; + +//! +//! Maximum number of elements in BoundingBoxFormat enum. +//! +//! \see BoundingBoxFormat +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 2; +} + +//! +//! \class INMSLayer +//! +//! \brief A non-maximum suppression layer in a network definition. +//! +//! The NMS algorithm iterates through a set of bounding boxes and their confidence scores, in decreasing +//! order of score. Boxes are selected if their score is above a given threshold, and their +//! intersection-over-union (IoU) with previously selected boxes is less than or equal to a given threshold. +//! This layer implements NMS per batch item and per class. +//! +//! Per batch item, boxes are initially sorted by their scores without regard to class. Only boxes up to a maximum of +//! the TopK limit are considered for selection (per batch). During selection, only overlapping boxes of the same class +//! are compared, so that overlapping boxes of different classes do not suppress each other. +//! +//! For each batch item, the ordering of candidate bounding boxes with the same score is unspecified, but the ordering +//! will be consistent across different runs for the same inputs. +//! +//! The layer has the following inputs, in order of input index: +//! +//! * Boxes contains the input bounding boxes. It is a linear tensor of type kFLOAT or kHALF. It has +//! shape [batchSize, numInputBoundingBoxes, numClasses, 4] if the boxes are per class, or +//! [batchSize, numInputBoundingBoxes, 4] if the same boxes are to be used for each class. +//! * Scores contains the per-box scores. It is a linear tensor of the same type as Boxes. It has shape +//! [batchSize, numInputBoundingBoxes, numClasses]. +//! * MaxOutputBoxesPerClass is the maximum number of output boxes per batch item per class. +//! It is a scalar (0D tensor) of type kINT32. +//! * IoUThreshold is the maximum IoU for selected boxes. It is a scalar (0D tensor) of type kFLOAT in the range +//! [0.0f, 1.0f]. It is an optional input with default 0.0f. +//! * ScoreThreshold is the value that a box score must exceed in order to be selected. It is a scalar (0D tensor) of +//! type kFLOAT. It is an optional +//! input with default 0.0f. +//! +//! The layer has the following outputs, in order of output index: +//! +//! * SelectedIndices contains the indices of the selected boxes. It is a linear tensor of type kINT32 or kINT64. It has +//! shape +//! [NumOutputBoxes, 3]. Each row contains a (batchIndex, classIndex, boxIndex) tuple. +//! The output boxes are sorted in order of increasing batchIndex and then in order of decreasing score within each +//! batchIndex. For each batchIndex, the ordering of output boxes with the same score is unspecified. If +//! MaxOutputBoxesPerClass is a constant input, the maximum number of output boxes is batchSize * numClasses * +//! min(numInputBoundingBoxes, MaxOutputBoxesPerClass). Otherwise, the maximum number of output boxes is batchSize * +//! numClasses * numInputBoundingBoxes. The maximum number of output boxes is used to determine the upper-bound on +//! allocated memory for this output tensor. +//! * NumOutputBoxes is the number of output boxes in SelectedIndices. It is a scalar (0D tensor) of type kINT32. +//! +//! \warning There is a hardware-dependent limit K such that only the K highest scoring boxes in each batch item +//! will be considered for selection. The value of K is 2000 for SM 5.3 and 6.2 devices, and 5000 otherwise. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class INMSLayer : public ILayer +{ +public: + //! + //! \brief Set the bounding box format parameter for the layer. + //! + //! The default value for the bounding box format parameter is kCORNER_PAIRS. + //! + //! \see BoundingBoxFormat + //! + //! \see getBoundingBoxFormat() + //! + void setBoundingBoxFormat(BoundingBoxFormat fmt) noexcept + { + mImpl->setBoundingBoxFormat(fmt); + } + + //! + //! \brief Get the bounding box format parameter for the layer. + //! + //! \see BoundingBoxFormat + //! + //! \see setBoundingBoxFormat() + //! + BoundingBoxFormat getBoundingBoxFormat() const noexcept + { + return mImpl->getBoundingBoxFormat(); + } + + //! + //! \brief Set the TopK box limit parameter for the layer. + //! + //! The TopK box limit is the maximum number of filtered boxes considered for selection per batch item. + //! The default value for the TopK box limit parameter is 2000 for SM 5.3 and 6.2 devices, and 5000 otherwise. + //! The TopK box limit must be less than or equal to {2000 for SM 5.3 and 6.2 devices, 5000 otherwise}. + //! + //! \see getTopKBoxLimit() + //! + void setTopKBoxLimit(int32_t limit) noexcept + { + mImpl->setTopKBoxLimit(limit); + } + + //! + //! \brief Get the TopK box limit parameter for the layer. + //! + //! \see setTopKBoxLimit() + //! + int32_t getTopKBoxLimit() const noexcept + { + return mImpl->getTopKBoxLimit(); + } + + //! + //! \brief Append or replace an input of this layer with a specific tensor + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor + //! + //! The indices are as follows: + //! + //! - 0: The required Boxes tensor. + //! - 1: The required Scores tensor. + //! - 2: The required MaxOutputBoxesPerClass tensor. + //! - 3: The optional IoUThreshold tensor. + //! - 4: The optional ScoreThreshold tensor. + //! + //! If this function is called for an index greater or equal to getNbInputs(), + //! then afterwards getNbInputs() returns index + 1, and any missing intervening + //! inputs are set to null. Note that only optional inputs can be missing. + //! + using ILayer::setInput; + + //! + //! \brief Set the indices type for the layer. + //! + //! \param type The DataType of the indices tensor. + //! + //! \return true if set successfully, false otherwise. + //! + //! Set the indices (the first output) type of the NMS layer. Valid values are DataType::kINT32 and + //! DataType::kINT64, otherwise an error occurs and the type is not updated. + //! + bool setIndicesType(DataType type) noexcept + { + return mImpl->setIndicesType(type); + } + + //! + //! \brief Return the NMS layer indices type. + //! + //! \return indices type set during layer creation or by setIndicesType(). + //! The return value is the indices type of the NMS layer. + //! The default value is DataType::kINT32. + //! + DataType getIndicesType() const noexcept + { + return mImpl->getIndicesType(); + } + +protected: + apiv::VNMSLayer* mImpl; + virtual ~INMSLayer() noexcept = default; +}; // class INMSLayer + +//! +//! \class IReverseSequenceLayer +//! +//! \brief A ReverseSequence layer in a network definition. +//! +//! This layer performs batch-wise reversal, which slices the input tensor along the axis batchAxis. For the +//! i-th slice, the operation reverses the first N elements, specified by the corresponding i-th value in +//! sequenceLens, along sequenceAxis and keeps the remaining elements unchanged. The output tensor will have +//! the same shape as the input tensor. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IReverseSequenceLayer : public ILayer +{ +public: + //! + //! \brief Set the batch axis. Default is 1. + //! + //! batchAxis should be between zero (inclusive) and the rank of input (exclusive), and different from + //! sequenceAxis. Otherwise, ErrorCode::kINVALID_ARGUMENT will be triggered. + //! + //! \see setBatchAxis() + //! + void setBatchAxis(int32_t batchAxis) noexcept + { + mImpl->setBatchAxis(batchAxis); + } + + //! + //! \brief Return the batch axis. Return 1 if no batch axis was set. + //! + //! \see getBatchAxis() + //! + int32_t getBatchAxis() const noexcept + { + return mImpl->getBatchAxis(); + } + + //! + //! \brief Set the sequence axis. Default is 0. + //! + //! sequenceAxis should be between zero (inclusive) and the rank of input (exclusive), and different from + //! batchAxis. Otherwise, ErrorCode::kINVALID_ARGUMENT will be triggered. + //! + //! \see setSequenceAxis() + //! + void setSequenceAxis(int32_t sequenceAxis) noexcept + { + mImpl->setSequenceAxis(sequenceAxis); + } + + //! + //! \brief Return the sequence axis. Return 0 if no sequence axis was set. + //! + //! \see getSequenceAxis() + //! + int32_t getSequenceAxis() const noexcept + { + return mImpl->getSequenceAxis(); + } + +protected: + apiv::VReverseSequenceLayer* mImpl; + virtual ~IReverseSequenceLayer() noexcept = default; +}; // class IReverseSequenceLayer + +//! +//! \class INormalizationLayer +//! +//! \brief A normalization layer in a network definition. +//! +//! The normalization layer performs the following operation: +//! +//! X - input Tensor +//! Y - output Tensor +//! S - scale Tensor +//! B - bias Tensor +//! +//! Y = (X - Mean(X, axes)) / Sqrt(Variance(X) + epsilon) * S + B +//! +//! Where Mean(X, axes) is a reduction over a set of axes, and Variance(X) = Mean((X - Mean(X, axes)) ^ 2, axes). +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class INormalizationLayer : public ILayer +{ +public: + //! + //! \brief Set the epsilon value used for the normalization calculation. + //! + //! The default value of \p eps is 1e-5F. + //! + //! \param eps The epsilon value used for the normalization calculation. + //! + void setEpsilon(float eps) noexcept + { + return mImpl->setEpsilon(eps); + } + + //! + //! \brief Get the epsilon value used for the normalization calculation. + //! + //! \return The epsilon value used for the normalization calculation. + //! + float getEpsilon() const noexcept + { + return mImpl->getEpsilon(); + } + + //! + //! \brief Set the reduction axes for the normalization calculation. + //! + //! \param axesMask The axes used for the normalization calculation. + //! + void setAxes(uint32_t axesMask) noexcept + { + return mImpl->setAxes(axesMask); + } + + //! + //! \brief Get the axes value used for the normalization calculation. + //! + //! \return The axes used for the normalization calculation. + //! + uint32_t getAxes() const noexcept + { + return mImpl->getAxes(); + } + + //! + //! \brief Set the number of groups used to split the channels in the normalization calculation. + //! + //! The input tensor channels are divided into \p nbGroups groups, and normalization is performed per group. + //! The channel dimension is considered to be the second dimension in a [N, C, H, W, ...] formatted tensor. + //! + //! The default \p nbGroups is 1. + //! + //! \warning It is an error to set \p nbGroups to a value that does not evenly divide into the number of channels + //! of the input tensor. + //! + //! \warning When \p nbGroups is != 1, it is expected that the provided axesMask will have all bits corresponding + //! to dimensions after the channel dimension set to 1, with all other bits set to 0. + //! + //! \param nbGroups The number of groups to split the channels into for the normalization calculation. + //! + void setNbGroups(int64_t nbGroups) noexcept + { + return mImpl->setNbGroups(nbGroups); + } + + //! + //! \brief Get the number of groups used to split the channels for the normalization calculation. + //! + //! \return The number of groups used to split the channel used for the normalization calculation. + //! + int64_t getNbGroups() const noexcept + { + return mImpl->getNbGroups(); + } + + //! + //! \brief Set the compute precision of this layer. + //! + //! \param type The datatype used for the compute precision of this layer. + //! + //! The method is used to avoid overflow errors by controlling the normalization computation in + //! mixed precision mode. The compute precision defaults to DataType::kFLOAT32. + //! To override this default, use this method to set the desired compute precision. + //! + //! For a weakly typed network: + //! + //! * Method setOutputType() can still be called to control the output data type. + //! + //! * Method setPrecision() can still be called. The input data is cast to that precision before + //! being cast to the compute precision. + //! + //! Strongly typed network rejects calls to this method since the compute precision is typically + //! controlled by casting the input tensors to the desired type. + //! + //! Only DataType::kFLOAT32 and DataType::kHALF are valid types for \p type. + //! + //! \deprecated Deprecated in TensorRT 10.16. Superseded by strong typing. + //! + TRT_DEPRECATED void setComputePrecision(DataType type) noexcept + { + return mImpl->setComputePrecision(type); + } + + //! + //! \brief Get the compute precision of this layer. + //! + //! \return The datatype used for the compute precision of this layer. + //! + //! \deprecated Deprecated in TensorRT 10.16. Superseded by strong typing. + //! + TRT_DEPRECATED DataType getComputePrecision() const noexcept + { + return mImpl->getComputePrecision(); + } + + //! + //! \brief Returns true if this layer was created through addNormalizationV2(). + //! + //! \return Whether the layer was created through addNormalizationV2(). + //! + TRT_NODISCARD bool isV2() const noexcept + { + return mImpl->isV2(); + } + +protected: + apiv::VNormalizationLayer* mImpl; + virtual ~INormalizationLayer() noexcept = default; +}; + + +//! +//! \class ISqueezeLayer +//! +//! \brief Layer that represents a squeeze operation, removing unit dimensions of the first input tensor +//! on a set of axes specified by the second input tensor. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class ISqueezeLayer : public ILayer +{ +public: + //! + //! \brief Append or replace an input of this layer with a specific tensor. + //! + //! \param index The index of the input to modify. + //! \param tensor The new input tensor. + //! + //! For a Squeeze layer, the values 0-1 are valid for index. + //! The indices are as follows: + //! + //! - 0: Input data tensor. + //! - 1: The axes to remove. Must resolve to a constant Int32 or Int64 1D shape tensor. + //! + using ILayer::setInput; + +protected: + apiv::VSqueezeLayer* mImpl; + virtual ~ISqueezeLayer() noexcept = default; +}; + +//! +//! \class IUnsqueezeLayer +//! +//! \brief Layer that represents an unsqueeze operation, which reshapes the first input tensor by inserting unit-length +//! dimensions to the output at the axes specified by the second input tensor. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IUnsqueezeLayer : public ILayer +{ +public: + //! + //! \brief Append or replace an input of this layer with a specific tensor. + //! + //! \param index The index of the input to modify. + //! \param tensor The new input tensor. + //! + //! For an Unsqueeze layer, the values 0-1 are valid for index. + //! The indices are as follows: + //! + //! - 0: Input data tensor. + //! - 1: The output axes at which unit-length dimensions are inserted. Must resolve to a constant Int32 or + //! Int64 1D shape tensor. + //! + using ILayer::setInput; + +protected: + apiv::VUnsqueezeLayer* mImpl; + virtual ~IUnsqueezeLayer() noexcept = default; +}; + +//! +//! \enum CumulativeOperation +//! +//! \brief Enumerates the cumulative operations that may be performed by a Cumulative layer. +//! +//! The table shows the initial value of each Cumulative operation. +//! +//! Operation | kFLOAT, kHALF, kBF16 | kINT32, kINT64 | +//! --------- | -------------------- | -------------- | +//! kSUM | +0.0 | 0 | +//! +enum class CumulativeOperation : int32_t +{ + kSUM = 0, //!< Calculate cumulative sum. +}; + +namespace impl +{ + +//! +//! \brief Maximum number of elements in CumulativeOperation enum. +//! +//! \see CumulativeOperation +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 1; +}; + +} // namespace impl + +//! +//! \class ICumulativeLayer +//! +//! \brief Layer that represents a cumulative operation across a tensor. +//! +//! It computes successive reductions across an axis of a tensor. The output +//! always has the same shape as the input. +//! +//! If the reduction operation is summation, then this is also known as +//! prefix-sum or cumulative sum. +//! +//! The operation has forward vs. reverse variants, and inclusive vs. exclusive variants. +//! +//! For example, let the input be a vector x of length n and the output be vector y. +//! Then y[j] = sum(x[...]) where ... denotes a sequence of indices from this table: +//! +//! | forward | reverse +//! ----------|-----------| --------- +//! inclusive | 0..j | j..n-1 +//! exclusive | 0..j-1 | j+1..n-1 +//! +//! For multidimensional tensors, the reductions apply across a specified axis. For +//! example, given a 2D input, a forward inclusive cumulative operation across axis 0 generates +//! cumulative sums within each column. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class ICumulativeLayer : public ILayer +{ +public: + //! + //! \brief Set the cumulative operation for the layer. + //! + //! \param op The reduction operation to be performed + //! + //! \return Whether \p op is valid and the operation successfully set + //! + //! \see getOperation(), CumulativeOperation + //! + bool setOperation(CumulativeOperation op) noexcept + { + return mImpl->setOperation(op); + } + + //! + //! \brief Get the cumulative operation for the layer. + //! + //! \return The reduction operation to be performed + //! + //! \see setOperation(), CumulativeOperation + //! + CumulativeOperation getOperation() const noexcept + { + return mImpl->getOperation(); + } + + //! + //! \brief Set whether it is an exclusive accumulation or inclusive accumulation. + //! + //! \param exclusive Whether the operation will exclude the element at the current index + //! + //! \see getExclusive + //! + void setExclusive(bool exclusive) noexcept + { + mImpl->setExclusive(exclusive); + } + + //! + //! \brief Get whether it is exclusive accumulation or inclusive accumulation. + //! + //! \return Whether the operation will exclude the element at the current index + //! + //! \see setExclusive + //! + bool getExclusive() const noexcept + { + return mImpl->getExclusive(); + } + + //! + //! \brief Specify whether the cumulative operation should be applied backward. + //! + //! \param reverse Whether the cumulative will run in the reverse direction from the last element + //! + //! \see getReverse + //! + void setReverse(bool reverse) noexcept + { + mImpl->setReverse(reverse); + } + + //! + //! \brief Get the boolean that specifies whether the cumulative operation should be applied backward. + //! + //! \return Whether the cumulative will run in the reverse direction from the last element + //! + //! \see setReverse + //! + bool getReverse() const noexcept + { + return mImpl->getReverse(); + } + +protected: + apiv::VCumulativeLayer* mImpl; + virtual ~ICumulativeLayer() noexcept = default; +}; + +//! +//! \enum AttentionNormalizationOp +//! +//! \brief Enumerates the operations that may be performed by the normalization in the attention subgraph. +//! +enum class AttentionNormalizationOp : int32_t +{ + kNONE + = 0, //!< Apply no normalization on the attention scores. Must be used with decomposable=True on pre-Blackwell GPUs + kSOFTMAX = 1, //!< Apply softmax normalization on the attention scores on the `s_kv` dimension. +}; + +namespace impl +{ +//! +//! Maximum number of elements in AttentionNormalizationOp enum. +//! +//! \see AttentionNormalizationOp +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 2; +}; + +} // namespace impl + +//! +//! \class IAttentionBoundaryLayer +//! +//! \brief This is a base class for Attention boundary layers. +//! +//! Boundary layers are used to demarcate the boundaries of IAttention. +//! Typically client code does not deal directly with the boundary layers. +//! However, they are indirectly visible via method `INetworkDefinition::getLayer(int32_t index)`. +//! +class IAttentionBoundaryLayer : public ILayer +{ +public: + //! + //! \brief Get a pointer to the IAttention associated with this boundary layer. + //! + IAttention* getAttention() const noexcept + { + return mBoundary->getAttention(); + } + +protected: + virtual ~IAttentionBoundaryLayer() noexcept = default; + apiv::VAttentionBoundaryLayer* mBoundary; +}; + +//! +//! \class IAttentionInputLayer +//! +//! \brief This layer represents an input to an attention subgraph. +//! +//! This layer is automatically created when an `IAttention` is created. Clients typically do not +//! deal with the layer directly, but instead specify its input via `addAttention` or `IAttention::setInput`. +//! +//! An IAttentionInputLayer has three to four inputs and one output. +//! +class IAttentionInputLayer : public IAttentionBoundaryLayer +{ +public: + //! + //! \brief Append or replace an input of this layer with a specific tensor + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor + //! + //! The indices are as follows: + //! + //! Input 0 is the input query tensor. + //! Input 1 is the input key tensor. + //! Input 2 is the input value tensor. + //! Input 3 is the optional mask tensor. setMask should be used instead of setInput + //! Input 4 is the optional normalizationQuantizeScale tensor. setNormalizationQuantizeScale should be used instead + //! of setInput + //! + using ILayer::setInput; + +protected: + virtual ~IAttentionInputLayer() noexcept = default; + apiv::VAttentionInputLayer* mImpl; +}; + +//! +//! \class IAttentionOutputLayer +//! +//! \brief This layer represents an output of an IAttention. +//! +//! This layer is automatically created when an `IAttention` is created. Clients typically do not +//! deal with the layer directly, but instead getting its output via `IAttention::getOutput`. +//! +//! An IAttentionOutputLayer has one input and one output. +//! +class IAttentionOutputLayer : public IAttentionBoundaryLayer +{ +public: +protected: + virtual ~IAttentionOutputLayer() noexcept = default; + apiv::VAttentionOutputLayer* mImpl; +}; + +//! +//! \class IAttention +//! +//! \brief Helper for constructing an attention that consumes query, key and value tensors. +//! +//! An attention subgraph implicitly includes three main components, two MatrixMultiply layers +//! known as BMM1 and BMM2, and one normalization operation which defaults to be a Softmax. +//! By default, IAttention is not decomposable and TensorRT will try to use a single fused kernel, which may be more +//! efficient than if the subgraph is expressed without IAttention. Setting the IAttention to decomposable=True can +//! allow IAttention to be decomposed to use multiple kernels if no fused kernel support found. +//! +//! Query Key Value Mask (optional) NormalizationQuantizeScale (optional) +//! | | | | | +//! | Transpose | | | +//! | | | | | +//! ----BMM1---- | | | +//! | | | | +//! *--------------------------- | +//! | | | +//! Normalization | | +//! | | | +//! *------------------------------------------------ +//! | | +//! -------BMM2------ +//! | +//! Output +//! +//! The attention has the following inputs, in order of input index: +//! +//! * Query contains the input query. It is a tensor of type kFLOAT, kHALF or kBF16 with +//! shape [batchSize, numHeadsQuery, sequenceLengthQuery, dimHead] +//! * Key contains the input key. It is a tensor of type kFLOAT, kHALF or kBF16 with +//! shape [batchSize, numHeadsKeyValue, sequenceLengthKeyValue, dimHead] +//! * Value contains the input value. It is a tensor of type kFLOAT, kHALF or kBF16 with +//! shape [batchSize, numHeadsKeyValue, sequenceLengthKeyValue, dimHead] +//! * Mask (optional) contains the mask value. It is a tensor of type kBOOL or the same data type of +//! BMM1 output with shape [batchSize, numHeadsQuery, sequenceLengthQuery, sequenceLengthKeyValue] +//! with batchSize and numHeadsQuery broadcastable. For a kBOOL mask, a True value indicates that the corresponding +//! position is allowed to attend. For other data types, the mask values will be added to the BMM1 output, known +//! as an add mask. +//! * NormalizationQuantizeScale (optional) contains the quantization scale for the attention normalization output. +//! It is a tensor of type kFLOAT, kHALF or kBF16 with dimension 0 or 1. +//! +//! \see +//! https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/work-with-transformers.html#multi-head-attention-fusion +//! for the complete matrix of fused kernel support. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IAttention : public INoCopy +{ +public: + //! + //! \brief Set the normalization operation for the attention. + //! + //! \see getNormalizationOperation(), AttentionNormalizationOp + //! + //! \return True if the normalization operation is set successfully, false otherwise. + //! + bool setNormalizationOperation(AttentionNormalizationOp op) noexcept + { + return mImpl->setNormalizationOperation(op); + } + + //! + //! \brief Get the normalization operation for the attention. + //! + //! \see setNormalizationOperation(), AttentionNormalizationOp + //! + //! \return The normalization operation for the attention. Default is kSOFTMAX. + //! + AttentionNormalizationOp getNormalizationOperation() const noexcept + { + return mImpl->getNormalizationOperation(); + } + + //! + //! \brief Set whether a mask will be used for the normalization operation. + //! + //! \param mask the mask tensor of type kBOOL or the same data type of BMM1 output with 4d shape broadcastable to + //! [batchSize, numHeadsQuery, sequenceLengthQuery, sequenceLengthKeyValue]. For a kBOOL mask, a True value + //! indicates that the corresponding position is allowed to attend. For other data types, the mask values will be + //! added to the BMM1 output, known as an add mask. + //! + //! \see getMask + //! + //! \return True if the mask is set successfully, false otherwise. + //! + bool setMask(ITensor& mask) noexcept + { + return mImpl->setMask(mask); + } + + //! + //! \brief Get the optional mask in attention. + //! + //! \see setMask + //! + //! \return The optional mask in attention, nullptr if no mask is set. + //! + ITensor* getMask() noexcept + { + return mImpl->getMask(); + } + + //! + //! \brief Set whether the attention will run a causal inference. + //! Cannot be used together with setMask(). + //! + //! \see getCausal + //! + //! \return True if the causal inference is set successfully, false otherwise. + //! + bool setCausal(bool isCausal) noexcept + { + return mImpl->setCausal(isCausal); + } + + //! + //! \brief Get whether the attention will run a causal inference. + //! + //! \see setCausal + //! + //! \return True if the attention will run a causal inference, false otherwise. Default is false. + //! + bool getCausal() const noexcept + { + return mImpl->getCausal(); + } + + //! + //! \brief Set whether the attention can be decomposed to use multiple kernels if no fused kernel support found. + //! + //! \see getDecomposable + //! + //! \return True if the decomposable attention is set successfully, false otherwise. + //! + bool setDecomposable(bool decomposable) noexcept + { + return mImpl->setDecomposable(decomposable); + } + + //! + //! \brief Get whether the attention can be decomposed to use multiple kernels if no fused kernel support found. + //! + //! \return True if the attention can be decomposed to use multiple kernels by the compiler, + //! false otherwise. Default is false. + //! + //! \see setDecomposable + //! + bool getDecomposable() const noexcept + { + return mImpl->getDecomposable(); + } + + //! + //! \brief Append or replace an input of this layer with a specific tensor. + //! + //! \param index the index of the input to modify. + //! \param input the new input tensor. + //! + //! The indices are as follows: + //! + //! Input 0 is the input query tensor. + //! Input 1 is the input key tensor. + //! Input 2 is the input value tensor. + //! + //! \return True if the input tensor is set successfully, false otherwise. + //! + bool setInput(int32_t index, ITensor& input) noexcept + { + return mImpl->setInput(index, input); + } + + //! + //! \brief Get the number of inputs of IAttention. IAttention has three inputs. + //! + //! \return The number of inputs of IAttention. + int32_t getNbInputs() const noexcept + { + return mImpl->getNbInputs(); + } + + //! + //! \brief Get the IAttention input corresponding to the given index. + //! + //! \param index The index of the input tensor. + //! + //! \return The input tensor, or nullptr if the index is out of range. + //! + ITensor* getInput(int32_t index) const noexcept + { + return mImpl->getInput(index); + } + + //! + //! \brief Get the number of outputs of a layer. IAttention has one output. + //! + int32_t getNbOutputs() const noexcept + { + return mImpl->getNbOutputs(); + } + + //! + //! \brief Get the IAttention output corresponding to the given index. IAttention has only one output. + //! + //! \param index The index of the output tensor. + //! + //! \return The indexed output tensor, or nullptr if the index is out of range. + //! + ITensor* getOutput(int32_t index) const noexcept + { + return mImpl->getOutput(index); + } + + //! + //! \brief Set the name of the attention. + //! + //! The name is used in error diagnostics. + //! This method copies the name string. + //! + //! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see getName() + //! + //! \return True if the name is set successfully, false otherwise. + //! + bool setName(char const* name) noexcept + { + return mImpl->setName(name); + } + + //! + //! \brief Return the name of the attention. + //! + //! \see setName() + //! + //! \return The name of the attention. + //! + char const* getName() const noexcept + { + return mImpl->getName(); + } + + //! + //! \brief Set the quantization scale for the attention normalization output. + //! + //! \param tensor for quantization scale. Data type must be DataType::kFLOAT, DataType::kHALF or DataType::kBF16. + //! Must be a 0-d or 1-d. + //! + //! \return True if the quantization scale is set successfully, false otherwise. + //! + //! \warning Must be used together with setNormalizationQuantizeToType to set normalization output datatype to + //! DataType::kFP8 or DataType::kINT8. + //! + bool setNormalizationQuantizeScale(ITensor& tensor) noexcept + { + return mImpl->setNormalizationQuantizeScale(tensor); + } + + //! + //! \brief Get the quantization scale for the attention normalization output. + //! + //! \return The quantization scale for the attention normalization output or nullptr if no quantization scale is + //! set. + //! + ITensor* getNormalizationQuantizeScale() const noexcept + { + return mImpl->getNormalizationQuantizeScale(); + } + + //! + //! \brief Set the datatype the attention normalization is quantized to. + //! + //! \param type the datatype the attention normalization is quantized to. Must be one of DataType::kFP8, + //! DataType::kINT8. + //! + //! \return True if the quantization to type is set successfully, false otherwise. + //! + bool setNormalizationQuantizeToType(DataType type) noexcept + { + return mImpl->setNormalizationQuantizeToType(type); + } + + //! + //! \brief Get the datatype the attention normalization is quantized to. + //! + //! \return The datatype the attention normalization is quantized to. + //! The default value is DataType::kFLOAT. + //! + //! \warning Must be used after normalization quantization to type is set by setNormalizationQuantizeToType. + DataType getNormalizationQuantizeToType() const noexcept + { + return mImpl->getNormalizationQuantizeToType(); + } + + //! + //! \brief Set the metadata for IAttention. + //! + //! The metadata is emitted in the JSON returned by IEngineInspector with + //! ProfilingVerbosity set to kDETAILED. + //! + //! \param metadata The per-layer metadata. + //! + //! \warning The string name must be null-terminated and be at most 4096 bytes including the terminator. + //! + //! \see getMetadata() + //! \see getLayerInformation() + //! + //! \return True if the metadata is set successfully, false otherwise. + //! + bool setMetadata(char const* metadata) noexcept + { + return mImpl->setMetadata(metadata); + } + + //! + //! \brief Get the metadata of IAttention. + //! + //! \return The metadata as a null-terminated C-style string. If setMetadata() has not been called, + //! an empty string "" will be returned as a default value. + //! + //! \see setMetadata() + //! + char const* getMetadata() const noexcept + { + return mImpl->getMetadata(); + } + + //! + //! \brief Set the number of ranks for multi-device attention execution. + //! + //! When nbRanks > 1, this hints attention to perform multi-device attention. + //! + //! \param nbRanks The number of ranks. Must be >= 1. + //! + //! \return True if successful, false otherwise. + //! + //! \see getNbRanks() + //! + bool setNbRanks(int32_t nbRanks) noexcept + { + return mImpl->setNbRanks(nbRanks); + } + + //! + //! \brief Get the number of ranks for multi-device execution. + //! + //! \return The number of ranks configured for multi-device attention. Default is 1. + //! + //! \see setNbRanks() + //! + int32_t getNbRanks() const noexcept + { + return mImpl->getNbRanks(); + } + +protected: + apiv::VAttention* mImpl; + virtual ~IAttention() noexcept = default; +}; + +//! \class IRotaryEmbeddingLayer +//! +//! \brief Layer that implements Rotary Position Embedding (RoPE) (https://arxiv.org/abs/2104.09864). +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IRotaryEmbeddingLayer : public ILayer +{ +public: + //! + //! \brief Set whether the input is in interleaved format, i.e., whether the 2-d vectors rotated are taken from adjacent 2 elements in the hidden dimension. The default value is false. + //! + //! \see getInterleaved + //! + void setInterleaved(bool interleaved) noexcept + { + mImpl->setInterleaved(interleaved); + } + + + //! + //! \brief Get whether the input is in interleaved format. The default value is false. + //! + //! \see setInterleaved + //! + TRT_NODISCARD bool getInterleaved() const noexcept + { + return mImpl->getInterleaved(); + } + + + //! + //! \brief Set the number of hidden dimensions participating in RoPE. The default value is 0, representing H, i.e., all hidden dimensions in each head. Must be non-negative and even. + //! + //! \see getRotaryEmbeddingDim + //! + TRT_NODISCARD bool setRotaryEmbeddingDim(int32_t rotaryEmbeddingDim) noexcept + { + return mImpl->setRotaryEmbeddingDim(rotaryEmbeddingDim); + } + + + //! + //! \brief Get the number of hidden dimensions participating in RoPE. The default value is 0, representing H, i.e., all hidden dimensions in each head. + //! + //! \see setRotaryEmbeddingDim + //! + TRT_NODISCARD int32_t getRotaryEmbeddingDim() const noexcept + { + return mImpl->getRotaryEmbeddingDim(); + } + + + //! + //! \brief Append or replace an input of this layer with a specific tensor + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor + //! + //! The indices are as follows: + //! + //! Input 0 is the input activation tensor. + //! Input 1 is the cosine cache tensor. + //! Input 2 is the sine cache tensor. + //! Input 3 (optional) is the positionIds tensor, which is used for indexing into the cosine and sine caches. + //! + using ILayer::setInput; + + +protected: + apiv::VRotaryEmbeddingLayer* mImpl; + virtual ~IRotaryEmbeddingLayer() noexcept = default; +}; + +//! +//! \enum KVCacheMode +//! +//! \brief Enumerates the KVCache modes that may be performed by a KVCacheUpdate layer. +//! +enum class KVCacheMode : int32_t +{ + kLINEAR = 0, //!< Linear mode. +}; + +namespace impl +{ +//! +//! Maximum number of elements in KVCacheMode enum. +//! +//! \see KVCacheMode +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 1; +}; + +} // namespace impl + +//! \class IKVCacheUpdateLayer +//! +//! \brief Layer that represents a KVCacheUpdate operation. +//! +//! The KVCacheUpdate layer is used to cache the key or value tensors for the attention mechanism. +//! K and V use separate KVCacheUpdate layers. +//! +//! An IKVCacheUpdateLayer has three inputs (`cache`, `update`, `writeIndices`) and one output. +//! In `kLINEAR` mode, for each batch element i, the layer copies the update tensor into the cache starting at +//! position `writeIndices[i]`. Assuming no out-of-bounds writes occur, the operation for each sequence position +//! s in [0, sequenceLength) is: +//! \code +//! output[i, :, writeIndices[i] + s, :] = update[i, :, s, :] +//! \endcode +//! +//! The output performs in-place updates on the cache tensor, so they must share the same device memory address. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IKVCacheUpdateLayer : public ILayer +{ +public: + //! + //! \brief Append or replace an input of this layer with a specific tensor. + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor. + //! + //! The indices are as follows: + //! + //! Input 0 is the input cache tensor. + //! Input 1 is the input update tensor. + //! Input 2 is the input writeIndices tensor. + //! + using ILayer::setInput; + + //! + //! \brief Set the mode of the KVCacheUpdate layer. + //! + //! \param cacheMode The mode of the KVCacheUpdate layer. For TensorRT 10.15, only `kLINEAR` mode is supported. + //! + //! \return True if cache mode is set successfully, false otherwise. + //! + bool setCacheMode(KVCacheMode cacheMode) noexcept + { + return mImpl->setCacheMode(cacheMode); + } + + //! + //! \brief Get the mode of the KVCacheUpdate layer. + //! + //! \return The mode of the KVCacheUpdate layer. + //! + KVCacheMode getCacheMode() const noexcept + { + return mImpl->getCacheMode(); + } + +protected: + apiv::VKVCacheUpdateLayer* mImpl; + virtual ~IKVCacheUpdateLayer() noexcept = default; +}; + +//! +//! \enum MoEActType +//! +//! \brief Enumerates the activation type for the MoE layer. +//! +enum class MoEActType : int32_t +{ + kNONE = 0, + kSILU = 1, +}; + +namespace impl +{ + +//! +//! Maximum number of elements in MoEActType enum. +//! +//! \see MoEActType +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 2; +}; + +} // namespace impl + +//! โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” +//! โ”‚ hiddenStates โ”‚โ”‚selectedExpertsForTokensโ”‚โ”‚scoresForSelectedExpertsโ”‚ +//! โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +//! โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚ +//! โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” +//! โ”‚ โ”‚ +//! โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ +//! โ”‚ โ”‚ โ”‚ Expert 0 โ”‚ โ”‚ MOE โ”‚ โ”‚ Expert i โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚ fcGate โ”‚ โ”‚ fcUp โ”‚โ”‚ โ”‚ โ”‚ fcGate โ”‚ โ”‚ fcUp โ”‚โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚โ”‚ โ”‚ +//! โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”˜โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”˜โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚activationโ”‚ โ”‚ โ”‚ โ”‚ โ”‚activationโ”‚ โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ ....... โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚ mul โ”‚ โ”‚ โ”‚ โ”‚ mul โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚ fcDown โ”‚ โ”‚ โ”‚ โ”‚ fcDown โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ โ”Œโ”€โ”€โ”€โ–ผโ”€โ”€โ”€โ”€โ” โ”‚ โ”‚ +//! โ”‚ โ”‚ โ”‚output 0โ”‚ โ”‚ โ”‚ โ”‚output iโ”‚ โ”‚ โ”‚ +//! โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ โ””โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”˜ โ”‚ โ”‚ +//! โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ +//! โ”‚ โ”‚ โ”‚ โ”‚ +//! โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ +//! โ”‚ โ”‚ โ”‚ +//! โ”‚ โ–ผ โ”‚ +//! โ”‚ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ +//! โ”‚ โ”‚ weightedSum โ”‚ โ”‚ +//! โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ฌโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ”‚ +//! โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”‚โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +//! โ–ผ +//! โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” +//! โ”‚ moeOutput โ”‚ +//! โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +//! \class IMoELayer +//! +//! \brief A MoE layer in a network definition. +//! Mixture of Experts (MoE) is a collection of experts with each expert specializing in processing different subsets of input data. +//! The key innovation lies in using a Router that selectively activates only the specific experts needed for a given input, rather than engaging the entire neural network for every task. +//! +//! Definition in the MoE layer: +//! \p fcDown, \p fcGate, \p fcUp are three linear layers. +//! fc(x) = x * w + b, where x is the input, w is the weight, b is the bias, * is the matrix multiplication. +//! \p activation is the activation function. +//! \p mul is the multiplication between the output of fc_up and the output of fc_gate. +//! \p weightedSum is the weighted sum of the output of the experts. +//! \p moeOutput is the output of the MoE layer. +//! +//! MoE is a collection of experts. +//! Each expert is a GLU (gated linear unit), which consists by \p fcGate, \p fcUp, \p fcDown, \p activation, \p mul. +//! +//! Definitions and Abbreviations: +//! \p batchSize: batch size +//! \p seqLen: sequence length +//! \p hiddenSize: the size of the hidden states +//! \p numExperts: the number of experts in the MoE layer +//! \p moeInterSize: the intermediate size of the MoE layer +//! \p topK: the number of experts to select for each token +//! +//! This layer takes several activation inputs: +//! 1. \p hiddenStates: the hidden states of the layer, with shape [batchSize, seqLen, hiddenSize] +//! 2. \p selectedExpertsForTokens: the top K experts selected for each token, with shape [batchSize, seqLen, topK] +//! 3. \p scoresForSelectedExperts: the scales for the selected experts per token, with shape [batchSize, seqLen, topK] +//! The MoE will take the selected experts and the corresponding scales for the selected experts to compute the output. +//! +//! The weights in the MoE layer: +//! 1. \p fcGateWeights with shape [numExperts, hiddenSize, moeInterSize]: the weight matrix for fcGate +//! 2. \p fcUpWeights with shape [numExperts, hiddenSize, moeInterSize]: the weight matrix for fcUp +//! 3. \p fcDownWeights with shape [numExperts, moeInterSize, hiddenSize]: the weight matrix for fcDown +//! +//! Several optional inputs are supported: +//! 1. \p fcGateBias: the bias for the fcGate, with shape [numExperts, moeInterSize] +//! 2. \p fcUpBias: the bias for the fcUp, with shape [numExperts, moeInterSize] +//! 3. \p fcDownBias: the bias for the fcDown, with shape [numExperts, hiddenSize] +//! All the bias are none by default. You must either set all the bias or none of them. +//! +//! 4. \p activation: the activation type for the MoE layer, currently only support SILU. +//! +//! MoE computation process description: +//! For each token, the MoE layer computation process is as follows: +//! +//! 1. Input processing: +//! - Receive \p hiddenStates: +//! - Receive \p selectedExpertsForTokens: +//! - Receive \p scoresForSelectedExperts: +//! +//! 2. Expert computation for each token: +//! - output_i = fcDown(fcUp(hiddenStates) * activation(fcGate(hiddenStates))) +//! +//! 3. Expert output aggregation: +//! For each token, firstly select all the experts that need to be activated to do the computation. +//! - calculate the selected expert's output according to expert id in \p selectedExpertsForTokens for each token +//! - Weighted sum of each expert's output according to weights in \p scoresForSelectedExperts for each token +//! - Final output for the token: moeOutput = ฮฃ(score_i * output_i) +//! The output of MoE has the same shape as the input \p hiddenStates. +//! +//! \warning MoE is only supported on Thor. And performance is limited when seqLen > 16. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IMoELayer : public ILayer +{ +public: + //! + //! \brief Set the weights of the experts when each expert is a GLU (gated linear unit). In each GLU, there are 3 linear layers and 1 activation function, so this function requires 3 weight tensors and 1 activation type. + //! + //! \param fcGateWeights The weights for the gate-projection layer of all experts in MoE. Shape: [numExperts, hiddenSize, moeInterSize]. + //! \param fcUpWeights The weights for the up-projection layer of all experts in MoE. Shape: [numExperts, hiddenSize, moeInterSize]. + //! \param fcDownWeights The weights for the down-projection layer of all experts in MoE. Shape: [numExperts, moeInterSize, hiddenSize]. + //! \param activationType The activation function to use for the MoE layer. Currently only kSILU is supported. + //! + //! \see setActivationType() + //! \see getActivationType() + //! + void setGatedWeights(ITensor& fcGateWeights, ITensor& fcUpWeights, ITensor& fcDownWeights, MoEActType activationType) noexcept + { + mImpl->setGatedWeights(fcGateWeights, fcUpWeights, fcDownWeights, activationType); + } + + //! + //! \brief Set the biases of the experts when each expert is a GLU (gated linear unit). In each GLU, there are 3 linear layers, so this function requires 3 bias tensors. + //! + //! \param fcGateBiases The biases for the gate-projection layer of all experts in MoE. Shape: [numExperts, moeInterSize]. + //! \param fcUpBiases The biases for the up-projection layer of all experts in MoE. Shape: [numExperts, moeInterSize]. + //! \param fcDownBiases The biases for the down-projection layer of all experts in MoE. Shape: [numExperts, hiddenSize]. + //! + void setGatedBiases(ITensor& fcGateBiases, ITensor& fcUpBiases, ITensor& fcDownBiases) noexcept + { + mImpl->setGatedBiases(fcGateBiases, fcUpBiases, fcDownBiases); + } + + //! + //! \brief Set the activation type for the MoE layer. + //! + //! \param activationType: the activation type for the MoE layer. + //! + //! \see getActivationType() + //! + void setActivationType(MoEActType activationType) noexcept + { + mImpl->setActivationType(activationType); + } + + //! + //! \brief Get the activation type for the MoE layer. + //! + //! \see setActivationType() + //! + //! \return the activation type for the MoE layer. + //! + MoEActType getActivationType() const noexcept + { + return mImpl->getActivationType(); + } + + //! + //! \brief Configure static quantization after the mul op. + //! โ”Œโ”€โ”€ fcGate โ”€โ”€ activation โ”€โ”€โ”€โ” + //! โ”‚ โ”‚ + //! hiddenStates โ”€โ”€โ”€โ”ค โ”œโ”€โ”€ mul โ”€โ”€ {Q โ”€โ”€ DQ} โ”€โ”€ fcDown โ”€โ”€ output + //! โ”‚ โ”‚ + //! โ””โ”€โ”€ fcUp โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + //! When using mul output static quantization, the user must provide: + //! \param fcDownActivationScale: the scale tensor. + //! \param dataType: the type that the activation is quantized to. + //! In addition, the user should also insert Q/DQ before the hiddenStates input of the MoE layer. The quantization method must be the same as the quantization method here. + //! + //! If setQuantizationDynamicDblQ is called, then previous calls to this function are overridden. + //! If setQuantizationToType is called, previous parameters set by this function are overridden. + //! + //! \see setQuantizationToType() + //! \see getQuantizationToType() + //! + void setQuantizationStatic(ITensor& fcDownActivationScale, DataType dataType) noexcept + { + mImpl->setQuantizationStatic(fcDownActivationScale, dataType); + } + + //! + //! \brief Configure dynamic quantization (with double quantization) after the mul op. + //! โ”Œโ”€โ”€ fcGate โ”€โ”€ activation โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€ DQ + //! โ”‚ โ”‚ โ”‚ โ”‚ + //! hiddenStates โ”€โ”€โ”€โ”ค โ”œโ”€โ”€ mul โ”€โ”€ {DynQ โ”€โ”€ DQ} โ”€โ”€ fcDown โ”€โ”€ output + //! โ”‚ โ”‚ + //! โ””โ”€โ”€ fcUp โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + //! When using mul output dynamic quantization (with double quantization), the user must provide: + //! \param fcDownActivationDblQScale: the double quantization scale tensor. + //! \param dataType: the type that the activation is quantized to. + //! \param blockShape: the blockShape used in quantization. + //! \param dynQOutputScaleType: the data type of the scale tensor. + //! In addition, the user should also insert DynQ/DQ/DQ before the hiddenStates input of the MoE layer. The quantization method must be the same as the quantization method here. + //! + //! If setQuantizationStatic is called, then previous calls to this function are overridden. + //! If setQuantizationToType, setQuantizationBlockShape or setDynQOutputScaleType is called, previous parameters set by this function are overridden. + //! + //! \see setQuantizationToType() + //! \see getQuantizationToType() + //! \see setQuantizationBlockShape() + //! \see getQuantizationBlockShape() + //! \see setDynQOutputScaleType() + //! \see getDynQOutputScaleType() + //! + void setQuantizationDynamicDblQ(ITensor& fcDownActivationDblQScale, DataType dataType, Dims const& blockShape, DataType dynQOutputScaleType) noexcept + { + mImpl->setQuantizationDynamicDblQ(fcDownActivationDblQScale, dataType, blockShape, dynQOutputScaleType); + } + + //! + //! \brief Set the data type the mul output is quantized to. + //! + //! \param type: the data type the mul output is quantized to. + //! The type must be one of DataType::kFP8, DataType::kFP4. + //! + //! Default: \p DataType::kFLOAT which means the MoE layer is not quantized. + //! + //! \see getQuantizationToType() + //! + void setQuantizationToType(DataType type) noexcept + { + mImpl->setQuantizationToType(type); + } + + //! + //! \brief Get the data type the mul in MoE layer is quantized to. + //! + //! \see setQuantizationToType() + //! + //! \return the data type the mul in MoE layer is quantized to. + //! + DataType getQuantizationToType() const noexcept + { + return mImpl->getQuantizationToType(); + } + + //! + //! \brief Set the block shape for the quantization of the Mul output. + //! + //! \param blockShape: the block shape for the quantization of the Mul output. + //! + //! The shape must have rank 3 and the dimensions representing block sizes for Mul output dimensions (batchSize, seqLen, moeInterSize) respectively. + //! For example, a shape of [1, 1, 16] means block quantization on the last (moeInterSize) axis. + //! -1 means a fully blocked dimension. + //! + //! \see getQuantizationBlockShape() + //! + void setQuantizationBlockShape(Dims const& blockShape) noexcept + { + mImpl->setQuantizationBlockShape(blockShape); + } + + //! + //! \brief Get the block shape for the quantization of the Mul output. + //! + //! \see setQuantizationBlockShape() + //! + //! \return the block shape for the quantization of the Mul output. + //! + Dims getQuantizationBlockShape() const noexcept + { + return mImpl->getQuantizationBlockShape(); + } + + //! + //! \brief Set the dynamic quantization output scale type. + //! + //! \param type: the dynamic quantization output scale type. + //! + //! \see getDynQOutputScaleType() + //! + void setDynQOutputScaleType(DataType type) noexcept + { + mImpl->setDynQOutputScaleType(type); + } + + //! + //! \brief Get the dynamic quantization output scale type. + //! + //! \see setDynQOutputScaleType() + //! + //! \return the dynamic quantization output scale type. + //! + DataType getDynQOutputScaleType() const noexcept + { + return mImpl->getDynQOutputScaleType(); + } + + //! + //! \brief Set the SwiGLU parameters. + //! + //! \param limit the SwiGLU parameter limit. + //! \param alpha the SwiGLU parameter alpha. + //! \param beta the SwiGLU parameter beta. + //! + //! Default: +inf, 1.0, 0.0 + //! + //! \see setSwigluParamLimit() + //! \see getSwigluParamLimit() + //! \see setSwigluParamAlpha() + //! \see getSwigluParamAlpha() + //! \see setSwigluParamBeta() + //! \see getSwigluParamBeta() + //! + void setSwigluParams(float limit, float alpha, float beta) noexcept + { + mImpl->setSwigluParams(limit, alpha, beta); + } + + //! + //! \brief Set the SwiGLU parameter limit. + //! + //! \param limit the SwiGLU parameter limit. + //! + //! Default: +inf + //! + //! \see getSwigluParamLimit() + //! + void setSwigluParamLimit(float limit) noexcept + { + mImpl->setSwigluParamLimit(limit); + } + + //! + //! \brief Get the SwiGLU parameter limit. + //! + //! \see setSwigluParamLimit() + //! + //! \return the SwiGLU parameter limit. + //! + float getSwigluParamLimit() const noexcept + { + return mImpl->getSwigluParamLimit(); + } + + //! + //! \brief Set the SwiGLU parameter alpha. + //! + //! \param alpha the SwiGLU parameter alpha. + //! + //! Default: 1.0 + //! + //! \see getSwigluParamAlpha() + //! + void setSwigluParamAlpha(float alpha) noexcept + { + mImpl->setSwigluParamAlpha(alpha); + } + + //! + //! \brief Get the SwiGLU parameter alpha. + //! + //! \see setSwigluParamAlpha() + //! + //! \return the SwiGLU parameter alpha. + //! + float getSwigluParamAlpha() const noexcept + { + return mImpl->getSwigluParamAlpha(); + } + + //! + //! \brief Set the SwiGLU parameter beta. + //! + //! \param beta the SwiGLU parameter beta. + //! + //! Default: 0.0 + //! + //! \see getSwigluParamBeta() + //! + void setSwigluParamBeta(float beta) noexcept + { + mImpl->setSwigluParamBeta(beta); + } + + //! + //! \brief Get the SwiGLU parameter beta. + //! + //! \see setSwigluParamBeta() + //! + //! \return the SwiGLU parameter beta. + //! + float getSwigluParamBeta() const noexcept + { + return mImpl->getSwigluParamBeta(); + } + + //! + //! \brief Set the input of the MoE layer. + //! + //! \param index the index of the input to modify. + //! \param tensor the new input tensor + //! + //! The indices are as follows: + //! + //! Input 0: hiddenStates: the input activations, with shape [batchSize, seqLen, hiddenSize] + //! Input 1: selectedExpertsForTokens: the selected experts for tokens, with shape [batchSize, seqLen, topK] + //! Input 2: scoresForSelectedExperts: the scores for selected experts, with shape [batchSize, seqLen, topK] + //! + void setInput(int32_t index, ITensor& tensor) noexcept + { + mImpl->setInput(index, tensor); + } + + using ILayer::setInput; + +protected: + virtual ~IMoELayer() noexcept = default; + apiv::VMoELayer* mImpl; +}; + +//! +//! \class IDistCollectiveLayer +//! +//! Implements a distributed collective operation. +//! +//! See \ref INetworkDefinition::addDistCollective for more details. +//! +class IDistCollectiveLayer : public ILayer +{ +protected: + virtual ~IDistCollectiveLayer() noexcept = default; + apiv::VDistCollectiveLayer* mImpl; +}; // class IDistCollectiveLayer + +//! +//! \class INetworkDefinition +//! +//! \brief A network definition for input to the builder. +//! +//! A network definition defines the structure of the network, and combined with a IBuilderConfig, is built +//! into an engine using an IBuilder. An INetworkDefinition can have all dimensions explicit, full dims mode, in the +//! network definition. The former mode, i.e. the implicit batch size mode, has been deprecated. +//! +//! A network with implicit batch dimensions returns the dimensions of a layer without the implicit dimension, +//! and instead the batch is specified at execute/enqueue time. If the network has all dimensions specified, then +//! the first dimension follows elementwise broadcast rules: if it is 1 for some inputs and is some value N for all +//! other inputs, then the first dimension of each output is N, and the inputs with 1 for the first dimension are +//! broadcast. Having divergent batch sizes across inputs to a layer is not supported. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class INetworkDefinition : public INoCopy +{ +public: + virtual ~INetworkDefinition() noexcept = default; + + //! + //! \brief Add an input tensor to the network. + //! + //! Each input and output tensor must have a unique name. + //! + //! For networks with wildcard dimensions, the volume + //! is based on the maxima specified by an IOptimizationProfile.Dimensions are normally non-negative integers. The + //! exception is that in networks with all explicit dimensions, -1 can be used as a wildcard for a dimension to + //! be specified at runtime. Input tensors with such a wildcard must have a corresponding entry in the + //! IOptimizationProfiles indicating the permitted extrema, and the input dimensions must be set by + //! IExecutionContext::setInputShape. Different IExecutionContext instances can have different dimensions. + //! Wildcard dimensions are only supported for EngineCapability::kSTANDARD. They are not + //! supported in safety contexts. DLA does not support Wildcard dimensions. + //! + //! Tensor dimensions are specified independent of format. For example, if a + //! tensor is formatted in "NHWC" or a vectorized format, the dimensions are + //! still specified in the order{N, C, H, W}. For 2D images with a channel + //! dimension, the last three dimensions are always {C,H,W}. For 3D images + //! with a channel dimension, the last four dimensions are always {C,D,H,W}. + //! + //! \param name The name of the tensor. + //! \param type The type of the data held in the tensor. + //! \param dimensions The dimensions of the tensor. + //! + //! \warning It is an error to specify a wildcard value on a dimension that is determined by trained parameters. + //! + //! \warning If run on DLA with explicit dimensions, only leading dimension can be a wildcard. And provided profile + //! must have same minimum, optimum, and maximum dimensions. + //! + //! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see ITensor + //! + //! \return The new tensor or nullptr if there is an error. + //! + ITensor* addInput(char const* name, DataType type, Dims const& dimensions) noexcept + { + return mImpl->addInput(name, type, dimensions); + } + + //! + //! \brief Mark a tensor as a network output. + //! + //! \param tensor The tensor to mark as an output tensor. + //! + //! \warning It is an error to mark a network input as an output. + //! \warning It is an error to mark a tensor inside an ILoop or an + //! IIfConditional as an output. + //! + void markOutput(ITensor& tensor) noexcept + { + mImpl->markOutput(tensor); + } + + //! + //! \brief Mark a tensor as a debug tensor. + //! + //! A debug tensor can be optionally emitted at runtime. + //! Note that tensor names are required to specify debug + //! tensors at runtime. + //! + //! \param tensor Tensor to be marked as debug + //! + //! \return True if tensor successfully marked (or was already marked), false otherwise. + //! + //! \see unmarkDebug(), IExecutionContext::setDebugListener(), ITensor::setName() + //! + bool markDebug(ITensor& tensor) noexcept + { + return mImpl->markDebug(tensor); + } + + //! + //! \brief Unmark a tensor as a debug tensor. + //! + //! Remove the marking of a tensor as a debug tensor. + //! + //! \param tensor Tensor to be unmarked as debug. + //! + //! \return True if tensor successfully unmarked (or was already unmarked), false otherwise. + //! + //! \see markDebug(), IExecutionContext::setDebugListener() + //! + bool unmarkDebug(ITensor& tensor) noexcept + { + return mImpl->unmarkDebug(tensor); + } + + //! + //! \brief Check if a tensor is marked as debug tensor. + //! + //! \return true if tensor is marked as debug tensor, false otherwise. + //! + bool isDebugTensor(ITensor const& tensor) const noexcept + { + return mImpl->isDebugTensor(tensor); + } + + //! + //! \brief Mark unfused tensors as debug tensors. + //! + //! Debug tensors can be optionally emitted at runtime. + //! Tensors that are fused by the optimizer will not be emitted. + //! Tensors marked this way will not prevent fusion like markDebug() does, thus preserving performance. + //! + //! \warning Tensors marked this way cannot be detected by isDebugTensor(). + //! \warning DebugListener can only get internal tensor names instead of the original tensor + //! names in the NetworkDefinition for tensors marked this way. But the names correspond to the + //! names obtained by IEngineInspector. + //! \warning There is no guarantee that all unfused tensors are marked. + //! + //! \return True if tensors were successfully marked (or were already marked), false otherwise. + //! + //! \see unmarkUnfusedTensorsAsDebugTensors(), markDebug(), IExecutionContext::setDebugListener() + //! + bool markUnfusedTensorsAsDebugTensors() noexcept + { + return mImpl->markUnfusedTensorsAsDebugTensors(); + } + + //! + //! \brief Undo the marking of unfused tensors as debug tensors. + //! + //! This has no effect on tensors marked by markDebug(). + //! + //! \return True if tensor successfully unmarked (or was already unmarked), false otherwise. + //! + //! \see markUnfusedTensorsAsDebugTensors(), IExecutionContext::setDebugListener() + //! + bool unmarkUnfusedTensorsAsDebugTensors() noexcept + { + return mImpl->unmarkUnfusedTensorsAsDebugTensors(); + } + + //! + //! \brief Add an activation layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param type The type of activation function to apply. + //! + //! Note that the setAlpha() and setBeta() methods must be used on the + //! output for activations that require these parameters. + //! + //! \see IActivationLayer ActivationType + //! + //! \warning Int32 and Int64 are valid only for activation type kRELU. + //! + //! \return The new activation layer, or nullptr if it could not be created. + //! + IActivationLayer* addActivation(ITensor& input, ActivationType type) noexcept + { + return mImpl->addActivation(input, type); + } + + //! + //! \brief Add a LRN layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param window The size of the window. + //! \param alpha The alpha value for the LRN computation. + //! \param beta The beta value for the LRN computation. + //! \param k The k value for the LRN computation. + //! + //! \see ILRNLayer + //! \warning Int32 tensors are not valid input tensors. + //! + //! \return The new LRN layer, or nullptr if it could not be created. + //! + ILRNLayer* addLRN(ITensor& input, int64_t window, float alpha, float beta, float k) noexcept + { + return mImpl->addLRN(input, window, alpha, beta, k); + } + + //! + //! \brief Add a Scale layer to the network. + //! + //! \param input The input tensor to the layer. + //! This tensor must have at least 4 dimensions. + //! \param mode The scaling mode. + //! \param shift The shift value. + //! \param scale The scale value. + //! \param power The power value. + //! + //! If the weights are available, then the size of weights are dependent on the ScaleMode. + //! For ScaleMode::kUNIFORM, the number of weights equals 1. + //! For ScaleMode::kCHANNEL, the number of weights equals the channel dimension. + //! For ScaleMode::kELEMENTWISE, the number of weights equals the product of the last three dimensions of the input. + //! + //! \see addScaleNd + //! \see IScaleLayer + //! \warning Int32 tensors are not valid input tensors. + //! + //! \return The new Scale layer, or nullptr if it could not be created. + //! + IScaleLayer* addScale(ITensor& input, ScaleMode mode, Weights shift, Weights scale, Weights power) noexcept + { + return mImpl->addScale(input, mode, shift, scale, power); + } + + //! + //! \brief Add a SoftMax layer to the network. + //! + //! \see ISoftMaxLayer + //! \warning Int32 tensors are not valid input tensors. + //! + //! \return The new SoftMax layer, or nullptr if it could not be created. + //! + ISoftMaxLayer* addSoftMax(ITensor& input) noexcept + { + return mImpl->addSoftMax(input); + } + + //! + //! \brief Add a concatenation layer to the network. + //! + //! \param inputs The input tensors to the layer. + //! \param nbInputs The number of input tensors. + //! + //! \see IConcatenationLayer + //! + //! \return The new concatenation layer, or nullptr if it could not be created. + //! + //! \warning All tensors must have the same dimensions except along the concatenation axis. + //! + IConcatenationLayer* addConcatenation(ITensor* const* inputs, int32_t nbInputs) noexcept + { + return mImpl->addConcatenation(inputs, nbInputs); + } + + //! + //! \brief Add an elementwise layer to the network. + //! + //! \param input1 The first input tensor to the layer. + //! \param input2 The second input tensor to the layer. + //! \param op The binary operation that the layer applies. + //! + //! The input tensors must have the same rank and compatible type. + //! Two types are compatible if they are the same type or are both in the set {kFLOAT, kHALF}. + //! For each dimension, their lengths must match, or one of them must be one. + //! In the latter case, the tensor is broadcast along that axis. + //! + //! The output tensor has the same rank as the inputs. + //! For each dimension, its length is the maximum of the lengths of the + //! corresponding input dimension. + //! + //! The inputs are shape tensors if the output is a shape tensor. + //! + //! \see IElementWiseLayer + //! + //! \return The new elementwise layer, or nullptr if it could not be created. + //! + IElementWiseLayer* addElementWise(ITensor& input1, ITensor& input2, ElementWiseOperation op) noexcept + { + return mImpl->addElementWise(input1, input2, op); + } + + //! + //! \brief Add a unary layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param operation The operation to apply. + //! + //! \see IUnaryLayer + //! + //! Generally the input must have a floating-point type (or kINT8 as a quantized float), + //! except for the following operations: + //! * kSIGN accepts a floating-point or Int32 tensor. + //! * kNOT requires a Bool tensor. + //! + //! The input is a shape tensor if the output is a shape tensor. + //! + //! \return The new unary layer, or nullptr if it could not be created + //! + IUnaryLayer* addUnary(ITensor& input, UnaryOperation operation) noexcept + { + return mImpl->addUnary(input, operation); + } + + //! + //! \brief Add a shuffle layer to the network. + //! + //! \param input The input tensor to the layer. + //! + //! \see IShuffleLayer + //! + //! \return The new shuffle layer, or nullptr if it could not be created. + //! + IShuffleLayer* addShuffle(ITensor& input) noexcept + { + return mImpl->addShuffle(input); + } + + //! + //! \brief Add a OneHot layer to the network. + //! + //! \param indices - tensor containing indices where on_value should be set. + //! \param values - a 2-element tensor, consisting of [off_value, on_value]. + //! \param depth - a shape tensor containing the width of the added one-hot dimension. + //! \param axis - the axis to add the one-hot encoding to. + //! + //! \see IOneHotLayer + //! + //! \return The new OneHot layer, or nullptr if it could not be created. + //! + IOneHotLayer* addOneHot(ITensor& indices, ITensor& values, ITensor& depth, int32_t axis) noexcept + { + return mImpl->addOneHot(indices, values, depth, axis); + } + + //! + //! \brief Get the number of layers in the network. + //! + //! \return The number of layers in the network. + //! + //! \see getLayer() + //! + int32_t getNbLayers() const noexcept + { + return mImpl->getNbLayers(); + } + + //! + //! \brief Get the layer specified by the given index. + //! + //! \param index The index of the layer. + //! + //! \return The layer, or nullptr if the index is out of range. + //! + //! \see getNbLayers() + //! + ILayer* getLayer(int32_t index) const noexcept + { + return mImpl->getLayer(index); + } + + //! + //! \brief Get the number of inputs in the network. + //! + //! \return The number of inputs in the network. + //! + //! \see getInput() + //! + int32_t getNbInputs() const noexcept + { + return mImpl->getNbInputs(); + } + + //! + //! \brief Get the input tensor specified by the given index. + //! + //! \param index The index of the input tensor. + //! + //! \return The input tensor, or nullptr if the index is out of range. + //! + //! \note adding inputs invalidates indexing here + //! + //! \see getNbInputs() + //! + ITensor* getInput(int32_t index) const noexcept + { + return mImpl->getInput(index); + } + + //! + //! \brief Get the number of outputs in the network. + //! + //! The outputs include those marked by markOutput or markOutputForShapes. + //! + //! \return The number of outputs in the network. + //! + //! \see getOutput() + //! + int32_t getNbOutputs() const noexcept + { + return mImpl->getNbOutputs(); + } + + //! + //! \brief Get the output tensor specified by the given index. + //! + //! \param index The index of the output tensor. + //! + //! \return The output tensor, or nullptr if the index is out of range. + //! + //! \note adding inputs invalidates indexing here + //! + //! \see getNbOutputs() + //! + ITensor* getOutput(int32_t index) const noexcept + { + return mImpl->getOutput(index); + } + + //! + //! \brief Add a reduce layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param operation The reduction operation to perform. + //! \param reduceAxes The reduction dimensions. + //! The bit in position i of bitmask reduceAxes corresponds to explicit dimension i if result. + //! E.g., the least significant bit corresponds to the first explicit dimension and the next to least + //! significant bit corresponds to the second explicit dimension. + //! \param keepDimensions The boolean that specifies whether or not to keep the reduced dimensions in the + //! output of the layer. + //! + //! The reduce layer works by performing an operation specified by \p operation to reduce the tensor \p input + //! across the axes specified by \p reduceAxes. + //! + //! \see IReduceLayer + //! + //! \warning If output is an Int32 or Int64 shape tensor, ReduceOperation::kAVG is unsupported. + //! + //! \return The new reduce layer, or nullptr if it could not be created. + //! + IReduceLayer* addReduce( + ITensor& input, ReduceOperation operation, uint32_t reduceAxes, bool keepDimensions) noexcept + { + return mImpl->addReduce(input, operation, reduceAxes, keepDimensions); + } + + //! + //! \brief Add a TopK layer to the network. + //! + //! The TopK layer has two outputs of the same dimensions. The first contains data values, + //! the second contains index positions for the values. Output values are sorted, largest first + //! for operation kMAX and smallest first for operation kMIN. + //! + //! Currently only values of K up to 3840 are supported. + //! + //! The default indices tensor (the second output) data type is DataType::kINT32. + //! + //! \param input The input tensor to the layer. + //! + //! \param op Operation to perform. + //! + //! \param k The number of elements to keep. For dynamic k, use the setInput() method to pass in k as a tensor + //! instead, which will override the static k value passed here in calculations. + //! + //! \param reduceAxes The reduction dimensions. + //! The bit in position i of bitmask reduceAxes corresponds to explicit dimension i of the result. + //! E.g., the least significant bit corresponds to the first explicit dimension and the next to least + //! significant bit corresponds to the second explicit dimension. Currently reduceAxes must specify + //! exactly one dimension, and it must be one of the last four dimensions. + //! + //! \see ITopKLayer + //! + //! \return The new TopK layer, or nullptr if it could not be created. + //! + //! \deprecated Deprecated in TensorRT 10.14. Superseded by five-argument addTopK. + //! + TRT_DEPRECATED ITopKLayer* addTopK(ITensor& input, TopKOperation op, int32_t k, uint32_t reduceAxes) noexcept + { + return mImpl->addTopK(input, op, k, reduceAxes); + } + + //! + //! \brief Add a TopK layer to the network. + //! + //! The TopK layer has two outputs of the same dimensions. The first contains data values, + //! the second contains index positions for the values. Output values are sorted, largest first + //! for operation kMAX and smallest first for operation kMIN. + //! + //! Currently only values of K up to 3840 are supported. + //! + //! \param input The input tensor to the layer. + //! + //! \param op Operation to perform. + //! + //! \param k The number of elements to keep. For dynamic k, use the setInput() method to pass in k as a tensor + //! instead, which will override the static k value passed here in calculations. + //! + //! \param reduceAxes The reduction dimensions. + //! The bit in position i of bitmask reduceAxes corresponds to explicit dimension i of the result. + //! E.g., the least significant bit corresponds to the first explicit dimension and the next to least + //! significant bit corresponds to the second explicit dimension. Currently reduceAxes must specify + //! exactly one dimension, and it must be one of the last four dimensions. + //! + //! \param indicesType Indices tensor (the second output) data type, must be DataType::kINT32 or DataType::kINT64. + //! + //! \see ITopKLayer + //! + //! \return The new TopK layer, or nullptr if it could not be created. + //! + ITopKLayer* addTopK(ITensor& input, TopKOperation op, int32_t k, uint32_t reduceAxes, DataType indicesType) noexcept + { + return mImpl->addTopKV2(input, op, k, reduceAxes, indicesType); + } + + //! + //! \brief Add gather with mode GatherMode::kDEFAULT and specified axis and nbElementWiseDims=0. + //! + //! \param data The tensor to gather values from. + //! \param indices The tensor to get indices from to populate the output tensor. + //! \param axis The axis in the data tensor to gather on. + //! + //! \see IGatherLayer + //! + //! \return The new gather layer, or nullptr if it could not be created. + //! + IGatherLayer* addGather(ITensor& data, ITensor& indices, int32_t axis) noexcept + { + return mImpl->addGather(data, indices, axis); + } + + //! + //! \brief Add gather with specified mode, axis=0 and nbElementWiseDims=0. + //! + //! \param data The tensor to gather values from. + //! \param indices The tensor to get indices from to populate the output tensor. + //! \param mode The gather mode. + //! + //! \see IGatherLayer + //! + //! \return The new gather layer, or nullptr if it could not be created. + //! + IGatherLayer* addGatherV2(ITensor& data, ITensor& indices, GatherMode mode) noexcept + { + return mImpl->addGatherV2(data, indices, mode); + } + + //! + //! \brief Add a RaggedSoftMax layer to the network. + //! + //! \param input The ZxS input tensor. + //! \param bounds The Zx1 bounds tensor. + //! + //! \see IRaggedSoftMaxLayer + //! + //! \warning The bounds tensor cannot have the last dimension be the wildcard character. + //! \warning Int32 tensors are not valid input tensors. + //! \warning The input and bounds tensors should be 3D tensors. + //! + //! \return The new RaggedSoftMax layer, or nullptr if it could not be created. + //! + IRaggedSoftMaxLayer* addRaggedSoftMax(ITensor& input, ITensor& bounds) noexcept + { + return mImpl->addRaggedSoftMax(input, bounds); + } + + //! + //! \brief Add a MatrixMultiply layer to the network. + //! + //! \param input0 The first input tensor (commonly A). + //! \param op0 The operation to apply to input0. + //! \param input1 The second input tensor (commonly B). + //! \param op1 The operation to apply to input1. + //! + //! The inputs are shape tensors if the output is a shape tensor. + //! + //! \see IMatrixMultiplyLayer + //! + //! \warning Int32 tensors are not valid input tensors. + //! + //! \return The new matrix multiply layer, or nullptr if it could not be created. + //! + IMatrixMultiplyLayer* addMatrixMultiply( + ITensor& input0, MatrixOperation op0, ITensor& input1, MatrixOperation op1) noexcept + { + return mImpl->addMatrixMultiply(input0, op0, input1, op1); + } + + //! + //! \brief Add a nonzero layer to the network. + //! + //! The default indices tensor (the first output) data type is DataType::kINT32. + //! + //! \param input The input tensor to the layer. + //! + //! \see INonZeroLayer + //! + //! \return The new nonzero layer, or nullptr if it could not be created. + //! + //! \deprecated Deprecated in TensorRT 10.14. Superseded by two-argument addNonZero. + //! + TRT_DEPRECATED INonZeroLayer* addNonZero(ITensor& input) noexcept + { + return mImpl->addNonZero(input); + } + + //! + //! \brief Add a nonzero layer to the network. + //! + //! \param input The input tensor to the layer. + //! + //! \param indicesType Indices tensor (the first output) data type, must be DataType::kINT32 or DataType::kINT64. + //! + //! \see INonZeroLayer + //! + //! \return The new nonzero layer, or nullptr if it could not be created. + //! + INonZeroLayer* addNonZero(ITensor& input, DataType indicesType) noexcept + { + return mImpl->addNonZeroV2(input, indicesType); + } + + //! + //! \brief Add a constant layer to the network. + //! + //! \param dimensions The dimensions of the constant. + //! \param weights The constant value, represented as weights. + //! + //! \see IConstantLayer + //! + //! \return The new constant layer, or nullptr if it could not be created. + //! + //! If weights.type is DataType::kINT32, the output is a tensor of 32-bit indices. + //! Otherwise the output is a tensor of real values and the output type will be + //! follow TensorRT's normal precision rules. + //! + //! If a wildcard dimension is used, the volume of the runtime dimensions must equal + //! the number of weights specified. + //! + //! \warning DataType::kUINT8 not supported. + //! + IConstantLayer* addConstant(Dims const& dimensions, Weights weights) noexcept + { + return mImpl->addConstant(dimensions, weights); + } + + //! + //! \brief Add an identity layer. + //! + //! \param input The input tensor to the layer. + //! + //! \see IIdentityLayer + //! + //! \return The new identity layer, or nullptr if it could not be created. + //! + IIdentityLayer* addIdentity(ITensor& input) noexcept + { + return mImpl->addIdentity(input); + } + + //! + //! \brief Add a cast layer. + //! + //! \param input The input tensor to the layer. + //! \param toType The DataType of the output tensor + //! + //! \see ICastLayer + //! + //! \return The new cast layer, or nullptr if it could not be created. + //! + ICastLayer* addCast(ITensor& input, DataType toType) noexcept + { + return mImpl->addCast(input, toType); + } + + //! + //! \brief remove a tensor from the network definition. + //! + //! \param tensor the tensor to remove + //! + //! It is illegal to remove a tensor that is the input or output of a layer. + //! if this method is called with such a tensor, a warning will be emitted on the log + //! and the call will be ignored. Its intended use is to remove detached tensors after + //! e.g. concatenating two networks with Layer::setInput(). + //! + void removeTensor(ITensor& tensor) noexcept + { + mImpl->removeTensor(tensor); + } + + //! + //! \brief unmark a tensor as a network output. + //! + //! \param tensor The tensor to unmark as an output tensor. + //! + //! see markOutput() + //! + void unmarkOutput(ITensor& tensor) noexcept + { + mImpl->unmarkOutput(tensor); + } + + //! + //! \brief Add a plugin layer to the network using the IPluginV2 interface. + //! + //! \param inputs The input tensors to the layer. + //! \param nbInputs The number of input tensors. + //! \param plugin The layer plugin. + //! + //! \see IPluginV2Layer + //! + //! \warning Dimension wildcard are only supported with IPluginV2DynamicExt or IPluginV2IOExt plugins. + //! \warning Int32 tensors are not valid input tensors. + //! + //! \return The new plugin layer, or nullptr if it could not be created. + //! + //! \deprecated Deprecated in TensorRT 10.8. Superseded by addPluginV3. + //! + TRT_DEPRECATED IPluginV2Layer* addPluginV2(ITensor* const* inputs, int32_t nbInputs, IPluginV2& plugin) noexcept + { + return mImpl->addPluginV2(inputs, nbInputs, plugin); + } + + //! + //! \brief Add a plugin layer implementing the IPluginV3 interface to the network. + //! + //! \param inputs The input tensors to the layer. + //! \param nbInputs The number of input tensors. + //! \param shapeInputs Shape tensor inputs to the layer. + //! \param nbShapeInputs The number of shape tensor inputs. + //! \param plugin The layer plugin. + //! + //! \see IPluginV3Layer + //! + //! \return The new plugin layer, or nullptr if it could not be created. + //! + IPluginV3Layer* addPluginV3(ITensor* const* inputs, int32_t nbInputs, ITensor* const* shapeInputs, + int32_t nbShapeInputs, IPluginV3& plugin) noexcept + { + return mImpl->addPluginV3(inputs, nbInputs, shapeInputs, nbShapeInputs, plugin); + } + + //! + //! \brief Add a slice layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param start The start offset + //! \param size The output dimension + //! \param stride The slicing stride + //! + //! Positive, negative, zero stride values, and combinations of them in different dimensions are allowed. + //! + //! \see ISliceLayer + //! + //! \return The new slice layer, or nullptr if it could not be created. + //! + ISliceLayer* addSlice(ITensor& input, Dims const& start, Dims const& size, Dims const& stride) noexcept + { + return mImpl->addSlice(input, start, size, stride); + } + + //! + //! \brief Sets the name of the network. + //! + //! \param name The name to assign to this network. + //! + //! Set the name of the network so that it can be associated with a built + //! engine. The \p name must be a null-terminated C-style string. + //! TensorRT makes no use of this string except storing it as part of the engine + //! so that it may be retrieved at runtime. + //! A name unique to the builder will be generated by default. + //! + //! This method copies the name string. + //! + //! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see INetworkDefinition::getName(), ISafeCudaEngine::getName() + //! + //! \return none + //! + void setName(char const* name) noexcept + { + mImpl->setName(name); + } + + //! + //! \brief Returns the name associated with the network. + //! + //! The memory pointed to by getName() is owned by the INetworkDefinition object. + //! + //! \see INetworkDefinition::setName() + //! + //! \return A null-terminated C-style string representing the name of the network. + //! + char const* getName() const noexcept + { + return mImpl->getName(); + } + + //! + //! \brief Add a shape layer to the network. + //! + //! \param input The input tensor to the layer. + //! + //! \see IShapeLayer + //! + //! \warning addShape is only supported when hasImplicitBatchDimensions is false. + //! + //! \return The new shape layer, or nullptr if it could not be created. + //! + IShapeLayer* addShape(ITensor& input) noexcept + { + return mImpl->addShape(input); + } + + //! + //! \brief Query whether the network was created with an implicit batch dimension. + //! + //! \return Always false since TensorRT 10.0 does not support an implicit batch dimension. + //! + //! \see createNetworkV2 + //! + //! \deprecated Deprecated in TensorRT 10.0. Implicit batch is not supported since TensorRT 10.0. + //! + TRT_DEPRECATED bool hasImplicitBatchDimension() const noexcept + { + return mImpl->hasImplicitBatchDimension(); + } + + //! + //! \brief Get the network definition creation flags for this network definition object. Defaults to 0. + //! + //! \return The network definition creation options as a bitmask. + //! + NetworkDefinitionCreationFlags getFlags() const noexcept + { + return mImpl->getFlags(); + } + + //! + //! \brief Returns true if the network definition creation flag is set + //! + //! \see getFlags() + //! + //! \return True if flag is set, false if unset. + //! + bool getFlag(NetworkDefinitionCreationFlag networkDefinitionCreationFlag) const noexcept + { + return mImpl->getFlag(networkDefinitionCreationFlag); + } + + //! + //! \brief Enable tensor's value to be computed by IExecutionContext::getShapeBinding. + //! + //! \return True if successful, false if tensor is already marked as an output. + //! + //! The tensor must be of type DataType::kINT32 and have no more than one dimension. + //! + //! \warning The tensor must have dimensions that can be determined to be constants at build time. + //! + //! \warning It is an error to mark a network input as a shape output. + //! + //! + bool markOutputForShapes(ITensor& tensor) noexcept + { + return mImpl->markOutputForShapes(tensor); + } + + //! + //! \brief Undo markOutputForShapes. + //! + //! \warning inputs to addShape cannot contain wildcard dimension values. + //! + //! \return True if successful, false if tensor is not marked as an output. + //! + bool unmarkOutputForShapes(ITensor& tensor) noexcept + { + return mImpl->unmarkOutputForShapes(tensor); + } + + //! + //! \brief Add a parametric ReLU layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param slope The slope tensor to the layer. This tensor should be unidirectionally broadcastable + //! to the input tensor. + //! + //! \see IParametricReLULayer + //! + //! \warning Tensors of type Int32, Int64, Bool, or UInt8 are not allowed as inputs. + //! + //! \return The new parametric ReLU layer, or nullptr if it could not be created. + //! + IParametricReLULayer* addParametricReLU(ITensor& input, ITensor& slope) noexcept + { + return mImpl->addParametricReLU(input, slope); + } + + //! + //! \brief Add a multi-dimension convolution layer to the network. + //! + //! \param input The input tensor to the convolution. + //! \param nbOutputMaps The number of output feature maps for the convolution. + //! \param kernelSize The multi-dimensions of the convolution kernel. + //! \param kernelWeights The kernel weights for the convolution. + //! \param biasWeights The bias weights for the convolution. Weights{} represents no bias. + //! + //! \see IConvolutionLayer + //! + //! \warning It is an error to specify a wildcard value for the 'C' dimension of the input tensor. + //! \warning Int32 tensors are not valid input tensors. + //! \warning Only 2D or 3D convolution is supported. + //! + //! \return The new convolution layer, or nullptr if it could not be created. + //! + IConvolutionLayer* addConvolutionNd( + ITensor& input, int64_t nbOutputMaps, Dims const& kernelSize, Weights kernelWeights, Weights biasWeights) noexcept + { + return mImpl->addConvolutionNd(input, nbOutputMaps, kernelSize, kernelWeights, biasWeights); + } + + //! + //! \brief Add a multi-dimension pooling layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param type The type of pooling to apply. + //! \param windowSize The size of the pooling window. + //! + //! \see IPoolingLayer PoolingType + //! + //! \warning Int32 tensors are not valid input tensors. + //! \warning Only 2D or 3D pooling is supported. + //! + //! \return The new pooling layer, or nullptr if it could not be created. + //! + IPoolingLayer* addPoolingNd(ITensor& input, PoolingType type, Dims const& windowSize) noexcept + { + return mImpl->addPoolingNd(input, type, windowSize); + } + + //! + //! \brief Add a multi-dimension deconvolution layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param nbOutputMaps The number of output feature maps. + //! \param kernelSize The multi-dimensions of the deconvolution kernel. + //! \param kernelWeights The kernel weights for the deconvolution. + //! \param biasWeights The bias weights for the deconvolution. Weights{} represents no bias. + //! + //! \see IDeconvolutionLayer + //! + //! \warning It is an error to specify a wildcard value for the 'C' dimension of the input tensor. + //! \warning Int32 tensors are not valid input tensors. + //! \warning Only 2D or 3D deconvolution is supported. + // + //! \return The new deconvolution layer, or nullptr if it could not be created. + //! + IDeconvolutionLayer* addDeconvolutionNd( + ITensor& input, int64_t nbOutputMaps, Dims kernelSize, Weights kernelWeights, Weights biasWeights) noexcept + { + return mImpl->addDeconvolutionNd(input, nbOutputMaps, kernelSize, kernelWeights, biasWeights); + } + + //! + //! \brief Add a multi-dimension scale layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param mode The scaling mode. + //! \param shift The shift value. + //! \param scale The scale value. + //! \param power The power value. + //! \param channelAxis The channel axis. + //! + //! If the weights are available, then the size of weights are dependent on the ScaleMode. + //! For ScaleMode::kUNIFORM, the number of weights equals 1. + //! For ScaleMode::kCHANNEL, the number of weights equals the channel dimension. + //! For ScaleMode::kELEMENTWISE, the number of weights equals the product of all input dimensions at channelAxis and + //! beyond. + //! + //! For example, if the inputs dimensions are [A,B,C,D,E,F], and channelAxis=2: + //! For ScaleMode::kUNIFORM, the number of weights is equal to 1. + //! For ScaleMode::kCHANNEL, the number of weights is C. + //! For ScaleMode::kELEMENTWISE, the number of weights is C*D*E*F. + //! + //! channelAxis can also be set explicitly using setChannelAxis(). + //! + //! \see IScaleLayer + //! \see setChannelAxis() + //! + //! \warning Int32 tensors are not valid input tensors. + //! \warning Only 2D or 3D scale is supported. + //! + //! \return The new Scale layer, or nullptr if it could not be created. + //! + IScaleLayer* addScaleNd( + ITensor& input, ScaleMode mode, Weights shift, Weights scale, Weights power, int32_t channelAxis) noexcept + { + return mImpl->addScaleNd(input, mode, shift, scale, power, channelAxis); + } + + //! + //! \brief Add a resize layer to the network. + //! + //! \param input The input tensor to the layer. + //! + //! \see IResizeLayer + //! + //! \warning Int32 tensors are not valid input tensors. + //! + //! \return The new resize layer, or nullptr if it could not be created. + //! + IResizeLayer* addResize(ITensor& input) noexcept + { + return mImpl->addResize(input); + } + + //! + //! \brief Add a loop to the network. + //! + //! An ILoop provides a way to specify a recurrent subgraph. + //! + //! \return Pointer to ILoop that can be used to add loop-boundary layers for the loop. + //! + //! \see ILoop + //! + ILoop* addLoop() noexcept + { + return mImpl->addLoop(); + } + + //! + //! \brief Add an if-then-else to the network. + //! + //! An IIfConditional provides a way to conditionally execute parts of the network. + //! + //! \return Pointer to the IIfConditional that can be used to add conditional-boundary layers + //! for the if-then-else. + //! + //! \see IIfConditional + //! + IIfConditional* addIfConditional() noexcept + { + return mImpl->addIfConditional(); + } + + //! + //! \brief Add a select layer to the network. + //! + //! \param condition The condition tensor to the layer. Must have type DataType::kBOOL. + //! \param thenInput The "then" input tensor to the layer. + //! \param elseInput The "else" input tensor to the layer. + //! + //! All three input tensors must have the same rank, and along each axis + //! must have the same length or a length of one. If the length is one, the tensor + //! is broadcast along that axis. The output tensor has the dimensions of the inputs AFTER + //! the broadcast rule is applied. For example, given: + //! + //! dimensions of condition: [1,1,5,9] + //! dimensions of thenInput: [1,1,5,9] + //! dimensions of elseInput: [1,3,1,9] + //! + //! the output dimensions are [1,3,5,9], and the output contents are defined by: + //! + //! output[0,i,j,k] = condition[0,0,j,k] ? thenInput[0,0,j,k] : elseInput[0,i,0,k] + //! + //! The output dimensions are not necessarily the max of the input dimensions if any input + //! is an empty tensor. For example, if in the preceding example, 5 is changed to 0: + //! + //! dimensions of condition: [1,1,0,9] + //! dimensions of thenInput: [1,1,0,9] + //! dimensions of elseInput: [1,3,1,9] + //! + //! then the output dimensions are [1,3,0,9]. + //! + //! The inputs are shape tensors if the output is a shape tensor. + //! + //! \see ISelectLayer + //! + //! \return The new select layer, or nullptr if it could not be created. + ISelectLayer* addSelect(ITensor& condition, ITensor& thenInput, ITensor& elseInput) noexcept + { + return mImpl->addSelect(condition, thenInput, elseInput); + } + + //! + //! \brief Add an assertion layer to the network. + //! + //! \param condition The input tensor to the layer. + //! \param message A message to print if the assertion fails. + //! + //! \see IAssertionLayer + //! + //! \return The new assertion layer, or nullptr if it could not be created. + //! + //! The input tensor must be a boolean shape tensor. + //! + IAssertionLayer* addAssertion(ITensor& condition, char const* message) noexcept + { + return mImpl->addAssertion(condition, message); + } + + //! + //! \brief Add a fill layer to the network. + //! + //! \param dimensions The output tensor dimensions if input 0 is missing. + //! \param op The fill operation that the layer applies. + //! + //! \warning For FillOperation::kLINSPACE, dimensions.nbDims must be 1 for static start/delta. If delta is provided + //! as a 1D tensor, the length of delta must match dimensions.nbDims. + //! + //! This layer is non-deterministic across subsequent calls as the same inputs will produce different + //! output tensors if \p op is either FillOperation::kRANDOM_UNIFORM or FillOperation::kRANDOM_NORMAL + //! due to random state being shared across calls. The output tensors generated are determinstic when + //! starting from the same initial state. + //! + //! \see IFillLayer + //! + //! \return The new fill layer, or nullptr if it could not be created. + //! + //! \deprecated Deprecated in TensorRT 9.0. Superseded by three-argument addFill. + //! + TRT_DEPRECATED IFillLayer* addFill(Dims const& dimensions, FillOperation op) noexcept + { + return mImpl->addFill(dimensions, op); + } + + //! + //! \brief Add a fill layer to the network. + //! + //! \param dimensions The output tensor dimensions if input 0 is missing. + //! \param op The fill operation that the layer applies. + //! \param outputType Optional output tensor data type, must be DataType::kFLOAT, DataType::kHALF, DataType::kINT32, + //! or DataType::kINT64. This parameter is only used for static alpha/beta. Future calls to set output type using + //! setToType or setOutputType must be consistent. + //! + //! \warning For FillOperation::kLINSPACE, dimensions.nbDims must be 1 for static start/delta. If delta is provided + //! as a 1D tensor, the length of delta must match dimensions.nbDims. + //! + //! This layer is non-deterministic across subsequent calls as the same inputs will produce different + //! output tensors if \p op is either FillOperation::kRANDOM_UNIFORM or FillOperation::kRANDOM_NORMAL + //! due to random state being shared across calls. The output tensors generated are deterministic when + //! starting from the same initial state. + //! + //! \see IFillLayer + //! + //! \return The new fill layer, or nullptr if it could not be created. + //! + IFillLayer* addFill(Dims const& dimensions, FillOperation op, DataType outputType) noexcept + { + return mImpl->addFillV2(dimensions, op, outputType); + } + + //! + //! \brief Add a padding layer to the network. Only 2D padding is currently supported. + //! + //! \param input The input tensor to the layer. + //! \param prePadding The padding to apply to the start of the tensor. + //! \param postPadding The padding to apply to the end of the tensor. + //! + //! \see IPaddingLayer + //! + //! \return The new padding layer, or nullptr if it could not be created. + //! + IPaddingLayer* addPaddingNd(ITensor& input, Dims const& prePadding, Dims const& postPadding) noexcept + { + return mImpl->addPaddingNd(input, prePadding, postPadding); + } + + //! + //! \brief Associate a name with all current uses of the given weights. + //! + //! The name must be set after the Weights are used in the network. + //! Lookup is associative. The name applies to all Weights with matching + //! type, value pointer, and count. If Weights with a matching value + //! pointer, but different type or count exists in the network, an + //! error message is issued, the name is rejected, and return false. + //! If the name has already been used for other weights, + //! return false. A nullptr causes the weights to become unnamed, + //! i.e. clears any previous name. + //! + //! \param weights The weights to be named. + //! \param name The name to associate with the weights. + //! + //! \return true on success. + //! + //! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. + //! + bool setWeightsName(Weights weights, char const* name) noexcept + { + return mImpl->setWeightsName(weights, name); + } + + //! + //! \brief Set the ErrorRecorder for this interface + //! + //! Assigns the ErrorRecorder to this interface. The ErrorRecorder will track all errors during execution. + //! This function will call incRefCount of the registered ErrorRecorder at least once. Setting + //! recorder to nullptr unregisters the recorder with the interface, resulting in a call to decRefCount if + //! a recorder has been registered. + //! + //! If an error recorder is not set, messages will be sent to the global log stream. + //! + //! \param recorder The error recorder to register with this interface. + // + //! \see getErrorRecorder() + //! + void setErrorRecorder(IErrorRecorder* recorder) noexcept + { + mImpl->setErrorRecorder(recorder); + } + + //! + //! \brief get the ErrorRecorder assigned to this interface. + //! + //! Retrieves the assigned error recorder object for the given class. + //! A nullptr will be returned if setErrorRecorder has not been called. + //! + //! \return A pointer to the IErrorRecorder object that has been registered. + //! + //! \see setErrorRecorder() + //! + IErrorRecorder* getErrorRecorder() const noexcept + { + return mImpl->getErrorRecorder(); + } + + //! + //! \brief Add a dequantization layer to the network. + //! + //! \param input The input tensor to be quantized. + //! \param scale A tensor with the scale value. + //! + //! \see IDequantizeLayer + //! + //! \p input tensor data type must be DataType::kINT8 or DataType::kFP8. + //! \p scale tensor data type must be DataType::kFLOAT. The subgraph which terminates with the \p scale tensor must + //! be a build-time constant. + //! + //! \return The new quantization layer, or nullptr if it could not be created. + //! + //! \deprecated Deprecated in TensorRT 9.0. Superseded by three-argument addDequantize. + //! + TRT_DEPRECATED IDequantizeLayer* addDequantize(ITensor& input, ITensor& scale) noexcept + { + return mImpl->addDequantize(input, scale); + } + + //! + //! \brief Add a dequantization layer to the network. + //! + //! \param input The input tensor to be dequantized. + //! \param scale A tensor with the scale value. + //! \param outputType Output tensor data type. + //! + //! \see IDequantizeLayer + //! + //! \p input tensor data type must be DataType::kINT8, DataType::kFP8, DataType::kINT4 or DataType::kFP4. + //! \p scale tensor data type must be one of the following: DataType::kFLOAT (default), DataType::kHALF, + //! DataType::kBF16 or DataType::kE8M0 (for MXFP8 quantization). + //! \p outputType output tensor data type must be DataType::kFLOAT (default), DataType::kHALF or DataType::kBF16. + //! Future calls to set output type using setToType or setOutputType must be consistent. For strongly typed + //! networks, if the scale type is DataType::kHALF or DataType::kBF16 the output type must match. + //! + //! \return The new quantization layer, or nullptr if it could not be created. + //! + IDequantizeLayer* addDequantize(ITensor& input, ITensor& scale, DataType outputType) noexcept + { + return mImpl->addDequantizeV2(input, scale, outputType); + } + + //! + //! \brief Add a Scatter layer to the network with specified mode and axis=0. + //! + //! \param data The input tensor to be updated with additional values. + //! \param indices indices of the elements to be updated. + //! \param updates values to be used for updates. + //! \param mode scatter mode. + //! + //! \see IScatterLayer + //! + //! \p indices tensor data type must be DataType::kINT32. + //! \p updates tensor data type must be the same as \p data + //! + //! \return The new Scatter layer, or nullptr if it could not be created. + //! + IScatterLayer* addScatter(ITensor& data, ITensor& indices, ITensor& updates, ScatterMode mode) noexcept + { + return mImpl->addScatter(data, indices, updates, mode); + } + + //! + //! \brief Add a quantization layer to the network. + //! + //! \param input The input tensor to be quantized. + //! \param scale A tensor with the scale value. + //! + //! \see IQuantizeLayer + //! + //! \p input tensor data type must be DataType::kFLOAT or DataType::kHALF. + //! \p scale tensor data type must be DataType::kFLOAT. The subgraph which terminates with the \p scale tensor must + //! be a build-time constant. + //! + //! \return The new quantization layer, or nullptr if it could not be created. + //! + //! \deprecated Deprecated in TensorRT 9.0. Superseded by three-argument addQuantize. + //! + TRT_DEPRECATED IQuantizeLayer* addQuantize(ITensor& input, ITensor& scale) noexcept + { + return mImpl->addQuantize(input, scale); + } + + //! + //! \brief Add a quantization layer to the network. + //! + //! \param input The input tensor to be quantized. + //! \param scale A tensor with the scale value. + //! \param outputType Output tensor data type. + //! + //! \see IQuantizeLayer + //! + //! \p input tensor data type must be DataType::kFLOAT, DataType::kHALF or DataType::kBF16. + //! \p scale tensor data type must be one of the following: DataType::kFLOAT (default), DataType::kHALF, + //! DataType::kBF16 or DataType::kE8M0 (for MXFP8 quantization). + //! \p outputType output tensor data type must be DataType::kINT8 (default), DataType::kFP8, DataType::kINT4 or + //! DataType::kFP4. + //! Future calls to set output type using setToType or setOutputType must be consistent. For strongly typed + //! networks, if the scale type is DataType::kHALF or DataType::kBF16 the output type must match. + //! + //! \return The new quantization layer, or nullptr if it could not be created. + //! + IQuantizeLayer* addQuantize(ITensor& input, ITensor& scale, DataType outputType) noexcept + { + return mImpl->addQuantizeV2(input, scale, outputType); + } + + //! + //! \brief Add a dynamic quantization layer to the network. + //! + //! This layer performs dynamic block quantization of its input tensor and outputs the + //! quantized data and the computed block scale-factors. + //! The blocked axis dimension size must be divisible by the block size. + //! + //! \param input The input tensor to be quantized. Its data type must be one of DataType::kFLOAT, + //! DataType::kHALF, or DataType::kBF16. Currently only 2D and 3D inputs are supported. + //! \param axis The axis that is sliced into blocks. The axis must be the last or second to last dimension. + //! \param blockSize The number of elements that are quantized using a shared scale factor. + //! Valid values are 16 (NVFP4 quantization) and 32 (MXFP8 quantization). + //! \param outputType The data type of the quantized output tensor, must be DataType::kFP4 (NVFP4 quantization) or + //! DataType::kFP8 (MXFP8 quantization). Future calls to set output type using setToType or setOutputType must be + //! consistent. + //! \param scaleType The data type of the scale factor used for quantizing the input data, must be DataType::kFP8 + //! (NVFP4 quantization) or DataType::kE8M0 (MXFP8 quantization). + //! + //! \return The new dynamic quantization layer, or nullptr if it could not be created. + //! + //! \see IDynamicQuantizeLayer + //! + TRT_DEPRECATED IDynamicQuantizeLayer* addDynamicQuantize( + ITensor& input, int32_t axis, int32_t blockSize, DataType outputType, DataType scaleType) noexcept + { + return mImpl->addDynamicQuantize(input, axis, blockSize, outputType, scaleType); + } + + //! + //! \brief Add a dynamic quantization layer to the network. + //! + //! This layer performs dynamic block quantization of its input tensor and outputs the + //! quantized data and the computed block scale factors. + //! + //! \param input The input tensor to be quantized. Its data type must be one of DataType::kFLOAT, + //! DataType::kHALF, or DataType::kBF16. + //! \param blockShape Defines the block shape for the quantization. Must match the input tensor rank. + //! \param outputType The data type of the quantized output tensor, must be DataType::kFP4, DataType::kFP8 or + //! DataType::kINT8. Future calls to set output type using setToType or setOutputType must be consistent. + //! \param scaleType The data type of the scale factor used for quantizing the input data, must be DataType::kFP8, + //! DataType::kE8M0 or DataType::kFLOAT. + //! + //! \return The new dynamic quantization layer, or nullptr if it could not be created. + //! + //! \see IDynamicQuantizeLayer + //! + IDynamicQuantizeLayer* addDynamicQuantizeV2( + ITensor& input, Dims const& blockShape, DataType outputType, DataType scaleType) noexcept + { + return mImpl->addDynamicQuantizeV2(input, blockShape, outputType, scaleType); + } + + //! + //! \brief Add an Einsum layer to the network. + //! + //! \param inputs The input tensors to the layer. + //! \param nbInputs The number of input tensors. + //! \param equation The equation of the layer + //! \see IEinsumLayer + //! + //! \return The new Einsum layer, or nullptr if it could not be created. + //! + IEinsumLayer* addEinsum(ITensor* const* inputs, int32_t nbInputs, char const* equation) noexcept + { + return mImpl->addEinsum(inputs, nbInputs, equation); + } + + //! + //! \brief Add a GridSample layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param grid The grid tensor to the layer. + //! + //! \see IGridSampleLayer + //! + //! Creates a GridSample layer with a InterpolationMode::kLINEAR, unaligned corners, + //! and SampleMode::kFILL for 4d-shape input tensors. + //! + //! \return The new GridSample layer, or nullptr if it could not be created. + //! + IGridSampleLayer* addGridSample(ITensor& input, ITensor& grid) noexcept + { + return mImpl->addGridSample(input, grid); + } + + //! + //! \brief Add a non-maximum suppression layer to the network. + //! + //! The default indices tensor (the first output) data type is DataType::kINT32. + //! + //! \param boxes The input boxes tensor to the layer. + //! + //! \param scores The input scores tensor to the layer. + //! + //! \param maxOutputBoxesPerClass The input maxOutputBoxesPerClass tensor to the layer. + //! + //! \see INMSLayer + //! + //! \return The new NMS layer, or nullptr if it could not be created. + //! + //! \deprecated Deprecated in TensorRT 10.14. Superseded by four-argument addNMS. + //! + TRT_DEPRECATED INMSLayer* addNMS(ITensor& boxes, ITensor& scores, ITensor& maxOutputBoxesPerClass) noexcept + { + return mImpl->addNMS(boxes, scores, maxOutputBoxesPerClass); + } + + //! + //! \brief Add a non-maximum suppression layer to the network. + //! + //! \param boxes The input boxes tensor to the layer. + //! + //! \param scores The input scores tensor to the layer. + //! + //! \param maxOutputBoxesPerClass The input maxOutputBoxesPerClass tensor to the layer. + //! + //! \param indicesType Indices tensor (the first output) data type, must be DataType::kINT32 or DataType::kINT64. + //! + //! \see INMSLayer + //! + //! \return The new NMS layer, or nullptr if it could not be created. + //! + INMSLayer* addNMS(ITensor& boxes, ITensor& scores, ITensor& maxOutputBoxesPerClass, DataType indicesType) noexcept + { + return mImpl->addNMSV2(boxes, scores, maxOutputBoxesPerClass, indicesType); + } + + //! + //! \brief Add a ReverseSequence layer to the network. + //! + //! \param input The input tensor to the layer. Must have rank >= 2. + //! + //! \param sequenceLens 1D tensor specifying lengths of sequences to reverse in a batch. The length of the + //! sequenceLens tensor must be equal to the size of the dimension in input tensor specified by batchAxis. + //! + //! \see IReverseSequenceLayer + //! + //! \return The new ReverseSequence layer, or nullptr if it could not be created. + //! + IReverseSequenceLayer* addReverseSequence(ITensor& input, ITensor& sequenceLens) noexcept + { + return mImpl->addReverseSequence(input, sequenceLens); + } + + //! + //! \brief Add a normalization layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param scale The scale tensor used to scale the normalized output. + //! \param bias The bias tensor used to scale the normalized output. + //! \param axesMask The axes on which to perform mean calculations. + //! The bit in position i of bitmask axesMask corresponds to explicit dimension i of the result. + //! E.g., the least significant bit corresponds to the first explicit dimension and the next to least + //! significant bit corresponds to the second explicit dimension. + //! + //! The normalization layer works by performing normalization of the tensor \p input on the specified \p axesMask. + //! The result is then scaled by multiplying with \p scale and adding \p bias. + //! + //! The shapes of \p scale and \p bias must be the same, and must have the same rank and be + //! unidirectionally broadcastable to the shape of \p input. Given a 4D NCHW input tensor, the expected shapes + //! for \p scale and \p bias are: + //! * [1, C, 1, 1] for InstanceNormalization + //! * [1, G, 1, 1] for GroupNormalization. Use addNormalizationV2() instead if [1, C, 1, 1] shapes for \p scale + //! and \p bias are required. + //! + //! \see INormalizationLayer + //! + //! \return The new normalization layer, or nullptr if it could not be created. + //! + //! \deprecated Deprecated in TensorRT 10.15. Superseded by addNormalizationV2(). + //! + TRT_DEPRECATED INormalizationLayer* addNormalization(ITensor& input, ITensor& scale, ITensor& bias, uint32_t axesMask) noexcept + { + return mImpl->addNormalization(input, scale, bias, axesMask); + } + + //! + //! \brief Add a cumulative layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param axis The axis tensor to apply the cumulative operation on. Currently, it must be a build-time constant 0D + //! shape tensor and must be in the range [-rank(input), rank(input)-1]. Negative value means counting dimensions + //! from the back. \param operation The reduction operation to perform. \param exclusive The boolean that specifies + //! whether it is an exclusive cumulative or inclusive cumulative. \param reverse The boolean that specifies whether + //! the cumulative operation should be applied backward. + //! + //! The cumulative layer works by performing the specified cumulative \p operation to the tensor \p input + //! on the axis specified by \p axis. + //! + //! \see ICumulativeLayer + //! + //! \return The new cumulative layer, or nullptr if it could not be created. + //! + ICumulativeLayer* addCumulative(ITensor& input, ITensor& axis, CumulativeOperation operation, bool exclusive, bool reverse) noexcept + { + return mImpl->addCumulative(input, axis, operation, exclusive, reverse); + } + + //! + //! \brief Add an attention to the network. + //! + //! \param query A 4d input query tensor to the layer. + //! \param key A 4d input key tensor to the layer. + //! \param value A 4d input value tensor to the layer. + //! \param normOp The normalization operation to perform. + //! \param causal Use causual inference or not. + //! + //! query must have shape [batchSize, numHeadsQuery, sequenceLengthQuery, dimHead]. + //! key and value must have shape [batchSize, numHeadsKeyValue, sequenceLengthKeyValue, dimHead]. + //! pastKey and pastValue must have shape [batchSize, numHeadsKeyValue, sequenceLengthKeyValue, dimHead]. + //! normOp defaults to kSOFTMAX isCausal defaults to false. + //! + //! By default, IAttention is not decomposable and TensorRT will try to use a single fused kernel, which may be more + //! efficient than if the subgraph is expressed without IAttention. Setting the IAttention to decomposable=True can + //! allow IAttention to be to use multiple kernels if no fused kernel support found. + //! + //! \see IAttention + //! + //! \return The new attention, or nullptr if it could not be created. + //! + IAttention* addAttention( + ITensor& query, ITensor& key, ITensor& value, AttentionNormalizationOp normOp, bool causal) noexcept + { + return mImpl->addAttention(query, key, value, normOp, causal); + } + + //! \brief Add a Rotary Position Embedding (RoPE) layer to the network. + //! + //! \param input The input activation tensor to the layer. The shape must be (batchSize, numHeads, sequenceLength, headSize). + //! \param cosCache The cosine cache tensor for use in RoPE computation. See the following explanation for the shape requirement. + //! \param sinCache The sine cache tensor for use in RoPE computation. See the following explanation for the shape requirement. + //! \param interleaved Whether the \p input is in interleaved format, i.e., whether the 2-d vectors rotated are taken from adjacent 2 elements in the hidden dimension. + //! \param rotaryEmbeddingDim The hidden dimension that participates in RoPE. + //! + //! The RotaryEmbedding layer applies RoPE to the \p input, using \p cosCache and \p sinCache. + //! An optional input, positionIds, can be provided using setInput with index 3. If provided, it is used to index into \p cosCache and \p sinCache. + //! + //! If \p positionIds is not provided, \p cosCache and \p sinCache must have shape (batchSize, sequenceLength, headSize / 2) if \p rotaryEmbeddingDim is 0, or (batchSize, sequenceLength, rotaryEmbeddingDim / 2) otherwise. + //! If \p positionIds is provided, \p cosCache and \p sinCache must have shape (maxPositionId+1, headSize / 2) if \p rotaryEmbeddingDim is 0, or (maxPositionId+1, rotaryEmbeddingDim / 2) otherwise. + //! \p positionIds, if provided, must have shape (batchSize, sequenceLength). + //! + //! \see IRotaryEmbeddingLayer + //! + //! \return The new RotaryEmbedding layer, or nullptr if it could not be created. + //! + IRotaryEmbeddingLayer* addRotaryEmbedding(ITensor& input, ITensor& cosCache, ITensor& sinCache, bool interleaved, int32_t rotaryEmbeddingDim) noexcept + { + return mImpl->addRotaryEmbedding(input, cosCache, sinCache, interleaved, rotaryEmbeddingDim); + } + + //! + //! \brief Add a KVCacheUpdate layer to the network. + //! + //! \param cache The key/value cache tensor for the layer. The user is responsible for properly allocating + //! and binding the tensor memory. + //! \param update The newly updated key/value tensor for the layer. + //! \param writeIndices The write indices tensor for key/value cache updates. + //! \param cacheMode The mode of the KVCacheUpdate layer. For TensorRT 10.15, only `kLINEAR` mode is supported. + //! + //! The expected tensor shapes are as follows: + //! - `cache`: [batchSize, numHeads, maxSequenceLength, headSize] + //! - `update`: [batchSize, numHeads, sequenceLength, headSize] + //! - `writeIndices`: [batchSize] + //! + //! The `cache` and `update` tensors must have the same data type, which can be DataType::kFLOAT, + //! DataType::kHALF, or DataType::kBF16. Quantized data types are not supported. + //! The `writeIndices` tensor must be DataType::kINT32 or DataType::kINT64. + //! + //! The layer performs in-place updates on the cache tensor. Therefore, the user must ensure that + //! the `cache` tensor and the corresponding output tensor share the same device memory address + //! before execution. + //! + //! \warning In `kLINEAR` mode, each update must satisfy the condition + //! `writeIndices[i] + sequenceLength <= maxSequenceLength`. Out-of-bound updates will be ignored silently. + //! + //! \see IKVCacheUpdateLayer + //! + //! \return The new KVCacheUpdate layer, or nullptr if it could not be created. + //! + IKVCacheUpdateLayer* addKVCacheUpdate( + ITensor& cache, ITensor& update, ITensor& writeIndices, KVCacheMode cacheMode) noexcept + { + return mImpl->addKVCacheUpdate(cache, update, writeIndices, cacheMode); + } + + //! \brief Add a MoE (Mixture of Experts) layer to the network. + //! + //! \param hiddenStates The hidden states tensor input to the MoE layer. Shape: [batchSize, seqLen, hiddenSize]. + //! \param selectedExpertsForTokens The tensor containing expert indices selected for each token. Shape: [batchSize, seqLen, topK]. + //! \param scoresForSelectedExperts The tensor containing scores computed for the selected experts. Shape: [batchSize, seqLen, topK]. + //! + //! \see IMoELayer + //! + //! \warning MoE is only supported on Thor. And performance is limited when seqLen > 16. + //! + //! \warning The number of selected experts per token could be inferred from the input \p selectedExpertsForTokens and should be consistent with the topK in the \p scoresForSelectedExperts. + //! + //! \return The new MoE layer, or nullptr if it could not be created. + //! + IMoELayer* addMoE(ITensor& hiddenStates, ITensor& selectedExpertsForTokens, ITensor& scoresForSelectedExperts) noexcept + { + return mImpl->addMoE(hiddenStates, selectedExpertsForTokens, scoresForSelectedExperts); + } + + //! + //! \brief Add a DistCollective layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param distCollectiveOp The collective operation to perform. See \ref CollectiveOperation for valid values. + //! \param reduceOp The reduction operation to perform, in case the collective operation is reduction type: kREDUCE, + //! kREDUCE_SCATTER or kALL_REDUCE. See \ref ReduceOperation for valid values. Use ReduceOperation::kNONE for a + //! CollectiveOperation which does not need a ReduceOperation + //! \param root The root rank of the collective operation. + //! Some CollectiveOperations, such as kBROADCAST and kREDUCE require specifying a root rank, with the following + //! semantics: + //! - kBROADCAST: the root rank sends, all other ranks receive data + //! - kREDUCE: the root rank receives reduced data, the other ranks send data + //! \param groups Pointer to a flat array of rank IDs in the communicator that defines a single group for this + //! layer. The DistCollective runner treats this array as the ordered list of participating ranks; only those ranks + //! take part in the collective, and the order defines the group-local rank (used to remap the root for root-based + //! ops). + //! \param groupSize The number of elements in the groups array. If groupSize is 0, all ranks participate and + //! groups can be nullptr. + //! \see IDistCollectiveLayer + //! + //! \return The new DistCollective layer, or nullptr if it could not be created. + //! + TRT_NODISCARD IDistCollectiveLayer* addDistCollective(ITensor& input, CollectiveOperation distCollectiveOp, + ReduceOperation reduceOp, int64_t root, int64_t* groups, int64_t groupSize) noexcept + { + return mImpl->addDistCollective(input, distCollectiveOp, reduceOp, root, groups, groupSize); + } + + //! + //! \brief Return the builder from which this INetworkDefinition was created. + //! + //! \see IBuilder::createNetworkV2 + //! + //! \return the builder + virtual IBuilder& getBuilder() const noexcept + { + return mImpl->getBuilder(); + } + + //! + //! \brief Mark weights as refittable when the builder flag kREFIT_INDIVIDUAL is set. + //! + //! \param name The name of the weights. + //! + //! \return True if the weights were successfully marked as refittable, false if the weights do not exist or cannot + //! be refitted. + //! + bool markWeightsRefittable(char const* name) noexcept + { + return mImpl->markWeightsRefittable(name); + } + + //! + //! \brief Unmark weights as refittable when the builder flag kREFIT_INDIVIDUAL is set. + //! + //! \param name The name of the weights. + //! + //! \return True if the weights were successfully marked as unrefittable, false if the weights do not exist. + //! + bool unmarkWeightsRefittable(char const* name) noexcept + { + return mImpl->unmarkWeightsRefittable(name); + } + + //! + //! \brief Whether the weight has been marked as refittable. + //! + //! \param name The name of the weights to check. + //! + //! \return True if the weights are marked as refittable, false if the weights do not exist or are marked as + //! non-refittable. + //! + bool areWeightsMarkedRefittable(char const* name) const noexcept + { + return mImpl->areWeightsMarkedRefittable(name); + } + + //! + //! \brief Add a squeeze layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param axes The axes to remove unit dimensions on. + //! + //! \see ISqueezeLayer + //! + //! Axes must be resolvable to a constant Int32 or Int64 1D shape tensor. + //! Values in axes must be unique and in the range of [-r, r-1], where r is the rank of the input tensor. + //! For each axis value, the corresponding dimension in the input tensor must be one. + //! + //! \return The new Squeeze layer, or nullptr if it could not be created. + //! + ISqueezeLayer* addSqueeze(ITensor& input, ITensor& axes) noexcept + { + return mImpl->addSqueeze(input, axes); + } + + //! + //! \brief Add an unsqueeze layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param axes The axes to add unit dimensions. + //! + //! \see IUnsqueezeLayer + //! + //! Axes must be resolvable to a constant Int32 or Int64 shape tensor. + //! Values in axes must be unique and in the range of [-r_final, r_final-1], where r_final + //! is the sum of rank(input) and len(axes). + //! + //! r_final must be less than Dims::MAX_DIMS. + //! + //! \return The new Unsqueeze layer, or nullptr if it could not be created + //! + IUnsqueezeLayer* addUnsqueeze(ITensor& input, ITensor& axes) noexcept + { + return mImpl->addUnsqueeze(input, axes); + } + + //! \brief Add a normalization layer to the network. + //! + //! \param input The input tensor to the layer. + //! \param scale The scale tensor used to scale the normalized output. + //! \param bias The bias tensor used to scale the normalized output. + //! \param axesMask The axes on which to perform mean calculations. + //! The bit in position i of bitmask axesMask corresponds to explicit dimension i of the result. + //! E.g., the least significant bit corresponds to the first explicit dimension and the next to least + //! significant bit corresponds to the second explicit dimension. + //! + //! The normalization layer works by performing normalization of the tensor \p input on the specified \p axesMask. + //! The result is then scaled by multiplying with \p scale and adding \p bias. + //! + //! The shapes of \p scale and \p bias are expected the be the same, and must have the same rank and be + //! unidirectionally broadcastable to the shape of \p input. In the case of InstanceNorm or GroupNorm, + //! the shapes of \p scale and \p bias are expected to be [1, C, 1, 1] in the case of a 4D NCHW input tensor. + //! + //! \see INormalizationLayer + //! + //! \return The new normalization layer, or nullptr if it could not be created. + //! + TRT_NODISCARD INormalizationLayer* addNormalizationV2(ITensor& input, ITensor& scale, ITensor& bias, uint32_t axesMask) noexcept + { + return mImpl->addNormalizationV2(input, scale, bias, axesMask); + } + +protected: + apiv::VNetworkDefinition* mImpl; +}; + +//! +//! \enum CalibrationAlgoType +//! +//! \brief Version of calibration algorithm to use. +//! +//! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. +//! +enum class CalibrationAlgoType : int32_t +{ + kLEGACY_CALIBRATION TRT_DEPRECATED_ENUM = 0, //!< Legacy calibration + kENTROPY_CALIBRATION TRT_DEPRECATED_ENUM = 1, //!< Legacy entropy calibration + kENTROPY_CALIBRATION_2 TRT_DEPRECATED_ENUM = 2, //!< Entropy calibration + kMINMAX_CALIBRATION TRT_DEPRECATED_ENUM = 3, //!< Minmax calibration +}; + +//! +//! Maximum number of elements in CalibrationAlgoType enum. +//! +//! \see DataType +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 4; +} + +//! +//! \class IInt8Calibrator +//! +//! \brief Application-implemented interface for calibration. +//! +//! Calibration is a step performed by the builder when deciding suitable scale factors for 8-bit inference. +//! +//! It must also provide a method for retrieving representative images which the calibration process can use to examine +//! the distribution of activations. It may optionally implement a method for caching the calibration result for reuse +//! on subsequent runs. +//! +//! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. +//! +class TRT_DEPRECATED IInt8Calibrator : public IVersionedInterface +{ +public: + //! + //! \brief Get the batch size used for calibration batches. + //! + //! \return The batch size. + //! + //! \deprecated Deprecated in TensorRT 10.0. Implicit batch support is removed in TensorRT 10.0. + //! + TRT_DEPRECATED virtual int32_t getBatchSize() const noexcept = 0; + + //! + //! \brief Get a batch of input for calibration. + //! + //! The batch size of the input must match the batch size returned by getBatchSize(). + //! + //! \param bindings An array of pointers to device memory that must be updated to point to device memory + //! containing each network input data. + //! \param names The names of the network input for each pointer in the binding array. + //! \param nbBindings The number of pointers in the bindings array. + //! + //! \return False if there are no more batches for calibration. + //! + //! \see getBatchSize() + //! + virtual bool getBatch(void* bindings[], char const* names[], int32_t nbBindings) noexcept = 0; + + //! + //! \brief Load a calibration cache. + //! + //! Calibration is potentially expensive, so it can be useful to generate the calibration data once, then use it on + //! subsequent builds of the network. The cache includes the regression cutoff and quantile values used to generate + //! it, and will not be used if these do not batch the settings of the current calibrator. However, the network + //! should also be recalibrated if its structure changes, or the input data set changes, and it is the + //! responsibility of the application to ensure this. + //! + //! \param length The length of the cached data, that should be set by the called function. If there is no data, + //! this should be zero. + //! + //! \return A pointer to the cache, or nullptr if there is no data. + //! + virtual void const* readCalibrationCache(std::size_t& length) noexcept = 0; + + //! + //! \brief Save a calibration cache. + //! + //! \param ptr A pointer to the data to cache. + //! \param length The length in bytes of the data to cache. + //! + //! \see readCalibrationCache() + //! + virtual void writeCalibrationCache(void const* ptr, std::size_t length) noexcept = 0; + + //! + //! \brief Get the algorithm used by this calibrator. + //! + //! \return The algorithm used by the calibrator. + //! + virtual CalibrationAlgoType getAlgorithm() noexcept = 0; + + ~IInt8Calibrator() noexcept override = default; +}; + +namespace v_1_0 +{ +class TRT_DEPRECATED IInt8EntropyCalibrator : public IInt8Calibrator +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"IInt8EntropyCalibrator", 1, 0}; + } + + //! + //! Signal that this is the entropy calibrator. + //! + CalibrationAlgoType getAlgorithm() noexcept override + { + return CalibrationAlgoType::kENTROPY_CALIBRATION; + } + + ~IInt8EntropyCalibrator() noexcept override = default; +}; +} // namespace v_1_0 + +//! +//! \class IInt8EntropyCalibrator +//! +//! \brief Entropy calibrator. +//! +//! This is the Legacy Entropy calibrator. It is less complicated than the legacy calibrator and +//! produces better results. +//! +//! \note To ensure compatibility of source code with future versions of TensorRT, use IEntropyCalibrator, not +//! v_1_0::IEntropyCalibrator +//! +//! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. +//! +using IInt8EntropyCalibrator = v_1_0::IInt8EntropyCalibrator; + +namespace v_1_0 +{ +class TRT_DEPRECATED IInt8EntropyCalibrator2 : public IInt8Calibrator +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"IInt8EntropyCalibrator2", 1, 0}; + } + + //! + //! Signal that this is the entropy calibrator 2. + //! + CalibrationAlgoType getAlgorithm() noexcept override + { + return CalibrationAlgoType::kENTROPY_CALIBRATION_2; + } + + ~IInt8EntropyCalibrator2() noexcept override = default; +}; +} // namespace v_1_0 + +//! +//! \class IInt8EntropyCalibrator2 +//! +//! \brief Entropy calibrator 2. +//! +//! This is the preferred calibrator. This is the required calibrator for DLA, as it supports per +//! activation tensor scaling. +//! +//! \note To ensure compatibility of source code with future versions of TensorRT, use IEntropyCalibrator2, not +//! v_1_0::IEntropyCalibrator2 +//! +//! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. +//! +using IInt8EntropyCalibrator2 = v_1_0::IInt8EntropyCalibrator2; + +namespace v_1_0 +{ +class TRT_DEPRECATED IInt8MinMaxCalibrator : public IInt8Calibrator +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"IInt8MinMaxCalibrator", 1, 0}; + } + + //! + //! Signal that this is the MinMax Calibrator. + //! + CalibrationAlgoType getAlgorithm() noexcept override + { + return CalibrationAlgoType::kMINMAX_CALIBRATION; + } + + ~IInt8MinMaxCalibrator() noexcept override = default; +}; +} // namespace v_1_0 + +//! +//! \class IInt8MinMaxCalibrator +//! +//! \brief MinMax Calibrator. +//! +//! It supports per activation tensor scaling. +//! +//! \note To ensure compatibility of source code with future versions of TensorRT, use IMinMaxCalibrator>, not +//! v_1_0::IMinMaxCalibrator +//! +//! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. +//! +using IInt8MinMaxCalibrator = v_1_0::IInt8MinMaxCalibrator; + +namespace v_1_0 +{ +class TRT_DEPRECATED IInt8LegacyCalibrator : public IInt8Calibrator +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"IInt8Calibrator", 1, 0}; + } + + //! + //! Signal that this is the legacy calibrator. + //! + CalibrationAlgoType getAlgorithm() noexcept override + { + return CalibrationAlgoType::kLEGACY_CALIBRATION; + } + + //! + //! \brief The quantile (between 0 and 1) that will be used to select the region maximum when the quantile method + //! is in use. + //! + //! See the user guide for more details on how the quantile is used. + //! + virtual double getQuantile() const noexcept = 0; + + //! + //! \brief The fraction (between 0 and 1) of the maximum used to define the regression cutoff when using regression + //! to determine the region maximum. + //! + //! See the user guide for more details on how the regression cutoff is used + //! + virtual double getRegressionCutoff() const noexcept = 0; + + //! + //! \brief Load a histogram. + //! + //! Histogram generation is potentially expensive, so it can be useful to generate the histograms once, then use + //! them when exploring the space of calibrations. The histograms should be regenerated if the network structure + //! changes, or the input data set changes, and it is the responsibility of the application to ensure this. + //! + //! \param length The length of the cached data, that should be set by the called function. If there is no data, + //! this should be zero. + //! + //! \return A pointer to the cache, or nullptr if there is no data. + //! + virtual void const* readHistogramCache(std::size_t& length) noexcept = 0; + + //! + //! \brief Save a histogram cache. + //! + //! \param ptr A pointer to the data to cache. + //! \param length The length in bytes of the data to cache. + //! + //! \see readHistogramCache() + //! + virtual void writeHistogramCache(void const* ptr, std::size_t length) noexcept = 0; + + ~IInt8LegacyCalibrator() noexcept override = default; +}; +} // namespace v_1_0 + +//! +//! \class IInt8LegacyCalibrator +//! +//! \brief Legacy calibrator. +//! +//! This calibrator requires user parameterization, +//! and is provided as a fallback option if the other calibrators yield poor results. +//! +//! \note To ensure compatibility of source code with future versions of TensorRT, use ILegacyCalibrator, not +//! v_1_0::ILegacyCalibrator +//! +//! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. +//! +using IInt8LegacyCalibrator = v_1_0::IInt8LegacyCalibrator; + +//! +//! \class IAlgorithmIOInfo +//! +//! \brief Carries information about input or output of the algorithm. +//! IAlgorithmIOInfo for all the input and output along with IAlgorithmVariant denotes the variation of algorithm +//! and can be used to select or reproduce an algorithm using IAlgorithmSelector::selectAlgorithms(). +//! \see IAlgorithmVariant, IAlgorithm, IAlgorithmSelector::selectAlgorithms() +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +//! \deprecated Deprecated in TensorRT 10.8. Please use editable mode in ITimingCache instead. +//! +class TRT_DEPRECATED IAlgorithmIOInfo : public INoCopy +{ +public: + //! + //! \brief Return DataType of the input/output of algorithm. + //! + //! \return the data type. + //! + DataType getDataType() const noexcept + { + return mImpl->getDataType(); + } + + //! + //! \brief Return strides of the input/output tensor of algorithm. + //! For vectorized formats, strides are given in units of vectors. + //! + //! \return the strides of the tensor. + //! + Dims getStrides() const noexcept + { + return mImpl->getStrides(); + } + + //! + //! \brief Return the index of the vectorized dimension or -1 for non-vectorized formats. + //! + //! \return the index of the vectorized dimension. + //! + int64_t getVectorizedDim() const noexcept + { + return mImpl->getVectorizedDim(); + } + + //! + //! \brief Return the number of components per element. + //! This is always 1 for non-vectorized formats. + //! + //! \return the number of components per element. + //! + int64_t getComponentsPerElement() const noexcept + { + return mImpl->getComponentsPerElement(); + } + +protected: + virtual ~IAlgorithmIOInfo() noexcept = default; + apiv::VAlgorithmIOInfo* mImpl; +}; + +//! +//! \class IAlgorithmVariant +//! +//! \brief provides a unique 128-bit identifier, which along with the input and output information +//! denotes the variation of algorithm and can be used to select or reproduce an algorithm, +//! using IAlgorithmSelector::selectAlgorithms() +//! \see IAlgorithmIOInfo, IAlgorithm, IAlgorithmSelector::selectAlgorithms() +//! \note A single implementation can have multiple tactics. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +//! \deprecated Deprecated in TensorRT 10.8. Please use editable mode in ITimingCache instead. +//! +class TRT_DEPRECATED IAlgorithmVariant : public INoCopy +{ +public: + //! + //! \brief Return implementation of the algorithm. + //! + int64_t getImplementation() const noexcept + { + return mImpl->getImplementation(); + } + + //! + //! \brief Return tactic of the algorithm. + //! + int64_t getTactic() const noexcept + { + return mImpl->getTactic(); + } + +protected: + virtual ~IAlgorithmVariant() noexcept = default; + apiv::VAlgorithmVariant* mImpl; +}; + +//! +//! \class IAlgorithmContext +//! +//! \brief Describes the context and requirements, that could be fulfilled by one or more instances of IAlgorithm. +//! \see IAlgorithm +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +//! \deprecated Deprecated in TensorRT 10.8. Please use editable mode in ITimingCache instead. +//! +class TRT_DEPRECATED IAlgorithmContext : public INoCopy +{ +public: + //! + //! \brief Return name of the algorithm node. + //! + //! This is a unique identifier for the IAlgorithmContext. + //! + char const* getName() const noexcept + { + return mImpl->getName(); + } + + //! + //! \brief Get the minimum / optimum / maximum dimensions for input or output tensor. + //! + //! \param index Index of the input or output of the algorithm. Incremental numbers assigned to indices of inputs + //! and the outputs. + //! \param select Which of the minimum, optimum, or maximum dimensions to be queried. + //! + Dims getDimensions(int32_t index, OptProfileSelector select) const noexcept + { + return mImpl->getDimensions(index, select); + } + + //! + //! \brief Return number of inputs of the algorithm. + //! + int32_t getNbInputs() const noexcept + { + return mImpl->getNbInputs(); + } + + //! + //! \brief Return number of outputs of the algorithm. + //! + int32_t getNbOutputs() const noexcept + { + return mImpl->getNbOutputs(); + } + +protected: + virtual ~IAlgorithmContext() noexcept = default; + apiv::VAlgorithmContext* mImpl; +}; + +//! +//! \class IAlgorithm +//! +//! \brief Describes a variation of execution of a layer. +//! An algorithm is represented by IAlgorithmVariant and the IAlgorithmIOInfo for each of its inputs and outputs. +//! An algorithm can be selected or reproduced using AlgorithmSelector::selectAlgorithms(). +//! +//! \see IAlgorithmIOInfo, IAlgorithmVariant, IAlgorithmSelector::selectAlgorithms() +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +//! \deprecated Deprecated in TensorRT 10.8. Please use editable mode in ITimingCache instead. +//! +class TRT_DEPRECATED IAlgorithm : public INoCopy +{ +public: + //! + //! \brief Returns the algorithm variant. + //! + IAlgorithmVariant const& getAlgorithmVariant() const noexcept + { + return mImpl->getAlgorithmVariant(); + } + + //! + //! \brief The time in milliseconds to execute the algorithm. + //! + float getTimingMSec() const noexcept + { + return mImpl->getTimingMSec(); + } + + //! + //! \brief The size of the GPU temporary memory in bytes which the algorithm uses at execution time. + //! + std::size_t getWorkspaceSize() const noexcept + { + return mImpl->getWorkspaceSize(); + } + + //! + //! \brief Returns the format of an Algorithm input or output. Algorithm inputs are incrementally numbered first, + //! followed by algorithm outputs. + //! + //! \param index Index of the input or output of the algorithm. Incremental numbers assigned to indices of inputs + //! and the outputs. + //! + //! \return a pointer to a IAlgorithmIOInfo interface or nullptr if index is out of range. + //! + IAlgorithmIOInfo const* getAlgorithmIOInfoByIndex(int32_t index) const noexcept + { + return mImpl->getAlgorithmIOInfoByIndex(index); + } + +protected: + virtual ~IAlgorithm() noexcept = default; + apiv::VAlgorithm* mImpl; +}; // IAlgorithm + +namespace v_1_0 +{ +class TRT_DEPRECATED IAlgorithmSelector : public IVersionedInterface +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"IAlgorithmSelector", 1, 0}; + } + //! + //! \brief Select Algorithms for a layer from the given list of algorithm choices. + //! + //! \return The number of choices selected from [0, nbChoices-1]. + //! \param context The context for which the algorithm choices are valid. + //! \param choices The list of algorithm choices to select for implementation of this layer. + //! \param nbChoices Number of algorithm choices. + //! \param selection The user writes indices of selected choices in to selection buffer which is of size nbChoices. + //! + //! \note TensorRT uses its default algorithm selection to choose from the list provided. + //! If return value is 0, TensorRT's default algorithm selection is used unless + //! BuilderFlag::kREJECT_EMPTY_ALGORITHMS is set. + //! The list of choices is valid only for this specific algorithm context. + //! + virtual int32_t selectAlgorithms(IAlgorithmContext const& context, IAlgorithm const* const* choices, + int32_t nbChoices, int32_t* selection) noexcept = 0; + + //! + //! \brief Called by TensorRT to report choices it made. + //! + //! \note For a given optimization profile, this call comes after all calls to selectAlgorithms. + //! algoChoices[i] is the choice that TensorRT made for algoContexts[i], for i in [0, nbAlgorithms-1] + //! + //! \param algoContexts The list of all algorithm contexts. + //! \param algoChoices The list of algorithm choices made by TensorRT + //! \param nbAlgorithms The size of algoContexts as well as algoChoices. + //! + virtual void reportAlgorithms(IAlgorithmContext const* const* algoContexts, IAlgorithm const* const* algoChoices, + int32_t nbAlgorithms) noexcept = 0; + + virtual ~IAlgorithmSelector() noexcept = default; +}; +} // namespace v_1_0 + +//! +//! \class IAlgorithmSelector +//! +//! \brief Interface implemented by application for selecting and reporting algorithms of a layer provided by the +//! builder. +//! \note A layer in context of algorithm selection may be different from ILayer in INetworkDefinition. +//! For example, an algorithm might be implementing a conglomeration of multiple ILayers in INetworkDefinition. +//! \note To ensure compatibility of source code with future versions of TensorRT, use IAlgorithmSelector, not +//! v_1_0::IAlgorithmSelector +//! +//! \deprecated Deprecated in TensorRT 10.8. Please use editable mode in ITimingCache instead. +//! +using IAlgorithmSelector = v_1_0::IAlgorithmSelector; + +//! +//! \brief Represents one or more QuantizationFlag values using binary OR +//! operations. +//! +//! \see IBuilderConfig::getQuantizationFlags(), IBuilderConfig::setQuantizationFlags() +//! +using QuantizationFlags = uint32_t; + +//! +//! \enum QuantizationFlag +//! +//! \brief List of valid flags for quantizing the network to int8 +//! +//! \see IBuilderConfig::setQuantizationFlag(), IBuilderConfig::getQuantizationFlag() +//! +//! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. +//! +enum class QuantizationFlag : int32_t +{ + //! Run int8 calibration pass before layer fusion. Only valid for IInt8LegacyCalibrator and + //! IInt8EntropyCalibrator. The builder always runs the int8 calibration pass before layer fusion for + //! IInt8MinMaxCalibrator and IInt8EntropyCalibrator2. Disabled by default. + kCALIBRATE_BEFORE_FUSION TRT_DEPRECATED_ENUM = 0 +}; + +//! +//! Maximum number of quantization flags in QuantizationFlag enum. +//! +//! \see QuantizationFlag +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 1; +} + +//! +//! \enum RuntimePlatform +//! +//! \brief Describes the intended runtime platform (operating system and CPU architecture) for the execution of the +//! TensorRT engine. TensorRT provides support for cross-platform engine compatibility when the target runtime +//! platform is different from the build platform. +//! +//! \note The cross-platform engine will not be able to run on the host platform it was built on. +//! +//! \note When building a cross-platform engine that also requires version forward compatibility, +//! kEXCLUDE_LEAN_RUNTIME must be set to exclude the target platform lean runtime. +//! +//! \note The cross-platform engine might have performance differences compared to the natively built engine on the +//! target platform. +//! +//! \see IBuilderConfig::setRuntimePlatform(), IBuilderConfig::getRuntimePlatform() +//! +enum class RuntimePlatform : int32_t +{ + //! No requirement for cross-platform compatibility. The engine constructed by TensorRT can only run on the + //! identical platform it was built on. + kSAME_AS_BUILD = 0, + + //! Designates the target platform for engine execution as Windows AMD64 system. Currently this flag can only be + //! enabled when building engines on Linux AMD64 platforms. + kWINDOWS_AMD64 = 1, + + +}; + +namespace impl +{ +//! +//! Maximum number of elements in RuntimePlatform enum. +//! +//! \see RuntimePlatform +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 2; +}; +} // namespace impl + +//! +//! \brief Represents one or more BuilderFlag values using binary OR +//! operations, e.g., 1U << BuilderFlag::kFP16 | 1U << BuilderFlag::kDEBUG. +//! +//! \see IBuilderConfig::setFlags(), IBuilderConfig::getFlags() +//! +using BuilderFlags = uint32_t; + +//! +//! \enum BuilderFlag +//! +//! \brief List of valid modes that the builder can enable when creating an engine from a network definition. +//! +//! \see IBuilderConfig::setFlags(), IBuilderConfig::getFlags() +//! +enum class BuilderFlag : int32_t +{ + //! Enable FP16 layer selection, with FP32 fallback. + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + kFP16 TRT_DEPRECATED_ENUM = 0, + + //! Enable Int8 layer selection, with FP32 fallback with FP16 fallback if kFP16 also specified. + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + kINT8 TRT_DEPRECATED_ENUM = 1, + + //! Enable debugging of layers via synchronizing after every layer. + kDEBUG = 2, + + //! Enable layers marked to execute on GPU if layer cannot execute on DLA. + kGPU_FALLBACK = 3, + + //! Enable building a refittable engine. + kREFIT = 4, + + //! Disable reuse of timing information across identical layers. + kDISABLE_TIMING_CACHE = 5, + + //! Allow (but not require) computations on tensors of type DataType::kFLOAT to use TF32. + //! TF32 computes inner products by rounding the inputs to 10-bit mantissas before + //! multiplying, but accumulates the sum using 23-bit mantissas. Enabled by default. + kTF32 = 6, + + //! Allow the builder to examine weights and use optimized functions when weights have suitable sparsity. + kSPARSE_WEIGHTS = 7, + + //! Change the allowed parameters in the EngineCapability::kSTANDARD flow to + //! match the restrictions that EngineCapability::kSAFETY check against for DeviceType::kGPU + //! and EngineCapability::kDLA_STANDALONE check against the DeviceType::kDLA case. This flag + //! is forced to true if EngineCapability::kSAFETY at build time if it is unset. + //! + //! This flag is only supported in NVIDIA Drive(R) products. + //! + //! \deprecated Deprecated in TensorRT 10.16. + //! In EngineCapability::kSTANDARD flow, safety restrictions are no longer supported. + //! In EngineCapability::kSAFETY and EngineCapability::kDLA_STANDALONE flows, restrictions are enforced natively. + //! This flag is retained for API compatibility but is ignored. + kSAFETY_SCOPE TRT_DEPRECATED_ENUM = 8, + + //! Require that layers execute in specified precisions. Build fails otherwise. + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + kOBEY_PRECISION_CONSTRAINTS TRT_DEPRECATED_ENUM = 9, + + //! Prefer that layers execute in specified precisions. + //! Fall back (with warning) to another precision if build would otherwise fail. + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + kPREFER_PRECISION_CONSTRAINTS TRT_DEPRECATED_ENUM = 10, + + //! Require that no reformats be inserted between a layer and a network I/O tensor + //! for which ITensor::setAllowedFormats was called. + //! Build fails if a reformat is required for functional correctness. + //! \deprecated Deprecated in TensorRT 10.7. Unneeded API. + kDIRECT_IO TRT_DEPRECATED_ENUM = 11, + + //! Fail if IAlgorithmSelector::selectAlgorithms returns an empty set of algorithms. + //! \deprecated Deprecated in TensorRT 10.10. Unneeded API due to IAlgorithmSelector deprecation. + kREJECT_EMPTY_ALGORITHMS TRT_DEPRECATED_ENUM = 12, + + //! Restrict to lean runtime operators to provide version forward compatibility + //! for the plan. + //! + //! This flag is only supported by NVIDIA Volta and later GPUs. + //! This flag is not supported in NVIDIA Drive(R) products. + kVERSION_COMPATIBLE = 13, + + //! Exclude lean runtime from the plan when version forward compatability is enabled. + //! By default, this flag is unset, so the lean runtime will be included in the plan. + //! + //! If BuilderFlag::kVERSION_COMPATIBLE is not set then the value of this flag will be ignored. + kEXCLUDE_LEAN_RUNTIME = 14, + + //! Enable plugins with FP8 input/output. + //! This flag is not supported when HardwareCompatibilityLevel::kAMPERE_PLUS is enabled. + //! \see HardwareCompatibilityLevel + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + kFP8 TRT_DEPRECATED_ENUM = 15, + + //! Emit error when a tactic being timed is not present in the timing cache. + //! This flag has an effect only when IBuilderConfig has an associated ITimingCache. + kERROR_ON_TIMING_CACHE_MISS = 16, + + //! Enable DataType::kBF16 layer selection, with FP32 fallback. + //! This flag is only supported by NVIDIA Ampere and later GPUs. + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + kBF16 TRT_DEPRECATED_ENUM = 17, + + //! Disable caching of JIT-compilation results during engine build. + //! By default, JIT-compiled code will be serialized as part of the timing cache, which may significantly increase + //! the cache size. Setting this flag prevents the code from being serialized. This flag has an effect only when + //! BuilderFlag::DISABLE_TIMING_CACHE is not set. + kDISABLE_COMPILATION_CACHE = 18, + + //! Strip the refittable weights from the engine plan file. + kSTRIP_PLAN = 19, + + //! \deprecated Deprecated in TensorRT 10.0. Superseded by kSTRIP_PLAN. + kWEIGHTLESS TRT_DEPRECATED_ENUM = kSTRIP_PLAN, + + //! Create a refittable engine under the assumption that the refit weights will be identical to those provided at + //! build time. The resulting engine will have the same performance as a non-refittable one. All refittable weights + //! can be refitted through the refit API, but if the refit weights are not identical to the build-time weights, + //! behavior is undefined. When used alongside 'kSTRIP_PLAN', this flag will result in a small plan file for which + //! weights are later supplied via refitting. This enables use of a single set of weights with different inference + //! backends, or with TensorRT plans for multiple GPU architectures. + kREFIT_IDENTICAL = 20, + + //! + //! \brief Enable weight streaming for the current engine. + //! + //! Weight streaming from the host enables execution of models that do not fit + //! in GPU memory by allowing TensorRT to intelligently stream network weights + //! from the CPU DRAM. Please see ICudaEngine::getMinimumWeightStreamingBudget + //! for the default memory budget when this flag is enabled. + //! + //! Enabling this feature changes the behavior of + //! IRuntime::deserializeCudaEngine to allocate the entire network's weights + //! on the CPU DRAM instead of GPU memory. Then, + //! ICudaEngine::createExecutionContext will determine the optimal split of + //! weights between the CPU and GPU and place weights accordingly. + //! + //! Future TensorRT versions may enable this flag by default. + //! + //! \warning Enabling this flag may marginally increase build time. + //! + //! \warning Enabling this feature will significantly increase the latency of + //! ICudaEngine::createExecutionContext. + //! + //! \see IRuntime::deserializeCudaEngine, + //! ICudaEngine::getMinimumWeightStreamingBudget, + //! ICudaEngine::setWeightStreamingBudget + //! + kWEIGHT_STREAMING = 21, + + //! Enable plugins with INT4 input/output. + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + kINT4 TRT_DEPRECATED_ENUM = 22, + + //! Enable building a refittable engine and provide fine-grained control. This allows + //! control over which weights are refittable or not using INetworkDefinition::markWeightsRefittable and + //! INetworkDefinition::unmarkWeightsRefittable. By default, all weights are non-refittable when this flag is + //! enabled. This flag cannot be used together with kREFIT or kREFIT_IDENTICAL. + kREFIT_INDIVIDUAL = 23, + + //! Disable floating-point optimizations: 0*x => 0, x-x => 0, or x/x => 1. These identities are + //! not true when x is a NaN or Inf, and thus might hide propagation or generation of NaNs. This flag is typically + //! used in combination with kSPARSE_WEIGHTS. + //! There are three valid sparsity configurations. + //! 1. Disable all sparsity. Both kSPARSE_WEIGHTS and kSTRICT_NANS are unset + //! 2. Enable sparsity only where it does not affect propagation/generation of NaNs. Both kSPARSE_WEIGHTS and + //! kSTRICT_NANS are set + //! 3. Enable all sparsity. kSPARSE_WEIGHTS is set and kSTRICT_NANS is unset + kSTRICT_NANS = 24, + + //! Enable memory monitor during build time. + kMONITOR_MEMORY = 25, + + //! Enable plugins with FP4 input/output. + //! \deprecated Deprecated in TensorRT 10.12. Superseded by strong typing. + kFP4 TRT_DEPRECATED_ENUM = 26, + + //! Enable editable timing cache. + kEDITABLE_TIMING_CACHE = 27, + + //! Enable distributive independence. + //! When BuilderFlag::kDISTRIBUTIVE_INDEPENDENCE is set and a layer documents axis i of an output as a distributive + //! axis, then the layer behaves exactly as if each evaluation across axis i was done using identical operations. + //! The definition of distributive axis is as follows: + //! For IMatrixMultiplyLayer: + //! All axes that are not one of the vector or matrix dimensions are distributive axes. + //! For layers that perform reduction: + //! All non-reduction axes are distributive axes. + //! For layers that perform einsum: + //! Let n be the leftmost reduction axis. The axes to the left of n are distributive axes. + kDISTRIBUTIVE_INDEPENDENCE = 28, + +#if ENABLE_FEATURE_DISABLE_RUNTIME_ALLOCATION + //! Build an engine that requires user allocation when creating an execution context. + //! This means that runtime allocation will not be enabled even when the tensor dimensions + //! exceed the limits for static allocation, and ensures that inference will support graph + //! capture unless the network includes operations such as data-dependent dynamic shapes + //! (INonZeroLayer, ITripLimitLayer, etc.) that require runtime allocation. If such operations + //! are present, the engine build will fail with an error message. + kREQUIRE_USER_ALLOCATION = 29, +#endif // ENABLE_FEATURE_DISABLE_RUNTIME_ALLOCATION + +}; + +//! +//! Maximum number of builder flags in BuilderFlag enum. +//! +//! \see BuilderFlag +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ +#if ENABLE_FEATURE_DISABLE_RUNTIME_ALLOCATION + return 30; +#else + return 29; +#endif // ENABLE_FEATURE_DISABLE_RUNTIME_ALLOCATION +} + +namespace v_1_0 +{ +//! +//! \struct TimingCacheKey +//! +//! \brief The key to retrieve timing cache entries. +//! +//! TimingCacheKey has two types of representation: binary and string. The conversion rule from binary to string is: +//! 1) Convert each uint8_t element in binary key into two hexadecimal ascii chars, e.g. 0xab -> "ab" +//! 2) Concat the ascii chars of all elements in sequence. The result should have exact 32 chars +//! 3) Add prefix "0x" to the string produced in step 2. +//! +//! \see ITimingCache::query(), ITimingCache::update() +//! +struct TimingCacheKey +{ + uint8_t data[16]; +}; + +//! +//! \struct Value +//! +//! \brief The values in the cache entry. +//! +//! \see ITimingCache::query(), ITimingCache::update() +//! +struct TimingCacheValue +{ + //! Hash of the selected tactic. + uint64_t tacticHash; + //! Timing of this tactic in milliseconds. Negative numbers and NaN are invalid values. + float timingMSec; + //! UINT64_MAX represents the invalid tactic hash. + static constexpr uint64_t kINVALID_TACTIC_HASH = UINT64_MAX; +}; +} // namespace v_1_0 + +//! +//! \class ITimingCache +//! +//! \brief Class to handle tactic timing info collected from builder. +//! +//! The timing cache is created or initialized by IBuilderConfig. It can be shared across builder instances +//! to reduce the builder wallclock time. +//! +//! \warning Rebuilding the same engine multiple times using the same timing cache will always yield a correct +//! engine but the selected tactics and formats may vary between generated engine instances, if weak typing is used. +//! +//! \see IBuilderConfig +//! +class ITimingCache : public INoCopy +{ +public: + virtual ~ITimingCache() noexcept = default; + + //! + //! \brief Serialize a timing cache to IHostMemory object. + //! + //! This function allows serialization of current timing cache. + //! + //! \return A pointer to a IHostMemory object that contains a serialized timing cache. + //! + //! \see IHostMemory + //! + nvinfer1::IHostMemory* serialize() const noexcept + { + return mImpl->serialize(); + } + + //! + //! \brief Combine input timing cache into local instance. + //! + //! This function allows combining entries in the input timing cache to local cache object. + //! + //! \param inputCache The input timing cache. + //! \param ignoreMismatch Whether or not to allow cache verification header mismatch. + //! + //! \return True if combined successfully, false otherwise. + //! + //! Append entries in input cache to local cache. Conflicting entries will be skipped + //! The input cache must be generated by a TensorRT build of exact same version, otherwise + //! combine will be skipped and return false. + //! ignoreMismatch must be set to true if combining a timing cache created from a + //! different device. + //! + //! \warning Combining caches generated from devices with different device properties may + //! lead to functional/performance bugs! + //! + bool combine(ITimingCache const& inputCache, bool ignoreMismatch) noexcept + { + return mImpl->combine(inputCache, ignoreMismatch); + } + + //! + //! \brief Empty the timing cache + //! + //! \return True if reset successfully, false otherwise. + //! + bool reset() noexcept + { + return mImpl->reset(); + } + + //! + //! \brief Query cache keys from Timing Cache. + //! + //! This function queries the entry count and writes the keys out. + //! + //! \param keyBuffer The buffer to store keys. + //! \param capacity The capacity of the buffer. + //! + //! \return The count of entries in the cache and fill keys if keyBuffer is non-null. + //! If an error occurs, -1 will be returned. + //! + //! Query the count of entries in the cache and write out cache keys if keyBuffer is provided. + //! Any key entries exceeding the capacity of the keyBuffer will not be copied. + //! + int64_t queryKeys(TimingCacheKey* keyBuffer, int64_t capacity) const noexcept + { + return mImpl->queryKeys(keyBuffer, capacity); + } + + //! + //! \brief Query value in a cache entry. + //! + //! The function queries the value in a specific cache entry. + //! + //! \param key The query key. + //! + //! \return Cache value if the key exists, otherwise an invalid value. + //! + //! Query the value of the given cache key. If the key exists, write the value out, + //! otherwise return an invalid value. + //! + TimingCacheValue query(TimingCacheKey const& key) const noexcept + { + return mImpl->query(key); + } + + //! + //! \brief Update values in a cache entry. + //! + //! The function updates the value in a specific cache entry. + //! + //! \param key The key to the entry to be updated. + //! \param value New cache value. + //! + //! \return True if update succeeds, otherwise false. + //! + //! Update the value of the given cache key. If the key does not exist, return false. + //! If the key exists and the new tactic timing is NaN, delete the cache entry and + //! return true. If tactic timing is not NaN and the new value is valid, override the + //! cache value and return true. False is returned when the new value is invalid. + //! If this layer cannot use the new tactic, build errors will be reported when + //! building the next engine. + //! + bool update(TimingCacheKey const& key, TimingCacheValue const& value) noexcept + { + return mImpl->update(key, value); + } + +protected: + apiv::VTimingCache* mImpl; +}; + +//! +//! \enum MemoryPoolType +//! +//! \brief The type for memory pools used by TensorRT. +//! +//! \see IBuilderConfig::setMemoryPoolLimit, IBuilderConfig::getMemoryPoolLimit +//! +enum class MemoryPoolType : int32_t +{ + //! + //! kWORKSPACE is used by TensorRT to store intermediate buffers within an operation. + //! This defaults to max device memory. Set to a smaller value to restrict tactics that use over the + //! threshold en masse. For more targeted removal of tactics use the IAlgorithmSelector + //! interface. + //! + kWORKSPACE = 0, + + //! + //! kDLA_MANAGED_SRAM is a fast software managed RAM used by DLA to communicate within a layer. + //! The size of this pool must be at least 4 KiB and must be a power of 2. + //! This defaults to 1 MiB. + //! Orin has capacity of 1 MiB per core. + //! + kDLA_MANAGED_SRAM = 1, + + //! + //! kDLA_LOCAL_DRAM is host RAM used by DLA to share intermediate tensor data across operations. + //! The size of this pool must be at least 4 KiB and must be a power of 2. + //! This defaults to 1 GiB. + //! + kDLA_LOCAL_DRAM = 2, + + //! + //! kDLA_GLOBAL_DRAM is host RAM used by DLA to store weights and metadata for execution. + //! The size of this pool must be at least 4 KiB and must be a power of 2. + //! This defaults to 512 MiB. + //! + kDLA_GLOBAL_DRAM = 3, + + //! + //! kTACTIC_DRAM is the device DRAM used by the optimizer to + //! run tactics. On embedded devices, where host and device memory are unified, this includes all host + //! memory required by TensorRT to build the network up to the point of each memory allocation. + //! This defaults to 75% of totalGlobalMem as reported by cudaGetDeviceProperties when + //! cudaGetDeviceProperties.embedded is true, and 100% otherwise. + //! + kTACTIC_DRAM = 4, + + //! + //! kTACTIC_SHARED_MEMORY defines the maximum sum of shared memory reserved by the driver and + //! used for executing CUDA kernels. Adjust this value to restrict tactics that exceed the + //! specified threshold en masse. The default value is device max capability. This value must + //! be less than 1GiB. + //! + //! The driver reserved shared memory can be queried from cuDeviceGetAttribute(&reservedShmem, + //! CU_DEVICE_ATTRIBUTE_RESERVED_SHARED_MEMORY_PER_BLOCK). + //! + //! Updating this flag will override the shared memory limit set by \ref HardwareCompatibilityLevel, + //! which defaults to 48KiB - reservedShmem. + //! + kTACTIC_SHARED_MEMORY = 5, +}; + +//! +//! Maximum number of memory pool types in the MemoryPoolType enum. +//! +//! \see MemoryPoolType +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 6; +} + +//! +//! \enum PreviewFeature +//! +//! \brief Define preview features +//! +//! Preview Features have been fully tested but are not yet as stable as other features in TensorRT. +//! They are provided as opt-in features for at least one release. +//! +enum class PreviewFeature : int32_t +{ + //! + //! Allows optimization profiles to be shared across execution contexts. + //! + //! \deprecated Deprecated in TensorRT 10.0. The default value for this flag is on and can not be changed. + //! + kPROFILE_SHARING_0806 TRT_DEPRECATED_ENUM = 0, + + //! + //! Allows plugin I/O to be aliased when using IPluginV3OneBuildV2 + //! + kALIASED_PLUGIN_IO_10_03 = 1, + + //! + //! Allows IExecutionContext::updateDeviceMemorySizeForShapes to resize runner internal activation memory. + //! Using this feature can reduce runtime memory requirement when the actual input tensor shapes are smaller than + //! the maximum input tensor dimensions. + //! + kRUNTIME_ACTIVATION_RESIZE_10_10 = 2, + + //! + //! Enabling multi-device mode in TRT. + //! Allows building an engine that contains multi-device enabled nodes, + //! such as IDistCollective. + //! + //! \note: The preview flag must be set if there are any layers in the + //! INetworkDefinition that need multi-device capabilities. Otherwise, an engine cannot be built. + kMULTIDEVICE_RUNTIME_10_16 = 3 +}; + +namespace impl +{ +//! +//! Maximum number of elements in PreviewFeature enum. +//! +//! \see PreviewFeature +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 4; +}; +} // namespace impl + +//! +//! \enum HardwareCompatibilityLevel +//! +//! \brief Describes requirements of compatibility with GPU architectures other than that of the GPU on which the engine +//! was built. +//! +//! \warning Note that compatibility with future hardware depends on CUDA forward compatibility support. +//! +enum class HardwareCompatibilityLevel : int32_t +{ + //! Do not require hardware compatibility with GPU architectures other than that of the GPU on which the engine was + //! built. + kNONE = 0, + + //! Require that the engine is compatible with Ampere and newer GPUs. This will limit the combined usage of driver + //! reserved and backend kernel max shared memory to 48KiB, may reduce the number of available tactics for each + //! layer, and may prevent some fusions from occurring. Thus this can decrease the performance, especially for tf32 + //! models. + //! This option will disable cuDNN, cuBLAS, and cuBLASLt as tactic sources. + //! + //! This option is only supported for engines built on NVIDIA Ampere and later GPUs. + //! + //! The driver reserved shared memory can be queried from cuDeviceGetAttribute(&reservedShmem, + //! CU_DEVICE_ATTRIBUTE_RESERVED_SHARED_MEMORY_PER_BLOCK). + //! + kAMPERE_PLUS = 1, + + //! Require that the engine is compatible with GPUs that have the same Compute Capability + //! (https://developer.nvidia.com/cuda-gpus) as the one it was built on. This may decrease the performance compared + //! to an engine with no compatibility. + //! + //! This option will disable cuDNN, cuBLAS, and cuBLASLt as tactic sources. + //! + //! This option is only supported for engines built on NVIDIA Turing and later GPUs. + //! + kSAME_COMPUTE_CAPABILITY = 2, +}; + +namespace impl +{ +//! +//! Maximum number of elements in HardwareCompatibilityLevel enum. +//! +//! \see HardwareCompatibilityLevel +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 3; +}; +} // namespace impl + + +//! +//! \enum TilingOptimizationLevel +//! +//! \brief Define the optimization levels for Tiling +//! +//! TensorRT will try tiling optimization for on-chip caching if non-zero level is set. +//! This level determines how much effort TensorRT would take to find a better solution for performance. +//! +enum class TilingOptimizationLevel : int32_t +{ + //! Do not apply any tiling strategy. + kNONE = 0, + + //! Use a fast algorithm and heuristic based strategy. Slightly increases engine build time. + kFAST = 1, + + //! Increase search space and use a mixed heuristic/profiling strategy. + //! Moderately increases engine build time. + kMODERATE = 2, + + //! Increase search space even wider. Significantly increases engine build time. + kFULL = 3 + +}; + +namespace impl +{ +//! +//! Maximum number of elements in TilingOptimizationLevel enum. +//! +//! \see TilingOptimizationLevel +//! +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 4; +}; +} // namespace impl + +namespace v_1_0 +{ +class IProgressMonitor : public IVersionedInterface +{ +public: + IProgressMonitor() = default; + virtual ~IProgressMonitor() noexcept = default; + + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"IProgressMonitor", 1, 0}; + } + + //! + //! \brief Signal that a phase of the optimizer has started. + //! + //! \param phaseName The name of this phase for tracking purposes. + //! \param parentPhase The parent phase that this phase belongs to, or nullptr if there is no parent. + //! \param nbSteps The number of steps that are involved in this phase. + //! + //! The phaseStart function signals to the application that the current phase is beginning, and that it has a + //! certain number of steps to perform. If \p phaseParent is nullptr, then the phaseStart is beginning an + //! independent phase, and if \p phaseParent is specified, then the current phase, specified by \p phaseName, is + //! within the scope of the parent phase. \p nbSteps will always be a positive number. The phaseStart function + //! implies that the first step is being executed. TensorRT will signal when each step is complete. + //! + //! Phase names are human readable English strings which are unique within a single phase hierarchy but which can be + //! reused once the previous instance has completed. Phase names and their hierarchies may change between versions + //! of TensorRT. + //! + //! \see phaseFinish + //! + virtual void phaseStart(char const* phaseName, char const* parentPhase, int32_t nbSteps) noexcept = 0; + + //! + //! \brief Signal that a step of an optimizer phase has finished. + //! + //! \param phaseName The name of the innermost phase being executed. + //! \param step The step number that was completed. + //! + //! The stepComplete function signals to the application that TensorRT has finished the current \p step for the + //! phase \p phaseName, and will move onto the next step if there is one. The application can return false for + //! TensorRT to exit the build early. The step value will increase on subsequent calls in the range [0, nbSteps). + //! + //! \return true to continue to the next step or false to stop the build. + //! + virtual bool stepComplete(char const* phaseName, int32_t step) noexcept = 0; + + //! + //! \brief Signal that a phase of the optimizer has finished. + //! + //! \param phaseName The name of the phase that has finished. + //! + //! The phaseFinish function signals to the application that the phase is complete. This function may be called + //! before all steps in the range [0, nbSteps) have been reported to stepComplete. This scenario can be triggered by + //! error handling, internal optimizations, or when stepComplete returns false to request cancellation of the build. + //! + //! \see phaseStart + //! + virtual void phaseFinish(char const* phaseName) noexcept = 0; + +}; // class IProgressMonitor +} // namespace v_1_0 + +//! +//! \class IProgressMonitor +//! +//! \brief Application-implemented progress reporting interface for TensorRT. +//! +//! The IProgressMonitor is a user-defined object that TensorRT uses to report back when an internal algorithm has +//! started or finished a phase to help provide feedback on the progress of the optimizer. +//! +//! The IProgressMonitor will trigger its start function when a phase is entered and will trigger its finish function +//! when that phase is exited. Each phase consists of one or more steps. When each step is completed, the stepComplete +//! function is triggered. This will allow an application using the builder to communicate progress relative to when the +//! optimization step is expected to complete. +//! +//! The implementation of IProgressMonitor must be thread-safe so that it can be called from multiple internal threads. +//! The lifetime of the IProgressMonitor must exceed the lifetime of all TensorRT objects that use it. +//! +//! \note To ensure compatibility of source code with future versions of TensorRT, use IProgressMonitor, not +//! v_1_0::IProgressMonitor +//! +using IProgressMonitor = v_1_0::IProgressMonitor; + +//! +//! \class IBuilderConfig +//! +//! \brief Holds properties for configuring a builder to produce an engine. +//! +//! \see BuilderFlags +//! +class IBuilderConfig : public INoCopy +{ +public: + virtual ~IBuilderConfig() noexcept = default; + + //! + //! \brief Set the number of averaging iterations used when timing layers. + //! + //! When timing layers, the builder minimizes over a set of average times for layer execution. This parameter + //! controls the number of iterations used in averaging. + //! + //! \see getAvgTimingIterations() + //! + virtual void setAvgTimingIterations(int32_t avgTiming) noexcept + { + mImpl->setAvgTimingIterations(avgTiming); + } + + //! + //! \brief Query the number of averaging iterations. + //! + //! By default the number of averaging iterations is 1. + //! + //! \see setAvgTimingIterations() + //! + int32_t getAvgTimingIterations() const noexcept + { + return mImpl->getAvgTimingIterations(); + } + + //! + //! \brief Configure the builder to target specified EngineCapability flow. + //! + //! The flow means a sequence of API calls that allow an application to set up a runtime, engine, + //! and execution context in order to run inference. + //! + //! The supported flows are specified in the EngineCapability enum. + //! + void setEngineCapability(EngineCapability capability) noexcept + { + mImpl->setEngineCapability(capability); + } + + //! + //! \brief Query EngineCapability flow configured for the builder. + //! + //! By default it returns EngineCapability::kSTANDARD. + //! + //! \see setEngineCapability() + //! + EngineCapability getEngineCapability() const noexcept + { + return mImpl->getEngineCapability(); + } + + //! + //! \brief Set Int8 Calibration interface. + //! + //! The calibrator is to minimize the information loss during the INT8 quantization process. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. + //! + TRT_DEPRECATED void setInt8Calibrator(IInt8Calibrator* calibrator) noexcept + { + mImpl->setInt8Calibrator(calibrator); + } + + //! + //! \brief Get Int8 Calibration interface. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. + //! + TRT_DEPRECATED IInt8Calibrator* getInt8Calibrator() const noexcept + { + return mImpl->getInt8Calibrator(); + } + + //! + //! \brief Set the build mode flags to turn on builder options for this network. + //! + //! The flags are listed in the BuilderFlags enum. + //! The flags set configuration options to build the network. + //! + //! \param builderFlags The build option for an engine. + //! + //! \note This function will override the previous set flags, rather than bitwise ORing the new flag. + //! + //! \see getFlags() + //! + void setFlags(BuilderFlags builderFlags) noexcept + { + mImpl->setFlags(builderFlags); + } + + //! + //! \brief Get the build mode flags for this builder config. Defaults to 0. + //! + //! \return The build options as a bitmask. + //! + //! \see setFlags() + //! + BuilderFlags getFlags() const noexcept + { + return mImpl->getFlags(); + } + + //! + //! \brief clear a single build mode flag. + //! + //! clears the builder mode flag from the enabled flags. + //! + //! \see setFlags() + //! + void clearFlag(BuilderFlag builderFlag) noexcept + { + mImpl->clearFlag(builderFlag); + } + + //! + //! \brief Set a single build mode flag. + //! + //! Add the input builder mode flag to the already enabled flags. + //! + //! \see setFlags() + //! + void setFlag(BuilderFlag builderFlag) noexcept + { + mImpl->setFlag(builderFlag); + } + + //! + //! \brief Returns true if the build mode flag is set + //! + //! \see getFlags() + //! + //! \return True if flag is set, false if unset. + //! + bool getFlag(BuilderFlag builderFlag) const noexcept + { + return mImpl->getFlag(builderFlag); + } + + //! + //! \brief Set the device that this layer must execute on. + //! + //! \param layer which layer to execute. + //! \param deviceType that this layer must execute on. + //! If DeviceType is not set or is reset, TensorRT will use the default DeviceType set in the builder. + //! + //! \note The device type for a layer must be compatible with the safety flow (if specified). + //! For example a layer cannot be marked for DLA execution while the builder is configured for kSAFETY. + //! + //! \see getDeviceType() + //! + void setDeviceType(ILayer const* layer, DeviceType deviceType) noexcept + { + mImpl->setDeviceType(layer, deviceType); + } + + //! + //! \brief Get the device that this layer executes on. + //! + //! \return Returns DeviceType of the layer. + //! + DeviceType getDeviceType(ILayer const* layer) const noexcept + { + return mImpl->getDeviceType(layer); + } + + //! + //! \brief whether the DeviceType has been explicitly set for this layer + //! + //! \return true if device type is not default + //! + //! \see setDeviceType() getDeviceType() resetDeviceType() + //! + bool isDeviceTypeSet(ILayer const* layer) const noexcept + { + return mImpl->isDeviceTypeSet(layer); + } + + //! + //! \brief reset the DeviceType for this layer + //! + //! \see setDeviceType() getDeviceType() isDeviceTypeSet() + //! + void resetDeviceType(ILayer const* layer) noexcept + { + mImpl->resetDeviceType(layer); + } + + //! + //! \brief Checks if a layer can run on DLA. + //! + //! \return status true if the layer can on DLA else returns false. + //! + bool canRunOnDLA(ILayer const* layer) const noexcept + { + return mImpl->canRunOnDLA(layer); + } + + //! + //! \brief Sets the DLA core used by the network. Defaults to -1. + //! + //! \param dlaCore The DLA core to execute the engine on, in the range [0,getNbDlaCores()). + //! + //! This function is used to specify which DLA core to use via indexing, if multiple DLA cores are available. + //! + //! \warning if getNbDLACores() returns 0, then this function does nothing. + //! + //! \see IRuntime::setDLACore() getDLACore() + //! + void setDLACore(int32_t dlaCore) noexcept + { + mImpl->setDLACore(dlaCore); + } + + //! + //! \brief Get the DLA core that the engine executes on. + //! + //! \return assigned DLA core or -1 for DLA not present or unset. + //! + int32_t getDLACore() const noexcept + { + return mImpl->getDLACore(); + } + + //! + //! \brief Sets the default DeviceType to be used by the builder. It ensures that all the layers that can run on + //! this device will run on it, unless setDeviceType is used to override the default DeviceType for a layer. + //! + //! \see getDefaultDeviceType() + //! + void setDefaultDeviceType(DeviceType deviceType) noexcept + { + mImpl->setDefaultDeviceType(deviceType); + } + + //! + //! \brief Get the default DeviceType which was set by setDefaultDeviceType. + //! + //! By default it returns DeviceType::kGPU. + //! + DeviceType getDefaultDeviceType() const noexcept + { + return mImpl->getDefaultDeviceType(); + } + + //! + //! \brief Resets the builder configuration to defaults. + //! + //! Useful for initializing a builder config object to its original state. + //! + void reset() noexcept + { + mImpl->reset(); + } + + //! + //! \brief Set the CUDA stream that is used to profile this network. + //! + //! \param stream The CUDA stream used for profiling by the builder. + //! + //! \see getProfileStream() + //! + void setProfileStream(const cudaStream_t stream) noexcept + { + return mImpl->setProfileStream(stream); + } + + //! + //! \brief Get the CUDA stream that is used to profile this network. + //! + //! \return The CUDA stream set by setProfileStream, nullptr if setProfileStream has not been called. + //! + //! \see setProfileStream() + //! + cudaStream_t getProfileStream() const noexcept + { + return mImpl->getProfileStream(); + } + + //! + //! \brief Add an optimization profile. + //! + //! This function must be called at least once if the network has dynamic or shape input tensors. + //! This function may be called at most once when building a refittable engine, as more than + //! a single optimization profile are not supported for refittable engines. + //! + //! \param profile The new optimization profile, which must satisfy profile->isValid() == true + //! + //! \return The index of the optimization profile (starting from 0) if the input is valid, or -1 if the input is + //! not valid. + //! + int32_t addOptimizationProfile(IOptimizationProfile const* profile) noexcept + { + return mImpl->addOptimizationProfile(profile); + } + + //! + //! \brief Get number of optimization profiles. + //! + //! This is one higher than the index of the last optimization profile that has be defined (or + //! zero, if none has been defined yet). + //! + //! \return The number of the optimization profiles. + //! + int32_t getNbOptimizationProfiles() const noexcept + { + return mImpl->getNbOptimizationProfiles(); + } + + //! + //! \brief Set verbosity level of layer information exposed in NVTX annotations and IEngineInspector. + //! + //! Control how much layer information will be exposed in NVTX annotations and IEngineInspector. + //! + //! \see ProfilingVerbosity, getProfilingVerbosity(), IEngineInspector + //! + void setProfilingVerbosity(ProfilingVerbosity verbosity) noexcept + { + mImpl->setProfilingVerbosity(verbosity); + } + + //! + //! \brief Get verbosity level of layer information exposed in NVTX annotations and IEngineInspector. + //! + //! Get the current setting of verbosity level of layer information exposed in + //! NVTX annotations and IEngineInspector. Default value is ProfilingVerbosity::kLAYER_NAMES_ONLY. + //! + //! \see ProfilingVerbosity, setProfilingVerbosity(), IEngineInspector + //! + ProfilingVerbosity getProfilingVerbosity() const noexcept + { + return mImpl->getProfilingVerbosity(); + } + + //! + //! \brief Set Algorithm Selector. + //! + //! \param selector The algorithm selector to be set in the build config. + //! + //! \deprecated Deprecated in TensorRT 10.8. Please use editable mode in ITimingCache instead. + //! + TRT_DEPRECATED void setAlgorithmSelector(IAlgorithmSelector* selector) noexcept + { + mImpl->setAlgorithmSelector(selector); + } + + //! + //! \brief Get Algorithm Selector. + //! + //! \deprecated Deprecated in TensorRT 10.8. Please use editable mode in ITimingCache instead. + //! + TRT_DEPRECATED IAlgorithmSelector* getAlgorithmSelector() const noexcept + { + return mImpl->getAlgorithmSelector(); + } + + //! + //! \brief Add a calibration profile. + //! + //! Calibration optimization profile must be set if int8 calibration is used to set scales for a network with + //! runtime dimensions. + //! + //! \param profile The new calibration profile, which must satisfy profile->isValid() == true or be nullptr. + //! MIN and MAX values will be overwritten by kOPT. + //! + //! \return True if the calibration profile was set correctly. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. + //! + TRT_DEPRECATED bool setCalibrationProfile(IOptimizationProfile const* profile) noexcept + { + return mImpl->setCalibrationProfile(profile); + } + + //! + //! \brief Get the current calibration profile. + //! + //! \return A pointer to the current calibration profile or nullptr if calibration profile is unset. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. + //! + TRT_DEPRECATED IOptimizationProfile const* getCalibrationProfile() noexcept + { + return mImpl->getCalibrationProfile(); + } + + //! + //! \brief Set the quantization flags. + //! + //! The flags are listed in the QuantizationFlag enum. + //! The flags set configuration options to quantize the network in int8. + //! + //! \param flags The quantization flags. + //! + //! \note This function will override the previous set flags, rather than bitwise ORing the new flag. + //! + //! \see getQuantizationFlags() + //! + //! \deprecated Deprecated in TensorRT 10.10. Superseded by explicit quantization. + //! + TRT_DEPRECATED void setQuantizationFlags(QuantizationFlags flags) noexcept + { + mImpl->setQuantizationFlags(flags); + } + + //! + //! \brief Get the quantization flags. + //! + //! \return The quantization flags as a bitmask. + //! + //! \see setQuantizationFlag() + //! + //! \deprecated Deprecated in TensorRT 10.10. Superseded by explicit quantization. + //! + TRT_DEPRECATED QuantizationFlags getQuantizationFlags() const noexcept + { + return mImpl->getQuantizationFlags(); + } + + //! + //! \brief clear a quantization flag. + //! + //! Clears the quantization flag from the enabled quantization flags. + //! + //! \see setQuantizationFlags() + //! + //! \deprecated Deprecated in TensorRT 10.10. Superseded by explicit quantization. + //! + TRT_DEPRECATED void clearQuantizationFlag(QuantizationFlag flag) noexcept + { + mImpl->clearQuantizationFlag(flag); + } + + //! + //! \brief Set a single quantization flag. + //! + //! Add the input quantization flag to the already enabled quantization flags. + //! + //! \see setQuantizationFlags() + //! + //! \deprecated Deprecated in TensorRT 10.10. Superseded by explicit quantization. + //! + TRT_DEPRECATED void setQuantizationFlag(QuantizationFlag flag) noexcept + { + mImpl->setQuantizationFlag(flag); + } + + //! + //! \brief Returns true if the quantization flag is set. + //! + //! \see getQuantizationFlags() + //! + //! \return True if quantization flag is set, false if unset. + //! + //! \deprecated Deprecated in TensorRT 10.10. Superseded by explicit quantization. + //! + TRT_DEPRECATED bool getQuantizationFlag(QuantizationFlag flag) const noexcept + { + return mImpl->getQuantizationFlag(flag); + } + + //! + //! \brief Set tactic sources. + //! + //! This bitset controls which tactic sources TensorRT is allowed to use for tactic + //! selection. + //! + //! Multiple tactic sources may be combined with a bitwise OR operation. For example, + //! to enable cublas and cublasLt as tactic sources, use a value of: + //! + //! 1U << static_cast(TacticSource::kCUBLAS) | 1U << + //! static_cast(TacticSource::kCUBLAS_LT) + //! + //! \see getTacticSources + //! + //! \return true if the tactic sources in the build configuration were updated. + //! The tactic sources in the build configuration will not be updated if the provided value is invalid. + //! + bool setTacticSources(TacticSources tacticSources) noexcept + { + return mImpl->setTacticSources(tacticSources); + } + + //! + //! \brief Get tactic sources. + //! + //! Get the tactic sources currently set in the engine build + //! configuration. + //! + //! \see setTacticSources() + //! + //! \return tactic sources + //! + TacticSources getTacticSources() const noexcept + { + return mImpl->getTacticSources(); + } + + //! + //! \brief Create timing cache + //! + //! Create ITimingCache instance from serialized raw data. The created timing cache doesn't belong to + //! a specific IBuilderConfig. It can be shared by multiple builder instances. Call setTimingCache() + //! before launching a builder to attach cache to builder instance. + //! The lifetime of the ITimingCache must exceed the lifetime of all builders that use it. + //! + //! \param blob A pointer to the raw data that contains serialized timing cache + //! \param size The size in bytes of the serialized timing cache. Size 0 means create a new cache from scratch + //! + //! \see setTimingCache + //! + //! \return the pointer to ITimingCache created + //! + nvinfer1::ITimingCache* createTimingCache(void const* blob, std::size_t size) const noexcept + { + return mImpl->createTimingCache(blob, size); + } + + //! + //! \brief Attach a timing cache to IBuilderConfig + //! + //! The timing cache has verification header to make sure the provided cache can be used in current environment. + //! A failure will be reported if the CUDA device property in the provided cache is different from current + //! environment. ignoreMismatch = true skips strict verification and allows loading cache created from a different + //! device. + //! + //! The cache must not be destroyed until after the engine is built. + //! + //! \param cache the timing cache to be used + //! \param ignoreMismatch whether or not allow using a cache that contains different CUDA device property + //! + //! \return true if set successfully, false otherwise + //! + //! \warning Using cache generated from devices with different CUDA device properties may lead to + //! functional/performance bugs. + //! + bool setTimingCache(ITimingCache const& cache, bool ignoreMismatch) noexcept + { + return mImpl->setTimingCache(cache, ignoreMismatch); + } + + //! + //! \brief Get the pointer to the timing cache from current IBuilderConfig + //! + //! \return pointer to the timing cache used in current IBuilderConfig + //! + nvinfer1::ITimingCache const* getTimingCache() const noexcept + { + return mImpl->getTimingCache(); + } + + //! + //! \brief Set the memory size for the memory pool. + //! + //! TensorRT layers access different memory pools depending on the operation. + //! This function sets in the IBuilderConfig the size limit, specified by \p poolSize, + //! for the corresponding memory pool, specified by \p pool. + //! TensorRT will build a plan file that is constrained by these limits or report + //! which constraint caused the failure. + //! + //! If the size of the pool, specified by \p poolSize, fails to meet the size requirements + //! for the pool, this function does nothing and emits the recoverable error, + //! ErrorCode::kINVALID_ARGUMENT, to the registered IErrorRecorder. + //! + //! If the size of the pool is larger than the maximum possible value for the + //! configuration, this function does nothing and emits ErrorCode::kUNSUPPORTED_STATE. + //! + //! If the pool does not exist on the requested device type when building + //! the network, a warning is emitted to the logger, and the memory pool + //! value is ignored. + //! + //! Refer to MemoryPoolType to see the size requirements for each pool. + //! + //! \param pool The memory pool to limit the available memory for. + //! \param poolSize The size of the pool in bytes. + //! + //! \see getMemoryPoolLimit, MemoryPoolType + //! + void setMemoryPoolLimit(MemoryPoolType pool, std::size_t poolSize) noexcept + { + mImpl->setMemoryPoolLimit(pool, poolSize); + } + + //! + //! \brief Get the memory size limit of the memory pool. + //! + //! Retrieve the memory size limit of the corresponding pool in bytes. + //! If setMemoryPoolLimit for the pool has not been called, this returns the default + //! value used by TensorRT. This default value is not necessarily the maximum possible + //! value for that configuration. + //! + //! \param pool The memory pool to get the limit for. + //! + //! \returns The size of the memory limit, in bytes, for the corresponding pool. + //! + //! \see setMemoryPoolLimit + //! + std::size_t getMemoryPoolLimit(MemoryPoolType pool) const noexcept + { + return mImpl->getMemoryPoolLimit(pool); + } + + //! + //! \brief Enable or disable a specific preview feature + //! + //! Allows enabling or disabling experimental features, which are not enabled by default in the + //! current release. + //! + //! Refer to PreviewFeature for additional information, and a list of the available features. + //! + //! \param feature the feature to enable / disable + //! \param enable true for enable, false for disable + //! + //! \see PreviewFeature, getPreviewFeature + //! + void setPreviewFeature(PreviewFeature feature, bool enable) noexcept + { + mImpl->setPreviewFeature(feature, enable); + } + + //! + //! \brief Get status of preview feature + //! + //! \param feature the feature to query + //! + //! \returns true if the \p feature is enabled, false otherwise + //! + //! \see PreviewFeature, setPreviewFeature + //! + bool getPreviewFeature(PreviewFeature feature) const noexcept + { + return mImpl->getPreviewFeature(feature); + } + + //! + //! \brief Set builder optimization level + //! + //! Set the builder optimization level. Setting a higher optimization + //! level allows the optimizer to spend more time searching for optimization opportunities. The + //! resulting engine may have better performance compared to an engine built with a lower optimization level. + //! + //! The default optimization level is 3. Valid values include integers from 0 to the maximum optimization level, + //! which is currently 5. Setting it to greater than the maximum level results in behavior identical to the + //! maximum level. + //! + //! Below are the descriptions about each builder optimization level: + //! + //! - Level 0: This enables the fastest compilation by disabling dynamic kernel generation and selecting the first + //! tactic that succeeds in execution. This will also not respect a timing cache. + //! - Level 1: Available tactics are sorted by heuristics, but only the top are tested to select the best. If a + //! dynamic kernel is generated its compile optimization is low. + //! - Level 2: Available tactics are sorted by heuristics, but only the fastest tactics are tested to select the + //! best. + //! - Level 3: Apply heuristics to see if a static precompiled kernel is applicable or if a new one has to be + //! compiled dynamically. + //! - Level 4: Always compiles a dynamic kernel. + //! - Level 5: Always compiles a dynamic kernel and compares it to static kernels. + //! + //! \param level The optimization level to set to. Must be non-negative. + //! + //! \see getBuilderOptimizationLevel + //! + void setBuilderOptimizationLevel(int32_t level) noexcept + { + mImpl->setBuilderOptimizationLevel(level); + } + + //! + //! \brief Get builder optimization level + //! + //! \returns the current builder optimization level + //! + //! \see setBuilderOptimizationLevel + //! + int32_t getBuilderOptimizationLevel() noexcept + { + return mImpl->getBuilderOptimizationLevel(); + } + + //! + //! \brief Set the hardware compatibility level. + //! + //! Hardware compatibility allows an engine to run on GPU + //! architectures other than that of the GPU where the engine was + //! built. + //! + //! The default hardware compatibility level is HardwareCompatibilityLevel::kNONE. + //! + //! \param hardwareCompatibilityLevel The level of hardware + //! compatibility. + //! + void setHardwareCompatibilityLevel(HardwareCompatibilityLevel hardwareCompatibilityLevel) noexcept + { + mImpl->setHardwareCompatibilityLevel(hardwareCompatibilityLevel); + } + + //! + //! \brief Get the hardware compatibility level. + //! + //! \return hardwareCompatibilityLevel The level of hardware + //! compatibility. + //! + //! \see setHardwareCompatibilityLevel() + //! + HardwareCompatibilityLevel getHardwareCompatibilityLevel() const noexcept + { + return mImpl->getHardwareCompatibilityLevel(); + } + + //! + //! \brief Set the plugin libraries to be serialized with version-compatible engines. + //! + //! Each entry in the list of libraries must be unique. + //! + //! \param paths The paths of plugin libraries. + //! \param nbPaths The number of paths. + //! + void setPluginsToSerialize(char const* const* paths, int32_t nbPaths) noexcept + { + mImpl->setPluginsToSerialize(paths, nbPaths); + } + + //! + //! \brief Get the plugin library path to be serialized with version-compatible engines. + //! + //! \param index Index of the plugin library path in the list. Should be in the range `[0, + //! getNbPluginsToSerialize())`. + //! + //! \return The path to the plugin library. + //! + char const* getPluginToSerialize(int32_t index) const noexcept + { + return mImpl->getPluginToSerialize(index); + } + + //! + //! \brief Get the number of plugin library paths to be serialized with version-compatible engines. + //! + //! \return The number of paths. + //! + int32_t getNbPluginsToSerialize() const noexcept + { + return mImpl->getNbPluginsToSerialize(); + } + + //! + //! \brief Set the maximum number of auxiliary streams that TRT is allowed to use. + //! + //! If the network contains operators that can run in parallel, TRT can execute them using auxiliary streams + //! in addition to the one provided to the IExecutionContext::enqueueV3() call. + //! + //! The default maximum number of auxiliary streams is determined by the heuristics in TensorRT on whether enabling + //! multi-stream would improve the performance. This behavior can be overridden by calling this API to set the + //! maximum number of auxiliary streams explicitly. Set this to 0 to enforce single-stream inference. + //! + //! The resulting engine may use fewer auxiliary streams than the maximum if the network does not contain enough + //! parallelism or if TensorRT determines that using more auxiliary streams does not help improve the performance. + //! + //! \note Allowing more auxiliary streams does not always give better performance since there will be + //! synchronizations overhead between streams. Using CUDA graphs at runtime can help reduce the overhead caused by + //! cross-stream synchronizations. + //! + //! \note Using more auxiliary leads to more memory usage at runtime since some activation memory blocks will not + //! be able to be reused. + //! + //! \param nbStreams The maximum number of auxiliary streams that TRT is allowed to use. + //! + //! \see getMaxAuxStreams(), ICudaEngine::getNbAuxStreams(), IExecutionContext::setAuxStreams() + //! + void setMaxAuxStreams(int32_t nbStreams) noexcept + { + mImpl->setMaxAuxStreams(nbStreams); + } + + //! + //! \brief Get the maximum number of auxiliary streams that TRT is allowed to use. + //! + //! \see setMaxAuxStreams() + //! + int32_t getMaxAuxStreams() const noexcept + { + return mImpl->getMaxAuxStreams(); + } + + //! + //! \brief Sets the progress monitor for building a network. + //! + //! \param monitor The progress monitor to assign to the IBuilderConfig. + //! + //! The progress monitor signals to the application when different phases of + //! the compiler are being executed. Setting to nullptr unsets the monitor so + //! that the application is not signaled. + //! + //! \see IBuilderConfig::getProgressMonitor + //! + void setProgressMonitor(IProgressMonitor* monitor) noexcept + { + return mImpl->setProgressMonitor(monitor); + } + + //! + //! \return The progress monitor set by the application or nullptr. + //! + //! \see IBuilderConfig::setProgressMonitor + //! + IProgressMonitor* getProgressMonitor() const noexcept + { + return mImpl->getProgressMonitor(); + } + + //! + //! \brief Set the target platform for runtime execution. + //! + //! Cross-platform compatibility allows an engine to be built and executed on different platforms. + //! + //! The default cross-platform target is RuntimePlatform::kSAME_AS_BUILD. + //! + //! \param runtimePlatform The target platform for runtime execution. + //! + //! \see IBuilderConfig::getRuntimePlatform() + //! + void setRuntimePlatform(RuntimePlatform runtimePlatform) noexcept + { + mImpl->setRuntimePlatform(runtimePlatform); + } + + //! + //! \brief Get the target platform for runtime execution. + //! + //! \return The target platform for runtime execution. + //! + //! \see IBuilderConfig::setRuntimePlatform() + //! + RuntimePlatform getRuntimePlatform() const noexcept + { + return mImpl->getRuntimePlatform(); + } + + //! + //! \brief Set the maximum number of tactics to time when there is a choice of tactics. + //! + //! This function controls the number of tactics timed when there are multiple tactics to choose from. + //! + //! \see getMaxNbTactics() + //! + void setMaxNbTactics(int32_t maxNbTactics) noexcept + { + mImpl->setMaxNbTactics(maxNbTactics); + } + + //! + //! \brief Query the maximum number of tactics timed when there is a choice. + //! + //! By default the value is -1, indicating TensorRT can determine the number of tactics based on its own heuristic. + //! + //! \see setMaxNbTactics() + //! + int32_t getMaxNbTactics() const noexcept + { + return mImpl->getMaxNbTactics(); + } + + //! + //! \brief Set the Tiling optimization level. + //! + //! Tiling allows TensorRT to try an on-chip caching strategy. + //! + //! The default getTilingOptimizationLevel is TilingOptimizationLevel::kNONE. + //! + //! \param level The level of Tiling optimization. + //! + //! \return True if successful, false otherwise + //! + bool setTilingOptimizationLevel(TilingOptimizationLevel level) noexcept + { + return mImpl->setTilingOptimizationLevel(level); + } + + //! + //! \brief Get the Tiling optimization level. + //! + //! \return TilingOptimizationLevel The level of Tiling optimization. + //! + //! \see setTilingOptimizationLevel() + //! + TilingOptimizationLevel getTilingOptimizationLevel() const noexcept + { + return mImpl->getTilingOptimizationLevel(); + } + + //! + //! \brief Set the L2 cache usage limit for Tiling optimization. + //! + //! Parameter for tiling optimization. This API only takes effect when TilingOptimizationLevel is not kNONE. + //! \note If setL2LimitForTiling() has not been called, TensorRT would choose a default value between 0 and L2 + //! capacity size. + //! + //! \param size The size of the L2 cache usage limit for Tiling optimization. + //! + //! \return True if successful, false otherwise + //! + bool setL2LimitForTiling(int64_t size) noexcept + { + return mImpl->setL2LimitForTiling(size); + } + + //! + //! \brief Get the L2 cache usage limit for tiling optimization. + //! + //! \return L2 cache usage limit for tiling optimization. + //! + //! \see setL2LimitForTiling() + //! + int64_t getL2LimitForTiling() const noexcept + { + return mImpl->getL2LimitForTiling(); + } + + //! + //! \brief Set a config string for remote auto tuning. + //! + //! Remote auto-tuning is supported only for engines built with EngineCapability::kSAFETY. + //! + //! \param config The config string to be used during remote auto tuning. + //! + //! \return True if successful, false otherwise + //! + bool setRemoteAutoTuningConfig(char const* config) noexcept + { + return mImpl->setRemoteAutoTuningConfig(config); + } + + //! + //! \brief Get a config string for remote auto tuning. + //! + //! \return The current string for remote auto tuning, or nullptr if not set. + //! + char const* getRemoteAutoTuningConfig() const noexcept + { + return mImpl->getRemoteAutoTuningConfig(); + } + +protected: + apiv::VBuilderConfig* mImpl; +}; + +//! +//! \brief Represents one or more NetworkDefinitionCreationFlag flags +//! using binary OR operations. +//! e.g., 1U << NetworkDefinitionCreationFlag::kSTRONGLY_TYPED +//! +//! \see IBuilder::createNetworkV2 +//! +using NetworkDefinitionCreationFlags = uint32_t; + +//! +//! \enum NetworkDefinitionCreationFlag +//! +//! \brief List of immutable network properties expressed at network creation time. +//! NetworkDefinitionCreationFlag is used with createNetworkV2() to specify immutable properties of the network. +//! +//! \see IBuilder::createNetworkV2 +//! +enum class NetworkDefinitionCreationFlag : int32_t +{ + //! Ignored because networks are always "explicit batch" in TensorRT 10.0. + //! + //! \deprecated Deprecated in TensorRT 10.0. + kEXPLICIT_BATCH TRT_DEPRECATED_ENUM = 0, + + //! Mark the network to be strongly typed. + //! Every tensor in the network has a data type defined in the network following only type inference rules and the + //! inputs/operator annotations. Setting layer precision and layer output types is not allowed, and the network + //! output types will be inferred based on the input types and the type inference rules. + kSTRONGLY_TYPED = 1, + //! If set, for a Python plugin with both AOT and JIT implementations, the JIT implementation will be used. + //! Any plugin-specific JIT/AOT specification may override this. + //! Cannot be used in conjunction with NetworkDefinitionCreationFlag::kPREFER_AOT_PYTHON_PLUGINS. + kPREFER_JIT_PYTHON_PLUGINS = 2, + + //! If set, for a Python plugin with both AOT and JIT implementations, the AOT implementation will be used. + //! Any plugin-specific JIT/AOT specification may override this. + //! Cannot be used in conjunction with NetworkDefinitionCreationFlag::kPREFER_JIT_PYTHON_PLUGINS. + kPREFER_AOT_PYTHON_PLUGINS = 3, +}; + +//! +//! Maximum number of elements in NetworkDefinitionCreationFlag enum. +//! +//! \see NetworkDefinitionCreationFlag +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 4; +} + +//! +//! \class IBuilder +//! +//! \brief Builds an engine from a network definition. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IBuilder : public INoCopy +{ +public: + virtual ~IBuilder() noexcept = default; + + //! + //! \brief Determine whether the platform has fast native fp16. + //! + //! \deprecated Deprecated in TensorRT 10.5. Please query data type support from CUDA directly. + //! + TRT_DEPRECATED bool platformHasFastFp16() const noexcept + { + return mImpl->platformHasFastFp16(); + } + + //! + //! \brief Determine whether the platform has fast native int8. + //! + //! \deprecated Deprecated in TensorRT 10.5. Please query data type support from CUDA directly. + //! + TRT_DEPRECATED bool platformHasFastInt8() const noexcept + { + return mImpl->platformHasFastInt8(); + } + + //! + //! \brief Get the maximum batch size DLA can support. + //! For any tensor the total volume of index dimensions combined(dimensions other than CHW) with the requested + //! batch size should not exceed the value returned by this function. + //! + //! \warning getMaxDLABatchSize does not work with dynamic shapes. + //! + int32_t getMaxDLABatchSize() const noexcept + { + return mImpl->getMaxDLABatchSize(); + } + + //! + //! \brief Return the number of DLA engines available to this builder. + //! + int32_t getNbDLACores() const noexcept + { + return mImpl->getNbDLACores(); + } + + //! + //! \brief Set the GPU allocator. + //! + //! \param allocator Set the GPU allocator to be used by the builder. All GPU memory acquired will use this + //! allocator. If NULL is passed, the default allocator will be used. + //! + //! Default: allocateAsync uses cudaMallocAsync if cudaDevAttrMemoryPoolsSupported returns true, otherwise falls + //! back to cudaMalloc. allocate always uses cudaMalloc. + //! + //! \note This allocator will be passed to any engines created via the builder; thus the lifetime of the allocator + //! must span the lifetime of those engines as + //! well as that of the builder. If nullptr is passed, the default allocator will be used. + //! + void setGpuAllocator(IGpuAllocator* allocator) noexcept + { + mImpl->setGpuAllocator(allocator); + } + + //! + //! \brief Create a builder configuration object. + //! + //! The caller owns the new IBuilderConfig, which must be destroyed with operator delete + //! before this IBuilder is destroyed. Destroying this IBuilder before destroying the + //! IBuilderConfig causes undefined behavior. + //! + //! \see IBuilderConfig + //! + nvinfer1::IBuilderConfig* createBuilderConfig() noexcept + { + return mImpl->createBuilderConfig(); + } + + //! + //! \brief Create a network definition object + //! + //! Creates a network definition object with immutable properties specified using the flags parameter. + //! + //! createNetworkV2 supports creating network with properties from NetworkDefinitionCreationFlags. + //! + //! CreateNetworkV2 supports dynamic shapes and explicit batch dimensions by default. + //! + //! createNetworkV2 with NetworkDefinitionCreationFlag::kSTRONGLY_TYPED flag supports creating a strongly typed plan + //! where tensor data types are inferred from network input types and operator type specification. + //! + //! The caller owns the new INetworkDefinition, which must be destroyed with operator delete + //! before this IBuilder is destroyed. Destroying this IBuilder before destroying the + //! INetworkDefinition causes undefined behavior. + //! + //! \param flags Bitset of NetworkDefinitionCreationFlags specifying network properties combined with bitwise OR, + //! e.g., 1U << NetworkDefinitionCreationFlag::kSTRONGLY_TYPED. + //! + //! \see INetworkDefinition, NetworkDefinitionCreationFlags + //! + nvinfer1::INetworkDefinition* createNetworkV2(NetworkDefinitionCreationFlags flags) noexcept + { + return mImpl->createNetworkV2(flags); + } + + //! + //! \brief Create a new optimization profile. + //! + //! If the network has any dynamic input tensors, the appropriate calls to setDimensions() must be made. + //! Likewise, if there are any shape input tensors, the appropriate calls to setShapeValues() are required. + //! The builder retains ownership of the created optimization profile and returns a raw pointer, i.e. the users + //! must not attempt to delete the returned pointer. + //! + //! \see IOptimizationProfile + //! + nvinfer1::IOptimizationProfile* createOptimizationProfile() noexcept + { + return mImpl->createOptimizationProfile(); + } + + //! + //! \brief Set the ErrorRecorder for this interface + //! + //! Assigns the ErrorRecorder to this interface. The ErrorRecorder will track all errors during execution. + //! This function will call incRefCount of the registered ErrorRecorder at least once. Setting + //! recorder to nullptr unregisters the recorder with the interface, resulting in a call to decRefCount if + //! a recorder has been registered. + //! + //! If an error recorder is not set, messages will be sent to the global log stream. + //! + //! \param recorder The error recorder to register with this interface. + //! + //! \see getErrorRecorder() + //! + void setErrorRecorder(IErrorRecorder* recorder) noexcept + { + mImpl->setErrorRecorder(recorder); + } + + //! + //! \brief get the ErrorRecorder assigned to this interface. + //! + //! Retrieves the assigned error recorder object for the given class. + //! A nullptr will be returned if setErrorRecorder has not been called. + //! + //! \return A pointer to the IErrorRecorder object that has been registered. + //! + //! \see setErrorRecorder() + //! + IErrorRecorder* getErrorRecorder() const noexcept + { + return mImpl->getErrorRecorder(); + } + + //! + //! \brief Resets the builder state to default values. + //! + void reset() noexcept + { + mImpl->reset(); + } + + //! + //! \brief Determine whether the platform has TF32 support. + //! + //! \deprecated Deprecated in TensorRT 10.5. Please query data type support from CUDA directly. + //! + TRT_DEPRECATED bool platformHasTf32() const noexcept + { + return mImpl->platformHasTf32(); + } + + //! + //! \brief Builds and serializes a network for the given INetworkDefinition and IBuilderConfig. + //! + //! This function allows building and serialization of a network without creating an engine. + //! + //! \param network Network definition. + //! \param config Builder configuration. + //! + //! \return A pointer to a IHostMemory object that contains a serialized network. + //! + //! \note This function will synchronize the CUDA stream returned by \p config.getProfileStream() before returning. + //! + //! \see INetworkDefinition, IBuilderConfig, IHostMemory + //! + nvinfer1::IHostMemory* buildSerializedNetwork(INetworkDefinition& network, IBuilderConfig& config) noexcept + { + return mImpl->buildSerializedNetwork(network, config); + } + + //! + //! \brief Builds and serializes a network into stream for the given INetworkDefinition and IBuilderConfig. + //! + //! This function allows building and serialization of a network without creating an engine. The engine is + //! finally serialized into the writer stream. + //! + //! \param network Network definition. + //! \param config Builder configuration. + //! \param writer Output writer stream. + //! + //! \return true if build succeed, otherwise false. + //! + //! \note This function will synchronize the CUDA stream returned by \p config.getProfileStream() before returning. + //! + //! \see INetworkDefinition, IBuilderConfig, IStreamWriter + //! + bool buildSerializedNetworkToStream( + INetworkDefinition& network, IBuilderConfig& config, IStreamWriter& writer) noexcept + { + return mImpl->buildSerializedNetworkToStream(network, config, writer); + } + + //! + //! \brief Extended form of buildSerializedNetwork that optionally permits getting the kernelText. + //! + //! Similar to two-argument form, except that if an engine with safe capability is successfully built + //! and there are kernels, sets kernelText to ..... Otherwise sets kernelText=nullptr. + //! + //! This function allows building and serialization of a network without creating an engine. + //! + //! \param network Network definition. + //! \param config Builder configuration. + //! \param kernelText A reference to a pointer to a IHostMemory object that will be set to the kernel CPP code text + //! + //! \return A pointer to a IHostMemory object that contains a serialized network. + //! + //! \note This function will synchronize the CUDA stream returned by \p config.getProfileStream() before returning. + //! + //! \see INetworkDefinition, IBuilderConfig, IHostMemory + //! + nvinfer1::IHostMemory* buildSerializedNetwork( + INetworkDefinition& network, IBuilderConfig& config, IHostMemory*& kernelText) noexcept + { + return mImpl->buildSerializedNetworkWithKernelText(network, config, kernelText); + } + + //! + //! \brief Builds a network for the given INetworkDefinition and IBuilderConfig. + //! + //! \param network Network definition. + //! \param config Builder configuration. + //! + //! \return A pointer to a ICudaEngine object that contains an engine. + //! + //! \note This function will synchronize the CUDA stream returned by \p config.getProfileStream() before returning. + //! + //! \note This function does not support \p BuilderFlag::kVERSION_COMPATIBLE. + //! Please use \p buildSerializedNetwork to get a version compatible engine. + //! + //! \see INetworkDefinition, IBuilderConfig, ICudaEngine + //! + nvinfer1::ICudaEngine* buildEngineWithConfig(INetworkDefinition& network, IBuilderConfig& config) noexcept + { + return mImpl->buildEngineWithConfig(network, config); + } + + //! + //! \brief Checks that a network is within the scope of the IBuilderConfig settings. + //! + //! \param network The network definition to check for configuration compliance. + //! \param config The configuration of the builder to use when checking \p network. + //! + //! Given an INetworkDefinition, \p network, and an IBuilderConfig, \p config, check if + //! the network falls within the constraints of the builder configuration based on the + //! EngineCapability, BuilderFlag, and DeviceType. If the network is within the constraints, + //! then the function returns true, and false if a violation occurs. This function reports + //! the conditions that are violated to the registered ErrorRecorder. + //! + //! \return True if network is within the scope of the restrictions specified by the builder config, + //! false otherwise. + //! + //! \note A `true` return value does not guarantee that engine building will succeed, as backends may reject it for + //! reasons not detectable with this fast validation. To definitively check whether a network can be built with a + //! given config, use \p buildEngineWithConfig or \p buildSerializedNetwork (depending on the engine capability). + //! + //! \note This function will synchronize the CUDA stream returned by \p config.getProfileStream() before returning. + //! + bool isNetworkSupported(INetworkDefinition const& network, IBuilderConfig const& config) const noexcept + { + return mImpl->isNetworkSupported(network, config); + } + + //! + //! \brief get the logger with which the builder was created + //! + //! \return the logger + //! + ILogger* getLogger() const noexcept + { + return mImpl->getLogger(); + } + + //! + //! \brief Set the maximum number of threads. + //! + //! \param maxThreads The maximum number of threads that can be used by the builder. + //! + //! \return True if successful, false otherwise. + //! + //! The default value is 1 and includes the current thread. + //! A value greater than 1 permits TensorRT to use multi-threaded algorithms. + //! A value less than 1 triggers a kINVALID_ARGUMENT error. + //! + bool setMaxThreads(int32_t maxThreads) noexcept + { + return mImpl->setMaxThreads(maxThreads); + } + + //! + //! \brief get the maximum number of threads that can be used by the builder. + //! + //! Retrieves the maximum number of threads that can be used by the builder. + //! + //! \return The maximum number of threads that can be used by the builder. + //! + //! \see setMaxThreads() + //! + int32_t getMaxThreads() const noexcept + { + return mImpl->getMaxThreads(); + } + + //! + //! \brief get the local plugin registry that can be used by the builder. + //! + //! \return The local plugin registry that can be used by the builder. + //! + IPluginRegistry& getPluginRegistry() noexcept + { + return mImpl->getPluginRegistry(); + } + +protected: + apiv::VBuilder* mImpl; +}; + +} // namespace nvinfer1 + +//! +//! Internal C entry point for creating IBuilder. +//! @private +//! +extern "C" TENSORRTAPI void* createInferBuilder_INTERNAL(void* logger, int32_t version) noexcept; + +namespace nvinfer1 +{ +namespace +{ + +//! +//! \brief Create an instance of an IBuilder class. +//! +//! \param logger The logging class for the builder. +//! +//! unnamed namespace avoids linkage surprises when linking objects built with different versions of this header. +//! +inline IBuilder* createInferBuilder(ILogger& logger) noexcept +{ + return static_cast(createInferBuilder_INTERNAL(&logger, NV_TENSORRT_VERSION)); +} + +} // namespace + +//! +//! \brief Return the plugin registry for building a Standard engine, or nullptr if no registry exists. +//! +//! Also return nullptr if the input argument is not EngineCapability::kSTANDARD. +//! Engine capabilities EngineCapability::kSTANDARD and EngineCapability::kSAFETY have distinct plugin registries. +//! Use IPluginRegistry::registerCreator from the registry to register plugins. +//! Plugins registered in a registry associated with a specific engine capability are only available when +//! building engines with that engine capability. +//! +//! There is no plugin registry for EngineCapability::kDLA_STANDALONE. +//! +extern "C" TENSORRTAPI nvinfer1::IPluginRegistry* getBuilderPluginRegistry( + nvinfer1::EngineCapability capability) noexcept; + +//! +//! \brief Set a custom directory path for loading internal TensorRT libraries when building engines. +//! +//! \param path The path to prepend to internal library names. +//! \return true if the path was successfully set, updated, or reset (by passing empty string). +//! false if it could not be set due to null pointer or an invalid path. +//! +//! This API take effect globally for all engines built by the current process. +//! +extern "C" TENSORRTAPI bool setInternalLibraryPath(AsciiChar const* path) noexcept; + +namespace safe +{ +//! Forward declaration +class IPluginRegistry; +} // namespace safe + +//! +//! \brief Return the plugin registry for building a Safety engine, or nullptr if no registry exists. +//! +//! Also return nullptr if the input argument is not EngineCapability::kSAFETY. +//! When building a Standard engine, use nvinfer1::getBuilderPluginRegistry(). +//! Use safe::IPluginRegistry::registerCreator from the registry to register plugins. +//! +extern "C" TRT_DEPRECATED_API nvinfer1::safe::IPluginRegistry* getBuilderSafePluginRegistry( + nvinfer1::EngineCapability capability) noexcept; + +} // namespace nvinfer1 + +#endif // NV_INFER_H diff --git a/tensorrt/include/NvInferImpl.h b/tensorrt/include/NvInferImpl.h new file mode 100644 index 0000000000000000000000000000000000000000..dc971415c267bb702579284024e15c2aa34e2ef7 --- /dev/null +++ b/tensorrt/include/NvInferImpl.h @@ -0,0 +1,1469 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NV_INFER_IMPL_H +#define NV_INFER_IMPL_H + +#include "NvInferLegacyDims.h" +#include "NvInferRuntimeCommon.h" + +// @cond SuppressDoxyWarnings + +namespace nvinfer1 +{ + +class ILogger; + +namespace v_1_0 +{ +class IProgressMonitor; +} // namespace v_1_0 +using IProgressMonitor = v_1_0::IProgressMonitor; + +namespace v_1_0 +{ +class IAlgorithmSelector; +} // namespace v_1_0 +using IAlgorithmSelector = v_1_0::IAlgorithmSelector; + +namespace v_1_0 +{ +class IProfiler; +} // namespace v_1_0 +using IProfiler = v_1_0::IProfiler; + +namespace v_1_0 +{ +class IOutputAllocator; +} // namespace v_1_0 +using IOutputAllocator = v_1_0::IOutputAllocator; + +namespace v_1_0 +{ +class IDebugListener; +} // namespace v_1_0 +using IDebugListener = v_1_0::IDebugListener; + +class IActivationLayer; +class IAlgorithm; +class IAlgorithmContext; +class IAlgorithmIOInfo; +class IAlgorithmVariant; +class IAssertionLayer; +class IAttention; +class IBuilder; +class IBuilderConfig; +class IConcatenationLayer; +class IConditionLayer; +class IConstantLayer; +class IConvolutionLayer; +class ICudaEngine; +class ICumulativeLayer; +class IDeconvolutionLayer; +class IDequantizeLayer; +class IDimensionExpr; +class IDynamicQuantizeLayer; +class IEinsumLayer; +class IElementWiseLayer; +class IEngineInspector; +class IExecutionContext; +class IFillLayer; +class IGatherLayer; +class IGridSampleLayer; +class IHostMemory; +class IIdentityLayer; +class ICastLayer; +class IIfConditional; +class IIfConditionalInputLayer; +class IIfConditionalOutputLayer; +class IInt8Calibrator; +class IIteratorLayer; +class IKVCacheUpdateLayer; +class ILayer; +class ILoop; +class ILoopOutputLayer; +class ILRNLayer; +class IMatrixMultiplyLayer; +class IMoELayer; +class IDistCollectiveLayer; +class INetworkDefinition; +class INormalizationLayer; +class INMSLayer; +class INonZeroLayer; +class IOneHotLayer; +class IOptimizationProfile; +class IPaddingLayer; +class IParametricReLULayer; +class IPlugin; +class IPluginExt; +class IPluginFactory; +class IPluginLayer; +class IPluginRegistry; +class IPluginV2Layer; +class IRotaryEmbeddingLayer; +class IRuntimeConfig; + +namespace v_1_0 +{ +class IPluginV3; +} // namespace v_1_0 +using IPluginV3 = v_1_0::IPluginV3; + +namespace v_1_0 +{ +class IStreamReader; +class IStreamWriter; +} // namespace v_1_0 +using IStreamReader = v_1_0::IStreamReader; +using IStreamWriter = v_1_0::IStreamWriter; +namespace v_1_0 +{ +class IStreamReaderV2; +} // namespace v_1_0 +using IStreamReaderV2 = v_1_0::IStreamReaderV2; + +class IPluginV3Layer; +class IPoolingLayer; +class IQuantizeLayer; +class IRaggedSoftMaxLayer; +class IRecurrenceLayer; +class IReduceLayer; +class IRefitter; +class IResizeLayer; +class IReverseSequenceLayer; +class IRuntime; +class IScaleLayer; +class IScatterLayer; +class ISelectLayer; +class ISerializationConfig; +class IShapeLayer; +class IShuffleLayer; +class ISliceLayer; +class ISoftMaxLayer; +class ISqueezeLayer; +class ITensor; + +namespace v_1_0 +{ +struct TimingCacheKey; +struct TimingCacheValue; +} // namespace v_1_0 +using TimingCacheKey = v_1_0::TimingCacheKey; +using TimingCacheValue = v_1_0::TimingCacheValue; + +class ITimingCache; +class ITopKLayer; +class ITripLimitLayer; +class IUnaryLayer; +class IUnsqueezeLayer; +struct Permutation; +class Weights; + +enum class ActivationType : int32_t; +enum class AttentionNormalizationOp : int32_t; +enum class BoundingBoxFormat : int32_t; +enum class BuilderFlag : int32_t; +enum class CalibrationAlgoType : int32_t; +enum class CumulativeOperation : int32_t; +enum class DeviceType : int32_t; +enum class DimensionOperation : int32_t; +enum class ElementWiseOperation : int32_t; +enum class EngineCapability : int32_t; +enum class FillOperation : int32_t; +enum class GatherMode : int32_t; +enum class KVCacheMode : int32_t; +enum class LayerInformationFormat : int32_t; +enum class LayerType : int32_t; +enum class LoopOutput : int32_t; +enum class MatrixOperation : int32_t; +enum class MemoryPoolType : int32_t; +enum class MoEActType : int32_t; +enum class NetworkDefinitionCreationFlag : int32_t; +enum class OptProfileSelector : int32_t; +enum class PaddingMode : int32_t; +enum class PoolingType : int32_t; +enum class ProfilingVerbosity : int32_t; +enum class QuantizationFlag : int32_t; +enum class ReduceOperation : int32_t; +enum class CollectiveOperation : int32_t; +enum class ResizeCoordinateTransformation : int32_t; +enum class InterpolationMode : int32_t; +enum class ResizeRoundMode : int32_t; +enum class ResizeSelector : int32_t; +enum class ScaleMode : int32_t; +enum class ScatterMode : int32_t; +enum class SampleMode : int32_t; +enum class SerializationFlag : int32_t; +enum class TensorIOMode : int32_t; +enum class TensorLocation : int32_t; +enum class TopKOperation : int32_t; +enum class TripLimit : int32_t; +enum class UnaryOperation : int32_t; +enum class WeightsRole : int32_t; +enum class PreviewFeature : int32_t; +enum class HardwareCompatibilityLevel : int32_t; +enum class ExecutionContextAllocationStrategy : int32_t; +enum class RuntimePlatform : int32_t; +enum class TilingOptimizationLevel : int32_t; +enum class EngineStat : int32_t; + + +using TacticSources = uint32_t; +using TensorFormats = uint32_t; +using BuilderFlags = uint32_t; +using NetworkDefinitionCreationFlags = uint32_t; +using QuantizationFlags = uint32_t; +using TempfileControlFlags = uint32_t; +using SerializationFlags = uint32_t; + +//! +//! \file NvInferImpl.h +//! +//! This file contains definitions for API methods that cross the shared library boundary. These +//! methods must not be called directly by applications; they should only be called through the +//! API classes. +//! + +namespace apiv +{ + +class VRoot +{ +public: + virtual ~VRoot() noexcept = default; +}; + +class VHostMemory : public VRoot +{ +public: + virtual void* data() const noexcept = 0; + virtual std::size_t size() const noexcept = 0; + virtual DataType type() const noexcept = 0; +}; + +class VDimensionExpr : public VRoot +{ +public: + virtual bool isConstant() const = 0; + virtual int64_t getConstantValue() const = 0; + virtual bool isSizeTensor() const = 0; +}; + +class VExprBuilder : public VRoot +{ +public: + virtual IDimensionExpr const* constant(int64_t value) = 0; + virtual IDimensionExpr const* operation( + DimensionOperation op, IDimensionExpr const& first, IDimensionExpr const& second) + = 0; + virtual IDimensionExpr const* declareSizeTensor( + int32_t outputIndex, IDimensionExpr const& opt, IDimensionExpr const& upper) + = 0; +}; + +class VRuntime : public VRoot +{ +public: + virtual IRuntime* getPImpl() noexcept = 0; + virtual nvinfer1::ICudaEngine* deserializeCudaEngine(void const* blob, std::size_t size) noexcept = 0; + virtual nvinfer1::ICudaEngine* deserializeCudaEngine(IStreamReader& streamReader) noexcept = 0; + virtual void setDLACore(int32_t dlaCore) noexcept = 0; + virtual int32_t getDLACore() const noexcept = 0; + virtual int32_t getNbDLACores() const noexcept = 0; + virtual void setGpuAllocator(IGpuAllocator* allocator) noexcept = 0; + virtual void setErrorRecorder(IErrorRecorder* recorder) noexcept = 0; + virtual IErrorRecorder* getErrorRecorder() const noexcept = 0; + virtual ILogger* getLogger() const noexcept = 0; + virtual bool setMaxThreads(int32_t maxThreads) noexcept = 0; + virtual int32_t getMaxThreads() const noexcept = 0; + virtual void setTemporaryDirectory(char const*) noexcept = 0; + virtual char const* getTemporaryDirectory() const noexcept = 0; + virtual void setTempfileControlFlags(TempfileControlFlags) noexcept = 0; + virtual TempfileControlFlags getTempfileControlFlags() const noexcept = 0; + virtual IPluginRegistry& getPluginRegistry() noexcept = 0; + virtual void setPluginRegistryParent(IPluginRegistry* parent) noexcept = 0; + virtual IRuntime* loadRuntime(char const* path) noexcept = 0; + virtual void setEngineHostCodeAllowed(bool allowed) noexcept = 0; + virtual bool getEngineHostCodeAllowed() const noexcept = 0; + // Added in TensorRT version 10.7 + virtual nvinfer1::ICudaEngine* deserializeCudaEngineV2(IStreamReaderV2& streamReader) noexcept = 0; +}; + +class VRefitter : public VRoot +{ +public: + virtual IRefitter* getPImpl() noexcept = 0; + virtual bool setWeights(char const* layerName, WeightsRole role, const Weights weights) noexcept = 0; + virtual bool refitCudaEngine() noexcept = 0; + virtual int32_t getMissing(int32_t size, char const** layerNames, WeightsRole* roles) noexcept = 0; + virtual int32_t getAll(int32_t size, char const** layerNames, WeightsRole* roles) noexcept = 0; + virtual bool setDynamicRange(char const* tensorName, float min, float max) noexcept = 0; + virtual float getDynamicRangeMin(char const* tensorName) const noexcept = 0; + virtual float getDynamicRangeMax(char const* tensorName) const noexcept = 0; + virtual int32_t getTensorsWithDynamicRange(int32_t size, char const** tensorNames) const noexcept = 0; + virtual void setErrorRecorder(IErrorRecorder* recorder) noexcept = 0; + virtual IErrorRecorder* getErrorRecorder() const noexcept = 0; + virtual bool setNamedWeights(char const* name, Weights weights) noexcept = 0; + virtual int32_t getMissingWeights(int32_t size, char const** weightsNames) noexcept = 0; + virtual int32_t getAllWeights(int32_t size, char const** weightsNames) noexcept = 0; + virtual ILogger* getLogger() const noexcept = 0; + virtual bool setMaxThreads(int32_t maxThreads) noexcept = 0; + virtual int32_t getMaxThreads() const noexcept = 0; + virtual bool setNamedWeightsWithLocation(char const* name, Weights weights, TensorLocation location) noexcept = 0; + virtual Weights getNamedWeights(char const* weightsName) const noexcept = 0; + virtual TensorLocation getWeightsLocation(char const* weightsName) const noexcept = 0; + virtual bool unsetNamedWeights(char const* weightsName) noexcept = 0; + virtual void setWeightsValidation(bool weightsValidation) noexcept = 0; + virtual bool getWeightsValidation() const noexcept = 0; + virtual bool refitCudaEngineAsync(cudaStream_t stream) noexcept = 0; + virtual Weights getWeightsPrototype(char const* weightsName) const noexcept = 0; +}; + +class VOptimizationProfile : public VRoot +{ +public: + virtual bool setDimensions(char const* inputName, OptProfileSelector select, Dims const& dims) noexcept = 0; + virtual Dims getDimensions(char const* inputName, OptProfileSelector select) const noexcept = 0; + virtual bool setShapeValues( + char const* inputName, OptProfileSelector select, int32_t const* values, int32_t nbValues) noexcept = 0; + virtual int32_t getNbShapeValues(char const* inputName) const noexcept = 0; + virtual int32_t const* getShapeValues(char const* inputName, OptProfileSelector select) const noexcept = 0; + virtual bool setExtraMemoryTarget(float target) noexcept = 0; + virtual float getExtraMemoryTarget() const noexcept = 0; + virtual bool isValid() const noexcept = 0; + // Added in TensorRT 10.11 + TRT_NODISCARD virtual bool setShapeValuesV2( + char const* inputName, OptProfileSelector select, int64_t const* values, int32_t nbValues) noexcept = 0; + TRT_NODISCARD virtual int64_t const* getShapeValuesV2( + char const* inputName, OptProfileSelector select) const noexcept = 0; +}; + +class VCudaEngine : public VRoot +{ +public: + virtual ICudaEngine* getPImpl() noexcept = 0; + virtual int32_t getNbLayers() const noexcept = 0; + virtual IHostMemory* serialize() const noexcept = 0; + virtual IExecutionContext* createExecutionContext(ExecutionContextAllocationStrategy strategy) noexcept = 0; + virtual IExecutionContext* createExecutionContextWithoutDeviceMemory() noexcept = 0; + virtual size_t getDeviceMemorySize() const noexcept = 0; + virtual bool isRefittable() const noexcept = 0; + virtual char const* getName() const noexcept = 0; + virtual int32_t getNbOptimizationProfiles() const noexcept = 0; + virtual int32_t const* getProfileTensorValues( + char const* tensorName, int32_t profileIndex, OptProfileSelector select) const noexcept = 0; + virtual EngineCapability getEngineCapability() const noexcept = 0; + virtual void setErrorRecorder(IErrorRecorder* recorder) noexcept = 0; + virtual IErrorRecorder* getErrorRecorder() const noexcept = 0; + virtual bool hasImplicitBatchDimension() const noexcept = 0; + virtual TacticSources getTacticSources() const noexcept = 0; + virtual ProfilingVerbosity getProfilingVerbosity() const noexcept = 0; + virtual IEngineInspector* createEngineInspector() const noexcept = 0; + virtual Dims getTensorShape(char const* tensorName) const noexcept = 0; + virtual DataType getTensorDataType(char const* tensorName) const noexcept = 0; + virtual TensorLocation getTensorLocation(char const* tensorName) const noexcept = 0; + virtual bool isShapeInferenceIO(char const* tensorName) const noexcept = 0; + virtual TensorIOMode getTensorIOMode(char const* tensorName) const noexcept = 0; + virtual int32_t getTensorBytesPerComponent(char const* tensorName) const noexcept = 0; + virtual int32_t getTensorComponentsPerElement(char const* tensorName) const noexcept = 0; + virtual TensorFormat getTensorFormat(char const* tensorName) const noexcept = 0; + virtual char const* getTensorFormatDesc(char const* tensorName) const noexcept = 0; + virtual int32_t getTensorVectorizedDim(char const* tensorName) const noexcept = 0; + virtual Dims getProfileShape( + char const* tensorName, int32_t profileIndex, OptProfileSelector select) const noexcept = 0; + virtual int32_t getNbIOTensors() const noexcept = 0; + virtual char const* getIOTensorName(int32_t index) const noexcept = 0; + virtual HardwareCompatibilityLevel getHardwareCompatibilityLevel() const noexcept = 0; + virtual int32_t getNbAuxStreams() const noexcept = 0; + + virtual int32_t getTensorBytesPerComponentV2(char const* tensorName, int32_t profileIndex) const noexcept = 0; + virtual int32_t getTensorComponentsPerElementV2(char const* tensorName, int32_t profileIndex) const noexcept = 0; + virtual TensorFormat getTensorFormatV2(char const* tensorName, int32_t profileIndex) const noexcept = 0; + virtual char const* getTensorFormatDescV2(char const* tensorName, int32_t profileIndex) const noexcept = 0; + virtual int32_t getTensorVectorizedDimV2(char const* tensorName, int32_t profileIndex) const noexcept = 0; + + virtual ISerializationConfig* createSerializationConfig() noexcept = 0; + virtual IHostMemory* serializeWithConfig(ISerializationConfig& config) const noexcept = 0; + + virtual size_t getDeviceMemorySizeForProfile(int32_t profileIndex) const noexcept = 0; + virtual IRefitter* createRefitter(ILogger& logger) noexcept = 0; + + virtual bool setWeightStreamingBudget(int64_t gpuMemoryBudget) noexcept = 0; + virtual int64_t getWeightStreamingBudget() const noexcept = 0; + virtual int64_t getMinimumWeightStreamingBudget() const noexcept = 0; + virtual int64_t getStreamableWeightsSize() const noexcept = 0; + + virtual bool isDebugTensor(char const* name) const noexcept = 0; + + // Added in TensorRT 10.1 + virtual bool setWeightStreamingBudgetV2(int64_t gpuMemoryBudget) noexcept = 0; + virtual int64_t getWeightStreamingBudgetV2() const noexcept = 0; + virtual int64_t getWeightStreamingAutomaticBudget() const noexcept = 0; + virtual int64_t getWeightStreamingScratchMemorySize() const noexcept = 0; + virtual int64_t getDeviceMemorySizeV2() const noexcept = 0; + virtual int64_t getDeviceMemorySizeForProfileV2(int32_t profileIndex) const noexcept = 0; + // Added in TensorRT 10.11 + TRT_NODISCARD virtual int64_t const* getProfileTensorValuesV2( + char const* tensorName, int32_t profileIndex, OptProfileSelector select) const noexcept = 0; + TRT_NODISCARD virtual IExecutionContext* createExecutionContextWithRuntimeConfig( + IRuntimeConfig* runtimeConfig) noexcept = 0; + TRT_NODISCARD virtual IRuntimeConfig* createRuntimeConfig() noexcept = 0; + TRT_NODISCARD virtual int64_t getEngineStat(EngineStat stat) const noexcept = 0; + // Added in TensorRT 10.15 + TRT_NODISCARD virtual char const* getAliasedInputTensor(char const* tensorName) const noexcept = 0; +}; + +class VExecutionContext : public VRoot +{ +public: + virtual IExecutionContext* getPImpl() noexcept = 0; + virtual void setDebugSync(bool sync) noexcept = 0; + virtual bool getDebugSync() const noexcept = 0; + virtual void setProfiler(IProfiler*) noexcept = 0; + virtual IProfiler* getProfiler() const noexcept = 0; + virtual ICudaEngine const& getEngine() const noexcept = 0; + virtual void setName(char const* name) noexcept = 0; + virtual char const* getName() const noexcept = 0; + virtual void setDeviceMemory(void* memory) noexcept = 0; + virtual int32_t getOptimizationProfile() const noexcept = 0; + virtual bool allInputDimensionsSpecified() const noexcept = 0; + virtual bool allInputShapesSpecified() const noexcept = 0; + virtual void setErrorRecorder(IErrorRecorder* recorder) noexcept = 0; + virtual IErrorRecorder* getErrorRecorder() const noexcept = 0; + virtual bool executeV2(void* const* bindings) noexcept = 0; + virtual bool setOptimizationProfileAsync(int32_t profileIndex, cudaStream_t stream) noexcept = 0; + virtual void setEnqueueEmitsProfile(bool enqueueEmitsProfile) noexcept = 0; + virtual bool getEnqueueEmitsProfile() const noexcept = 0; + virtual bool reportToProfiler() const noexcept = 0; + virtual bool setInputShape(char const* tensorName, Dims const& dims) noexcept = 0; + virtual Dims getTensorShape(char const* tensorName) const noexcept = 0; + virtual Dims getTensorStrides(char const* tensorName) const noexcept = 0; + virtual bool setTensorAddress(char const* tensorName, void* data) noexcept = 0; + virtual void const* getTensorAddress(char const* tensorName) const noexcept = 0; + virtual bool setInputTensorAddress(char const* tensorName, void const* data) noexcept = 0; + virtual bool setOutputTensorAddress(char const* tensorName, void* data) noexcept = 0; + virtual int32_t inferShapes(int32_t nbMaxNames, char const** tensorNames) noexcept = 0; + virtual bool setInputConsumedEvent(cudaEvent_t event) noexcept = 0; + virtual cudaEvent_t getInputConsumedEvent() const noexcept = 0; + virtual void* getOutputTensorAddress(char const* tensorName) const noexcept = 0; + virtual bool setOutputAllocator(char const* tensorName, IOutputAllocator* outputAllocator) noexcept = 0; + virtual IOutputAllocator* getOutputAllocator(char const* name) noexcept = 0; + virtual int64_t getMaxOutputSize(char const* tensorName) const noexcept = 0; + virtual bool setTemporaryStorageAllocator(IGpuAllocator* allocator) noexcept = 0; + virtual IGpuAllocator* getTemporaryStorageAllocator() const noexcept = 0; + virtual bool enqueueV3(cudaStream_t stream) noexcept = 0; + virtual void setPersistentCacheLimit(size_t size) noexcept = 0; + virtual size_t getPersistentCacheLimit() const noexcept = 0; + virtual bool setNvtxVerbosity(ProfilingVerbosity verbosity) noexcept = 0; + virtual ProfilingVerbosity getNvtxVerbosity() const noexcept = 0; + virtual void setAuxStreams(cudaStream_t* auxStreams, int32_t nbStreams) noexcept = 0; + virtual bool setDebugListener(IDebugListener* listener) noexcept = 0; + virtual IDebugListener* getDebugListener() noexcept = 0; + virtual bool setTensorDebugState(char const* name, bool flag) noexcept = 0; + virtual bool getDebugState(char const* name) const noexcept = 0; + virtual bool setAllTensorsDebugState(bool flag) noexcept = 0; + virtual size_t updateDeviceMemorySizeForShapes() noexcept = 0; + virtual void setDeviceMemoryV2(void* memory, int64_t size) noexcept = 0; + TRT_NODISCARD virtual IRuntimeConfig* getRuntimeConfig() const noexcept = 0; + virtual bool setUnfusedTensorsDebugState(bool flag) noexcept = 0; + virtual bool getUnfusedTensorsDebugState() const noexcept = 0; +#if ENABLE_FEATURE_DISABLE_RUNTIME_ALLOCATION + virtual bool isStreamCapturable(cudaStream_t stream) const noexcept = 0; +#endif // ENABLE_FEATURE_DISABLE_RUNTIME_ALLOCATION + virtual bool setCommunicator(void* communicator) noexcept = 0; +}; + +class VEngineInspector : public VRoot +{ +public: + virtual IEngineInspector* getPImpl() noexcept = 0; + virtual bool setExecutionContext(IExecutionContext const* context) noexcept = 0; + virtual IExecutionContext const* getExecutionContext() const noexcept = 0; + virtual char const* getLayerInformation(int32_t layerIndex, LayerInformationFormat format) const noexcept = 0; + virtual char const* getEngineInformation(LayerInformationFormat format) const noexcept = 0; + virtual void setErrorRecorder(IErrorRecorder* recorder) noexcept = 0; + virtual IErrorRecorder* getErrorRecorder() const noexcept = 0; +}; + +class VTensor : public VRoot +{ +public: + virtual void setName(char const* name) noexcept = 0; + virtual char const* getName() const noexcept = 0; + virtual void setDimensions(Dims const& dimensions) noexcept = 0; + virtual Dims getDimensions() const noexcept = 0; + virtual void setType(DataType type) noexcept = 0; + virtual DataType getType() const noexcept = 0; + virtual bool setDynamicRange(float min, float max) noexcept = 0; + virtual bool isNetworkInput() const noexcept = 0; + virtual bool isNetworkOutput() const noexcept = 0; + virtual void setBroadcastAcrossBatch(bool broadcastAcrossBatch) noexcept = 0; + virtual bool getBroadcastAcrossBatch() const noexcept = 0; + virtual TensorLocation getLocation() const noexcept = 0; + virtual void setLocation(TensorLocation location) noexcept = 0; + virtual bool dynamicRangeIsSet() const noexcept = 0; + virtual void resetDynamicRange() noexcept = 0; + virtual float getDynamicRangeMin() const noexcept = 0; + virtual float getDynamicRangeMax() const noexcept = 0; + virtual void setAllowedFormats(TensorFormats formats) noexcept = 0; + virtual TensorFormats getAllowedFormats() const noexcept = 0; + virtual bool isShapeTensor() const noexcept = 0; + virtual bool isExecutionTensor() const noexcept = 0; + virtual void setDimensionName(int32_t index, char const* name) noexcept = 0; + virtual char const* getDimensionName(int32_t index) const noexcept = 0; +}; + +class VLayer : public VRoot +{ +public: + virtual LayerType getType() const noexcept = 0; + virtual void setName(char const* name) noexcept = 0; + virtual char const* getName() const noexcept = 0; + virtual int32_t getNbInputs() const noexcept = 0; + virtual ITensor* getInput(int32_t index) const noexcept = 0; + virtual int32_t getNbOutputs() const noexcept = 0; + virtual ITensor* getOutput(int32_t index) const noexcept = 0; + virtual void setInput(int32_t index, ITensor& tensor) noexcept = 0; + virtual void setPrecision(DataType dataType) noexcept = 0; + virtual DataType getPrecision() const noexcept = 0; + virtual bool precisionIsSet() const noexcept = 0; + virtual void resetPrecision() noexcept = 0; + virtual void setOutputType(int32_t index, DataType dataType) noexcept = 0; + virtual DataType getOutputType(int32_t index) const noexcept = 0; + virtual bool outputTypeIsSet(int32_t index) const noexcept = 0; + virtual void resetOutputType(int32_t index) noexcept = 0; + virtual void setMetadata(char const* docString) noexcept = 0; + virtual char const* getMetadata() const noexcept = 0; + virtual bool setNbRanks(int32_t nbRanks) noexcept = 0; + virtual int32_t getNbRanks() const noexcept = 0; +}; + +class VConvolutionLayer : public VRoot +{ +public: + virtual void setNbOutputMaps(int64_t nbOutputMaps) noexcept = 0; + virtual int64_t getNbOutputMaps() const noexcept = 0; + virtual void setNbGroups(int64_t nbGroups) noexcept = 0; + virtual int64_t getNbGroups() const noexcept = 0; + virtual void setKernelWeights(Weights weights) noexcept = 0; + virtual Weights getKernelWeights() const noexcept = 0; + virtual void setBiasWeights(Weights weights) noexcept = 0; + virtual Weights getBiasWeights() const noexcept = 0; + virtual void setPrePadding(Dims const& padding) noexcept = 0; + virtual Dims getPrePadding() const noexcept = 0; + virtual void setPostPadding(Dims const& padding) noexcept = 0; + virtual Dims getPostPadding() const noexcept = 0; + virtual void setPaddingMode(PaddingMode paddingMode) noexcept = 0; + virtual PaddingMode getPaddingMode() const noexcept = 0; + virtual void setKernelSizeNd(Dims const& kernelSize) noexcept = 0; + virtual Dims getKernelSizeNd() const noexcept = 0; + virtual void setStrideNd(Dims const& stride) noexcept = 0; + virtual Dims getStrideNd() const noexcept = 0; + virtual void setPaddingNd(Dims const& padding) noexcept = 0; + virtual Dims getPaddingNd() const noexcept = 0; + virtual void setDilationNd(Dims const& dilation) noexcept = 0; + virtual Dims getDilationNd() const noexcept = 0; +}; + +class VActivationLayer : public VRoot +{ +public: + virtual void setActivationType(ActivationType type) noexcept = 0; + virtual ActivationType getActivationType() const noexcept = 0; + virtual void setAlpha(float alpha) noexcept = 0; + virtual void setBeta(float beta) noexcept = 0; + virtual float getAlpha() const noexcept = 0; + virtual float getBeta() const noexcept = 0; +}; + +class VPoolingLayer : public VRoot +{ +public: + virtual void setPoolingType(PoolingType type) noexcept = 0; + virtual PoolingType getPoolingType() const noexcept = 0; + virtual void setBlendFactor(float blendFactor) noexcept = 0; + virtual float getBlendFactor() const noexcept = 0; + virtual void setAverageCountExcludesPadding(bool exclusive) noexcept = 0; + virtual bool getAverageCountExcludesPadding() const noexcept = 0; + virtual void setPrePadding(Dims const& padding) noexcept = 0; + virtual Dims getPrePadding() const noexcept = 0; + virtual void setPostPadding(Dims const& padding) noexcept = 0; + virtual Dims getPostPadding() const noexcept = 0; + virtual void setPaddingMode(PaddingMode paddingMode) noexcept = 0; + virtual PaddingMode getPaddingMode() const noexcept = 0; + virtual void setWindowSizeNd(Dims const& windowSize) noexcept = 0; + virtual Dims getWindowSizeNd() const noexcept = 0; + virtual void setStrideNd(Dims const& stride) noexcept = 0; + virtual Dims getStrideNd() const noexcept = 0; + virtual void setPaddingNd(Dims const& padding) noexcept = 0; + virtual Dims getPaddingNd() const noexcept = 0; +}; + +class VLRNLayer : public VRoot +{ +public: + virtual void setWindowSize(int64_t windowSize) noexcept = 0; + virtual int64_t getWindowSize() const noexcept = 0; + virtual void setAlpha(float alpha) noexcept = 0; + virtual float getAlpha() const noexcept = 0; + virtual void setBeta(float beta) noexcept = 0; + virtual float getBeta() const noexcept = 0; + virtual void setK(float k) noexcept = 0; + virtual float getK() const noexcept = 0; +}; + +class VScaleLayer : public VRoot +{ +public: + virtual void setMode(ScaleMode mode) noexcept = 0; + virtual ScaleMode getMode() const noexcept = 0; + virtual void setShift(Weights shift) noexcept = 0; + virtual Weights getShift() const noexcept = 0; + virtual void setScale(Weights scale) noexcept = 0; + virtual Weights getScale() const noexcept = 0; + virtual void setPower(Weights power) noexcept = 0; + virtual Weights getPower() const noexcept = 0; + virtual int32_t getChannelAxis() const noexcept = 0; + virtual void setChannelAxis(int32_t channelAxis) noexcept = 0; +}; + +class VSoftMaxLayer : public VRoot +{ +public: + virtual void setAxes(uint32_t axes) noexcept = 0; + virtual uint32_t getAxes() const noexcept = 0; +}; + +class VConcatenationLayer : public VRoot +{ +public: + virtual void setAxis(int32_t axis) noexcept = 0; + virtual int32_t getAxis() const noexcept = 0; +}; + +class VDeconvolutionLayer : public VRoot +{ +public: + virtual void setNbOutputMaps(int64_t nbOutputMaps) noexcept = 0; + virtual int64_t getNbOutputMaps() const noexcept = 0; + virtual void setNbGroups(int64_t nbGroups) noexcept = 0; + virtual int64_t getNbGroups() const noexcept = 0; + virtual void setKernelWeights(Weights weights) noexcept = 0; + virtual Weights getKernelWeights() const noexcept = 0; + virtual void setBiasWeights(Weights weights) noexcept = 0; + virtual Weights getBiasWeights() const noexcept = 0; + virtual void setPrePadding(Dims const& padding) noexcept = 0; + virtual Dims getPrePadding() const noexcept = 0; + virtual void setPostPadding(Dims const& padding) noexcept = 0; + virtual Dims getPostPadding() const noexcept = 0; + virtual void setPaddingMode(PaddingMode paddingMode) noexcept = 0; + virtual PaddingMode getPaddingMode() const noexcept = 0; + virtual void setKernelSizeNd(Dims const& kernelSize) noexcept = 0; + virtual Dims getKernelSizeNd() const noexcept = 0; + virtual void setStrideNd(Dims const& stride) noexcept = 0; + virtual Dims getStrideNd() const noexcept = 0; + virtual void setPaddingNd(Dims const& padding) noexcept = 0; + virtual Dims getPaddingNd() const noexcept = 0; + virtual void setDilationNd(Dims const& dilation) noexcept = 0; + virtual Dims getDilationNd() const noexcept = 0; +}; + +class VElementWiseLayer : public VRoot +{ +public: + virtual void setOperation(ElementWiseOperation op) noexcept = 0; + virtual ElementWiseOperation getOperation() const noexcept = 0; +}; + +class VGatherLayer : public VRoot +{ +public: + virtual void setGatherAxis(int32_t axis) noexcept = 0; + virtual int32_t getGatherAxis() const noexcept = 0; + virtual void setNbElementWiseDims(int32_t k) noexcept = 0; + virtual int32_t getNbElementWiseDims() const noexcept = 0; + virtual void setMode(GatherMode mode) noexcept = 0; + virtual GatherMode getMode() const noexcept = 0; +}; + +class VPluginLayer : public VRoot +{ +public: + virtual IPlugin& getPlugin() noexcept = 0; +}; + +class VPluginV2Layer : public VRoot +{ +public: + virtual IPluginV2& getPlugin() noexcept = 0; +}; + +class VPluginV3Layer : public VRoot +{ +public: + virtual IPluginV3& getPlugin() noexcept = 0; +}; + +class VUnaryLayer : public VRoot +{ +public: + virtual void setOperation(UnaryOperation op) noexcept = 0; + virtual UnaryOperation getOperation() const noexcept = 0; +}; + +class VReduceLayer : public VRoot +{ +public: + virtual void setOperation(ReduceOperation op) noexcept = 0; + virtual ReduceOperation getOperation() const noexcept = 0; + virtual void setReduceAxes(uint32_t reduceAxes) noexcept = 0; + virtual uint32_t getReduceAxes() const noexcept = 0; + virtual void setKeepDimensions(bool keepDimensions) noexcept = 0; + virtual bool getKeepDimensions() const noexcept = 0; +}; + +class VPaddingLayer : public VRoot +{ +public: + virtual void setPrePaddingNd(Dims const& padding) noexcept = 0; + virtual Dims getPrePaddingNd() const noexcept = 0; + virtual void setPostPaddingNd(Dims const& padding) noexcept = 0; + virtual Dims getPostPaddingNd() const noexcept = 0; +}; + +class VShuffleLayer : public VRoot +{ +public: + virtual void setFirstTranspose(Permutation const& permutation) noexcept = 0; + virtual Permutation const& getFirstTranspose() const noexcept = 0; + virtual void setReshapeDimensions(Dims const& dimensions) noexcept = 0; + virtual Dims getReshapeDimensions() const noexcept = 0; + virtual void setSecondTranspose(Permutation const& permutation) noexcept = 0; + virtual Permutation const& getSecondTranspose() const noexcept = 0; + virtual void setZeroIsPlaceholder(bool zeroIsPlaceholder) noexcept = 0; + virtual bool getZeroIsPlaceholder() const noexcept = 0; +}; + +class VSliceLayer : public VRoot +{ +public: + virtual void setStart(Dims const& start) noexcept = 0; + virtual Dims getStart() const noexcept = 0; + virtual void setSize(Dims const& size) noexcept = 0; + virtual Dims getSize() const noexcept = 0; + virtual void setStride(Dims const& stride) noexcept = 0; + virtual Dims getStride() const noexcept = 0; + virtual void setMode(SampleMode mode) noexcept = 0; + virtual SampleMode getMode() const noexcept = 0; + virtual void setAxes(Dims const& axes) noexcept = 0; + virtual Dims getAxes() const noexcept = 0; +}; + +class VShapeLayer : public VRoot +{ +public: +}; + +class VTopKLayer : public VRoot +{ +public: + virtual void setOperation(TopKOperation op) noexcept = 0; + virtual TopKOperation getOperation() const noexcept = 0; + virtual void setK(int32_t k) noexcept = 0; + virtual int32_t getK() const noexcept = 0; + virtual void setReduceAxes(uint32_t reduceAxes) noexcept = 0; + virtual uint32_t getReduceAxes() const noexcept = 0; + virtual bool setIndicesType(DataType type) noexcept = 0; + virtual DataType getIndicesType() const noexcept = 0; +}; + +class VMatrixMultiplyLayer : public VRoot +{ +public: + virtual void setOperation(int32_t index, MatrixOperation op) noexcept = 0; + virtual MatrixOperation getOperation(int32_t index) const noexcept = 0; +}; + +class VNonZeroLayer : public VRoot +{ +public: + virtual bool setIndicesType(DataType type) noexcept = 0; + virtual DataType getIndicesType() const noexcept = 0; +}; + +class VRaggedSoftMaxLayer : public VRoot +{ +public: +}; + +class VIdentityLayer : public VRoot +{ +public: +}; + +class VCastLayer : public VRoot +{ +public: + virtual void setToType(DataType toType) noexcept = 0; + virtual DataType getToType() const noexcept = 0; +}; + +class VConstantLayer : public VRoot +{ +public: + virtual void setWeights(Weights weights) noexcept = 0; + virtual Weights getWeights() const noexcept = 0; + virtual void setDimensions(Dims const& dimensions) noexcept = 0; + virtual Dims getDimensions() const noexcept = 0; +}; + +class VParametricReLULayer : public VRoot +{ +public: +}; + +class VResizeLayer : public VRoot +{ +public: + virtual void setOutputDimensions(Dims const& dimensions) noexcept = 0; + virtual Dims getOutputDimensions() const noexcept = 0; + virtual void setScales(float const* scales, int32_t nbScales) noexcept = 0; + virtual int32_t getScales(int32_t size, float* scales) const noexcept = 0; + virtual void setResizeMode(InterpolationMode interpolationMode) noexcept = 0; + virtual InterpolationMode getResizeMode() const noexcept = 0; + virtual void setCoordinateTransformation(ResizeCoordinateTransformation coordTransform) noexcept = 0; + virtual ResizeCoordinateTransformation getCoordinateTransformation() const noexcept = 0; + virtual void setSelectorForSinglePixel(ResizeSelector selector) noexcept = 0; + virtual ResizeSelector getSelectorForSinglePixel() const noexcept = 0; + virtual void setNearestRounding(ResizeRoundMode value) noexcept = 0; + virtual ResizeRoundMode getNearestRounding() const noexcept = 0; + virtual void setCubicCoeff(float value) noexcept = 0; + virtual float getCubicCoeff() const noexcept = 0; + virtual void setExcludeOutside(bool value) noexcept = 0; + virtual bool getExcludeOutside() const noexcept = 0; +}; + +class VLoopBoundaryLayer : public VRoot +{ +public: + virtual ILoop* getLoop() const noexcept = 0; +}; + +class VRecurrenceLayer : public VRoot +{ +public: +}; + +class VLoopOutputLayer : public VRoot +{ +public: + virtual LoopOutput getLoopOutput() const noexcept = 0; + virtual void setAxis(int32_t axis) noexcept = 0; + virtual int32_t getAxis() const noexcept = 0; +}; + +class VTripLimitLayer : public VRoot +{ +public: + virtual TripLimit getTripLimit() const noexcept = 0; +}; + +class VIteratorLayer : public VRoot +{ +public: + virtual void setAxis(int32_t axis) noexcept = 0; + virtual int32_t getAxis() const noexcept = 0; + virtual void setReverse(bool reverse) noexcept = 0; + virtual bool getReverse() const noexcept = 0; +}; +class VLoop : public VRoot +{ +public: + virtual IRecurrenceLayer* addRecurrence(ITensor& initialValue) noexcept = 0; + virtual ITripLimitLayer* addTripLimit(ITensor& tensor, TripLimit limit) noexcept = 0; + virtual IIteratorLayer* addIterator(ITensor& tensor, int32_t axis = 0, bool reverse = false) noexcept = 0; + virtual ILoopOutputLayer* addLoopOutput(ITensor& tensor, LoopOutput outputKind, int32_t axis = 0) noexcept = 0; + virtual void setName(char const* name) noexcept = 0; + virtual char const* getName() const noexcept = 0; +}; + +class VConditionalBoundaryLayer : public VRoot +{ +public: + virtual IIfConditional* getConditional() const noexcept = 0; +}; + +class VConditionLayer : public VRoot +{ +public: +}; + +class VConditionalInputLayer : public VRoot +{ +public: +}; + +class VConditionalOutputLayer : public VRoot +{ +public: +}; + +class VIfConditional : public VRoot +{ +public: + virtual IConditionLayer* setCondition(ITensor& tensor) noexcept = 0; + virtual IIfConditionalInputLayer* addInput(ITensor& tensor) noexcept = 0; + virtual IIfConditionalOutputLayer* addOutput(ITensor& trueTensor, ITensor& falseTensor) noexcept = 0; + virtual void setName(char const* name) noexcept = 0; + virtual char const* getName() const noexcept = 0; +}; + +class VAttentionBoundaryLayer : public VRoot +{ +public: + virtual IAttention* getAttention() const noexcept = 0; +}; + +class VAttentionInputLayer : public VRoot +{ +public: +}; + +class VAttentionOutputLayer : public VRoot +{ +public: +}; + +class VAttention : public VRoot +{ +public: + TRT_NODISCARD virtual bool setInput(int32_t index, ITensor& input) noexcept = 0; + TRT_NODISCARD virtual int32_t getNbInputs() const noexcept = 0; + TRT_NODISCARD virtual ITensor* getInput(int32_t index) const noexcept = 0; + TRT_NODISCARD virtual int32_t getNbOutputs() const noexcept = 0; + TRT_NODISCARD virtual ITensor* getOutput(int32_t index) const noexcept = 0; + TRT_NODISCARD virtual bool setName(char const* name) noexcept = 0; + TRT_NODISCARD virtual char const* getName() const noexcept = 0; + TRT_NODISCARD virtual bool setNormalizationOperation(AttentionNormalizationOp op) noexcept = 0; + TRT_NODISCARD virtual AttentionNormalizationOp getNormalizationOperation() const noexcept = 0; + TRT_NODISCARD virtual bool setCausal(bool isCausal) noexcept = 0; + TRT_NODISCARD virtual bool getCausal() const noexcept = 0; + TRT_NODISCARD virtual bool setMask(ITensor& mask) noexcept = 0; + TRT_NODISCARD virtual ITensor* getMask() const noexcept = 0; + TRT_NODISCARD virtual bool setDecomposable(bool decomposable) noexcept = 0; + TRT_NODISCARD virtual bool getDecomposable() const noexcept = 0; + TRT_NODISCARD virtual bool setNormalizationQuantizeScale(ITensor& tensor) noexcept = 0; + TRT_NODISCARD virtual ITensor* getNormalizationQuantizeScale() const noexcept = 0; + TRT_NODISCARD virtual bool setNormalizationQuantizeToType(DataType type) noexcept = 0; + TRT_NODISCARD virtual DataType getNormalizationQuantizeToType() const noexcept = 0; + TRT_NODISCARD virtual bool setMetadata(char const* docString) noexcept = 0; + TRT_NODISCARD virtual char const* getMetadata() const noexcept = 0; + TRT_NODISCARD virtual bool setNbRanks(int32_t nbRanks) noexcept = 0; + TRT_NODISCARD virtual int32_t getNbRanks() const noexcept = 0; +}; // class VAttention + +class VSelectLayer : public VRoot +{ +}; + +class VAssertionLayer : public VRoot +{ +public: + virtual void setMessage(char const* message) noexcept = 0; + virtual char const* getMessage() const noexcept = 0; +}; + +class VFillLayer : public VRoot +{ +public: + virtual void setDimensions(Dims const& dimensions) noexcept = 0; + virtual Dims getDimensions() const noexcept = 0; + virtual void setOperation(FillOperation op) noexcept = 0; + virtual FillOperation getOperation() const noexcept = 0; + virtual void setAlpha(double alpha) noexcept = 0; + virtual double getAlpha() const noexcept = 0; + virtual void setBeta(double beta) noexcept = 0; + virtual double getBeta() const noexcept = 0; + virtual void setAlphaInt64(int64_t alpha) noexcept = 0; + virtual int64_t getAlphaInt64() const noexcept = 0; + virtual void setBetaInt64(int64_t beta) noexcept = 0; + virtual int64_t getBetaInt64() const noexcept = 0; + virtual bool isAlphaBetaInt64() const noexcept = 0; + virtual DataType getToType() const noexcept = 0; + virtual void setToType(DataType toType) noexcept = 0; +}; + +class VQuantizeLayer : public VRoot +{ +public: + virtual int32_t getAxis() const noexcept = 0; + virtual void setAxis(int32_t axis) noexcept = 0; + virtual DataType getToType() const noexcept = 0; + virtual void setToType(DataType toType) noexcept = 0; + virtual Dims getBlockShape() const noexcept = 0; + virtual bool setBlockShape(Dims const& blockShape) noexcept = 0; +}; + +class VDequantizeLayer : public VRoot +{ +public: + virtual int32_t getAxis() const noexcept = 0; + virtual void setAxis(int32_t axis) noexcept = 0; + virtual DataType getToType() const noexcept = 0; + virtual void setToType(DataType toType) noexcept = 0; + virtual Dims getBlockShape() const noexcept = 0; + virtual bool setBlockShape(Dims const& blockShape) noexcept = 0; +}; + +class VDynamicQuantizeLayer : public VRoot +{ +public: + TRT_DEPRECATED virtual int32_t getAxis() const noexcept = 0; + TRT_DEPRECATED virtual void setAxis(int32_t axis) noexcept = 0; + TRT_DEPRECATED virtual int32_t getBlockSize() const noexcept = 0; + TRT_DEPRECATED virtual void setBlockSize(int32_t axis) noexcept = 0; + virtual DataType getScaleType() const noexcept = 0; + virtual void setScaleType(DataType axis) noexcept = 0; + virtual DataType getToType() const noexcept = 0; + virtual void setToType(DataType toType) noexcept = 0; + virtual Dims getBlockShape() const noexcept = 0; + virtual void setBlockShape(Dims const& blockShape) noexcept = 0; +}; + +class VScatterLayer : public VRoot +{ +public: + virtual void setMode(ScatterMode mode) noexcept = 0; + virtual ScatterMode getMode() const noexcept = 0; + virtual void setAxis(int32_t axis) noexcept = 0; + virtual int32_t getAxis() const noexcept = 0; +}; // class VScatterLayer + +class VEinsumLayer : public VRoot +{ +public: + virtual bool setEquation(char const* equation) noexcept = 0; + virtual char const* getEquation() const noexcept = 0; +}; + +class VOneHotLayer : public VRoot +{ +public: + virtual int32_t getAxis() const noexcept = 0; + virtual void setAxis(int32_t axis) noexcept = 0; +}; // class VOneHotLayer + +class VGridSampleLayer : public VRoot +{ +public: + virtual void setInterpolationMode(InterpolationMode mode) noexcept = 0; + virtual InterpolationMode getInterpolationMode() const noexcept = 0; + virtual void setAlignCorners(bool alignCorners) noexcept = 0; + virtual bool getAlignCorners() const noexcept = 0; + virtual bool setSampleMode(SampleMode mode) noexcept = 0; + virtual SampleMode getSampleMode() const noexcept = 0; +}; // class VGridSampleLayer + +class VNMSLayer : public VRoot +{ +public: + virtual void setBoundingBoxFormat(BoundingBoxFormat fmt) noexcept = 0; + virtual BoundingBoxFormat getBoundingBoxFormat() const noexcept = 0; + virtual void setTopKBoxLimit(int32_t limit) noexcept = 0; + virtual int32_t getTopKBoxLimit() const noexcept = 0; + virtual bool setIndicesType(DataType type) noexcept = 0; + virtual DataType getIndicesType() const noexcept = 0; +}; // class VNMSLayer + +class VReverseSequenceLayer : public VRoot +{ +public: + virtual void setBatchAxis(int32_t batchAxis) noexcept = 0; + virtual int32_t getBatchAxis() const noexcept = 0; + + virtual void setSequenceAxis(int32_t sequenceAxis) noexcept = 0; + virtual int32_t getSequenceAxis() const noexcept = 0; +}; // class VReverseSequenceLayer + +class VNormalizationLayer : public VRoot +{ +public: + virtual void setEpsilon(float eps) noexcept = 0; + virtual float getEpsilon() const noexcept = 0; + virtual void setAxes(uint32_t axesMask) noexcept = 0; + virtual uint32_t getAxes() const noexcept = 0; + virtual void setNbGroups(int64_t nbGroups) noexcept = 0; + virtual int64_t getNbGroups() const noexcept = 0; + virtual void setComputePrecision(DataType type) noexcept = 0; + virtual DataType getComputePrecision() const noexcept = 0; + virtual bool isV2() const noexcept = 0; +}; // class VNormalizationLayer + +class VSqueezeLayer : public VRoot +{ +}; + +class VUnsqueezeLayer : public VRoot +{ +}; + +class VCumulativeLayer : public VRoot +{ +public: + virtual bool setOperation(CumulativeOperation op) noexcept = 0; + virtual CumulativeOperation getOperation() const noexcept = 0; + virtual void setExclusive(bool exclusive) noexcept = 0; + virtual bool getExclusive() const noexcept = 0; + virtual void setReverse(bool reverse) noexcept = 0; + virtual bool getReverse() const noexcept = 0; +}; // class VCumulativeLayer + +class VRotaryEmbeddingLayer : public VRoot +{ +public: + virtual void setInterleaved(bool interleaved) noexcept = 0; + virtual bool getInterleaved() const noexcept = 0; + virtual bool setRotaryEmbeddingDim(int32_t rotaryEmbeddingDim) noexcept = 0; + virtual int32_t getRotaryEmbeddingDim() const noexcept = 0; + virtual void setInput(int32_t index, ITensor& input) noexcept = 0; +}; // class VRotaryEmbeddingLayer + +class VKVCacheUpdateLayer : public VRoot +{ +public: + TRT_NODISCARD virtual bool setCacheMode(KVCacheMode cacheMode) noexcept = 0; + TRT_NODISCARD virtual KVCacheMode getCacheMode() const noexcept = 0; +}; // class VKVCacheUpdateLayer + +class VMoELayer : public VRoot +{ +public: + virtual void setGatedWeights(ITensor& fcGateWeights, ITensor& fcUpWeights, ITensor& fcDownWeights, MoEActType activationType) noexcept = 0; + virtual void setGatedBiases(ITensor& fcGateBiases, ITensor& fcUpBiases, ITensor& fcDownBiases) noexcept = 0; + virtual void setActivationType(MoEActType activationType) noexcept = 0; + virtual MoEActType getActivationType() const noexcept = 0; + virtual void setQuantizationStatic(ITensor& fcDownActivationScale, DataType dataType) noexcept = 0; + virtual void setQuantizationDynamicDblQ(ITensor& fcDownActivationDblQScale, DataType dataType, Dims const& blockShape, DataType dynQOutputScaleType) noexcept = 0; + virtual void setQuantizationToType(DataType type) noexcept = 0; + virtual DataType getQuantizationToType() const noexcept = 0; + virtual void setQuantizationBlockShape(Dims const& blockShape) noexcept = 0; + virtual Dims getQuantizationBlockShape() const noexcept = 0; + virtual void setDynQOutputScaleType(DataType type) noexcept = 0; + virtual DataType getDynQOutputScaleType() const noexcept = 0; + virtual void setSwigluParams(float limit, float alpha, float beta) noexcept = 0; + virtual void setSwigluParamLimit(float limit) noexcept = 0; + virtual float getSwigluParamLimit() const noexcept = 0; + virtual void setSwigluParamAlpha(float alpha) noexcept = 0; + virtual float getSwigluParamAlpha() const noexcept = 0; + virtual void setSwigluParamBeta(float beta) noexcept = 0; + virtual float getSwigluParamBeta() const noexcept = 0; + virtual void setInput(int32_t index, ITensor& tensor) noexcept = 0; +}; // class VMoELayer + + +class VNetworkDefinition : public VRoot +{ +public: + virtual ITensor* addInput(char const* name, DataType type, Dims const& dimensions) noexcept = 0; + virtual void markOutput(ITensor& tensor) noexcept = 0; + virtual IActivationLayer* addActivation(ITensor& input, ActivationType type) noexcept = 0; + virtual ILRNLayer* addLRN(ITensor& input, int64_t window, float alpha, float beta, float k) noexcept = 0; + virtual IScaleLayer* addScale( + ITensor& input, ScaleMode mode, Weights shift, Weights scale, Weights power) noexcept = 0; + virtual ISoftMaxLayer* addSoftMax(ITensor& input) noexcept = 0; + virtual IConcatenationLayer* addConcatenation(ITensor* const* inputs, int32_t nbInputs) noexcept = 0; + virtual IElementWiseLayer* addElementWise(ITensor& input1, ITensor& input2, ElementWiseOperation op) noexcept = 0; + virtual IUnaryLayer* addUnary(ITensor& input, UnaryOperation operation) noexcept = 0; + virtual IShuffleLayer* addShuffle(ITensor& input) noexcept = 0; + virtual int32_t getNbLayers() const noexcept = 0; + virtual ILayer* getLayer(int32_t index) const noexcept = 0; + virtual int32_t getNbInputs() const noexcept = 0; + virtual ITensor* getInput(int32_t index) const noexcept = 0; + virtual int32_t getNbOutputs() const noexcept = 0; + virtual ITensor* getOutput(int32_t index) const noexcept = 0; + virtual IReduceLayer* addReduce( + ITensor& input, ReduceOperation operation, uint32_t reduceAxes, bool keepDimensions) noexcept + = 0; + virtual ITopKLayer* addTopK(ITensor& input, TopKOperation op, int32_t k, uint32_t reduceAxes) noexcept = 0; + virtual IGatherLayer* addGather(ITensor& data, ITensor& indices, int32_t axis) noexcept = 0; + virtual IRaggedSoftMaxLayer* addRaggedSoftMax(ITensor& input, ITensor& bounds) noexcept = 0; + virtual IMatrixMultiplyLayer* addMatrixMultiply( + ITensor& input0, MatrixOperation op0, ITensor& input1, MatrixOperation op1) noexcept = 0; + virtual IConstantLayer* addConstant(Dims const& dimensions, Weights weights) noexcept = 0; + virtual IIdentityLayer* addIdentity(ITensor& input) noexcept = 0; + virtual void removeTensor(ITensor& tensor) noexcept = 0; + virtual void unmarkOutput(ITensor& tensor) noexcept = 0; + virtual IPluginV2Layer* addPluginV2(ITensor* const* inputs, int32_t nbInputs, IPluginV2& plugin) noexcept = 0; + virtual IPluginV3Layer* addPluginV3(ITensor* const* inputs, int32_t nbInputs, ITensor* const* shapeInputs, + int32_t nbShapeInputs, IPluginV3& plugin) noexcept = 0; + virtual ISliceLayer* addSlice(ITensor& input, Dims const& start, Dims const& size, Dims const& stride) noexcept = 0; + virtual void setName(char const* name) noexcept = 0; + virtual char const* getName() const noexcept = 0; + virtual IShapeLayer* addShape(ITensor& input) noexcept = 0; + virtual bool hasImplicitBatchDimension() const noexcept = 0; + virtual bool markOutputForShapes(ITensor& tensor) noexcept = 0; + virtual bool unmarkOutputForShapes(ITensor& tensor) noexcept = 0; + virtual IParametricReLULayer* addParametricReLU(ITensor& input, ITensor& slope) noexcept = 0; + virtual IConvolutionLayer* addConvolutionNd( + ITensor& input, int64_t nbOutputMaps, Dims const& kernelSize, Weights kernelWeights, Weights biasWeights) noexcept + = 0; + virtual IPoolingLayer* addPoolingNd(ITensor& input, PoolingType type, Dims const& windowSize) noexcept = 0; + virtual IDeconvolutionLayer* addDeconvolutionNd( + ITensor& input, int64_t nbOutputMaps, Dims const& kernelSize, Weights kernelWeights, Weights biasWeights) noexcept + = 0; + virtual IScaleLayer* addScaleNd( + ITensor& input, ScaleMode mode, Weights shift, Weights scale, Weights power, int32_t channelAxis) noexcept = 0; + virtual IResizeLayer* addResize(ITensor& input) noexcept = 0; + virtual ILoop* addLoop() noexcept = 0; + virtual ISelectLayer* addSelect(ITensor& condition, ITensor& thenInput, ITensor& elseInput) noexcept = 0; + virtual IFillLayer* addFill(Dims const& dimensions, FillOperation op) noexcept = 0; + virtual IPaddingLayer* addPaddingNd(ITensor& input, Dims const& prePadding, Dims const& postPadding) noexcept = 0; + virtual bool setWeightsName(Weights weights, char const* name) noexcept = 0; + virtual void setErrorRecorder(IErrorRecorder* recorder) noexcept = 0; + virtual IErrorRecorder* getErrorRecorder() const noexcept = 0; + virtual IDequantizeLayer* addDequantize(ITensor& input, ITensor& scale) noexcept = 0; + virtual IQuantizeLayer* addQuantize(ITensor& input, ITensor& scale) noexcept = 0; + virtual IGatherLayer* addGatherV2(ITensor& data, ITensor& indices, GatherMode mode) noexcept = 0; + virtual IIfConditional* addIfConditional() noexcept = 0; + virtual IScatterLayer* addScatter(ITensor& data, ITensor& indices, ITensor& updates, ScatterMode mode) noexcept = 0; + virtual IEinsumLayer* addEinsum(ITensor* const* inputs, int32_t nbInputs, char const* equation) noexcept = 0; + virtual IAssertionLayer* addAssertion(ITensor& condition, char const* message) noexcept = 0; + virtual IOneHotLayer* addOneHot(ITensor& indices, ITensor& values, ITensor& depth, int32_t axis) noexcept = 0; + virtual INonZeroLayer* addNonZero(ITensor& input) noexcept = 0; + virtual IGridSampleLayer* addGridSample(ITensor& input, ITensor& grid) noexcept = 0; + virtual INMSLayer* addNMS(ITensor& boxes, ITensor& scores, ITensor& maxOutputBoxesPerClass) noexcept = 0; + virtual IReverseSequenceLayer* addReverseSequence(ITensor& input, ITensor& sequenceLens) noexcept = 0; + virtual INormalizationLayer* addNormalization( + ITensor& input, ITensor& scale, ITensor& bias, uint32_t axesMask) noexcept = 0; + virtual ICastLayer* addCast(ITensor& input, DataType toType) noexcept = 0; + virtual IBuilder& getBuilder() const noexcept = 0; + virtual NetworkDefinitionCreationFlags getFlags() const noexcept = 0; + virtual bool getFlag(NetworkDefinitionCreationFlag networkDefinitionCreationFlag) const noexcept = 0; + virtual IQuantizeLayer* addQuantizeV2(ITensor& input, ITensor& scale, DataType outputType) noexcept = 0; + virtual IDequantizeLayer* addDequantizeV2(ITensor& input, ITensor& scale, DataType outputType) noexcept = 0; + virtual IFillLayer* addFillV2(Dims const& dimensions, FillOperation op, DataType outputType) noexcept = 0; + virtual bool markDebug(ITensor& tensor) noexcept = 0; + virtual bool unmarkDebug(ITensor& tensor) noexcept = 0; + virtual bool isDebugTensor(ITensor const& tensor) const noexcept = 0; + virtual bool markWeightsRefittable(char const* name) noexcept = 0; + virtual bool unmarkWeightsRefittable(char const* name) noexcept = 0; + virtual bool areWeightsMarkedRefittable(char const* name) const noexcept = 0; + virtual ISqueezeLayer* addSqueeze(ITensor& input, ITensor& axes) noexcept = 0; + virtual IUnsqueezeLayer* addUnsqueeze(ITensor& input, ITensor& axes) noexcept = 0; + virtual IDynamicQuantizeLayer* addDynamicQuantize( + ITensor& input, int32_t axis, int32_t blockSize, DataType toType, DataType scaleType) noexcept = 0; + virtual ICumulativeLayer* addCumulative( + ITensor& input, ITensor& axis, CumulativeOperation operation, bool exclusive, bool reverse) noexcept = 0; + virtual bool markUnfusedTensorsAsDebugTensors() noexcept = 0; + virtual bool unmarkUnfusedTensorsAsDebugTensors() noexcept = 0; + virtual ITopKLayer* addTopKV2( + ITensor& input, TopKOperation op, int32_t k, uint32_t reduceAxes, DataType indicesType) noexcept = 0; + virtual INonZeroLayer* addNonZeroV2(ITensor& input, DataType indicesType) noexcept = 0; + virtual INMSLayer* addNMSV2( + ITensor& boxes, ITensor& scores, ITensor& maxOutputBoxesPerClass, DataType indicesType) noexcept = 0; + virtual IAttention* addAttention( + ITensor& query, ITensor& key, ITensor& value, AttentionNormalizationOp normOp, bool isCausal) noexcept = 0; + virtual IRotaryEmbeddingLayer* addRotaryEmbedding(ITensor& input, ITensor& cosCache, ITensor& sinCache, + bool interleaved, int32_t rotaryEmbeddingDim) noexcept = 0; + virtual IDynamicQuantizeLayer* addDynamicQuantizeV2( + ITensor& input, Dims const& blockShape, DataType toType, DataType scaleType) noexcept = 0; + virtual IKVCacheUpdateLayer* addKVCacheUpdate( + ITensor& cache, ITensor& update, ITensor& writeIndices, KVCacheMode cacheMode) noexcept = 0; + virtual INormalizationLayer* addNormalizationV2( + ITensor& input, ITensor& scale, ITensor& bias, uint32_t axesMask) noexcept = 0; + virtual IMoELayer* addMoE( + ITensor& hiddenStates, ITensor& selectedExpertsForTokens, ITensor& scoresForSelectedExperts) noexcept = 0; + virtual IDistCollectiveLayer* addDistCollective(ITensor& input, CollectiveOperation distCollectiveOp, + ReduceOperation reduceOp, int64_t root, int64_t* groups, int64_t groupSize) noexcept = 0; +}; + +class VAlgorithmIOInfo : public VRoot +{ +public: + virtual DataType getDataType() const noexcept = 0; + virtual Dims getStrides() const noexcept = 0; + virtual int64_t getVectorizedDim() const noexcept = 0; + virtual int64_t getComponentsPerElement() const noexcept = 0; +}; + +class VAlgorithmVariant : public VRoot +{ +public: + virtual int64_t getImplementation() const noexcept = 0; + virtual int64_t getTactic() const noexcept = 0; +}; + +class VAlgorithmContext : public VRoot +{ +public: + virtual char const* getName() const noexcept = 0; + virtual Dims getDimensions(int32_t index, OptProfileSelector select) const noexcept = 0; + virtual int32_t getNbInputs() const noexcept = 0; + virtual int32_t getNbOutputs() const noexcept = 0; +}; + +class VAlgorithm : public VRoot +{ +public: + virtual IAlgorithmVariant const& getAlgorithmVariant() const noexcept = 0; + virtual float getTimingMSec() const noexcept = 0; + virtual std::size_t getWorkspaceSize() const noexcept = 0; + virtual IAlgorithmIOInfo const* getAlgorithmIOInfoByIndex(int32_t index) const noexcept = 0; +}; + + +class VTimingCache : public VRoot +{ +public: + virtual nvinfer1::IHostMemory* serialize() const noexcept = 0; + virtual bool combine(ITimingCache const& inputCache, bool ignoreMismatch) noexcept = 0; + virtual bool reset() noexcept = 0; + virtual int64_t queryKeys(TimingCacheKey* keyBuffer, int64_t capacity) const noexcept = 0; + virtual TimingCacheValue query(TimingCacheKey const& key) const noexcept = 0; + virtual bool update(TimingCacheKey const& key, TimingCacheValue const& value) noexcept = 0; +}; + +class VBuilderConfig : public VRoot +{ +public: + virtual void setAvgTimingIterations(int32_t avgTiming) noexcept = 0; + virtual int32_t getAvgTimingIterations() const noexcept = 0; + virtual void setEngineCapability(EngineCapability capability) noexcept = 0; + virtual EngineCapability getEngineCapability() const noexcept = 0; + virtual void setInt8Calibrator(IInt8Calibrator* calibrator) noexcept = 0; + virtual IInt8Calibrator* getInt8Calibrator() const noexcept = 0; + virtual void setFlags(BuilderFlags builderFlags) noexcept = 0; + virtual BuilderFlags getFlags() const noexcept = 0; + virtual void clearFlag(BuilderFlag builderFlag) noexcept = 0; + virtual void setFlag(BuilderFlag builderFlag) noexcept = 0; + virtual bool getFlag(BuilderFlag builderFlag) const noexcept = 0; + virtual void setDeviceType(ILayer const* layer, DeviceType deviceType) noexcept = 0; + virtual DeviceType getDeviceType(ILayer const* layer) const noexcept = 0; + virtual bool isDeviceTypeSet(ILayer const* layer) const noexcept = 0; + virtual void resetDeviceType(ILayer const* layer) noexcept = 0; + virtual bool canRunOnDLA(ILayer const* layer) const noexcept = 0; + virtual void setDLACore(int32_t dlaCore) noexcept = 0; + virtual int32_t getDLACore() const noexcept = 0; + virtual void setDefaultDeviceType(DeviceType deviceType) noexcept = 0; + virtual DeviceType getDefaultDeviceType() const noexcept = 0; + virtual void reset() noexcept = 0; + virtual void setProfileStream(const cudaStream_t stream) noexcept = 0; + virtual cudaStream_t getProfileStream() const noexcept = 0; + virtual int32_t addOptimizationProfile(IOptimizationProfile const* profile) noexcept = 0; + virtual int32_t getNbOptimizationProfiles() const noexcept = 0; + virtual void setProfilingVerbosity(ProfilingVerbosity verbosity) noexcept = 0; + virtual ProfilingVerbosity getProfilingVerbosity() const noexcept = 0; + virtual void setAlgorithmSelector(IAlgorithmSelector* selector) noexcept = 0; + virtual IAlgorithmSelector* getAlgorithmSelector() const noexcept = 0; + virtual bool setCalibrationProfile(IOptimizationProfile const* profile) noexcept = 0; + virtual IOptimizationProfile const* getCalibrationProfile() noexcept = 0; + virtual void setQuantizationFlags(QuantizationFlags flags) noexcept = 0; + virtual QuantizationFlags getQuantizationFlags() const noexcept = 0; + virtual void clearQuantizationFlag(QuantizationFlag flag) noexcept = 0; + virtual void setQuantizationFlag(QuantizationFlag flag) noexcept = 0; + virtual bool getQuantizationFlag(QuantizationFlag flag) const noexcept = 0; + virtual bool setTacticSources(TacticSources tacticSources) noexcept = 0; + virtual TacticSources getTacticSources() const noexcept = 0; + virtual nvinfer1::ITimingCache* createTimingCache(void const* blob, std::size_t size) const noexcept = 0; + virtual bool setTimingCache(ITimingCache const& cache, bool ignoreMismatch) noexcept = 0; + virtual nvinfer1::ITimingCache const* getTimingCache() const noexcept = 0; + virtual void setMemoryPoolLimit(MemoryPoolType pool, std::size_t poolSize) noexcept = 0; + virtual std::size_t getMemoryPoolLimit(MemoryPoolType pool) const noexcept = 0; + virtual void setPreviewFeature(PreviewFeature feature, bool enable) noexcept = 0; + virtual bool getPreviewFeature(PreviewFeature feature) const noexcept = 0; + virtual void setBuilderOptimizationLevel(int32_t level) noexcept = 0; + virtual int32_t getBuilderOptimizationLevel() const noexcept = 0; + virtual void setHardwareCompatibilityLevel(HardwareCompatibilityLevel hardwareCompatibilityLevel) noexcept = 0; + virtual HardwareCompatibilityLevel getHardwareCompatibilityLevel() const noexcept = 0; + virtual void setPluginsToSerialize(char const* const* paths, int32_t nbPaths) noexcept = 0; + virtual char const* getPluginToSerialize(int32_t index) const noexcept = 0; + virtual int32_t getNbPluginsToSerialize() const noexcept = 0; + virtual void setMaxAuxStreams(int32_t nbStreams) noexcept = 0; + virtual int32_t getMaxAuxStreams() const noexcept = 0; + virtual void setProgressMonitor(IProgressMonitor* monitor) noexcept = 0; + virtual IProgressMonitor* getProgressMonitor() const noexcept = 0; + virtual void setRuntimePlatform(RuntimePlatform runtimePlatform) noexcept = 0; + virtual RuntimePlatform getRuntimePlatform() const noexcept = 0; + virtual void setMaxNbTactics(int32_t maxTactics) noexcept = 0; + virtual int32_t getMaxNbTactics() const noexcept = 0; + virtual bool setTilingOptimizationLevel(TilingOptimizationLevel level) noexcept = 0; + virtual TilingOptimizationLevel getTilingOptimizationLevel() const noexcept = 0; + virtual bool setL2LimitForTiling(int64_t size) noexcept = 0; + virtual int64_t getL2LimitForTiling() const noexcept = 0; + virtual bool setRemoteAutoTuningConfig(char const* config) noexcept = 0; + virtual char const* getRemoteAutoTuningConfig() const noexcept = 0; +}; + +class VSerializationConfig : public VRoot +{ +public: + virtual bool setFlags(SerializationFlags serializationFlags) noexcept = 0; + virtual SerializationFlags getFlags() const noexcept = 0; + virtual bool clearFlag(SerializationFlag serializationFlag) noexcept = 0; + virtual bool setFlag(SerializationFlag serializationFlag) noexcept = 0; + virtual bool getFlag(SerializationFlag serializationFlag) const noexcept = 0; +}; + +class VBuilder : public VRoot +{ +public: + virtual bool platformHasFastFp16() const noexcept = 0; + virtual bool platformHasFastInt8() const noexcept = 0; + virtual int32_t getMaxDLABatchSize() const noexcept = 0; + virtual int32_t getNbDLACores() const noexcept = 0; + virtual void setGpuAllocator(IGpuAllocator* allocator) noexcept = 0; + virtual nvinfer1::IBuilderConfig* createBuilderConfig() noexcept = 0; + virtual nvinfer1::INetworkDefinition* createNetworkV2(NetworkDefinitionCreationFlags flags) noexcept = 0; + virtual nvinfer1::IOptimizationProfile* createOptimizationProfile() noexcept = 0; + virtual void setErrorRecorder(IErrorRecorder* recorder) noexcept = 0; + virtual IErrorRecorder* getErrorRecorder() const noexcept = 0; + virtual void reset() noexcept = 0; + virtual bool platformHasTf32() const noexcept = 0; + virtual nvinfer1::IHostMemory* buildSerializedNetwork( + INetworkDefinition& network, IBuilderConfig& config) noexcept = 0; + virtual bool isNetworkSupported(INetworkDefinition const& network, IBuilderConfig const& config) const noexcept = 0; + virtual ILogger* getLogger() const noexcept = 0; + virtual bool setMaxThreads(int32_t maxThreads) noexcept = 0; + virtual int32_t getMaxThreads() const noexcept = 0; + virtual IPluginRegistry& getPluginRegistry() noexcept = 0; + virtual ICudaEngine* buildEngineWithConfig(INetworkDefinition& network, IBuilderConfig& config) noexcept = 0; + virtual bool buildSerializedNetworkToStream( + INetworkDefinition& network, IBuilderConfig& config, IStreamWriter& writer) noexcept = 0; + virtual nvinfer1::IHostMemory* buildSerializedNetworkWithKernelText( + INetworkDefinition& network, IBuilderConfig& config, IHostMemory*& kernelText) noexcept + = 0; +}; + +class VRuntimeConfig : public VRoot +{ +public: + virtual IRuntimeConfig* getPImpl() noexcept = 0; + virtual void setExecutionContextAllocationStrategy(ExecutionContextAllocationStrategy strategy) noexcept = 0; + virtual ExecutionContextAllocationStrategy getExecutionContextAllocationStrategy() const noexcept = 0; +}; + + +class VDistCollectiveLayer : public VRoot +{ +}; // VDistCollectiveLayer + +} // namespace apiv +} // namespace nvinfer1 + +// @endcond + +#endif // NV_INFER_RUNTIME_IMPL_H diff --git a/tensorrt/include/NvInferLegacyDims.h b/tensorrt/include/NvInferLegacyDims.h new file mode 100644 index 0000000000000000000000000000000000000000..098fcb9a1f27514cd0b297b6e57c2d88f1545744 --- /dev/null +++ b/tensorrt/include/NvInferLegacyDims.h @@ -0,0 +1,206 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NV_INFER_LEGACY_DIMS_H +#define NV_INFER_LEGACY_DIMS_H + +#define NV_INFER_INTERNAL_INCLUDE 1 +#include "NvInferRuntimeBase.h" // IWYU pragma: exports +#undef NV_INFER_INTERNAL_INCLUDE + +//! +//! \file NvInferLegacyDims.h +//! +//! This file contains declarations of legacy dimensions types which use channel +//! semantics in their names, and declarations on which those types rely. +//! + +//! +//! \namespace nvinfer1 +//! +//! \brief The TensorRT API version 1 namespace. +//! +namespace nvinfer1 +{ +//! +//! \class Dims2 +//! +//! \brief Descriptor for two-dimensional data. +//! +class Dims2 : public Dims +{ +public: + //! + //! \brief Construct an empty Dims2 object. + //! + Dims2() + : Dims2(0, 0) + { + } + + //! + //! \brief Construct a Dims2 from 2 elements. + //! + //! \param d0 The first element. + //! \param d1 The second element. + //! + Dims2(int64_t d0, int64_t d1) + { + nbDims = 2; + d[0] = d0; + d[1] = d1; + for (int64_t i{nbDims}; i < Dims::MAX_DIMS; ++i) + { + d[i] = 0; + } + } +}; + +//! +//! \class DimsHW +//! +//! \brief Descriptor for two-dimensional spatial data. +//! +class DimsHW : public Dims2 +{ +public: + //! + //! \brief Construct an empty DimsHW object. + //! + DimsHW() + : Dims2() + { + } + + //! + //! \brief Construct a DimsHW given height and width. + //! + //! \param height the height of the data + //! \param width the width of the data + //! + DimsHW(int64_t height, int64_t width) + : Dims2(height, width) + { + } + + //! + //! \brief Get the height. + //! + //! \return The height. + //! + int64_t& h() + { + return d[0]; + } + + //! + //! \brief Get the height. + //! + //! \return The height. + //! + int64_t h() const + { + return d[0]; + } + + //! + //! \brief Get the width. + //! + //! \return The width. + //! + int64_t& w() + { + return d[1]; + } + + //! + //! \brief Get the width. + //! + //! \return The width. + //! + int64_t w() const + { + return d[1]; + } +}; + +//! +//! \class Dims3 +//! +//! \brief Descriptor for three-dimensional data. +//! +class Dims3 : public Dims2 +{ +public: + //! + //! \brief Construct an empty Dims3 object. + //! + Dims3() + : Dims3(0, 0, 0) + { + } + + //! + //! \brief Construct a Dims3 from 3 elements. + //! + //! \param d0 The first element. + //! \param d1 The second element. + //! \param d2 The third element. + //! + Dims3(int64_t d0, int64_t d1, int64_t d2) + : Dims2(d0, d1) + { + nbDims = 3; + d[2] = d2; + } +}; + +//! +//! \class Dims4 +//! +//! \brief Descriptor for four-dimensional data. +//! +class Dims4 : public Dims3 +{ +public: + //! + //! \brief Construct an empty Dims4 object. + //! + Dims4() + : Dims4(0, 0, 0, 0) + { + } + + //! + //! \brief Construct a Dims4 from 4 elements. + //! + //! \param d0 The first element. + //! \param d1 The second element. + //! \param d2 The third element. + //! \param d3 The fourth element. + //! + Dims4(int64_t d0, int64_t d1, int64_t d2, int64_t d3) + : Dims3(d0, d1, d2) + { + nbDims = 4; + d[3] = d3; + } +}; + +} // namespace nvinfer1 + +#endif // NV_INFER_LEGCY_DIMS_H diff --git a/tensorrt/include/NvInferPlugin.h b/tensorrt/include/NvInferPlugin.h new file mode 100644 index 0000000000000000000000000000000000000000..c391483f742e0951dc0e3f71fbc3f5a8d61a47cc --- /dev/null +++ b/tensorrt/include/NvInferPlugin.h @@ -0,0 +1,42 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NV_INFER_PLUGIN_H +#define NV_INFER_PLUGIN_H + +#include "NvInfer.h" +#include "NvInferPluginUtils.h" +//! +//! \file NvInferPlugin.h +//! +//! This is the API for the Nvidia provided TensorRT plugins. +//! + +extern "C" +{ + //! + //! \brief Initialize and register all the existing TensorRT plugins to the Plugin Registry with an optional + //! namespace. The plugin library author should ensure that this function name is unique to the library. This + //! function should be called once before accessing the Plugin Registry. + //! \param logger Logger object to print plugin registration information + //! \param libNamespace Namespace used to register all the plugins in this library + //! + TENSORRTAPI bool initLibNvInferPlugins(void* logger, char const* libNamespace); + +} // extern "C" + +#endif // NV_INFER_PLUGIN_H diff --git a/tensorrt/include/NvInferPluginBase.h b/tensorrt/include/NvInferPluginBase.h new file mode 100644 index 0000000000000000000000000000000000000000..2add45a73826a1fe9844be3b375f4682a3803cc1 --- /dev/null +++ b/tensorrt/include/NvInferPluginBase.h @@ -0,0 +1,291 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NV_INFER_PLUGIN_BASE_H +#define NV_INFER_PLUGIN_BASE_H + +#if !defined(NV_INFER_INTERNAL_INCLUDE) +static_assert(false, "Do not directly include this file. Include NvInferRuntime.h or NvInferPluginUtils.h"); +#endif + +#define NV_INFER_INTERNAL_INCLUDE 1 +#include "NvInferRuntimeBase.h" // IWYU pragma: exports +#undef NV_INFER_INTERNAL_INCLUDE +namespace nvinfer1 +{ + +//! +//! \enum PluginFieldType +//! +//! \brief The possible field types for custom layer. +//! +enum class PluginFieldType : int32_t +{ + //! FP16 field type. + kFLOAT16 = 0, + //! FP32 field type. + kFLOAT32 = 1, + //! FP64 field type. + kFLOAT64 = 2, + //! INT8 field type. + kINT8 = 3, + //! INT16 field type. + kINT16 = 4, + //! INT32 field type. + kINT32 = 5, + //! char field type. + kCHAR = 6, + //! nvinfer1::Dims field type. + kDIMS = 7, + //! Unknown field type. + kUNKNOWN = 8, + //! BF16 field type. + kBF16 = 9, + //! INT64 field type. + kINT64 = 10, + //! FP8 field type. + kFP8 = 11, + //! INT4 field type. + kINT4 = 12, + //! FP4 field type. + kFP4 = 13, +}; + +//! +//! \class PluginField +//! +//! \brief Structure containing plugin attribute field names and associated data +//! This information can be parsed to decode necessary plugin metadata +//! +//! +class PluginField +{ +public: + //! Plugin field attribute name + AsciiChar const* name; + //! Plugin field attribute data + void const* data; + //! Plugin field attribute type + PluginFieldType type; + //! Number of data entries in the Plugin attribute + int32_t length; + + PluginField(AsciiChar const* const name_ = nullptr, void const* const data_ = nullptr, + PluginFieldType const type_ = PluginFieldType::kUNKNOWN, int32_t const length_ = 0) noexcept + : name(name_) + , data(data_) + , type(type_) + , length(length_) + { + } +}; + +//! +//! \struct PluginFieldCollection +//! +//! \brief Plugin field collection struct. +//! +struct PluginFieldCollection +{ + //! Number of PluginField entries. + int32_t nbFields{}; + //! Pointer to PluginField entries. + PluginField const* fields{}; +}; + +//! +//! \enum TensorRTPhase +//! +//! \brief Indicates a phase of operation of TensorRT +//! +enum class TensorRTPhase : int32_t +{ + //! Build phase of TensorRT + kBUILD = 0, + //! Execution phase of TensorRT + kRUNTIME = 1 +}; + +//! +//! \enum PluginCapabilityType +//! +//! \brief Enumerates the different capability types a IPluginV3 object may have +//! +enum class PluginCapabilityType : int32_t +{ + //! Core capability. Every IPluginV3 object must have this. + kCORE = 0, + //! Build capability. IPluginV3 objects provided to TensorRT build phase must have this. + kBUILD = 1, + //! Runtime capability. IPluginV3 objects provided to TensorRT build and execution phases must have this. + kRUNTIME = 2 +}; + +namespace v_1_0 +{ +class IPluginCapability : public IVersionedInterface +{ +}; + +class IPluginResource : public IVersionedInterface +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"IPluginResource", 1, 0}; + } + //! + //! \brief Free the underlying resource + //! + //! This will only be called for IPluginResource objects that were produced from IPluginResource::clone() + //! + //! The IPluginResource object on which release() is called must still be in a clone-able state + //! after release() returns + //! + //! \return 0 for success, else non-zero + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: No; this method is not required to be thread-safe + //! + virtual int32_t release() noexcept = 0; + + //! + //! \brief Clone the resource object + //! + //! \note Resource initialization (if any) may be skipped for non-cloned objects since only clones will be + //! registered by TensorRT + //! + //! \return Pointer to cloned object. nullptr if there was an issue. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes; this method is required to be thread-safe and may be called from multiple threads. + //! + virtual IPluginResource* clone() noexcept = 0; + + ~IPluginResource() noexcept override = default; + + IPluginResource() = default; + IPluginResource(IPluginResource const&) = default; + IPluginResource(IPluginResource&&) = default; + IPluginResource& operator=(IPluginResource const&) & = default; + IPluginResource& operator=(IPluginResource&&) & = default; +}; // class IPluginResource + +class IPluginCreatorInterface : public IVersionedInterface +{ +public: + ~IPluginCreatorInterface() noexcept override = default; + +protected: + IPluginCreatorInterface() = default; + IPluginCreatorInterface(IPluginCreatorInterface const&) = default; + IPluginCreatorInterface(IPluginCreatorInterface&&) = default; + IPluginCreatorInterface& operator=(IPluginCreatorInterface const&) & = default; + IPluginCreatorInterface& operator=(IPluginCreatorInterface&&) & = default; +}; + +class IPluginV3 : public IVersionedInterface +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"PLUGIN", 1, 0}; + } + + //! \brief Return a pointer to plugin object implementing the specified PluginCapabilityType. + //! + //! \note IPluginV3 objects added for the build phase (through addPluginV3()) must return valid objects for + //! PluginCapabilityType::kCORE, PluginCapabilityType::kBUILD and PluginCapabilityType::kRUNTIME. + //! + //! \note IPluginV3 objects added for the runtime phase must return valid objects for + //! PluginCapabilityType::kCORE and PluginCapabilityType::kRUNTIME. + //! + //! \see TensorRTPhase + //! \see IPluginCreatorV3One::createPlugin() + //! + virtual IPluginCapability* getCapabilityInterface(PluginCapabilityType type) noexcept = 0; + + //! + //! \brief Clone the plugin object. This copies over internal plugin parameters and returns a new plugin object with + //! these parameters. The cloned object must be in a fully initialized state. + //! + //! \note The cloned object must return valid objects through getCapabilityInterface() for at least the same + //! PluginCapabilityTypes as the original object. + //! + //! \return A cloned plugin object in an initialized state with the same parameters as the current object. + //! nullptr must be returned if the cloning fails. + //! + virtual IPluginV3* clone() noexcept = 0; +}; +} // namespace v_1_0 + +//! +//! \class IPluginResource +//! +//! \brief Interface for plugins to define custom resources that could be shared through the plugin registry +//! +//! \see IPluginRegistry::acquirePluginResource +//! \see IPluginRegistry::releasePluginResource +//! +using IPluginResource = v_1_0::IPluginResource; + +//! +//! \class IPluginCreatorInterface +//! +//! \brief Base class for all plugin creator versions. +//! +//! \see IPluginCreator and IPluginRegistry +//! +using IPluginCreatorInterface = v_1_0::IPluginCreatorInterface; + +//! +//! \class IPluginV3 +//! +//! \brief Plugin class for the V3 generation of user-implemented layers. +//! +//! IPluginV3 acts as a wrapper around the plugin capability interfaces that define the actual behavior of the plugin. +//! +//! \see IPluginCapability +//! \see IPluginCreatorV3One +//! \see IPluginRegistry +//! +using IPluginV3 = v_1_0::IPluginV3; + +//! +//! \class IPluginCapability +//! +//! \brief Base class for plugin capability interfaces +//! +//! IPluginCapability represents a split in TensorRT V3 plugins to sub-objects that expose different types of +//! capabilites a plugin may have, as opposed to a single interface which defines all capabilities and behaviors of a +//! plugin. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +//! \see PluginCapabilityType +//! +using IPluginCapability = v_1_0::IPluginCapability; +} // namespace nvinfer1 + +#endif /* NV_INFER_PLUGIN_BASE_H */ diff --git a/tensorrt/include/NvInferPluginUtils.h b/tensorrt/include/NvInferPluginUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..a4020ab13b18b78559c92c2e48eea8321e6e38ea --- /dev/null +++ b/tensorrt/include/NvInferPluginUtils.h @@ -0,0 +1,204 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NV_INFER_PLUGIN_UTILS_H +#define NV_INFER_PLUGIN_UTILS_H + +#include "NvInferRuntimeCommon.h" + +//! +//! \file NvInferPluginUtils.h +//! +//! This is the API for the Nvidia provided TensorRT plugin utilities. +//! It lists all the parameters utilized by the TensorRT plugins. +//! + +namespace nvinfer1 +{ +namespace plugin +{ + +//! +//! \struct PriorBoxParameters +//! +//! \brief The PriorBox plugin layer generates the prior boxes of designated sizes and aspect ratios across all +//! dimensions (H x W). +//! +//! PriorBoxParameters defines a set of parameters for creating the PriorBox plugin layer. +//! +struct PriorBoxParameters +{ + float *minSize; //!< Minimum box size in pixels. Can not be nullptr. + float *maxSize; //!< Maximum box size in pixels. Can be nullptr. + float *aspectRatios; //!< Aspect ratios of the boxes. Can be nullptr. + int32_t numMinSize; //!< Number of elements in minSize. Must be larger than 0. + int32_t numMaxSize; //!< Number of elements in maxSize. Can be 0 or same as numMinSize. + int32_t numAspectRatios; //!< Number of elements in aspectRatios. Can be 0. + bool flip; //!< If true, will flip each aspect ratio. For example, + //!< if there is an aspect ratio "r", the aspect ratio "1.0/r" will be generated as well. + bool clip; //!< If true, will clip the prior so that it is within [0,1]. + float variance[4]; //!< Variance for adjusting the prior boxes. + int32_t imgH; //!< Image height. If 0, then the H dimension of the data tensor will be used. + int32_t imgW; //!< Image width. If 0, then the W dimension of the data tensor will be used. + float stepH; //!< Step in H. If 0, then (float)imgH/h will be used where h is the H dimension of the 1st input tensor. + float stepW; //!< Step in W. If 0, then (float)imgW/w will be used where w is the W dimension of the 1st input tensor. + float offset; //!< Offset to the top left corner of each cell. +}; + +//! +//! \struct RPROIParams +//! +//! \brief RPROIParams is used to create the RPROIPlugin instance. +//! +struct RPROIParams +{ + int32_t poolingH; //!< Height of the output in pixels after ROI pooling on feature map. + int32_t poolingW; //!< Width of the output in pixels after ROI pooling on feature map. + int32_t featureStride; //!< Feature stride; ratio of input image size to feature map size. + //!< Assuming that max pooling layers in the neural network use square filters. + int32_t preNmsTop; //!< Number of proposals to keep before applying NMS. + int32_t nmsMaxOut; //!< Number of remaining proposals after applying NMS. + int32_t anchorsRatioCount; //!< Number of anchor box ratios. + int32_t anchorsScaleCount; //!< Number of anchor box scales. + float iouThreshold; //!< IoU (Intersection over Union) threshold used for the NMS step. + float minBoxSize; //!< Minimum allowed bounding box size before scaling, used for anchor box calculation. + float spatialScale; //!< Spatial scale between the input image and the last feature map. +}; + +//! +//! \struct GridAnchorParameters +//! +//! \brief The Anchor Generator plugin layer generates the prior boxes of designated sizes and aspect ratios across all dimensions (H x W). +//! GridAnchorParameters defines a set of parameters for creating the plugin layer for all feature maps. +//! +struct GridAnchorParameters +{ + float minSize; //!< Scale of anchors corresponding to finest resolution. + float maxSize; //!< Scale of anchors corresponding to coarsest resolution. + float* aspectRatios; //!< List of aspect ratios to place on each grid point. + int32_t numAspectRatios; //!< Number of elements in aspectRatios. + int32_t H; //!< Height of feature map to generate anchors for. + int32_t W; //!< Width of feature map to generate anchors for. + float variance[4]; //!< Variance for adjusting the prior boxes. +}; + +//! +//! \enum CodeTypeSSD +//! +//! \brief The type of encoding used for decoding the bounding boxes and loc_data. +//! +//! \deprecated Deprecated in TensorRT 10.0. DetectionOutput plugin is deprecated. +//! +enum class CodeTypeSSD : int32_t +{ + CORNER TRT_DEPRECATED_ENUM = 0, //!< Use box corners. + CENTER_SIZE TRT_DEPRECATED_ENUM = 1, //!< Use box centers and size. + CORNER_SIZE TRT_DEPRECATED_ENUM = 2, //!< Use box centers and size. + TF_CENTER TRT_DEPRECATED_ENUM = 3 //!< Use box centers and size but flip x and y coordinates. +}; + +//! +//! \struct DetectionOutputParameters +//! +//! \brief The DetectionOutput plugin layer generates the detection output +//! based on location and confidence predictions by doing non maximum suppression. +//! +//! This plugin first decodes the bounding boxes based on the anchors generated. +//! It then performs non_max_suppression on the decoded bounding boxes. +//! DetectionOutputParameters defines a set of parameters for creating the DetectionOutput plugin layer. +//! +//! \deprecated Deprecated in TensorRT 10.0. DetectionOutput plugin is deprecated. +//! +struct TRT_DEPRECATED DetectionOutputParameters +{ + bool shareLocation; //!< If true, bounding box are shared among different classes. + bool varianceEncodedInTarget; //!< If true, variance is encoded in target. + //!< Otherwise we need to adjust the predicted offset accordingly. + int32_t backgroundLabelId; //!< Background label ID. If there is no background class, set it as -1. + int32_t numClasses; //!< Number of classes to be predicted. + int32_t topK; //!< Number of boxes per image with top confidence scores that are fed + //!< into the NMS algorithm. + int32_t keepTopK; //!< Number of total bounding boxes to be kept per image after NMS step. + float confidenceThreshold; //!< Only consider detections whose confidences are larger than a threshold. + float nmsThreshold; //!< Threshold to be used in NMS. + CodeTypeSSD codeType; //!< Type of coding method for bbox. + int32_t inputOrder[3]; //!< Specifies the order of inputs {loc_data, conf_data, priorbox_data}. + bool confSigmoid; //!< Set to true to calculate sigmoid of confidence scores. + bool isNormalized; //!< Set to true if bounding box data is normalized by the network. + bool isBatchAgnostic{true}; //!< Defaults to true. Set to false if prior boxes are unique per batch. +}; + +//! +//! \brief When performing yolo9000, softmaxTree is helping to do softmax on confidence scores, +//! for element to get the precise classification through word-tree structured classification definition. +//! +struct softmaxTree +{ + int32_t* leaf; + int32_t n; + int32_t* parent; + int32_t* child; + int32_t* group; + char** name; + int32_t groups; + int32_t* groupSize; + int32_t* groupOffset; +}; + +//! +//! \brief The Region plugin layer performs region proposal calculation. +//! +//! Generate 5 bounding boxes per cell (for yolo9000, generate 3 bounding boxes per cell). +//! For each box, calculating its probabilities of objects detections from 80 pre-defined classifications +//! (yolo9000 has 9418 pre-defined classifications, and these 9418 items are organized as work-tree structure). +//! RegionParameters defines a set of parameters for creating the Region plugin layer. +//! +struct RegionParameters +{ + int32_t num; //!< Number of predicted bounding box for each grid cell. + int32_t coords; //!< Number of coordinates for a bounding box. + int32_t classes; //!< Number of classifications to be predicted. + softmaxTree* smTree; //!< Helping structure to do softmax on confidence scores. +}; + +//! +//! \brief The NMSParameters are used by the BatchedNMSPlugin for performing +//! the non_max_suppression operation over boxes for object detection networks. +//! +//! \deprecated Deprecated in TensorRT 10.0. BatchedNMSPlugin plugin is deprecated. +//! +struct TRT_DEPRECATED NMSParameters +{ + bool shareLocation; //!< If set to true, the boxes inputs are shared across all classes. + //!< If set to false, the boxes input should account for per class box data. + int32_t backgroundLabelId; //!< Label ID for the background class. + //!< If there is no background class, set it as -1 + int32_t numClasses; //!< Number of classes in the network. + int32_t topK; //!< Number of bounding boxes to be fed into the NMS step. + int32_t keepTopK; //!< Number of total bounding boxes to be kept per image after NMS step. + //!< Should be less than or equal to the topK value. + float scoreThreshold; //!< Scalar threshold for score (low scoring boxes are removed). + float iouThreshold; //!< A scalar threshold for IOU (new boxes that have high IOU overlap + //!< with previously selected boxes are removed). + bool isNormalized; //!< Set to false, if the box coordinates are not normalized, + //!< i.e. not in the range [0,1]. Defaults to false. +}; + +} // namespace plugin +} // namespace nvinfer1 + +#endif // NV_INFER_PLUGIN_UTILS_H diff --git a/tensorrt/include/NvInferRuntime.h b/tensorrt/include/NvInferRuntime.h new file mode 100644 index 0000000000000000000000000000000000000000..a863d47ae0a395c8cc69ca187be0bad86090d4b7 --- /dev/null +++ b/tensorrt/include/NvInferRuntime.h @@ -0,0 +1,5786 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NV_INFER_RUNTIME_H +#define NV_INFER_RUNTIME_H + +//! +//! \file NvInferRuntime.h +//! +//! This is the top-level API file for TensorRT extended runtime library. +//! + +#include "NvInferImpl.h" // IWYU pragma: export +#define NV_INFER_INTERNAL_INCLUDE 1 +#include "NvInferPluginBase.h" // IWYU pragma: export +#undef NV_INFER_INTERNAL_INCLUDE +#include "NvInferRuntimeCommon.h" // IWYU pragma: export + +namespace nvinfer1 +{ + +class IExecutionContext; //!< Forward declaration of IExecutionContext for use by other interfaces. +class ICudaEngine; //!< Forward declaration of ICudaEngine for use by other interfaces. +class IPluginFactory; //!< Forward declaration of IPluginFactory for use by other interfaces. +class IEngineInspector; //!< Forward declaration of IEngineInspector for use by other interfaces. + +//! +//! \class INoCopy +//! +//! \brief Base class for all TensorRT interfaces that are implemented by the TensorRT libraries +//! +//! Objects of such classes are not movable or copyable, and should only be manipulated +//! via pointers. +//! + +class INoCopy +{ +protected: + INoCopy() = default; + virtual ~INoCopy() = default; + INoCopy(INoCopy const& other) = delete; + INoCopy& operator=(INoCopy const& other) = delete; + INoCopy(INoCopy&& other) = delete; + INoCopy& operator=(INoCopy&& other) = delete; +}; + +//! +//! \enum EngineCapability +//! +//! \brief List of supported engine capability flows. +//! +//! \details The EngineCapability determines the restrictions of a network during build time and what runtime +//! it targets. EngineCapability::kSTANDARD does not provide any restrictions on functionality and the resulting +//! serialized engine can be executed with TensorRT's standard runtime APIs in the nvinfer1 namespace. +//! EngineCapability::kSAFETY provides a restricted subset of network operations that are safety certified and the +//! resulting serialized engine can be executed with TensorRT's safe runtime APIs in the nvinfer2::safe namespace. +//! EngineCapability::kDLA_STANDALONE provides a restricted subset of network operations that are DLA compatible and the +//! resulting serialized engine can be executed using standalone DLA runtime APIs. See sampleCudla for an example of +//! integrating cuDLA APIs with TensorRT APIs. +//! +enum class EngineCapability : int32_t +{ + //! + //! Standard: TensorRT flow without targeting the safety runtime. + //! This flow supports both DeviceType::kGPU and DeviceType::kDLA. + //! + kSTANDARD = 0, + + //! + //! Safety: TensorRT flow with restrictions targeting the safety runtime. + //! See safety documentation for list of supported layers and formats. + //! This flow supports only DeviceType::kGPU. + //! + //! This flag is only supported in NVIDIA Drive(R) products. + kSAFETY = 1, + + //! + //! DLA Standalone: TensorRT flow with restrictions targeting external, to TensorRT, DLA runtimes. + //! See DLA documentation for list of supported layers and formats. + //! This flow supports only DeviceType::kDLA. + //! + kDLA_STANDALONE = 2, +}; + +namespace impl +{ +//! Maximum number of elements in EngineCapability enum. \see EngineCapability +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 3; +}; +} // namespace impl + +//! +//! \class Weights +//! +//! \brief An array of weights used as a layer parameter. +//! +//! When using the DLA, the cumulative size of all Weights used in a network +//! must be less than 512MB in size. If the build option kGPU_FALLBACK is specified, +//! then multiple DLA sub-networks may be generated from the single original network. +//! +//! The weights are held by reference until the engine has been built. Therefore the data referenced +//! by \p values field should be preserved until the build is complete. +//! +//! The term "empty weights" refers to Weights with weight coefficients ( \p count == 0 and \p values == nullptr). +//! +class Weights +{ +public: + DataType type; //!< The type of the weights. + void const* values; //!< The weight values, in a contiguous array. + int64_t count; //!< The number of weights in the array. +}; + +//! +//! \class IHostMemory +//! +//! \brief Class to handle library allocated memory that is accessible to the user. +//! +//! The memory allocated via the host memory object is owned by the library and will +//! be de-allocated when the destroy method is called. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IHostMemory : public INoCopy +{ +public: + virtual ~IHostMemory() noexcept = default; + + //! A pointer to the raw data that is owned by the library. + void* data() const noexcept + { + return mImpl->data(); + } + + //! The size in bytes of the data that was allocated. + std::size_t size() const noexcept + { + return mImpl->size(); + } + + //! The type of the memory that was allocated. + DataType type() const noexcept + { + return mImpl->type(); + } + +protected: + apiv::VHostMemory* mImpl; +}; + +//! +//! \enum DimensionOperation +//! +//! \brief An operation on two IDimensionExpr, which represent integer expressions used in dimension computations. +//! +//! For example, given two IDimensionExpr x and y and an IExprBuilder& eb, +//! eb.operation(DimensionOperation::kSUM, x, y) creates a representation of x+y. +//! +//! \see IDimensionExpr, IExprBuilder +//! +enum class DimensionOperation : int32_t +{ + kSUM = 0, //!< Sum of the two operands. + kPROD = 1, //!< Product of the two operands. + kMAX = 2, //!< Maximum of the two operands. + kMIN = 3, //!< Minimum of the two operands. + kSUB = 4, //!< Substract the second element from the first. + kEQUAL = 5, //!< 1 if operands are equal, 0 otherwise. + kLESS = 6, //!< 1 if first operand is less than second operand, 0 otherwise. + kFLOOR_DIV = 7, //!< Floor division of the first element by the second. + kCEIL_DIV = 8 //!< Division rounding up +}; + +//! Maximum number of elements in DimensionOperation enum. \see DimensionOperation +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 9; +} + +//! +//! \enum TensorLocation +//! +//! \brief The location for tensor data storage, device or host. +//! +enum class TensorLocation : int32_t +{ + kDEVICE = 0, //!< Data stored on device. + kHOST = 1, //!< Data stored on host. +}; + +namespace impl +{ +//! Maximum number of elements in TensorLocation enum. \see TensorLocation +template <> +struct EnumMaxImpl +{ + static constexpr int32_t kVALUE = 2; +}; +} // namespace impl + +//! +//! \class IDimensionExpr +//! +//! \brief An IDimensionExpr represents an integer expression constructed from constants, +//! input dimensions, and binary operations. These expressions are can be used +//! in overrides of IPluginV2DynamicExt::getOutputDimensions or IPluginV3OneBuild::getOutputShapes() to define output +//! dimensions in terms of input dimensions. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +//! \see DimensionOperation, IPluginV2DynamicExt::getOutputDimensions, IPluginV3OneBuild::getOutputShapes() +//! +class IDimensionExpr : public INoCopy +{ +public: + //! + //! \brief Return true if expression is a build-time constant. + //! + bool isConstant() const noexcept + { + return mImpl->isConstant(); + } + + //! + //! \brief Get the value of the constant. + //! + //! If isConstant(), returns value of the constant. + //! If !isConstant(), return std::numeric_limits::min(). + //! + int64_t getConstantValue() const noexcept + { + return mImpl->getConstantValue(); + } + +protected: + apiv::VDimensionExpr* mImpl; + virtual ~IDimensionExpr() noexcept = default; + +public: + //! + //! \brief Return true if this denotes the value of a size tensor. + //! + //! \return True if this was created with method IExprBuilder::declareSizeTensor, false otherwise + //! + bool isSizeTensor() const noexcept + { + return mImpl->isSizeTensor(); + } +}; + +//! +//! \class IExprBuilder +//! +//! \brief Object for constructing IDimensionExpr. +//! +//! There is no public way to construct an IExprBuilder. It appears as an argument to +//! method IPluginV2DynamicExt::getOutputDimensions() and IPluginV3OneBuild::getOutputShapes(). Overrides of that +//! method can use that IExprBuilder argument to construct expressions that define output dimensions in terms of input +//! dimensions. +//! +//! Clients should assume that any values constructed by the IExprBuilder are destroyed +//! after IPluginV2DynamicExt::getOutputDimensions() or IPluginV3OneBuild::getOutputShapes() returns. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +//! \see IDimensionExpr +//! +class IExprBuilder : public INoCopy +{ +public: + //! + //! \brief Return pointer to IDimensionExpr for given value. + //! + IDimensionExpr const* constant(int64_t value) noexcept + { + return mImpl->constant(value); + } + + //! + //! \brief Get the operation. + //! + //! Return pointer to IDimensionExpr that represents the given operation applied to first and second. + //! Returns nullptr if op is not a valid DimensionOperation. + //! + IDimensionExpr const* operation( + DimensionOperation op, IDimensionExpr const& first, IDimensionExpr const& second) noexcept + { + return mImpl->operation(op, first, second); + } + +protected: + apiv::VExprBuilder* mImpl; + virtual ~IExprBuilder() noexcept = default; + +public: + //! + //! \brief Declare a size tensor at the given output index, with the specified auto-tuning formula and upper bound. + //! + //! A size tensor allows a plugin to have output dimensions that cannot be computed solely from input dimensions. + //! For example, suppose a plugin implements the equivalent of INonZeroLayer for 2D input. The plugin can + //! have one output for the indices of non-zero elements, and a second output containing the number of non-zero + //! elements. Suppose the input has size [M,N] and has K non-zero elements. The plugin can write K to the second + //! output. When telling TensorRT that the first output has shape [2,K], plugin uses IExprBuilder::constant() and + //! IExprBuilder::declareSizeTensor(1,...) to create the IDimensionExpr that respectively denote 2 and K. + //! + //! TensorRT also needs to know the value of K to use for auto-tuning and an upper bound on K so that it can + //! allocate memory for the output tensor. In the example, supposed typically half of the plugin's input elements + //! are non-zero, and all the elements might be nonzero. then using M*N/2 might be a good expression for the opt + //! parameter, and M*N for the upper bound. IDimensionsExpr for these expressions can be constructed from + //! IDimensionsExpr for the input dimensions. + //! + //! \param outputIndex index of a plugin output that is a size tensor. + //! \param opt formula for computing auto-tuning value. Must not depend on a size tensor. + //! \param upper Upper bound on the size tensor. + //! + //! \return IDimensionExpr denoting the value of the size tensor. + //! + //! \see IPluginV3OneBuild::getOutputShapes() + //! + IDimensionExpr const* declareSizeTensor(int32_t outputIndex, IDimensionExpr const& opt, IDimensionExpr const& upper) + { + return mImpl->declareSizeTensor(outputIndex, opt, upper); + } +}; + +//! +//! \class DimsExprs +//! +//! \brief Analog of class Dims with expressions instead of constants for the dimensions. +//! +class DimsExprs +{ +public: + int32_t nbDims; //!< The number of dimensions. + IDimensionExpr const* d[Dims::MAX_DIMS]; //!< The extent of each dimension. +}; + +//! +//! \struct DynamicPluginTensorDesc +//! +//! \brief Summarizes tensors that a plugin might see for an input or output. +//! +struct DynamicPluginTensorDesc +{ + //! Information required to interpret a pointer to tensor data, except that desc.dims has -1 in place of any runtime dimension. + PluginTensorDesc desc; + + //! Lower bounds on tensorโ€™s dimensions + Dims min; + + //! Upper bounds on tensorโ€™s dimensions + Dims max; + + //! Optimum value of tensorโ€™s dimensions specified for auto-tuning + Dims opt; +}; + +//! +//! \class IPluginV2DynamicExt +//! +//! \brief Similar to IPluginV2Ext, but with support for dynamic shapes. +//! +//! Clients should override the public methods, including the following inherited methods: +//! +//! * virtual int32_t getNbOutputs() const noexcept = 0; +//! +//! * virtual DataType getOutputDataType(int32_t index, DataType const* inputTypes, +//! int32_t nbInputs) const noexcept = 0; +//! +//! * virtual size_t getSerializationSize() const noexcept = 0; +//! +//! * virtual void serialize(void* buffer) const noexcept = 0; +//! +//! * virtual void destroy() noexcept = 0; +//! +//! * virtual void setPluginNamespace(char const* pluginNamespace) noexcept = 0; +//! +//! * virtual char const* getPluginNamespace() const noexcept = 0; +//! +//! For weakly typed networks, the inputTypes will always be DataType::kFLOAT or DataType::kINT32, +//! and the returned type is canonicalized to DataType::kFLOAT if it is DataType::kHALF or DataType:kINT8. +//! For strongly typed networks, inputTypes are inferred from previous operations, and getOutputDataType +//! specifies the returned type based on the inputTypes. +//! Details about the floating-point precision are elicited later by method supportsFormatCombination. +//! +//! \deprecated Deprecated in TensorRT 10.0. Please implement IPluginV3 instead. +//! +class TRT_DEPRECATED IPluginV2DynamicExt : public nvinfer1::IPluginV2Ext +{ +public: + IPluginV2DynamicExt* clone() const noexcept override = 0; + + //! + //! \brief Get expressions for computing dimensions of an output tensor from dimensions of the input tensors. + //! + //! \param outputIndex The index of the output tensor + //! \param inputs Expressions for dimensions of the input tensors + //! \param nbInputs The number of input tensors + //! \param exprBuilder Object for generating new expressions + //! + //! This function is called by the implementations of IBuilder during analysis of the network. + //! + //! Example #1: A plugin has a single output that transposes the last two dimensions of the plugin's single input. + //! The body of the override of getOutputDimensions can be: + //! + //! DimsExprs output(inputs[0]); + //! std::swap(output.d[output.nbDims-1], output.d[output.nbDims-2]); + //! return output; + //! + //! Example #2: A plugin concatenates its two inputs along the first dimension. + //! The body of the override of getOutputDimensions can be: + //! + //! DimsExprs output(inputs[0]); + //! output.d[0] = exprBuilder.operation(DimensionOperation::kSUM, *inputs[0].d[0], *inputs[1].d[0]); + //! return output; + //! + virtual DimsExprs getOutputDimensions( + int32_t outputIndex, DimsExprs const* inputs, int32_t nbInputs, IExprBuilder& exprBuilder) noexcept = 0; + + //! + //! \brief Limit on number of format combinations accepted. + //! + static constexpr int32_t kFORMAT_COMBINATION_LIMIT = 100; + + //! + //! \brief Return true if plugin supports the format and datatype for the input/output indexed by pos. + //! + //! For this method inputs are numbered 0..(nbInputs-1) and outputs are numbered nbInputs..(nbInputs+nbOutputs-1). + //! Using this numbering, pos is an index into InOut, where 0 <= pos < nbInputs+nbOutputs. + //! + //! TensorRT invokes this method to ask if the input/output indexed by pos supports the format/datatype specified + //! by inOut[pos].format and inOut[pos].type. The override should return true if that format/datatype at inOut[pos] + //! are supported by the plugin. If support is conditional on other input/output formats/datatypes, the plugin can + //! make its result conditional on the formats/datatypes in inOut[0..pos-1], which will be set to values + //! that the plugin supports. The override should not inspect inOut[pos+1..nbInputs+nbOutputs-1], + //! which will have invalid values. In other words, the decision for pos must be based on inOut[0..pos] only. + //! + //! Some examples: + //! + //! * A definition for a plugin that supports only FP16 NCHW: + //! + //! return inOut[pos].format == TensorFormat::kLINEAR && inOut[pos].type == DataType::kHALF; + //! + //! * A definition for a plugin that supports only FP16 NCHW for its two inputs, + //! and FP32 NCHW for its single output: + //! + //! return inOut[pos].format == TensorFormat::kLINEAR && (inOut[pos].type == (pos < 2 ? DataType::kHALF : + //! DataType::kFLOAT)); + //! + //! * A definition for a "polymorphic" plugin with two inputs and one output that supports + //! any format or type, but the inputs and output must have the same format and type: + //! + //! return pos == 0 || (inOut[pos].format == inOut.format[0] && inOut[pos].type == inOut[0].type); + //! + //! Warning: TensorRT will stop asking for formats once it finds kFORMAT_COMBINATION_LIMIT on combinations. + //! + virtual bool supportsFormatCombination( + int32_t pos, PluginTensorDesc const* inOut, int32_t nbInputs, int32_t nbOutputs) noexcept = 0; + + //! + //! \brief Configure the plugin. + //! + //! configurePlugin() can be called multiple times in both the build and execution phases. The build phase happens + //! before initialize() is called and only occurs during creation of an engine by IBuilder. The execution phase + //! happens after initialize() is called and occurs during both creation of an engine by IBuilder and execution + //! of an engine by IExecutionContext. + //! + //! Build phase: + //! IPluginV2DynamicExt->configurePlugin is called when a plugin is being prepared for profiling but not for any + //! specific input size. This provides an opportunity for the plugin to make algorithmic choices on the basis of + //! input and output formats, along with the bound of possible dimensions. The min and max value of the + //! DynamicPluginTensorDesc correspond to the kMIN and kMAX value of the current profile that the plugin is being + //! profiled for, with the desc.dims field corresponding to the dimensions of plugin specified at network creation. + //! Wildcard dimensions will exist during this phase in the desc.dims field. + //! + //! Execution phase: + //! IPluginV2DynamicExt->configurePlugin is called when a plugin is being prepared for executing the plugin for a + //! specific dimensions. This provides an opportunity for the plugin to change algorithmic choices based on the + //! explicit input dimensions stored in desc.dims field. + //! * IBuilder will call this function once per profile, with desc.dims resolved to the values specified by the + //! kOPT + //! field of the current profile. Wildcard dimensions will not exist during this phase. + //! * IExecutionContext will call this during the next subsequent instance enqueue[V2]() or execute[V2]() if: + //! - The batch size is changed from previous call of execute()/enqueue() if hasImplicitBatchDimension() returns + //! true. + //! - The optimization profile is changed via setOptimizationProfileAsync(). + //! - An input execution binding is changed via setInputShape(). + //! \warning The execution phase is timing critical during IExecutionContext but is not part of the timing loop when + //! called from IBuilder. Performance bottlenecks of configurePlugin won't show up during engine building but will + //! be visible during execution after calling functions that trigger layer resource updates. + //! + //! \param in The input tensors attributes that are used for configuration. + //! \param nbInputs Number of input tensors. + //! \param out The output tensors attributes that are used for configuration. + //! \param nbOutputs Number of output tensors. + //! + virtual void configurePlugin(DynamicPluginTensorDesc const* in, int32_t nbInputs, + DynamicPluginTensorDesc const* out, int32_t nbOutputs) noexcept = 0; + + //! + //! \brief Find the workspace size required by the layer. + //! + //! This function is called after the plugin is configured, and possibly during execution. + //! The result should be a sufficient workspace size to deal with inputs and outputs of the given size + //! or any smaller problem. + //! + //! \return The workspace size. + //! + virtual size_t getWorkspaceSize(PluginTensorDesc const* inputs, int32_t nbInputs, PluginTensorDesc const* outputs, + int32_t nbOutputs) const noexcept = 0; + + //! + //! \brief Execute the layer. + //! + //! \param inputDesc how to interpret the memory for the input tensors. + //! \param outputDesc how to interpret the memory for the output tensors. + //! \param inputs The memory for the input tensors. + //! \param outputs The memory for the output tensors. + //! \param workspace Workspace for execution. + //! \param stream The stream in which to execute the kernels. + //! + //! \return 0 for success, else non-zero (which will cause engine termination). + //! + virtual int32_t enqueue(PluginTensorDesc const* inputDesc, PluginTensorDesc const* outputDesc, + void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept = 0; + +protected: + //! + //! \brief Return the API version with which this plugin was built. The + //! upper byte reserved by TensorRT and is used to differentiate this from IPluginV2. + //! + //! Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with + //! plugins. + //! + int32_t getTensorRTVersion() const noexcept override + { + return (static_cast(PluginVersion::kV2_DYNAMICEXT) << 24 | (NV_TENSORRT_VERSION & 0xFFFFFF)); + } + + virtual ~IPluginV2DynamicExt() noexcept {} + +private: + // Following are obsolete base class methods, and must not be implemented or used. + + //! + //! \brief Set plugin configuration + //! + void configurePlugin(Dims const*, int32_t, Dims const*, int32_t, DataType const*, DataType const*, bool const*, + bool const*, PluginFormat, int32_t) noexcept final + { + } + + //! + //! \brief Check if provided data type is supported + //! + bool supportsFormat(DataType, PluginFormat) const noexcept final + { + return false; + } + + //! + //! \brief Get output dimensions. + //! + Dims getOutputDimensions(int32_t, Dims const*, int32_t) noexcept final + { + return Dims{-1, {}}; + } + + //! + //! \brief Is output broadcasted across batch. + //! + //! \warning Expected to return false as implicit batch support was removed in TensorRT 10.0. + //! + //! \deprecated Deprecated in TensorRT 10.0. Implicit batch support is removed in TensorRT 10.0. + //! + TRT_DEPRECATED bool isOutputBroadcastAcrossBatch(int32_t, bool const*, int32_t) const noexcept final + { + return false; + } + + //! + //! \brief Can output broadcasted across batch. + //! + //! \warning Expected to return false as implicit batch support was removed in TensorRT 10.0. + //! + //! \deprecated Deprecated in TensorRT 10.0. Implicit batch support is removed in TensorRT 10.0. + //! + TRT_DEPRECATED bool canBroadcastInputAcrossBatch(int32_t) const noexcept final + { + return true; + } + + //! + //! \brief Get required workspace size in bytes. + //! + size_t getWorkspaceSize(int32_t) const noexcept final + { + return 0; + } + + //! + //! \brief Run inference. + //! + int32_t enqueue(int32_t, void const* const*, void* const*, void*, cudaStream_t) noexcept final + { + return 1; + } +}; + +namespace v_1_0 +{ +class IStreamReader : public IVersionedInterface +{ +public: + //! + //! TensorRT never calls the destructor for an IStreamReader defined by the + //! application. + //! + ~IStreamReader() override = default; + IStreamReader() = default; + + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"IStreamReader", 1, 0}; + } + + //! + //! \brief Read the next number of bytes in the stream. + //! + //! \param destination The memory to write to + //! \param nbBytes The number of bytes to read + //! + //! \returns The number of bytes read. Negative values will be considered an automatic error. + //! + virtual int64_t read(void* destination, int64_t nbBytes) = 0; + +protected: + IStreamReader(IStreamReader const&) = default; + IStreamReader(IStreamReader&&) = default; + IStreamReader& operator=(IStreamReader const&) & = default; + IStreamReader& operator=(IStreamReader&&) & = default; +}; + +class IStreamWriter : public IVersionedInterface +{ +public: + //! + //! TensorRT never calls the destructor for an IStreamWriter defined by the + //! application. + //! + ~IStreamWriter() override = default; + IStreamWriter() = default; + + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept final + { + return InterfaceInfo{"IStreamWriter", 1, 0}; + } + + //! + //! \brief write nbBytes of data into the stream. + //! + //! \param data The data to be written to stream + //! \param nbBytes The number of bytes to write + //! + //! \returns The number of bytes written. A value that is negative or less than nBytes indicates that an error + //! occurred and TensorRT will give up on writing to the stream. + //! + virtual int64_t write(void const* data, int64_t nbBytes) = 0; + +protected: + IStreamWriter(IStreamWriter const&) = default; + IStreamWriter(IStreamWriter&&) = default; + IStreamWriter& operator=(IStreamWriter const&) & = default; + IStreamWriter& operator=(IStreamWriter&&) & = default; +}; +} // namespace v_1_0 + +//! +//! \class IStreamReader +//! +//! \brief Application-implemented class for reading data in a stream-based manner. +//! +//! \note To ensure compatibility of source code with future versions of TensorRT, use IStreamReader, not +//! v_1_0::IStreamReader +//! +using IStreamReader = v_1_0::IStreamReader; + +//! +//! \class IStreamWriter +//! +//! \brief Application-implemented class for writing data in a stream-based manner. +//! +//! \note To ensure compatibility of source code with future versions of TensorRT, use IStreamWriter, not +//! v_1_0::IStreamWriter +//! +using IStreamWriter = v_1_0::IStreamWriter; + +//! +//! \enum SeekPosition +//! \brief Controls the seek mode of IStreamReaderV2. +//! +enum class SeekPosition : int32_t +{ + //! From the beginning of the file. + kSET = 0, + + //! From the current position of the file. + kCUR = 1, + + //! From the tail of the file. + kEND = 2, +}; + +namespace v_1_0 +{ +class IStreamReaderV2 : public IVersionedInterface +{ +public: + //! + //! TensorRT never calls the destructor for an IStreamReaderV2 defined by the + //! application. + //! + ~IStreamReaderV2() override = default; + IStreamReaderV2() = default; + + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"IStreamReaderV2", 1, 0}; + } + + //! + //! \brief Read the next number of bytes in the stream asynchronously. + //! + //! \param destination The memory to write to, call cudaPointerGetAttributes to get the memory location + //! \param nbBytes The number of bytes to read + //! \param stream The CUDA stream used to do the copy + //! + //! \returns The number of bytes read. Negative values indicate an unrecoverable error. + //! A zero indicates that the end of the stream has been reached. + //! + virtual int64_t read(void* destination, int64_t nbBytes, cudaStream_t stream) noexcept = 0; + + //! + //! \brief Sets the position of the stream to the given offset. + //! + //! \param offset The number of bytes to offset from where. + //! \param where The position from where the offset is added. \see SeekPosition + //! + //! \returns True if the position is updated successfully. + //! + virtual bool seek(int64_t offset, SeekPosition where) noexcept = 0; + +protected: + IStreamReaderV2(IStreamReaderV2 const&) = default; + IStreamReaderV2(IStreamReaderV2&&) = default; + IStreamReaderV2& operator=(IStreamReaderV2 const&) & = default; + IStreamReaderV2& operator=(IStreamReaderV2&&) & = default; +}; +} // namespace v_1_0 + +//! +//! \class IStreamReaderV2 +//! +//! \brief Application-implemented class for reading data in a stream-based manner asynchronously. Intended for use with +//! the GDS API for optimizing load times. +//! +//! \note To ensure compatibility of source code with future versions of TensorRT, use IStreamReaderV2, not +//! v_1_0::IStreamReaderV2 +//! +using IStreamReaderV2 = v_1_0::IStreamReaderV2; + +//! +//! \class IPluginResourceContext +//! +//! \brief Interface for plugins to access per context resources provided by TensorRT +//! +//! There is no public way to construct an IPluginResourceContext. It appears as an argument to +//! IPluginV3OneRuntime::attachToContext(). Overrides of that method can use the IPluginResourceContext object to access +//! any available per context resources. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +//! \see IPluginV3OneRuntime::attachToContext() +//! +class IPluginResourceContext +{ +public: + //! \brief Get the GPU allocator associated with the resource context + //! + //! \see IPluginV3OneRuntime::attachToContext() + //! + virtual IGpuAllocator* getGpuAllocator() const noexcept = 0; + + //! \brief Get the error recorder associated with the resource context + //! + //! \see IPluginV3OneRuntime::attachToContext() + //! + virtual IErrorRecorder* getErrorRecorder() const noexcept = 0; + virtual ~IPluginResourceContext() noexcept = default; + +protected: + IPluginResourceContext() = default; + IPluginResourceContext(IPluginResourceContext const&) = default; + IPluginResourceContext(IPluginResourceContext&&) = default; + IPluginResourceContext& operator=(IPluginResourceContext const&) & = default; + IPluginResourceContext& operator=(IPluginResourceContext&&) & = default; +}; + +namespace v_1_0 +{ +class IPluginV3OneCore : public IPluginCapability +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"PLUGIN_V3ONE_CORE", 1, 0}; + } + + //! + //! \brief Return the plugin name. Should match the plugin name returned by the corresponding plugin creator. + //! + //! \see IPluginCreatorV3One::getPluginName() + //! + //! \warning The string returned must be NULL-terminated and have a length of 1024 bytes or less including the + //! NULL terminator. + //! + virtual AsciiChar const* getPluginName() const noexcept = 0; + + //! + //! \brief Return the plugin version. Should match the plugin version returned by the corresponding plugin creator. + //! + //! \see IPluginCreatorV3One::getPluginVersion() + //! + //! \warning The string returned must be NULL-terminated and have a length of 1024 bytes or less including the + //! NULL terminator. + //! + virtual AsciiChar const* getPluginVersion() const noexcept = 0; + + //! + //! \brief Return the namespace of the plugin object. Should match the plugin namespace returned by the + //! corresponding plugin creator. + //! + //! \see IPluginCreatorV3One::getPluginNamespace() + //! + //! \warning The string returned must be NULL-terminated and have a length of 1024 bytes or less including the + //! NULL terminator. + //! + virtual AsciiChar const* getPluginNamespace() const noexcept = 0; +}; + +class IPluginV3OneBuild : public IPluginCapability +{ +public: + //! + //! \brief The default maximum number of format combinations that will be timed by TensorRT during the build phase + //! + //! \see getFormatCombinationLimit + //! + static constexpr int32_t kDEFAULT_FORMAT_COMBINATION_LIMIT = 100; + + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"PLUGIN_V3ONE_BUILD", 1, 0}; + } + + //! + //! \brief Configure the plugin. + //! + //! configurePlugin() can be called multiple times in the build phase during creation of an engine by IBuilder. + //! + //! configurePlugin() is called when a plugin is being prepared for profiling but not for any + //! specific input size. This provides an opportunity for the plugin to make algorithmic choices on the basis of + //! input and output formats, along with the bound of possible dimensions. The min, opt and max value of the + //! DynamicPluginTensorDesc correspond to the kMIN, kOPT and kMAX value of the current profile that the plugin is + //! being profiled for, with the desc.dims field corresponding to the dimensions of plugin specified at network + //! creation. Wildcard dimensions may exist during this phase in the desc.dims field. + //! + //! \param in The input tensors attributes that are used for configuration. + //! \param nbInputs Number of input tensors. + //! \param out The output tensors attributes that are used for configuration. + //! \param nbOutputs Number of output tensors. + //! + //! \return 0 for success, else non-zero (which will cause engine termination, if invoked by TensorRT). + //! + virtual int32_t configurePlugin(DynamicPluginTensorDesc const* in, int32_t nbInputs, + DynamicPluginTensorDesc const* out, int32_t nbOutputs) noexcept = 0; + + //! + //! \brief Provide the data types of the plugin outputs if the input tensors have the data types provided. + //! + //! \param outputTypes Pre-allocated array to which the output data types should be written. + //! \param nbOutputs The number of output tensors. This matches the value returned from getNbOutputs(). + //! \param inputTypes The input data types. + //! \param nbInputs The number of input tensors. + //! + //! \return 0 for success, else non-zero (which will cause engine termination). The returned code will be reported + //! through the error recorder. + //! + //! \note Provide `DataType::kFLOAT`s if the layer has no inputs. The data type for any size tensor outputs must be + //! `DataType::kINT32`. The returned data types must each have a format that is supported by the plugin. + //! + //! \warning DataType:kBOOL and DataType::kUINT8 are not supported. + //! + virtual int32_t getOutputDataTypes( + DataType* outputTypes, int32_t nbOutputs, const DataType* inputTypes, int32_t nbInputs) const noexcept = 0; + + //! + //! \brief Provide expressions for computing dimensions of the output tensors from dimensions of the input tensors. + //! + //! \param inputs Expressions for dimensions of the input tensors + //! \param nbInputs The number of input tensors + //! \param shapeInputs Expressions for values of the shape tensor inputs + //! \param nbShapeInputs The number of shape tensor inputs + //! \param outputs Pre-allocated array to which the output dimensions must be written + //! \param nbOutputs Number of outputs. + //! \param exprBuilder Object for generating new dimension expressions + //! + //! \note Any size tensor outputs must be declared to be 0D. + //! + //! \note The declaration of shapeInputs as DimsExprs is slightly abusive, because the "dimensions" + //! are actually the values of the shape tensor. For example, if the input shape tensor + //! is a 2x3 matrix, the DimsExprs will have six "dimensions": the three values from the first + //! row of the matrix followed by the three values from the second row of the matrix. + //! + //! \return 0 for success, else non-zero (which will cause engine termination). Returned code will be reported + //! through the error recorder. + //! + virtual int32_t getOutputShapes(DimsExprs const* inputs, int32_t nbInputs, DimsExprs const* shapeInputs, + int32_t nbShapeInputs, DimsExprs* outputs, int32_t nbOutputs, IExprBuilder& exprBuilder) noexcept = 0; + + //! + //! \brief Return true if plugin supports the format and datatype for the input/output indexed by pos. + //! + //! For this method inputs are numbered 0.. (nbInputs - 1) and outputs are numbered nbInputs.. (nbInputs + nbOutputs + //! - 1). Using this numbering, pos is an index into InOut, where 0 <= pos < nbInputs + nbOutputs - 1. + //! + //! TensorRT invokes this method to ask if the input/output indexed by pos supports the format/datatype specified + //! by inOut[pos].format and inOut[pos].type. The override should return true if that format/datatype at inOut[pos] + //! are supported by the plugin. If support is conditional on other input/output formats/datatypes, the plugin can + //! make its result conditional on the formats/datatypes in inOut[0.. pos - 1], which will be set to values + //! that the plugin supports. The override should not inspect inOut[pos1.. nbInputs + nbOutputs - 1], + //! which will have invalid values. In other words, the decision for pos must be based on inOut[0..pos] only. + //! + //! Some examples: + //! + //! * A definition for a plugin that supports only FP16 NCHW: + //! + //! return inOut.format[pos] == TensorFormat::kLINEAR && inOut.type[pos] == DataType::kHALF; + //! + //! * A definition for a plugin that supports only FP16 NCHW for its two inputs, + //! and FP32 NCHW for its single output: + //! + //! return inOut.format[pos] == TensorFormat::kLINEAR && (inOut.type[pos] == pos < 2 ? DataType::kHALF : + //! DataType::kFLOAT); + //! + //! * A definition for a "polymorphic" plugin with two inputs and one output that supports + //! any format or type, but the inputs and output must have the same format and type: + //! + //! return pos == 0 || (inOut.format[pos] == inOut.format[0] && inOut.type[pos] == inOut.type[0]); + //! + //! \warning TensorRT will stop querying once it finds getFormatCombinationLimit() of combinations. + //! + //! \see getFormatCombinationLimit + //! + virtual bool supportsFormatCombination( + int32_t pos, DynamicPluginTensorDesc const* inOut, int32_t nbInputs, int32_t nbOutputs) noexcept = 0; + + //! + //! \brief Get the number of outputs from the plugin. + //! + //! \return The number of outputs, which must be a positive integer. + //! + virtual int32_t getNbOutputs() const noexcept = 0; + + //! + //! \brief Find the workspace size required by the layer. + //! + //! This function is called after the plugin is configured, and possibly during execution. + //! The result should be a sufficient workspace size to deal with inputs and outputs of the given size + //! or any smaller problem. + //! + //! \return The workspace size. + //! + virtual size_t getWorkspaceSize(DynamicPluginTensorDesc const* inputs, int32_t nbInputs, + DynamicPluginTensorDesc const* outputs, int32_t nbOutputs) const noexcept + { + return 0; + } + + //! + //! \brief Query for any custom tactics that the plugin intends to use + //! + //! This method queries for the set of tactics T(f) supported by the plugin for the format combination f indicated + //! by the immediately preceding call to configurePlugin(). It is guaranteed to be called after configurePlugin(). + //! + //! For each format combination provided through configurePlugin(), up to a maximum of getFormatCombinationLimit(), + //! the plugin will be timed for each tactic advertised through this method for that format combination. i.e. The + //! plugin will be timed \f$N = \sum_{i=0}^{i getFormatCombinationLimit() + //! goto done + //! configurePlugin(...) + //! for each tactic in getValidTactics(...) + //! time tactic + //! done: + //! + //! + //! \param tactics Pre-allocated buffer to which the tactic values should be written + //! \param nbTactics The number of tactics advertised through getNbTactics() + //! + //! \note The provided tactic values must be unique and non-zero. The tactic value 0 is reserved for the default + //! tactic attached to each format combination. + //! + //! \return 0 for success, else non-zero (which will cause engine termination). The returned code will be reported + //! through the error recorder. + //! + virtual int32_t getValidTactics(int32_t* tactics, int32_t nbTactics) noexcept + { + return 0; + } + + //! + //! \brief Query for the number of custom tactics the plugin intends to use + //! + virtual int32_t getNbTactics() noexcept + { + return 0; + } + + //! + //! \brief Called to query the suffix to use for the timing cache ID. May be called anytime after plugin creation. + //! + //! \return Suffix to use for timing cache ID, considering only the creation state of the plugin. + //! Returning nullptr will disable timing caching for the plugin altogether. + //! + //! \note If timing caching is enabled for the plugin (by returning non-null), the I/O shape and format information + //! will be automatically considered to form the prefix of the timing cache ID. Therefore, only other factors + //! determining the creation state of the plugin, such as its attribute values, should be considered to compose the + //! return value. + //! + virtual char const* getTimingCacheID() noexcept + { + return nullptr; + } + + //! + //! \brief Return the maximum number of format combinations that will be timed by TensorRT during the build phase + //! + virtual int32_t getFormatCombinationLimit() noexcept + { + return kDEFAULT_FORMAT_COMBINATION_LIMIT; + } + + //! + //! \brief Query for a string representing the configuration of the plugin. May be called anytime after + //! plugin creation. + //! + //! \return A string representing the plugin's creation state, especially with regard to its attribute values. + //! + virtual char const* getMetadataString() noexcept + { + return nullptr; + } +}; + +class IPluginV3OneRuntime : public IPluginCapability +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"PLUGIN_V3ONE_RUNTIME", 1, 0}; + } + + //! + //! \brief Set the tactic to be used in the subsequent call to enqueue(). If no custom tactics were advertised, this + //! will have a value of 0, which is designated as the default tactic. + //! + //! \return 0 for success, else non-zero (which will cause engine termination). The returned code will be reported + //! through the error recorder. + //! + virtual int32_t setTactic(int32_t tactic) noexcept + { + return 0; + } + + //! + //! \brief Called when a plugin is being prepared for execution for specific dimensions. This could + //! happen multiple times in the execution phase, both during creation of an engine by IBuilder and execution of an + //! engine by IExecutionContext. + //! * IBuilder will call this function once per profile, with `in` resolved to the values specified by the + //! kOPT field of the current profile. + //! * IExecutionContext will call this during the next subsequent instance of enqueueV3() or executeV2() if: + //! - The optimization profile is changed via setOptimizationProfile() or setOptimizationProfileAsync(). + //! - An input binding is changed via setInputTensorAddress() or setTensorAddress() or setInputShape(). + //! \warning The execution phase is timing critical during IExecutionContext but is not part of the timing loop when + //! called from IBuilder. Performance bottlenecks of onShapeChange() will not show up during engine building but + //! will be visible during execution if any triggering functions are called. + //! + //! \param in The input tensors attributes that are used for configuration. + //! \param nbInputs Number of input tensors. + //! \param out The output tensors attributes that are used for configuration. + //! \param nbOutputs Number of output tensors. + //! + virtual int32_t onShapeChange( + PluginTensorDesc const* in, int32_t nbInputs, PluginTensorDesc const* out, int32_t nbOutputs) noexcept = 0; + + //! + //! \brief Execute the layer. + //! + //! \param inputDesc how to interpret the memory for the input tensors. + //! \param outputDesc how to interpret the memory for the output tensors. + //! \param inputs The memory for the input tensors. + //! \param outputs The memory for the output tensors. + //! \param workspace Workspace for execution. + //! \param stream The stream in which to execute the kernels. + //! + //! \return 0 for success, else non-zero (which will cause engine termination). The returned code will be reported + //! through the error recorder. + //! + virtual int32_t enqueue(PluginTensorDesc const* inputDesc, PluginTensorDesc const* outputDesc, + void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept = 0; + + //! + //! \brief Clone the plugin, attach the cloned plugin object to a execution context and grant the cloned plugin + //! access to some context resources. + //! + //! This function is called automatically for each plugin when a new execution context is created. The plugin may + //! use resources provided by the IPluginResourceContext until the plugin is deleted by TensorRT. + //! + //! If the plugin needs per-context resources, it can be allocated here. + //! + //! \param context A resource context that exposes methods to get access to execution context specific resources. + //! A different resource context is guaranteed for each different execution context to which the + //! plugin is attached. + //! \see IPluginResourceContext + //! + //! \note This method should clone the entire IPluginV3 object, not just the runtime interface + //! + //! \return A clone of the IPluginV3 object whose runtime interface on which this method is invoked, which has + //! attached to the provided resource context. + //! + virtual IPluginV3* attachToContext(IPluginResourceContext* context) noexcept = 0; + + //! + //! \brief Get the plugin fields which should be serialized. + //! + //! \note The set of plugin fields returned does not necessarily need to match that advertised through + //! getFieldNames() of the corresponding plugin creator. + + //! \note To serialize arbitrary plugin data, use a PluginField of + //! PluginFieldType::kUNKNOWN, with the length of the PluginField set to the correct number of bytes. + //! + virtual PluginFieldCollection const* getFieldsToSerialize() noexcept = 0; +}; +} // namespace v_1_0 + +namespace v_2_0 +{ + +class IPluginV3OneBuild : public v_1_0::IPluginV3OneBuild +{ +public: + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"PLUGIN_V3ONE_BUILD", 2, 0}; + } + + //! + //! \brief Communicates to TensorRT that the output at the specified output index is aliased to the input at the + //! returned index + //! + //! Enables read-modify-write behavior in plugins. TensorRT may insert copies to facilitate this capability. + //! + //! \return An integer denoting the index of the input which is aliased to the output at outputIndex. + //! Returning -1 indicates that the output is not aliased to any input. Otherwise, the valid range for + //! return value is [0, nbInputs - 1]. + //! + //! \note A given plugin input can only be aliased to a single plugin output. + //! + //! \note This API will only be called and have an effect when PreviewFeature::kALIASED_PLUGIN_IO_10_03 is turned + //! on. + //! + //! \warning If an input is not shallow copyable, a copy inserted by TensorRT may not work as intended. Therefore, + //! using this feature with tensors requiring deep copies is not supported. + //! + //! \warning If a given tensor is requested to be aliased by two different plugins, this may result in divergent + //! copies of the tensor after writes from each plugin. e.g. In the below example, t1 and t2 could be divergent. + //! + //! +-----+ +--------+ + //! +->|Copy +--> t* ---->|Plugin0 +--> t1 + //! | +-----+ +--------+ + //! t + //! | +-----+ +--------+ + //! +->|Copy +--> t** --->|Plugin1 +--> t2 + //! +-----+ +--------+ + //! + virtual int32_t getAliasedInput(int32_t outputIndex) noexcept + { + return -1; + } +}; + +} // namespace v_2_0 + +//! +//! \class IPluginV3OneCore +//! +//! \brief A plugin capability interface that enables the core capability (PluginCapabilityType::kCORE). +//! +//! \see IPluginCapability +//! \see PluginCapabilityType +//! \see IPluginV3::getCapabilityInterface() +//! +using IPluginV3OneCore = v_1_0::IPluginV3OneCore; + +//! +//! \class IPluginV3OneBuild +//! +//! \brief A plugin capability interface that enables the build capability (PluginCapabilityType::kBUILD). Exposes +//! methods that allow the expression of the build time properties and behavior of a plugin. +//! +//! \see IPluginCapability +//! \see PluginCapabilityType +//! \see IPluginV3::getCapabilityInterface() +//! +using IPluginV3OneBuild = v_1_0::IPluginV3OneBuild; + +//! +//! \class IPluginV3OneRuntime +//! +//! \brief A plugin capability interface that enables the runtime capability (PluginCapabilityType::kRUNTIME). Exposes +//! methods that allow the expression of the runtime properties and behavior of a plugin. +//! +//! \see IPluginCapability +//! \see PluginCapabilityType +//! \see IPluginV3::getCapabilityInterface() +//! +using IPluginV3OneRuntime = v_1_0::IPluginV3OneRuntime; + +//! +//! \class IPluginV3OneBuildV2 +//! +//! \brief A plugin capability interface that extends IPluginV3OneBuild by providing I/O aliasing functionality. +//! +//! \see IPluginV3OneBuild +//! +using IPluginV3OneBuildV2 = v_2_0::IPluginV3OneBuild; + +namespace v_1_0 +{ +class IProfiler +{ +public: + //! + //! \brief Layer time reporting callback. + //! + //! \param layerName The name of the layer, set when constructing the network definition. If the engine is built + //! with profiling verbosity set to kNONE, the layerName is the decimal index of the layer. + //! \param ms The time in milliseconds to execute the layer. + //! + virtual void reportLayerTime(char const* layerName, float ms) noexcept = 0; + + virtual ~IProfiler() noexcept {} +}; +} // namespace v_1_0 + +//! +//! \class IProfiler +//! +//! \brief Application-implemented interface for profiling. +//! +//! When this class is added to an execution context, the profiler will be called once per layer for each invocation of +//! executeV2()/enqueueV3(). +//! +//! It is not recommended to run inference with profiler enabled when the inference execution time is critical since the +//! profiler may affect execution time negatively. +//! +using IProfiler = v_1_0::IProfiler; + +//! +//! \enum WeightsRole +//! +//! \brief How a layer uses particular Weights. +//! +//! The power weights of an IScaleLayer are omitted. Refitting those is not supported. +//! +enum class WeightsRole : int32_t +{ + kKERNEL = 0, //!< kernel for IConvolutionLayer or IDeconvolutionLayer + kBIAS = 1, //!< bias for IConvolutionLayer or IDeconvolutionLayer + kSHIFT = 2, //!< shift part of IScaleLayer + kSCALE = 3, //!< scale part of IScaleLayer + kCONSTANT = 4, //!< weights for IConstantLayer + kANY = 5, //!< Any other weights role +}; + +//! Maximum number of elements in WeightsRole enum. \see WeightsRole +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 6; +} + +//! +//! \enum DeviceType +//! \brief The device that this layer/network will execute on. +//! +//! +enum class DeviceType : int32_t +{ + kGPU = 0, //!< GPU Device + kDLA = 1, //!< DLA Core +}; + +//! Maximum number of elements in DeviceType enum. \see DeviceType +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 2; +} + +//! +//! \enum TempfileControlFlag +//! +//! \brief Flags used to control TensorRT's behavior when creating executable temporary files. +//! +//! On some platforms the TensorRT runtime may need to create files in a temporary directory or use platform-specific +//! APIs to create files in-memory to load temporary DLLs that implement runtime code. These flags allow the +//! application to explicitly control TensorRT's use of these files. This will preclude the use of certain TensorRT +//! APIs for deserializing and loading lean runtimes. +//! +enum class TempfileControlFlag : int32_t +{ + //! Allow creating and loading files in-memory (or unnamed files). + kALLOW_IN_MEMORY_FILES = 0, + + //! Allow creating and loading named files in a temporary directory on the filesystem. + //! + //! \see IRuntime::setTemporaryDirectory() + kALLOW_TEMPORARY_FILES = 1, +}; + +//! Maximum number of elements in TempfileControlFlag enum. \see TempfileControlFlag +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 2; +} + +//! +//! \brief Represents a collection of one or more TempfileControlFlag values combined using bitwise-OR operations. +//! +//! \see TempfileControlFlag, +//! IRuntime::setTempfileControlFlags(), +//! IRuntime::getTempfileControlFlags() +using TempfileControlFlags = uint32_t; + +//! +//! \enum TensorFormat +//! +//! \brief Format of the input/output tensors. +//! +//! This enum is used by both plugins and network I/O tensors. +//! +//! \see IPluginV2::supportsFormat(), safe::ICudaEngine::getBindingFormat() +//! +//! Many of the formats are **vector-major** or **vector-minor**. These formats specify +//! a vector dimension and scalars per vector. +//! For example, suppose that the tensor has has dimensions [M,N,C,H,W], +//! the vector dimension is C and there are V scalars per vector. +//! +//! * A **vector-major** format splits the vectorized dimension into two axes in the +//! memory layout. The vectorized dimension is replaced by an axis of length ceil(C/V) +//! and a new dimension of length V is appended. For the example tensor, the memory layout +//! is equivalent to an array with dimensions [M][N][ceil(C/V)][H][W][V]. +//! Tensor coordinate (m,n,c,h,w) maps to array location [m][n][c/V][h][w][c\%V]. +//! +//! * A **vector-minor** format moves the vectorized dimension to become the last axis +//! in the memory layout. For the example tensor, the memory layout is equivalent to an +//! array with dimensions [M][N][H][W][ceil(C/V)*V]. Tensor coordinate (m,n,c,h,w) maps +//! array location subscript [m][n][h][w][c]. +//! +//! In interfaces that refer to "components per element", that's the value of V above. +//! +//! For more information about data formats, see the topic "Data Format Description" located in the +//! TensorRT Developer Guide. +//! https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/advanced.html#i-o-formats +//! +enum class TensorFormat : int32_t +{ + //! Memory layout is similar to an array in C or C++. + //! The stride of each dimension is the product of the dimensions after it. + //! The last dimension has unit stride. + //! + //! This format supports all TensorRT types. + //! For DLA usage, the tensor sizes are limited to C,H,W in the range [1,8192]. + kLINEAR = 0, + + //! Vector-major format with two scalars per vector. + //! Vector dimension is third to last. + //! + //! This format requires FP16 or BF16 and at least three dimensions. + kCHW2 = 1, + + //! Vector-minor format with eight scalars per vector. + //! Vector dimension is third to last. + //! This format requires FP16 or BF16 and at least three dimensions. + kHWC8 = 2, + + //! Vector-major format with four scalars per vector. + //! Vector dimension is third to last. + //! + //! This format requires INT8 and at least three dimensions. + //! For INT8, the length of the vector dimension must be a build-time constant. + //! + //! Deprecated usage: + //! + //! If running on the DLA, this format can be used for acceleration + //! with the caveat that C must be less than or equal to 4. + //! If used as DLA input and the build option kGPU_FALLBACK is not specified, + //! it needs to meet line stride requirement of DLA format. Column stride in + //! bytes must be a multiple of 64 on Orin. + kCHW4 = 3, + + //! Vector-major format with 16 scalars per vector. + //! Vector dimension is third to last. + //! + //! This format is only supported by DLA and requires FP16 and at least three dimensions. + //! This format maps to the native feature format for FP16, + //! and the tensor sizes are limited to C,H,W in the range [1,8192]. + kCHW16 = 4, + + //! Vector-major format with 32 scalars per vector. + //! Vector dimension is third to last. + //! + //! This format requires INT8, FP32, or FP16 and at least three dimensions. + //! + //! For DLA usage, this format maps to the native feature format for INT8, + //! and the tensor sizes are limited to C,H,W in the range [1,8192]. + kCHW32 = 5, + + //! Vector-minor format with eight scalars per vector. + //! Vector dimension is fourth to last. + //! + //! This format requires FP16 or BF16 and at least four dimensions. + kDHWC8 = 6, + + //! Vector-major format with 32 scalars per vector. + //! Vector dimension is fourth to last. + //! + //! This format requires FP16 or INT8 and at least four dimensions. + kCDHW32 = 7, + + //! Vector-minor format where channel dimension is third to last and unpadded. + //! + //! This format requires either FP32 or UINT8 and at least three dimensions. + kHWC = 8, + + //! DLA planar format. For a tensor with dimension {N, C, H, W}, the W axis + //! always has unit stride. The stride for stepping along the H axis is + //! rounded up to 64 bytes. + //! + //! The memory layout is equivalent to a C array with dimensions + //! [N][C][H][roundUp(W, 64/elementSize)] where elementSize is + //! 2 for FP16 and 1 for Int8, with the tensor coordinates (n, c, h, w) + //! mapping to array subscript [n][c][h][w]. + kDLA_LINEAR = 9, + + //! DLA image format. For a tensor with dimension {N, C, H, W} the C axis + //! always has unit stride. The stride for stepping along the H axis is rounded up + //! to 64 bytes on Orin. C can only be 1, 3 or 4. + //! If C == 1, it will map to grayscale format. + //! If C == 3 or C == 4, it will map to color image format. And if C == 3, + //! the stride for stepping along the W axis needs to be padded to 4 in elements. + //! + //! When C is {1, 3, 4}, then C' is {1, 4, 4} respectively, + //! the memory layout is equivalent to a C array with dimensions + //! [N][H][roundUp(W, 64/C'/elementSize)][C'] on Orin + //! where elementSize is 2 for FP16 + //! and 1 for Int8. The tensor coordinates (n, c, h, w) mapping to array + //! subscript [n][h][w][c]. + kDLA_HWC4 = 10, + + //! Vector-minor format with 16 scalars per vector. + //! Vector dimension is third to last. + //! + //! This requires FP16, INT8 or FP8 and at least three dimensions. + kHWC16 = 11, + + //! Vector-minor format with one scalar per vector. + //! Vector dimension is fourth to last. + //! + //! This format requires FP32 and at least four dimensions. + kDHWC = 12 +}; + +namespace impl +{ +//! Maximum number of elements in TensorFormat enum. \see TensorFormat +template <> +struct EnumMaxImpl +{ + //! Declaration of kVALUE that represents the maximum number of elements in the TensorFormat enum. + static constexpr int32_t kVALUE = 13; +}; +} // namespace impl + +//! +//! \enum AllocatorFlag +//! +//! \brief Allowed type of memory allocation. +//! +enum class AllocatorFlag : int32_t +{ + //! TensorRT may call realloc() on this allocation. + kRESIZABLE = 0, +}; + +namespace impl +{ +//! Maximum number of elements in AllocatorFlag enum. \see AllocatorFlag +template <> +struct EnumMaxImpl +{ + //! Declaration of kVALUE that represents the maximum number of elements in the AllocatorFlag enum. + static constexpr int32_t kVALUE = 1; +}; +} // namespace impl + +using AllocatorFlags = uint32_t; + +//! DO NOT REFER TO namespace v_1_0 IN CODE. ALWAYS USE nvinfer1 INSTEAD. +//! The name v_1_0 may change in future versions of TensorRT. + +//! +//! \class ILogger +//! +//! \brief Application-implemented logging interface for the builder, refitter and runtime. +//! +//! The logger used to create an instance of IBuilder, IRuntime or IRefitter is used for all objects created through +//! that interface. The logger must be valid until all objects created are released. +//! +//! The Logger object implementation must be thread safe. All locking and synchronization is pushed to the +//! interface implementation and TensorRT does not hold any synchronization primitives when calling the interface +//! functions. +//! +class ILogger +{ +public: + //! + //! \enum Severity + //! + //! \brief The severity corresponding to a log message. + //! + enum class Severity : int32_t + { + //! An internal error has occurred. Execution is unrecoverable. + kINTERNAL_ERROR = 0, + //! An application error has occurred. + kERROR = 1, + //! An application error has been discovered, but TensorRT has recovered or fallen back to a default. + kWARNING = 2, + //! Informational messages with instructional information. + kINFO = 3, + //! Verbose messages with debugging information. + kVERBOSE = 4, + }; + + //! + //! \brief A callback implemented by the application to handle logging messages; + //! + //! \param severity The severity of the message. + //! \param msg A null-terminated log message. + //! + //! \warning Loggers used in the safety certified runtime must set a maximum message length and truncate + //! messages exceeding this length. It is up to the implementer of the derived class to define + //! a suitable limit that will prevent buffer overruns, resource exhaustion, and other security + //! vulnerabilities in their implementation. The TensorRT safety certified runtime will never + //! emit messages longer than 1024 bytes. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when multiple execution contexts are used during runtime, or if the same logger is used + //! for multiple runtimes, builders, or refitters. + //! + virtual void log(Severity severity, AsciiChar const* msg) noexcept = 0; + + ILogger() = default; + virtual ~ILogger() = default; + +protected: + // @cond SuppressDoxyWarnings + ILogger(ILogger const&) = default; + ILogger(ILogger&&) = default; + ILogger& operator=(ILogger const&) & = default; + ILogger& operator=(ILogger&&) & = default; + // @endcond +}; + +namespace impl +{ +//! Maximum number of elements in ILogger::Severity enum. \see ILogger::Severity +template <> +struct EnumMaxImpl +{ + //! Declaration of kVALUE that represents the maximum number of elements in the ILogger::Severity enum. + static constexpr int32_t kVALUE = 5; +}; +} // namespace impl + +namespace v_1_0 +{ + +class IGpuAllocator : public IVersionedInterface +{ +public: + //! + //! \brief A thread-safe callback implemented by the application to handle acquisition of GPU memory. + //! + //! \param size The size of the memory block required (in bytes). + //! \param alignment The required alignment of memory. Alignment will be zero + //! or a power of 2 not exceeding the alignment guaranteed by cudaMalloc. + //! Thus this allocator can be safely implemented with cudaMalloc/cudaFree. + //! An alignment value of zero indicates any alignment is acceptable. + //! \param flags Reserved for future use. In the current release, 0 will be passed. + //! + //! \return If the allocation was successful, the start address of a device memory block of the requested size. + //! If an allocation request of size 0 is made, nullptr must be returned. + //! If an allocation request cannot be satisfied, nullptr must be returned. + //! If a non-null address is returned, it is guaranteed to have the specified alignment. + //! + //! \note The implementation must guarantee thread safety for concurrent allocate/reallocate/deallocate + //! requests. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads. + //! + //! \deprecated Deprecated in TensorRT 10.0. Superseded by allocateAsync + //! + TRT_DEPRECATED virtual void* allocate( + uint64_t const size, uint64_t const alignment, AllocatorFlags const flags) noexcept = 0; + + ~IGpuAllocator() override = default; + IGpuAllocator() = default; + + //! + //! \brief A thread-safe callback implemented by the application to resize an existing allocation. + //! + //! Only allocations which were allocated with AllocatorFlag::kRESIZABLE will be resized. + //! + //! Options are one of: + //! * resize in place leaving min(oldSize, newSize) bytes unchanged and return the original address + //! * move min(oldSize, newSize) bytes to a new location of sufficient size and return its address + //! * return nullptr, to indicate that the request could not be fulfilled. + //! + //! If nullptr is returned, TensorRT will assume that resize() is not implemented, and that the + //! allocation at baseAddr is still valid. + //! + //! This method is made available for use cases where delegating the resize + //! strategy to the application provides an opportunity to improve memory management. + //! One possible implementation is to allocate a large virtual device buffer and + //! progressively commit physical memory with cuMemMap. CU_MEM_ALLOC_GRANULARITY_RECOMMENDED + //! is suggested in this case. + //! + //! TensorRT may call realloc to increase the buffer by relatively small amounts. + //! + //! \param baseAddr the address of the original allocation, which will have been returned by previously calling + //! allocate() or reallocate() on the same object. + //! \param alignment The alignment used by the original allocation. This will be the same value that was previously + //! passed to the allocate() or reallocate() call that returned baseAddr. + //! \param newSize The new memory size required (in bytes). + //! + //! \return The address of the reallocated memory, or nullptr. If a non-null address is returned, it is + //! guaranteed to have the specified alignment. + //! + //! \note The implementation must guarantee thread safety for concurrent allocate/reallocate/deallocate + //! requests. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads. + //! + virtual void* reallocate(void* const /*baseAddr*/, uint64_t /*alignment*/, uint64_t /*newSize*/) noexcept + { + return nullptr; + } + + //! + //! \brief A thread-safe callback implemented by the application to handle release of GPU memory. + //! + //! TensorRT may pass a nullptr to this function if it was previously returned by allocate(). + //! + //! \param memory A memory address that was previously returned by an allocate() or reallocate() call of the same + //! allocator object. + //! + //! \return True if the acquired memory is released successfully. + //! + //! \note The implementation must guarantee thread safety for concurrent allocate/reallocate/deallocate + //! requests. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads. + //! \deprecated Deprecated in TensorRT 10.0. Superseded by deallocateAsync + //! + TRT_DEPRECATED virtual bool deallocate(void* const memory) noexcept = 0; + + //! + //! \brief A thread-safe callback implemented by the application to handle stream-ordered acquisition of GPU memory. + //! + //! The default behavior is to call method allocate(), which is synchronous and thus loses + //! any performance benefits of asynchronous allocation. If you want the benefits of asynchronous + //! allocation, see discussion of IGpuAsyncAllocator vs. IGpuAllocator in the documentation + //! for nvinfer1::IGpuAllocator. + //! + //! \param size The size of the memory block required (in bytes). + //! \param alignment The required alignment of memory. Alignment will be zero + //! or a power of 2 not exceeding the alignment guaranteed by cudaMalloc. + //! Thus this allocator can be safely implemented with cudaMalloc/cudaFree. + //! An alignment value of zero indicates any alignment is acceptable. + //! \param flags Reserved for future use. In the current release, 0 will be passed. + //! \param stream specifies the cudaStream for asynchronous usage. + //! + //! \return If the allocation was successful, the start address of a device memory block of the requested size. + //! If an allocation request of size 0 is made, nullptr must be returned. + //! If an allocation request cannot be satisfied, nullptr must be returned. + //! If a non-null address is returned, it is guaranteed to have the specified alignment. + //! + //! \note The implementation must guarantee thread safety for concurrent allocate/reallocate/deallocate + //! requests. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads. + //! + virtual void* allocateAsync( + uint64_t const size, uint64_t const alignment, AllocatorFlags const flags, cudaStream_t /*stream*/) noexcept + { + return allocate(size, alignment, flags); + } + //! + //! \brief A thread-safe callback implemented by the application to handle stream-ordered release of GPU memory. + //! + //! The default behavior is to call method deallocate(), which is synchronous and thus loses + //! any performance benefits of asynchronous deallocation. If you want the benefits of asynchronous + //! deallocation, see discussion of IGpuAsyncAllocator vs. IGpuAllocator in the documentation + //! for nvinfer1::IGpuAllocator. + //! + //! TensorRT may pass a nullptr to this function if it was previously returned by allocate(). + //! + //! \param memory A memory address that was previously returned by an allocate() or reallocate() call of the same + //! allocator object. + //! \param stream specifies the cudaStream for asynchronous usage. + //! + //! \return True if the acquired memory is released successfully. + //! + //! \note The implementation must guarantee thread safety for concurrent allocate/reallocate/deallocate + //! requests. + //! + //! \note The implementation is not required to be asynchronous. It is permitted to synchronize, + //! albeit doing so will lose the performance advantage of asynchronous deallocation. + //! Either way, it is critical that it not actually free the memory until the current + //! stream position is reached. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads. + //! + virtual bool deallocateAsync(void* const memory, cudaStream_t /*stream*/) noexcept + { + return deallocate(memory); + } + + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return {"IGpuAllocator", 1, 0}; + } + +protected: + // @cond SuppressDoxyWarnings + IGpuAllocator(IGpuAllocator const&) = default; + IGpuAllocator(IGpuAllocator&&) = default; + IGpuAllocator& operator=(IGpuAllocator const&) & = default; + IGpuAllocator& operator=(IGpuAllocator&&) & = default; + // @endcond +}; + +} // namespace v_1_0 + +//! +//! \class IGpuAllocator +//! +//! \brief Application-implemented class for controlling allocation on the GPU. +//! +//! \warning The lifetime of an IGpuAllocator object must exceed that of all objects that use it. +//! +//! This class is intended as a base class for allocators that implement synchronous allocation. +//! If you want the benefits of asynchronous allocation, you can do either of: +//! +//! * Derive your class from IGpuAllocator and override all four of its virtual methods +//! for allocation/deallocation, including the two deprecated methods. +//! +//! * Derive your class from IGpuAsyncAllocator and override its two pure virtual +//! methods for allocation/deallocation. +//! +//! The latter style is preferred because it does not tie code to deprecated methods. +//! +//! \see IGpuAsyncAllocator. +//! +using IGpuAllocator = v_1_0::IGpuAllocator; + + +//! +//! \class IRuntime +//! +//! \brief Allows a serialized functionally unsafe engine to be deserialized. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IRuntime : public INoCopy +{ +public: + virtual ~IRuntime() noexcept = default; + + //! + //! \brief Sets the DLA core used by the network. Defaults to -1. + //! + //! \param dlaCore The DLA core to execute the engine on, in the range [0,getNbDlaCores()). + //! + //! This function is used to specify which DLA core to use via indexing, if multiple DLA cores are available. + //! + //! \warning if getNbDLACores() returns 0, then this function does nothing. + //! + //! \see getDLACore() + //! + void setDLACore(int32_t dlaCore) noexcept + { + mImpl->setDLACore(dlaCore); + } + + //! + //! \brief Get the DLA core that the engine executes on. + //! + //! \return assigned DLA core or -1 for DLA not present or unset. + //! + int32_t getDLACore() const noexcept + { + return mImpl->getDLACore(); + } + + //! + //! \brief Returns number of DLA hardware cores accessible or 0 if DLA is unavailable. + //! + int32_t getNbDLACores() const noexcept + { + return mImpl->getNbDLACores(); + } + + //! + //! \brief Set the GPU allocator. + //! + //! \param allocator Set the GPU allocator to be used by the runtime. All GPU memory acquired will use this + //! allocator. If NULL is passed, the default allocator will be used. + //! + //! Default: allocateAsync uses cudaMallocAsync if cudaDevAttrMemoryPoolsSupported returns true, otherwise falls + //! back to cudaMalloc. allocate always uses cudaMalloc. + //! + //! If nullptr is passed, the default allocator will be used. + //! + void setGpuAllocator(IGpuAllocator* allocator) noexcept + { + mImpl->setGpuAllocator(allocator); + } + + //! + //! \brief Set the ErrorRecorder for this interface + //! + //! Assigns the ErrorRecorder to this interface. The ErrorRecorder will track all errors during execution. + //! This function will call incRefCount of the registered ErrorRecorder at least once. Setting + //! recorder to nullptr unregisters the recorder with the interface, resulting in a call to decRefCount if + //! a recorder has been registered. + //! + //! If an error recorder is not set, messages will be sent to the global log stream. + //! + //! \param recorder The error recorder to register with this interface. + // + //! \see getErrorRecorder() + //! + void setErrorRecorder(IErrorRecorder* recorder) noexcept + { + mImpl->setErrorRecorder(recorder); + } + + //! + //! \brief get the ErrorRecorder assigned to this interface. + //! + //! Retrieves the assigned error recorder object for the given class. A nullptr will be returned if + //! an error handler has not been set. + //! + //! \return A pointer to the IErrorRecorder object that has been registered. + //! + //! \see setErrorRecorder() + //! + IErrorRecorder* getErrorRecorder() const noexcept + { + return mImpl->getErrorRecorder(); + } + + //! + //! \brief Deserialize an engine from host memory. + //! + //! If an error recorder has been set for the runtime, it will also be passed to the engine. + //! + //! \warning Destroying the IRuntime before destroying all associated ICudaEngine instances results in undefined + //! behavior. + //! + //! \param blob The memory that holds the serialized engine. + //! \param size The size of the memory. + //! + //! \return The engine, or nullptr if it could not be deserialized. + //! + ICudaEngine* deserializeCudaEngine(void const* blob, std::size_t size) noexcept + { + return mImpl->deserializeCudaEngine(blob, size); + } + + //! + //! \brief Deserialize an engine from a stream. + //! + //! If an error recorder has been set for the runtime, it will also be passed to the + //! engine. + //! + //! This deserialization path will reduce host memory usage when weight streaming is enabled. + //! + //! \warning Destroying the IRuntime before destroying all associated ICudaEngine instances results in undefined + //! behavior. + //! + //! \param streamReader a read-only stream from which TensorRT will deserialize a + //! previously serialized engine. + //! + //! \return The engine, or nullptr if it could not be deserialized. + //! + //! \deprecated Deprecated in TensorRT 10.7. Superseded by deserializeCudaEngine that takes an IStreamReaderV2 + //! instead of IStreamReader. + //! + TRT_DEPRECATED ICudaEngine* deserializeCudaEngine(IStreamReader& streamReader) + { + return mImpl->deserializeCudaEngine(streamReader); + } + + //! + //! \brief Deserialize an engine from a stream. IStreamReaderV2 is expected to support reading to both host and + //! device pointers. + //! + //! If an error recorder has been set for the runtime, it will also be passed to the + //! engine. + //! + //! This deserialization path will reduce engine load time when applied with GDS (GPU Direct storage), or when + //! weight streaming is enabled. + //! + //! \warning Destroying the IRuntime before destroying all associated ICudaEngine instances results in undefined + //! behavior. + //! + //! \param streamReader a read-only stream from which TensorRT will deserialize a previously serialized engine. + //! + //! \return The engine, or nullptr if it could not be deserialized. The pointer may not be valid immediately after + //! the function returns. + //! + ICudaEngine* deserializeCudaEngine(IStreamReaderV2& streamReader) + { + return mImpl->deserializeCudaEngineV2(streamReader); + } + + //! + //! \brief get the logger with which the runtime was created + //! + //! \return the logger + //! + ILogger* getLogger() const noexcept + { + return mImpl->getLogger(); + } + + //! + //! \brief Set the maximum number of threads. + //! + //! \param maxThreads The maximum number of threads that can be used by the runtime. + //! \return True if successful, false otherwise. + //! + //! The default value is 1 and includes the current thread. + //! A value greater than 1 permits TensorRT to use multi-threaded algorithms. + //! A value less than 1 triggers a kINVALID_ARGUMENT error. + //! + bool setMaxThreads(int32_t maxThreads) noexcept + { + return mImpl->setMaxThreads(maxThreads); + } + + //! + //! \brief Get the maximum number of threads that can be used by the runtime. + //! + //! Retrieves the maximum number of threads that can be used by the runtime. + //! + //! \return The maximum number of threads that can be used by the runtime. + //! + //! \see setMaxThreads() + //! + int32_t getMaxThreads() const noexcept + { + return mImpl->getMaxThreads(); + } + + //! + //! \brief Set the directory that will be used by this runtime for temporary files. + //! + //! On some platforms the TensorRT runtime may need to create and use temporary files + //! with read/write/execute permissions to implement runtime functionality. + //! + //! \param path Path to the temporary directory for use, or nullptr. + //! + //! If path is nullptr, then TensorRT will use platform-specific heuristics to pick + //! a default temporary directory if required: + //! + //! - On UNIX/Linux platforms, TensorRT will first try the TMPDIR environment variable, then fall back to /tmp + //! - On Windows, TensorRT will try the TEMP environment variable. + //! + //! See the TensorRT Developer Guide for more information. + //! + //! The default value is nullptr. + //! + //! \warning If path is not nullptr, it must be a non-empty string representing a relative + //! or absolute path in the format expected by the host operating system. + //! + //! \warning The string path must be null-terminated, and be at most 4096 bytes including the + //! terminator. Note that the operating system may have stricter path length requirements. + //! + //! \warning The process using TensorRT must have rwx permissions for the temporary directory, + //! and the directory shall be configured to disallow other users from modifying created files + //! (e.g. on Linux, if the directory is shared with other users, the sticky bit must be set). + //! + //! \see getTemporaryDirectory() + //! + void setTemporaryDirectory(char const* path) noexcept + { + return mImpl->setTemporaryDirectory(path); + } + + //! + //! \brief Get the directory that will be used by this runtime for temporary files. + //! + //! \returns A path to the temporary directory in use, or nullptr if no path is specified. + //! + //! \see setTemporaryDirectory() + char const* getTemporaryDirectory() const noexcept + { + return mImpl->getTemporaryDirectory(); + } + + //! + //! \brief Set the tempfile control flags for this runtime. + //! + //! \param flags The flags to set. + //! + //! The default value is all flags set, i.e. + //! + //! (1U << static_cast(kALLOW_IN_MEMORY_FILES)) | (1U << static_cast(kALLOW_TEMPORARY_FILES)) + //! + //! \see TempfileControlFlag, TempfileControlFlags, getTempfileControlFlags() + //! + void setTempfileControlFlags(TempfileControlFlags flags) noexcept + { + return mImpl->setTempfileControlFlags(flags); + } + + //! + //! \brief Get the tempfile control flags for this runtime. + //! + //! \return The flags currently set. + //! + //! \see TempfileControlFlag, TempfileControlFlags, setTempfileControlFlags() + //! + TempfileControlFlags getTempfileControlFlags() const noexcept + { + return mImpl->getTempfileControlFlags(); + } + + //! + //! \brief Get the local plugin registry that can be used by the runtime. + //! + //! \return The local plugin registry that can be used by the runtime. + //! + IPluginRegistry& getPluginRegistry() noexcept + { + return mImpl->getPluginRegistry(); + } + + //! + //! \brief Load IRuntime from the file. + //! + //! This method loads a runtime library from a shared library file. The runtime can then be used to execute + //! a plan file built with BuilderFlag::kVERSION_COMPATIBLE and BuilderFlag::kEXCLUDE_LEAN_RUNTIME both set + //! and built with the same version of TensorRT as the loaded runtime library. + //! + //! \param path Path to the runtime lean library. + //! + //! \return the runtime library, or nullptr if it could not be loaded + //! + //! \warning The path string must be null-terminated, and be at most 4096 bytes including the terminator. + //! + IRuntime* loadRuntime(char const* path) noexcept + { + return mImpl->loadRuntime(path); + } + + //! + //! \brief Set whether the runtime is allowed to deserialize engines with host executable code. + //! + //! \param allowed Whether the runtime is allowed to deserialize engines with host executable code. + //! + //! The default value is false. + //! + void setEngineHostCodeAllowed(bool allowed) noexcept + { + return mImpl->setEngineHostCodeAllowed(allowed); + } + + //! + //! \brief Get whether the runtime is allowed to deserialize engines with host executable code. + //! + //! \return Whether the runtime is allowed to deserialize engines with host executable code. + //! + bool getEngineHostCodeAllowed() const noexcept + { + return mImpl->getEngineHostCodeAllowed(); + } + + +protected: + apiv::VRuntime* mImpl; +}; + +//! +//! \class IRefitter +//! +//! \brief Updates weights in an engine. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IRefitter : public INoCopy +{ +public: + virtual ~IRefitter() noexcept = default; + + //! + //! \brief Specify new weights for a layer of given name. + //! Returns true on success, or false if new weights are rejected. + //! Possible reasons for rejection are: + //! + //! * There is no such layer by that name. + //! * The layer does not have weights with the specified role. + //! * The count of weights is inconsistent with the layerโ€™s original specification. + //! * The type of weights is inconsistent with the layerโ€™s original specification. + //! + //! Modifying the weights before method refitCudaEngine or refitCudaEngineAsync returns will result in undefined + //! behavior. + //! + //! \warning The string layerName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + bool setWeights(char const* layerName, WeightsRole role, Weights weights) noexcept + { + return mImpl->setWeights(layerName, role, weights); + } + + //! + //! \brief Refits associated engine. + //! + //! \return True on success, or false if new weights validation fails or getMissingWeights() != 0 before the call. + //! If false is returned, a subset of weights may have been refitted. + //! + //! The behavior is undefined if the engine has pending enqueued work. + //! Provided weights on CPU or GPU can be unset and released, or updated after refitCudaEngine returns. + //! + //! IExecutionContexts associated with the engine remain valid for use afterwards. There is no need to set the same + //! weights repeatedly for multiple refit calls as the weights memory can be updated directly instead. + //! + bool refitCudaEngine() noexcept + { + return mImpl->refitCudaEngine(); + } + + //! + //! \brief Get description of missing weights. + //! + //! For example, if some Weights have been set, but the engine was optimized + //! in a way that combines weights, any unsupplied Weights in the combination + //! are considered missing. + //! + //! \param size The number of items that can be safely written to a non-null layerNames or roles. + //! \param layerNames Where to write the layer names. + //! \param roles Where to write the weights roles. + //! + //! \return The number of missing Weights. + //! + //! If layerNames!=nullptr, each written pointer points to a string owned by + //! the engine being refit, and becomes invalid when the engine is destroyed. + //! + int32_t getMissing(int32_t size, char const** layerNames, WeightsRole* roles) noexcept + { + return mImpl->getMissing(size, layerNames, roles); + } + + //! + //! \brief Get description of all weights that could be refit. + //! + //! \param size The number of items that can be safely written to a non-null layerNames or roles. + //! \param layerNames Where to write the layer names. + //! \param roles Where to write the weights roles. + //! + //! \return The number of Weights that could be refit. + //! + //! If layerNames!=nullptr, each written pointer points to a string owned by + //! the engine being refit, and becomes invalid when the engine is destroyed. + //! + int32_t getAll(int32_t size, char const** layerNames, WeightsRole* roles) noexcept + { + return mImpl->getAll(size, layerNames, roles); + } + + //! + //! Update dynamic range for a tensor. + //! + //! \param tensorName The name of an ITensor in the network. + //! \param min The minimum of the dynamic range for the tensor. + //! \param max The maximum of the dynamic range for the tensor. + //! + //! \return True if successful; false otherwise. + //! + //! Returns false if there is no Int8 engine tensor derived from + //! a network tensor of that name. If successful, then getMissing + //! may report that some weights need to be supplied. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. + //! + TRT_DEPRECATED bool setDynamicRange(char const* tensorName, float min, float max) noexcept + { + return mImpl->setDynamicRange(tensorName, min, max); + } + + //! + //! \brief Get minimum of dynamic range. + //! + //! \return Minimum of dynamic range. + //! + //! If the dynamic range was never set, returns the minimum computed during calibration. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. + //! + TRT_DEPRECATED float getDynamicRangeMin(char const* tensorName) const noexcept + { + return mImpl->getDynamicRangeMin(tensorName); + } + + //! + //! \brief Get maximum of dynamic range. + //! + //! \return Maximum of dynamic range. + //! + //! If the dynamic range was never set, returns the maximum computed during calibration. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. + //! + TRT_DEPRECATED float getDynamicRangeMax(char const* tensorName) const noexcept + { + return mImpl->getDynamicRangeMax(tensorName); + } + + //! + //! \brief Get names of all tensors that have refittable dynamic ranges. + //! + //! \param size The number of items that can be safely written to a non-null tensorNames. + //! \param tensorNames Where to write the layer names. + //! + //! \return The number of Weights that could be refit. + //! + //! If tensorNames!=nullptr, each written pointer points to a string owned by + //! the engine being refit, and becomes invalid when the engine is destroyed. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by explicit quantization. + //! + TRT_DEPRECATED int32_t getTensorsWithDynamicRange(int32_t size, char const** tensorNames) const noexcept + { + return mImpl->getTensorsWithDynamicRange(size, tensorNames); + } + + //! + //! \brief Set the ErrorRecorder for this interface + //! + //! Assigns the ErrorRecorder to this interface. The ErrorRecorder will track all errors during execution. + //! This function will call incRefCount of the registered ErrorRecorder at least once. Setting + //! recorder to nullptr unregisters the recorder with the interface, resulting in a call to decRefCount if + //! a recorder has been registered. + //! + //! If an error recorder is not set, messages will be sent to the global log stream. + //! + //! \param recorder The error recorder to register with this interface. + // + //! \see getErrorRecorder() + //! + void setErrorRecorder(IErrorRecorder* recorder) noexcept + { + mImpl->setErrorRecorder(recorder); + } + + //! + //! \brief Get the ErrorRecorder assigned to this interface. + //! + //! Retrieves the assigned error recorder object for the given class. A nullptr will be returned if + //! an error handler has not been set. + //! + //! \return A pointer to the IErrorRecorder object that has been registered. + //! + //! \see setErrorRecorder() + //! + IErrorRecorder* getErrorRecorder() const noexcept + { + return mImpl->getErrorRecorder(); + } + + //! + //! \brief Specify new weights of given name. + //! + //! \param name The name of the weights to be refit. + //! \param weights The new weights to associate with the name. + //! + //! Returns true on success, or false if new weights are rejected. + //! Possible reasons for rejection are: + //! + //! * The name of weights is nullptr or does not correspond to any refittable weights. + //! * The count of the weights is inconsistent with the count returned from calling getWeightsPrototype() with the + //! same name. + //! * The type of the weights is inconsistent with the type returned from calling getWeightsPrototype() with the + //! same name. + //! + //! Modifying the weights before method refitCudaEngine or refitCudaEngineAsync returns will result in undefined + //! behavior. + //! + //! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. + //! + bool setNamedWeights(char const* name, Weights weights) noexcept + { + return mImpl->setNamedWeights(name, weights); + } + + //! + //! \brief Get names of missing weights. + //! + //! For example, if some Weights have been set, but the engine was optimized + //! in a way that combines weights, any unsupplied Weights in the combination + //! are considered missing. + //! + //! \param size The number of weights names that can be safely written to. + //! \param weightsNames The names of the weights to be updated, or nullptr for unnamed weights. + //! + //! \return The number of missing Weights. + //! + //! If layerNames!=nullptr, each written pointer points to a string owned by + //! the engine being refit, and becomes invalid when the engine is destroyed. + //! + int32_t getMissingWeights(int32_t size, char const** weightsNames) noexcept + { + return mImpl->getMissingWeights(size, weightsNames); + } + + //! + //! \brief Get names of all weights that could be refit. + //! + //! \param size The number of weights names that can be safely written to. + //! \param weightsNames The names of the weights to be updated, or nullptr for unnamed weights. + //! + //! \return The number of Weights that could be refit. + //! + //! If layerNames!=nullptr, each written pointer points to a string owned by + //! the engine being refit, and becomes invalid when the engine is destroyed. + //! + int32_t getAllWeights(int32_t size, char const** weightsNames) noexcept + { + return mImpl->getAllWeights(size, weightsNames); + } + + //! + //! \brief get the logger with which the refitter was created + //! + //! \return the logger + //! + ILogger* getLogger() const noexcept + { + return mImpl->getLogger(); + } + + //! + //! \brief Set the maximum number of threads. + //! + //! \param maxThreads The maximum number of threads that can be used by the refitter. + //! + //! \return True if successful, false otherwise. + //! + //! The default value is 1 and includes the current thread. + //! A value greater than 1 permits TensorRT to use multi-threaded algorithms. + //! A value less than 1 triggers a kINVALID_ARGUMENT error. + //! + bool setMaxThreads(int32_t maxThreads) noexcept + { + return mImpl->setMaxThreads(maxThreads); + } + + //! + //! \brief get the maximum number of threads that can be used by the refitter. + //! + //! Retrieves the maximum number of threads that can be used by the refitter. + //! + //! \return The maximum number of threads that can be used by the refitter. + //! + //! \see setMaxThreads() + //! + int32_t getMaxThreads() const noexcept + { + return mImpl->getMaxThreads(); + } + + //! + //! \brief Specify new weights on a specified device of given name. + //! + //! \param name The name of the weights to be refitted. + //! \param weights The new weights on the specified device. + //! \param location The location (host vs. device) of the new weights. + //! + //! \return True on success, or false if new weights are rejected. + //! Possible reasons for rejection are: + //! + //! * The name of the weights is nullptr or does not correspond to any refittable weights. + //! * The count of the weights is inconsistent with the count returned from calling getWeightsPrototype() with the + //! same name. + //! * The type of the weights is inconsistent with the type returned from calling getWeightsPrototype() with the + //! same name. + //! + //! It is allowed to provide some weights on CPU and others on GPU. + //! Modifying the weights before the method refitCudaEngine() or refitCudaEngineAsync() completes will result in + //! undefined behavior. + //! + //! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. + //! + bool setNamedWeights(char const* name, Weights weights, TensorLocation location) noexcept + { + return mImpl->setNamedWeightsWithLocation(name, weights, location); + } + + //! + //! \brief Get weights associated with the given name. + //! + //! \param weightsName The name of the weights to be refitted. + //! + //! \return Weights associated with the given name. + //! + //! If the weights were never set, returns null weights and reports an error to the refitter errorRecorder. + //! + //! \warning The string weightsName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + Weights getNamedWeights(char const* weightsName) const noexcept + { + return mImpl->getNamedWeights(weightsName); + } + + //! + //! \brief Get location for the weights associated with the given name. + //! + //! \param weightsName The name of the weights to be refitted. + //! + //! \return Location for the weights associated with the given name. + //! + //! If the weights were never set, returns TensorLocation::kHOST and reports an error to the refitter errorRecorder. + //! + //! \warning The string weightsName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + TensorLocation getWeightsLocation(char const* weightsName) const noexcept + { + return mImpl->getWeightsLocation(weightsName); + } + + //! + //! \brief Unset weights associated with the given name. + //! + //! \param weightsName The name of the weights to be refitted. + //! + //! \return False if the weights were never set, returns true otherwise. + //! + //! Unset weights before releasing them. + //! + //! \warning The string weightsName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + bool unsetNamedWeights(char const* weightsName) noexcept + { + return mImpl->unsetNamedWeights(weightsName); + } + + //! + //! \brief Set whether to validate weights during refitting. + //! + //! \param weightsValidation Indicate whether to validate weights during refitting. + //! + //! When set to true, TensorRT will validate weights during FP32 to FP16/BF16 weights conversions or + //! sparsifying weights in the refit call. If provided weights are not proper for some weights transformations, + //! TensorRT will issue a warning and continue the transformation for minor issues (such as overflow during + //! narrowing conversion), or issue an error and stop the refitting process for severe issues (such as sparsifying + //! dense weights). By default the flag is true. Set the flag to false for faster refitting performance. + //! + void setWeightsValidation(bool weightsValidation) noexcept + { + return mImpl->setWeightsValidation(weightsValidation); + } + + //! + //! \brief Get whether to validate weights values during refitting. + //! + bool getWeightsValidation() const noexcept + { + return mImpl->getWeightsValidation(); + } + + //! + //! \brief Enqueue weights refitting of the associated engine on the given stream. + //! + //! \param stream The stream to enqueue the weights updating task. + //! + //! \return True on success, or false if new weights validation fails or getMissingWeights() != 0 before the call. + //! If false is returned, a subset of weights may have been refitted. + //! + //! The behavior is undefined if the engine has pending enqueued work on a different stream from the provided one. + //! Provided weights on CPU can be unset and released, or updated after refitCudaEngineAsync returns. + //! Freeing or updating of the provided weights on GPU can be enqueued on the same stream after refitCudaEngineAsync + //! returns. + //! + //! IExecutionContexts associated with the engine remain valid for use afterwards. There is no need to set the same + //! weights repeatedly for multiple refit calls as the weights memory can be updated directly instead. The weights + //! updating task should use the same stream as the one used for the refit call. + //! + bool refitCudaEngineAsync(cudaStream_t stream) noexcept + { + return mImpl->refitCudaEngineAsync(stream); + } + + //! + //! \brief Get the Weights prototype associated with the given name. + //! + //! \param weightsName The name of the weights to be refitted. + //! + //! \return Weights prototype associated with the given name. + //! + //! The type and count of weights prototype is the same as weights used for engine building. The values property + //! is nullptr for weights prototypes. The count of the weights prototype is -1 when the name of the weights is + //! nullptr or does not correspond to any refittable weights. + //! + //! \warning The string weightsName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + Weights getWeightsPrototype(char const* weightsName) const noexcept + { + return mImpl->getWeightsPrototype(weightsName); + } + +protected: + apiv::VRefitter* mImpl; +}; + +//! +//! \enum OptProfileSelector +//! +//! \brief When setting or querying optimization profile parameters (such as shape tensor inputs or dynamic dimensions), +//! select whether we are interested in the minimum, optimum, or maximum values for these parameters. +//! The minimum and maximum specify the permitted range that is supported at runtime, while the optimum value +//! is used for the kernel selection. This should be the "typical" value that is expected to occur at runtime. +//! +//! \see IOptimizationProfile::setDimensions(), IOptimizationProfile::setShapeValuesV2(), IOptimizationProfile::setShapeValues() +//! +enum class OptProfileSelector : int32_t +{ + kMIN = 0, //!< This is used to set or get the minimum permitted value for dynamic dimensions etc. + kOPT = 1, //!< This is used to set or get the value that is used in the optimization (kernel selection). + kMAX = 2 //!< This is used to set or get the maximum permitted value for dynamic dimensions etc. +}; + +//! +//! \brief Number of different values of OptProfileSelector enum. +//! +//! \see OptProfileSelector +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 3; +} + +//! +//! \class IOptimizationProfile +//! \brief Optimization profile for dynamic input dimensions and shape tensors. +//! +//! When building an ICudaEngine from an INetworkDefinition that has dynamically resizable inputs (at least +//! one input tensor has one or more of its dimensions specified as -1) or shape input tensors, users need to specify +//! at least one optimization profile. Optimization profiles are numbered 0, 1, ... +//! The first optimization profile that has been defined (with index 0) will be used by the ICudaEngine whenever no +//! optimization profile has been selected explicitly. If none of the inputs are dynamic, the default optimization +//! profile will be generated automatically unless it is explicitly provided by the user (this is possible but not +//! required in this case). If more than a single optimization profile is defined, users may set a target how +//! much additional weight space should be maximally allocated to each additional profile (as a fraction of the +//! maximum, unconstrained memory). +//! +//! Users set optimum input tensor dimensions, as well as minimum and maximum input tensor dimensions. The builder +//! selects the kernels that result in the lowest runtime for the optimum input tensor dimensions, and are valid for +//! all input tensor sizes in the valid range between minimum and maximum dimensions. A runtime error will be raised +//! if the input tensor dimensions fall outside the valid range for this profile. Likewise, users provide minimum, +//! optimum, and maximum values for all shape tensor input values. +//! +//! \see IBuilderConfig::addOptimizationProfile() +//! +class IOptimizationProfile : public INoCopy +{ +public: + //! + //! \brief Set the minimum / optimum / maximum dimensions for a dynamic input tensor. + //! + //! This function must be called three times (for the minimum, optimum, and maximum) for any network input tensor + //! that has dynamic dimensions. If minDims, optDims, and maxDims are the minimum, optimum, and maximum dimensions, + //! and networkDims are the dimensions for this input tensor that are provided to the INetworkDefinition object, + //! then the following conditions must all hold: + //! + //! (1) minDims.nbDims == optDims.nbDims == maxDims.nbDims == networkDims.nbDims + //! (2) 0 <= minDims.d[i] <= optDims.d[i] <= maxDims.d[i] for i = 0, ..., networkDims.nbDims-1 + //! (3) if networkDims.d[i] != -1, then minDims.d[i] == optDims.d[i] == maxDims.d[i] == networkDims.d[i] + //! + //! This function may (but need not be) called for an input tensor that does not have dynamic dimensions. In this + //! case, the third argument must always equal networkDims. + //! + //! \param inputName The input tensor name + //! \param select Whether to set the minimum, optimum, or maximum dimensions + //! \param dims The minimum, optimum, or maximum dimensions for this input tensor + //! + //! \return false if an inconsistency was detected (e.g. the rank does not match another dimension that was + //! previously set for the same input), true if no inconsistency was detected. Note that inputs can be + //! validated only partially; a full validation is performed at engine build time. + //! + //! \warning If run on DLA, minimum, optimum, and maximum dimensions must to be the same. + //! + //! \warning The string inputName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + bool setDimensions(char const* inputName, OptProfileSelector select, Dims const& dims) noexcept + { + return mImpl->setDimensions(inputName, select, dims); + } + + //! + //! \brief Get the minimum / optimum / maximum dimensions for a dynamic input tensor. + //! + //! If the dimensions have not been previously set via setDimensions(), return an invalid Dims with nbDims == -1. + //! + //! \warning The string inputName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + Dims getDimensions(char const* inputName, OptProfileSelector select) const noexcept + { + return mImpl->getDimensions(inputName, select); + } + + //! + //! \brief Set the minimum / optimum / maximum values for an input shape tensor. + //! + //! This function must be called three times for every input tensor t that is a shape tensor (t.isShape() == true). + //! This implies that the dimensions of t are fixed at network definition time and the volume does not exceed 64. + //! This function must not be called for any input tensor that is not a shape tensor. + //! + //! Each time this function is called for the same input tensor, the same nbValues must be supplied (either 1 + //! if the tensor rank is 0, or dims.d[0] if the rank is 1). Furthermore, if minVals, optVals, maxVals are the + //! minimum, optimum, and maximum values, it must be true that minVals[i] <= optVals[i] <= maxVals[i] for + //! i = 0, ..., nbValues - 1. Execution of the network must be valid for the optVals. + //! + //! Shape tensors are tensors that contribute to shape calculations in some way. While input shape tensors can be + //! type kINT32 or kINT64, the values used to set the minimum, optimum, and maximum values must fit in int32_t. + //! + //! Examples: + //! + //! * A shape tensor used as the second input to IShuffleLayer can contain a -1 wildcard. + //! The corresponding minVal[i] should be -1. + //! + //! * A shape tensor used as the stride input to ISliceLayer can contain any valid strides. + //! The values could be positive, negative, or zero. + //! + //! * A shape tensor subtracted from zero to compute the size input of an ISliceLayer can + //! contain any non-positive values that yield a valid slice operation. + //! + //! Tightening the minVals and maxVals bounds to cover only values that are necessary may help optimization. + //! + //! \param inputName The input tensor name + //! \param select Whether to set the minimum, optimum, or maximum input values. + //! \param values An array of length nbValues containing the minimum, optimum, or maximum shape tensor elements. + //! For multidimensional tensors, the array is in row-major order. + //! \param nbValues The length of the value array, which must equal the number of shape tensor elements (>= 1) + //! + //! \return false if an inconsistency was detected (e.g. nbValues does not match a previous call for the same + //! tensor), else true. As for setDimensions(), a full validation can only be performed at engine build + //! time. + //! + //! \warning If run on DLA, minimum, optimum, and maximum shape values must to be the same. + //! + //! \warning The string inputName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \warning When setShapeValuesV2 is called after setShapeValues, a following call to getShapeValues will + //! return nullptr. Vice versa, a call to setShapeValues undoes the effects of setShapeValuesV2. + //! + //! \deprecated Deprecated in TensorRT 10.11. Superseded by setShapeValuesV2(). + //! + TRT_DEPRECATED bool setShapeValues( + char const* inputName, OptProfileSelector select, int32_t const* values, int32_t nbValues) noexcept + { + return mImpl->setShapeValues(inputName, select, values, nbValues); + } + + //! + //! \brief Get the number of values for an input shape tensor. + //! + //! This will return the number of shape values if setShapeValues() has been called before for this input tensor. + //! Otherwise, return -1. + //! + //! \warning The string inputName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + int32_t getNbShapeValues(char const* inputName) const noexcept + { + return mImpl->getNbShapeValues(inputName); + } + + //! + //! \brief Get the minimum / optimum / maximum values for an input shape tensor. + //! + //! If the shape values have not been set previously with setShapeValues(), this returns nullptr. + //! + //! \warning The string inputName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \deprecated Deprecated in TensorRT 10.11. Superseded by getShapeValuesV2(). + //! + TRT_DEPRECATED int32_t const* getShapeValues(char const* inputName, OptProfileSelector select) const noexcept + { + return mImpl->getShapeValues(inputName, select); + } + + //! + //! \brief Set a target for extra GPU memory that may be used by this profile. + //! + //! \param target Additional memory that the builder should aim to maximally allocate for this profile, as a + //! fraction of the memory it would use if the user did not impose any constraints on memory. This + //! unconstrained case is the default; it corresponds to target == 1.0. If target == 0.0, the builder + //! aims to create the new optimization profile without allocating any additional weight memory. + //! Valid inputs lie between 0.0 and 1.0. This parameter is only a hint, and TensorRT does not guarantee + //! that the target will be reached. This parameter is ignored for the first (default) optimization profile + //! that is defined. + //! + //! \return true if the input is in the valid range (between 0 and 1 inclusive), else false. + //! + bool setExtraMemoryTarget(float target) noexcept + { + return mImpl->setExtraMemoryTarget(target); + } + + //! + //! \brief Get the extra memory target that has been defined for this profile. + //! + //! This defaults to 1.0F. + //! + //! \return the valid value set by setExtraMemoryTarget or 1.0F. + //! + float getExtraMemoryTarget() const noexcept + { + return mImpl->getExtraMemoryTarget(); + } + + //! + //! \brief Check whether the optimization profile can be passed to an IBuilderConfig object. + //! + //! This function performs partial validation, by e.g. checking that whenever one of the minimum, optimum, or + //! maximum dimensions of a tensor have been set, the others have also been set and have the same rank, as + //! well as checking that the optimum dimensions are always as least as large as the minimum dimensions, and + //! that the maximum dimensions are at least as large as the optimum dimensions. Some validation steps require + //! knowledge of the network definition and are deferred to engine build time. + //! + //! + //! \return true if the optimization profile is valid and may be passed to an IBuilderConfig, else false. + //! + bool isValid() const noexcept + { + return mImpl->isValid(); + } + + //! + //! \brief Set the minimum / optimum / maximum values for an input shape tensor. + //! + //! This function must be called three times for every input tensor t that is a shape tensor (t.isShape() == true). + //! This implies that the dimensions of t are fixed at network definition time and the volume does not exceed 64. + //! This function must not be called for any input tensor that is not a shape tensor. + //! + //! Each time this function is called for the same input tensor, the same nbValues must be supplied (either 1 + //! if the tensor rank is 0, or dims.d[0] if the rank is 1). Furthermore, if minVals, optVals, maxVals are the + //! minimum, optimum, and maximum values, it must be true that minVals[i] <= optVals[i] <= maxVals[i] for + //! i = 0, ..., nbValues - 1. Execution of the network must be valid for the optVals. + //! + //! Shape tensors are tensors that contribute to shape calculations in some way. While input shape tensors can be + //! type kINT32 or kINT64, the values used to set the minimum, optimum, and maximum values must fit in int64_t. + //! + //! Examples: + //! + //! * A shape tensor used as the second input to IShuffleLayer can contain a -1 wildcard. + //! The corresponding minVal[i] should be -1. + //! + //! * A shape tensor used as the stride input to ISliceLayer can contain any valid strides. + //! The values could be positive, negative, or zero. + //! + //! * A shape tensor subtracted from zero to compute the size input of an ISliceLayer can + //! contain any non-positive values that yield a valid slice operation. + //! + //! Tightening the minVals and maxVals bounds to cover only values that are necessary may help optimization. + //! + //! \param inputName The input tensor name + //! \param select Whether to set the minimum, optimum, or maximum input values. + //! \param values An array of length nbValues containing the minimum, optimum, or maximum shape tensor elements. + //! For multidimensional tensors, the array is in row-major order. + //! \param nbValues The length of the value array, which must equal the number of shape tensor elements (>= 1) + //! + //! \return false if an inconsistency was detected (e.g. nbValues does not match a previous call for the same + //! tensor), else true. As for setDimensions(), a full validation can only be performed at engine build + //! time. + //! + //! \warning If run on DLA, minimum, optimum, and maximum shape values must to be the same. + //! + //! \warning The string inputName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \warning When setShapeValues is called after setShapeValuesV2, input shape would be overwritten as 32 bit + //! and getShapeValuesV2 would return nullptr. + //! + bool setShapeValuesV2( + char const* inputName, OptProfileSelector select, int64_t const* values, int32_t nbValues) noexcept + { + return mImpl->setShapeValuesV2(inputName, select, values, nbValues); + } + + //! + //! \brief Get the minimum / optimum / maximum values for an input shape tensor. + //! + //! If the shape values have not been set previously with setShapeValuesV2(), this returns nullptr. + //! + //! \warning The string inputName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + int64_t const* getShapeValuesV2(char const* inputName, OptProfileSelector select) const noexcept + { + return mImpl->getShapeValuesV2(inputName, select); + } + +protected: + apiv::VOptimizationProfile* mImpl; + virtual ~IOptimizationProfile() noexcept = default; +}; + +//! +//! \enum TacticSource +//! +//! \brief List of tactic sources for TensorRT. +//! +//! \see TacticSources, IBuilderConfig::setTacticSources(), IBuilderConfig::getTacticSources() +//! +enum class TacticSource : int32_t +{ + //! cuBLAS tactics. Disabled by default. + //! \note Disabling kCUBLAS will cause the cuBLAS handle passed to plugins in attachToContext to be null. + //! \deprecated Deprecated in TensorRT 10.0. + kCUBLAS TRT_DEPRECATED_ENUM = 0, + + //! cuBLAS LT tactics. Disabled by default. + //! \deprecated Deprecated in TensorRT 9.0. + kCUBLAS_LT TRT_DEPRECATED_ENUM = 1, + + //! cuDNN tactics. Disabled by default. + //! \note Disabling kCUDNN will cause the cuDNN handle passed to plugins in attachToContext to be null. + //! \deprecated Deprecated in TensorRT 10.0. + kCUDNN TRT_DEPRECATED_ENUM = 2, + + //! Enables convolution tactics implemented with edge mask tables. These tactics tradeoff memory for performance by + //! consuming additional memory space proportional to the input size. + //! Enabled by default. + kEDGE_MASK_CONVOLUTIONS = 3, + + //! Enables convolution tactics implemented with source-code JIT fusion. The engine building time may increase + //! when this is enabled. Enabled by default. + kJIT_CONVOLUTIONS = 4, +}; + +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 5; +} //!< Maximum number of tactic sources in TacticSource enum. \see TacticSource + +//! +//! \brief Represents a collection of one or more TacticSource values +//! combine using bitwise-OR operations. +//! +//! \see IBuilderConfig::setTacticSources(), IBuilderConfig::getTacticSources() +//! +using TacticSources = uint32_t; + +//! +//! \enum ProfilingVerbosity +//! +//! \brief List of verbosity levels of layer information exposed in NVTX annotations and in IEngineInspector. +//! +//! \see IBuilderConfig::setProfilingVerbosity(), +//! IBuilderConfig::getProfilingVerbosity(), +//! IEngineInspector +//! +enum class ProfilingVerbosity : int32_t +{ + kLAYER_NAMES_ONLY = 0, //!< Print only the layer names. This is the default setting. + kNONE = 1, //!< Do not print any layer information. + kDETAILED = 2, //!< Print detailed layer information including layer names and layer parameters. +}; + +//! Maximum number of profile verbosity levels in ProfilingVerbosity enum. \see ProfilingVerbosity +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 3; +} + +//! +//! \brief Represents one or more SerializationFlag values using binary OR +//! operations, e.g., 1U << SerializationFlag::kEXCLUDE_LEAN_RUNTIME +//! +//! \see ISerializationConfig::setFlags(), ISerializationConfig::getFlags() +//! +using SerializationFlags = uint32_t; + +//! +//! \enum SerializationFlag +//! +//! \brief List of valid flags that the engine can enable when serializing the bytes. +//! +//! \see ISerializationConfig::setFlags(), ISerializationConfig::getFlags() +//! +enum class SerializationFlag : int32_t +{ + kEXCLUDE_WEIGHTS = 0, //!< Exclude the weights that can be refitted. + kEXCLUDE_LEAN_RUNTIME = 1, //!< Exclude the lean runtime. + kINCLUDE_REFIT = 2, //!< Remain refittable if originally so. +}; + +//! Maximum number of serialization flags in SerializationFlag enum. \see SerializationFlag +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 3; +} + +//! +//! \class ISerializationConfig +//! +//! \brief Holds properties for configuring an engine to serialize the binary. +//! +//! \see SerializationFlag +//! +class ISerializationConfig : public INoCopy +{ +public: + virtual ~ISerializationConfig() noexcept = default; + + //! + //! \brief Set the serialization flags to turn on for this config. + //! + //! The flags are listed in the SerializationFlag enum. + //! + //! \param serializationFlags The serialization flags for an engine. + //! + //! \note This function will override the previous set flags, rather than bitwise ORing the new flag. + //! + //! \see getFlags() + //! + bool setFlags(SerializationFlags serializationFlags) noexcept + { + return mImpl->setFlags(serializationFlags); + } + + //! + //! \brief Get the serialization flags for this config. + //! + //! \return The serialization flags as a bitmask. + //! + //! \see setFlags() + //! + SerializationFlags getFlags() const noexcept + { + return mImpl->getFlags(); + } + + //! + //! \brief clear a serialization flag. + //! + //! clears the serialization flag from the config. + //! + //! \see setFlags() + //! + bool clearFlag(SerializationFlag serializationFlag) noexcept + { + return mImpl->clearFlag(serializationFlag); + } + + //! + //! \brief Set a serialization flag. + //! + //! Add the input serialization flag to the already enabled flags. + //! + //! \see setFlags() + //! + bool setFlag(SerializationFlag serializationFlag) noexcept + { + return mImpl->setFlag(serializationFlag); + } + + //! + //! \brief Returns true if the serialization flag is set + //! + //! \see getFlags() + //! + //! \return True if flag is set, false if unset. + //! + bool getFlag(SerializationFlag serializationFlag) const noexcept + { + return mImpl->getFlag(serializationFlag); + } + +protected: + apiv::VSerializationConfig* mImpl; +}; + +//! +//! \enum ExecutionContextAllocationStrategy +//! +//! \brief Different memory allocation behaviors for IExecutionContext. +//! +//! IExecutionContext requires a block of device memory for internal activation tensors during inference. The user can +//! either let the execution context manage the memory in various ways or allocate the memory themselves. +//! +//! \see ICudaEngine::createExecutionContext() +//! \see IExecutionContext::setDeviceMemory() +//! +enum class ExecutionContextAllocationStrategy : int32_t +{ + kSTATIC = 0, //!< Default static allocation with the maximum size across all profiles. + kON_PROFILE_CHANGE = 1, //!< Reallocate for a profile when it's selected. + kUSER_MANAGED = 2, //!< The user supplies custom allocation to the execution context. +}; + +//! +//! \brief Maximum number of memory allocation strategies in ExecutionContextAllocationStrategy enum. +//! +//! \see ExecutionContextAllocationStrategy +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 3; +} + + +//! \class IRuntimeConfig +//! +//! \brief A class for runtime configuration. This class is used during execution context creation. +//! +//! \see IRuntime, IBuilderConfig +//! +class IRuntimeConfig : public INoCopy +{ +public: + virtual ~IRuntimeConfig() noexcept = default; + + //! + //! \brief Set the execution context allocation strategy. Default value is kSTATIC. + //! + //! \param strategy The execution context allocation strategy. + //! + void setExecutionContextAllocationStrategy(ExecutionContextAllocationStrategy strategy) noexcept + { + return mImpl->setExecutionContextAllocationStrategy(strategy); + } + + //! + //! \brief Get the execution context allocation strategy. + //! + //! \return The execution context allocation strategy. + //! + ExecutionContextAllocationStrategy getExecutionContextAllocationStrategy() const noexcept + { + return mImpl->getExecutionContextAllocationStrategy(); + } + + +protected: + apiv::VRuntimeConfig* mImpl; +}; // class IRuntimeConfig + +//! +//! \enum EngineStat +//! +//! \brief The kind of engine statistics that queried from the ICudaEngine. +//! +//! \see ICudaEngine::getEngineStat() +//! \see BuilderFlag::kSTRIP_PLAN +//! +enum class EngineStat : int32_t +{ + //! Return the total weight size in bytes. + kTOTAL_WEIGHTS_SIZE = 0, + + //! Return the stripped weight size in bytes for engines built with BuilderFlag::kSTRIP_PLAN. + kSTRIPPED_WEIGHTS_SIZE = 1, +}; + +//! +//! \brief Maximum number of engine statistic kinds in EngineStat enum. +//! +//! \see EngineStat +//! +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 2; +} + +//! +//! \class ICudaEngine +//! +//! \brief An engine for executing inference on a built network, with functionally unsafe features. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class ICudaEngine : public INoCopy +{ +public: + virtual ~ICudaEngine() noexcept = default; + + //! + //! \brief Get shape of an input or output tensor. + //! + //! \param tensorName The name of an input or output tensor. + //! + //! \return shape of the tensor, with -1 in place of each dynamic runtime dimension, + //! or Dims{-1, {}} if the provided name does not map to an input or output tensor. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + Dims getTensorShape(char const* tensorName) const noexcept + { + return mImpl->getTensorShape(tensorName); + } + + //! + //! \brief Determine the required data type for a buffer from its tensor name. + //! + //! \param tensorName The name of an input or output tensor. + //! + //! \return The type of the data in the buffer, or DataType::kFLOAT if the provided name does not map to an input or + //! output tensor. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + DataType getTensorDataType(char const* tensorName) const noexcept + { + return mImpl->getTensorDataType(tensorName); + } + + //! + //! \brief Get the number of layers in the network. + //! + //! The number of layers in the network is not necessarily the number in the original network definition, as layers + //! may be combined or eliminated as the engine is optimized. This value can be useful when building per-layer + //! tables, such as when aggregating profiling data over a number of executions. + //! + //! \return The number of layers in the network. + //! + int32_t getNbLayers() const noexcept + { + return mImpl->getNbLayers(); + } + + //! + //! \brief Serialize the network to a stream. + //! + //! \return A IHostMemory object that contains the serialized engine. + //! + //! The network may be deserialized with IRuntime::deserializeCudaEngine(). + //! + //! \see IRuntime::deserializeCudaEngine() + //! + IHostMemory* serialize() const noexcept + { + return mImpl->serialize(); + } + + //! + //! \brief Create an execution context and specify the strategy for allocating internal activation memory. + //! + //! The default value for the allocation strategy is ExecutionContextAllocationStrategy::kSTATIC, which means the + //! context will pre-allocate a block of device memory that is sufficient for all profiles. The newly created + //! execution context will be assigned optimization profile 0. If an error recorder has been set for the engine, it + //! will also be passed to the execution context. + //! + //! \see IExecutionContext + //! \see IExecutionContext::setOptimizationProfileAsync() + //! \see ExecutionContextAllocationStrategy + //! + IExecutionContext* createExecutionContext( + ExecutionContextAllocationStrategy strategy = ExecutionContextAllocationStrategy::kSTATIC) noexcept + { + return mImpl->createExecutionContext(strategy); + } + + //! + //! \brief Get whether an input or output tensor must be on GPU or CPU. + //! + //! \param tensorName The name of an input or output tensor. + //! + //! \return TensorLocation::kDEVICE if tensorName must be on GPU, or TensorLocation::kHOST if on CPU, or + //! TensorLocation::kDEVICE if the provided name does not map to an input or output tensor. + //! + //! The location is established at build time. E.g. shape tensors inputs are typically required to be on the CPU. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + TensorLocation getTensorLocation(char const* tensorName) const noexcept + { + return mImpl->getTensorLocation(tensorName); + } + + //! + //! \brief True if tensor is required as input for shape calculations or is output from shape calculations. + //! + //! Return true for either of the following conditions: + //! + //! * The tensor is a network input, and its value is required for IExecutionContext::getTensorShape() + //! to return the shape of a network output. + //! + //! * The tensor is a network output, and inferShape() will compute its values. + //! + //! For example, if a network uses an input tensor "foo" as an addend to an IElementWiseLayer + //! that computes the "reshape dimensions" for IShuffleLayer, then isShapeInferenceIO("foo") == true. + //! If the network copies said input tensor "foo" to an output "bar", then + //! isShapeInferenceIO("bar") == true and IExecutionContext::inferShapes() will write to "bar". + //! + bool isShapeInferenceIO(char const* tensorName) const noexcept + { + return mImpl->isShapeInferenceIO(tensorName); + } + + //! + //! \brief Determine whether a tensor is an input or output tensor. + //! + //! \param tensorName The name of an input or output tensor. + //! + //! \return kINPUT if tensorName is an input, kOUTPUT if tensorName is an output, or kNONE if neither. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + TensorIOMode getTensorIOMode(char const* tensorName) const noexcept + { + return mImpl->getTensorIOMode(tensorName); + } + + //! + //! \brief Get the input tensor name that an output tensor should alias with. + //! + //! Some operations (e.g., KVCacheUpdate) require that certain output tensors share memory with input tensors. + //! This method returns the name of the input tensor that a given output tensor should alias with. + //! + //! \param tensorName The name of an output tensor. + //! + //! \return The name of the input tensor to alias with, or nullptr if tensorName is not an output tensor or + //! the output does not alias with any input. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the + //! terminator. + //! + TRT_NODISCARD char const* getAliasedInputTensor(char const* tensorName) const noexcept + { + return mImpl->getAliasedInputTensor(tensorName); + } + + //! + //! \brief create an execution context without any device memory allocated + //! + //! The memory for execution of this device context must be supplied by the application. + //! + //! \deprecated Deprecated in TensorRT 10.0. Superseded by createExecutionContext() with parameter. + //! + TRT_DEPRECATED IExecutionContext* createExecutionContextWithoutDeviceMemory() noexcept + { + return mImpl->createExecutionContextWithoutDeviceMemory(); + } + + //! + //! \brief Create an execution context with TensorRT JIT runtime config. + //! + //! \param runtimeConfig The runtime config for TensorRT JIT. + //! + //! \see IRuntimeConfig + //! + IExecutionContext* createExecutionContext(IRuntimeConfig* runtimeConfig) noexcept + { + return mImpl->createExecutionContextWithRuntimeConfig(runtimeConfig); + } + + //! + //! \brief Create a runtime config for TensorRT JIT. + //! The caller is responsible for ownership of the returned IRuntimeConfig object. + //! + //! \return A IRuntimeConfig object. + //! + //! \see IRuntimeConfig + //! + IRuntimeConfig* createRuntimeConfig() noexcept + { + return mImpl->createRuntimeConfig(); + } + + //! + //! \brief Return the maximum device memory required by the context over all profiles. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by getDeviceMemorySizeV2(). + //! + //! \see IExecutionContext::setDeviceMemory() + //! + TRT_DEPRECATED size_t getDeviceMemorySize() const noexcept + { + return mImpl->getDeviceMemorySize(); + } + + //! + //! \brief Return the maximum device memory required by the context for a profile. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by getDeviceMemorySizeForProfileV2(int32_t). + //! + //! \see IExecutionContext::setDeviceMemoryV2() + //! + TRT_DEPRECATED size_t getDeviceMemorySizeForProfile(int32_t profileIndex) const noexcept + { + return mImpl->getDeviceMemorySizeForProfile(profileIndex); + } + + //! + //! \brief Return the maximum device memory required by the context over all profiles. + //! + //! This API is stateful, so its call returns different values based on the following calls: + //! * setWeightStreamingBudget() + //! * setWeightStreamingBudgetV2() + //! + //! \see IExecutionContext::setDeviceMemoryV2() + //! \see setWeightStreamingBudget() + //! \see setWeightStreamingBudgetV2() + //! + int64_t getDeviceMemorySizeV2() const noexcept + { + return mImpl->getDeviceMemorySizeV2(); + } + + //! + //! \brief Return the maximum device memory required by the context for a profile. + //! + //! This API is stateful, so its call returns different values based on the following calls: + //! * setWeightStreamingBudget() + //! * setWeightStreamingBudgetV2() + //! + //! \see IExecutionContext::setDeviceMemoryV2() + //! \see setWeightStreamingBudget() + //! \see setWeightStreamingBudgetV2() + //! + int64_t getDeviceMemorySizeForProfileV2(int32_t profileIndex) const noexcept + { + return mImpl->getDeviceMemorySizeForProfileV2(profileIndex); + } + + //! + //! \brief Return true if an engine can be refit. + //! + //! \see nvinfer1::createInferRefitter() + //! + bool isRefittable() const noexcept + { + return mImpl->isRefittable(); + } + + //! + //! \brief Return the number of bytes per component of an element, or -1 if the + //! tensor is not vectorized or provided name does not map to an input or output tensor. + //! + //! The vector component size is returned if getTensorVectorizedDim(tensorName) != -1. + //! + //! \param tensorName The name of an input or output tensor. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! \warning The function can only return the result of profile 0, and issues a warning message when there are + //! multiple profiles in the engine, use getTensorBytesPerComponent with profileIndex when there are multiple + //! profiles. + //! + //! \see getTensorVectorizedDim() + //! \see getTensorBytesPerComponent(tensorName, profileIndex) + //! + int32_t getTensorBytesPerComponent(char const* tensorName) const noexcept + { + return mImpl->getTensorBytesPerComponent(tensorName); + } + + //! + //! \brief Return the number of bytes per component of an element given of given profile, or -1 if the tensor is not + //! vectorized or provided name does not map to an input or output tensor. + //! + //! The vector component size is returned if getTensorVectorizedDim(tensorName, profileIndex) != -1. + //! + //! \param tensorName The name of an input or output tensor. + //! \param profileIndex The profile index to query + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see getTensorVectorizedDim(tensorName, profileIndex) + //! + int32_t getTensorBytesPerComponent(char const* tensorName, int32_t profileIndex) const noexcept + { + return mImpl->getTensorBytesPerComponentV2(tensorName, profileIndex); + } + + //! + //! \brief Return the number of components included in one element, or -1 if tensor is + //! not vectorized or if the provided name does not map to an input or output tensor. + //! + //! The number of elements in the vectors is returned if getTensorVectorizedDim(tensorName) != -1. + //! + //! \param tensorName The name of an input or output tensor. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! \warning The function can only return the result of profile 0, and issues a warning message when there + //! are multiple profiles in the engine, use getTensorComponentsPerElement with profileIndex when there are + //! multiple profiles. + //! + //! \see getTensorVectorizedDim() + //! \see getTensorComponentsPerElement(tensorName, profileIndex) + //! + int32_t getTensorComponentsPerElement(char const* tensorName) const noexcept + { + return mImpl->getTensorComponentsPerElement(tensorName); + } + + //! + //! \brief Return the number of components included in one element of given profile, or -1 if tensor is not + //! vectorized or the provided name does not map to an input or output tensor. + //! + //! The number of elements in the vectors is returned if getTensorVectorizedDim(tensorName, profileIndex) != -1. + //! + //! \param tensorName The name of an input or output tensor. + //! \param profileIndex The profile index to query + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see getTensorVectorizedDim(tensorName, profileIndex) + //! + int32_t getTensorComponentsPerElement(char const* tensorName, int32_t profileIndex) const noexcept + { + return mImpl->getTensorComponentsPerElementV2(tensorName, profileIndex); + } + + //! + //! \brief Return the tensor format, or TensorFormat::kLINEAR if the provided name does not map to an input or + //! output tensor. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! \warning This API can only return the tensor format of profile 0, and issues a warning message when there are + //! multiple profiles in the engine, use getTensorFormat with profileIndex when there are multiple profiles. + //! + //! \see getTensorFormat(tensorName, profileIndex) + //! + TensorFormat getTensorFormat(char const* tensorName) const noexcept + { + return mImpl->getTensorFormat(tensorName); + } + + //! + //! \brief Return the tensor format of given profile, or TensorFormat::kLINEAR if the provided name does not map to + //! an input or output tensor. + //! + //! \param tensorName The name of an input or output tensor. + //! \param profileIndex The profile index to query the format for. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + TensorFormat getTensorFormat(char const* tensorName, int32_t profileIndex) const noexcept + { + return mImpl->getTensorFormatV2(tensorName, profileIndex); + } + + //! + //! \brief Return the human readable description of the tensor format, or empty string if the provided name does not + //! map to an input or output tensor. + //! + //! The description includes the order, vectorization, data type, and strides. + //! Examples are shown as follows: + //! Example 1: kCHW + FP32 + //! "Row-major linear FP32 format" + //! Example 2: kCHW2 + FP16 + //! "Two-wide channel vectorized row-major FP16 format" + //! Example 3: kHWC8 + FP16 + Line Stride = 32 + //! "Channel major FP16 format where C % 8 == 0 and H Stride % 32 == 0" + //! + //! \param tensorName The name of an input or output tensor. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! \warning The function can only return the result of profile 0, and issues a warning message when there are + //! multiple profiles in the engine, use getTensorFormatDesc with profileIndex when there are multiple profiles. + //! + char const* getTensorFormatDesc(char const* tensorName) const noexcept + { + return mImpl->getTensorFormatDesc(tensorName); + } + + //! + //! \brief Return the human readable description of the tensor format of given profile, or empty string if the + //! provided name does not map to an input or output tensor. + //! + //! The description includes the order, vectorization, data type, and strides. + //! Examples are shown as follows: + //! Example 1: kCHW + FP32 + //! "Row-major linear FP32 format" + //! Example 2: kCHW2 + FP16 + //! "Two-wide channel vectorized row-major FP16 format" + //! Example 3: kHWC8 + FP16 + Line Stride = 32 + //! "Channel major FP16 format where C % 8 == 0 and H Stride % 32 == 0" + //! + //! \param tensorName The name of an input or output tensor. + //! \param profileIndex The profile index to query the format for. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + char const* getTensorFormatDesc(char const* tensorName, int32_t profileIndex) const noexcept + { + return mImpl->getTensorFormatDescV2(tensorName, profileIndex); + } + + //! + //! \brief Return the dimension index that the buffer is vectorized, or -1 if the provided name does not + //! map to an input or output tensor. + //! + //! Specifically -1 is returned if scalars per vector is 1. + //! + //! \param tensorName The name of an input or output tensor. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! \warning The function can only return the result of profile 0, and issues a warning message when there are + //! multiple profiles in the engine, use getTensorVectorizedDim with profileIndex when there are multiple profiles. + //! + int32_t getTensorVectorizedDim(char const* tensorName) const noexcept + { + return mImpl->getTensorVectorizedDim(tensorName); + } + + //! + //! \brief Return the dimension index that the buffer is vectorized of given profile, or -1 if the provided name + //! does not map to an input or output tensor. + //! + //! Specifically -1 is returned if scalars per vector is 1. + //! + //! \param tensorName The name of an input. + //! \param profileIndex The profile index to query the format for. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + int32_t getTensorVectorizedDim(char const* tensorName, int32_t profileIndex) const noexcept + { + return mImpl->getTensorVectorizedDimV2(tensorName, profileIndex); + } + + //! + //! \brief Returns the name of the network associated with the engine. + //! + //! The name is set during network creation and is retrieved after + //! building or deserialization. + //! + //! \see INetworkDefinition::setName(), INetworkDefinition::getName() + //! + //! \return A null-terminated C-style string representing the name of the network. + //! + char const* getName() const noexcept + { + return mImpl->getName(); + } + + //! + //! \brief Get the number of optimization profiles defined for this engine. + //! + //! \return Number of optimization profiles. It is always at least 1. + //! + //! \see IExecutionContext::setOptimizationProfileAsync() + int32_t getNbOptimizationProfiles() const noexcept + { + return mImpl->getNbOptimizationProfiles(); + } + + //! + //! \brief Get the minimum / optimum / maximum dimensions for an input tensor given its name under an optimization + //! profile. + //! + //! \param tensorName The name of an input tensor. + //! + //! \param profileIndex The profile index, which must be between 0 and getNbOptimizationProfiles()-1. + //! + //! \param select Whether to query the minimum, optimum, or maximum dimensions for this input tensor. + //! + //! \return The minimum / optimum / maximum dimensions for an input tensor in this profile. + //! If the profileIndex is invalid or provided name does not map to an input tensor, return Dims{-1, {}} + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + Dims getProfileShape(char const* tensorName, int32_t profileIndex, OptProfileSelector select) const noexcept + { + return mImpl->getProfileShape(tensorName, profileIndex, select); + } + + //! + //! \brief Get the minimum / optimum / maximum values (not dimensions) for an input tensor given + //! its name under an optimization profile. These correspond to the values set using + //! IOptimizationProfile::setShapeValues when the engine was built. + //! + //! \param tensorName The name of an input tensor. + //! + //! \param profileIndex The profile index, which must be between 0 and getNbOptimizationProfiles()-1. + //! + //! \param select Whether to query the minimum, optimum, or maximum values for this input tensor. + //! + //! \return The minimum / optimum / maximum values for an input tensor in this profile. If the profileIndex is + //! invalid or the provided name does not map to an input tensor, or the tensor is not a shape binding, return + //! nullptr. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \deprecated Deprecated in TensorRT 10.11. Superseded by getProfileTensorValuesV2(). + //! \warning If input shapes are set with setShapeValuesV2, getProfileTensorValues will return nullptr + //! + TRT_DEPRECATED int32_t const* getProfileTensorValues( + char const* tensorName, int32_t profileIndex, OptProfileSelector select) const noexcept + { + return mImpl->getProfileTensorValues(tensorName, profileIndex, select); + } + + //! + //! \brief Determine what execution capability this engine has. + //! + //! If the engine has EngineCapability::kSTANDARD, then all engine functionality is valid. + //! If the engine has EngineCapability::kSAFETY, then only the functionality in safe engine is valid. + //! If the engine has EngineCapability::kDLA_STANDALONE, then only serialize, destroy, and const-accessor functions + //! are valid. + //! + //! \return The EngineCapability flag that the engine was built for. + //! + EngineCapability getEngineCapability() const noexcept + { + return mImpl->getEngineCapability(); + } + + //! + //! \brief Set the ErrorRecorder for this interface + //! + //! Assigns the ErrorRecorder to this interface. The ErrorRecorder will track all errors during execution. + //! This function will call incRefCount of the registered ErrorRecorder at least once. Setting + //! recorder to nullptr unregisters the recorder with the interface, resulting in a call to decRefCount if + //! a recorder has been registered. + //! + //! If an error recorder is not set, messages will be sent to the global log stream. + //! + //! \param recorder The error recorder to register with this interface. + //! + //! \see getErrorRecorder() + //! + void setErrorRecorder(IErrorRecorder* recorder) noexcept + { + return mImpl->setErrorRecorder(recorder); + } + + //! + //! \brief Get the ErrorRecorder assigned to this interface. + //! + //! Retrieves the assigned error recorder object for the given class. A nullptr will be returned if + //! an error handler has not been set. + //! + //! \return A pointer to the IErrorRecorder object that has been registered. + //! + //! \see setErrorRecorder() + //! + IErrorRecorder* getErrorRecorder() const noexcept + { + return mImpl->getErrorRecorder(); + } + + //! + //! \brief Query whether the engine was built with an implicit batch dimension. + //! + //! \return Always false since TensorRT 10.0 does not support an implicit batch dimension. + //! + //! \see createNetworkV2 + //! + //! \deprecated Deprecated in TensorRT 10.0. Implicit batch is no supported since TensorRT 10.0. + //! + TRT_DEPRECATED bool hasImplicitBatchDimension() const noexcept + { + return mImpl->hasImplicitBatchDimension(); + } + + //! + //! \brief return the tactic sources required by this engine. + //! + //! The value returned is equal to zero or more tactics sources set + //! at build time via setTacticSources() in IBuilderConfig. Sources + //! set by the latter but not returned by \ref ICudaEngine::getTacticSources + //! do not reduce overall engine execution time, and can be removed from + //! future builds to reduce build time. + //! + //! \see IBuilderConfig::setTacticSources() + //! + TacticSources getTacticSources() const noexcept + { + return mImpl->getTacticSources(); + } + + //! + //! \brief Return the \ref ProfilingVerbosity the builder config was set to when the engine was built. + //! + //! \return the profiling verbosity the builder config was set to when the engine was built. + //! + //! \see IBuilderConfig::setProfilingVerbosity() + //! + ProfilingVerbosity getProfilingVerbosity() const noexcept + { + return mImpl->getProfilingVerbosity(); + } + + //! + //! \brief Create a new engine inspector which prints the layer information in an engine or an execution context. + //! + //! \see IEngineInspector. + //! + IEngineInspector* createEngineInspector() const noexcept + { + return mImpl->createEngineInspector(); + } + + //! + //! \brief Return number of IO tensors. + //! + //! It is the number of input and output tensors for the network from which the engine was built. + //! The names of the IO tensors can be discovered by calling getIOTensorName(i) for i in 0 to getNbIOTensors()-1. + //! + //! \see getIOTensorName() + //! + int32_t getNbIOTensors() const noexcept + { + return mImpl->getNbIOTensors(); + } + + //! + //! \brief Return name of an IO tensor. + //! + //! \param index value between 0 and getNbIOTensors()-1 + //! + //! \see getNbIOTensors() + //! + char const* getIOTensorName(int32_t index) const noexcept + { + return mImpl->getIOTensorName(index); + } + + //! + //! \brief Return the hardware compatibility level of this engine. + //! + //! \return hardwareCompatibilityLevel The level of hardware + //! compatibility. + //! + HardwareCompatibilityLevel getHardwareCompatibilityLevel() const noexcept + { + return mImpl->getHardwareCompatibilityLevel(); + } + + //! + //! \brief Return the number of auxiliary streams used by this engine. + //! + //! This number will be less than or equal to the maximum allowed number of auxiliary streams set by + //! IBuilderConfig::setMaxAuxStreams() API call when the engine was built. + //! + //! \return the number of auxiliary streams used by this engine. + //! + //! \see IBuilderConfig::setMaxAuxStreams(), IExecutionContext::setAuxStreams() + //! + int32_t getNbAuxStreams() const noexcept + { + return mImpl->getNbAuxStreams(); + } + + //! + //! \brief Create a serialization configuration object. + //! + //! \see ISerializationConfig + //! + ISerializationConfig* createSerializationConfig() noexcept + { + return mImpl->createSerializationConfig(); + } + + //! + //! \brief Serialize the network to a stream with the provided SerializationConfig. + //! + //! \return An IHostMemory object that contains the serialized engine. + //! + //! The network may be deserialized with IRuntime::deserializeCudaEngine(). + //! Serializing plan file with SerializationFlag::kEXCLUDE_WEIGHTS requires building the engine with kREFIT, + //! kREFIT_IDENTICAL or kREFIT_INDIVIDUAL. + //! + //! The only applicable scenario for SerializationFlag::kINCLUDE_REFIT is when serializing weight-stripping + //! engines without kEXCLUDE_WEIGHTS. By default, the resulting serialized engine is unrefittable. Setting + //! SerializationFlag::kINCLUDE_REFIT ensures that the serialized engine remains refittable. + //! + //! \see IRuntime::deserializeCudaEngine() + //! + IHostMemory* serializeWithConfig(ISerializationConfig& config) const noexcept + { + return mImpl->serializeWithConfig(config); + } + + //! + //! \brief Limit the maximum amount of GPU memory usable for network weights + //! in bytes. + //! + //! \param gpuMemoryBudget This parameter may take on 3 types of values: + //! -1: Allows TensorRT to choose the budget according to the streamable weights size. + //! Free CUDA memory will be queried at createExecutionContext() and accordingly: + //! * If streamable weights all fit: weight streaming is not required and disabled. + //! * Otherwise: Budget is set to getMinimumWeightStreamingBudget + //! 0: (default) Disables weight streaming. The execution may fail if the network is too large for GPU memory. + //! >0: The maximum bytes of GPU memory that weights can occupy. It must be bounded by + //! [getMinimumWeightStreamingBudget, free GPU memory)]. + //! + //! By setting a weight limit, users can expect a GPU memory usage reduction + //! of (total bytes for network weights) - gpuMemoryBudget bytes. Maximum memory savings occur + //! when gpuMemoryBudget is set to getMinimumWeightStreamingBudget(). Creating additional + //! IExecutionContexts will increase memory usage by O(getMinimumStreamingBudget()). + //! + //! Streaming larger amounts of memory will likely result in lower performance + //! except in some boundary cases where streaming weights allows the user to + //! run larger batch sizes. The higher throughput offsets the increased + //! latency in these cases. Tuning the value of the memory limit is + //! recommended for best performance. + //! + //! \warning GPU memory for the weights is allocated in this call and will be deallocated by enabling weight + //! streaming or destroying the ICudaEngine. + //! + //! \warning BuilderFlag::kWEIGHT_STREAMING must be set during engine building. + //! + //! \warning The weights streaming budget cannot be modified while there are active IExecutionContexts. + //! + //! \return true if the memory limit is valid and the call was successful, false otherwise. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by setWeightStreamingBudgetV2(). + //! + //! \see BuilderFlag::kWEIGHT_STREAMING + //! \see getWeightStreamingBudget() + //! \see getMinimumWeightStreamingBudget() + //! \see getStreamableWeightsSize() + //! + TRT_DEPRECATED bool setWeightStreamingBudget(int64_t gpuMemoryBudget) noexcept + { + return mImpl->setWeightStreamingBudget(gpuMemoryBudget); + } + + //! + //! \brief Returns the current weight streaming device memory budget in bytes. + //! + //! \warning BuilderFlag::kWEIGHT_STREAMING must be set during engine building. + //! + //! \returns The weight streaming budget in bytes. Please see setWeightStreamingBudget() for the possible + //! values. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by getWeightStreamingBudgetV2(). + //! + //! \see BuilderFlag::kWEIGHT_STREAMING, + //! \see setWeightStreamingBudget() + //! \see getMinimumWeightStreamingBudget() + //! \see getStreamableWeightsSize() + //! + TRT_DEPRECATED int64_t getWeightStreamingBudget() const noexcept + { + return mImpl->getWeightStreamingBudget(); + } + + //! + //! \brief The minimum number of bytes of GPU memory required by network + //! weights for successful weight streaming. + //! + //! This is a positive integer for engines with streamable weights because a + //! staging buffer on the GPU is required to temporarily hold the streamed + //! weights. The size of the staging buffer is determined by TensorRT and must + //! be at least as large as the size of the largest streamable weight in the + //! network. + //! + //! \warning BuilderFlag::kWEIGHT_STREAMING must be set during engine building. + //! + //! \returns The minimum number of bytes of GPU memory required for streaming. + //! + //! \deprecated Deprecated in TensorRT 10.1. The minimum budget is 0 in the V2 APIs. + //! + //! \see setWeightStreamingBudget() + //! + TRT_DEPRECATED int64_t getMinimumWeightStreamingBudget() const noexcept + { + return mImpl->getMinimumWeightStreamingBudget(); + } + + //! + //! \brief Get the total size in bytes of all streamable weights. + //! + //! The set of streamable weights is a subset of all network weights. The + //! total size may exceed free GPU memory. + //! + //! \returns The total size in bytes of all streamable weights. + //! Returns 0 if BuilderFlag::kWEIGHT_STREAMING is unset during engine building. + //! + //! \see setWeightStreamingBudget() + //! + int64_t getStreamableWeightsSize() const noexcept + { + return mImpl->getStreamableWeightsSize(); + } + + //! + //! \brief Limit the maximum amount of GPU memory usable for network weights in bytes. + //! + //! \param gpuMemoryBudget This parameter must be a non-negative value. + //! 0: Only small amounts of scratch memory will required to run the model. + //! >= getStreamableWeightsSize (default): Disables weight streaming. + //! The execution may fail if the network is too large for GPU memory. + //! + //! By setting a weight limit, users can expect a GPU memory usage reduction on the order + //! of (total bytes for network weights) - gpuMemoryBudget bytes. Maximum memory savings occur + //! when gpuMemoryBudget is set to 0. Each IExecutionContext will require getWeightStreamingScratchMemorySize() + //! bytes of additional device memory if the engine is streaming its weights (budget < getStreamableWeightsSize()). + //! + //! Streaming larger amounts of memory will likely result in lower performance + //! except in some boundary cases where streaming weights allows the user to + //! run larger batch sizes. The higher throughput offsets the increased + //! latency in these cases. Tuning the value of the memory limit is + //! recommended for best performance. + //! + //! \warning GPU memory for the weights is allocated in this call and will be deallocated by enabling weight + //! streaming or destroying the ICudaEngine. + //! + //! \warning BuilderFlag::kWEIGHT_STREAMING must be set during engine building. + //! + //! \warning The weights streaming budget cannot be modified while there are active IExecutionContexts. + //! + //! \warning Using the V2 weight streaming APIs with V1 APIs (setWeightStreamingBudget(), + //! getWeightStreamingBudget(), getWeightStreamingMinimumBudget()) leads to undefined behavior. + //! + //! \return true if the memory limit is valid and the call was successful, false otherwise. + //! + //! \see BuilderFlag::kWEIGHT_STREAMING + //! \see getWeightStreamingBudgetV2() + //! \see getWeightStreamingScratchMemorySize() + //! \see getWeightStreamingAutomaticBudget() + //! \see getStreamableWeightsSize() + //! + bool setWeightStreamingBudgetV2(int64_t gpuMemoryBudget) noexcept + { + return mImpl->setWeightStreamingBudgetV2(gpuMemoryBudget); + } + + //! + //! \brief Returns the current weight streaming device memory budget in bytes. + //! + //! \warning BuilderFlag::kWEIGHT_STREAMING must be set during engine building. + //! + //! \returns The weight streaming budget in bytes. Please see setWeightStreamingBudgetV2() for the possible + //! return values. Returns getStreamableWeightsSize() if weight streaming is disabled. + //! + //! \see BuilderFlag::kWEIGHT_STREAMING + //! \see setWeightStreamingBudget() + //! \see getMinimumWeightStreamingBudget() + //! \see getStreamableWeightsSize() + //! + int64_t getWeightStreamingBudgetV2() const noexcept + { + return mImpl->getWeightStreamingBudgetV2(); + } + + //! + //! \brief TensorRT automatically determines a device memory budget for the model to run. The budget is close to the + //! current free memory size, leaving some space for other memory needs in the user's application. If the budget + //! exceeds the size obtained from getStreamableWeightsSize(), it is capped to that size, effectively disabling + //! weight streaming. Since TensorRT lacks information about the user's allocations, the remaining memory size might + //! be larger than required, leading to wasted memory, or smaller than required, causing an out-of-memory error. For + //! optimal memory allocation, it is recommended to manually calculate and set the budget. + //! + //! \warning BuilderFlag::kWEIGHT_STREAMING must be set during engine building. + //! + //! \warning The return value may change between TensorRT minor versions. + //! + //! \warning Setting the returned budget with V1 APIs (setWeightStreamingBudget()) will lead to undefined behavior. + //! Please use V2 APIs. + //! + //! \returns The weight streaming budget in bytes. Please set with setWeightStreamingBudgetV2(). + //! + //! \see BuilderFlag::kWEIGHT_STREAMING + //! \see setWeightStreamingBudgetV2() + //! + int64_t getWeightStreamingAutomaticBudget() const noexcept + { + return mImpl->getWeightStreamingAutomaticBudget(); + } + + //! + //! \brief Returns the size of the scratch memory required by the current weight streaming budget. + //! + //! Weight streaming requires small amounts of scratch memory on the GPU to stage CPU weights right before + //! execution. This value is typically much smaller than the total streamable weights size. Each IExecutionContext + //! will then allocate this additional memory or the user can provide the additional memory through + //! getDeviceMemorySizeV2() and IExecutionContext::setDeviceMemoryV2(). + //! + //! The return value of this call depends on + //! 1. setWeightStreamingBudget() + //! 2. setWeightStreamingBudgetV2() + //! + //! \warning BuilderFlag::kWEIGHT_STREAMING must be set during engine building. + //! + //! \returns The weight streaming scratch memory in bytes. Returns 0 if weight streaming is disabled. + //! + //! \see BuilderFlag::kWEIGHT_STREAMING + //! \see setWeightStreamingBudgetV2() + //! \see getStreamableWeightsSize() + //! \see getDeviceMemorySizeV2() + //! \see getDeviceMemorySizeForProfileV2() + //! \see IExecutionContext::setDeviceMemoryV2() + //! + int64_t getWeightStreamingScratchMemorySize() const noexcept + { + return mImpl->getWeightStreamingScratchMemorySize(); + } + + //! + //! \brief Check if a tensor is marked as a debug tensor. + //! + //! Determine whether the given name corresponds to a debug tensor. + //! + //! \returns True if tensor is a debug tensor, false otherwise. + //! + //! \see INetworkDefinition::markDebug + //! + bool isDebugTensor(char const* name) const noexcept + { + return mImpl->isDebugTensor(name); + } + + //! + //! \brief Get the minimum / optimum / maximum values (not dimensions) for an input tensor given + //! its name under an optimization profile. These correspond to the values set using + //! IOptimizationProfile::setShapeValuesV2 when the engine was built. + //! + //! \param tensorName The name of an input tensor. + //! + //! \param profileIndex The profile index, which must be between 0 and getNbOptimizationProfiles()-1. + //! + //! \param select Whether to query the minimum, optimum, or maximum values for this input tensor. + //! + //! \return The minimum / optimum / maximum values for an input tensor in this profile. If the profileIndex is + //! invalid or the provided name does not map to an input tensor, or the tensor is not a shape binding, return + //! nullptr. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \warning If input shapes are set with setShapeValues, getProfileTensorValuesV2 will return nullptr + //! + int64_t const* getProfileTensorValuesV2( + char const* tensorName, int32_t profileIndex, OptProfileSelector select) const noexcept + { + return mImpl->getProfileTensorValuesV2(tensorName, profileIndex, select); + } + + //! + //! \brief Get engine statistics according to the given enum value. + //! + //! \param stat The kind of statistics to query. + //! + //! If stat is kTOTAL_WEIGHTS_SIZE, the return value is the total weights size in bytes in the engine. + //! If stat is kSTRIPPED_WEIGHTS_SIZE, the return value is the stripped weight size in bytes for engines + //! built with BuilderFlag::kSTRIP_PLAN. + //! + //! When the BuilderFlag::kWEIGHT_STREAMING flag is enabled, engine weights may not be fully copied to the device. + //! The reported total weight size reflects the sum of all weights utilized by the engine, + //! which does not necessarily correspond to the actual GPU memory allocated. + //! + //! \return The kind of statistics specified by EngineStat. + //! + //! \warning if kSTRIPPED_WEIGHTS_SIZE is passed to query a normal engine, this function will + //! return -1 to indicate invalid enum value. + //! + //! \see EngineStat + //! \see BuilderFlag::kWEIGHT_STREAMING + //! \see setWeightStreamingBudget() + //! \see getStreamableWeightsSize() + //! + int64_t getEngineStat(EngineStat stat) const noexcept + { + return mImpl->getEngineStat(stat); + } + +protected: + apiv::VCudaEngine* mImpl; +}; + +namespace v_1_0 +{ +class IOutputAllocator : public IVersionedInterface +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return {"IOutputAllocator", 1, 0}; + } + + //! + //! \brief Return a pointer to memory for an output tensor, or nullptr if memory cannot be allocated. + //! If the requested memory size exceeds the currentMemory size, the currentMemory can be freed as well. + //! If currentMemory is known to be big enough, one option is to return currentMemory. + //! + //! \param tensorName name of the output tensor. + //! \param currentMemory points to the address set by IExecutionContext::setTensorAddress. + //! \param size number of bytes required. Always positive, even for an empty tensor. + //! \param alignment required alignment of the allocation. + //! + //! \return A pointer to memory to use for the output tensor or nullptr. + //! + //! + //! To preallocate memory and have the engine fail if the preallocation is not big enough, + //! use IExecutionContext::setTensorAddress to set a pointer to the preallocated memory, + //! and have reallocateOutput return nullptr if that memory is not big enough. + //! + //! \deprecated Deprecated in TensorRT 10.0. Superseded by reallocateOutputAsync with cudaStream_t argument + //! + TRT_DEPRECATED virtual void* reallocateOutput( + char const* tensorName, void* currentMemory, uint64_t size, uint64_t alignment) noexcept + { + return nullptr; + } + + //! + //! \brief Return a pointer to memory for an output tensor, or nullptr if memory cannot be allocated. + //! If the requested memory size exceeds the currentMemory size, the currentMemory can be freed as well. + //! If currentMemory is known to be big enough, one option is to return currentMemory. + //! + //! \param tensorName name of the output tensor. + //! \param currentMemory points to the address set by IExecutionContext::setTensorAddress. + //! \param size number of bytes required. Always positive, even for an empty tensor. + //! \param alignment required alignment of the allocation. + //! \param stream The stream in which to execute the kernels. + //! + //! \return A pointer to memory to use for the output tensor or nullptr. + //! + //! To preallocate memory and have the engine fail if the preallocation is not big enough, + //! use IExecutionContext::setTensorAddress to set a pointer to the preallocated memory, + //! and have reallocateOutputAsync return nullptr if that memory is not big enough. + //! + //! The default definition exists for sake of backward compatibility with earlier versions of TensorRT. + //! Eventually this method will become a pure virtual method that requires an override, and method + //! reallocateOutput() will disappear. Code moving away from TensorRT 9.x should override method + //! reallocateOutputAsync() and NOT override method reallocateOutput(). + //! + virtual void* reallocateOutputAsync( + char const* tensorName, void* currentMemory, uint64_t size, uint64_t alignment, cudaStream_t /*stream*/) + { + return reallocateOutput(tensorName, currentMemory, size, alignment); + } + + //! + //! \brief Called by TensorRT when the shape of the output tensor is known. + //! + //! Called by TensorRT sometime between when it calls reallocateOutput and enqueueV3 returns. + //! + //! \param dims dimensions of the output + //! \param tensorName name of the tensor + //! + virtual void notifyShape(char const* tensorName, Dims const& dims) noexcept = 0; +}; +} // namespace v_1_0 + +//! +//! \class IOutputAllocator +//! +//! \brief Callback from ExecutionContext::enqueueV3() +//! +//! \see IExecutionContext::enqueueV3() +//! +using IOutputAllocator = v_1_0::IOutputAllocator; + +namespace v_1_0 +{ +class IDebugListener : public IVersionedInterface +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return {"IDebugListener", 1, 0}; + } + + //! + //! \brief Callback function that is called when a debug tensorโ€™s value is updated and the debug state of the tensor + //! is set to true. Content in the given address is only guaranteed to be valid for the duration of the callback. + //! + //! \param location TensorLocation of the tensor. + //! \param addr pointer to buffer. + //! \param type data Type of the tensor. + //! \param shape shape of the tensor. + //! \param name name of the tensor. + //! \param stream CUDA stream object. + //! + //! \return True on success, false otherwise. + //! + virtual bool processDebugTensor(void const* addr, TensorLocation location, DataType type, Dims const& shape, + char const* name, cudaStream_t stream) + = 0; + + ~IDebugListener() override = default; +}; +} // namespace v_1_0 + +//! +//! \class IDebugListener +//! +//! \brief User-implemented callback for notification when value of a debug tensor is updated. +//! +using IDebugListener = v_1_0::IDebugListener; + +//! +//! \class IExecutionContext +//! +//! \brief Context for executing inference using an engine, with functionally unsafe features. +//! +//! Multiple execution contexts may exist for one ICudaEngine instance, allowing the same +//! engine to be used for the execution of multiple batches simultaneously. If the engine supports +//! dynamic shapes, each execution context in concurrent use must use a separate optimization profile. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +class IExecutionContext : public INoCopy +{ +public: + virtual ~IExecutionContext() noexcept = default; + + //! + //! \brief Set the debug sync flag. + //! + //! If this flag is set to true, the engine will log the successful execution for each kernel during executeV2(). It + //! has no effect when using enqueueV3(). + //! + //! \see getDebugSync() + //! + void setDebugSync(bool sync) noexcept + { + mImpl->setDebugSync(sync); + } + + //! + //! \brief Get the debug sync flag. + //! + //! \see setDebugSync() + //! + bool getDebugSync() const noexcept + { + return mImpl->getDebugSync(); + } + + //! + //! \brief Set the profiler. + //! + //! \see IProfiler getProfiler() + //! + void setProfiler(IProfiler* profiler) noexcept + { + mImpl->setProfiler(profiler); + } + + //! + //! \brief Get the profiler. + //! + //! \see IProfiler setProfiler() + //! + IProfiler* getProfiler() const noexcept + { + return mImpl->getProfiler(); + } + + //! + //! \brief Get the associated engine. + //! + //! \see ICudaEngine + //! + ICudaEngine const& getEngine() const noexcept + { + return mImpl->getEngine(); + } + + //! + //! \brief Set the name of the execution context. + //! + //! This method copies the name string. + //! + //! \warning The string name must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see getName() + //! + void setName(char const* name) noexcept + { + mImpl->setName(name); + } + + //! + //! \brief Return the name of the execution context. + //! + //! \see setName() + //! + char const* getName() const noexcept + { + return mImpl->getName(); + } + + //! + //! \brief Set the device memory for use by this execution context. + //! + //! The memory must be aligned with CUDA memory alignment property (using cudaGetDeviceProperties()), and its size + //! must be large enough for performing inference with the given network inputs. getDeviceMemorySize() and + //! getDeviceMemorySizeForProfile() report upper bounds of the size. Setting memory to nullptr is acceptable if the + //! reported size is 0. If using enqueueV3() to run the network, the memory is in use from the invocation of + //! enqueueV3() until network execution is complete. If using executeV2(), it is in use until executeV2() returns. + //! Releasing or otherwise using the memory for other purposes, including using it in another execution context + //! running in parallel, during this time will result in undefined behavior. + //! + //! \deprecated Deprecated in TensorRT 10.1. Superseded by setDeviceMemoryV2(). + //! + //! \warning Weight streaming related scratch memory will be allocated by TensorRT if the memory is set by this API. + //! Please use setDeviceMemoryV2() instead. + //! + //! \see ICudaEngine::getDeviceMemorySize() + //! \see ICudaEngine::getDeviceMemorySizeForProfile() + //! \see ExecutionContextAllocationStrategy + //! \see ICudaEngine::createExecutionContext() + //! \see ICudaEngine::createExecutionContextWithoutDeviceMemory() + //! + void setDeviceMemory(void* memory) noexcept + { + mImpl->setDeviceMemory(memory); + } + + //! + //! \brief Set the device memory and its corresponding size for use by this execution context. + //! + //! The memory must be aligned with CUDA memory alignment property (using cudaGetDeviceProperties()), and its size + //! must be large enough for performing inference with the given network inputs. getDeviceMemorySize() and + //! getDeviceMemorySizeForProfile() report upper bounds of the size. Setting memory to nullptr is acceptable if the + //! reported size is 0. If using enqueueV3() to run the network, the memory is in use from the invocation of + //! enqueueV3() until network execution is complete. If using executeV2(), it is in use until executeV2() returns. + //! Releasing or otherwise using the memory for other purposes, including using it in another execution context + //! running in parallel, during this time will result in undefined behavior. + //! + //! \see ICudaEngine::getDeviceMemorySizeV2() + //! \see ICudaEngine::getDeviceMemorySizeForProfileV2() + //! \see ExecutionContextAllocationStrategy + //! \see ICudaEngine::createExecutionContext() + //! \see ICudaEngine::createExecutionContextWithoutDeviceMemory() + //! + void setDeviceMemoryV2(void* memory, int64_t size) noexcept + { + return mImpl->setDeviceMemoryV2(memory, size); + } + + //! + //! \brief Return the strides of the buffer for the given tensor name. + //! + //! The strides are in units of elements, not components or bytes. + //! For example, for TensorFormat::kHWC8, a stride of one spans 8 scalars. + //! + //! Note that strides can be different for different execution contexts + //! with dynamic shapes. + //! + //! If the provided name does not map to an input or output tensor, or there are dynamic dimensions that have not + //! been set yet, return Dims{-1, {}} + //! + //! \param tensorName The name of an input or output tensor. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + Dims getTensorStrides(char const* tensorName) const noexcept + { + return mImpl->getTensorStrides(tensorName); + } + +public: + //! + //! \brief Get the index of the currently selected optimization profile. + //! + //! If the profile index has not been set yet (implicitly to 0 if no other execution context has been set to + //! profile 0, or explicitly for all subsequent contexts), an invalid value of -1 will be returned + //! and all calls to enqueueV3()/executeV2() will fail until a valid profile index has been set. + //! This behavior is deprecated in TensorRT 8.6, all profiles will default to optimization + //! profile 0 and -1 will no longer be returned. + //! + int32_t getOptimizationProfile() const noexcept + { + return mImpl->getOptimizationProfile(); + } + + //! + //! \brief Set shape of given input. + //! + //! \param tensorName The name of an input tensor. + //! \param dims The shape of an input tensor. + //! + //! \return True on success, false if the provided name does not map to an input tensor, or if some other error + //! occurred. + //! + //! Each dimension must agree with the network dimension unless the latter was -1. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + bool setInputShape(char const* tensorName, Dims const& dims) noexcept + { + return mImpl->setInputShape(tensorName, dims); + } + + //! + //! \brief Return the shape of the given input or output. + //! + //! \param tensorName The name of an input or output tensor. + //! + //! Return Dims{-1, {}} if the provided name does not map to an input or output tensor. + //! Otherwise return the shape of the input or output tensor. + //! + //! A dimension in an input tensor will have a -1 wildcard value if all the following are true: + //! * setInputShape() has not yet been called for this tensor + //! * The dimension is a runtime dimension that is not implicitly constrained to be a single value. + //! + //! A dimension in an output tensor will have a -1 wildcard value if the dimension depends + //! on values of execution tensors OR if all the following are true: + //! * It is a runtime dimension. + //! * setInputShape() has NOT been called for some input tensor(s) with a runtime shape. + //! * setTensorAddress() has NOT been called for some input tensor(s) with isShapeInferenceIO() = true. + //! + //! An output tensor may also have -1 wildcard dimensions if its shape depends on values of tensors supplied to + //! enqueueV3(). + //! + //! If the request is for the shape of an output tensor with runtime dimensions, + //! all input tensors with isShapeInferenceIO() = true should have their value already set, + //! since these values might be needed to compute the output shape. + //! + //! Examples of an input dimension that is implicitly constrained to a single value: + //! * The optimization profile specifies equal min and max values. + //! * The dimension is named and only one value meets the optimization profile requirements + //! for dimensions with that name. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + Dims getTensorShape(char const* tensorName) const noexcept + { + return mImpl->getTensorShape(tensorName); + } + + //! + //! \brief Whether all dynamic dimensions of input tensors have been specified + //! + //! \return True if all dynamic dimensions of input tensors have been specified + //! by calling setInputShape(). + //! + //! Trivially true if network has no dynamically shaped input tensors. + //! + //! Does not work with name-base interfaces eg. IExecutionContext::setInputShape(). Use + //! IExecutionContext::inferShapes() instead. + //! + bool allInputDimensionsSpecified() const noexcept + { + return mImpl->allInputDimensionsSpecified(); + } + + //! + //! \brief Whether all input shape bindings have been specified + //! + //! \return True if all input shape bindings have been specified by setInputShapeBinding(). + //! + //! Trivially true if network has no input shape bindings. + //! + //! Does not work with name-base interfaces eg. IExecutionContext::setInputShape(). Use + //! IExecutionContext::inferShapes() instead. + //! + //! \deprecated Deprecated in TensorRT 10.0. setInputShapeBinding() is removed since TensorRT 10.0. + //! + TRT_DEPRECATED bool allInputShapesSpecified() const noexcept + { + return mImpl->allInputShapesSpecified(); + } + + //! + //! \brief Set the ErrorRecorder for this interface + //! + //! Assigns the ErrorRecorder to this interface. The ErrorRecorder will track all errors during execution. + //! This function will call incRefCount of the registered ErrorRecorder at least once. Setting + //! recorder to nullptr unregisters the recorder with the interface, resulting in a call to decRefCount if + //! a recorder has been registered. + //! + //! If an error recorder is not set, messages will be sent to the global log stream. + //! + //! \param recorder The error recorder to register with this interface. + //! + //! \see getErrorRecorder() + //! + void setErrorRecorder(IErrorRecorder* recorder) noexcept + { + mImpl->setErrorRecorder(recorder); + } + + //! + //! \brief Get the ErrorRecorder assigned to this interface. + //! + //! Retrieves the assigned error recorder object for the given class. A nullptr will be returned if + //! an error handler has not been set. + //! + //! \return A pointer to the IErrorRecorder object that has been registered. + //! + //! \see setErrorRecorder() + //! + IErrorRecorder* getErrorRecorder() const noexcept + { + return mImpl->getErrorRecorder(); + } + + //! + //! \brief Synchronously execute a network. + //! + //! This method requires an array of input and output buffers. The mapping + //! from indices to tensor names can be queried using ICudaEngine::getIOTensorName(). + //! + //! \param bindings An array of pointers to input and output buffers for the network. + //! + //! \return True if execution succeeded. + //! + //! \see ICudaEngine::getIOTensorName() + //! + bool executeV2(void* const* bindings) noexcept + { + return mImpl->executeV2(bindings); + } + + //! + //! \brief Select an optimization profile for the current context with async + //! semantics. + //! + //! \param profileIndex Index of the profile. The value must lie between 0 and + //! getEngine().getNbOptimizationProfiles() - 1 + //! + //! \param stream A CUDA stream on which the cudaMemcpyAsyncs may be + //! enqueued + //! + //! When an optimization profile is switched via this API, TensorRT may + //! require that data is copied via cudaMemcpyAsync. It is the + //! applicationโ€™s responsibility to guarantee that synchronization between + //! the profile sync stream and the enqueue stream occurs. + //! + //! The selected profile will be used in subsequent calls to executeV2()/enqueueV3(). + //! If the associated CUDA engine has inputs with dynamic shapes, the optimization profile must + //! be set with its corresponding profileIndex before calling execute or enqueue. The newly created execution + //! context will be assigned optimization profile 0. + //! + //! If the associated CUDA engine does not have inputs with dynamic shapes, + //! this method need not be called, in which case the default profile index + //! of 0 will be used. + //! + //! setOptimizationProfileAsync() must be called before calling + //! setInputShape() for all dynamic input + //! tensors or input shape tensors, which in turn must be called before + //! executeV2()/enqueueV3(). + //! + //! \warning This function will trigger layer resource updates on the next call of + //! executeV2()/enqueueV3(), possibly resulting in performance bottlenecks. + //! + //! \warning Not synchronizing the stream used at enqueue with the stream + //! used to set optimization profile asynchronously using this API will + //! result in undefined behavior. + //! + //! \return true if the call succeeded, else false (e.g. input out of range) + //! + //! \see ICudaEngine::getNbOptimizationProfiles() + bool setOptimizationProfileAsync(int32_t profileIndex, cudaStream_t stream) noexcept + { + return mImpl->setOptimizationProfileAsync(profileIndex, stream); + } + + //! + //! \brief Set whether enqueue emits layer timing to the profiler + //! + //! If set to true (default), enqueue is synchronous and does layer timing profiling implicitly if + //! there is a profiler attached. + //! If set to false, enqueue will be asynchronous if there is a profiler attached. An extra method + //! reportToProfiler() needs to be called to obtain the profiling data and report to the profiler attached. + //! + //! \see IExecutionContext::getEnqueueEmitsProfile() + //! \see IExecutionContext::reportToProfiler() + //! + void setEnqueueEmitsProfile(bool enqueueEmitsProfile) noexcept + { + mImpl->setEnqueueEmitsProfile(enqueueEmitsProfile); + } + + //! + //! \brief Get the enqueueEmitsProfile state. + //! + //! \return The enqueueEmitsProfile state. + //! + //! \see IExecutionContext::setEnqueueEmitsProfile() + //! + bool getEnqueueEmitsProfile() const noexcept + { + return mImpl->getEnqueueEmitsProfile(); + } + + //! + //! \brief Calculate layer timing info for the current optimization profile in IExecutionContext + //! and update the profiler after one iteration of inference launch. + //! + //! If IExecutionContext::getEnqueueEmitsProfile() returns true, the enqueue function will calculate layer timing + //! implicitly if a profiler is provided. This function returns true and does nothing. + //! + //! If IExecutionContext::getEnqueueEmitsProfile() returns false, the enqueue function will record the CUDA event + //! timers if a profiler is provided. But it will not perform the layer timing calculation. + //! IExecutionContext::reportToProfiler() needs to be called explicitly to calculate layer timing for the previous + //! inference launch. + //! + //! In the CUDA graph launch scenario, it will record the same set of CUDA events + //! as in regular enqueue functions if the graph is captured from an IExecutionContext with profiler enabled. + //! This function needs to be called after graph launch to report the layer timing info to the profiler. + //! + //! \warning profiling CUDA graphs is only available from CUDA 11.1 onwards. + //! \warning reportToProfiler uses the stream of the previous enqueue call, so the stream must be live otherwise + //! behavior is undefined. + //! + //! \return true if the call succeeded, else false (e.g. profiler not provided, in CUDA graph capture mode, etc.) + //! + //! \see IExecutionContext::setEnqueueEmitsProfile() + //! \see IExecutionContext::getEnqueueEmitsProfile() + //! + bool reportToProfiler() const noexcept + { + return mImpl->reportToProfiler(); + } + + //! + //! \brief Set memory address for given input or output tensor. + //! + //! \param tensorName The name of an input or output tensor. + //! \param data The pointer (void*) to the data owned by the user. + //! + //! \return True on success, false if error occurred. + //! + //! An address defaults to nullptr. + //! Pass data=nullptr to reset to the default state. + //! + //! Return false if the provided name does not map to an input or output tensor. + //! + //! If an input pointer has type (void const*), use setInputTensorAddress() instead. + //! + //! Before calling enqueueV3(), each input must have a non-null address and + //! each output must have a non-null address or an IOutputAllocator to set it later. + //! + //! If the TensorLocation of the tensor is kHOST: + //! - The pointer must point to a host buffer of sufficient size. + //! - Data representing shape values is not copied until enqueueV3 is invoked. + //! + //! If the TensorLocation of the tensor is kDEVICE: + //! - The pointer must point to a device buffer of sufficient size and alignment, or + //! - Be nullptr if the tensor is an output tensor that will be allocated by IOutputAllocator. + //! + //! If getTensorShape(name) reports a -1 for any dimension of an output after all + //! input shapes have been set, use setOutputAllocator() to associate an IOutputAllocator + //! to which the dimensions will be reported when known. + //! + //! Calling both setTensorAddress and setOutputAllocator() for the same output is allowed, + //! and can be useful for preallocating memory, and then reallocating if it's not big enough. + //! + //! The pointer must have at least 256-byte alignment. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see setInputTensorAddress() setOutputTensorAddress() getTensorShape() setOutputAllocator() IOutputAllocator + //! + bool setTensorAddress(char const* tensorName, void* data) noexcept + { + return mImpl->setTensorAddress(tensorName, data); + } + + //! + //! \brief Get memory address bound to given input or output tensor, or nullptr if the provided name does not map to + //! an input or output tensor. + //! + //! \param tensorName The name of an input or output tensor. + //! + //! Use method getOutputTensorAddress() if a non-const pointer for an output tensor is required. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see getOutputTensorAddress() + //! + void const* getTensorAddress(char const* tensorName) const noexcept + { + return mImpl->getTensorAddress(tensorName); + } + + //! + //! \brief Set the memory address for a given output tensor. + //! + //! \param tensorName The name of an output tensor. + //! \param data The pointer to the buffer to which to write the output. + //! + //! \return True on success, false if the provided name does not map to an output tensor, does not meet alignment + //! requirements, or some other error occurred. + //! + //! Output addresses can also be set using method setTensorAddress. This method is provided for applications which + //! prefer to use different methods for setting input and output tensors. + //! + //! See setTensorAddress() for alignment and data type constraints. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see setTensorAddress() + //! + bool setOutputTensorAddress(char const* tensorName, void* data) noexcept + { + return mImpl->setOutputTensorAddress(tensorName, data); + } + + //! + //! \brief Set memory address for given input. + //! + //! \param tensorName The name of an input tensor. + //! \param data The pointer (void const*) to the const data owned by the user. + //! + //! \return True on success, false if the provided name does not map to an input tensor, does not meet alignment + //! requirements, or some other error occurred. + //! + //! Input addresses can also be set using method setTensorAddress, which requires a (void*). + //! + //! See description of method setTensorAddress() for alignment and data type constraints. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see setTensorAddress() + //! + bool setInputTensorAddress(char const* tensorName, void const* data) noexcept + { + return mImpl->setInputTensorAddress(tensorName, data); + } + + //! + //! \brief Get memory address for given output. + //! + //! \param tensorName The name of an output tensor. + //! + //! \return Raw output data pointer (void*) for given output tensor, or nullptr if the provided name does not map to + //! an output tensor. + //! + //! If only a (void const*) pointer is needed, an alternative is to call method getTensorAddress(). + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see getTensorAddress() + //! + void* getOutputTensorAddress(char const* tensorName) const noexcept + { + return mImpl->getOutputTensorAddress(tensorName); + } + + //! + //! \brief Run shape calculations. + //! + //! \param nbMaxNames Maximum number of names to write to tensorNames. + //! When the return value is a positive value n and tensorNames != nullptr, + //! the names of min(n,nbMaxNames) insufficiently specified input tensors are + //! written to tensorNames. + //! + //! \param tensorNames Buffer in which to place names of insufficiently specified input tensors. + //! + //! \return 0 on success. + //! Positive value n if n input tensors were not sufficiently specified. + //! -1 for other errors. + //! + //! An input tensor is insufficiently specified if either of the following is true: + //! + //! * It has dynamic dimensions and its runtime dimensions have not yet + //! been specified via IExecutionContext::setInputShape. + //! + //! * isShapeInferenceIO(t)=true and the tensor's address has not yet been set. + //! + //! If an output tensor has isShapeInferenceIO(t)=true and its address has been specified, + //! then its value is written. + //! + //! Returns -1 if tensorNames == nullptr and nbMaxNames != 0. + //! Returns -1 if nbMaxNames < 0. + //! Returns -1 if a tensor's dimensions are invalid, e.g. a tensor ends up with a negative dimension. + //! + int32_t inferShapes(int32_t nbMaxNames, char const** tensorNames) noexcept + { + return mImpl->inferShapes(nbMaxNames, tensorNames); + } + + //! + //! \brief Recompute the internal activation buffer sizes based on the current input shapes, and return the total + //! amount of memory required. + //! + //! Users can allocate the device memory based on the size returned and provided the memory to TRT with + //! IExecutionContext::setDeviceMemory(). Must specify all input shapes and the optimization profile to use before + //! calling this function, otherwise the partition will be invalidated. + //! + //! \return Total amount of memory required on success, 0 if error occurred. + //! + //! \see IExecutionContext::setDeviceMemory() + //! + size_t updateDeviceMemorySizeForShapes() noexcept + { + return mImpl->updateDeviceMemorySizeForShapes(); + } + + //! + //! \brief Mark input as consumed. + //! + //! \param event The CUDA event that is triggered after all input tensors have been consumed. + //! + //! \warning The set event must be valid during the inference. + //! + //! \return True on success, false if error occurred. + //! + //! Passing event==nullptr removes whatever event was set, if any. + //! + bool setInputConsumedEvent(cudaEvent_t event) noexcept + { + return mImpl->setInputConsumedEvent(event); + } + + //! + //! \brief The event associated with consuming the input. + //! + //! \return The CUDA event. Nullptr will be returned if the event is not set yet. + //! + cudaEvent_t getInputConsumedEvent() const noexcept + { + return mImpl->getInputConsumedEvent(); + } + + //! + //! \brief Set output allocator to use for output tensor of given name. + //! Pass nullptr to outputAllocator to unset. + //! The allocator is called by enqueueV3(). + //! + //! \param tensorName The name of an output tensor. + //! \param outputAllocator IOutputAllocator for the tensors. + //! + //! \return True if success, false if the provided name does not map to an output or, if some other error occurred. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see enqueueV3() IOutputAllocator + //! + bool setOutputAllocator(char const* tensorName, IOutputAllocator* outputAllocator) noexcept + { + return mImpl->setOutputAllocator(tensorName, outputAllocator); + } + + //! + //! \brief Get output allocator associated with output tensor of given name, or nullptr if the provided name does + //! not map to an output tensor. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + //! \see IOutputAllocator + //! + IOutputAllocator* getOutputAllocator(char const* tensorName) const noexcept + { + return mImpl->getOutputAllocator(tensorName); + } + + //! + //! \brief Get upper bound on an output tensor's size, in bytes, based on + //! the current optimization profile and input dimensions. + //! + //! If the profile or input dimensions are not yet set, or the provided name + //! does not map to an output, returns -1. + //! + //! \param tensorName The name of an output tensor. + //! + //! \return Upper bound in bytes. + //! + //! \warning The string tensorName must be null-terminated, and be at most 4096 bytes including the terminator. + //! + int64_t getMaxOutputSize(char const* tensorName) const noexcept + { + return mImpl->getMaxOutputSize(tensorName); + } + + //! + //! \brief Specify allocator to use for internal temporary storage. + //! + //! This allocator is used only by enqueueV3() for temporary storage whose size cannot be + //! predicted ahead of enqueueV3(). It is not used for output tensors, because memory + //! allocation for those is allocated by the allocator set by setOutputAllocator(). + //! All memory allocated is freed by the time enqueueV3() returns. + //! + //! \param allocator pointer to allocator to use. Pass nullptr to revert to using TensorRT's + //! default allocator. + //! + //! \return True on success, false if error occurred. + //! + //! \see enqueueV3() setOutputAllocator() + //! + bool setTemporaryStorageAllocator(IGpuAllocator* allocator) noexcept + { + return mImpl->setTemporaryStorageAllocator(allocator); + } + + //! + //! \brief Get allocator set by setTemporaryStorageAllocator. + //! + //! Returns a nullptr if a nullptr was passed with setTemporaryStorageAllocator(). + //! + IGpuAllocator* getTemporaryStorageAllocator() const noexcept + { + return mImpl->getTemporaryStorageAllocator(); + } + + //! + //! \brief Enqueue inference on a stream. + //! + //! \param stream A CUDA stream on which the inference kernels will be enqueued. + //! + //! \return True if the kernels were enqueued successfully, false otherwise. + //! + //! Modifying or releasing memory that has been registered for the tensors before stream + //! synchronization or the event passed to setInputConsumedEvent has been being triggered results in undefined + //! behavior. + //! Input tensor can be released after the setInputConsumedEvent whereas output tensors require stream + //! synchronization. + //! + //! \warning Using default stream may lead to performance issues due to additional cudaDeviceSynchronize() calls by + //! TensorRT to ensure correct synchronizations. Please use non-default stream instead. + //! + //! \warning If the Engine is streaming weights, enqueueV3 will become synchronous, and + //! the graph will not be capturable. + //! + bool enqueueV3(cudaStream_t stream) noexcept + { + return mImpl->enqueueV3(stream); + } + + //! + //! \brief Set the maximum size for persistent cache usage. + //! + //! This function sets the maximum persistent L2 cache that this execution context may use for activation caching. + //! Activation caching is not supported on all architectures - see "How TensorRT uses Memory" in the developer guide + //! for details + //! + //! \param size the size of persistent cache limitation in bytes. + //! The default is 0 Bytes. + //! + //! \see getPersistentCacheLimit + void setPersistentCacheLimit(size_t size) noexcept + { + mImpl->setPersistentCacheLimit(size); + } + + //! + //! \brief Get the maximum size for persistent cache usage. + //! + //! \returns The size of the persistent cache limit + //! + //! \see setPersistentCacheLimit + size_t getPersistentCacheLimit() const noexcept + { + return mImpl->getPersistentCacheLimit(); + } + + //! + //! \brief Set the verbosity of the NVTX markers in the execution context. + //! + //! Building with kDETAILED verbosity will generally increase latency in enqueueV3(). Call this method + //! to select NVTX verbosity in this execution context at runtime. + //! + //! The default is the verbosity with which the engine was built, and the verbosity may not be raised above that + //! level. + //! + //! This function does not affect how IEngineInspector interacts with the engine. + //! + //! \param verbosity The verbosity of the NVTX markers. + //! + //! \return True if the NVTX verbosity is set successfully. False if the provided verbosity level is higher than the + //! profiling verbosity of the corresponding engine. + //! + //! \see getNvtxVerbosity() + //! \see ICudaEngine::getProfilingVerbosity() + //! + bool setNvtxVerbosity(ProfilingVerbosity verbosity) noexcept + { + return mImpl->setNvtxVerbosity(verbosity); + } + + //! + //! \brief Get the NVTX verbosity of the execution context. + //! + //! \return The current NVTX verbosity of the execution context. + //! + //! \see setNvtxVerbosity() + //! + ProfilingVerbosity getNvtxVerbosity() const noexcept + { + return mImpl->getNvtxVerbosity(); + } + + //! + //! \brief Set the auxiliary streams that TensorRT should launch kernels on in the next enqueueV3() call. + //! + //! If set, TensorRT will launch the kernels that are supposed to run on the auxiliary streams using the streams + //! provided by the user with this API. If this API is not called before the enqueueV3() call, then TensorRT will + //! use the auxiliary streams created by TensorRT internally. + //! + //! TensorRT will always insert event synchronizations between the main stream provided via enqueueV3() call and the + //! auxiliary streams: + //! - At the beginning of the enqueueV3() call, TensorRT will make sure that all the auxiliary streams wait on + //! the activities on the main stream. + //! - At the end of the enqueueV3() call, TensorRT will make sure that the main stream wait on the activities on + //! all the auxiliary streams. + //! + //! \param auxStreams The pointer to an array of cudaStream_t with the array length equal to nbStreams. + //! \param nbStreams The number of auxiliary streams provided. If nbStreams is greater than + //! `engine->getNbAuxStreams()`, then only the first `engine->getNbAuxStreams()` streams will be used. If + //! `nbStreams` is less than `engine->getNbAuxStreams()`, such as setting `nbStreams` to 0, then TensorRT + //! will use the provided streams for the first `nbStreams` auxiliary streams, and will create additional + //! streams internally for the rest of the auxiliary streams. + //! + //! \note The provided auxiliary streams must not be the default stream and must all be different to avoid + //! deadlocks. + //! + //! \see enqueueV3(), IBuilderConfig::setMaxAuxStreams(), ICudaEngine::getNbAuxStreams() + //! + void setAuxStreams(cudaStream_t* auxStreams, int32_t nbStreams) noexcept + { + mImpl->setAuxStreams(auxStreams, nbStreams); + } + + //! + //! \brief Set DebugListener for this execution context. + //! + //! \param listener DebugListener for this execution context. + //! + //! \return true if succeed, false if failure. + //! + bool setDebugListener(IDebugListener* listener) noexcept + { + return mImpl->setDebugListener(listener); + } + + //! + //! \brief Get the DebugListener of this execution context. + //! + //! \return DebugListener of this execution context. + //! + IDebugListener* getDebugListener() noexcept + { + return mImpl->getDebugListener(); + } + + //! + //! \brief Set debug state of tensor given the tensor name. + //! + //! Turn the debug state of a tensor on or off. + //! A tensor with the parameter tensor name must exist in the network, and the tensor must have + //! been marked as a debug tensor during build time. Otherwise, an error is thrown. + //! + //! \param name Name of target tensor. + //! + //! \param flag True if turning on debug state, false if turning off debug state of tensor + //! The default is off. + //! + //! \return True if successful, false otherwise. + //! + bool setTensorDebugState(char const* name, bool flag) noexcept + { + return mImpl->setTensorDebugState(name, flag); + } + + //! + //! \brief Get the debug state. + //! + //! \param name Name of target tensor. + //! + //! \return true if there is a debug tensor with the given name and it has debug state turned on. + //! + bool getDebugState(char const* name) const noexcept + { + return mImpl->getDebugState(name); + } + + //! + //! \brief Get the runtime config object used during execution context creation. + //! + //! \return The runtime config object. + //! + IRuntimeConfig* getRuntimeConfig() const noexcept + { + return mImpl->getRuntimeConfig(); + } + + //! \brief Turn the debug state of all debug tensors on or off. + //! + //! \param flag true if turning on debug state, false if turning off debug state. + //! + //! \return true if successful, false otherwise. + //! + //! The default is off. + //! + bool setAllTensorsDebugState(bool flag) noexcept + { + return mImpl->setAllTensorsDebugState(flag); + } + + //! + //! \brief Turn the debug state of unfused tensors on or off. + //! + //! The default is off. + //! + //! \param flag true if turning on debug state, false if turning off debug state. + //! + //! \return true if successful, false otherwise. + //! + //! \see INetworkDefinition::markUnfusedTensorsAsDebugTensors() + //! + bool setUnfusedTensorsDebugState(bool flag) noexcept + { + return mImpl->setUnfusedTensorsDebugState(flag); + } + + //! + //! \brief Get the debug state of unfused tensors. + //! + //! \return true if unfused tensors debug state is on. False if unfused tensors debug state is off. + //! + bool getUnfusedTensorsDebugState() const noexcept + { + return mImpl->getUnfusedTensorsDebugState(); + } +#if ENABLE_FEATURE_DISABLE_RUNTIME_ALLOCATION + //! + //! \brief Check if a subsequent call to enqueueV3 is graph-capturable on the provided stream. + //! + //! \param stream The stream to check. + //! + //! \return true if a subsequent call to enqueueV3 is graph-capturable on the provided stream. + //! Reasons why graph capture may fail include: + //! - blocking runtime allocation due to large dynamically sized tensors that cannot be + //! statically allocated, + //! - dynamically shaped tensors whose size contains on the tensor contents, like the output + //! of an INonZeroLayer, + //! - conditional control flow depending on the contents of on-device tensors, like an + //! ITripLimitLayer whose input tensor resides on the device, + //! - engines that have been built for weight streaming. + //! + //! \note If this API returns false, enqueueV3 may not be called on a capturable stream + //! (i.e. users may not call cudaStreamBeingCapture before starting inference). Otherwise, + //! inference will fail with an error message. + bool isStreamCapturable(cudaStream_t stream) const noexcept { + return mImpl->isStreamCapturable(stream); + } +#endif // ENABLE_FEATURE_DISABLE_RUNTIME_ALLOCATION + + //! + //! \brief Set the NCCL communicator for the execution context. + //! + //! \param communicator A pointer to the communicator that is used by the execution context. The communicator is + //! expected to be already initialized with `ncclCommInitRank` and castable to `ncclComm_t`. + //! + //! The communicator must be uniform across all multi-device instances or undefined + //! behavior occurs. + //! + //! \warning The lifetime of the communicator must be longer than the execution contexts it is attached to. + //! + //! \return True if the communicator was set successfully, false otherwise. + //! + bool setCommunicator(void* communicator) noexcept + { + return mImpl->setCommunicator(communicator); + } + +protected: + apiv::VExecutionContext* mImpl; +}; // class IExecutionContext + +//! +//! \enum LayerInformationFormat +//! +//! \brief The format in which the IEngineInspector prints the layer information. +//! +//! \see IEngineInspector::getLayerInformation(), IEngineInspector::getEngineInformation() +//! +enum class LayerInformationFormat : int32_t +{ + kONELINE = 0, //!< Print layer information in one line per layer. + kJSON = 1, //!< Print layer information in JSON format. +}; + +//! Maximum number of layer information formats in LayerInformationFormat enum. +//! \see LayerInformationFormat +template <> +constexpr inline int32_t EnumMax() noexcept +{ + return 2; +} + +//! +//! \class IEngineInspector +//! +//! \brief An engine inspector which prints out the layer information of an engine or an execution context. +//! +//! The amount of printed information depends on the profiling verbosity setting of the builder config when the engine +//! is built: +//! - ProfilingVerbosity::kLAYER_NAMES_ONLY: only layer names will be printed. +//! - ProfilingVerbosity::kNONE: no layer information will be printed. +//! - ProfilingVerbosity::kDETAILED: layer names and layer parameters will be printed. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +//! \see ProfilingVerbosity, IEngineInspector +//! +class IEngineInspector : public INoCopy +{ +public: + virtual ~IEngineInspector() noexcept = default; + + //! + //! \brief Set an execution context as the inspection source. + //! + //! Setting the execution context and specifying all the input shapes allows the inspector + //! to calculate concrete dimensions for any dynamic shapes and display their format information. + //! Otherwise, values dependent on input shapes will be displayed as -1 and format information + //! will not be shown. + //! + //! Passing nullptr will remove any association with an execution context. + //! + //! \return Whether the action succeeds. + //! + bool setExecutionContext(IExecutionContext const* context) noexcept + { + return mImpl->setExecutionContext(context); + } + + //! + //! \brief Get the context currently being inspected. + //! + //! \return The pointer to the context currently being inspected. + //! + //! \see setExecutionContext() + //! + IExecutionContext const* getExecutionContext() const noexcept + { + return mImpl->getExecutionContext(); + } + + //! + //! \brief Get a string describing the information about a specific layer in the current engine or the execution + //! context. + //! + //! \param layerIndex the index of the layer. It must lie in range [0, engine.getNbLayers()). + //! + //! \param format the format the layer information should be printed in. + //! + //! \return A null-terminated C-style string describing the information about a specific layer in the current + //! engine or the execution context. + //! + //! \warning The content of the returned string may change when another execution context has + //! been set, or when another getLayerInformation() or getEngineInformation() has been called. + //! + //! \warning In a multi-threaded environment, this function must be protected from other threads changing the + //! inspection source. If the inspection source changes, the data that is being pointed to can change. + //! Copy the string to another buffer before releasing the lock in order to guarantee consistency. + //! + //! \see LayerInformationFormat + //! + char const* getLayerInformation(int32_t layerIndex, LayerInformationFormat format) const noexcept + { + return mImpl->getLayerInformation(layerIndex, format); + } + + //! + //! \brief Get a string describing the information about all the layers in the current engine or the execution + //! context. + //! + //! \param format the format the layer information should be printed in. + //! + //! \return A null-terminated C-style string describing the information about all the layers in the current + //! engine or the execution context. + //! + //! \warning The content of the returned string may change when another execution context has + //! been set, or when another getLayerInformation() or getEngineInformation() has been called. + //! + //! \warning In a multi-threaded environment, this function must be protected from other threads changing the + //! inspection source. If the inspection source changes, the data that is being pointed to can change. + //! Copy the string to another buffer before releasing the lock in order to guarantee consistency. + //! + //! \see LayerInformationFormat + //! + char const* getEngineInformation(LayerInformationFormat format) const noexcept + { + return mImpl->getEngineInformation(format); + } + + //! + //! \brief Set the ErrorRecorder for this interface + //! + //! Assigns the ErrorRecorder to this interface. The ErrorRecorder will track all errors during execution. + //! This function will call incRefCount of the registered ErrorRecorder at least once. Setting + //! recorder to nullptr unregisters the recorder with the interface, resulting in a call to decRefCount if + //! a recorder has been registered. + //! + //! If an error recorder is not set, messages will be sent to the global log stream. + //! + //! \param recorder The error recorder to register with this interface. + //! + //! \see getErrorRecorder() + //! + void setErrorRecorder(IErrorRecorder* recorder) noexcept + { + mImpl->setErrorRecorder(recorder); + } + + //! + //! \brief Get the ErrorRecorder assigned to this interface. + //! + //! Retrieves the assigned error recorder object for the given class. A nullptr will be returned if + //! an error handler has not been set. + //! + //! \return A pointer to the IErrorRecorder object that has been registered. + //! + //! \see setErrorRecorder() + //! + IErrorRecorder* getErrorRecorder() const noexcept + { + return mImpl->getErrorRecorder(); + } + +protected: + apiv::VEngineInspector* mImpl; +}; // class IEngineInspector + +} // namespace nvinfer1 + +//! +//! Internal C entry point for creating IRuntime. +//! @private +//! +extern "C" TENSORRTAPI void* createInferRuntime_INTERNAL(void* logger, int32_t version) noexcept; + +//! +//! Internal C entry point for creating IRefitter. +//! @private +//! +extern "C" TENSORRTAPI void* createInferRefitter_INTERNAL(void* engine, void* logger, int32_t version) noexcept; + +//! +//! \brief Return the plugin registry +//! +extern "C" TENSORRTAPI nvinfer1::IPluginRegistry* getPluginRegistry() noexcept; + +//! +//! \brief Return the logger object. +//! \note the global logger is used only by standalone functions which have no associated builder, runtime +//! or refitter. +//! +extern "C" TENSORRTAPI nvinfer1::ILogger* getLogger() noexcept; + +namespace nvinfer1 +{ +namespace // unnamed namespace avoids linkage surprises when linking objects built with different versions of this + // header. +{ +//! +//! \brief Create an instance of an IRuntime class. +//! +//! \param logger The logging class for the runtime. +//! +inline IRuntime* createInferRuntime(ILogger& logger) noexcept +{ + return static_cast(createInferRuntime_INTERNAL(&logger, NV_TENSORRT_VERSION)); +} + +//! +//! \brief Create an instance of an IRefitter class. +//! +//! \param engine The engine class for the refitter. +//! \param logger The logging class for the refitter. +//! +inline IRefitter* createInferRefitter(ICudaEngine& engine, ILogger& logger) noexcept +{ + return static_cast(createInferRefitter_INTERNAL(&engine, &logger, NV_TENSORRT_VERSION)); +} + +} // namespace + +//! +//! \brief Register the plugin creator to the registry +//! The static registry object will be instantiated when the plugin library is +//! loaded. This static object will register all creators available in the +//! library to the registry. +//! +//! \warning Statically registering plugins should be avoided in the automotive +//! safety context as the application developer should first register an error recorder +//! with the plugin registry via IPluginRegistry::setErrorRecorder() before using +//! IPluginRegistry::registerCreator() or other methods. +//! +template +class PluginRegistrar +{ +public: + PluginRegistrar() + { + getPluginRegistry()->registerCreator(instance, ""); + } + +private: + //! Plugin instance. + T instance{}; +}; + +} // namespace nvinfer1 + +#define REGISTER_TENSORRT_PLUGIN(name) \ + static nvinfer1::PluginRegistrar pluginRegistrar##name {} + +namespace nvinfer1 +{ +//! +//! \class ILoggerFinder +//! +//! \brief A virtual base class to find a logger. +//! Allows a plugin to find an instance of a logger if it needs to emit a log message. +//! A pointer to an instance of this class is passed to a plugin shared library on initialization when that plugin +//! is serialized as part of a version-compatible plan. See the plugin chapter in the developer guide for details. +//! +class ILoggerFinder +{ +public: + //! + //! \brief Get the logger used by the engine or execution context which called the plugin method. + //! + //! \warning Must be called from the thread in which the plugin method was called. + //! + //! \return A pointer to the logger. + //! + virtual ILogger* findLogger() = 0; + +protected: + virtual ~ILoggerFinder() = default; +}; + +//! DO NOT REFER TO namespace v_1_0 IN CODE. ALWAYS USE nvinfer1 INSTEAD. +//! The name v_1_0 may change in future versions of TensorRT. +namespace v_1_0 +{ + +class IGpuAsyncAllocator : public IGpuAllocator +{ +public: + IGpuAsyncAllocator() = default; + ~IGpuAsyncAllocator() override = default; + + //! + //! \brief A thread-safe callback implemented by the application to handle stream-ordered asynchronous + //! acquisition of GPU memory. + //! + //! \param size The size of the memory block required (in bytes). + //! \param alignment The required alignment of memory. Alignment will be zero + //! or a power of 2 not exceeding the alignment guaranteed by cudaMalloc. + //! Thus this allocator can be safely implemented with cudaMalloc/cudaFree. + //! An alignment value of zero indicates any alignment is acceptable. + //! \param flags Reserved for future use. In the current release, 0 will be passed. + //! + //! \param stream Specifies the cudastream for the asynchronous allocation. If nullptr or 0 is + //! passed, the default stream will be used. + //! + //! \return If the allocation was successful, the start address of a device memory block of the requested size. + //! If an allocation request of size 0 is made, nullptr must be returned. + //! If an allocation request cannot be satisfied, nullptr must be returned. + //! If a non-null address is returned, it is guaranteed to have the specified alignment. + //! + //! \note The implementation must guarantee thread safety for concurrent allocateAsync/deallocateAsync + //! requests. + //! + //! \note The implementation is not required to be asynchronous. It is permitted to synchronize, + //! albeit doing so will lose the performance advantage of asynchronous allocation. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads. + //! + void* allocateAsync(uint64_t const size, uint64_t const alignment, AllocatorFlags const flags, + cudaStream_t /*stream*/) noexcept override = 0; + + //! + //! \brief A thread-safe callback implemented by the application to handle stream-ordered asynchronous + //! release of GPU memory. + //! + //! TensorRT may pass a nullptr to this function if it was previously returned by allocate(). + //! + //! \param memory A memory address that was previously returned by an allocate() or reallocate() call of the same + //! allocator object. + //! + //! \param stream Specifies the cudastream for the asynchronous deallocation. If nullptr or 0 is + //! passed, the default stream will be used. + //! + //! \return True if the acquired memory is released successfully. + //! + //! \note The implementation must guarantee thread safety for concurrent allocateAsync/deallocateAsync + //! requests. + //! + //! \note The implementation is not required to be asynchronous. It is permitted to synchronize, + //! albeit doing so will lose the performance advantage of asynchronous deallocation. + //! Either way, it is critical that it not actually free the memory until the current + //! stream position is reached. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads. + bool deallocateAsync(void* const memory, cudaStream_t /*stream*/) noexcept override = 0; + + //! + //! \brief A thread-safe callback implemented by the application to handle acquisition of GPU memory. + //! + //! \param size The size of the memory block required (in bytes). + //! \param alignment The required alignment of memory. Alignment will be zero + //! or a power of 2 not exceeding the alignment guaranteed by cudaMalloc. + //! Thus this allocator can be safely implemented with cudaMalloc/cudaFree. + //! An alignment value of zero indicates any alignment is acceptable. + //! \param flags Reserved for future use. In the current release, 0 will be passed. + //! + //! \return If the allocation was successful, the start address of a device memory block of the requested size. + //! If an allocation request of size 0 is made, nullptr must be returned. + //! If an allocation request cannot be satisfied, nullptr must be returned. + //! If a non-null address is returned, it is guaranteed to have the specified alignment. + //! + //! \note The implementation must guarantee thread safety for concurrent allocateAsync/deallocateAsync/reallocate + //! requests. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads. + //! \deprecated Deprecated in TensorRT 10.0. Superseded by allocateAsync + //! + TRT_DEPRECATED void* allocate( + uint64_t const size, uint64_t const alignment, AllocatorFlags const flags) noexcept override + { + return allocateAsync(size, alignment, flags, nullptr); + } + + //! + //! \brief A thread-safe callback implemented by the application to handle release of GPU memory. + //! + //! TensorRT may pass a nullptr to this function if it was previously returned by allocate(). + //! + //! \param memory A memory address that was previously returned by an allocate() or reallocate() call of the same + //! allocator object. + //! + //! \return True if the acquired memory is released successfully. + //! + //! \note The implementation must guarantee thread safety for concurrent allocate/reallocate/deallocate + //! requests. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads. + //! \deprecated Deprecated in TensorRT 10.0. Superseded by deallocateAsync + //! + TRT_DEPRECATED bool deallocate(void* const memory) noexcept override + { + return deallocateAsync(memory, nullptr); + } + + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return {"IGpuAllocator", 1, 0}; + } +}; + +class IPluginCreatorV3One : public IPluginCreatorInterface +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"PLUGIN CREATOR_V3ONE", 1, 0}; + } + + //! + //! \brief Return a plugin object. Return nullptr in case of error. + //! + //! \param name A NULL-terminated name string of length 1024 or less, including the NULL terminator. + //! \param fc A pointer to a collection of fields needed for constructing the plugin. + //! \param phase The TensorRT phase in which the plugin is being created + //! + //! When the phase is TensorRTPhase::kRUNTIME, the PluginFieldCollection provided for serialization by the plugin's + //! runtime interface will be passed as fc. + //! + //! \note The returned plugin object must be in an initialized state + //! + //! \note If invoked by the user (e.g. with TensorRTPhase::kBUILD, to add to the network defintion with + //! addPluginV3()), it is the user's responsibility to delete the plugin object. If invoked by TensorRT (e.g. during + //! engine deserialization), TensorRT will delete any objects it creates. + //! + virtual IPluginV3* createPlugin( + AsciiChar const* name, PluginFieldCollection const* fc, TensorRTPhase phase) noexcept = 0; + + //! + //! \brief Return a list of fields that need to be passed to createPlugin() when creating a plugin for use in the + //! TensorRT build phase. + //! + //! \see PluginFieldCollection + //! + virtual PluginFieldCollection const* getFieldNames() noexcept = 0; + + //! + //! \brief Return the plugin name. + //! + //! \warning The string returned must be NULL-terminated and have a length of 1024 bytes or less including + //! the NULL terminator. + //! + virtual AsciiChar const* getPluginName() const noexcept = 0; + + //! + //! \brief Return the plugin version. + //! + //! \warning The string returned must be NULL-terminated and have a length of 1024 bytes or less including + //! the NULL terminator. + //! + virtual AsciiChar const* getPluginVersion() const noexcept = 0; + + //! + //! \brief Return the plugin namespace. + //! + //! \warning The string returned must be NULL-terminated and have a length of 1024 bytes or less including + //! the NULL terminator. + //! + virtual AsciiChar const* getPluginNamespace() const noexcept = 0; + + IPluginCreatorV3One() = default; + virtual ~IPluginCreatorV3One() = default; + +protected: + IPluginCreatorV3One(IPluginCreatorV3One const&) = default; + IPluginCreatorV3One(IPluginCreatorV3One&&) = default; + IPluginCreatorV3One& operator=(IPluginCreatorV3One const&) & = default; + IPluginCreatorV3One& operator=(IPluginCreatorV3One&&) & = default; +}; + +} // namespace v_1_0 + +//! +//! \class IGpuAsyncAllocator +//! +//! \brief Application-implemented class for controlling asynchronous (stream ordered) memory allocation on the GPU. +//! +//! \warning The lifetime of an IGpuAsyncAllocator object must exceed that of all objects that use it. +//! +//! The advantage of deriving from IGpuAsyncAllocator instead of IGpuAllocator is that you only have +//! to override two methods: allocateAsync() and deallocateAsync() to implement an allocator with +//! asynchronous capability, whereas deriving from IGpuAllocator requires overriding four methods, +//! including two deprecated methods. +//! +//! \see IGpuAllocator +using IGpuAsyncAllocator = v_1_0::IGpuAsyncAllocator; + +//! +//! \class IPluginCreatorV3One +//! +//! \brief A plugin creator class capable of producing IPluginV3 objects +//! +//! \see IPluginV3 +//! \see IPluginRegistry +//! +using IPluginCreatorV3One = v_1_0::IPluginCreatorV3One; + +} // namespace nvinfer1 + +//! +//! \brief Return the library major version number. +//! +extern "C" TENSORRTAPI int32_t getInferLibMajorVersion() noexcept; +//! +//! \brief Return the library minor version number. +//! +extern "C" TENSORRTAPI int32_t getInferLibMinorVersion() noexcept; +//! +//! \brief Return the library patch version number. +//! +extern "C" TENSORRTAPI int32_t getInferLibPatchVersion() noexcept; +//! +//! \brief Return the library build version number. +//! +extern "C" TENSORRTAPI int32_t getInferLibBuildVersion() noexcept; + +#endif // NV_INFER_RUNTIME_H diff --git a/tensorrt/include/NvInferRuntimeBase.h b/tensorrt/include/NvInferRuntimeBase.h new file mode 100644 index 0000000000000000000000000000000000000000..8c72e4a0d969d09b0dac74a9be5ddcf8778d452b --- /dev/null +++ b/tensorrt/include/NvInferRuntimeBase.h @@ -0,0 +1,689 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NV_INFER_RUNTIME_BASE_H +#define NV_INFER_RUNTIME_BASE_H + +#include "NvInferVersion.h" +#include +#include +#include + +// Items that are marked as deprecated will be removed in a future release. +#if __cplusplus >= 201402L +#define TRT_DEPRECATED [[deprecated]] +#define TRT_DEPRECATED_BECAUSE(REASON) [[deprecated(REASON)]] +#define TRT_DEPRECATED_ENUM TRT_DEPRECATED +#ifdef _MSC_VER +#define TRT_DEPRECATED_API __declspec(dllexport) +#else +#define TRT_DEPRECATED_API [[deprecated]] __attribute__((visibility("default"))) +#endif +#else +#ifdef _MSC_VER +#define TRT_DEPRECATED +#define TRT_DEPRECATED_ENUM +#define TRT_DEPRECATED_API __declspec(dllexport) +#else +#define TRT_DEPRECATED __attribute__((deprecated)) +#define TRT_DEPRECATED_ENUM +#define TRT_DEPRECATED_API __attribute__((deprecated, visibility("default"))) +#endif +#define TRT_DEPRECATED_BECAUSE(REASON) TRT_DEPRECATED +#endif + +//! A stand-in for `[[nodiscard]]` and `[[nodiscard(REASON)]]` that works with older compilers. +#if __cplusplus >= 201907L +#define TRT_NODISCARD [[nodiscard]] +#define TRT_NODISCARD_BECAUSE(REASON) [[nodiscard(REASON)]] +#elif __cplusplus >= 201603L +#define TRT_NODISCARD [[nodiscard]] +#define TRT_NODISCARD_BECAUSE(REASON) [[nodiscard]] +#else +#define TRT_NODISCARD +#define TRT_NODISCARD_BECAUSE(REASON) +#endif + +// Defines which symbols are exported. +#ifdef TENSORRT_BUILD_LIB +#ifdef _MSC_VER +//On Windows, exports are controlled by .def files; not by dllexport. +#define TENSORRTAPI +#else +#define TENSORRTAPI __attribute__((visibility("default"))) +#endif +#else +#define TENSORRTAPI +#endif +#define TRTNOEXCEPT +//! +//! \file NvInferRuntimeBase.h +//! +//! This file contains common definitions, data structures and interfaces shared between the standard and safe runtime. +//! +//! \warning Do not directly include this file. Instead include one of: +//! * NvInferRuntime.h (for the standard runtime) +//! * NvInferPluginUtils.h (for plugin utilities) +//! +#if !defined(NV_INFER_INTERNAL_INCLUDE) +static_assert(false, "Do not directly include this file. Include NvInferRuntime.h or NvInferPluginUtils.h"); +#endif + +//! Forward declare some CUDA types to avoid an include dependency. + +extern "C" +{ + //! Forward declaration of cublasContext to use in other interfaces. + struct cublasContext; + //! Forward declaration of cudnnContext to use in other interfaces. + struct cudnnContext; +} + +//! Construct a single integer denoting TensorRT version. +//! Usable in preprocessor expressions. +#define NV_TENSORRT_VERSION_INT(major, minor, patch) ((major) *10000L + (minor) *100L + (patch) *1L) + +//! TensorRT version as a single integer. +//! Usable in preprocessor expressions. +#define NV_TENSORRT_VERSION NV_TENSORRT_VERSION_INT(NV_TENSORRT_MAJOR, NV_TENSORRT_MINOR, NV_TENSORRT_PATCH) + +//! +//! \namespace nvinfer1 +//! +//! \brief The TensorRT API version 1 namespace. +//! +namespace nvinfer1 +{ +//! char_t is the type used by TensorRT to represent all valid characters. +using char_t = char; + +//! AsciiChar is the type used by TensorRT to represent valid ASCII characters. +//! This type is widely used in automotive safety context. +using AsciiChar = char_t; + +//! Forward declare IErrorRecorder for use in other interfaces. +namespace v_1_0 +{ +class IErrorRecorder; +} // namespace v_1_0 +using IErrorRecorder = v_1_0::IErrorRecorder; + +namespace impl +{ +//! Declaration of EnumMaxImpl struct to store maximum number of elements in an enumeration type. +template +struct EnumMaxImpl; +} // namespace impl + +//! Maximum number of elements in an enumeration type. +template +constexpr int32_t EnumMax() noexcept +{ + return impl::EnumMaxImpl::kVALUE; +} + +//! +//! \enum DataType +//! \brief The type of weights and tensors. +//! The datatypes other than kBOOL, kINT32, and kINT64 are "activation datatypes," +//! as they often represent values corresponding to inference results. +//! +enum class DataType : int32_t +{ + //! 32-bit floating point format. + kFLOAT = 0, + + //! IEEE 16-bit floating-point format -- has a 5 bit exponent and 11 bit significand. + kHALF = 1, + + //! Signed 8-bit integer representing a quantized floating-point value. + kINT8 = 2, + + //! Signed 32-bit integer format. + kINT32 = 3, + + //! 8-bit boolean. 0 = false, 1 = true, other values undefined. + kBOOL = 4, + + //! Unsigned 8-bit integer format. + //! Cannot be used to represent quantized floating-point values. + //! Use the IdentityLayer to convert kUINT8 network-level inputs to {kFLOAT, kHALF} prior + //! to use with other TensorRT layers, or to convert intermediate output + //! before kUINT8 network-level outputs from {kFLOAT, kHALF} to kUINT8. + //! kUINT8 conversions are only supported for {kFLOAT, kHALF}. + //! kUINT8 to {kFLOAT, kHALF} conversion will convert the integer values + //! to equivalent floating point values. + //! {kFLOAT, kHALF} to kUINT8 conversion will convert the floating point values + //! to integer values by truncating towards zero. This conversion has undefined behavior for + //! floating point values outside the range [0.0F, 256.0F) after truncation. + //! kUINT8 conversions are not supported for {kINT8, kINT32, kBOOL}. + kUINT8 = 5, + + //! Signed 8-bit floating point with + //! 1 sign bit, 4 exponent bits, 3 mantissa bits, and exponent-bias 7. + kFP8 = 6, + + //! Brain float -- has an 8 bit exponent and 8 bit significand. + kBF16 = 7, + + //! Signed 64-bit integer type. + kINT64 = 8, + + //! Signed 4-bit integer type. + kINT4 = 9, + + //! 4-bit floating point type + //! 1 bit sign, 2 bit exponent, 1 bit mantissa + kFP4 = 10, + + //! Unsigned representation of exponent-only 8-bit floating point type for quantization scales + kE8M0 = 11, +}; + +namespace impl +{ +//! Maximum number of elements in DataType enum. \see DataType +template <> +struct EnumMaxImpl +{ + //! Declaration of kVALUE that represents the maximum number of elements in the DataType enum. + static constexpr int32_t kVALUE = 12; +}; +} // namespace impl + +//! +//! \class Dims +//! \brief Structure to define the dimensions of a tensor. +//! +//! TensorRT can also return an "invalid dims" structure. This structure is +//! represented by nbDims == -1 and d[i] == 0 for all i. +//! +//! TensorRT can also return an "unknown rank" dims structure. This structure is +//! represented by nbDims == -1 and d[i] == -1 for all i. +//! +class Dims64 +{ +public: + //! The maximum rank (number of dimensions) supported for a tensor. + static constexpr int32_t MAX_DIMS{8}; + + //! The rank (number of dimensions). + int32_t nbDims; + + //! The extent of each dimension. + int64_t d[MAX_DIMS]; +}; + +//! +//! Alias for Dims64. +//! +using Dims = Dims64; + +using InterfaceKind = char const*; + +//! +//! \class InterfaceInfo +//! +//! \brief Version information associated with a TRT interface +//! +class InterfaceInfo +{ +public: + InterfaceKind kind; + int32_t major; + int32_t minor; +}; + +//! +//! \enum APILanguage +//! +//! \brief Programming language used in the implementation of a TRT interface +//! +enum class APILanguage : int32_t +{ + kCPP = 0, + kPYTHON = 1 +}; + +namespace impl +{ +//! Maximum number of elements in APILanguage enum. \see APILanguage +template <> +struct EnumMaxImpl +{ + //! Declaration of kVALUE that represents the maximum number of elements in the APILanguage enum. + static constexpr int32_t kVALUE = 2; +}; +} // namespace impl + +//! +//! \class IVersionedInterface +//! +//! \brief An Interface class for version control. +//! +class IVersionedInterface +{ +public: + //! + //! \brief The language used to build the implementation of this Interface. + //! + //! Applications must not override this method. + //! + virtual APILanguage getAPILanguage() const noexcept + { + return APILanguage::kCPP; + } + + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + virtual InterfaceInfo getInterfaceInfo() const noexcept = 0; + + virtual ~IVersionedInterface() noexcept = default; + +protected: + IVersionedInterface() = default; + IVersionedInterface(IVersionedInterface const&) = default; + IVersionedInterface(IVersionedInterface&&) = default; + IVersionedInterface& operator=(IVersionedInterface const&) & = default; + IVersionedInterface& operator=(IVersionedInterface&&) & = default; +}; + +//! +//! \enum ErrorCode +//! +//! \brief Error codes that can be returned by TensorRT during execution. +//! +enum class ErrorCode : int32_t +{ + //! + //! Execution completed successfully. + //! + kSUCCESS = 0, + + //! + //! An error that does not fall into any other category. This error is included for forward compatibility. + //! + kUNSPECIFIED_ERROR = 1, + + //! + //! A non-recoverable TensorRT error occurred. TensorRT is in an invalid internal state when this error is + //! emitted and any further calls to TensorRT will result in undefined behavior. + //! + kINTERNAL_ERROR = 2, + + //! + //! An argument passed to the function is invalid in isolation. + //! This is a violation of the API contract. + //! + kINVALID_ARGUMENT = 3, + + //! + //! An error occurred when comparing the state of an argument relative to other arguments. For example, the + //! dimensions for concat differ between two tensors outside of the channel dimension. This error is triggered + //! when an argument is correct in isolation, but not relative to other arguments. This is to help to distinguish + //! from the simple errors from the more complex errors. + //! This is a violation of the API contract. + //! + kINVALID_CONFIG = 4, + + //! + //! An error occurred when performing an allocation of memory on the host or the device. + //! A memory allocation error is normally fatal, but in the case where the application provided its own memory + //! allocation routine, it is possible to increase the pool of available memory and resume execution. + //! + kFAILED_ALLOCATION = 5, + + //! + //! One, or more, of the components that TensorRT relies on did not initialize correctly. + //! This is a system setup issue. + //! + kFAILED_INITIALIZATION = 6, + + //! + //! An error occurred during execution that caused TensorRT to end prematurely, either an asynchronous error, + //! user cancellation, or other execution errors reported by CUDA/DLA. In a dynamic system, the + //! data can be thrown away and the next frame can be processed or execution can be retried. + //! This is either an execution error or a memory error. + //! + kFAILED_EXECUTION = 7, + + //! + //! An error occurred during execution that caused the data to become corrupted, but execution finished. Examples + //! of this error are NaN squashing or integer overflow. In a dynamic system, the data can be thrown away and the + //! next frame can be processed or execution can be retried. + //! This is either a data corruption error, an input error, or a range error. + //! This is not used in safety but may be used in standard. + //! + kFAILED_COMPUTATION = 8, + + //! + //! TensorRT was put into a bad state by incorrect sequence of function calls. An example of an invalid state is + //! specifying a layer to be DLA only without GPU fallback, and that layer is not supported by DLA. This can occur + //! in situations where a service is optimistically executing networks for multiple different configurations + //! without checking proper error configurations, and instead throwing away bad configurations caught by TensorRT. + //! This is a violation of the API contract, but can be recoverable. + //! + //! Example of a recovery: + //! GPU fallback is disabled and conv layer with large filter(63x63) is specified to run on DLA. This will fail due + //! to DLA not supporting the large kernel size. This can be recovered by either turning on GPU fallback + //! or setting the layer to run on the GPU. + //! + kINVALID_STATE = 9, + + //! + //! An error occurred due to the network not being supported on the device due to constraints of the hardware or + //! system. An example is running an unsafe layer in a safety certified context, or a resource requirement for the + //! current network is greater than the capabilities of the target device. The network is otherwise correct, but + //! the network and hardware combination is problematic. This can be recoverable. + //! Examples: + //! * Scratch space requests larger than available device memory and can be recovered by increasing allowed + //! workspace size. + //! * Tensor size exceeds the maximum element count and can be recovered by reducing the maximum batch size. + //! + kUNSUPPORTED_STATE = 10, + +}; + +namespace impl +{ +//! Maximum number of elements in ErrorCode enum. \see ErrorCode +template <> +struct EnumMaxImpl +{ + //! Declaration of kVALUE + static constexpr int32_t kVALUE = 11; +}; +} // namespace impl + +namespace v_1_0 +{ +class IErrorRecorder : public IVersionedInterface +{ +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"IErrorRecorder", 1, 0}; + } + + //! + //! \brief A typedef of a C-style string for reporting error descriptions. + //! + using ErrorDesc = char const*; + + //! + //! \brief The length limit for an error description in bytes, excluding the '\0' string terminator. + //! Only applicable to safe runtime. + //! General error recorder implementation can use any size appropriate for the use case. + //! + static constexpr size_t kMAX_DESC_LENGTH{127U}; + + //! + //! \brief A typedef of a 32-bit integer for reference counting. + //! + using RefCount = int32_t; + + IErrorRecorder() = default; + ~IErrorRecorder() noexcept override = default; + + // Public API used to retrieve information from the error recorder. + + //! + //! \brief Return the number of errors + //! + //! Determines the number of errors that occurred between the current point in execution + //! and the last time that the clear() was executed. Due to the possibility of asynchronous + //! errors occurring, a TensorRT API can return correct results, but still register errors + //! with the Error Recorder. The value of getNbErrors() must increment by 1 after each reportError() + //! call until clear() is called, or the maximum number of errors that can be stored is exceeded. + //! + //! \return Returns the number of errors detected, or 0 if there are no errors. + //! If the upper bound of errors that can be stored is exceeded, the upper bound value must + //! be returned. + //! + //! For example, if the error recorder can store up to 16 error descriptions but reportError() has + //! been called 20 times, getNbErrors() must return 16. + //! + //! \see clear(), hasOverflowed() + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when multiple execution contexts are used during runtime. + //! + virtual int32_t getNbErrors() const noexcept = 0; + + //! + //! \brief Returns the ErrorCode enumeration. + //! + //! \param errorIdx A 32-bit integer that indexes into the error array. + //! + //! The errorIdx specifies what error code from 0 to getNbErrors()-1 that the application + //! wants to analyze and return the error code enum. + //! + //! \return Returns the enum corresponding to errorIdx if errorIdx is in range (between 0 and getNbErrors()-1). + //! ErrorCode::kUNSPECIFIED_ERROR must be returned if errorIdx is not in range. + //! + //! \see getErrorDesc(), ErrorCode + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when multiple execution contexts are used during runtime. + //! + virtual ErrorCode getErrorCode(int32_t errorIdx) const noexcept = 0; + + //! + //! \brief Returns a null-terminated C-style string description of the error. + //! + //! \param errorIdx A 32-bit integer that indexes into the error array. + //! + //! For the error specified by the idx value, return the string description of the error. The + //! error string is a null-terminated C-style string. In the safety context there is a + //! constant length requirement to remove any dynamic memory allocations and the error message + //! will be truncated if it exceeds kMAX_DESC_LENGTH bytes. + //! The format of the string is " - ". + //! + //! \return Returns a string representation of the error along with a description of the error if errorIdx is in + //! range (between 0 and getNbErrors()-1). An empty string will be returned if errorIdx is not in range. + //! + //! \see getErrorCode() + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when multiple execution contexts are used during runtime. + //! + virtual ErrorDesc getErrorDesc(int32_t errorIdx) const noexcept = 0; + + //! + //! \brief Determine if the error stack has overflowed. + //! + //! In the case when the number of errors is large, this function is used to query if one or more + //! errors have been dropped due to lack of storage capacity. This is especially important in the + //! automotive safety case where the internal error handling mechanisms cannot allocate memory. + //! + //! \return true if errors have been dropped due to overflowing the error stack. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when multiple execution contexts are used during runtime. + //! + virtual bool hasOverflowed() const noexcept = 0; + + //! + //! \brief Clear the error stack on the error recorder. + //! + //! Removes all the tracked errors by the error recorder. The implementation must guarantee that after + //! this function is called, and as long as no error occurs, the next call to getNbErrors will return + //! zero and hasOverflowed will return false. + //! + //! \see getNbErrors(), hasOverflowed() + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when multiple execution contexts are used during runtime. + //! + virtual void clear() noexcept = 0; + + // API used by TensorRT to report Error information to the application. + + //! + //! \brief Report an error to the error recorder with the corresponding enum and description. + //! + //! \param val The error code enum that is being reported. + //! \param desc The string description of the error, which will be a NULL-terminated string. + //! For safety use cases its length is limited to kMAX_DESC_LENGTH bytes + //! (excluding the NULL terminator) and descriptions that exceed this limit will be silently truncated. + //! + //! Report an error to the user that has a given value and human readable description. The function returns false + //! if processing can continue, which implies that the reported error is not fatal. This does not guarantee that + //! processing continues, but provides a hint to TensorRT. + //! The desc C-string data is only valid during the call to reportError and may be immediately deallocated by the + //! caller when reportError returns. The implementation must not store the desc pointer in the ErrorRecorder object + //! or otherwise access the data from desc after reportError returns. + //! + //! \return True if the error is determined to be fatal and processing of the current function must end. + //! + //! \warning If the error recorder's maximum number of storable errors is exceeded, the error description will be + //! silently dropped and the value returned by getNbErrors() will not be incremented. However, the return + //! value will still signal whether the error must be considered fatal. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when multiple execution contexts are used during runtime. + //! + virtual bool reportError(ErrorCode val, ErrorDesc desc) noexcept = 0; + + //! + //! \brief Increments the refcount for the current ErrorRecorder. + //! + //! Increments the reference count for the object by one and returns the current value. This reference count allows + //! the application to know that an object inside of TensorRT has taken a reference to the ErrorRecorder. TensorRT + //! guarantees that every call to IErrorRecorder::incRefCount() will be paired with a call to + //! IErrorRecorder::decRefCount() when the reference is released. It is undefined behavior to destruct the + //! ErrorRecorder when incRefCount() has been called without a corresponding decRefCount(). + //! + //! \return The reference counted value after the increment completes. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when multiple execution contexts are used during runtime. + //! + virtual RefCount incRefCount() noexcept = 0; + + //! + //! \brief Decrements the refcount for the current ErrorRecorder. + //! + //! Decrements the reference count for the object by one and returns the current value. This reference count allows + //! the application to know that an object inside of TensorRT has taken a reference to the ErrorRecorder. TensorRT + //! guarantees that every call to IErrorRecorder::decRefCount() will be preceded by a call to + //! IErrorRecorder::incRefCount(). It is undefined behavior to destruct the ErrorRecorder when incRefCount() has been + //! called without a corresponding decRefCount(). + //! + //! \return The reference counted value after the decrement completes. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when multiple execution contexts are used during runtime. + //! + virtual RefCount decRefCount() noexcept = 0; + +protected: + // @cond SuppressDoxyWarnings + IErrorRecorder(IErrorRecorder const&) = default; + IErrorRecorder(IErrorRecorder&&) = default; + IErrorRecorder& operator=(IErrorRecorder const&) & = default; + IErrorRecorder& operator=(IErrorRecorder&&) & = default; + // @endcond +}; // class IErrorRecorder +} // namespace v_1_0 + +//! +//! \class IErrorRecorder +//! +//! \brief Reference counted application-implemented error reporting interface for TensorRT objects. +//! +//! The error reporting mechanism is a user-defined object that interacts with the internal state of the object +//! that it is assigned to in order to determine information about abnormalities in execution. The error recorder +//! gets both an error enum that is more descriptive than pass/fail and also a string description that gives more +//! detail on the exact failure modes. In the safety context, the error strings are all limited to 128 bytes +//! or less in length, including the NULL terminator. +//! +//! The ErrorRecorder gets passed along to any class that is created from another class that has an ErrorRecorder +//! assigned to it. For example, assigning an ErrorRecorder to an IBuilder allows all INetwork's, ILayer's, and +//! ITensor's to use the same error recorder. For functions that have their own ErrorRecorder accessor functions. +//! This allows registering a different error recorder or de-registering of the error recorder for that specific +//! object. +//! +//! ErrorRecorder objects that are used in the safety runtime must define an implementation-dependent upper limit +//! of errors whose information can be stored, and drop errors above this upper limit. The limit must fit in int32_t. +//! The IErrorRecorder::hasOverflowed() method is used to signal that one or more errors have been dropped. +//! +//! The ErrorRecorder object implementation must be thread safe. All locking and synchronization is pushed to the +//! interface implementation and TensorRT does not hold any synchronization primitives when calling the interface +//! functions. +//! +//! The lifetime of the ErrorRecorder object must exceed the lifetime of all TensorRT objects that use it. +//! +using IErrorRecorder = v_1_0::IErrorRecorder; + +//! +//! \enum TensorIOMode +//! +//! \brief Definition of tensor IO Mode. +//! +enum class TensorIOMode : int32_t +{ + //! Tensor is not an input or output. + kNONE = 0, + + //! Tensor is input to the engine. + kINPUT = 1, + + //! Tensor is output by the engine. + kOUTPUT = 2 +}; + +namespace impl +{ +//! Maximum number of elements in TensorIOMode enum. \see TensorIOMode +template <> +struct EnumMaxImpl +{ + // Declaration of kVALUE that represents maximum number of elements in TensorIOMode enum + static constexpr int32_t kVALUE = 3; +}; +} // namespace impl +} // namespace nvinfer1 + +//! +//! \brief Return the library version number. +//! +//! The format is as for TENSORRT_VERSION: (MAJOR * 100 + MINOR) * 100 + PATCH +//! +extern "C" TENSORRTAPI int32_t getInferLibVersion() noexcept; + +#endif // NV_INFER_RUNTIME_BASE_H diff --git a/tensorrt/include/NvInferRuntimeCommon.h b/tensorrt/include/NvInferRuntimeCommon.h new file mode 100644 index 0000000000000000000000000000000000000000..756db0d69a73aafa60c073179e4f1abdc23257f6 --- /dev/null +++ b/tensorrt/include/NvInferRuntimeCommon.h @@ -0,0 +1,322 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NV_INFER_RUNTIME_COMMON_H +#define NV_INFER_RUNTIME_COMMON_H + +//! +//! \file NvInferRuntimeCommon.h +//! +//! This file provides the nvinfer1::IPluginRegistry interface, which will be moved to the NvInferRuntime.h header +//! in a future release. +//! +//! \warning This file will be removed in a future release. +//! +//! \warning Do not directly include this file. Instead include NvInferRuntime.h +//! +#define NV_INFER_INTERNAL_INCLUDE 1 +#include "NvInferPluginBase.h" +#undef NV_INFER_INTERNAL_INCLUDE +#include "NvInferRuntimePlugin.h" + +namespace nvinfer1 +{ +//! +//! \class IPluginRegistry +//! +//! \brief Single registration point for all plugins in an application. It is +//! used to find plugin implementations during engine deserialization. +//! Internally, the plugin registry is considered to be a singleton so all +//! plugins in an application are part of the same global registry. +//! Note that the plugin registry is only supported for plugins of type +//! IPluginV2 and should also have a corresponding IPluginCreator implementation. +//! +//! \see IPluginV2 and IPluginCreator +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +//! \warning In the automotive safety context, be sure to call IPluginRegistry::setErrorRecorder() to register +//! an error recorder with the registry before using other methods in the registry. +//! +class IPluginRegistry +{ +public: + //! + //! \brief Pointer for plugin library handle. + //! + using PluginLibraryHandle = void*; + + //! + //! \brief Register a plugin creator implementing IPluginCreator. Returns false if any plugin creator with the same + //! name, version or namespace is already registered. + //! + //! \warning The string pluginNamespace must be 1024 bytes or less including the NULL terminator and must be NULL + //! terminated. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes; calls to this method will be synchronized by a mutex. + //! + //! \deprecated Deprecated in TensorRT 10.0. Superseded by + //! IPluginRegistry::registerCreator(IPluginCreatorInterface&, AsciiChar const* const). + //! + TRT_DEPRECATED virtual bool registerCreator( + IPluginCreator& creator, AsciiChar const* const pluginNamespace) noexcept = 0; + + //! + //! \brief Return all the registered plugin creators and the number of + //! registered plugin creators. Returns nullptr if none found. + //! + //! \warning If any plugin creators are registered or deregistered after calling this function, the returned pointer + //! is not guaranteed to be valid thereafter. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: No + //! + //! \deprecated Deprecated in TensorRT 10.0. Superseded by IPluginRegistry::getAllCreators(int32_t* const). + //! + TRT_DEPRECATED virtual IPluginCreator* const* getPluginCreatorList(int32_t* const numCreators) const noexcept = 0; + + //! + //! \brief Return plugin creator based on plugin name, version, and + //! namespace associated with plugin during network creation. + //! + //! \warning The strings pluginName, pluginVersion, and pluginNamespace must be 1024 bytes or less including the + //! NULL terminator and must be NULL terminated. + //! + //! \warning Returns nullptr if a plugin creator with matching name, version, and namespace is found, but is not a + //! descendent of IPluginCreator + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes + //! + //! \deprecated Deprecated in TensorRT 10.0. Superseded by IPluginRegistry::getCreator(AsciiChar const* const, + //! AsciiChar const* const, AsciiChar const* const). + //! + TRT_DEPRECATED virtual IPluginCreator* getPluginCreator(AsciiChar const* const pluginName, + AsciiChar const* const pluginVersion, AsciiChar const* const pluginNamespace = "") noexcept = 0; + + // @cond SuppressDoxyWarnings + IPluginRegistry() = default; + IPluginRegistry(IPluginRegistry const&) = delete; + IPluginRegistry(IPluginRegistry&&) = delete; + IPluginRegistry& operator=(IPluginRegistry const&) & = delete; + IPluginRegistry& operator=(IPluginRegistry&&) & = delete; + // @endcond + +protected: + virtual ~IPluginRegistry() noexcept = default; + +public: + //! + //! \brief Set the ErrorRecorder for this interface + //! + //! Assigns the ErrorRecorder to this interface. The ErrorRecorder will track all errors during execution. + //! This function will call incRefCount of the registered ErrorRecorder at least once. Setting + //! recorder to nullptr unregisters the recorder with the interface, resulting in a call to decRefCount if + //! a recorder has been registered. + //! + //! \param recorder The error recorder to register with this interface. + //! + //! \see getErrorRecorder() + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: No + //! + virtual void setErrorRecorder(IErrorRecorder* const recorder) noexcept = 0; + + //! + //! \brief Get the ErrorRecorder assigned to this interface. + //! + //! Retrieves the assigned error recorder object for the given class. A default error recorder does not exist, + //! so a nullptr will be returned if setErrorRecorder has not been called, or an ErrorRecorder has not been + //! inherited. + //! + //! \return A pointer to the IErrorRecorder object that has been registered. + //! + //! \see setErrorRecorder() + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes + //! + virtual IErrorRecorder* getErrorRecorder() const noexcept = 0; + + //! + //! \brief Deregister a previously registered plugin creator implementing IPluginCreator. + //! + //! Since there may be a desire to limit the number of plugins, + //! this function provides a mechanism for removing plugin creators registered in TensorRT. + //! The plugin creator that is specified by \p creator is removed from TensorRT and no longer tracked. + //! + //! \return True if the plugin creator was deregistered, false if it was not found in the registry or otherwise + //! could not be deregistered. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes + //! + //! \deprecated Deprecated in TensorRT 10.0. Superseded by + //! IPluginRegistry::deregisterCreator(IPluginCreatorInterface const&). + //! + TRT_DEPRECATED virtual bool deregisterCreator(IPluginCreator const& creator) noexcept = 0; + + //! + //! \brief Return whether the parent registry will be searched if a plugin is not found in this registry + //! default: true + //! + //! \return bool variable indicating whether parent search is enabled. + //! + //! \see setParentSearchEnabled + //! + virtual bool isParentSearchEnabled() const = 0; + + //! + //! \brief Set whether the parent registry will be searched if a plugin is not found in this registry. + //! + //! \param enabled The bool variable indicating whether parent search is enabled. + //! + //! \see isParentSearchEnabled + //! + virtual void setParentSearchEnabled(bool const enabled) = 0; + + //! + //! \brief Load and register a shared library of plugins. + //! + //! \param pluginPath the plugin library path. + //! + //! \return The loaded plugin library handle. The call will fail and return + //! nullptr if any of the plugins are already registered. + //! + virtual PluginLibraryHandle loadLibrary(AsciiChar const* pluginPath) noexcept = 0; + + //! + //! \brief Deregister plugins associated with a library. Any resources acquired when the library + //! was loaded will be released. + //! + //! \param handle the plugin library handle to deregister. + //! + virtual void deregisterLibrary(PluginLibraryHandle handle) noexcept = 0; + + //! + //! \brief Register a plugin creator. Returns false if a plugin creator with the same type + //! is already registered. + //! + //! \warning The string pluginNamespace must be 1024 bytes or less including the NULL terminator and must be NULL + //! terminated. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes; calls to this method will be synchronized by a mutex. + //! + virtual bool registerCreator(IPluginCreatorInterface& creator, AsciiChar const* const pluginNamespace) noexcept = 0; + + //! + //! \brief Return all registered plugin creators. Returns nullptr if none found. + //! + //! \warning If any plugin creators are registered or deregistered after calling this function, the returned pointer + //! is not guaranteed to be valid thereafter. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: No + //! + virtual IPluginCreatorInterface* const* getAllCreators(int32_t* const numCreators) const noexcept = 0; + + //! + //! \brief Return a registered plugin creator based on plugin name, version, and namespace associated with the + //! plugin during network creation. + //! + //! \warning The strings pluginName, pluginVersion, and pluginNamespace must be 1024 bytes or less including the + //! NULL terminator and must be NULL terminated. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes + //! + virtual IPluginCreatorInterface* getCreator(AsciiChar const* const pluginName, AsciiChar const* const pluginVersion, + AsciiChar const* const pluginNamespace = "") noexcept = 0; + + //! + //! \brief Deregister a previously registered plugin creator. + //! + //! Since there may be a desire to limit the number of plugins, + //! this function provides a mechanism for removing plugin creators registered in TensorRT. + //! The plugin creator that is specified by \p creator is removed from TensorRT and no longer tracked. + //! + //! \return True if the plugin creator was deregistered, false if it was not found in the registry or otherwise + //! could not be deregistered. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes + //! + virtual bool deregisterCreator(IPluginCreatorInterface const& creator) noexcept = 0; + + //! + //! \brief Get a plugin resource + //! \param key Key for identifying the resource. Cannot be null. + //! \param resource A plugin resource object. The object will only need to be valid until this method returns, as + //! only a clone of this object will be registered by TRT. Cannot be null. + //! + //! \return Registered plugin resource object + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes; calls to this method will be synchronized by a mutex. + //! + virtual IPluginResource* acquirePluginResource(AsciiChar const* key, IPluginResource* resource) noexcept = 0; + + //! + //! \brief Decrement reference count for the resource with this key + //! If reference count goes to zero after decrement, release() will be invoked on the resource, the key will + //! be deregistered and the resource object will be deleted + //! + //! \param key Key that was used to register the resource. Cannot be null. + //! + //! \return 0 for success, else non-zero + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes; calls to this method will be synchronized by a mutex. + //! + virtual int32_t releasePluginResource(AsciiChar const* key) noexcept = 0; + + //! + //! \brief Return all registered plugin creators by searching starting from the current registry and following + //! parent registries recursively as long as isParentSearchEnabled() returns true. + //! + //! \param[out] numCreators Pointer to an integer where the number of registered plugin creators will be stored. + //! + //! \return A pointer to an array of IPluginCreatorInterface pointers. Returns nullptr if no creators are found. + //! + //! \warning If any plugin creators are registered or deregistered after calling this function, the returned pointer + //! is not guaranteed to remain valid. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: No + //! + virtual IPluginCreatorInterface* const* getAllCreatorsRecursive(int32_t* const numCreators) noexcept = 0; +}; + +} // namespace nvinfer1 + +#endif /* NV_INFER_RUNTIME_COMMON_H */ diff --git a/tensorrt/include/NvInferRuntimePlugin.h b/tensorrt/include/NvInferRuntimePlugin.h new file mode 100644 index 0000000000000000000000000000000000000000..cd0250f6402fc10b66107a5fa919f9e9532e056d --- /dev/null +++ b/tensorrt/include/NvInferRuntimePlugin.h @@ -0,0 +1,980 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NV_INFER_RUNTIME_PLUGIN_H +#define NV_INFER_RUNTIME_PLUGIN_H + +#define NV_INFER_INTERNAL_INCLUDE 1 +#include "NvInferPluginBase.h" +#undef NV_INFER_INTERNAL_INCLUDE + +//! +//! \file NvInferRuntimePlugin.h +//! +//! This file contains common definitions, data structures and interfaces that relate to plugins and are shared +//! between the standard and safe runtime. +//! +//! \warning Do not directly include this file. Instead include NvInferRuntime.h +//! + +//! +//! \namespace nvinfer1 +//! +//! \brief The TensorRT API version 1 namespace. +//! +namespace nvinfer1 +{ + +enum class TensorFormat : int32_t; +namespace v_1_0 +{ +class IGpuAllocator; +} // namespace v_1_0 +using IGpuAllocator = v_1_0::IGpuAllocator; + +//! +//! \brief PluginFormat is reserved for backward compatibility. +//! +//! \see IPluginV2::supportsFormat() +//! +using PluginFormat = TensorFormat; + +//! +//! \brief Bit at the plugin version to identify that it is a plugin. +//! +static constexpr int32_t kPLUGIN_VERSION_PYTHON_BIT = 0x40; + +//! +//! \struct PluginTensorDesc +//! +//! \brief Fields that a plugin might see for an input or output. +//! +//! Scale is only valid when data type is DataType::kINT8. TensorRT will set +//! the value to -1.0F if it is invalid. +//! +//! \see IPluginV2IOExt::supportsFormatCombination +//! \see IPluginV2IOExt::configurePlugin +//! +struct PluginTensorDesc +{ + //! Dimensions. + Dims dims; + //! \warning DataType:kBOOL and DataType::kUINT8 are not supported. + DataType type; + //! Tensor format. + TensorFormat format; + //! Scale for INT8 data type. + float scale; +}; + +//! +//! \struct PluginVersion +//! +//! \brief Definition of plugin versions. +//! +//! Tag for plug-in versions. Used in upper byte of getTensorRTVersion(). +//! +//! \deprecated Deprecated in TensorRT 10.10. PluginVersion is used only in relation to IPluginV2-descendent plugin +//! interfaces, which are all deprecated. +//! +enum class PluginVersion : uint8_t +{ + //! IPluginV2 + kV2 TRT_DEPRECATED_ENUM = 0, + //! IPluginV2Ext + kV2_EXT TRT_DEPRECATED_ENUM = 1, + //! IPluginV2IOExt + kV2_IOEXT TRT_DEPRECATED_ENUM = 2, + //! IPluginV2DynamicExt + kV2_DYNAMICEXT TRT_DEPRECATED_ENUM = 3, + //! IPluginV2DynamicExt-based Python plugins + kV2_DYNAMICEXT_PYTHON TRT_DEPRECATED_ENUM = kPLUGIN_VERSION_PYTHON_BIT | 3 +}; + +//! +//! \enum PluginCreatorVersion +//! +//! \brief Enum to identify version of the plugin creator. +//! +//! \deprecated Deprecated in TensorRT 10.10. PluginCreatorVersion is used only in relation to plugin creators based +//! off IPluginCreator, which is deprecated. +//! +enum class PluginCreatorVersion : int32_t +{ + //! IPluginCreator + kV1 TRT_DEPRECATED_ENUM = 0, + //! IPluginCreator-based Python plugin creators + kV1_PYTHON TRT_DEPRECATED_ENUM = kPLUGIN_VERSION_PYTHON_BIT +}; + +//! +//! \class IPluginV2 +//! +//! \brief Plugin class for user-implemented layers. +//! +//! Plugins are a mechanism for applications to implement custom layers. When +//! combined with IPluginCreator it provides a mechanism to register plugins and +//! look up the Plugin Registry during de-serialization. +//! +//! \see IPluginCreator +//! \see IPluginRegistry +//! +//! \deprecated Deprecated in TensorRT 8.5. Implement IPluginV3 instead. +//! +class TRT_DEPRECATED IPluginV2 +{ +public: + //! + //! \brief Return the API version with which this plugin was built. + //! + //! Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with + //! plugins. + //! + //! \return The TensorRT version in the format (major * 100 + minor) * 100 + patch. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, the implementation provided here is safe to call from any thread. + //! + virtual int32_t getTensorRTVersion() const noexcept + { + return NV_TENSORRT_VERSION; + } + + //! + //! \brief Return the plugin type. Should match the plugin name returned by the corresponding plugin creator + //! + //! \see IPluginCreator::getPluginName() + //! + //! \warning The string returned must be NULL-terminated and have a length of 1024 bytes or less including the + //! NULL terminator. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual AsciiChar const* getPluginType() const noexcept = 0; + + //! + //! \brief Return the plugin version. Should match the plugin version returned by the corresponding plugin creator + //! + //! \see IPluginCreator::getPluginVersion() + //! + //! \warning The string returned must be NULL-terminated and have a length of 1024 bytes or less including the + //! NULL terminator. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual AsciiChar const* getPluginVersion() const noexcept = 0; + + //! + //! \brief Get the number of outputs from the layer. + //! + //! \return The number of outputs, which is a positive integer. + //! + //! This function is called by the implementations of INetworkDefinition and IBuilder. In particular, it is called + //! prior to any call to initialize(). + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual int32_t getNbOutputs() const noexcept = 0; + + //! + //! \brief Get the dimension of an output tensor. + //! + //! \param index The index of the output tensor. Will lie in the valid range (between 0 and getNbOutputs()-1 + //! inclusive). + //! \param inputs The input tensor dimensions. Will be the start address of a Dims array of length nbInputDims. + //! \param nbInputDims The number of input tensors. Will be a non-negative integer. + //! + //! \return The output tensor dimensions if the index is in the valid range. + //! An invalid value of Dims{-1, {}} must be returned if the index is not in the valid range. + //! + //! This function is called by the implementations of INetworkDefinition and IBuilder. In particular, it is called + //! prior to any call to initialize(). + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + //! \note In any non-IPluginV2DynamicExt plugin, batch size must not be included in the returned dimensions, + //! even if the plugin is expected to be run in a network with explicit batch mode enabled. + //! Please see the TensorRT Developer Guide for more details on how plugin inputs and outputs behave. + //! + virtual Dims getOutputDimensions(int32_t index, Dims const* inputs, int32_t nbInputDims) noexcept = 0; + + //! + //! \brief Check format support. + //! + //! \param type DataType requested. + //! \param format PluginFormat requested. + //! + //! \return true if the plugin supports the type-format combination. + //! + //! This function is called by the implementations of INetworkDefinition, IBuilder, and + //! safe::ICudaEngine/ICudaEngine. In particular, it is called when creating an engine and when deserializing an + //! engine. + //! + //! \warning for the format field, the values PluginFormat::kCHW4, PluginFormat::kCHW16, and PluginFormat::kCHW32 + //! will not be passed in, this is to keep backward compatibility with TensorRT 5.x series. Use PluginV2IOExt + //! or PluginV2DynamicExt for other PluginFormats. + //! + //! \warning DataType:kBOOL and DataType::kUINT8 are not supported. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual bool supportsFormat(DataType type, PluginFormat format) const noexcept = 0; + + //! + //! \brief Configure the layer. + //! + //! This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make + //! algorithm choices on the basis of its weights, dimensions, and maximum batch size. + //! + //! \param inputDims The input tensor dimensions. Will be the start address of a Dims array of length nbInputs. + //! \param nbInputs The number of inputs. Will be a non-negative integer. + //! \param outputDims The output tensor dimensions. Will be the start address of a Dims array of length nbOutputs. + //! \param nbOutputs The number of outputs. Will be a positive integer identical to the return value of + //! getNbOutputs(). + //! \param type The data type selected for the engine. + //! \param format The format selected for the engine. + //! \param maxBatchSize The maximum batch size. Will be a positive integer. + //! + //! The dimensions passed here do not include the outermost batch size (i.e. for 2D image networks, they will be + //! 3-dimensional CHW dimensions). + //! + //! \warning for the format field, the values PluginFormat::kCHW4, PluginFormat::kCHW16, and PluginFormat::kCHW32 + //! will not be passed in, this is to keep backward compatibility with TensorRT 5.x series. Use PluginV2IOExt + //! or PluginV2DynamicExt for other PluginFormats. + //! + //! \warning DataType:kBOOL and DataType::kUINT8 are not supported. + //! + //! \see clone() + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. However, TensorRT + //! will not call this method from two threads simultaneously on a given clone of a plugin. + //! + virtual void configureWithFormat(Dims const* inputDims, int32_t nbInputs, Dims const* outputDims, int32_t nbOutputs, + DataType type, PluginFormat format, int32_t maxBatchSize) noexcept + = 0; + + //! + //! \brief Initialize the layer for execution. This is called when the engine is created. + //! + //! \return 0 for success, else non-zero (which will cause engine termination). + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin or when using multiple + //! execution contexts using this plugin. + //! + virtual int32_t initialize() noexcept = 0; + + //! + //! \brief Release resources acquired during plugin layer initialization. This is called when the engine is + //! destroyed. + //! + //! \see initialize() + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin or when using multiple + //! execution contexts using this plugin. However, TensorRT will not call this method from + //! two threads simultaneously on a given clone of a plugin. + //! + virtual void terminate() noexcept = 0; + + //! + //! \brief Find the workspace size required by the layer. + //! + //! This function is called during engine startup, after initialize(). The workspace size returned must be + //! sufficient for any batch size up to the maximum. + //! + //! \param maxBatchSize The maximum batch size, which will be a positive integer. + //! + //! \return The workspace size in bytes, i.e. the device memory size that the plugin requires for its internal + //! computations. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. However, TensorRT + //! will not call this method from two threads simultaneously on a given clone of a plugin. + //! + virtual size_t getWorkspaceSize(int32_t maxBatchSize) const noexcept = 0; + + //! + //! \brief Execute the layer. + //! + //! \param batchSize The number of inputs in the batch. + //! \param inputs The memory for the input tensors. Will be an array of device addresses corresponding to input + //! tensors of length nbInputs, where nbInputs is the second parameter passed to configureWithFormat(). + //! The i-th input tensor will have the dimensions inputDims[i], where inputDims is the first parameter + //! that was passed to configureWithFormat(). + //! \param outputs The memory for the output tensors. Will be an array of device addresses corresponding to output + //! tensors of length getNbOutputs(). + //! \param workspace Workspace for execution. Will be the start address of a device buffer whose length will be at + //! least getWorkspaceSize(batchSize). + //! \param stream The stream in which to execute the kernels. This will be a valid CUDA stream. + //! + //! \return 0 for success, else non-zero (which will cause engine termination). + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when multiple execution contexts are used during runtime. + //! + virtual int32_t enqueue(int32_t batchSize, void const* const* inputs, void* const* outputs, void* workspace, + cudaStream_t stream) noexcept + = 0; + + //! + //! \brief Find the size of the serialization buffer required to store the plugin configuration in a binary file. + //! + //! \return The size of the serialization buffer in bytes. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual size_t getSerializationSize() const noexcept = 0; + + //! + //! \brief Serialize the layer. + //! + //! \param buffer A pointer to a host buffer to serialize data. Size of buffer will be at least as large as the + //! value returned by getSerializationSize. + //! + //! \see getSerializationSize() + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual void serialize(void* buffer) const noexcept = 0; + + //! + //! \brief Destroy the plugin object. This will be called when the network, builder or engine is destroyed. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual void destroy() noexcept = 0; + + //! + //! \brief Clone the plugin object. This copies over internal plugin parameters and returns a new plugin object with + //! these parameters. + //! + //! The TensorRT runtime calls clone() to clone the plugin when an execution context is created for an engine, + //! after the engine has been created. The runtime does not call initialize() on the cloned plugin, + //! so the cloned plugin must be created in an initialized state. + //! + //! \return A cloned plugin object in an initialized state with the same parameters as the current object. + //! nullptr must be returned if the cloning fails, e.g. because of resource exhaustion. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin or when creating multiple + //! execution contexts. + //! + virtual IPluginV2* clone() const noexcept = 0; + + //! + //! \brief Set the namespace that this plugin object belongs to. Ideally, all plugin + //! objects from the same plugin library must have the same namespace. + //! + //! \param pluginNamespace The namespace for the plugin object. + //! + //! \warning The string pluginNamespace will be NULL-terminated and have a length of 1024 bytes or less including the + //! NULL terminator. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual void setPluginNamespace(AsciiChar const* pluginNamespace) noexcept = 0; + + //! + //! \brief Return the namespace of the plugin object. + //! + //! \return The namespace string that was passed to setPluginNamespace(), possibly after truncation to 1024 bytes + //! if a longer string was passed. An empty string must be returned as default value. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual AsciiChar const* getPluginNamespace() const noexcept = 0; + + // @cond SuppressDoxyWarnings + IPluginV2() = default; + virtual ~IPluginV2() noexcept = default; +// @endcond + +protected: +// @cond SuppressDoxyWarnings + IPluginV2(IPluginV2 const&) = default; + IPluginV2(IPluginV2&&) = default; + IPluginV2& operator=(IPluginV2 const&) & = default; + IPluginV2& operator=(IPluginV2&&) & = default; +// @endcond +}; + +//! +//! \class IPluginV2Ext +//! +//! \brief Plugin class for user-implemented layers. +//! +//! Plugins are a mechanism for applications to implement custom layers. This +//! interface provides additional capabilities to the IPluginV2 interface by +//! supporting different output data types and broadcast across batches. +//! +//! \see IPluginV2 +//! +//! \deprecated Deprecated in TensorRT 8.5. Implement IPluginV3 instead. +//! +class TRT_DEPRECATED IPluginV2Ext : public IPluginV2 +{ +public: + //! + //! \brief Return the DataType of the plugin output at the requested index. + //! + //! \param index The output tensor index in the valid range between 0 and getNbOutputs()-1. + //! \param inputTypes The data types of the input tensors, stored in an array of length nbInputs. + //! \param nbInputs The number of input tensors. Will be a non-negative integer. + //! + //! \return The data type of the output tensor with the provided index if the input tensors have the data types + //! provided in inputTypes, provided the output tensor index is in the valid range. DataType::kFLOAT must be + //! returned if the index is not in the valid range. + //! + //! The default behavior must be to return the type of the first input, or DataType::kFLOAT if the layer has no + //! inputs. The returned data type must have a format that is supported by the plugin. + //! + //! \see supportsFormat() + //! + //! \warning DataType:kBOOL and DataType::kUINT8 are not supported. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual nvinfer1::DataType getOutputDataType( + int32_t index, nvinfer1::DataType const* inputTypes, int32_t nbInputs) const noexcept + = 0; + + //! + //! \brief Return true if the output tensor is broadcast across a batch. + //! + //! \param outputIndex The index of the output tensor, which will be in the valid range between 0 and + //! nbOutputs()-1. + //! \param inputIsBroadcasted A boolean array of length nbInputs. The i-th element will be true if and only if + //! the tensor for the ith input is broadcast across a batch. + //! \param nbInputs The number of inputs. Will be a non-negative integer. + //! + //! The values in inputIsBroadcasted refer to broadcasting at the semantic level, + //! i.e. are unaffected by whether method canBroadcastInputAcrossBatch requests + //! physical replication of the values. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + //! \deprecated Deprecated in TensorRT 10.0. Implicit batch support is removed in TensorRT 10.0. + //! + TRT_DEPRECATED virtual bool isOutputBroadcastAcrossBatch( + int32_t outputIndex, bool const* inputIsBroadcasted, int32_t nbInputs) const noexcept + = 0; + + //! + //! \brief Return true if the plugin can use an input tensor that is broadcast across batch without replication. + //! + //! \param inputIndex Index of input that could be broadcast. Will be in the valid range between 0 and + //! nbInputs - 1 where nbInputs is the maximum number of input tensors supported by this plugin. + //! + //! \return true if the index is in the valid range and the plugin is able to broadcast a single copy of this + //! input tensor across the batch. False otherwise. + //! + //! For each input whose tensor is semantically broadcast across a batch, + //! TensorRT calls this method before calling configurePlugin. + //! If canBroadcastInputAcrossBatch returns true, TensorRT will not replicate the input tensor; + //! i.e., there will be a single copy that the plugin must share across the batch. + //! If it returns false, TensorRT will replicate the input tensor + //! so that it appears like a non-broadcasted tensor. + //! + //! This method is called only for inputs that can be broadcast. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + //! \deprecated Deprecated in TensorRT 10.0. Implicit batch support is removed in TensorRT 10.0. + //! + TRT_DEPRECATED virtual bool canBroadcastInputAcrossBatch(int32_t inputIndex) const noexcept = 0; + + //! + //! \brief Configure the layer with input and output data types. + //! + //! This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make + //! algorithm choices on the basis of its weights, dimensions, data types and maximum batch size. + //! + //! \param inputDims The input tensor dimensions. Will be an array of length nbInputs. + //! \param nbInputs The number of inputs. Will be a non-negative integer. + //! \param outputDims The output tensor dimensions. Will be an array of length nbOutputs. + //! \param nbOutputs The number of outputs. Will be a positive integer. + //! \param inputTypes The data types selected for the plugin inputs. Will be an array of length nbInputs. + //! \param outputTypes The data types selected for the plugin outputs. Will be an array of length nbOutputs. + //! \param inputIsBroadcast True for each input that the plugin must broadcast across the batch. + //! Will be an array of length nbInputs. + //! \param outputIsBroadcast True for each output that TensorRT will broadcast across the batch. + //! Will be an array of length nbOutputs. + //! \param floatFormat The format selected for the engine for the floating point inputs/outputs. + //! \param maxBatchSize The maximum batch size. Will be a positive integer. + //! + //! The dimensions passed here do not include the outermost batch size (i.e. for 2D image networks, they will be + //! 3-dimensional CHW dimensions). When inputIsBroadcast or outputIsBroadcast is true, the outermost batch size for + //! that input or output must be treated as if it is one. + //! Index 'i' of inputIsBroadcast is true only if the input is semantically broadcast across the batch and + //! calling canBroadcastInputAcrossBatch with argument 'i' returns true. + //! Index 'i' of outputIsBroadcast is true only if calling isOutputBroadcastAcrossBatch with argument 'i' + //! returns true. + //! + //! \warning for the floatFormat field, the values PluginFormat::kCHW4, PluginFormat::kCHW16, and + //! PluginFormat::kCHW32 will not be passed in, this is to keep backward compatibility with TensorRT 5.x series. Use + //! PluginV2IOExt or PluginV2DynamicExt for other PluginFormats. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. However, TensorRT + //! will not call this method from two threads simultaneously on a given clone of a plugin. + //! + virtual void configurePlugin(Dims const* inputDims, int32_t nbInputs, Dims const* outputDims, int32_t nbOutputs, + DataType const* inputTypes, DataType const* outputTypes, bool const* inputIsBroadcast, + bool const* outputIsBroadcast, PluginFormat floatFormat, int32_t maxBatchSize) noexcept + = 0; + + IPluginV2Ext() = default; + ~IPluginV2Ext() override = default; + + //! + //! \brief Attach the plugin object to an execution context and grant the plugin the access to some context + //! resources. + //! + //! \param cudnn The cuDNN context handle of the execution context. Will be a valid cuDNN context handle, or + //! nullptr if TacticSource::kCUDNN is disabled. + //! \param cublas The cuBLAS context handle of the execution context. Will be a valid cuBLAS context handle, or + //! nullptr if TacticSource::kCUBLAS is disabled. + //! \param allocator The allocator used by the execution context + //! + //! This function is called automatically for each plugin when a new execution context is created. If the context + //! was created without resources, this method is not called until the resources are assigned. It is also called if + //! new resources are assigned to the context. + //! + //! If the plugin needs per-context resource, it can be allocated here. + //! The plugin can also get context-owned cuDNN and cuBLAS context here. + //! + //! \note The TacticSource::kCUDNN and TacticSource::kCUBLAS flag is disabled by default. + //! The allocator pointer is unique to each building or execution context instance having overlapping lifetimes. + //! It can be used as a key to manage resources across plugin instances sharing the same context. + //! Plugins attached to different contexts will have different handles as their execution will not overlap. + //! + //! \see TacticSources + //! \see getPluginCudnnHandle(void* executionContextIdentifier) + //! \see getPluginCublasHandle(void* excecutionContextIdentifier) + //! + //! \note In the automotive safety context, the cuDNN and cuBLAS parameters will be nullptr because cuDNN and cuBLAS + //! are not used by the safe runtime. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual void attachToContext( + cudnnContext* /*cudnn*/, cublasContext* /*cublas*/, IGpuAllocator* /*allocator*/) noexcept + { + } + + //! + //! \brief Detach the plugin object from its execution context. + //! + //! This function is called automatically for each plugin when an execution context is destroyed or the context + //! resources are unassigned from the context. + //! + //! If the plugin owns per-context resource, it can be released here. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual void detachFromContext() noexcept {} + + //! + //! \brief Clone the plugin object. This copies over internal plugin parameters as well and returns a new plugin + //! object with these parameters. If the source plugin is pre-configured with configurePlugin(), the returned object + //! must also be pre-configured. The returned object must allow attachToContext() with a new execution context. + //! Cloned plugin objects can share the same per-engine immutable resource (e.g. weights) with the source object + //! (e.g. via ref-counting) to avoid duplication. + //! + //! \return A pointer to a cloned plugin object if cloning was successful, otherwise nullptr. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + IPluginV2Ext* clone() const noexcept override = 0; + +protected: + // @cond SuppressDoxyWarnings + IPluginV2Ext(IPluginV2Ext const&) = default; + IPluginV2Ext(IPluginV2Ext&&) = default; + IPluginV2Ext& operator=(IPluginV2Ext const&) & = default; + IPluginV2Ext& operator=(IPluginV2Ext&&) & = default; +// @endcond + + //! + //! \brief Return the API version with which this plugin was built. The + //! upper byte reserved by TensorRT and is used to differentiate this from IPluginV2. + //! + //! \return In the lower three bytes, the TensorRT version in the format + //! (major * 100 + minor) * 100 + patch. + //! In the upper byte, the value 1. + //! + //! Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with + //! plugins. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, the implementation provided here is safe to call from any thread. + //! + int32_t getTensorRTVersion() const noexcept override + { + return static_cast((static_cast(PluginVersion::kV2_EXT) << 24U) + | (static_cast(NV_TENSORRT_VERSION) & 0xFFFFFFU)); + } + + //! + //! \brief Derived classes must not implement this. In a C++11 API it would be final. + //! + //! IPluginV2Ext::configureWithFormat() is a NOP operation for all classes derived from IPluginV2Ext. + //! These classes call configurePlugin() instead. + //! + void configureWithFormat(Dims const* /*inputDims*/, int32_t /*nbInputs*/, Dims const* /*outputDims*/, + int32_t /*nbOutputs*/, DataType /*type*/, PluginFormat /*format*/, int32_t /*maxBatchSize*/) noexcept override + { + } +}; + +//! +//! \class IPluginV2IOExt +//! +//! \brief Plugin class for user-implemented layers. +//! +//! Plugins are a mechanism for applications to implement custom layers. This interface provides additional +//! capabilities to the IPluginV2Ext interface by extending different I/O data types and tensor formats. +//! +//! \see IPluginV2Ext +//! +//! \deprecated Deprecated in TensorRT 10.0. Implement IPluginV3 instead. +//! +class TRT_DEPRECATED IPluginV2IOExt : public IPluginV2Ext +{ +public: + //! + //! \brief Configure the layer. + //! + //! This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make + //! algorithm choices on the basis of the provided I/O PluginTensorDesc. + //! + //! \param in The input tensors attributes that are used for configuration. + //! \param nbInput Number of input tensors. + //! \param out The output tensors attributes that are used for configuration. + //! \param nbOutput Number of output tensors. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. However, TensorRT + //! will not call this method from two threads simultaneously on a given clone of a plugin. + //! + virtual void configurePlugin( + PluginTensorDesc const* in, int32_t nbInput, PluginTensorDesc const* out, int32_t nbOutput) noexcept + = 0; + + //! + //! \brief Return true if plugin supports the format and datatype for the input/output indexed by pos. + //! + //! For this method inputs are numbered 0..(nbInputs-1) and outputs are numbered nbInputs..(nbInputs+nbOutputs-1). + //! Using this numbering, pos is an index into InOut, where 0 <= pos < nbInputs+nbOutputs. + //! + //! TensorRT invokes this method to ask if the input/output indexed by pos supports the format/datatype specified + //! by inOut[pos].format and inOut[pos].type. The override must return true if that format/datatype at inOut[pos] + //! are supported by the plugin. If support is conditional on other input/output formats/datatypes, the plugin can + //! make its result conditional on the formats/datatypes in inOut[0..pos-1], which will be set to values + //! that the plugin supports. The override must not inspect inOut[pos+1..nbInputs+nbOutputs-1], + //! which will have invalid values. In other words, the decision for pos must be based on inOut[0..pos] only. + //! + //! Some examples: + //! + //! * A definition for a plugin that supports only FP16 NCHW: + //! + //! return inOut.format[pos] == TensorFormat::kLINEAR && inOut.type[pos] == DataType::kHALF; + //! + //! * A definition for a plugin that supports only FP16 NCHW for its two inputs, + //! and FP32 NCHW for its single output: + //! + //! return inOut.format[pos] == TensorFormat::kLINEAR && + //! (inOut.type[pos] == (pos < 2 ? DataType::kHALF : DataType::kFLOAT)); + //! + //! * A definition for a "polymorphic" plugin with two inputs and one output that supports + //! any format or type, but the inputs and output must have the same format and type: + //! + //! return pos == 0 || (inOut.format[pos] == inOut.format[0] && inOut.type[pos] == inOut.type[0]); + //! + //! Warning: TensorRT will stop asking for formats once it finds kFORMAT_COMBINATION_LIMIT on combinations. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin. + //! + virtual bool supportsFormatCombination( + int32_t pos, PluginTensorDesc const* inOut, int32_t nbInputs, int32_t nbOutputs) const noexcept + = 0; + + // @cond SuppressDoxyWarnings + IPluginV2IOExt() = default; + ~IPluginV2IOExt() override = default; +// @endcond + +protected: +// @cond SuppressDoxyWarnings + IPluginV2IOExt(IPluginV2IOExt const&) = default; + IPluginV2IOExt(IPluginV2IOExt&&) = default; + IPluginV2IOExt& operator=(IPluginV2IOExt const&) & = default; + IPluginV2IOExt& operator=(IPluginV2IOExt&&) & = default; +// @endcond + + //! + //! \brief Return the API version with which this plugin was built. The upper byte is reserved by TensorRT and is + //! used to differentiate this from IPluginV2 and IPluginV2Ext. + //! + //! Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with + //! plugins. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, the implementation provided here is safe to call from any thread. + //! + int32_t getTensorRTVersion() const noexcept override + { + return static_cast((static_cast(PluginVersion::kV2_IOEXT) << 24U) + | (static_cast(NV_TENSORRT_VERSION) & 0xFFFFFFU)); + } + +private: + // Following are obsolete base class methods, and must not be implemented or used. + + //! + //! \brief Set plugin configuration. + //! + void configurePlugin(Dims const*, int32_t, Dims const*, int32_t, DataType const*, DataType const*, bool const*, + bool const*, PluginFormat, int32_t) noexcept final + { + } + + //! + //! \brief Check if provided data type is supported. + //! + bool supportsFormat(DataType, PluginFormat) const noexcept final + { + return false; + } +}; + +namespace v_1_0 +{ +class TRT_DEPRECATED IPluginCreator : public IPluginCreatorInterface +{ +public: + //! + //! \brief Return the plugin name. + //! + //! \warning The string returned must be NULL-terminated and have a length of 1024 bytes or less including + //! the NULL terminator. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin or when deserializing + //! multiple engines concurrently sharing plugins. + //! + virtual AsciiChar const* getPluginName() const noexcept = 0; + + //! + //! \brief Return the plugin version. + //! + //! \warning The string returned must be NULL-terminated and have a length of 1024 bytes or less including + //! the NULL terminator. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin or when deserializing + //! multiple engines concurrently sharing plugins. + //! + virtual AsciiChar const* getPluginVersion() const noexcept = 0; + + //! + //! \brief Return a list of fields that need to be passed to createPlugin. + //! + //! \see PluginFieldCollection + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin or when deserializing + //! multiple engines concurrently sharing plugins. + //! + virtual PluginFieldCollection const* getFieldNames() noexcept = 0; + + //! + //! \brief Return a plugin object. Return nullptr in case of error. + //! + //! \param name A NULL-terminated name string of length 1024 or less, including the NULL terminator. + //! \param fc A pointer to a collection of fields needed for constructing the plugin. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin or when deserializing + //! multiple engines concurrently sharing plugins. + //! + virtual IPluginV2* createPlugin(AsciiChar const* name, PluginFieldCollection const* fc) noexcept = 0; + + //! + //! \brief Called during deserialization of plugin layer. Return a plugin object. + //! + //! \param name A NULL-terminated name string of length 1024 or less, including the NULL terminator. + //! \param serialData The start address of a byte array with the serialized plugin representation. + //! \param serialLength The length in bytes of the byte array with the serialized plugin representation. + //! + //! \return A deserialized plugin object + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin or when deserializing + //! multiple engines concurrently sharing plugins. + //! + virtual IPluginV2* deserializePlugin(AsciiChar const* name, void const* serialData, size_t serialLength) noexcept + = 0; + + //! + //! \brief Set the namespace of the plugin creator based on the plugin + //! library it belongs to. This can be set while registering the plugin creator. + //! + //! \param pluginNamespace A NULL-terminated namespace string of length 1024 or less, including the NULL terminator + //! + //! \see IPluginRegistry::registerCreator() + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin or when deserializing + //! multiple engines concurrently sharing plugins. + //! + virtual void setPluginNamespace(AsciiChar const* pluginNamespace) noexcept = 0; + + //! + //! \brief Return the namespace of the plugin creator object. + //! + //! \warning The string returned must be NULL-terminated and have a length of 1024 bytes or less including the + //! NULL terminator. + //! + //! \usage + //! - Allowed context for the API call + //! - Thread-safe: Yes, this method is required to be thread-safe and may be called from multiple threads + //! when building networks on multiple devices sharing the same plugin or when deserializing + //! multiple engines concurrently sharing plugins. + //! + virtual AsciiChar const* getPluginNamespace() const noexcept = 0; + + IPluginCreator() = default; + ~IPluginCreator() override = default; + +protected: + // @cond SuppressDoxyWarnings + IPluginCreator(IPluginCreator const&) = default; + IPluginCreator(IPluginCreator&&) = default; + IPluginCreator& operator=(IPluginCreator const&) & = default; + IPluginCreator& operator=(IPluginCreator&&) & = default; + // @endcond +public: + //! + //! \brief Return version information associated with this interface. Applications must not override this method. + //! + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"PLUGIN CREATOR_V1", 1, 0}; + } +}; +} // namespace v_1_0 + +//! +//! \class IPluginCreator +//! +//! \brief Plugin creator class for user implemented layers. +//! +//! \see IPlugin and IPluginFactory +//! +//! \deprecated Deprecated in TensorRT 10.0. Please implement IPluginCreatorV3One +//! along with IPluginV3 plugins instead. +//! +using IPluginCreator = v_1_0::IPluginCreator; + +} // namespace nvinfer1 + +#endif // NV_INFER_RUNTIME_PLUGIN_H diff --git a/tensorrt/include/NvInferVersion.h b/tensorrt/include/NvInferVersion.h new file mode 100644 index 0000000000000000000000000000000000000000..8465d3ec88804e0b15caa3a804157dcbf94d4e86 --- /dev/null +++ b/tensorrt/include/NvInferVersion.h @@ -0,0 +1,45 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +//! +//! \file NvInferVersion.h +//! +//! Defines the TensorRT version +//! +#ifndef NV_INFER_VERSION_H +#define NV_INFER_VERSION_H + +#define TRT_MAJOR_ENTERPRISE 10 +#define TRT_MINOR_ENTERPRISE 16 +#define TRT_PATCH_ENTERPRISE 1 +#define TRT_BUILD_ENTERPRISE 11 +#define NV_TENSORRT_MAJOR TRT_MAJOR_ENTERPRISE //!< TensorRT major version. +#define NV_TENSORRT_MINOR TRT_MINOR_ENTERPRISE //!< TensorRT minor version. +#define NV_TENSORRT_PATCH TRT_PATCH_ENTERPRISE //!< TensorRT patch version. +#define NV_TENSORRT_BUILD TRT_BUILD_ENTERPRISE //!< TensorRT build number. + +#define NV_TENSORRT_LWS_MAJOR 0 //!< TensorRT LWS major version. +#define NV_TENSORRT_LWS_MINOR 0 //!< TensorRT LWS minor version. +#define NV_TENSORRT_LWS_PATCH 0 //!< TensorRT LWS patch version. + +#define NV_TENSORRT_RELEASE_TYPE_EARLY_ACCESS 0 //!< An early access release +#define NV_TENSORRT_RELEASE_TYPE_RELEASE_CANDIDATE 1 //!< A release candidate +#define NV_TENSORRT_RELEASE_TYPE_GENERAL_AVAILABILITY 2 //!< A final release + +#define NV_TENSORRT_RELEASE_TYPE NV_TENSORRT_RELEASE_TYPE_GENERAL_AVAILABILITY //!< TensorRT release type + +#endif // NV_INFER_VERSION_H diff --git a/tensorrt/include/NvOnnxConfig.h b/tensorrt/include/NvOnnxConfig.h new file mode 100644 index 0000000000000000000000000000000000000000..26d8b93598429a16f1304a8af339c517314ce3fa --- /dev/null +++ b/tensorrt/include/NvOnnxConfig.h @@ -0,0 +1,198 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NV_OnnxConfig_H +#define NV_OnnxConfig_H + +#include "NvInfer.h" + +namespace nvonnxparser +{ + +//! +//! \mainpage +//! +//! This is the API documentation for the Configuration Manager for Open Neural Network Exchange (ONNX) Parser for Nvidia TensorRT Inference Engine. +//! It provides information on individual functions, classes +//! and methods. Use the index on the left to navigate the documentation. +//! +//! Please see the accompanying user guide and samples for higher-level information and general advice on using ONNX Parser and TensorRT. +//! + +//! +//! \file NvOnnxConfig.h +//! +//! This is the API file for the Configuration Manager for ONNX Parser for Nvidia TensorRT. +//! + +//! +//! \class IOnnxConfig +//! \brief Configuration Manager Class. +//! +class IOnnxConfig +{ +public: + virtual ~IOnnxConfig() noexcept = default; + //! + //! \typedef Verbosity + //! + //! \brief Defines Verbosity level. + //! + typedef int32_t Verbosity; + + //! + //! \brief Set the Model Data Type. + //! + //! Sets the Model DataType, one of the following: float -d 32 (default), half precision -d 16, and int8 -d 8 data + //! types. + //! + //! \see getModelDtype() + //! + virtual void setModelDtype(const nvinfer1::DataType) noexcept = 0; + + //! + //! \brief Get the Model Data Type. + //! + //! \return the data type of the model. + //! + //! \see setModelDtype() and DataType + //! + virtual nvinfer1::DataType getModelDtype() const noexcept = 0; + + //! + //! \brief Get the Model FileName. + //! + //! \return Return the Model Filename, as a null-terminated C-style string. + //! + //! \see setModelFileName() + //! + virtual char const* getModelFileName() const noexcept = 0; + + //! + //! \brief Set the Model File Name. + //! + //! The Model File name contains the Network Description in ONNX pb format. + //! + //! This method copies the name string. + //! + //! \param onnxFilename The name. + //! + //! \see getModelFileName() + //! + virtual void setModelFileName(char const* onnxFilename) noexcept = 0; + + //! + //! \brief Get the Verbosity Level. + //! + //! \return The Verbosity Level. + //! + //! \see addVerbosity(), reduceVerbosity() + //! + virtual Verbosity getVerbosityLevel() const noexcept = 0; + + //! + //! \brief Increase the Verbosity Level. + //! + //! \return The Verbosity Level. + //! + //! \see reduceVerbosity(), setVerbosity(Verbosity) + //! + virtual void addVerbosity() noexcept = 0; + + //! + //! \brief Reduce the Verbosity Level. + //! + //! \see addVerbosity(), setVerbosity(Verbosity) + //! + virtual void reduceVerbosity() noexcept = 0; + + //! + //! \brief Set to specific verbosity Level. + //! + //! \see addVerbosity(), reduceVerbosity() + //! + virtual void setVerbosityLevel(Verbosity) noexcept = 0; + + //! + //! \brief Returns the File Name of the Network Description as a Text File. + //! + //! \return Return the name of the file containing the network description converted to a plain text, used for + //! debugging purposes. + //! + //! \see setTextFilename() + //! + virtual char const* getTextFileName() const noexcept = 0; + + //! + //! \brief Set the File Name of the Network Description as a Text File. + //! + //! This API allows setting a file name for the network description in plain text, equivalent of the ONNX protobuf. + //! + //! This method copies the name string. + //! + //! \param textFileName Name of the file. + //! + //! \see getTextFilename() + //! + virtual void setTextFileName(char const* textFileName) noexcept = 0; + + //! + //! \brief Get the File Name of the Network Description as a Text File, including the weights. + //! + //! \return Return the name of the file containing the network description converted to a plain text, used for + //! debugging purposes. + //! + //! \see setFullTextFilename() + //! + virtual char const* getFullTextFileName() const noexcept = 0; + + //! + //! \brief Set the File Name of the Network Description as a Text File, including the weights. + //! + //! This API allows setting a file name for the network description in plain text, equivalent of the ONNX protobuf. + //! + //! This method copies the name string. + //! + //! \param fullTextFileName Name of the file. + //! + //! \see getFullTextFilename() + //! + virtual void setFullTextFileName(char const* fullTextFileName) noexcept = 0; + + //! + //! \brief Get whether the layer information will be printed. + //! + //! \return Returns whether the layer information will be printed. + //! + //! \see setPrintLayerInfo() + //! + virtual bool getPrintLayerInfo() const noexcept = 0; + + //! + //! \brief Set whether the layer information will be printed. + //! + //! \see getPrintLayerInfo() + //! + virtual void setPrintLayerInfo(bool) noexcept = 0; + +}; // class IOnnxConfig + +TENSORRTAPI IOnnxConfig* createONNXConfig(); + +} // namespace nvonnxparser + +#endif diff --git a/tensorrt/include/NvOnnxParser.h b/tensorrt/include/NvOnnxParser.h new file mode 100644 index 0000000000000000000000000000000000000000..435619df81d9e8971163f46319832e46549e4669 --- /dev/null +++ b/tensorrt/include/NvOnnxParser.h @@ -0,0 +1,657 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef NV_ONNX_PARSER_H +#define NV_ONNX_PARSER_H + +#include "NvInfer.h" +#include +#include +#include + +//! +//! \file NvOnnxParser.h +//! +//! This is the API for the ONNX Parser +//! + +#define NV_ONNX_PARSER_MAJOR 0 +#define NV_ONNX_PARSER_MINOR 1 +#define NV_ONNX_PARSER_PATCH 0 + +static constexpr int32_t NV_ONNX_PARSER_VERSION + = ((NV_ONNX_PARSER_MAJOR * 10000) + (NV_ONNX_PARSER_MINOR * 100) + NV_ONNX_PARSER_PATCH); + +//! +//! \typedef SubGraph_t +//! +//! \brief The data structure containing the parsing capability of +//! a set of nodes in an ONNX graph. +//! +typedef std::pair, bool> SubGraph_t; + +//! +//! \typedef SubGraphCollection_t +//! +//! \brief The data structure containing all SubGraph_t partitioned +//! out of an ONNX graph. +//! +typedef std::vector SubGraphCollection_t; + +//! +//! \namespace nvonnxparser +//! +//! \brief The TensorRT ONNX parser API namespace +//! +namespace nvonnxparser +{ + +//! \return the numerical value of the highest-valued enumerator for type T. +//! It must be specialized for each enum type that uses it. +//! {$nv-internal-release begin} +//! \note This is different from nvinfer1::EnumMax(), which is one more than that. +//! {$nv-internal-release end} +template +constexpr int32_t EnumMax() noexcept = delete; + +//! +//! \enum ErrorCode +//! +//! \brief The type of error that the parser or refitter may return +//! +enum class ErrorCode : int +{ + kSUCCESS = 0, + kINTERNAL_ERROR = 1, + kMEM_ALLOC_FAILED = 2, + kMODEL_DESERIALIZE_FAILED = 3, + kINVALID_VALUE = 4, + kINVALID_GRAPH = 5, + kINVALID_NODE = 6, + kUNSUPPORTED_GRAPH = 7, + kUNSUPPORTED_NODE = 8, + kUNSUPPORTED_NODE_ATTR = 9, + kUNSUPPORTED_NODE_INPUT = 10, + kUNSUPPORTED_NODE_DATATYPE = 11, + kUNSUPPORTED_NODE_DYNAMIC = 12, + kUNSUPPORTED_NODE_SHAPE = 13, + kREFIT_FAILED = 14 +}; + +//! Specialization. See `nvonnxparser::EnumMax()` for details. +template <> +constexpr int32_t EnumMax() noexcept +{ + return 14; +} + +//! +//! \brief Represents one or more OnnxParserFlag values using binary OR +//! operations, e.g., 1U << OnnxParserFlag::kNATIVE_INSTANCENORM +//! +//! \see IParser::setFlags() and IParser::getFlags() +//! +using OnnxParserFlags + = uint32_t; + +enum class OnnxParserFlag : int32_t +{ + //! Parse the ONNX model into the INetworkDefinition with the intention of using TensorRT's native layer + //! implementation over the plugin implementation for InstanceNormalization nodes. + //! This flag is required when building version-compatible or hardware-compatible engines. + //! This flag is set to be ON by default. + kNATIVE_INSTANCENORM = 0, + //! Enable UINT8 as a quantization data type and asymmetric quantization with non-zero zero-point values + //! in Quantize and Dequantize nodes. This flag is set to be OFF by default. + //! The resulting engine must be built targeting DLA version >= 3.16. + kENABLE_UINT8_AND_ASYMMETRIC_QUANTIZATION_DLA = 1, + //! Parse the ONNX model with per-node validation for DLA. If the model is not fully supported by DLA, then + //! parsing will fail. If this flag is set, isSubGraphSupported() will also return capability in the context of DLA + //! support. When this flag is set, a valid IBuilderConfig must be provided to the parser via setBuilderConfig(). + // This flag is set to be OFF by default. + kREPORT_CAPABILITY_DLA = 2, + //! Allow a loaded plugin with the same name as an ONNX operator type to override the default ONNX implementation, + //! even if the plugin namespace attribute is not set. + //! Useful for custom plugins that replace standard ONNX operators, such as alternative implementations for better + //! performance. This flag is set to be OFF by default. + kENABLE_PLUGIN_OVERRIDE = 3, + //! Opportunistically rewrite or modify layers to make them more amenable to running on DLA. + kADJUST_FOR_DLA = 4 +}; + +//! Specialization. See `nvonnxparser::EnumMax()` for details. +template <> +constexpr int32_t EnumMax() noexcept +{ + return 3; +} + +//! +//! \class IParserError +//! +//! \brief an object containing information about an error +//! +class IParserError +{ +public: + //! + //!\brief the error code. + //! + virtual ErrorCode code() const = 0; + //! + //!\brief description of the error. + //! + virtual char const* desc() const = 0; + //! + //!\brief source file in which the error occurred. + //! + virtual char const* file() const = 0; + //! + //!\brief source line at which the error occurred. + //! + virtual int line() const = 0; + //! + //!\brief source function in which the error occurred. + //! + virtual char const* func() const = 0; + //! + //!\brief index of the ONNX model node in which the error occurred. + //! + virtual int node() const = 0; + //! + //!\brief name of the node in which the error occurred. + //! + virtual char const* nodeName() const = 0; + //! + //!\brief name of the node operation in which the error occurred. + //! + virtual char const* nodeOperator() const = 0; + //! + //!\brief A list of the local function names, from the top level down, constituting the current + //! stack trace in which the error occurred. A top-level node that is not inside any + //! local function would return a nullptr. + //! + virtual char const* const* localFunctionStack() const = 0; + //! + //!\brief The size of the stack of local functions at the point where the error occurred. + //! A top-level node that is not inside any local function would correspond to + // a stack size of 0. + //! + virtual int32_t localFunctionStackSize() const = 0; + +protected: + virtual ~IParserError() {} +}; + +//! +//! \class IParser +//! +//! \brief an object for parsing ONNX models into a TensorRT network definition +//! +//! \warning If the ONNX model has a graph output with the same name as a graph input, +//! the output will be renamed by prepending "__". +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IParser +{ +public: + //! + //! \brief Parse a serialized ONNX model into the TensorRT network. + //! This method has very limited diagnostics. If parsing the serialized model + //! fails for any reason (e.g. unsupported IR version, unsupported opset, etc.) + //! it the user responsibility to intercept and report the error. + //! To obtain a better diagnostic, use the parseFromFile method below. + //! + //! \param serialized_onnx_model Pointer to the serialized ONNX model. Can be freed after this function returns. + //! \param serialized_onnx_model_size Size of the serialized ONNX model + //! in bytes + //! \param model_path Absolute path to the model file for loading external weights if required + //! \return true if the model was parsed successfully + //! \see getNbErrors() getError() + //! + virtual bool parse( + void const* serialized_onnx_model, size_t serialized_onnx_model_size, const char* model_path = nullptr) noexcept + = 0; + + //! + //! \brief Parse an onnx model file, which can be a binary protobuf or a text onnx model + //! calls parse method inside. + //! + //! \param onnxModelFile name + //! \param verbosity Level + //! + //! \return true if the model was parsed successfully + //! + //! + virtual bool parseFromFile(const char* onnxModelFile, int verbosity) noexcept = 0; + + //! + //! [DEPRECATED] Deprecated in TensorRT 10.1. See supportsModelV2. + //! + //! \brief Check whether TensorRT supports a particular ONNX model. + //! If the function returns True, one can proceed to engine building + //! without having to call \p parse or \p parseFromFile. + //! + //! \param serialized_onnx_model Pointer to the serialized ONNX model. Can be freed after this function returns. + //! \param serialized_onnx_model_size Size of the serialized ONNX model + //! in bytes + //! \param sub_graph_collection Container to hold supported subgraphs + //! \param model_path Absolute path to the model file for loading external weights if required + //! \return true if the model is supported + //! + TRT_DEPRECATED virtual bool supportsModel(void const* serialized_onnx_model, size_t serialized_onnx_model_size, + SubGraphCollection_t& sub_graph_collection, const char* model_path = nullptr) noexcept = 0; + + //! + //! [DEPRECATED] Deprecated in TensorRT 10.13. See loadInitializer(). + //! + //!\brief Parse a serialized ONNX model into the TensorRT network + //! with consideration of user provided weights + //! + //! \param serialized_onnx_model Pointer to the serialized ONNX model. Can be freed after this function returns. + //! \param serialized_onnx_model_size Size of the serialized ONNX model + //! in bytes + //! \return true if the model was parsed successfully + //! \see getNbErrors() getError() + //! + TRT_DEPRECATED virtual bool parseWithWeightDescriptors( + void const* serialized_onnx_model, size_t serialized_onnx_model_size) noexcept = 0; + + //! + //!\brief Returns whether the specified operator may be supported by the + //! parser. + //! + //! Note that a result of true does not guarantee that the operator will be + //! supported in all cases (i.e., this function may return false-positives). + //! + //! \param op_name The name of the ONNX operator to check for support + //! + virtual bool supportsOperator(const char* op_name) const noexcept = 0; + + //! + //!\brief Get the number of errors that occurred during prior calls to + //! \p parse + //! + //! \see getError() clearErrors() IParserError + //! + virtual int getNbErrors() const noexcept = 0; + + //! + //!\brief Get an error that occurred during prior calls to \p parse + //! + //! \see getNbErrors() clearErrors() IParserError + //! + virtual IParserError const* getError(int index) const noexcept = 0; + + //! + //!\brief Clear errors from prior calls to \p parse + //! + //! \see getNbErrors() getError() IParserError + //! + virtual void clearErrors() noexcept = 0; + + virtual ~IParser() noexcept = default; + + //! + //! \brief Query the plugin libraries needed to implement operations used by the parser in a version-compatible + //! engine. + //! + //! This provides a list of plugin libraries on the filesystem needed to implement operations + //! in the parsed network. If you are building a version-compatible engine using this network, + //! provide this list to IBuilderConfig::setPluginsToSerialize to serialize these plugins along + //! with the version-compatible engine, or, if you want to ship these plugin libraries externally + //! to the engine, ensure that IPluginRegistry::loadLibrary is used to load these libraries in the + //! appropriate runtime before deserializing the corresponding engine. + //! + //! \param[out] nbPluginLibs Returns the number of plugin libraries in the array, or -1 if there was an error. + //! \return Array of `nbPluginLibs` C-strings describing plugin library paths on the filesystem if nbPluginLibs > 0, + //! or nullptr otherwise. This array is owned by the IParser, and the pointers in the array are only valid until + //! the next call to parse(), supportsModel(), parseFromFile(), or parseWithWeightDescriptors(). + //! + virtual char const* const* getUsedVCPluginLibraries(int64_t& nbPluginLibs) const noexcept = 0; + + //! + //! \brief Set the parser flags. + //! + //! The flags are listed in the OnnxParserFlag enum. + //! + //! \param OnnxParserFlags The flags used when parsing an ONNX model. + //! + //! \note This function will override the previous set flags, rather than bitwise ORing the new flag. + //! + //! \see getFlags() + //! + virtual void setFlags(OnnxParserFlags onnxParserFlags) noexcept = 0; + + //! + //! \brief Get the parser flags. Defaults to 0. + //! + //! \return The parser flags as a bitmask. + //! + //! \see setFlags() + //! + virtual OnnxParserFlags getFlags() const noexcept = 0; + + //! + //! \brief clear a parser flag. + //! + //! clears the parser flag from the enabled flags. + //! + //! \see setFlags() + //! + virtual void clearFlag(OnnxParserFlag onnxParserFlag) noexcept = 0; + + //! + //! \brief Set a single parser flag. + //! + //! Add the input parser flag to the already enabled flags. + //! + //! \see setFlags() + //! + virtual void setFlag(OnnxParserFlag onnxParserFlag) noexcept = 0; + + //! + //! \brief Returns true if the parser flag is set + //! + //! \see getFlags() + //! + //! \return True if flag is set, false if unset. + //! + virtual bool getFlag(OnnxParserFlag onnxParserFlag) const noexcept = 0; + + //! + //!\brief Return the i-th output ITensor object for the ONNX layer "name". + //! + //! Return the i-th output ITensor object for the ONNX layer "name". + //! If "name" is not found or i is out of range, return nullptr. + //! In the case of multiple nodes sharing the same name this function will return + //! the output tensors of the first instance of the node in the ONNX graph. + //! + //! \param name The name of the ONNX layer. + //! + //! \param i The index of the output. i must be in range [0, layer.num_outputs). + //! + virtual nvinfer1::ITensor const* getLayerOutputTensor(char const* name, int64_t i) noexcept = 0; + + //! + //! \brief Check whether TensorRT supports a particular ONNX model. + //! If the function returns True, one can proceed to engine building + //! without having to call \p parse or \p parseFromFile. + //! Results can be queried through \p getNbSubgraphs, \p isSubgraphSupported, + //! \p getSubgraphNodes. + //! + //! \param serializedOnnxModel Pointer to the serialized ONNX model. Can be freed after this function returns. + //! \param serializedOnnxModelSize Size of the serialized ONNX model in bytes + //! \param modelPath Absolute path to the model file for loading external weights if required + //! \return true if the model is supported + //! + virtual bool supportsModelV2( + void const* serializedOnnxModel, size_t serializedOnnxModelSize, char const* modelPath = nullptr) noexcept = 0; + + //! + //! \brief Get the number of subgraphs. Calling this function before calling \p supportsModelV2 results in undefined + //! behavior. + //! + //! + //! \return Number of subgraphs. + //! + virtual int64_t getNbSubgraphs() noexcept = 0; + + //! + //! \brief Returns whether the subgraph is supported. Calling this function before calling \p supportsModelV2 + //! results in undefined behavior. + //! + //! + //! \param index Index of the subgraph. + //! \return Whether the subgraph is supported. + //! + virtual bool isSubgraphSupported(int64_t const index) noexcept = 0; + + //! + //! \brief Get the nodes of the specified subgraph. Calling this function before calling \p supportsModelV2 results + //! in undefined behavior. + //! + //! + //! \param index Index of the subgraph. + //! \param subgraphLength Returns the length of the subgraph as reference. + //! + //! \return Pointer to the subgraph nodes array. This pointer is owned by the Parser. + //! + virtual int64_t* getSubgraphNodes(int64_t const index, int64_t& subgraphLength) noexcept = 0; + + //! + //! \brief Load a serialized ONNX model into the parser. Unlike the parse(), parseFromFile(), or + //! parseWithWeightDescriptors() functions, this function does not immediately convert the model into a TensorRT + //! INetworkDefinition. Using this function allows users to provide their own initializers for the ONNX model + //! through the loadInitializer() function. + //! + //! Only one model can be loaded at a time. Subsequent calls to loadModelProto() will result in an error. + //! + //! To begin the conversion of the model into a TensorRT INetworkDefinition, use parseModelProto(). + //! + //! \param serializedOnnxModel Pointer to the serialized ONNX model. Can be freed after this function returns. + //! \param serializedOnnxModelSize Size of the serialized ONNX model in bytes. + //! \param modelPath Absolute path to the model file for loading external weights if required. + //! \return true if the model was loaded successfully + //! \see getNbErrors() getError() + //! + virtual bool loadModelProto( + void const* serializedOnnxModel, size_t serializedOnnxModelSize, char const* modelPath = nullptr) noexcept = 0; + + //! + //! \brief Prompt the ONNX parser to load an initializer with user-provided binary data. + //! The lifetime of the data must exceed the lifetime of the parser. + //! + //! All user-provided initializers must be provided prior to calling refitModelProto(). + //! + //! This function can be called multiple times to specify the names of multiple initializers. + //! + //! Calling this function with an initializer previously specified will overwrite the previous instance. + //! + //! + //! This function will return false if initializer validation fails. Possible validation errors are: + //! * This function was called prior to loadModelProto(). + //! * The requested initializer was not found in the model. + //! * The size of the data provided is different from the corresponding initializer in the model. + //! + //! \param name Name of the initializer. + //! \param data Binary data containing the values of the initializer. + //! \param size Size of the initializer in bytes. + //! \return true if the initializer was loaded successfully + //! \see loadModelProto() + //! + virtual bool loadInitializer(char const* name, void const* data, size_t size) noexcept = 0; + + //! \brief Begin the parsing and conversion process of the loaded ONNX model into a TensorRT INetworkDefinition. + //! + //! \return true if conversion was successful + //! \see getNbErrors() getError() loadModelProto() loadModelProtoFromFile() + //! + virtual bool parseModelProto() noexcept = 0; + + //! + //! \brief Set the BuilderConfig for the parser. + //! + //! \return true if the IBuilderConfig was set successfully, false otherwise. + //! + virtual bool setBuilderConfig(const nvinfer1::IBuilderConfig* const builderConfig) noexcept = 0; +}; + +//! +//! \class IParserRefitter +//! +//! \brief An interface designed to refit weights from an ONNX model. +//! +//! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. +//! +class IParserRefitter +{ +public: + //! + //! \brief Load a serialized ONNX model from memory and perform weight refit. + //! + //! \param serializedOnnxModel Pointer to the serialized ONNX model + //! \param serializedOnnxModelSize Size of the serialized ONNX model + //! in bytes + //! \param modelPath Absolute path to the model file for loading external weights if required + //! \return true if all the weights in the engine were refit successfully. + //! + //! The serialized ONNX model must be identical to the one used to generate the engine + //! that will be refit. + //! + virtual bool refitFromBytes( + void const* serializedOnnxModel, size_t serializedOnnxModelSize, char const* modelPath = nullptr) noexcept + = 0; + + //! + //! \brief Load and parse a ONNX model from disk and perform weight refit. + //! + //! \param onnxModelFile Path to the ONNX model to load from disk. + //! + //! \return true if the model was loaded successfully, and if all the weights in the engine were refit successfully. + //! + //! The provided ONNX model must be identical to the one used to generate the engine + //! that will be refit. + //! + virtual bool refitFromFile(char const* onnxModelFile) noexcept = 0; + + //! + //!\brief Get the number of errors that occurred during prior calls to \p refitFromBytes or \p refitFromFile + //! + //! \see getError() IParserError + //! + virtual int32_t getNbErrors() const noexcept = 0; + + //! + //!\brief Get an error that occurred during prior calls to \p refitFromBytes or \p refitFromFile + //! + //! \see getNbErrors() IParserError + //! + virtual IParserError const* getError(int32_t index) const noexcept = 0; + + //! + //!\brief Clear errors from prior calls to \p refitFromBytes or \p refitFromFile + //! + //! \see getNbErrors() getError() IParserError + //! + virtual void clearErrors() = 0; + + virtual ~IParserRefitter() noexcept = default; + + //! + //! \brief Load a serialized ONNX model into the parser. Unlike the refit(), or refitFromFile() + //! functions, this function does not immediately begin the refit process. Using this function + //! allows users to provide their own initializers for the ONNX model through the loadInitializer() function. + //! + //! Only one model can be loaded at a time. Subsequent calls to loadModelProto() will result in an error. + //! + //! To begin the refit process, use refitModelProto(). + //! + //! \param serializedOnnxModel Pointer to the serialized ONNX model. Can be freed after this function returns. + //! \param serializedOnnxModelSize Size of the serialized ONNX model in bytes. + //! \param modelPath Absolute path to the model file for loading external weights if required. + //! \return true if the model was loaded successfully + //! \see getNbErrors() getError() + //! + virtual bool loadModelProto( + void const* serializedOnnxModel, size_t serializedOnnxModelSize, char const* modelPath = nullptr) noexcept = 0; + + //! + //! \brief Prompt the ONNX refitter to load an initializer with user-provided binary data. + //! The lifetime of the data must exceed the lifetime of the refitter. + //! + //! All user-provided initializers must be provided prior to calling refitModelProto(). + //! + //! This function can be called multiple times to specify the names of multiple initializers. + //! + //! Calling this function with an initializer previously specified will overwrite the previous instance. + //! + //! This function will return false if initializer validation fails. Possible validation errors are: + //! * This function was called prior to loadModelProto() + //! * The requested initializer was not found in the model. + //! * The size of the data provided is different from the corresponding initializer in the model. + //! + //! \param name Name of the initializer. + //! \param data Binary data containing the values of the initializer. + //! \param size Size of the initializer in bytes. + //! \return true if the initializer was loaded successfully + //! \see loadModelProto() + //! + virtual bool loadInitializer(char const* name, void const* data, size_t size) noexcept = 0; + + //! \brief Begin the refit process from the loaded ONNX model. + //! + //! \return true if refit was successful + //! \see getNbErrors() getError() loadModelProto() + //! + virtual bool refitModelProto() noexcept = 0; +}; + +} // namespace nvonnxparser + +extern "C" TENSORRTAPI void* createNvOnnxParser_INTERNAL(void* network, void* logger, int version) noexcept; +extern "C" TENSORRTAPI void* createNvOnnxParserRefitter_INTERNAL( + void* refitter, void* logger, int32_t version) noexcept; +extern "C" TENSORRTAPI int getNvOnnxParserVersion() noexcept; + +namespace nvonnxparser +{ + +namespace +{ + +//! +//! \brief Create a new parser object +//! +//! \param network The network definition that the parser will write to +//! \param logger The logger to use +//! \return a new parser object or NULL if an error occurred +//! +//! Any input dimensions that are constant should not be changed after parsing, +//! because correctness of the translation may rely on those constants. +//! Changing a dynamic input dimension, i.e. one that translates to -1 in +//! TensorRT, to a constant is okay if the constant is consistent with the model. +//! Each instance of the parser is designed to only parse one ONNX model once. +//! +//! \see IParser +//! +inline IParser* createParser(nvinfer1::INetworkDefinition& network, nvinfer1::ILogger& logger) noexcept +{ + return static_cast(createNvOnnxParser_INTERNAL(&network, &logger, NV_ONNX_PARSER_VERSION)); +} + +//! +//! \brief Create a new ONNX refitter object +//! +//! \param refitter The Refitter object used to refit the model +//! \param logger The logger to use +//! \return a new ParserRefitter object or NULL if an error occurred +//! +//! \see IParserRefitter +//! +inline IParserRefitter* createParserRefitter(nvinfer1::IRefitter& refitter, nvinfer1::ILogger& logger) noexcept +{ + return static_cast( + createNvOnnxParserRefitter_INTERNAL(&refitter, &logger, NV_ONNX_PARSER_VERSION)); +} + +} // namespace + +} // namespace nvonnxparser + +#endif // NV_ONNX_PARSER_H diff --git a/tensorrt/include/impl/NvInferPythonPlugin.h b/tensorrt/include/impl/NvInferPythonPlugin.h new file mode 100644 index 0000000000000000000000000000000000000000..3d0842ee3267fa07e6a88e042a2f3065fabebb65 --- /dev/null +++ b/tensorrt/include/impl/NvInferPythonPlugin.h @@ -0,0 +1,594 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TRT_PYTHON_IMPL_PLUGIN_H +#define TRT_PYTHON_IMPL_PLUGIN_H + +#include "NvInfer.h" + +//! +//! \file NvInferPythonPlugin.h +//! +//! This file contains definitions for supporting the `tensorrt.plugin` Python module +//! +//! \warning None of the defintions here are part of the TensorRT C++ API and may not follow semantic versioning rules. +//! TensorRT clients must not utilize them directly. +//! + +namespace nvinfer1 +{ + +//! \enum PluginArgType +//! \brief Numeric type of an extra kernel input argument in an AOT Python plugin +enum class PluginArgType : int32_t +{ + //! Integer argument + kINT = 0, +}; + +//! \enum PluginArgDataType +//! \brief Data type of an extra kernel input argument in an AOT Python plugin +enum class PluginArgDataType : int32_t +{ + //! 8-bit signed integer + kINT8 = 0, + //! 16-bit signed integer + kINT16 = 1, + //! 32-bit signed integer + kINT32 = 2, +}; +//! \class ISymExpr +//! \brief Generic interface for a scalar symbolic expression implementable by a Python plugin / TensorRT Python backend +class ISymExpr +{ +public: + //! \brief Get the type of the symbolic expression + virtual PluginArgType getType() const noexcept = 0; + //! \brief Get the data type of the symbolic expression + virtual PluginArgDataType getDataType() const noexcept = 0; + //! \brief Underlying symbolic expression + virtual void* getExpr() noexcept = 0; +}; + +//! Impl class for ISymExprs +class ISymExprsImpl +{ +public: + virtual ISymExpr* getSymExpr(int32_t index) const noexcept = 0; + virtual bool setSymExpr(int32_t index, ISymExpr* symExpr) noexcept = 0; + virtual int32_t getNbSymExprs() const noexcept = 0; + virtual bool setNbSymExprs(int32_t count) noexcept = 0; + + virtual ~ISymExprsImpl() noexcept = default; +}; + +//! \class ISymExprs +//! \brief Allows for a sequence of symbolic expressions to be communicated to the TensorRT backend +//! \note Clients must not implement this class. +//! \see ISymExpr +class ISymExprs +{ +public: + //! \brief Get the symbolic expression at the given index + //! \return A pointer to the symbolic expression or nullptr if the index is out of range + ISymExpr* getSymExpr(int32_t index) const noexcept + { + return mImpl->getSymExpr(index); + } + + //! \brief Set the symbolic expression at the given index + //! \return true if the index is in range and the symbolic expression was set successfully, false otherwise + bool setSymExpr(int32_t index, ISymExpr* symExpr) noexcept + { + return mImpl->setSymExpr(index, symExpr); + } + + //! \brief Get the number of symbolic expressions + int32_t getNbSymExprs() const noexcept + { + return mImpl->getNbSymExprs(); + } + + //! \brief Set the number of symbolic expressions + //! \return true if the number of symbolic expressions was set successfully, false otherwise + bool setNbSymExprs(int32_t count) noexcept + { + return mImpl->setNbSymExprs(count); + } + +protected: + ISymExprsImpl* mImpl{nullptr}; + virtual ~ISymExprs() noexcept = default; +}; + +//! \enum QuickPluginCreationRequest +//! \brief Communicates preference when a quickly deployable plugin is to be added to the network +enum class QuickPluginCreationRequest : int32_t +{ + //! No preference specified + kUNKNOWN = 0, + //! JIT plugin is preferred + kPREFER_JIT = 1, + //! AOT plugin is preferred + kPREFER_AOT = 2, + //! JIT plugin must be used. TensorRT should fail if a JIT implementation cannot be found. + kSTRICT_JIT = 3, + //! AOT plugin must be used. TensorRT should fail if an AOT implementation cannot be found. + kSTRICT_AOT = 4, +}; + +//! Impl class for IKernelLaunchParams +class IKernelLaunchParamsImpl +{ +public: + virtual ISymExpr* getGridX() noexcept = 0; + virtual bool setGridX(ISymExpr* gridX) noexcept = 0; + + virtual ISymExpr* getGridY() noexcept = 0; + virtual bool setGridY(ISymExpr* gridY) noexcept = 0; + + virtual ISymExpr* getGridZ() noexcept = 0; + virtual bool setGridZ(ISymExpr* gridZ) noexcept = 0; + + virtual ISymExpr* getBlockX() noexcept = 0; + virtual bool setBlockX(ISymExpr* blockX) noexcept = 0; + + virtual ISymExpr* getBlockY() noexcept = 0; + virtual bool setBlockY(ISymExpr* blockY) noexcept = 0; + + virtual ISymExpr* getBlockZ() noexcept = 0; + virtual bool setBlockZ(ISymExpr* blockZ) noexcept = 0; + + virtual ISymExpr* getSharedMem() noexcept = 0; + virtual bool setSharedMem(ISymExpr* sharedMem) noexcept = 0; + + virtual ~IKernelLaunchParamsImpl() noexcept = default; +}; + +//! \class IKernelLaunchParams +//! \brief Allows for kernel launch parameters to be communicated to the TensorRT backend +//! \note Clients must not implement this class. +class IKernelLaunchParams +{ +public: + //! Get the X dimension of the grid + ISymExpr* getGridX() noexcept + { + return mImpl->getGridX(); + } + + //! \brief Set the X dimension of the grid + //! \return true if the grid's X dimension was set successfully, false otherwise + bool setGridX(ISymExpr* gridX) noexcept + { + return mImpl->setGridX(gridX); + } + + //! Get the Y dimension of the grid + ISymExpr* getGridY() noexcept + { + return mImpl->getGridY(); + } + + //! \brief Set the Y dimension of the grid + //! \return true if the grid's Y dimension was set successfully, false otherwise + bool setGridY(ISymExpr* gridY) noexcept + { + return mImpl->setGridY(gridY); + } + + //! Get the Z dimension of the grid + ISymExpr* getGridZ() noexcept + { + return mImpl->getGridZ(); + } + + //! \brief Set the Z dimension of the grid + //! \return true if the grid's Z dimension was set successfully, false otherwise + bool setGridZ(ISymExpr* gridZ) noexcept + { + return mImpl->setGridZ(gridZ); + } + + //! \brief Get the X dimension of each thread block + ISymExpr* getBlockX() noexcept + { + return mImpl->getBlockX(); + } + + //! \brief Set the X dimension of each thread block + //! \return true if each thread block's X dimension was set successfully, false otherwise + bool setBlockX(ISymExpr* blockX) noexcept + { + return mImpl->setBlockX(blockX); + } + + //! \brief Get the Y dimension of each thread block + ISymExpr* getBlockY() noexcept + { + return mImpl->getBlockY(); + } + + //! \brief Set the Y dimension of each thread block + //! \return true if each thread block's Y dimension was set successfully, false otherwise + bool setBlockY(ISymExpr* blockY) noexcept + { + return mImpl->setBlockY(blockY); + } + + //! \brief Get the Z dimension of each thread block + ISymExpr* getBlockZ() noexcept + { + return mImpl->getBlockZ(); + } + + //! \brief Set the Z dimension of each thread block + //! \return true if each thread block's Z dimension was set successfully, false otherwise + bool setBlockZ(ISymExpr* blockZ) noexcept + { + return mImpl->setBlockZ(blockZ); + } + + //! \brief Get the dynamic shared-memory per thread block in bytes + ISymExpr* getSharedMem() noexcept + { + return mImpl->getSharedMem(); + } + + //! \brief Set the dynamic shared-memory per thread block in bytes + //! \return true if the dynamic shared-memory per thread block was set successfully, false otherwise + bool setSharedMem(ISymExpr* sharedMem) noexcept + { + return mImpl->setSharedMem(sharedMem); + } + +protected: + IKernelLaunchParamsImpl* mImpl{nullptr}; + virtual ~IKernelLaunchParams() noexcept = default; +}; + +namespace v_1_0 +{ + +class IPluginV3QuickCore : public IPluginCapability +{ +public: + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"PLUGIN_V3QUICK_CORE", 1, 0}; + } + + virtual AsciiChar const* getPluginName() const noexcept = 0; + + virtual AsciiChar const* getPluginVersion() const noexcept = 0; + + virtual AsciiChar const* getPluginNamespace() const noexcept = 0; +}; + +class IPluginV3QuickBuild : public IPluginCapability +{ +public: + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"PLUGIN_V3QUICK_BUILD", 1, 0}; + } + + //! + //! \brief Provide the data types of the plugin outputs if the input tensors have the data types provided. + //! + //! \param outputTypes Pre-allocated array to which the output data types should be written. + //! \param nbOutputs The number of output tensors. This matches the value returned from getNbOutputs(). + //! \param inputTypes The input data types. + //! \param inputRanks Ranks of the input tensors + //! \param nbInputs The number of input tensors. + //! + //! \return 0 for success, else non-zero + //! + virtual int32_t getOutputDataTypes(DataType* outputTypes, int32_t nbOutputs, DataType const* inputTypes, + int32_t const* inputRanks, int32_t nbInputs) const noexcept = 0; + + //! + //! \brief Provide expressions for computing dimensions of the output tensors from dimensions of the input tensors. + //! + //! \param inputs Expressions for dimensions of the input tensors + //! \param nbInputs The number of input tensors + //! \param shapeInputs Expressions for values of the shape tensor inputs + //! \param nbShapeInputs The number of shape tensor inputs + //! \param outputs Pre-allocated array to which the output dimensions must be written + //! \param exprBuilder Object for generating new dimension expressions + //! + //! \return 0 for success, else non-zero + //! + virtual int32_t getOutputShapes(DimsExprs const* inputs, int32_t nbInputs, DimsExprs const* shapeInputs, + int32_t nbShapeInputs, DimsExprs* outputs, int32_t nbOutputs, IExprBuilder& exprBuilder) noexcept = 0; + + //! + //! \brief Configure the plugin. Behaves similarly to `IPluginV3OneBuild::configurePlugin()` + //! + //! \return 0 for success, else non-zero + //! + virtual int32_t configurePlugin(DynamicPluginTensorDesc const* in, int32_t nbInputs, + DynamicPluginTensorDesc const* out, int32_t nbOutputs) noexcept = 0; + + //! + //! \brief Get number of format combinations supported by the plugin for the I/O characteristics indicated by + //! `inOut`. + //! + virtual int32_t getNbSupportedFormatCombinations( + DynamicPluginTensorDesc const* inOut, int32_t nbInputs, int32_t nbOutputs) noexcept = 0; + + //! + //! \brief Write all format combinations supported by the plugin for the I/O characteristics indicated by `inOut` to + //! `supportedCombinations`. It is guaranteed to have sufficient memory allocated for (nbInputs + nbOutputs) * + //! getNbSupportedFormatCombinations() `PluginTensorDesc`s. + //! + //! \return 0 for success, else non-zero + //! + virtual int32_t getSupportedFormatCombinations(DynamicPluginTensorDesc const* inOut, int32_t nbInputs, + int32_t nbOutputs, PluginTensorDesc* supportedCombinations, int32_t nbFormatCombinations) noexcept = 0; + + //! + //! \brief Get the number of outputs from the plugin. + //! + virtual int32_t getNbOutputs() const noexcept = 0; + + //! + //! \brief Communicates to TensorRT that the output at the specified output index is aliased to the input at the + //! returned index. Behaves similary to `v_2_0::IPluginV3OneBuild.getAliasedInput()`. + //! + virtual int32_t getAliasedInput(int32_t outputIndex) noexcept + { + return -1; + } + + //! + //! \brief Query for any custom tactics that the plugin intends to use specific to the I/O characteristics indicated + //! by the immediately preceding call to `configurePlugin()`. + //! + //! \return 0 for success, else non-zero + //! + virtual int32_t getValidTactics(int32_t* tactics, int32_t nbTactics) noexcept + { + return 0; + } + + //! + //! \brief Query for number of custom tactics related to the `getValidTactics()` call. + //! + virtual int32_t getNbTactics() noexcept + { + return 0; + } + + //! + //! \brief Called to query the suffix to use for the timing cache ID. May be called anytime after plugin creation. + //! + virtual char const* getTimingCacheID() noexcept + { + return nullptr; + } + + //! + //! \brief Query for a string representing the configuration of the plugin. May be called anytime after + //! plugin creation. + //! + virtual char const* getMetadataString() noexcept + { + return nullptr; + } +}; + +class IPluginV3QuickAOTBuild : public IPluginV3QuickBuild +{ +public: + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"PLUGIN_V3QUICKAOT_BUILD", 1, 0}; + } + + //! \brief Get the launch parameters for the kernel to be used for the specified input and output types/formats and + //! any corresponding custom tactics. + //! If custom tactics are being advertised by the plugin, the corresponding tactic is the one specified by + //! the immediately preceding call to setTactic(). + //! + //! \param inputs Expressions for dimensions of the input tensors + //! \param inOut The input and output tensors' attributes + //! \param nbInputs The number of input tensors + //! \param nbOutputs The number of output tensors + //! \param launchParams Interface which allows the specification of kernel launch parameters as symbolic expressions + //! of the input dimensions + //! \param extraArgs Interface which allows the specification of any scalar arguments to be + //! passed to the kernel, as symbolic expressions of the input dimensions + //! \param exprBuilder Object for generating new symbolic expressions + //! + //! \return 0 for success, else non-zero + //! + virtual int32_t getLaunchParams(DimsExprs const* inputs, DynamicPluginTensorDesc const* inOut, int32_t nbInputs, + int32_t nbOutputs, IKernelLaunchParams* launchParams, ISymExprs* extraArgs, + IExprBuilder& exprBuilder) noexcept = 0; + + //! + //! \brief Get the compiled form for the kernel to be used for the specified input and output types/formats and any + //! corresponding custom tactics. + //! If custom tactics are being advertised by the plugin, the corresponding tactic is the one specified by + //! the immediately preceding call to setTactic(). + //! + //! \param in The input tensors' attributes that are used for configuration. + //! \param nbInputs Number of input tensors. + //! \param out The output tensors' attributes that are used for configuration. + //! \param nbOutputs Number of output tensors. + //! \param kernelName The name for the kernel. + //! \param compiledKernel Compiled form of the kernel. + //! \param compiledKernelSize The size of the compiled kernel. + //! + //! \return 0 for success, else non-zero + //! + virtual int32_t getKernel(PluginTensorDesc const* in, int32_t nbInputs, PluginTensorDesc const* out, + int32_t nbOutputs, const char** kernelName, char** compiledKernel, int32_t* compiledKernelSize) noexcept = 0; + + //! + //! \brief Set the tactic to be used in the subsequent call to enqueue(). Behaves similar to + //! IPluginV3OneRuntime::setTactic() + //! + //! \return 0 for success, else non-zero + //! + virtual int32_t setTactic(int32_t tactic) noexcept + { + return 0; + } +}; + +class IPluginV3QuickRuntime : public IPluginCapability +{ +public: + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"PLUGIN_V3QUICK_RUNTIME", 1, 0}; + } + + //! + //! \brief Set the tactic to be used in the subsequent call to enqueue(). Behaves similar to + //! `IPluginV3OneRuntime::setTactic()`. + //! + //! \return 0 for success, else non-zero + //! + virtual int32_t setTactic(int32_t tactic) noexcept + { + return 0; + } + + //! + //! \brief Execute the plugin. + //! + //! \param inputDesc how to interpret the memory for the input tensors. + //! \param outputDesc how to interpret the memory for the output tensors. + //! \param inputs The memory for the input tensors. + //! \param inputStrides Strides for input tensors. + //! \param outputStrides Strides for output tensors. + //! \param outputs The memory for the output tensors. + //! \param nbInputs Number of input tensors. + //! \param nbOutputs Number of output tensors. + //! \param stream The stream in which to execute the kernels. + //! + //! \return 0 for success, else non-zero + //! + virtual int32_t enqueue(PluginTensorDesc const* inputDesc, PluginTensorDesc const* outputDesc, + void const* const* inputs, void* const* outputs, Dims const* inputStrides, Dims const* outputStrides, + int32_t nbInputs, int32_t nbOutputs, cudaStream_t stream) noexcept = 0; + + //! + //! \brief Get the plugin fields which should be serialized. + //! + virtual PluginFieldCollection const* getFieldsToSerialize() noexcept = 0; +}; + +class IPluginCreatorV3Quick : public IPluginCreatorInterface +{ +public: + InterfaceInfo getInterfaceInfo() const noexcept override + { + return InterfaceInfo{"PLUGIN CREATOR_V3QUICK", 1, 0}; + } + + //! + //! \brief Return a plugin object. Return nullptr in case of error. + //! + //! \param name A NULL-terminated name string of length 1024 or less, including the NULL terminator. + //! \param namespace A NULL-terminated name string of length 1024 or less, including the NULL terminator. + //! \param fc A pointer to a collection of fields needed for constructing the plugin. + //! \param phase The TensorRT phase in which the plugin is being created + //! \param quickPluginCreationRequest Whether a JIT or AOT plugin should be created + //! + virtual IPluginV3* createPlugin(AsciiChar const* name, AsciiChar const* nspace, PluginFieldCollection const* fc, + TensorRTPhase phase, QuickPluginCreationRequest quickPluginCreationRequest) noexcept = 0; + + //! + //! \brief Return a list of fields that need to be passed to createPlugin() when creating a plugin for use in the + //! TensorRT build phase. + //! + virtual PluginFieldCollection const* getFieldNames() noexcept = 0; + + virtual AsciiChar const* getPluginName() const noexcept = 0; + + virtual AsciiChar const* getPluginVersion() const noexcept = 0; + + virtual AsciiChar const* getPluginNamespace() const noexcept = 0; + + IPluginCreatorV3Quick() = default; + virtual ~IPluginCreatorV3Quick() = default; + +protected: + IPluginCreatorV3Quick(IPluginCreatorV3Quick const&) = default; + IPluginCreatorV3Quick(IPluginCreatorV3Quick&&) = default; + IPluginCreatorV3Quick& operator=(IPluginCreatorV3Quick const&) & = default; + IPluginCreatorV3Quick& operator=(IPluginCreatorV3Quick&&) & = default; +}; + +} // namespace v_1_0 + +//! +//! \class IPluginV3QuickCore +//! +//! \brief Provides core capability (`IPluginCapability::kCORE`) for quickly-deployable TRT plugins +//! +//! \warning This class is strictly for the purpose of supporting quickly-deployable TRT Python plugins and is not part +//! of the public TensorRT C++ API. Users must not inherit from this class. +//! +using IPluginV3QuickCore = v_1_0::IPluginV3QuickCore; + +//! +//! \class IPluginV3QuickBuild +//! +//! \brief Provides build capability (`IPluginCapability::kBUILD`) for quickly-deployable TRT plugins +//! +//! \warning This class is strictly for the purpose of supporting quickly-deployable TRT Python plugins and is not part +//! of the public TensorRT C++ API. Users must not inherit from this class. +//! +using IPluginV3QuickBuild = v_1_0::IPluginV3QuickBuild; + +//! +//! \class IPluginV3QuickAOTBuild +//! +//! \brief Provides additional build capabilities for AOT quickly-deployable TRT plugins. Descends from +//! IPluginV3QuickBuild. +//! +//! \warning This class is strictly for the purpose of supporting quickly-deployable TRT Python plugins and is not part +//! of the public TensorRT C++ API. Users must not inherit from this class. +//! +using IPluginV3QuickAOTBuild = v_1_0::IPluginV3QuickAOTBuild; + +//! +//! \class IPluginV3QuickRuntime +//! +//! \brief Provides runtime capability (`IPluginCapability::kRUNTIME`) for JIT quickly-deployable TRT plugins +//! +//! \warning This class is strictly for the purpose of supporting quickly-deployable TRT Python plugins and is not part +//! of the public TensorRT C++ API. Users must not inherit from this class. +//! +using IPluginV3QuickRuntime = v_1_0::IPluginV3QuickRuntime; + +//! +//! \class IPluginCreatorV3Quick +//! +//! \warning This class is strictly for the purpose of supporting quickly-deployable TRT Python plugins and is not part +//! of the public TensorRT C++ API. Users must not inherit from this class. +//! +using IPluginCreatorV3Quick = v_1_0::IPluginCreatorV3Quick; + +} // namespace nvinfer1 + +#endif // TRT_PYTHON_IMPL_PLUGIN_H diff --git a/tensorrt/lib/nvinfer_dispatch_10.lib b/tensorrt/lib/nvinfer_dispatch_10.lib new file mode 100644 index 0000000000000000000000000000000000000000..34398a43d0ca03915efe58b376e6b9be0d99b040 Binary files /dev/null and b/tensorrt/lib/nvinfer_dispatch_10.lib differ diff --git a/tensorrt/lib/nvinfer_plugin_10.lib b/tensorrt/lib/nvinfer_plugin_10.lib new file mode 100644 index 0000000000000000000000000000000000000000..2929d24b1cf998a6e5ff15bbd1c6fcee48e324ff Binary files /dev/null and b/tensorrt/lib/nvinfer_plugin_10.lib differ diff --git a/tensorrt/lib/nvinfer_vc_plugin_10.lib b/tensorrt/lib/nvinfer_vc_plugin_10.lib new file mode 100644 index 0000000000000000000000000000000000000000..113b96e83e8bc5b2c477adac285c63c303d937c4 Binary files /dev/null and b/tensorrt/lib/nvinfer_vc_plugin_10.lib differ diff --git a/tensorrt/lib/nvonnxparser_10.lib b/tensorrt/lib/nvonnxparser_10.lib new file mode 100644 index 0000000000000000000000000000000000000000..aeb8cfd77d93b52d24a939bd66cb0b70b709c6cf Binary files /dev/null and b/tensorrt/lib/nvonnxparser_10.lib differ diff --git a/vulkan/Bin/SPIRV.dll b/vulkan/Bin/SPIRV.dll new file mode 100644 index 0000000000000000000000000000000000000000..41e0f3b2562cf488f49185f604d29d49cda49a77 Binary files /dev/null and b/vulkan/Bin/SPIRV.dll differ diff --git a/vulkan/Bin/VkLayer_api_dump.json b/vulkan/Bin/VkLayer_api_dump.json new file mode 100644 index 0000000000000000000000000000000000000000..97d6fc5d8ddc8a44d347edcc1a94b8408ce64c7f --- /dev/null +++ b/vulkan/Bin/VkLayer_api_dump.json @@ -0,0 +1,334 @@ +{ + "file_format_version": "1.2.0", + "layer": { + "name": "VK_LAYER_LUNARG_api_dump", + "type": "GLOBAL", + "library_path": ".\\VkLayer_api_dump.dll", + "api_version": "1.4.341", + "implementation_version": "2", + "description": "LunarG API dump layer", + "introduction": "The API Dump utility layer prints API calls, parameters, and values to the identified output stream.", + "url": "https://vulkan.lunarg.com/doc/sdk/latest/windows/api_dump_layer.html", + "platforms": [ + "WINDOWS", + "LINUX", + "MACOS", + "ANDROID" + ], + "instance_extensions": [ + { + "name": "VK_EXT_layer_settings", + "spec_version": "2" + } + ], + "device_extensions": [ + { + "name": "VK_EXT_tooling_info", + "spec_version": "1", + "entrypoints": [ + "vkGetPhysicalDeviceToolPropertiesEXT" + ] + } + ], + "features": { + "presets": [ + { + "label": "Console Output", + "description": "Output API dump to stdout", + "platforms": [ + "WINDOWS", + "LINUX", + "MACOS", + "ANDROID" + ], + "status": "STABLE", + "settings": [ + { + "key": "output_format", + "value": "text" + }, + { + "key": "log_filename", + "value": "stdout" + }, + { + "key": "file", + "value": false + } + ] + }, + { + "label": "Text Output", + "description": "Output API dump to a text file", + "platforms": [ + "WINDOWS", + "LINUX", + "MACOS", + "ANDROID" + ], + "status": "STABLE", + "settings": [ + { + "key": "output_format", + "value": "text" + }, + { + "key": "log_filename", + "value": "${VK_LOCAL}/vk_apidump.txt" + }, + { + "key": "file", + "value": true + } + ] + }, + { + "label": "HTML Output", + "description": "Output API dump to a HTML file", + "platforms": [ + "WINDOWS", + "LINUX", + "MACOS", + "ANDROID" + ], + "status": "STABLE", + "settings": [ + { + "key": "output_format", + "value": "html" + }, + { + "key": "log_filename", + "value": "${VK_LOCAL}/vk_apidump.html" + }, + { + "key": "file", + "value": true + } + ] + }, + { + "label": "JSON Output", + "description": "Output API dump to a JSON file", + "platforms": [ + "WINDOWS", + "LINUX", + "MACOS", + "ANDROID" + ], + "status": "STABLE", + "settings": [ + { + "key": "output_format", + "value": "json" + }, + { + "key": "log_filename", + "value": "${VK_LOCAL}/vk_apidump.json" + }, + { + "key": "file", + "value": true + } + ] + } + ], + "settings": [ + { + "key": "output_range", + "env": "VK_APIDUMP_OUTPUT_RANGE", + "label": "Output Range", + "description": "Comma separated list of frames to output or a range of frames with a start, count, and optional interval separated by a dash. A count of 0 will output every frame after the start of the range. Example: \"5-8-2\" will output frame 5, continue until frame 13, dumping every other frame. Example: \"3,8-2\" will output frames 3, 8, and 9.", + "type": "STRING", + "default": "0-0" + }, + { + "key": "output_format", + "env": "VK_APIDUMP_OUTPUT_FORMAT", + "label": "Output Format", + "description": "Specifies the format used for output; can be HTML, JSON, or Text (default -- outputs plain text)", + "type": "ENUM", + "flags": [ + { + "key": "text", + "label": "Text", + "description": "Plain text" + }, + { + "key": "html", + "label": "HTML", + "description": "HTML" + }, + { + "key": "json", + "label": "JSON", + "description": "Json" + } + ], + "default": "text", + "settings": [ + { + "key": "pre_dump", + "env": "VK_APIDUMP_PRE_DUMP", + "label": "Pre-dump the parameters.", + "description": "Setting this to true causes parameters to calls be logged before the function as well as after the function.", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "output_format", + "value": "text" + } + ] + } + } + ] + }, + { + "key": "file", + "label": "Output to File", + "description": "Setting this to true indicates that output should be written to file instead of stdout", + "type": "BOOL", + "default": false, + "settings": [ + { + "key": "log_filename", + "env": "VK_APIDUMP_LOG_FILENAME", + "label": "Log Filename", + "description": "Specifies the file to dump to when output files are enabled", + "type": "SAVE_FILE", + "filter": "*.txt,*.html,*.json", + "default": "stdout", + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "file", + "value": true + } + ] + } + } + ] + }, + { + "key": "flush", + "env": "VK_APIDUMP_FLUSH", + "label": "Log Flush After Write", + "description": "Setting this to true causes IO to be flushed after each API call that is written", + "type": "BOOL", + "default": true + }, + { + "key": "name_size", + "label": "Name Size", + "description": "The number of characters the name of a variable should consume, assuming more are not required", + "type": "INT", + "default": 32, + "range": { + "min": 1 + }, + "unit": "chars" + }, + { + "key": "show_types", + "label": "Show Types", + "description": "Dump types in addition to values", + "type": "BOOL", + "default": true, + "settings": [ + { + "key": "type_size", + "label": "Type Size", + "description": "The number of characters the name of a type should consume, assuming more are not required", + "type": "INT", + "default": 0, + "range": { + "min": 0 + }, + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "show_types", + "value": true + } + ] + } + } + ] + }, + { + "key": "timestamp", + "env": "VK_APIDUMP_TIMESTAMP", + "label": "Show Timestamp", + "description": "Show the timestamp of function calls since start in microseconds", + "type": "BOOL", + "default": false + }, + { + "key": "show_shader", + "label": "Show Shader", + "description": "Dump the shader binary code in pCode as an hex array which can be viewed in https://www.khronos.org/spir/visualizer/", + "type": "BOOL", + "default": false + }, + { + "key": "detailed", + "env": "VK_APIDUMP_DETAILED", + "label": "Show Parameter Details", + "description": "Dump parameter details in addition to API calls", + "type": "BOOL", + "default": true + }, + { + "key": "no_addr", + "env": "VK_APIDUMP_NO_ADDR", + "label": "Hide Addresses", + "description": "Dump \"address\" in place of hex addresses", + "type": "BOOL", + "default": false + }, + { + "key": "use_spaces", + "label": "Use Spaces", + "description": "Setting this to true causes all tab characters to be replaced with spaces", + "type": "BOOL", + "default": true, + "settings": [ + { + "key": "indent_size", + "label": "Indent Size", + "description": "Specifies the number of spaces that a tab is equal to", + "type": "INT", + "default": 4, + "range": { + "min": 1, + "max": 16 + }, + "unit": "chars", + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "use_spaces", + "value": true + } + ] + } + } + ] + }, + { + "key": "show_thread_and_frame", + "label": "Show Thread and Frame", + "description": "Show the thread and frame of each function called", + "type": "BOOL", + "default": true + } + ] + } + } +} diff --git a/vulkan/Bin/VkLayer_crash_diagnostic.json b/vulkan/Bin/VkLayer_crash_diagnostic.json new file mode 100644 index 0000000000000000000000000000000000000000..2ca58f88144860cb7253ac3a2996cd95e7b13944 --- /dev/null +++ b/vulkan/Bin/VkLayer_crash_diagnostic.json @@ -0,0 +1,423 @@ +{ + "file_format_version": "1.2.0", + "layer": { + "name": "VK_LAYER_LUNARG_crash_diagnostic", + "type": "GLOBAL", + "status": "BETA", + "library_path": ".\\VkLayer_crash_diagnostic.dll", + "api_version": "1.4.341", + "implementation_version": "1", + "description": "Crash Diagnostic Layer is a crash/hang debugging tool that helps determines GPU progress in a Vulkan application.", + "platforms": [ "WINDOWS", "LINUX", "ANDROID" ], + "url": "https://vulkan.lunarg.com/doc/sdk/latest/windows/crash_diagnostic_layer.html", + "enable_environment": { + "VK_CRASH_DIAGNOSTIC_ENABLE": "1" + }, + "disable_environment": { + "VK_CRASH_DIAGNOSTIC_DISABLE": "1" + }, + "instance_extensions": [ + { + "name": "VK_EXT_debug_report", + "spec_version": "10" + }, + { + "name": "VK_EXT_debug_utils", + "spec_version": "1" + }, + { + "name": "VK_EXT_layer_settings", + "spec_version": "2" + } + ], + "device_extensions": [ + { + "name": "VK_EXT_debug_report", + "spec_version": "10", + "entrypoints": [ + "vkCreateDebugReportCallbackEXT", + "vkDebugReportMessageEXT", + "vkDestroyDebugReportCallbackEXT" + ] + }, + { + "name": "VK_EXT_tooling_info", + "spec_version": "1", + "entrypoints": [ + "vkGetPhysicalDeviceToolPropertiesEXT" + ] + } + ], + "features": { + "settings": [ + { + "key": "state", + "label": "State Tracking", + "description": "Control of state tracking.", + "type": "GROUP", + "expanded": true, + "settings": [ + { + "key": "sync_after_commands", + "env": "CDL_SYNC_AFTER_COMMANDS", + "label": "Synchronize commands", + "description": "Add pipeline barriers after instrumented commands to help identify whether a Vulkan crash is due to a synchronization issue.", + "detailed": "VK_KHR_dynamic_rendering usage is required for this feature otherwise Validation layer errors will be generated.", + "type": "BOOL", + "default": false, + "messages": [ + { + "key": "sync_after_commands_msg0", + "title": "'Synchronize commands' is incompatible with Vulkan RenderPass", + "version": 1, + "description": "'Synchronize commands' add pipeline barriers after instrumented commands. This feature is not supported for Vulkan application using Vulkan RenderPass which would generate Validation layer errors.", + "informative": "Are you sure you want to enable 'Synchronize commands'?", + "severity": "WARNING", + "conditions": [ + { + "setting": { + "key": "sync_after_commands", + "value": true + } + } + ], + "actions": { + "default": "BUTTON0", + "BUTTON0": { + "type": "OK", + "changes": [ + { + "setting": { + "key": "sync_after_commands", + "value": true + } + } + ] + }, + "BUTTON1": { + "type": "CANCEL", + "changes": [ + { + "setting": { + "key": "sync_after_commands", + "value": false + } + } + ] + } + } + } + ] + }, + { + "key": "instrument_all_commands", + "env": "CDL_INSTRUMENT_ALL_COMMANDS", + "label": "Add markers on each Vulkan command", + "description": "Include completion markers around every vulkan command", + "detailed": "This may allow more accuratute fault locations at the expense of larger command buffers and reduced performance", + "type": "BOOL", + "default": false + } + ] + }, + { + "key": "dump", + "label": "Crash dump file", + "description": "Control of dump files", + "type": "GROUP", + "expanded": true, + "settings": [ + { + "key": "output_path", + "env": "CDL_OUTPUT_PATH", + "label": "Output Path", + "description": "The directory where dump files and shader binaries are written.", + "type": "SAVE_FOLDER", + "default": "" + }, + { + "key": "triggers", + "label": "Crash Dump Triggers", + "description": "Additional crash detection methods beyond a VK_ERROR_DEVICE_LOST error returned by the Vulkan driver.", + "type": "GROUP", + "expanded": true, + "settings": [ + { + "key": "trigger_watchdog_timeout", + "env": "CDL_TRIGGER_WATCHDOG_TIMEOUT", + "label": "Watchdog Timer", + "description": "If the application fails to submit new commands within a set time, a crash dump will be created as if the a lost device error was encountered.", + "type": "BOOL", + "default": true, + "settings": [ + { + "key": "watchdog_timeout_ms", + "env": "CDL_WATCHDOG_TIMEOUT_MS", + "label": "Watchdog Timeout (ms)", + "description": "Time in milliseconds the application has to submit a new command before creating a crash dump.", + "type": "INT", + "default": 30000, + "range": { + "min": 1 + }, + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "trigger_watchdog_timeout", + "value": true + } + ] + } + } + ] + } + ] + }, + { + "key": "track_semaphores", + "env": "CDL_TRACK_SEMAPHORES", + "label": "Dump semaphores", + "description": "Include semaphore state reporting in runtime logging and crash dump files", + "type": "BOOL", + "default": false + }, + { + "key": "dump_queue_submits", + "env": "CDL_DUMP_QUEUE_SUBMITS", + "label": "Dump queue submissions", + "description": "Control which queue submissions are included in the dump.", + "type": "ENUM", + "default": "running", + "flags": [ + { + "key": "running", + "label": "Running", + "description": "Submissions that were executing at the time of the dump." + }, + { + "key": "pending", + "label": "Pending", + "description": "Submissions executing or pending at the time of the dump." + } + ] + }, + { + "key": "dump_command_buffers", + "env": "CDL_DUMP_COMMAND_BUFFERS", + "label": "Dump command buffers", + "description": "Control which command buffers are included in the dump.", + "type": "ENUM", + "default": "running", + "flags": [ + { + "key": "running", + "label": "Running", + "description": "Command Buffers that were executing at the time of the dump." + }, + { + "key": "pending", + "label": "Pending", + "description": "Command Buffers executing or pending at the time of the dump." + }, + { + "key": "all", + "label": "All", + "description": "All known Command Buffers." + } + ] + }, + { + "key": "dump_commands", + "env": "CDL_DUMP_COMMANDS", + "label": "Dump commands", + "description": "Control which commands are included in the dump.", + "type": "ENUM", + "default": "running", + "flags": [ + { + "key": "running", + "label": "Running", + "description": "Command Buffers that were executing at the time of the dump." + }, + { + "key": "pending", + "label": "Pending", + "description": "Commands executing or pending at the time of the dump." + }, + { + "key": "all", + "label": "All", + "description": "All known Commands." + } + ] + }, + { + "key": "dump_shaders", + "env": "CDL_DUMP_SHADERS", + "label": "Dump shaders", + "description": "Control of shader dumping.", + "type": "ENUM", + "default": "off", + "flags": [ + { + "key": "off", + "label": "Off", + "description": "Never dump shaders." + }, + { + "key": "on_crash", + "label": "On Crash", + "description": "Dump currently bound shaders after a crash is detected." + }, + { + "key": "on_bind", + "label": "On Bind", + "description": "Dump only bound shaders." + }, + { + "key": "all", + "label": "All", + "description": "Dump all shaders." + } + ] + } + ] + }, + { + "key": "logging", + "label": "Logging", + "description": "Control of logging", + "type": "GROUP", + "expanded": true, + "settings": [ + { + "key": "log_file", + "env": "CDL_LOG_FILE", + "label": "Log Filename", + "description": "none = no logging, stderr or stdout = to the console, otherwise an absolute or relative path.", + "filter": "*.txt", + "type": "SAVE_FILE", + "default": "stderr", + "platforms": [ + "WINDOWS", + "LINUX", + "MACOS" + ] + }, + { + "key": "message_group", + "label": "Log Message Areas", + "description": "", + "type": "GROUP", + "expanded": true, + "settings": [ + { + "key": "trace_on", + "env": "CDL_TRACE_ON", + "label": "Log main submission and synchronization Vulkan commands", + "description": "Add Vulkan API calls intercepted by the layer will be logged to the console.", + "detailed": "This includes: vkDeviceWaitIdle, vkQueueWaitIdle, vkQueuePresentKHR, vkWaitForFences, vkGetFenceStatus, vkAcquireNextImageKHR, vkCreateCommandPool, vkDestroyCommandPool, vkResetCommandPool, vkAllocateCommandBuffers, vkFreeCommandBuffers, vkQueueSubmit, vkQueueSubmit2, vkQueueSubmit2KHR and vkQueueBindSparse", + "type": "BOOL", + "default": false + }, + { + "key": "trace_all_semaphores", + "env": "CDL_TRACE_ALL_SEMAPHORES", + "label": "Log Vulkan commands that use semaphores", + "description": "Add logging messages about every vulkan command that uses semaphores", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "track_semaphores", + "value": true + } + ] + }, + "messages": [ + { + "key": "trace_all_semaphores_msg0", + "title": "'Log Vulkan commands that use semaphores' requires 'Track semaphores' to be enabled", + "version": 1, + "description": "'Log Vulkan commands that use semaphores' requires 'Track semaphores' to be enabled", + "informative": "Are you sure you want to enable 'Log Vulkan commands that use semaphores'?", + "severity": "INFO", + "conditions": [ + { + "setting": { + "key": "track_semaphores", + "value": false + } + } + ], + "actions": { + "default": "BUTTON0", + "BUTTON0": { + "type": "OK", + "changes": [ + { + "setting": { + "key": "track_semaphores", + "value": true + } + } + ] + }, + "BUTTON1": { + "type": "CANCEL", + "changes": [ + { + "setting": { + "key": "track_semaphores", + "value": false + } + } + ] + } + } + } + ] + } + ] + }, + { + "key": "message_severity", + "label": "Message Severity", + "description": "Comma-delineated list of options specifying the types of log messages to be reported.", + "type": "FLAGS", + "flags": [ + { + "key": "error", + "label": "Error", + "description": "Report errors such as device lost or setup problems in the layer." + }, + { + "key": "warn", + "label": "Warning", + "description": "Report non-fatal problems that may interfere with operation of the layer" + }, + { + "key": "info", + "label": "Info", + "description": "Report informational messages." + }, + { + "key": "verbose", + "label": "Debug", + "description": "For layer development. Report messages for debugging layer behavior.", + "view": "DEBUG" + } + ], + "default": [ + "error" + ] + } + ] + } + ] + } + } +} diff --git a/vulkan/Bin/VkLayer_gfxreconstruct.json b/vulkan/Bin/VkLayer_gfxreconstruct.json new file mode 100644 index 0000000000000000000000000000000000000000..9818bb3f9621011b227410a52f9602ba272bde03 --- /dev/null +++ b/vulkan/Bin/VkLayer_gfxreconstruct.json @@ -0,0 +1,498 @@ +{ + "file_format_version": "1.2.0", + "layer": { + "name": "VK_LAYER_LUNARG_gfxreconstruct", + "type": "GLOBAL", + "library_path": ".\\VkLayer_gfxreconstruct.dll", + "api_version": "1.4.341", + "implementation_version": "4194309", + "description": "GFXReconstruct Capture Layer Version 1.0.5", + "introduction": "The GFXReconstruct Capture Layer intercepts Vulkan API calls and logs them to a capture file. Other tools in the GFXReconstruct suite can inspect, modify, or replay capture files generated by this component.", + "platforms": [ "WINDOWS", "LINUX", "ANDROID", "MACOS" ], + "url": "https://vulkan.lunarg.com/doc/sdk/latest/windows/capture_tools.html", + "status": "STABLE", + "device_extensions": [ + { + "name": "VK_EXT_tooling_info", + "spec_version": "1", + "entrypoints": [ "vkGetPhysicalDeviceToolPropertiesEXT" ] + } + ], + "disable_environment": { + "GFXRECON_DISABLE": "" + }, + "features": { + "presets": [ + { + "label": "Initialization", + "description": "Capture the first two frames.", + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "settings": [ + { + "key": "capture_trigger", + "value": "" + }, + { + "key": "capture_frames", + "value": "1-2" + } + ] + }, + { + "label": "Range", + "description": "Capture a range of frames between presses of the F5 key.", + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "settings": [ + { + "key": "capture_trigger", + "value": "F5" + }, + { + "key": "capture_frames", + "value": "" + } + ] + } + ], + "settings": [ + { + "key": "capture_trigger", + "env": "GFXRECON_CAPTURE_TRIGGER", + "label": "Hotkey Capture Trigger", + "description": "Specify a hotkey (any one of F1-F12, TAB, CONTROL) that will be used to start/stop capture. Example: F3 will set the capture trigger to F3 hotkey. One capture file will be generated for each pair of start/stop hotkey presses. Default is: Empty string (hotkey capture trigger is disabled).", + "platforms" : [ "WINDOWS", "LINUX", "MACOS" ], + "type": "ENUM", + "flags": [ + { + "key": "", + "label": "None", + "description": "" + }, + { + "key": "F1", + "label": "F1", + "description": "F1 Key" + }, + { + "key": "F2", + "label": "F2", + "description": "F2 Key" + }, + { + "key": "F3", + "label": "F3", + "description": "F3 Key" + }, + { + "key": "F4", + "label": "F4", + "description": "F4 Key" + }, + { + "key": "F5", + "label": "F5", + "description": "F5 Key" + }, + { + "key": "F6", + "label": "F6", + "description": "F6 Key" + }, + { + "key": "F7", + "label": "F7", + "description": "F7 Key" + }, + { + "key": "F8", + "label": "F8", + "description": "F8 Key" + }, + { + "key": "F9", + "label": "F9", + "description": "F9 Key" + }, + { + "key": "F10", + "label": "F10", + "description": "F10 Key" + }, + { + "key": "F11", + "label": "F11", + "description": "F11 Key" + }, + { + "key": "F12", + "label": "F12", + "description": "F12 Key" + }, + { + "key": "TAB", + "label": "TAB", + "description": "TAB Key" + }, + { + "key": "CONTROL", + "label": "CONTROL", + "description": "CONTROL Key" + } + ], + "default": "" + }, + { + "key": "capture_trigger_frames", + "env": "GFXRECON_CAPTURE_TRIGGER_FRAMES", + "label": "Hotkey Capture Frame Limit", + "description": "Specify a limit on the number of frames to be captured via hotkey. Example: 1 will capture exactly one frame when the trigger key is pressed. Default is: Empty string (no limit)", + "type": "STRING", + "default": "" + }, + { + "key": "capture_frames", + "env": "GFXRECON_CAPTURE_FRAMES", + "label": "Capture Specific Frames", + "description": "Specify one or more comma-separated frame ranges to capture. Each range will be written to its own file. A frame range can be specified as a single value, to specify a single frame to capture, or as two hyphenated values, to specify the first and last frame to capture. Frame ranges should be specified in ascending order and cannot overlap. Note that frame numbering is 1-based (i.e. the first frame is frame 1). Example: 200,301-305 will create two capture files, one containing a single frame and one containing five frames. Default is: Empty string (all frames are captured).", + "type": "STRING", + "default": "" + }, + { + "key": "capture_process_name", + "env": "GFXRECON_CAPTURE_PROCESS_NAME", + "label": "Capture Specific process", + "description": "Specify one process name to capture.", + "type": "STRING", + "default": "" + }, + { + "key": "capture_file", + "env": "GFXRECON_CAPTURE_FILE", + "label": "Capture File Name", + "description": "Path to use when creating the capture file. Default is: gfxrecon_capture.gfxr", + "type": "SAVE_FILE", + "filter": "*.gfxr", + "default": "gfxrecon_capture.gfxr", + "settings": [ + { + "key": "capture_file_timestamp", + "env": "GFXRECON_CAPTURE_FILE_TIMESTAMP", + "label": "Capture File Name with Timestamp", + "description": "Add a timestamp (yyyymmddThhmmss) postfix to the capture file name.", + "type": "BOOL", + "default": true + }, + { + "key": "capture_file_flush", + "env": "GFXRECON_CAPTURE_FILE_FLUSH", + "label": "Capture File Flush After Write", + "description": "Flush output stream after each packet is written to the capture file. Default is: false.", + "type": "BOOL", + "default": false + } + ] + }, + { + "key": "capture_compression_type", + "env": "GFXRECON_CAPTURE_COMPRESSION_TYPE", + "label": "Compression Format", + "description": "Compression format to use with the capture file. Valid values are: LZ4, ZLIB, ZSTD, and NONE. Default is: LZ4", + "type": "ENUM", + "flags": [ + { + "key": "LZ4", + "label": "LZ4", + "description": "Fastest compression algorithm that favors performance over compression ratio." + }, + { + "key": "ZLIB", + "label": "ZLIB", + "description": "Widely used compression algorithm with better compression ratio than LZ4 but worse performance." + }, + { + "key": "ZSTD", + "label": "ZSTD", + "description": "Newer compression algorithm with better compression ratio and performance than ZLIB, though not as fast as LZ4." + }, + { + "key": "NONE", + "label": "NONE", + "description": "No compression" + } + ], + "default": "LZ4" + }, + { + "key": "capture_environment", + "env": "GFXRECON_CAPTURE_ENVIRONMENT", + "label": "Capture Environment", + "description": "List of environment variables to capture.", + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "type": "LIST", + "default": [] + }, + { + "key": "memory_tracking_mode", + "env": "GFXRECON_MEMORY_TRACKING_MODE", + "label": "Memory Tracking Mode", + "description": "Specifies the memory tracking mode to use for detecting modifications to mapped Vulkan memory objects. Available options are: page_guard, assisted, and unassisted.", + "type": "ENUM", + "flags": [ + { + "key": "page_guard", + "label": "page_guard", + "description": "Tracks modifications to individual memory pages, which are written to the capture file on calls to vkFlushMappedMemoryRanges, vkUnmapMemory, and vkQueueSubmit. Tracking modifications requires allocating shadow memory for all mapped memory." + }, + { + "key": "assisted", + "label": "assisted", + "description": "Expects the application to call vkFlushMappedMemoryRanges after memory is modified; the memory ranges specified to the vkFlushMappedMemoryRanges call will be written to the capture file during the call." + }, + { + "key": "unassisted", + "label": "unassisted", + "description": "Writes the full content of mapped memory to the capture file on calls to vkUnmapMemory and vkQueueSubmit. It is very inefficient and may be unusable with real-world applications that map large amounts of memory." + } + ], + "default": "page_guard", + "settings": [ + { + "key": "page_guard_copy_on_map", + "env": "GFXRECON_PAGE_GUARD_COPY_ON_MAP", + "label": "Page Guard Copy on Map", + "description": "When the page_guard memory tracking mode is enabled, copies the content of the mapped memory to the shadow memory immediately after the memory is mapped.", + "type": "BOOL", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "memory_tracking_mode", + "value": "page_guard" + } + ] + } + }, + { + "key": "page_guard_separate_read", + "env": "GFXRECON_PAGE_GUARD_SEPARATE_READ", + "label": "Page Guard Separate Read Tracking", + "description": "When the page_guard memory tracking mode is enabled, copies the content of pages accessed for read from mapped memory to shadow memory on each read. Can overwrite unprocessed shadow memory content when an application is reading from and writing to the same page.", + "type": "BOOL", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "memory_tracking_mode", + "value": "page_guard" + } + ] + } + }, + { + "key": "page_guard_external_memory", + "env": "GFXRECON_PAGE_GUARD_EXTERNAL_MEMORY", + "label": "Page Guard External Memory", + "description": "When the page_guard memory tracking mode is enabled, use the VK_EXT_external_memory_host extension to eliminate the need for shadow memory allocations. For each memory allocation from a host visible memory type, the capture layer will create an allocation from system memory, which it can monitor for write access, and provide that allocation to vkAllocateMemory as external memory. Only available on Windows.", + "platforms": [ "WINDOWS" ], + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "memory_tracking_mode", + "value": "page_guard" + } + ] + } + }, + { + "key": "page_guard_persistent_memory", + "env": "GFXRECON_PAGE_GUARD_PERSISTENT_MEMORY", + "label": "Page Guard Persistent Memory", + "description": "When the page_guard memory tracking mode is enabled, this option changes the way that the shadow memory used to detect modifications to mapped memory is allocated. The default behavior is to allocate and copy the mapped memory range on map and free the allocation on unmap. When this option is enabled, an allocation with a size equal to that of the object being mapped is made once on the first map and is not freed until the object is destroyed. This option is intended to be used with applications that frequently map and unmap large memory ranges, to avoid frequent allocation and copy operations that can have a negative impact on performance. This option is ignored when GFXRECON_PAGE_GUARD_EXTERNAL_MEMORY is enabled.", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "memory_tracking_mode", + "value": "page_guard" + }, + { + "key": "page_guard_external_memory", + "value": false + } + ] + } + }, + { + "key": "page_guard_align_buffer_sizes", + "env": "GFXRECON_PAGE_GUARD_ALIGN_BUFFER_SIZES", + "label": "Page Guard Align Buffer Sizes", + "description": "When the page_guard memory tracking mode is enabled, this option overrides the Vulkan API calls that report buffer memory properties to report that buffer sizes and alignments must be a multiple of the system page size. This option is intended to be used with applications that perform CPU writes and GPU writes/copies to different buffers that are bound to the same page of mapped memory, which may result in data being lost when copying pages from the page_guard shadow allocation to the real allocation. This data loss can result in visible corruption during capture. Forcing buffer sizes and alignments to a multiple of the system page size prevents multiple buffers from being bound to the same page, avoiding data loss from simultaneous CPU writes to the shadow allocation and GPU writes to the real allocation for different buffers bound to the same page. This option is only available for the Vulkan API.", + "type": "BOOL", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "memory_tracking_mode", + "value": "page_guard" + }, + { + "key": "page_guard_external_memory", + "value": false + } + ] + } + }, + { + "key": "page_guard_unblock_sigsegv", + "env": "GFXRECON_PAGE_GUARD_UNBLOCK_SIGSEGV", + "label": "Page Guard unblock SIGSEGV from thread's signal mask", + "description": "When the page_guard memory tracking mode is enabled, this option overrides enables SIGSEGV in thread's signal mask in the case that it has been disabled.", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "memory_tracking_mode", + "value": "page_guard" + }, + { + "key": "page_guard_external_memory", + "value": false + } + ] + } + } + ] + }, + { + "key": "log", + "label": "Log", + "description": "Control log messages.", + "type": "GROUP", + "settings": [ + { + "key": "log_level", + "env": "GFXRECON_LOG_LEVEL", + "label": "Level", + "description": "Specify the minimum severity level of messages to log. Options are: debug, info, warning, error, and fatal. The specified level and all higher levels will be enabled for logging. For example, choosing the warning level will also enable the error and fatal levels.", + "type": "ENUM", + "flags": [ + { + "key": "debug", + "label": "debug, info, warning, error, fatal", + "description": "" + }, + { + "key": "info", + "label": "info, warning, error, fatal", + "description": "" + }, + { + "key": "warning", + "label": "warning, error, fatal", + "description": "" + }, + { + "key": "error", + "label": "error, fatal", + "description": "" + }, + { + "key": "fatal", + "label": "fatal", + "description": "" + } + ], + "default": "info" + }, + { + "key": "log_detailed", + "env": "GFXRECON_LOG_DETAILED", + "label": "Log Name and Line Number", + "description": "Include name and line number from the file responsible.", + "type": "BOOL", + "default": false + }, + { + "key": "log_allow_indents", + "env": "GFXRECON_LOG_ALLOW_INDENTS", + "label": "Apply additional indentation", + "description": "Apply additional indentation formatting to log messages.", + "type": "BOOL", + "default": false + }, + { + "key": "log_output_to_console", + "env": "GFXRECON_LOG_OUTPUT_TO_CONSOLE", + "label": "Log Output to Console / stdout", + "description": "Log messages will be written to stdout.", + "type": "BOOL", + "default": true + }, + { + "key": "log_break_on_error", + "env": "GFXRECON_LOG_BREAK_ON_ERROR", + "label": "Trigger Debug Break on Error", + "description": "Trigger a debug break when logging an error.", + "type": "BOOL", + "default": false + }, + { + "key": "log_output_to_os_debug_string", + "env": "GFXRECON_LOG_OUTPUT_TO_OS_DEBUG_STRING", + "label": "Log Output to Debug Console", + "description": "Windows only option. Log messages will be written to the Debug Console with OutputDebugStringA", + "platforms": [ "WINDOWS" ], + "type": "BOOL", + "default": false + }, + { + "key": "log_file", + "env": "GFXRECON_LOG_FILE", + "label": "Log File", + "description": "When set, log messages will be written to a file at the specified path. Default is: Empty string (file logging disabled).", + "type": "SAVE_FILE", + "filter": "*.txt", + "default": "", + "settings": [ + { + "key": "log_file_flush_after_write", + "env": "GFXRECON_LOG_FILE_FLUSH_AFTER_WRITE", + "label": "Log File Flush After Write", + "description": "Flush the log file to disk after each write when true.", + "type": "BOOL", + "default": false + }, + { + "key": "log_file_keep_open", + "env": "GFXRECON_LOG_FILE_KEEP_OPEN", + "label": "Log File Keep Open", + "description": "Keep the log file open between log messages when true, or close and reopen the log file for each message when false.", + "type": "BOOL", + "default": true + }, + { + "key": "log_file_create_new", + "env": "GFXRECON_LOG_FILE_CREATE_NEW", + "label": "Log File Overwrite", + "description": "Specifies that log file initialization should overwrite an existing file when true, or append to an existing file when false.", + "type": "BOOL", + "default": true + } + ] + } + ] + } + ] + } + } +} diff --git a/vulkan/Bin/VkLayer_khronos_profiles.json b/vulkan/Bin/VkLayer_khronos_profiles.json new file mode 100644 index 0000000000000000000000000000000000000000..5969453cd22b84a694feac45bdebc879e5b025c9 --- /dev/null +++ b/vulkan/Bin/VkLayer_khronos_profiles.json @@ -0,0 +1,1211 @@ +{ + "file_format_version": "1.2.1", + "layer": { + "name": "VK_LAYER_KHRONOS_profiles", + "type": "GLOBAL", + "library_path": ".\\VkLayer_khronos_profiles.dll", + "api_version": "1.4.341", + "implementation_version": "1.3.0", + "status": "STABLE", + "description": "Khronos Profiles layer", + "introduction": "The Khronos Profiles layer helps test across a wide range of hardware capabilities without requiring a physical copy of every device.", + "url": "https://vulkan.lunarg.com/doc/sdk/latest/windows/profiles_layer.html", + "instance_extensions": [ + { + "name": "VK_EXT_layer_settings", + "spec_version": "2" + } + ], + "device_extensions": [ + { + "name": "VK_EXT_tooling_info", + "spec_version": "1", + "entrypoints": [ + "vkGetPhysicalDeviceToolPropertiesEXT" + ] + } + ], + "features": { + "presets": [ + { + "label": "Emulate a Vulkan Conformant Profile", + "description": "Emulate a Conformant Vulkan Profile", + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "status": "STABLE", + "settings": [ + { + "key": "profile_emulation", + "value": true + }, + { + "key": "profile_dirs", + "value": "${VULKAN_CONTENT}/VK_LAYER_KHRONOS_profiles" + }, + { + "key": "profile_name", + "value": "VP_LUNARG_desktop_baseline_2023" + }, + { + "key": "simulate_capabilities", + "value": [ "SIMULATE_API_VERSION_BIT", "SIMULATE_FEATURES_BIT", "SIMULATE_PROPERTIES_BIT" ] + }, + { + "key": "emulate_portability", + "value": false + } + ] + }, + { + "label": "Emulate a Vulkan Portability Profile", + "description": "Emulate a Vulkan Profile with Vulkan Portability", + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "status": "STABLE", + "settings": [ + { + "key": "profile_emulation", + "value": true + }, + { + "key": "profile_dirs", + "value": "${VULKAN_CONTENT}/VK_LAYER_KHRONOS_profiles" + }, + { + "key": "profile_name", + "value": "VP_LUNARG_desktop_baseline_2023" + }, + { + "key": "simulate_capabilities", + "value": [ "SIMULATE_API_VERSION_BIT", "SIMULATE_FEATURES_BIT", "SIMULATE_PROPERTIES_BIT" ] + }, + { + "key": "emulate_portability", + "value": true + }, + { + "key": "vertexAttributeAccessBeyondStride", + "value": true + }, + { + "key": "separateStencilMaskRef", + "value": true + }, + { + "key": "mutableComparisonSamplers", + "value": true + }, + { + "key": "multisampleArrayImage", + "value": true + }, + { + "key": "imageViewFormatSwizzle", + "value": true + }, + { + "key": "imageViewFormatReinterpretation", + "value": true + }, + { + "key": "events", + "value": true + }, + { + "key": "constantAlphaColorBlendFactors", + "value": true + } + ] + } + ], + "settings": [ + { + "key": "profile_emulation", + "label": "Emulate a Vulkan Profile", + "description": "Emulate a Vulkan Profile on the Developer System", + "status": "STABLE", + "type": "BOOL", + "default": true, + "settings": [ + { + "key": "profile_dirs", + "label": "Loading Directory", + "description": "Select the location all the profiles.", + "type": "LOAD_FOLDER", + "format": "PROFILE", + "default": "", + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "profile_emulation", + "value": true + } + ] + } + }, + { + "key": "profile_name", + "label": "Name", + "description": "Name of the profile specified by the profile file to use.", + "type": "ENUM", + "flags": [], + "default": "${VP_DEFAULT}", + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "profile_emulation", + "value": true + } + ] + } + }, + { + "key": "profile_validation", + "label": "Schema Validation", + "description": "Validate the profile files against the Vulkan SDK profile schema if the file is found.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "profile_emulation", + "value": true + } + ] + } + } + ] + }, + { + "key": "simulate_capabilities", + "label": "Simulate Profile Capabilities", + "description": "Control of the simulated capabilities of the Vulkan physical device from the selected Vulkan Profile.", + "status": "STABLE", + "type": "FLAGS", + "flags": [ + { + "key": "SIMULATE_API_VERSION_BIT", + "label": "Version", + "description": "The Vulkan device will report the API version from the selected Profile. It also overrides the `api-version` set in VkPhysicalDeviceProperties." + }, + { + "key": "SIMULATE_FEATURES_BIT", + "label": "Features", + "description": "The Vulkan device will report the features from the selected Profile.", + "settings": [ + { + "key": "default_feature_values", + "label": "Unspecified Features", + "description": "Feature values when not specified in the select Vulkan profiles.", + "status": "STABLE", + "type": "ENUM", + "default": "DEFAULT_FEATURE_VALUES_DEVICE", + "flags": [ + { + "key": "DEFAULT_FEATURE_VALUES_DEVICE", + "label": "Use Device Values", + "description": "When a feature is not mentioned in the select Vulkan profiles, set is to the physical device value." + }, + { + "key": "DEFAULT_FEATURE_VALUES_FALSE", + "label": "Use False", + "description": "When a feature is not mentioned in the select Vulkan profiles, set it to false." + } + ] + }, + { + "key": "unknown_feature_values", + "label": "Unknown Features", + "description": "Feature values when unrecongnized by Vulkan profiles.", + "status": "STABLE", + "type": "ENUM", + "default": "UNKNOWN_FEATURE_VALUES_UNCHANGED", + "flags": [ + { + "key": "UNKNOWN_FEATURE_VALUES_UNCHANGED", + "label": "Unmodified", + "description": "When a feature is not recognized by Vulkan profiles, skip it." + }, + { + "key": "UNKNOWN_FEATURE_VALUES_DEVICE", + "label": "Use Device Values", + "description": "When a feature is not recognized by Vulkan profiles, attempt to load device values." + } + ] + } + ] + }, + { + "key": "SIMULATE_PROPERTIES_BIT", + "label": "Properties", + "description": "The Vulkan device will report the properties from the selected Profile." + }, + { + "key": "SIMULATE_EXTENSIONS_BIT", + "label": "Device Extensions", + "description": "The Vulkan device will report the extensions from the selected Profile." + }, + { + "key": "SIMULATE_FORMATS_BIT", + "label": "Formats", + "description": "The Vulkan device will report the formats from the selected Profile." + }, + { + "key": "SIMULATE_VIDEO_CAPABILITIES_BIT", + "label": "Video Capabilities", + "description": "The Vulkan device will report video capabilities from the selected Profile." + }, + { + "key": "SIMULATE_VIDEO_FORMATS_BIT", + "label": "Video Formats", + "description": "The Vulkan device will report video formats from the selected Profile." + } + ], + "default": [ "SIMULATE_API_VERSION_BIT", "SIMULATE_FEATURES_BIT", "SIMULATE_PROPERTIES_BIT" ] + }, + { + "key": "emulate_portability", + "label": "Emulate VK_KHR_portability_subset", + "description": "Emulate the VK_KHR_portability_subset extension on the device.", + "status": "STABLE", + "platforms": [ + "WINDOWS", + "LINUX" + ], + "type": "BOOL", + "default": true, + "settings": [ + { + "key": "constantAlphaColorBlendFactors", + "label": "constantAlphaColorBlendFactors", + "description": "Indicates whether this implementation supports constant alpha Blend Factors used as source or destination color Blending.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "events", + "label": "events", + "description": "Indicates whether this implementation supports synchronization using Events", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "imageViewFormatReinterpretation", + "label": "imageViewFormatReinterpretation", + "description": "Indicates whether this implementation supports a VkImageView being created with a texel format containing a different number of components, or a different number of bits in each component, than the texel format of the underlying VkImage.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "imageViewFormatSwizzle", + "label": "imageViewFormatSwizzle", + "description": "Indicates whether this implementation supports remapping format components using VkImageViewCreateInfo::components.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "imageView2DOn3DImage", + "label": "imageView2DOn3DImage", + "description": "Indicates whether this implementation supports a VkImage being created with the VK_IMAGE_CREATE_2D_ARRAY_COMPATIBLE_BIT flag set, permitting a 2D or 2D array image view to be created on a 3D VkImage.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "multisampleArrayImage", + "label": "multisampleArrayImage", + "description": "Indicates whether this implementation supports a VkImage being created as a 2D array with multiple samples per texel.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "mutableComparisonSamplers", + "label": "mutableComparisonSamplers", + "description": "Indicates whether this implementation allows descriptors with comparison samplers to be updated.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "pointPolygons", + "label": "pointPolygons", + "description": "Indicates whether this implementation supports Rasterization using a point Polygon Mode.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "samplerMipLodBias", + "label": "samplerMipLodBias", + "description": "Indicates whether this implementation supports setting a mipmap LOD bias value when creating a sampler.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "separateStencilMaskRef", + "label": "separateStencilMaskRef", + "description": "Indicates whether this implementation supports separate front and back Stencil Test reference values.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "shaderSampleRateInterpolationFunctions", + "label": "shaderSampleRateInterpolationFunctions", + "description": "Indicates whether this implementation supports fragment shaders which use the InterpolationFunction capability.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "tessellationIsolines", + "label": "tessellationIsolines", + "description": "Indicates whether this implementation supports isoline output from the Tessellation stage of a graphics pipeline. This member is only meaningful if tessellationShader are supported.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "triangleFans", + "label": "triangleFans", + "description": "Indicates whether this implementation supports Triangle Fans primitive topology.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + } + }, + { + "key": "vertexAttributeAccessBeyondStride", + "label": "vertexAttributeAccessBeyondStride", + "description": "Indicates whether this implementation supports accessing a vertex input attribute beyond the stride of the corresponding vertex input binding.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "emulate_portability", + "value": true + } + ] + }, + "settings": [ + { + "key": "minVertexInputBindingStrideAlignment", + "label": "minVertexInputBindingStrideAlignment", + "description": "Indicates whether this implementation supports accessing a vertex input attribute beyond the stride of the corresponding vertex input binding.", + "type": "INT", + "default": 4, + "platforms": [ "WINDOWS", "LINUX" ], + "dependence": { + "mode": "ALL", + "settings": [ + { + "key": "vertexAttributeAccessBeyondStride", + "value": false + } + ] + } + } + ] + } + ] + }, + { + "key": "exclude_device_extensions", + "label": "Exclude Device Extensions", + "description": "Removes the listed device extensions from being reported by the Vulkan physical device.", + "status": "STABLE", + "type": "LIST", + "list": [ + "VK_KHR_16bit_storage", + "VK_KHR_8bit_storage", + "VK_KHR_acceleration_structure", + "VK_KHR_bind_memory2", + "VK_KHR_buffer_device_address", + "VK_KHR_copy_commands2", + "VK_KHR_create_renderpass2", + "VK_KHR_dedicated_allocation", + "VK_KHR_deferred_host_operations", + "VK_KHR_depth_stencil_resolve", + "VK_KHR_descriptor_update_template", + "VK_KHR_device_group", + "VK_KHR_display_swapchain", + "VK_KHR_draw_indirect_count", + "VK_KHR_driver_properties", + "VK_KHR_dynamic_rendering", + "VK_KHR_external_fence", + "VK_KHR_external_fence_fd", + "VK_KHR_external_fence_win32", + "VK_KHR_external_memory", + "VK_KHR_external_memory_fd", + "VK_KHR_external_memory_win32", + "VK_KHR_external_semaphore", + "VK_KHR_external_semaphore_fd", + "VK_KHR_external_semaphore_win32", + "VK_KHR_format_feature_flags2", + "VK_KHR_fragment_shading_rate", + "VK_KHR_get_memory_requirements2", + "VK_KHR_image_format_list", + "VK_KHR_imageless_framebuffer", + "VK_KHR_incremental_present", + "VK_KHR_maintenance1", + "VK_KHR_maintenance2", + "VK_KHR_maintenance3", + "VK_KHR_maintenance4", + "VK_KHR_multiview", + "VK_KHR_performance_query", + "VK_KHR_pipeline_executable_properties", + "VK_KHR_pipeline_library", + "VK_KHR_portability_subset", + "VK_KHR_present_id", + "VK_KHR_present_wait", + "VK_KHR_push_descriptor", + "VK_KHR_ray_query", + "VK_KHR_ray_tracing_pipeline", + "VK_KHR_relaxed_block_layout", + "VK_KHR_sampler_mirror_clamp_to_edge", + "VK_KHR_sampler_ycbcr_conversion", + "VK_KHR_separate_depth_stencil_layouts", + "VK_KHR_shader_atomic_int64", + "VK_KHR_shader_clock", + "VK_KHR_shader_draw_parameters", + "VK_KHR_shader_float16_int8", + "VK_KHR_shader_float_controls", + "VK_KHR_shader_integer_dot_product", + "VK_KHR_shader_non_semantic_info", + "VK_KHR_shader_subgroup_extended_types", + "VK_KHR_shader_subgroup_uniform_control_flow", + "VK_KHR_shader_terminate_invocation", + "VK_KHR_shared_presentable_image", + "VK_KHR_spirv_1_4", + "VK_KHR_storage_buffer_storage_class", + "VK_KHR_swapchain", + "VK_KHR_swapchain_mutable_format", + "VK_KHR_synchronization2", + "VK_KHR_timeline_semaphore", + "VK_KHR_uniform_buffer_standard_layout", + "VK_KHR_variable_pointers", + "VK_KHR_video_decode_queue", + "VK_KHR_video_encode_queue", + "VK_KHR_video_queue", + "VK_KHR_vulkan_memory_model", + "VK_KHR_win32_keyed_mutex", + "VK_KHR_workgroup_memory_explicit_layout", + "VK_KHR_zero_initialize_workgroup_memory", + "VK_EXT_4444_formats", + "VK_EXT_astc_decode_mode", + "VK_EXT_blend_operation_advanced", + "VK_EXT_border_color_swizzle", + "VK_EXT_buffer_device_address", + "VK_EXT_calibrated_timestamps", + "VK_EXT_color_write_enable", + "VK_EXT_conditional_rendering", + "VK_EXT_conservative_rasterization", + "VK_EXT_custom_border_color", + "VK_EXT_debug_marker", + "VK_EXT_depth_clip_control", + "VK_EXT_depth_clip_enable", + "VK_EXT_depth_range_unrestricted", + "VK_EXT_descriptor_indexing", + "VK_EXT_device_memory_report", + "VK_EXT_discard_rectangles", + "VK_EXT_display_control", + "VK_EXT_extended_dynamic_state", + "VK_EXT_extended_dynamic_state2", + "VK_EXT_external_memory_dma_buf", + "VK_EXT_external_memory_host", + "VK_EXT_filter_cubic", + "VK_EXT_fragment_density_map", + "VK_EXT_fragment_density_map2", + "VK_EXT_fragment_shader_interlock", + "VK_EXT_full_screen_exclusive", + "VK_EXT_global_priority", + "VK_EXT_global_priority_query", + "VK_EXT_hdr_metadata", + "VK_EXT_host_query_reset", + "VK_EXT_image_drm_format_modifier", + "VK_EXT_image_robustness", + "VK_EXT_image_view_min_lod", + "VK_EXT_index_type_uint8", + "VK_EXT_inline_uniform_block", + "VK_EXT_line_rasterization", + "VK_EXT_load_store_op_none", + "VK_EXT_memory_budget", + "VK_EXT_memory_priority", + "VK_EXT_multi_draw", + "VK_EXT_pageable_device_local_memory", + "VK_EXT_pci_bus_info", + "VK_EXT_physical_device_drm", + "VK_EXT_pipeline_creation_cache_control", + "VK_EXT_pipeline_creation_feedback", + "VK_EXT_post_depth_coverage", + "VK_EXT_primitive_topology_list_restart", + "VK_EXT_private_data", + "VK_EXT_provoking_vertex", + "VK_EXT_queue_family_foreign", + "VK_EXT_rgba10x6_formats", + "VK_EXT_robustness2", + "VK_EXT_sample_locations", + "VK_EXT_sampler_filter_minmax", + "VK_EXT_scalar_block_layout", + "VK_EXT_separate_stencil_usage", + "VK_EXT_shader_atomic_float", + "VK_EXT_shader_atomic_float2", + "VK_EXT_shader_demote_to_helper_invocation", + "VK_EXT_shader_image_atomic_int64", + "VK_EXT_shader_stencil_export", + "VK_EXT_shader_subgroup_ballot", + "VK_EXT_shader_subgroup_vote", + "VK_EXT_shader_viewport_index_layer", + "VK_EXT_subgroup_size_control", + "VK_EXT_texel_buffer_alignment", + "VK_EXT_texture_compression_astc_hdr", + "VK_EXT_tooling_info", + "VK_EXT_transform_feedback", + "VK_EXT_validation_cache", + "VK_EXT_vertex_attribute_divisor", + "VK_EXT_vertex_input_dynamic_state", + "VK_EXT_video_decode_h264", + "VK_EXT_video_decode_h265", + "VK_EXT_video_encode_h264", + "VK_EXT_video_encode_h265", + "VK_EXT_ycbcr_2plane_444_formats", + "VK_EXT_ycbcr_image_arrays", + "VK_AMD_buffer_marker", + "VK_AMD_device_coherent_memory", + "VK_AMD_display_native_hdr", + "VK_AMD_draw_indirect_count", + "VK_AMD_gcn_shader", + "VK_AMD_gpu_shader_half_float", + "VK_AMD_gpu_shader_int16", + "VK_AMD_memory_overallocation_behavior", + "VK_AMD_mixed_attachment_samples", + "VK_AMD_negative_viewport_height", + "VK_AMD_pipeline_compiler_control", + "VK_AMD_rasterization_order", + "VK_AMD_shader_ballot", + "VK_AMD_shader_core_properties", + "VK_AMD_shader_core_properties2", + "VK_AMD_shader_explicit_vertex_parameter", + "VK_AMD_shader_fragment_mask", + "VK_AMD_shader_image_load_store_lod", + "VK_AMD_shader_info", + "VK_AMD_shader_trinary_minmax", + "VK_AMD_texture_gather_bias_lod", + "VK_ANDROID_external_memory_android_hardware_buffer", + "VK_ARM_rasterization_order_attachment_access", + "VK_FUCHSIA_buffer_collection", + "VK_FUCHSIA_external_memory", + "VK_FUCHSIA_external_semaphore", + "VK_GGP_frame_token", + "VK_GOOGLE_decorate_string", + "VK_GOOGLE_display_timing", + "VK_GOOGLE_hlsl_functionality1", + "VK_GOOGLE_user_type", + "VK_HUAWEI_invocation_mask", + "VK_HUAWEI_subpass_shading", + "VK_IMG_filter_cubic", + "VK_IMG_format_pvrtc", + "VK_INTEL_performance_query", + "VK_INTEL_shader_integer_functions2", + "VK_NV_acquire_winrt_display", + "VK_NV_clip_space_w_scaling", + "VK_NV_compute_shader_derivatives", + "VK_NV_cooperative_matrix", + "VK_NV_corner_sampled_image", + "VK_NV_coverage_reduction_mode", + "VK_NV_dedicated_allocation", + "VK_NV_dedicated_allocation_image_aliasing", + "VK_NV_device_diagnostic_checkpoints", + "VK_NV_device_diagnostics_config", + "VK_NV_device_generated_commands", + "VK_NV_external_memory", + "VK_NV_external_memory_rdma", + "VK_NV_external_memory_win32", + "VK_NV_fill_rectangle", + "VK_NV_fragment_coverage_to_color", + "VK_NV_fragment_shader_barycentric", + "VK_NV_fragment_shading_rate_enums", + "VK_NV_framebuffer_mixed_samples", + "VK_NV_geometry_shader_passthrough", + "VK_NV_glsl_shader", + "VK_NV_inherited_viewport_scissor", + "VK_NV_mesh_shader", + "VK_NV_ray_tracing", + "VK_NV_ray_tracing_motion_blur", + "VK_NV_representative_fragment_test", + "VK_NV_sample_mask_override_coverage", + "VK_NV_scissor_exclusive", + "VK_NV_shader_image_footprint", + "VK_NV_shader_sm_builtins", + "VK_NV_shader_subgroup_partitioned", + "VK_NV_shading_rate_image", + "VK_NV_viewport_array2", + "VK_NV_viewport_swizzle", + "VK_NV_win32_keyed_mutex", + "VK_QCOM_render_pass_shader_resolve", + "VK_QCOM_render_pass_store_ops", + "VK_QCOM_render_pass_transform", + "VK_QCOM_rotated_copy_commands", + "VK_VALVE_mutable_descriptor_type", + "VK_QCOM_fragment_density_map_offset", + "VK_NV_linear_color_attachment", + "VK_KHR_global_priority" + ], + "default": [] + }, + { + "key": "exclude_formats", + "label": "Exclude Formats", + "description": "Removes the format feature flags from being reported by the Vulkan physical device.", + "status": "STABLE", + "type": "LIST", + "list": [ + "VK_FORMAT_R4G4_UNORM_PACK8", + "VK_FORMAT_R4G4B4A4_UNORM_PACK16", + "VK_FORMAT_B4G4R4A4_UNORM_PACK16", + "VK_FORMAT_R5G6B5_UNORM_PACK16", + "VK_FORMAT_B5G6R5_UNORM_PACK16", + "VK_FORMAT_R5G5B5A1_UNORM_PACK16", + "VK_FORMAT_B5G5R5A1_UNORM_PACK16", + "VK_FORMAT_A1R5G5B5_UNORM_PACK16", + "VK_FORMAT_R8_UNORM", + "VK_FORMAT_R8_SNORM", + "VK_FORMAT_R8_USCALED", + "VK_FORMAT_R8_SSCALED", + "VK_FORMAT_R8_UINT", + "VK_FORMAT_R8_SINT", + "VK_FORMAT_R8_SRGB", + "VK_FORMAT_R8G8_UNORM", + "VK_FORMAT_R8G8_SNORM", + "VK_FORMAT_R8G8_USCALED", + "VK_FORMAT_R8G8_SSCALED", + "VK_FORMAT_R8G8_UINT", + "VK_FORMAT_R8G8_SINT", + "VK_FORMAT_R8G8_SRGB", + "VK_FORMAT_R8G8B8_UNORM", + "VK_FORMAT_R8G8B8_SNORM", + "VK_FORMAT_R8G8B8_USCALED", + "VK_FORMAT_R8G8B8_SSCALED", + "VK_FORMAT_R8G8B8_UINT", + "VK_FORMAT_R8G8B8_SINT", + "VK_FORMAT_R8G8B8_SRGB", + "VK_FORMAT_B8G8R8_UNORM", + "VK_FORMAT_B8G8R8_SNORM", + "VK_FORMAT_B8G8R8_USCALED", + "VK_FORMAT_B8G8R8_SSCALED", + "VK_FORMAT_B8G8R8_UINT", + "VK_FORMAT_B8G8R8_SINT", + "VK_FORMAT_B8G8R8_SRGB", + "VK_FORMAT_R8G8B8A8_UNORM", + "VK_FORMAT_R8G8B8A8_SNORM", + "VK_FORMAT_R8G8B8A8_USCALED", + "VK_FORMAT_R8G8B8A8_SSCALED", + "VK_FORMAT_R8G8B8A8_UINT", + "VK_FORMAT_R8G8B8A8_SINT", + "VK_FORMAT_R8G8B8A8_SRGB", + "VK_FORMAT_B8G8R8A8_UNORM", + "VK_FORMAT_B8G8R8A8_SNORM", + "VK_FORMAT_B8G8R8A8_USCALED", + "VK_FORMAT_B8G8R8A8_SSCALED", + "VK_FORMAT_B8G8R8A8_UINT", + "VK_FORMAT_B8G8R8A8_SINT", + "VK_FORMAT_B8G8R8A8_SRGB", + "VK_FORMAT_A8B8G8R8_UNORM_PACK32", + "VK_FORMAT_A8B8G8R8_SNORM_PACK32", + "VK_FORMAT_A8B8G8R8_USCALED_PACK32", + "VK_FORMAT_A8B8G8R8_SSCALED_PACK32", + "VK_FORMAT_A8B8G8R8_UINT_PACK32", + "VK_FORMAT_A8B8G8R8_SINT_PACK32", + "VK_FORMAT_A8B8G8R8_SRGB_PACK32", + "VK_FORMAT_A2R10G10B10_UNORM_PACK32", + "VK_FORMAT_A2R10G10B10_SNORM_PACK32", + "VK_FORMAT_A2R10G10B10_USCALED_PACK32", + "VK_FORMAT_A2R10G10B10_SSCALED_PACK32", + "VK_FORMAT_A2R10G10B10_UINT_PACK32", + "VK_FORMAT_A2R10G10B10_SINT_PACK32", + "VK_FORMAT_A2B10G10R10_UNORM_PACK32", + "VK_FORMAT_A2B10G10R10_SNORM_PACK32", + "VK_FORMAT_A2B10G10R10_USCALED_PACK32", + "VK_FORMAT_A2B10G10R10_SSCALED_PACK32", + "VK_FORMAT_A2B10G10R10_UINT_PACK32", + "VK_FORMAT_A2B10G10R10_SINT_PACK32", + "VK_FORMAT_R16_UNORM", + "VK_FORMAT_R16_SNORM", + "VK_FORMAT_R16_USCALED", + "VK_FORMAT_R16_SSCALED", + "VK_FORMAT_R16_UINT", + "VK_FORMAT_R16_SINT", + "VK_FORMAT_R16_SFLOAT", + "VK_FORMAT_R16G16_UNORM", + "VK_FORMAT_R16G16_SNORM", + "VK_FORMAT_R16G16_USCALED", + "VK_FORMAT_R16G16_SSCALED", + "VK_FORMAT_R16G16_UINT", + "VK_FORMAT_R16G16_SINT", + "VK_FORMAT_R16G16_SFLOAT", + "VK_FORMAT_R16G16B16_UNORM", + "VK_FORMAT_R16G16B16_SNORM", + "VK_FORMAT_R16G16B16_USCALED", + "VK_FORMAT_R16G16B16_SSCALED", + "VK_FORMAT_R16G16B16_UINT", + "VK_FORMAT_R16G16B16_SINT", + "VK_FORMAT_R16G16B16_SFLOAT", + "VK_FORMAT_R16G16B16A16_UNORM", + "VK_FORMAT_R16G16B16A16_SNORM", + "VK_FORMAT_R16G16B16A16_USCALED", + "VK_FORMAT_R16G16B16A16_SSCALED", + "VK_FORMAT_R16G16B16A16_UINT", + "VK_FORMAT_R16G16B16A16_SINT", + "VK_FORMAT_R16G16B16A16_SFLOAT", + "VK_FORMAT_R32_UINT", + "VK_FORMAT_R32_SINT", + "VK_FORMAT_R32_SFLOAT", + "VK_FORMAT_R32G32_UINT", + "VK_FORMAT_R32G32_SINT", + "VK_FORMAT_R32G32_SFLOAT", + "VK_FORMAT_R32G32B32_UINT", + "VK_FORMAT_R32G32B32_SINT", + "VK_FORMAT_R32G32B32_SFLOAT", + "VK_FORMAT_R32G32B32A32_UINT", + "VK_FORMAT_R32G32B32A32_SINT", + "VK_FORMAT_R32G32B32A32_SFLOAT", + "VK_FORMAT_R64_UINT", + "VK_FORMAT_R64_SINT", + "VK_FORMAT_R64_SFLOAT", + "VK_FORMAT_R64G64_UINT", + "VK_FORMAT_R64G64_SINT", + "VK_FORMAT_R64G64_SFLOAT", + "VK_FORMAT_R64G64B64_UINT", + "VK_FORMAT_R64G64B64_SINT", + "VK_FORMAT_R64G64B64_SFLOAT", + "VK_FORMAT_R64G64B64A64_UINT", + "VK_FORMAT_R64G64B64A64_SINT", + "VK_FORMAT_R64G64B64A64_SFLOAT", + "VK_FORMAT_B10G11R11_UFLOAT_PACK32", + "VK_FORMAT_E5B9G9R9_UFLOAT_PACK32", + "VK_FORMAT_D16_UNORM", + "VK_FORMAT_X8_D24_UNORM_PACK32", + "VK_FORMAT_D32_SFLOAT", + "VK_FORMAT_S8_UINT", + "VK_FORMAT_D16_UNORM_S8_UINT", + "VK_FORMAT_D24_UNORM_S8_UINT", + "VK_FORMAT_D32_SFLOAT_S8_UINT", + "VK_FORMAT_BC1_RGB_UNORM_BLOCK", + "VK_FORMAT_BC1_RGB_SRGB_BLOCK", + "VK_FORMAT_BC1_RGBA_UNORM_BLOCK", + "VK_FORMAT_BC1_RGBA_SRGB_BLOCK", + "VK_FORMAT_BC2_UNORM_BLOCK", + "VK_FORMAT_BC2_SRGB_BLOCK", + "VK_FORMAT_BC3_UNORM_BLOCK", + "VK_FORMAT_BC3_SRGB_BLOCK", + "VK_FORMAT_BC4_UNORM_BLOCK", + "VK_FORMAT_BC4_SNORM_BLOCK", + "VK_FORMAT_BC5_UNORM_BLOCK", + "VK_FORMAT_BC5_SNORM_BLOCK", + "VK_FORMAT_BC6H_UFLOAT_BLOCK", + "VK_FORMAT_BC6H_SFLOAT_BLOCK", + "VK_FORMAT_BC7_UNORM_BLOCK", + "VK_FORMAT_BC7_SRGB_BLOCK", + "VK_FORMAT_ETC2_R8G8B8_UNORM_BLOCK", + "VK_FORMAT_ETC2_R8G8B8_SRGB_BLOCK", + "VK_FORMAT_ETC2_R8G8B8A1_UNORM_BLOCK", + "VK_FORMAT_ETC2_R8G8B8A1_SRGB_BLOCK", + "VK_FORMAT_ETC2_R8G8B8A8_UNORM_BLOCK", + "VK_FORMAT_ETC2_R8G8B8A8_SRGB_BLOCK", + "VK_FORMAT_EAC_R11_UNORM_BLOCK", + "VK_FORMAT_EAC_R11_SNORM_BLOCK", + "VK_FORMAT_EAC_R11G11_UNORM_BLOCK", + "VK_FORMAT_EAC_R11G11_SNORM_BLOCK", + "VK_FORMAT_ASTC_4x4_UNORM_BLOCK", + "VK_FORMAT_ASTC_4x4_SRGB_BLOCK", + "VK_FORMAT_ASTC_5x4_UNORM_BLOCK", + "VK_FORMAT_ASTC_5x4_SRGB_BLOCK", + "VK_FORMAT_ASTC_5x5_UNORM_BLOCK", + "VK_FORMAT_ASTC_5x5_SRGB_BLOCK", + "VK_FORMAT_ASTC_6x5_UNORM_BLOCK", + "VK_FORMAT_ASTC_6x5_SRGB_BLOCK", + "VK_FORMAT_ASTC_6x6_UNORM_BLOCK", + "VK_FORMAT_ASTC_6x6_SRGB_BLOCK", + "VK_FORMAT_ASTC_8x5_UNORM_BLOCK", + "VK_FORMAT_ASTC_8x5_SRGB_BLOCK", + "VK_FORMAT_ASTC_8x6_UNORM_BLOCK", + "VK_FORMAT_ASTC_8x6_SRGB_BLOCK", + "VK_FORMAT_ASTC_8x8_UNORM_BLOCK", + "VK_FORMAT_ASTC_8x8_SRGB_BLOCK", + "VK_FORMAT_ASTC_10x5_UNORM_BLOCK", + "VK_FORMAT_ASTC_10x5_SRGB_BLOCK", + "VK_FORMAT_ASTC_10x6_UNORM_BLOCK", + "VK_FORMAT_ASTC_10x6_SRGB_BLOCK", + "VK_FORMAT_ASTC_10x8_UNORM_BLOCK", + "VK_FORMAT_ASTC_10x8_SRGB_BLOCK", + "VK_FORMAT_ASTC_10x10_UNORM_BLOCK", + "VK_FORMAT_ASTC_10x10_SRGB_BLOCK", + "VK_FORMAT_ASTC_12x10_UNORM_BLOCK", + "VK_FORMAT_ASTC_12x10_SRGB_BLOCK", + "VK_FORMAT_ASTC_12x12_UNORM_BLOCK", + "VK_FORMAT_ASTC_12x12_SRGB_BLOCK", + "VK_FORMAT_G8B8G8R8_422_UNORM", + "VK_FORMAT_B8G8R8G8_422_UNORM", + "VK_FORMAT_G8_B8_R8_3PLANE_420_UNORM", + "VK_FORMAT_G8_B8R8_2PLANE_420_UNORM", + "VK_FORMAT_G8_B8_R8_3PLANE_422_UNORM", + "VK_FORMAT_G8_B8R8_2PLANE_422_UNORM", + "VK_FORMAT_G8_B8_R8_3PLANE_444_UNORM", + "VK_FORMAT_R10X6_UNORM_PACK16", + "VK_FORMAT_R10X6G10X6_UNORM_2PACK16", + "VK_FORMAT_R10X6G10X6B10X6A10X6_UNORM_4PACK16", + "VK_FORMAT_G10X6B10X6G10X6R10X6_422_UNORM_4PACK16", + "VK_FORMAT_B10X6G10X6R10X6G10X6_422_UNORM_4PACK16", + "VK_FORMAT_G10X6_B10X6_R10X6_3PLANE_420_UNORM_3PACK16", + "VK_FORMAT_G10X6_B10X6R10X6_2PLANE_420_UNORM_3PACK16", + "VK_FORMAT_G10X6_B10X6_R10X6_3PLANE_422_UNORM_3PACK16", + "VK_FORMAT_G10X6_B10X6R10X6_2PLANE_422_UNORM_3PACK16", + "VK_FORMAT_G10X6_B10X6_R10X6_3PLANE_444_UNORM_3PACK16", + "VK_FORMAT_R12X4_UNORM_PACK16", + "VK_FORMAT_R12X4G12X4_UNORM_2PACK16", + "VK_FORMAT_R12X4G12X4B12X4A12X4_UNORM_4PACK16", + "VK_FORMAT_G12X4B12X4G12X4R12X4_422_UNORM_4PACK16", + "VK_FORMAT_B12X4G12X4R12X4G12X4_422_UNORM_4PACK16", + "VK_FORMAT_G12X4_B12X4_R12X4_3PLANE_420_UNORM_3PACK16", + "VK_FORMAT_G12X4_B12X4R12X4_2PLANE_420_UNORM_3PACK16", + "VK_FORMAT_G12X4_B12X4_R12X4_3PLANE_422_UNORM_3PACK16", + "VK_FORMAT_G12X4_B12X4R12X4_2PLANE_422_UNORM_3PACK16", + "VK_FORMAT_G12X4_B12X4_R12X4_3PLANE_444_UNORM_3PACK16", + "VK_FORMAT_G16B16G16R16_422_UNORM", + "VK_FORMAT_B16G16R16G16_422_UNORM", + "VK_FORMAT_G16_B16_R16_3PLANE_420_UNORM", + "VK_FORMAT_G16_B16R16_2PLANE_420_UNORM", + "VK_FORMAT_G16_B16_R16_3PLANE_422_UNORM", + "VK_FORMAT_G16_B16R16_2PLANE_422_UNORM", + "VK_FORMAT_G16_B16_R16_3PLANE_444_UNORM", + "VK_FORMAT_G8_B8R8_2PLANE_444_UNORM", + "VK_FORMAT_G10X6_B10X6R10X6_2PLANE_444_UNORM_3PACK16", + "VK_FORMAT_G12X4_B12X4R12X4_2PLANE_444_UNORM_3PACK16", + "VK_FORMAT_G16_B16R16_2PLANE_444_UNORM", + "VK_FORMAT_A4R4G4B4_UNORM_PACK16", + "VK_FORMAT_A4B4G4R4_UNORM_PACK16", + "VK_FORMAT_ASTC_4x4_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_5x4_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_5x5_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_6x5_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_6x6_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_8x5_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_8x6_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_8x8_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_10x5_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_10x6_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_10x8_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_10x10_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_12x10_SFLOAT_BLOCK", + "VK_FORMAT_ASTC_12x12_SFLOAT_BLOCK", + "VK_FORMAT_PVRTC1_2BPP_UNORM_BLOCK_IMG", + "VK_FORMAT_PVRTC1_4BPP_UNORM_BLOCK_IMG", + "VK_FORMAT_PVRTC2_2BPP_UNORM_BLOCK_IMG", + "VK_FORMAT_PVRTC2_4BPP_UNORM_BLOCK_IMG", + "VK_FORMAT_PVRTC1_2BPP_SRGB_BLOCK_IMG", + "VK_FORMAT_PVRTC1_4BPP_SRGB_BLOCK_IMG", + "VK_FORMAT_PVRTC2_2BPP_SRGB_BLOCK_IMG", + "VK_FORMAT_PVRTC2_4BPP_SRGB_BLOCK_IMG" + ], + "default": [] + }, + { + "key": "debug_actions", + "label": "Debug Actions", + "description": "This indicates what action is to be taken when a layer wants to report information", + "status": "STABLE", + "type": "FLAGS", + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "flags": [ + { + "key": "DEBUG_ACTION_STDOUT_BIT", + "label": "Log to stdout", + "description": "Log messages using the stdout." + }, + { + "key": "DEBUG_ACTION_OUTPUT_BIT", + "label": "Log to OutputDebugString", + "description": "Log messages using the Windows OutputDebugString for Vulkan Studio output display.", + "platforms": [ "WINDOWS" ] + }, + { + "key": "DEBUG_ACTION_FILE_BIT", + "label": "Log to File", + "description": "Log messages to a file.", + "settings": [ + { + "key": "debug_filename", + "label": "Log Filename", + "description": "Specifies the output filename", + "type": "SAVE_FILE", + "default": "profiles_layer_log.txt", + "dependence": { + "mode": "ANY", + "settings": [ + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_STDOUT_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_OUTPUT_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_BREAKPOINT_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_STDOUT_BIT", "DEBUG_ACTION_OUTPUT_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_STDOUT_BIT", "DEBUG_ACTION_BREAKPOINT_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_OUTPUT_BIT", "DEBUG_ACTION_BREAKPOINT_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_STDOUT_BIT", "DEBUG_ACTION_OUTPUT_BIT", "DEBUG_ACTION_BREAKPOINT_BIT" ] + } + ] + } + }, + { + "key": "debug_file_clear", + "label": "Clear Log at Launch", + "description": "Discard the content of the log file between each layer run", + "type": "BOOL", + "default": true, + "dependence": { + "mode": "ANY", + "settings": [ + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_STDOUT_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_OUTPUT_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_BREAKPOINT_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_STDOUT_BIT", "DEBUG_ACTION_OUTPUT_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_STDOUT_BIT", "DEBUG_ACTION_BREAKPOINT_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_OUTPUT_BIT", "DEBUG_ACTION_BREAKPOINT_BIT" ] + }, + { + "key": "debug_actions", + "value": [ "DEBUG_ACTION_FILE_BIT", "DEBUG_ACTION_STDOUT_BIT", "DEBUG_ACTION_OUTPUT_BIT", "DEBUG_ACTION_BREAKPOINT_BIT" ] + } + ] + } + } + ], + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "expanded": true + }, + { + "key": "DEBUG_ACTION_BREAKPOINT_BIT", + "label": "Break", + "description": "Trigger a breakpoint if a debugger is in use." + } + ], + "default": [ "DEBUG_ACTION_STDOUT_BIT" ] + }, + { + "key": "debug_fail_on_error", + "label": "Fail on Error", + "description": "If the device doesn't support the capabilities of the selected Profile, the layer fail to load. When enabled, vkEnumeratePhysicalDevices will fail when the selected Profile is not supported.", + "status": "STABLE", + "type": "BOOL", + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "default": false + }, + { + "key": "debug_reports", + "label": "Message Types", + "description": "This is a comma-delineated list of options telling the layer what types of messages it should report back", + "status": "STABLE", + "type": "FLAGS", + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "flags": [ + { + "key": "DEBUG_REPORT_NOTIFICATION_BIT", + "label": "Notification", + "description": "Report notifications." + }, + { + "key": "DEBUG_REPORT_WARNING_BIT", + "label": "Warning", + "description": "Report warnings from using the API in a manner which may lead to undefined behavior or to warn the user of common trouble spots. A warning does NOT necessarily signify illegal application behavior." + }, + { + "key": "DEBUG_REPORT_ERROR_BIT", + "label": "Error", + "description": "Report errors in API usage." + }, + { + "key": "DEBUG_REPORT_DEBUG_BIT", + "label": "Debug", + "description": "Report debug information for the Profiles Layer development.", + "view": "DEBUG" + } + ], + "default": [ + "DEBUG_REPORT_NOTIFICATION_BIT", + "DEBUG_REPORT_WARNING_BIT", + "DEBUG_REPORT_ERROR_BIT" + ] + } + ] + } + } +} diff --git a/vulkan/Bin/VkLayer_khronos_shader_object.json b/vulkan/Bin/VkLayer_khronos_shader_object.json new file mode 100644 index 0000000000000000000000000000000000000000..d04f38a3987d4f555809f59f288fffd360dc895c --- /dev/null +++ b/vulkan/Bin/VkLayer_khronos_shader_object.json @@ -0,0 +1,98 @@ +{ + "file_format_version" : "1.2.0", + "layer": { + "name": "VK_LAYER_KHRONOS_shader_object", + "type": "GLOBAL", + "library_path": ".\\VkLayer_khronos_shader_object.dll", + "api_version": "1.4.341", + "implementation_version": "1", + "description": "Khronos Shader object layer", + "instance_extensions": [ + { + "name": "VK_EXT_layer_settings", + "spec_version": "2" + } + ], + "device_extensions": [ + { + "name": "VK_EXT_shader_object", + "spec_version": "1", + "entrypoints": [ + "vkCmdBindShadersEXT", + "vkCmdBindVertexBuffers2EXT", + "vkCmdSetAlphaToCoverageEnableEXT", + "vkCmdSetAlphaToOneEnableEXT", + "vkCmdSetColorBlendAdvancedEXT", + "vkCmdSetColorBlendEnableEXT", + "vkCmdSetColorBlendEquationEXT", + "vkCmdSetColorWriteMaskEXT", + "vkCmdSetConservativeRasterizationModeEXT", + "vkCmdSetCoverageModulationModeNV", + "vkCmdSetCoverageModulationTableEnableNV", + "vkCmdSetCoverageModulationTableNV", + "vkCmdSetCoverageReductionModeNV", + "vkCmdSetCoverageToColorEnableNV", + "vkCmdSetCoverageToColorLocationNV", + "vkCmdSetCullModeEXT", + "vkCmdSetDepthBiasEnableEXT", + "vkCmdSetDepthBoundsTestEnableEXT", + "vkCmdSetDepthClampEnableEXT", + "vkCmdSetDepthClipEnableEXT", + "vkCmdSetDepthClipNegativeOneToOneEXT", + "vkCmdSetDepthCompareOpEXT", + "vkCmdSetDepthTestEnableEXT", + "vkCmdSetDepthWriteEnableEXT", + "vkCmdSetExtraPrimitiveOverestimationSizeEXT", + "vkCmdSetFrontFaceEXT", + "vkCmdSetLineRasterizationModeEXT", + "vkCmdSetLineStippleEnableEXT", + "vkCmdSetLogicOpEXT", + "vkCmdSetLogicOpEnableEXT", + "vkCmdSetPatchControlPointsEXT", + "vkCmdSetPolygonModeEXT", + "vkCmdSetPrimitiveRestartEnableEXT", + "vkCmdSetPrimitiveTopologyEXT", + "vkCmdSetProvokingVertexModeEXT", + "vkCmdSetRasterizationSamplesEXT", + "vkCmdSetRasterizationStreamEXT", + "vkCmdSetRasterizerDiscardEnableEXT", + "vkCmdSetRepresentativeFragmentTestEnableNV", + "vkCmdSetSampleLocationsEnableEXT", + "vkCmdSetSampleMaskEXT", + "vkCmdSetScissorWithCountEXT", + "vkCmdSetShadingRateImageEnableNV", + "vkCmdSetStencilOpEXT", + "vkCmdSetStencilTestEnableEXT", + "vkCmdSetTessellationDomainOriginEXT", + "vkCmdSetVertexInputEXT", + "vkCmdSetViewportSwizzleNV", + "vkCmdSetViewportWScalingEnableNV", + "vkCmdSetViewportWithCountEXT", + "vkCreateShadersEXT", + "vkDestroyShaderEXT", + "vkGetShaderBinaryDataEXT" + ] + } + ], + "features": { + "settings": [ + { + "key": "force_enable", + "env": "VK_SHADER_OBJECT_FORCE_ENABLE", + "label": "Force Enable", + "description": "Force the layer to be active even if the underlying driver also implements the shader object extension.", + "type": "BOOL", + "default": false + }, + { + "key": "disable_pipeline_pre_caching", + "env": "VK_SHADER_OBJECT_DISABLE_PIPELINE_PRE_CACHING", + "label": "Disable Pipeline Pre-Caching", + "description": "Disable the layer from pre-caching pipelines, reducing the memory overhead.", + "type": "BOOL", + "default": false + } + ] + } + } +} diff --git a/vulkan/Bin/VkLayer_khronos_synchronization2.json b/vulkan/Bin/VkLayer_khronos_synchronization2.json new file mode 100644 index 0000000000000000000000000000000000000000..40f8886e181f9ed64174e91a679faedee3fb5db1 --- /dev/null +++ b/vulkan/Bin/VkLayer_khronos_synchronization2.json @@ -0,0 +1,49 @@ +{ + "file_format_version": "1.2.0", + "layer": { + "name": "VK_LAYER_KHRONOS_synchronization2", + "type": "GLOBAL", + "library_path": ".\\VkLayer_khronos_synchronization2.dll", + "api_version": "1.4.341", + "implementation_version": "1", + "description": "Khronos Synchronization2 layer", + "introduction": "The VK_LAYER_KHRONOS_synchronization2 extension layer implements the VK_KHR_synchronization2 extension.", + "url": "https://vulkan.lunarg.com/doc/sdk/latest/windows/synchronization2_layer.html", + "status": "STABLE", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "instance_extensions": [ + { + "name": "VK_EXT_layer_settings", + "spec_version": "2" + } + ], + "device_extensions": [ + { + "name": "VK_KHR_synchronization2", + "spec_version": "1", + "entrypoints": [ + "vkCmdSetEvent2KHR", + "vkCmdResetEvent2KHR", + "vkCmdWaitEvents2KHR", + "vkCmdPipelineBarrier2KHR", + "vkCmdWriteTimestamp2KHR", + "vkQueueSubmit2KHR", + "vkCmdWriteBufferMarker2AMD", + "vkGetQueueCheckpointData2NV" + ] + } + ], + "features": { + "settings": [ + { + "key": "force_enable", + "env": "VK_SYNCHRONIZATION2_FORCE_ENABLE", + "label": "Force Enable", + "description": "Force the layer to be active even if the underlying driver also implements the synchonization2 extension.", + "type": "BOOL", + "default": false + } + ] + } + } +} diff --git a/vulkan/Bin/VkLayer_khronos_validation.json b/vulkan/Bin/VkLayer_khronos_validation.json new file mode 100644 index 0000000000000000000000000000000000000000..07492e8527a448c20207150d80c8554d1e8990be --- /dev/null +++ b/vulkan/Bin/VkLayer_khronos_validation.json @@ -0,0 +1,1475 @@ +{ + "file_format_version": "1.2.0", + "layer": { + "name": "VK_LAYER_KHRONOS_validation", + "prefix": "vvl", + "type": "GLOBAL", + "library_path": ".\\VkLayer_khronos_validation.dll", + "api_version": "1.4.341", + "implementation_version": "1", + "description": "Khronos Validation Layer", + "introduction": "The main, comprehensive Khronos validation layer.\n\nVulkan is an Explicit API, enabling direct control over how GPUs actually work. By design, minimal error checking is done inside a Vulkan driver. Applications have full control and responsibility for correct operation. Any errors in how Vulkan is used can result in a crash. \n\nThe Khronos Validation Layer can be enabled to assist development by enabling developers to verify their applications correctly use the Vulkan API.", + "platforms": [ + "WINDOWS", + "LINUX", + "ANDROID", + "MACOS" + ], + "url": "https://vulkan.lunarg.com/doc/sdk/latest/windows/khronos_validation_layer.html", + "instance_extensions": [ + { + "name": "VK_EXT_debug_report", + "spec_version": "9" + }, + { + "name": "VK_EXT_debug_utils", + "spec_version": "1" + }, + { + "name": "VK_EXT_layer_settings", + "spec_version": "2" + }, + { + "name": "VK_EXT_validation_features", + "spec_version": "2" + } + ], + "device_extensions": [ + { + "name": "VK_EXT_debug_marker", + "spec_version": "4", + "entrypoints": [ + "vkDebugMarkerSetObjectTagEXT", + "vkDebugMarkerSetObjectNameEXT", + "vkCmdDebugMarkerBeginEXT", + "vkCmdDebugMarkerEndEXT", + "vkCmdDebugMarkerInsertEXT" + ] + }, + { + "name": "VK_EXT_validation_cache", + "spec_version": "1", + "entrypoints": [ + "vkCreateValidationCacheEXT", + "vkDestroyValidationCacheEXT", + "vkGetValidationCacheDataEXT", + "vkMergeValidationCachesEXT" + ] + }, + { + "name": "VK_EXT_tooling_info", + "spec_version": "1", + "entrypoints": [ + "vkGetPhysicalDeviceToolPropertiesEXT" + ] + } + ], + "features": { + "presets": [ + { + "label": "Standard", + "description": "Good default validation setup that balance validation coverage and performance.", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "status": "STABLE", + "settings": [ + { "key": "validate_core", "value": true }, + { "key": "check_image_layout", "value": true }, + { "key": "check_command_buffer", "value": true }, + { "key": "check_object_in_use", "value": true }, + { "key": "check_query", "value": true }, + { "key": "check_shaders", "value": true }, + { "key": "check_shaders_caching", "value": true }, + { "key": "unique_handles", "value": true }, + { "key": "object_lifetime", "value": true }, + { "key": "stateless_param", "value": true }, + { "key": "legacy_detection", "value": false }, + { "key": "thread_safety", "value": false }, + { "key": "validate_sync", "value": false }, + { "key": "printf_enable", "value": false }, + { "key": "gpuav_enable", "value": false }, + { "key": "validate_best_practices", "value": false }, + { "key": "report_flags", "value": [ "error", "warn" ] }, + { "key": "debug_action", "value": [ "VK_DBG_LAYER_ACTION_LOG_MSG", "VK_DBG_LAYER_ACTION_DEBUG_OUTPUT" ] }, + { "key": "enable_message_limit", "value": true } + ] + }, + { + "label": "Reduced-Overhead", + "description": "Disables some checks in the interest of better performance.", + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "status": "STABLE", + "settings": [ + { "key": "validate_core", "value": true }, + { "key": "check_image_layout", "value": false }, + { "key": "check_command_buffer", "value": false }, + { "key": "check_object_in_use", "value": false }, + { "key": "check_query", "value": false }, + { "key": "check_shaders", "value": true }, + { "key": "check_shaders_caching", "value": true }, + { "key": "unique_handles", "value": false }, + { "key": "object_lifetime", "value": true }, + { "key": "stateless_param", "value": true }, + { "key": "legacy_detection", "value": false }, + { "key": "thread_safety", "value": false }, + { "key": "validate_sync", "value": false }, + { "key": "printf_enable", "value": false }, + { "key": "gpuav_enable", "value": false }, + { "key": "validate_best_practices", "value": false }, + { "key": "report_flags", "value": [ "error" ] }, + { "key": "debug_action", "value": [ "VK_DBG_LAYER_ACTION_LOG_MSG" ] }, + { "key": "enable_message_limit", "value": true } + ] + }, + { + "label": "Best Practices", + "description": "Provides warnings on valid API usage that is potential API misuse.", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "status": "STABLE", + "settings": [ + { "key": "validate_core", "value": false }, + { "key": "check_image_layout", "value": false }, + { "key": "check_command_buffer", "value": false }, + { "key": "check_object_in_use", "value": false }, + { "key": "check_query", "value": false }, + { "key": "check_shaders", "value": false }, + { "key": "check_shaders_caching", "value": false }, + { "key": "unique_handles", "value": false }, + { "key": "object_lifetime", "value": false }, + { "key": "stateless_param", "value": false }, + { "key": "legacy_detection", "value": false }, + { "key": "thread_safety", "value": false }, + { "key": "validate_sync", "value": false }, + { "key": "printf_enable", "value": false }, + { "key": "gpuav_enable", "value": false }, + { "key": "validate_best_practices", "value": true }, + { "key": "report_flags", "value": [ "error", "warn", "perf" ] }, + { "key": "debug_action", "value": [ "VK_DBG_LAYER_ACTION_LOG_MSG", "VK_DBG_LAYER_ACTION_DEBUG_OUTPUT" ] }, + { "key": "enable_message_limit", "value": true } + ] + }, + { + "label": "Synchronization", + "description": "Identify resource access conflicts due to missing or incorrect synchronization operations between actions reading or writing the same regions of memory.", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "status": "STABLE", + "settings": [ + { "key": "validate_core", "value": false }, + { "key": "check_image_layout", "value": false }, + { "key": "check_command_buffer", "value": false }, + { "key": "check_object_in_use", "value": false }, + { "key": "check_query", "value": false }, + { "key": "check_shaders", "value": false }, + { "key": "check_shaders_caching", "value": false }, + { "key": "unique_handles", "value": true }, + { "key": "object_lifetime", "value": false }, + { "key": "stateless_param", "value": false }, + { "key": "legacy_detection", "value": false }, + { "key": "thread_safety", "value": true }, + { "key": "validate_sync", "value": true }, + { "key": "printf_enable", "value": false }, + { "key": "gpuav_enable", "value": false }, + { "key": "validate_best_practices", "value": false }, + { "key": "report_flags", "value": [ "error" ] }, + { "key": "debug_action", "value": [ "VK_DBG_LAYER_ACTION_LOG_MSG", "VK_DBG_LAYER_ACTION_DEBUG_OUTPUT" ] }, + { "key": "enable_message_limit", "value": true } + ] + }, + { + "label": "GPU-Assisted", + "description": "Check for API usage errors at shader execution time.", + "platforms": [ "WINDOWS", "LINUX" ], + "status": "STABLE", + "settings": [ + { "key": "validate_core", "value": false }, + { "key": "check_image_layout", "value": false }, + { "key": "check_command_buffer", "value": false }, + { "key": "check_object_in_use", "value": false }, + { "key": "check_query", "value": false }, + { "key": "check_shaders", "value": false }, + { "key": "check_shaders_caching", "value": false }, + { "key": "unique_handles", "value": false }, + { "key": "object_lifetime", "value": false }, + { "key": "stateless_param", "value": false }, + { "key": "legacy_detection", "value": false }, + { "key": "thread_safety", "value": false }, + { "key": "validate_sync", "value": false }, + { "key": "printf_enable", "value": false }, + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_shader_instrumentation", "value": true }, + { "key": "gpuav_select_instrumented_shaders", "value": false }, + { "key": "gpuav_buffers_validation", "value": true }, + { "key": "validate_best_practices", "value": false }, + { "key": "report_flags", "value": [ "error", "warn" ] }, + { "key": "debug_action", "value": [ "VK_DBG_LAYER_ACTION_LOG_MSG", "VK_DBG_LAYER_ACTION_DEBUG_OUTPUT" ] }, + { "key": "enable_message_limit", "value": true } + ] + }, + { + "label": "Debug Printf", + "description": "Debug shader code by \"printing\" any values of interest to the debug callback or stdout.", + "platforms": [ "WINDOWS", "LINUX" ], + "status": "STABLE", + "settings": [ + { "key": "validate_core", "value": false }, + { "key": "check_image_layout", "value": false }, + { "key": "check_command_buffer", "value": false }, + { "key": "check_object_in_use", "value": false }, + { "key": "check_query", "value": false }, + { "key": "check_shaders", "value": false }, + { "key": "check_shaders_caching", "value": false }, + { "key": "unique_handles", "value": false }, + { "key": "object_lifetime", "value": false }, + { "key": "stateless_param", "value": false }, + { "key": "legacy_detection", "value": false }, + { "key": "thread_safety", "value": false }, + { "key": "validate_sync", "value": false }, + { "key": "gpuav_enable", "value": false }, + { "key": "printf_enable", "value": true }, + { "key": "validate_best_practices", "value": false }, + { "key": "report_flags", "value": [ "error", "warn", "info" ] }, + { "key": "debug_action", "value": [] }, + { "key": "enable_message_limit", "value": false } + ] + }, + { + "label": "Legacy Detection Only", + "description": "Turn off everything but legacy detection.", + "platforms": [ "WINDOWS", "LINUX" ], + "status": "STABLE", + "settings": [ + { "key": "validate_core", "value": false }, + { "key": "check_image_layout", "value": false }, + { "key": "check_command_buffer", "value": false }, + { "key": "check_object_in_use", "value": false }, + { "key": "check_query", "value": false }, + { "key": "check_shaders", "value": false }, + { "key": "check_shaders_caching", "value": false }, + { "key": "unique_handles", "value": false }, + { "key": "object_lifetime", "value": false }, + { "key": "stateless_param", "value": false }, + { "key": "legacy_detection", "value": true }, + { "key": "thread_safety", "value": false }, + { "key": "validate_sync", "value": false }, + { "key": "gpuav_enable", "value": false }, + { "key": "printf_enable", "value": false }, + { "key": "validate_best_practices", "value": false }, + { "key": "report_flags", "value": [ "warn" ] }, + { "key": "debug_action", "value": [ "VK_DBG_LAYER_ACTION_LOG_MSG", "VK_DBG_LAYER_ACTION_DEBUG_OUTPUT" ] }, + { "key": "enable_message_limit", "value": false } + ] + } + ], + "settings": [ + { + "key": "validation_control", + "label": "Validation Areas", + "description": "Control of the Validation layer validation", + "type": "GROUP", + "expanded": true, + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "settings": [ + { + "key": "fine_grained_locking", + "env": "VK_LAYER_FINE_GRAINED_LOCKING", + "label": "Fine Grained Locking", + "description": "Enable fine grained locking for Core Validation, which should improve performance in multithreaded applications. This setting allows the optimization to be disabled for debugging.", + "url": "https://github.com/KhronosGroup/Vulkan-ValidationLayers/blob/main/docs/fine_grained_locking_usage.md", + "type": "BOOL", + "default": true + }, + { + "key": "validate_core", + "label": "Core", + "description": "The main, heavy-duty validation checks. This may be valuable early in the development cycle to reduce validation output while correcting parameter/object usage errors.", + "url": "https://github.com/KhronosGroup/Vulkan-ValidationLayers/blob/main/docs/core_checks.md", + "type": "BOOL", + "default": true, + "settings": [ + { + "key": "check_image_layout", + "label": "Image Layout", + "description": "Check that the layout of each image subresource is correct whenever it is used by a command buffer. These checks are very CPU intensive for some applications.", + "type": "BOOL", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_core", "value": true } + ] + } + }, + { + "key": "check_command_buffer", + "label": "Command Buffer State", + "description": "Check that all Vulkan objects used by a command buffer have not been destroyed. These checks can be CPU intensive for some applications.", + "type": "BOOL", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_core", "value": true } + ] + } + }, + { + "key": "check_object_in_use", + "label": "Object in Use", + "description": "Check that Vulkan objects are not in use by a command buffer when they are destroyed.", + "type": "BOOL", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_core", "value": true } + ] + } + }, + { + "key": "check_query", + "label": "Query", + "description": "Checks for commands that use VkQueryPool objects.", + "type": "BOOL", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_core", "value": true } + ] + } + }, + { + "key": "check_shaders", + "label": "Shader", + "description": "This will validate the contents of the SPIR-V which can be CPU intensive during application start up. This does internal checks as well as calling spirv-val. (Same effect using VK_VALIDATION_FEATURE_DISABLE_SHADERS_EXT)", + "type": "BOOL", + "default": true, + "expanded": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_core", "value": true } + ] + }, + "settings": [ + { + "key": "check_shaders_caching", + "label": "Caching", + "description": "Creates an internal instance of VK_EXT_validation_cache and upon vkDestroyInstance, will cache the shader validation so sequential usage of the validation layers will be skipped.", + "type": "BOOL", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_core", "value": true }, + { "key": "check_shaders", "value": true } + ] + } + }, + { + "key": "debug_disable_spirv_val", + "label": "Disable spirv-val", + "view": "DEBUG", + "description": "Allows normal shader validation to run, but removes just spirv-val for performance reasons", + "type": "BOOL", + "default": false + } + ] + } + ], + "messages": [ + { + "key": "validate_core_msg0", + "title": "Validation layer performance issue", + "version": 1, + "description": "Both 'Core validation' and 'GPU-assisted validation' are enabled. This is going to require a lot of CPU resource causing the Vulkan application to run very slowly.", + "informative": "Are you sure you want to enable 'Core Validation'?", + "severity": "WARNING", + "conditions": [ + { "setting": { "key": "validate_core", "value": true }}, + { "setting": { "key": "gpuav_enable", "value": true }} + ], + "actions": { + "default": "BUTTON0", + "BUTTON0": { + "type": "OK", + "changes": [ + { "setting": { "key": "validate_core", "value": true }}, + { "setting": { "key": "gpuav_enable", "value": true }} + ] + }, + "BUTTON1": { + "type": "CANCEL", + "changes": [ + { "setting": { "key": "validate_core", "value": false }} + ] + } + } + } + ] + }, + { + "key": "unique_handles", + "label": "Handle Wrapping", + "description": "Handle wrapping checks. Disable this feature if you are experiencing crashes when creating new extensions or developing new Vulkan objects/structures.", + "url": "https://github.com/KhronosGroup/Vulkan-ValidationLayers/blob/main/docs/handle_wrapping.md", + "type": "BOOL", + "default": true + }, + { + "key": "object_lifetime", + "label": "Object Lifetime", + "description": "Object tracking checks. This may not always be necessary late in a development cycle.", + "url": "https://github.com/KhronosGroup/Vulkan-ValidationLayers/blob/main/docs/object_lifetimes.md", + "type": "BOOL", + "default": true + }, + { + "key": "stateless_param", + "label": "Stateless Parameter", + "description": "Stateless parameter checks. This may not always be necessary late in a development cycle.", + "url": "https://github.com/KhronosGroup/Vulkan-ValidationLayers/blob/main/docs/stateless_validation.md", + "type": "BOOL", + "default": true + }, + { + "key": "legacy_detection", + "label": "Legacy Detection", + "description": "Give warnings when using legacy parts of the API.", + "type": "BOOL", + "default": false + }, + { + "key": "thread_safety", + "label": "Thread Safety", + "description": "Thread checks. In order to not degrade performance, it might be best to run your program with thread-checking disabled most of the time, enabling it occasionally for a quick sanity check or when debugging difficult application behaviors.", + "url": "https://github.com/KhronosGroup/Vulkan-ValidationLayers/blob/main/docs/thread_safety.md", + "type": "BOOL", + "default": true + }, + { + "key": "validate_sync", + "label": "Synchronization", + "description": "Check for resource access conflicts caused by missing or incorrectly used synchronization operations.", + "url": "${LUNARG_SDK}/synchronization_usage.html", + "type": "BOOL", + "default": false, + "expanded": true, + "settings": [ + { + "key": "syncval_submit_time_validation", + "label": "Submit time validation", + "description": "Enable synchronization validation on the boundary between submitted command buffers. This also validates accesses from presentation operations. This option can incur a significant performance cost.", + "type": "BOOL", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_sync", "value": true } + ] + } + }, + { + "key": "syncval_shader_accesses_heuristic", + "label": "Shader accesses heuristic", + "description": "Take into account memory accesses performed by the shader based on SPIR-V static analysis. Warning: can produce false-positives, can ignore certain types of accesses, does not support VK_EXT_descriptor_buffer.", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_sync", "value": true } + ] + } + }, + { + "key": "syncval_load_op_after_store_op_validation", + "label": "LoadOp after StoreOp validation", + "description": "Detect LoadOp after StoreOp hazards between render pass instances.", + "view": "HIDDEN", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_sync", "value": true } + ] + } + }, + { + "key": "syncval_reporting", + "label": "Error messages", + "description": "Options to configure synchronization validation error reporting", + "type": "GROUP", + "expanded": true, + "settings": [ + { + "key": "syncval_message_extra_properties", + "label": "Extra properties", + "description": "Append a section of key-value properties to the error message. Useful for filtering errors.", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_sync", "value": true } + ] + } + } + ] + } + ] + }, + { + "key": "printf_only_preset", + "label": "Debug Printf only preset", + "description": "A single, quick setting to turn on only DebugPrintf and turn off everything else", + "type": "BOOL", + "default": false, + "view": "DEBUG", + "platforms": [ "WINDOWS", "LINUX" ] + }, + { + "key": "printf_enable", + "label": "Debug Printf", + "description": "Enable DebugPrintf and will print anything use NonSemantic.DebugPrintf in their SPIR-V", + "type": "BOOL", + "url": "${LUNARG_SDK}/debug_printf.html", + "default": false, + "platforms": [ "WINDOWS", "LINUX" ], + "settings": [ + { + "key": "printf_to_stdout", + "label": "Redirect Printf messages to stdout", + "description": "Enable redirection of Debug Printf messages from the debug callback to stdout", + "type": "BOOL", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "printf_enable", "value": true } + ] + }, + "messages": [ + { + "key": "printf_enable_msg3", + "title": "Debug Printf without 'info' level message severity flag", + "version": 1, + "description": "Enabling Debug Printf output redirection to the debug callback, but 'info' level message severity is disabled, so printf message won't be shown.", + "informative": "Adding 'info' level to 'Message Severity'", + "severity": "INFORMATION", + "conditions": [ + { "setting": { "key": "printf_enable", "value": true }}, + { "setting": { "key": "printf_to_stdout", "value": false }}, + { "setting": { "key": "report_flags", "value": ["info"]}, "operator": "NOT" } + ], + "actions": { + "default": "BUTTON0", + "BUTTON0": { + "type": "OK", + "changes": [ + { "setting": { "key": "report_flags", "value": ["info"]}, "operator": "APPEND" } + ] + } + } + } + ] + }, + { + "key": "printf_verbose", + "label": "Printf verbose", + "description": "Will print out handles, instruction location, position in command buffer, and more", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "printf_enable", "value": true } + ] + } + }, + { + "key": "printf_buffer_size", + "label": "Printf buffer size", + "description": "Set the size in bytes of the buffer per VkCommandBuffer to hold the messages (Each message is about 50 bytes)", + "type": "INT", + "default": 1024, + "range": { + "min": 128, + "max": 1048576 + }, + "unit": "bytes", + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "printf_enable", "value": true } + ] + } + } + ], + "messages": [ + { + "key": "printf_enable_msg1", + "title": "Warning: Debug Printf without info level message severity flag", + "version": 1, + "description": "Enabling Debug Printf, with output directed to the debug callback, but info level message severity is disabled, so printf message won't be shown.", + "informative": "Adding 'info' level to 'Message Severity'", + "severity": "INFORMATION", + "conditions": [ + { "setting": { "key": "printf_enable", "value": true }}, + { "setting": { "key": "printf_to_stdout", "value": false }}, + { "setting": { "key": "report_flags", "value": ["info"]}, "operator": "NOT" } + ], + "actions": { + "default": "BUTTON0", + "BUTTON0": { + "type": "OK", + "changes": [ + { "setting": { "key": "report_flags", "value": ["info"] }, "operator": "APPEND" } + ] + } + } + } + ] + }, + { + "key": "gpuav_enable", + "label": "GPU Assisted Validation", + "description": "Enable validation that cannot be done the CPU and needs hooks into the GPU execution", + "url": "${LUNARG_SDK}/gpu_validation.html", + "type": "BOOL", + "default": false, + "expanded": true, + "platforms": [ "WINDOWS", "LINUX" ], + "settings": [ + { + "key": "gpuav_safe_mode", + "label": "Safe Mode", + "description": "Will have GPU-AV try and prevent crashes, but will be much slower to validate. If using Safe Mode, consider using selective shader instrumentation, to only instrument the shaders/pipelines causing issues.", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true } + ] + } + }, + { + "key": "gpuav_force_on_robustness", + "label": "Force on robustness features", + "description": "This will enable all possible robustness features for the app at device creation time. This can be used to quickly detect if with robustness, your issue disappears. GPU-AV will also skip validating things already covered by robustness, so turning on should reduce GPU-AV performance overhead.", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true } + ] + } + }, + { + "key": "gpuav_shader_instrumentation", + "label": "Shader instrumentation", + "description": "Instrument shaders to validate descriptors, descriptor indexing, buffer device addresses and ray queries. Warning: will considerably slow down shader executions.", + "url": "https://github.com/KhronosGroup/Vulkan-ValidationLayers/blob/main/docs/gpu_av_shader_instrumentation.md", + "type": "BOOL", + "expanded": true, + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true } + ] + }, + "settings": [ + { + "key": "gpuav_select_instrumented_shaders", + "label": "Enable instrumenting shaders selectively", + "description": "Select which shaders to instrument by passing a VkValidationFeaturesEXT struct with GPU-AV enabled in the VkShaderModuleCreateInfo pNext or using a regex matching a shader/pipeline debug name. Because this only validates the selected shaders, it will allow GPU-AV to run much faster.", + "url": "https://github.com/KhronosGroup/Vulkan-ValidationLayers/blob/main/docs/gpu_av_selective_shader.md", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_shader_instrumentation", "value": true } + ] + }, + "settings": [ + { + "key": "gpuav_shaders_to_instrument", + "label": "Shader/pipeline name regexes", + "description": "Any shader or pipeline library debug name (set with vkSetDebugUtilsObjectNameEXT) fully matching any listed regular expression will be instrumented when creating pipelines with those shaders or libraries. Regex grammar: Modified ECMAScript. No support for shader objects yet. Warning: instrumentation being performed at final pipeline creation time, if shaders modules have already been destroyed at this step (possible when using pipeline libraries), they won't be found by this regex selection system. In this case, consider naming pipeline libraries instead.", + "type": "LIST", + "default": [], + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_shader_instrumentation", "value": true }, + { "key": "gpuav_select_instrumented_shaders", "value": true } + ] + } + } + ] + }, + { + "key": "gpuav_descriptor_checks", + "label": "Descriptors indexing", + "description": "Enable descriptors and buffer out of bounds validation when using descriptor indexing", + "url": "https://github.com/KhronosGroup/Vulkan-ValidationLayers/blob/main/docs/gpu_av_descriptor_indexing.md", + "type": "BOOL", + "view": "DEBUG", + "expanded": true, + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_shader_instrumentation", "value": true } + ] + } + }, + { + "key": "gpuav_post_process_descriptor_indexing", + "label": "Post process descriptor indexing", + "description": "Track which descriptor indexes were used in shader to run normal validation afterwards", + "url": "https://github.com/KhronosGroup/Vulkan-ValidationLayers/blob/main/docs/gpu_av_post_process.md", + "type": "BOOL", + "view": "DEBUG", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_shader_instrumentation", "value": true } + ] + } + }, + { + "key": "gpuav_buffer_address_oob", + "label": "Out of bounds buffer device addresses", + "type": "BOOL", + "view": "DEBUG", + "default": true, + "expanded": true, + "description": "Check for invalid access using buffer device address", + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_shader_instrumentation", "value": true } + ] + } + }, + { + "key": "gpuav_mesh_shading", + "label": "Mesh Shading SPIR-V instructions", + "description": "Enable shader instrumentation on VK_EXT_mesh_shader", + "type": "BOOL", + "view": "DEBUG", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_shader_instrumentation", "value": true } + ] + } + }, + { + "key": "gpuav_validate_ray_query", + "label": "RayQuery SPIR-V instructions", + "description": "Enable shader instrumentation on OpRayQueryInitializeKHR", + "type": "BOOL", + "view": "DEBUG", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_shader_instrumentation", "value": true } + ] + } + }, + { + "key": "gpuav_vertex_attribute_fetch_oob", + "label": "Out of bounds vertex attribute fetching", + "description": "Validate that no vertex attribute fetching is out of bonds", + "type": "BOOL", + "view": "DEBUG", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_shader_instrumentation", "value": true } + ] + } + }, + { + "key": "gpuav_shader_sanitizer", + "label": "Shader Sanitizer", + "description": "Things invalid to do in SPIR-V but don't have a dedicated check (example: dividing an int by zero, reaching OpUnreachable, invalid casting, overflow/undeflow). When in safe mode, will modify the invalid usage.", + "type": "BOOL", + "view": "DEBUG", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_shader_instrumentation", "value": true } + ] + } + } + ] + }, + { + "key": "gpuav_buffers_validation", + "label": "Buffer content validation", + "description": "Validate buffers containing parameters used in indirect Vulkan commands, or used in copy commands", + "type": "BOOL", + "view": "DEBUG", + "default": true, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true } + ] + }, + "settings": [ + { + "key": "gpuav_indirect_draws_buffers", + "label": "Indirect draws parameters", + "type": "BOOL", + "default": true, + "description": "Validate buffers containing draw parameters used in indirect draw commands. Includes vkCmdDrawMeshTasks* calls as well", + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_buffers_validation", "value": true } + ] + } + }, + { + "key": "gpuav_indirect_dispatches_buffers", + "label": "Indirect dispatches parameters", + "type": "BOOL", + "default": true, + "description": "Validate buffers containing dispatch parameters used in indirect dispatch commands", + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_buffers_validation", "value": true } + ] + } + }, + { + "key": "gpuav_indirect_trace_rays_buffers", + "label": "Indirect trace rays parameters", + "type": "BOOL", + "default": true, + "description": "Validate buffers containing ray tracing parameters used in indirect ray tracing commands", + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_buffers_validation", "value": true } + ] + } + }, + { + "key": "gpuav_buffer_copies", + "label": "Buffer copies", + "type": "BOOL", + "default": true, + "description": "Validate copies involving a VkBuffer. Right now only validates copy buffer to image.", + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_buffers_validation", "value": true } + ] + } + }, + { + "key": "gpuav_copy_memory_indirect", + "label": "VK_KHR_copy_memory_indirect", + "type": "BOOL", + "default": true, + "description": "Validate VK_KHR_copy_memory_indirect.", + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_buffers_validation", "value": true } + ] + } + }, + { + "key": "gpuav_index_buffers", + "label": "Index buffers", + "type": "BOOL", + "default": true, + "description": "Validate that indexed draws do not fetch indices outside of the bounds of the index buffer.", + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_buffers_validation", "value": true } + ] + } + }, + { + "key": "gpuav_acceleration_structures_builds", + "label": "Acceleration structures builds", + "type": "BOOL", + "default": true, + "description": "Validate acceleration structures references in TLAS build commands.", + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true }, + { "key": "gpuav_buffers_validation", "value": true } + ] + } + } + ] + }, + { + "key": "gpuav_debug_settings", + "label": "Developer Debug Settings", + "description": "GPU-AV debug settings", + "type": "GROUP", + "view": "DEBUG", + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true } + ] + }, + "settings": [ + { + "key": "gpuav_debug_disable_all", + "label": "Disable all of GPU-AV", + "description": "Acts as a VkValidationFeatureDisableEXT to override the VK_VALIDATION_FEATURE_ENABLE_GPU_ASSISTED_EXT passed by the user", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true } + ] + } + }, + { + "key": "gpuav_debug_validate_instrumented_shaders", + "label": "Validate instrumented shaders", + "description": "Run spirv-val after doing shader instrumentation", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true } + ] + } + }, + { + "key": "gpuav_debug_dump_instrumented_shaders", + "label": "Dump instrumented shaders", + "description": "Will dump the instrumented shaders (before and after) to working directory", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true } + ] + } + }, + { + "key": "gpuav_debug_max_instrumentations_count", + "label": "Limit how many time a pass can instrument the SPIR-V", + "description": "Zero is same as unlimited", + "type": "INT", + "default": 0, + "range": { + "min": 0 + }, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true } + ] + } + }, + { + "key": "gpuav_debug_print_instrumentation_info", + "label": "Print SPIR-V instrumentation info", + "description": "Prints verbose information about the instrumentation of the SPIR-V", + "type": "BOOL", + "default": false, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "gpuav_enable", "value": true } + ] + } + } + ] + } + ], + "messages": [ + { + "key": "gpuav_enable_msg0", + "title": "Validation layer performance warning", + "version": 1, + "description": "Both 'Core validation' and 'GPU-assisted validation' are enabled. This is going to require a lot of CPU resource causing the Vulkan application to run very slowly.", + "informative": "Are you sure you want to enable 'GPU Assisted Validation'?", + "severity": "WARNING", + "conditions": [ + { "setting": { "key": "validate_core", "value": true }}, + { "setting": { "key": "gpuav_enable", "value": true }} + ], + "actions": { + "default": "BUTTON0", + "BUTTON0": { + "type": "OK", + "changes": [ + { "setting": { "key": "validate_core", "value": true }}, + { "setting": { "key": "gpuav_enable", "value": true }} + ] + }, + "BUTTON1": { + "type": "CANCEL", + "changes": [ + { "setting": { "key": "gpuav_enable", "value": false }} + ] + } + } + } + ] + }, + { + "key": "validate_best_practices", + "label": "Best Practices", + "description": "Outputs warnings related to common misuse of the API, but which are not explicitly prohibited by the specification.", + "url": "${LUNARG_SDK}/best_practices.html", + "type": "BOOL", + "default": false, + "expanded": true, + "status": "STABLE", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "settings": [ + { + "key": "validate_best_practices_arm", + "label": "ARM-specific best practices", + "description": "Outputs warnings for spec-conforming but non-ideal code on ARM GPUs.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_best_practices", "value": true } + ] + } + }, + { + "key": "validate_best_practices_amd", + "label": "AMD-specific best practices", + "description": "Outputs warnings for spec-conforming but non-ideal code on AMD GPUs.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_best_practices", "value": true } + ] + } + }, + { + "key": "validate_best_practices_img", + "label": "IMG-specific best practices", + "description": "Outputs warnings for spec-conforming but non-ideal code on Imagination GPUs.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_best_practices", "value": true } + ] + } + }, + { + "key": "validate_best_practices_nvidia", + "label": "NVIDIA-specific best practices", + "description": "Outputs warnings for spec-conforming but non-ideal code on NVIDIA GPUs.", + "type": "BOOL", + "default": false, + "platforms": [ "WINDOWS", "LINUX", "ANDROID" ], + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "validate_best_practices", "value": true } + ] + } + } + ] + } + ] + }, + { + "key": "debug_action", + "label": "Debug Action", + "description": "Specifies what action is to be taken when a layer reports information", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "type": "FLAGS", + "flags": [ + { + "key": "VK_DBG_LAYER_ACTION_LOG_MSG", + "label": "Log Message", + "description": "Log a txt message to stdout or to a log filename.", + "settings": [ + { + "key": "log_filename", + "label": "Log Filename", + "description": "Specifies the output filename", + "type": "SAVE_FILE", + "default": "stdout", + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "debug_action", "value": [ "VK_DBG_LAYER_ACTION_LOG_MSG" ] } + ] + } + } + ] + }, + { + "key": "VK_DBG_LAYER_ACTION_CALLBACK", + "label": "Callback", + "description": "Call user defined callback function(s) that have been registered via the VK_EXT_debug_report extension. Since application must register callback, this is a NOOP for the settings file.", + "view": "HIDDEN" + }, + { + "key": "VK_DBG_LAYER_ACTION_DEBUG_OUTPUT", + "label": "Debug Output", + "description": "Log a txt message using the Windows OutputDebugString function.", + "platforms": [ "WINDOWS" ] + }, + { + "key": "VK_DBG_LAYER_ACTION_BREAK", + "label": "Break", + "description": "Trigger a breakpoint if a debugger is in use." + } + ], + "default": [ + "VK_DBG_LAYER_ACTION_LOG_MSG", "VK_DBG_LAYER_ACTION_DEBUG_OUTPUT" + ] + }, + { + "key": "report_flags", + "label": "Message Severity", + "description": "Comma-delineated list of options specifying the types of messages to be reported", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "type": "FLAGS", + "flags": [ + { + "key": "info", + "label": "Info", + "description": "Report informational messages.", + "messages": [ + { + "key": "printf_enable_msg2", + "title": "Warning: Debug Printf without info level message severity flag", + "version": 1, + "description": "Disabling info level message severity is disabled, but Debug Printf output is directed to the debug callback, so printf message won't be shown.", + "informative": "Adding 'info' level to 'Message Severity'", + "severity": "INFORMATION", + "conditions": [ + { "setting": { "key": "printf_enable", "value": true }}, + { "setting": { "key": "printf_to_stdout", "value": false }}, + { "setting": { "key": "report_flags", "value": ["info"]}, "operator": "NOT" } + ], + "actions": { + "default": "BUTTON0", + "BUTTON0": { + "type": "OK", + "changes": [ + { "setting": {"key": "report_flags", "value": ["info"]}, "operator": "APPEND" } + ] + } + } + } + ] + }, + { + "key": "warn", + "label": "Warning", + "description": "Report warnings from using the API in a manner which may lead to undefined behavior or to warn the user of common trouble spots. A warning does NOT necessarily signify illegal application behavior." + }, + { + "key": "perf", + "label": "Performance", + "description": "Report usage of the API that may cause suboptimal performance." + }, + { + "key": "error", + "label": "Error", + "description": "Report errors in API usage." + }, + { + "key": "debug", + "label": "Debug", + "description": "For layer development. Report messages for debugging layer behavior.", + "view": "DEBUG" + } + ], + "default": [ + "error" + ] + }, + { + "key": "enable_message_limit", + "label": "Limit Duplicated Messages", + "description": "Enable limiting of duplicate messages.", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "type": "BOOL", + "default": true, + "settings": [ + { + "key": "duplicate_message_limit", + "env": "VK_LAYER_DUPLICATE_MESSAGE_LIMIT", + "label": "Max Duplicated Messages", + "description": "Maximum number of times any single validation message should be reported.", + "type": "INT", + "default": 10, + "range": { + "min": 1 + }, + "dependence": { + "mode": "ALL", + "settings": [ + { "key": "enable_message_limit", "value": true } + ] + } + } + ] + }, + { + "key": "message_id_filter", + "label": "Mute Message VUIDs", + "description": "List of VUIDs and VUID identifiers which are to be IGNORED by the validation layer", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "type": "LIST", + "env": "VK_LAYER_MESSAGE_ID_FILTER", + "default": [] + }, + { + "key": "message_format", + "label": "Message Format", + "description": "Specifies how error messages are reported", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "type": "GROUP", + "expanded": true, + "settings": [ + { + "key": "message_format_json", + "label": "JSON", + "description": "Display Validation as JSON (VkDebugUtilsMessengerCallbackDataEXT::pMessage will contain JSON)", + "type": "BOOL", + "default": false + }, + { + "key": "message_format_display_application_name", + "label": "Display Application Name", + "description": "Useful when running multiple instances to know which instance the message is from.", + "type": "BOOL", + "default": false + } + ] + }, + { + "key": "custom_stype_list", + "label": "Provide Custom SType", + "type": "LIST", + "description": "specify custom pNext structs using a vector of strings", + "view": "HIDDEN", + "default": [] + }, + { + "key": "disables", + "label": "Disables", + "description": "Specify areas of validation to be disabled", + "type": "FLAGS", + "status": "DEPRECATED", + "view": "HIDDEN", + "env": "VK_LAYER_DISABLES", + "flags": [ + { + "key": "VK_VALIDATION_FEATURE_DISABLE_THREAD_SAFETY_EXT", + "label": "Thread Safety", + "description": "Thread checks. In order to not degrade performance, it might be best to run your program with thread-checking disabled most of the time, enabling it occasionally for a quick sanity check or when debugging difficult application behaviors.", + "deprecated_by_key": "thread_safety" + }, + { + "key": "VK_VALIDATION_FEATURE_DISABLE_API_PARAMETERS_EXT", + "label": "Stateless Parameter", + "description": "Stateless parameter checks. This may not always be necessary late in a development cycle.", + "deprecated_by_key": "stateless_param" + }, + { + "key": "VK_VALIDATION_FEATURE_DISABLE_OBJECT_LIFETIMES_EXT", + "label": "Object Lifetime", + "description": "Object tracking checks. This may not always be necessary late in a development cycle.", + "deprecated_by_key": "object_lifetime" + }, + { + "key": "VK_VALIDATION_FEATURE_DISABLE_CORE_CHECKS_EXT", + "label": "Core", + "description": "The main, heavy-duty validation checks. This may be valuable early in the development cycle to reduce validation output while correcting parameter/object usage errors.", + "deprecated_by_key": "validate_core" + }, + { + "key": "VK_VALIDATION_FEATURE_DISABLE_UNIQUE_HANDLES_EXT", + "label": "Handle Wrapping", + "description": "Handle wrapping checks. Disable this feature if you are experiencing crashes when creating new extensions or developing new Vulkan objects/structures.", + "deprecated_by_key": "unique_handles" + }, + { + "key": "VK_VALIDATION_FEATURE_DISABLE_SHADERS_EXT", + "label": "Shader Validation", + "description": "Shader checks. These checks can be CPU intensive during application start up, especially if Shader Validation Caching is also disabled.", + "deprecated_by_key": "check_shaders" + }, + { + "key": "VALIDATION_CHECK_DISABLE_COMMAND_BUFFER_STATE", + "label": "Command Buffer State", + "description": "Check that all Vulkan objects used by a command buffer have not been destroyed. These checks can be CPU intensive for some applications.", + "deprecated_by_key": "check_command_buffer" + }, + { + "key": "VALIDATION_CHECK_DISABLE_IMAGE_LAYOUT_VALIDATION", + "label": "Image Layout", + "description": "Check that the layout of each image subresource is correct whenever it is used by a command buffer. These checks are very CPU intensive for some applications.", + "deprecated_by_key": "check_image_layout" + }, + { + "key": "VALIDATION_CHECK_DISABLE_QUERY_VALIDATION", + "label": "Query", + "description": "Checks for commands that use VkQueryPool objects.", + "deprecated_by_key": "check_query" + }, + { + "key": "VALIDATION_CHECK_DISABLE_OBJECT_IN_USE", + "label": "Object in Use", + "description": "Check that Vulkan objects are not in use by a command buffer when they are destroyed.", + "deprecated_by_key": "check_object_in_use" + }, + { + "key": "VK_VALIDATION_FEATURE_DISABLE_SHADER_VALIDATION_CACHE_EXT", + "label": "Shader Validation Caching", + "description": "Disable caching of shader validation results.", + "deprecated_by_key": "check_shaders_caching" + } + ], + "default": [ + "VK_VALIDATION_FEATURE_DISABLE_THREAD_SAFETY_EXT" + ] + }, + { + "key": "enables", + "label": "Enables", + "description": "Setting an option here will enable specialized areas of validation", + "type": "FLAGS", + "status": "DEPRECATED", + "view": "HIDDEN", + "env": "VK_LAYER_ENABLES", + "flags": [ + { + "key": "VK_VALIDATION_FEATURE_ENABLE_SYNCHRONIZATION_VALIDATION_EXT", + "label": "Synchronization", + "description": "This feature reports resource access conflicts due to missing or incorrect synchronization operations between actions (Draw, Copy, Dispatch, Blit) reading or writing the same regions of memory.", + "url": "${LUNARG_SDK}/synchronization_usage.html", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "deprecated_by_key": "validate_sync" + }, + { + "key": "VK_VALIDATION_FEATURE_ENABLE_DEBUG_PRINTF_EXT", + "label": "Debug Printf", + "description": "Enables processing of debug printf instructions in shaders and sending debug strings to the debug callback.", + "url": "${LUNARG_SDK}/debug_printf.html", + "platforms": [ "WINDOWS", "LINUX" ], + "deprecated_by_key": "printf_enable" + }, + { + "key": "VK_VALIDATION_FEATURE_ENABLE_GPU_ASSISTED_EXT", + "label": "GPU-Assisted", + "description": "Check for API usage errors at shader execution time.", + "url": "${LUNARG_SDK}/gpu_validation.html", + "platforms": [ "WINDOWS", "LINUX" ], + "deprecated_by_key": "gpuav_enable" + }, + { + "key": "VK_VALIDATION_FEATURE_ENABLE_GPU_ASSISTED_RESERVE_BINDING_SLOT_EXT", + "label": "Reserve Descriptor Set Binding Slot", + "description": "Specifies that the validation layers reserve a descriptor set binding slot for their own use. The layer reports a value for VkPhysicalDeviceLimits::maxBoundDescriptorSets that is one less than the value reported by the device. If the device supports the binding of only one descriptor set, the validation layer does not perform GPU-assisted validation.", + "platforms": [ "WINDOWS", "LINUX" ], + "deprecated_by_key": "gpuav_enable" + }, + { + "key": "VK_VALIDATION_FEATURE_ENABLE_BEST_PRACTICES_EXT", + "label": "Best Practices", + "description": "Activating this feature enables the output of warnings related to common misuse of the API, but which are not explicitly prohibited by the specification.", + "url": "${LUNARG_SDK}/best_practices.html", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "deprecated_by_key": "validate_best_practices" + }, + { + "key": "VALIDATION_CHECK_ENABLE_VENDOR_SPECIFIC_ARM", + "label": "ARM-specific best practices", + "description": "Activating this feature enables the output of warnings related to ARM-specific misuse of the API, but which are not explicitly prohibited by the specification.", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "deprecated_by_key": "validate_best_practices_arm" + }, + { + "key": "VALIDATION_CHECK_ENABLE_VENDOR_SPECIFIC_AMD", + "label": "AMD-specific best practices", + "description": "Adds check for spec-conforming but non-ideal code on AMD GPUs.", + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "deprecated_by_key": "validate_best_practices_amd" + }, + { + "key": "VALIDATION_CHECK_ENABLE_VENDOR_SPECIFIC_IMG", + "label": "IMG-specific best practices", + "description": "Adds check for spec-conforming but non-ideal code on Imagination GPUs.", + "platforms": [ "WINDOWS", "LINUX", "MACOS" ], + "deprecated_by_key": "validate_best_practices_img" + }, + { + "key": "VALIDATION_CHECK_ENABLE_VENDOR_SPECIFIC_NVIDIA", + "label": "NVIDIA-specific best practices", + "description": "Activating this feature enables the output of warnings related to NVIDIA-specific misuse of the API, but which are not explicitly prohibited by the specification.", + "platforms": [ "WINDOWS", "LINUX", "ANDROID" ], + "deprecated_by_key": "validate_best_practices_nvidia" + }, + { + "key": "VALIDATION_CHECK_ENABLE_VENDOR_SPECIFIC_ALL", + "label": "Hardware specific best practices", + "description": "Activating this feature enables all vendor specific best practices.", + "platforms": [ "WINDOWS", "LINUX", "ANDROID" ] + } + ], + "default": [] + } + ] + } + } +} diff --git a/vulkan/Bin/VkLayer_monitor.json b/vulkan/Bin/VkLayer_monitor.json new file mode 100644 index 0000000000000000000000000000000000000000..07a58c6a46a54d0b6f0400f5b1c164c45652685f --- /dev/null +++ b/vulkan/Bin/VkLayer_monitor.json @@ -0,0 +1,29 @@ +{ + "file_format_version": "1.0.0", + "layer": { + "name": "VK_LAYER_LUNARG_monitor", + "type": "GLOBAL", + "library_path": ".\\VkLayer_monitor.dll", + "api_version": "1.4.341", + "implementation_version": "1", + "description": "Execution Monitoring Layer", + "introduction": "The monitor utility layer displays the real-time frame rate in frames-per-second in the application's title bar.", + "url": "https://vulkan.lunarg.com/doc/sdk/latest/windows/monitor_layer.html", + "platforms": [ "WINDOWS", "LINUX" ], + "instance_extensions": [ + { + "name": "VK_EXT_layer_settings", + "spec_version": "2" + } + ], + "device_extensions": [ + { + "name": "VK_EXT_tooling_info", + "spec_version": "1", + "entrypoints": [ + "vkGetPhysicalDeviceToolPropertiesEXT" + ] + } + ] + } +} diff --git a/vulkan/Bin/VkLayer_screenshot.json b/vulkan/Bin/VkLayer_screenshot.json new file mode 100644 index 0000000000000000000000000000000000000000..fa265545e8fbc24a016d36e57463048b1e7ba083 --- /dev/null +++ b/vulkan/Bin/VkLayer_screenshot.json @@ -0,0 +1,99 @@ +{ + "file_format_version" : "1.2.0", + "layer": { + "name": "VK_LAYER_LUNARG_screenshot", + "type": "GLOBAL", + "library_path": ".\\VkLayer_screenshot.dll", + "api_version": "1.4.341", + "implementation_version": "1", + "description": "LunarG image capture layer", + "introduction": "The screenshot layer records frames to image files.", + "url": "https://vulkan.lunarg.com/doc/sdk/latest/windows/screenshot_layer.html", + "platforms": [ "WINDOWS", "LINUX", "MACOS", "ANDROID" ], + "instance_extensions": [ + { + "name": "VK_EXT_layer_settings", + "spec_version": "2" + } + ], + "device_extensions": [ + { + "name": "VK_EXT_tooling_info", + "spec_version": "1", + "entrypoints": [ + "vkGetPhysicalDeviceToolPropertiesEXT" + ] + } + ], + "features": { + "settings": [ + { + "key": "frames", + "env": "VK_SCREENSHOT_FRAMES", + "label": "Frames", + "description": "Specifies list of frames to output as screenshots. It is specified as a comma-separated list of frames or a range of frames with a start, count, and optional interval separated by a dash. Setting the variable to \"all\" will output every frame. Example: \"5-8-2\" will output frame 5, continue until frame 13, dumping every other frame. Example: \"3,8-2\" will output frames 3, 8, and 9. If it is not set or it set to an empty string, no screenshots are created.", + "type": "STRING", + "default": "" + }, + { + "key": "dir", + "env": "VK_SCREENSHOT_DIR", + "label": "Directory", + "description": "Specifies the directory in which to create the screenshot files. If it is not set or is set to an empty string, the files will be created in the current working directory.", + "type": "SAVE_FOLDER", + "default": "" + }, + { + "key": "format", + "env": "VK_SCREENSHOT_FORMAT", + "label": "Format", + "description": "Specify a color space for the output. If it is not set, set to null, or set to USE_SWAPCHAIN_COLORSPACE the format will be set to use the same color space as the swapchain object.", + "type": "ENUM", + "flags": [ + { + "key": "UNORM", + "label": "UNORM", + "description": "" + }, + { + "key": "SNORM", + "label": "SNORM", + "description": "" + }, + { + "key": "USCALED", + "label": "USCALED", + "description": "" + }, + { + "key": "SSCALED", + "label": "SSCALED", + "description": "" + }, + { + "key": "UINT", + "label": "UINT", + "description": "" + }, + { + "key": "SINT", + "label": "SINT", + "description": "" + }, + { + "key": "SRGB", + "label": "SRGB", + "description": "" + }, + { + "key": "USE_SWAPCHAIN_COLORSPACE", + "label": "USE_SWAPCHAIN_COLORSPACE", + "description": "" + } + ], + "default": "USE_SWAPCHAIN_COLORSPACE" + } + ] + } + } +} diff --git a/vulkan/Bin/gfxrecon-capture-vulkan.py b/vulkan/Bin/gfxrecon-capture-vulkan.py new file mode 100644 index 0000000000000000000000000000000000000000..9032b01baa79794dc8c29f7ba9471ee12cd537ca --- /dev/null +++ b/vulkan/Bin/gfxrecon-capture-vulkan.py @@ -0,0 +1,351 @@ +#!/usr/bin/env python3 +# +# Copyright (c) 2020 LunarG, Inc. +# +# 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. +# +# +# Utility for capturing Vulkan API calls using gfxreconstruct + +'''Capture a trace of a Vulkan binary. +''' + +import argparse +import os +import platform +import shutil +import sys +import subprocess + +if sys.version_info < (3, 7): + print("Error: Python 3.7 or later is required; you're using:", sys.version) + sys.exit(1) + +def usage_message(): + '''Output the script usage message + + We use a custom usage message because Python's arg parser doesn't provide + a very good usage message for positional arguments. + (The default usage message for positional args is as an ellipse. We would + rather have " []".) + + :return: A well formatted usage message string + ''' + message = [ + 'usage gfxrecon-capture-vulkan.py [-h]', + ' [-w dir]', + ' [-o capture_file]', + ' [-f capture_frames]', + ' [--no-file-timestamp]', + ' [--trigger {F1-F12,TAB,CTRL}]', + ' [--trigger-frames frame_count]', + ' [--compression-type {LZ4,ZLIB,ZSTD,NONE}]', + ' [--file-flush]', + ' [--log-level {debug,info,warn,error,fatal}]', + ' [--log-timestamps]', + ' [--log-file ]', + ' [--memory-tracking-mode {page_guard,assisted,unassisted}]', + ' [--capture-layer ', + ] + if sys.platform == 'win32': + message.append(' [--log-debugview]') + message.append(' []') + return '\n'.join(message) + + +class SmartFormatter(argparse.HelpFormatter): + '''Used by the argument parser to assist in breaking argument help text + into multiple lines. + + It is used whenever the help text starts with 'R|'. + ''' + + def _split_lines(self, text, width): + '''Split the argument help into multiple lines + + In addition to the default behavior it will also break lines that start + with R|. + ''' + if text.startswith('R|'): + return text[2:].splitlines() + return argparse.HelpFormatter._split_lines(self, text, width) + + +def print_error_and_exit(msg): + '''Print error message and exit with non-zero status + ''' + print(os.path.basename(__file__) + ' error: ' + msg) + sys.exit(1) + + +def set_env_var(name, value): + '''Set an environment variable to a given value or remove it from the + environment if None + ''' + if value is not None: + os.environ[name] = value + elif name in os.environ: + del os.environ[name] + + +def create_argument_parser(): + '''Create the argument parser used to parse command line arguments + + Create an argument parser with a custom description and useage. + Add command line arguments and options to the parser. + ''' + TRIGGER_KEY_CHOICES = ['F1', 'F2', 'F3', 'F4', 'F5', 'F6', + 'F7', 'F8', 'F9', 'F10', 'F11', 'F12', 'TAB', 'CTRL'] + COMPRESSION_CHOICES = ['LZ4', 'ZLIB', 'ZSTD', 'NONE'] + LOG_LEVEL_CHOICES = ['debug', 'info', 'warn', 'error', 'fatal'] + MEMORY_TRACKING_MODE_CHOICES = ['page_guard', 'assisted', 'unassisted'] + + parser = argparse.ArgumentParser( + prog=os.path.basename(sys.argv[0]), + description=__doc__, + usage=usage_message(), + allow_abbrev=False, + formatter_class=argparse.RawTextHelpFormatter) + + # Common optional args + # All arguments default to None, indicating they should be unset in the capture environment + parser.add_argument( + '-w', '--working-dir', dest='working_dir', metavar='', + help='Set CWD to this directory before running the program') + parser.add_argument( + '-o', '--capture-file', dest='capture_file', metavar='', + default='gfxrecon_capture.gfxr', + help='Name of the capture file, default is gfxrecon_capture.gfxr') + parser.add_argument( + '-f', '--capture-frames', dest='capture_frames', + metavar='', + help='List of frames to capture, default is all frames') + parser.add_argument( + '--no-file-timestamp', dest='file_timestamp', action='store_const', const='false', + help='Do not add a timestamp to the capture file name') + parser.add_argument( + '--trigger', dest='trigger', choices=TRIGGER_KEY_CHOICES, + help='Specify a hotkey to start/stop capture') + parser.add_argument('--trigger-frames', dest='trigger_frames', metavar='', + help='Specify a limit on the number of frames captured via hotkey') + parser.add_argument( + '--compression-type', dest='compression_type', choices=COMPRESSION_CHOICES, + help='Specify the type of compression to use in the capture file, default is LZ4') + parser.add_argument( + '--file-flush', dest='file_flush', action='store_const', const='true', + help='Flush output stream after each packet is written to capture file') + parser.add_argument( + '--log-level', dest='log_level', choices=LOG_LEVEL_CHOICES, + help='Specify highest level message to log, default is info') + parser.add_argument( + '--log-timestamps', dest='log_timestamps', action='store_const', const='true', + help='Output a timestamp in front of each log message.') + parser.add_argument( + '--log-file', dest='log_file', metavar='', + help='Write log messages to a file at the specified path. Default is: Empty string (file logging disabled)') + parser.add_argument( + '--log-debugview', dest='log_debug_view', action='store_const', const='true', + help='Log messages with OutputDebugStringA' if sys.platform == 'win32' else argparse.SUPPRESS) + parser.add_argument( + '--memory-tracking-mode', dest='memory_tracking_mode', choices=MEMORY_TRACKING_MODE_CHOICES, + help='\n'.join([ + 'R|Method used to track changes to memory mapped objects:', + ' - page_guard: use pageguard to track changes (default)', + ' - assisted: application will call vkFlushMappedMemoryRanges', + ' - for memory to be written to the capture file', + ' - unassisted: all mapped memory will be written to the', + ' - capture file during VkQueueSubmit and VkUnmapMemory'])) + parser.add_argument( + '--capture-layer', dest='capture_layer', metavar='', + default=None, + help='\n'.join([ + 'The path to the capture layer.', + '', + 'The path specified must contain both the layer JSON, and the', + 'layer library', + 'It is recommended to use an absolute path for this option.'])) + + # Required args + parser.add_argument( + 'program_and_args', metavar=' []', nargs=argparse.REMAINDER, + help='Program to capture, optionally followed by program arguments') + + return parser + + +def print_args(args): + '''Print command line argument values. + + Used for debug. + ''' + print('working-dir', args.working_dir) + print('capture-file', os.path.abspath(args.capture_file)) + print('capture-frames', args.capture_frames) + print('no-file-timestamp', args.file_timestamp) + print('trigger', args.trigger) + print('trigger-frames', args.trigger_frames) + print('compression-type', args.compression_type) + print('file-flush', args.file_flush) + print('log-level', args.log_level) + print('log-file', args.log_file) + print('log-debugview', args.log_debug_view) + print('memory-tracking-mode', args.memory_tracking_mode) + print('program_and_args', args.program_and_args) + + +def get_command_path(args): + '''Get the full path to the command to execute + ''' + # Replace ~ or ~user with the user's home path before calling shutil.which() + programName = os.path.expanduser(args.program_and_args[0]) + return programName, shutil.which(programName) + + +def validate_args(args): + '''Validate command line arguments + ''' + # Verify working_dir exists and is a directory. + if args.working_dir is not None: + if (not os.path.isdir(args.working_dir)): + print_error_and_exit('Working directory ' + + args.working_dir + ' does not exist') + + # Make sure program was specified. arg parser doesn't allow specifying program_and_args + # as required. + if len(args.program_and_args) == 0: + print('usage: ' + usage_message()) + print_error_and_exit(' must be specified') + + # Verify programName exists and is executable. + programName, programWhich = get_command_path(args) + if programWhich is None: + print_error_and_exit('Cannot find program ' + + programName + ' to execute') + + # Verify capture_file directory exists and is a valid directory. + capture_dir = os.path.dirname(os.path.abspath(args.capture_file)) + if (not os.path.exists(capture_dir)): + print_error_and_exit('Capture file output directory ' + + capture_dir + ' does not exist') + if (not os.path.isdir(capture_dir)): + print_error_and_exit('Capture file output directory ' + + capture_dir + ' is not a valid directory') + + # Verify the capture layer paht is a valid directory + if (args.capture_layer is not None) and (not os.path.isdir(args.capture_layer)): + print_error_and_exit('Capture layer path is not a directory') + + +def set_env_vars(args): + '''Set environment variables for capture layer + ''' + # Set VK_INSTANCE_LAYERS + # If gfxr layer is not already in VK_INSTANCE_LAYER, append gfxr layer + # to VK_INSTANCE_LAYERS + if os.getenv('VK_INSTANCE_LAYERS') is None: + os.environ['VK_INSTANCE_LAYERS'] = 'VK_LAYER_LUNARG_gfxreconstruct' + elif (not ('VK_LAYER_LUNARG_gfxreconstruct' in os.getenv('VK_INSTANCE_LAYERS'))): + os.environ['VK_INSTANCE_LAYERS'] = os.environ['VK_INSTANCE_LAYERS'] + \ + os.pathsep + 'VK_LAYER_LUNARG_gfxreconstruct' + if args.capture_layer is not None: + # Prefix the layer path provided by the user to the layer search path + path_delimiter = ':' + if 'windows' == platform.system().lower(): + path_delimiter = ';' + vk_layer_path = '' + if 'VK_LAYER_PATH' in os.environ: + vk_layer_path = os.environ['VK_LAYER_PATH'] + os.environ['VK_LAYER_PATH'] = path_delimiter.join([ + args.capture_layer, vk_layer_path]) + + # Set GFXRECON_* capture options + # The capture layer will validate these options and generate errors as needed + set_env_var('GFXRECON_CAPTURE_FILE', os.path.abspath(args.capture_file)) + set_env_var('GFXRECON_CAPTURE_FRAMES', args.capture_frames) + set_env_var('GFXRECON_CAPTURE_FILE_TIMESTAMP', args.file_timestamp) + set_env_var('GFXRECON_CAPTURE_TRIGGER', args.trigger) + set_env_var('GFXRECON_CAPTURE_TRIGGER_FRAMES', args.trigger_frames) + set_env_var('GFXRECON_CAPTURE_COMPRESSION_TYPE', args.compression_type) + set_env_var('GFXRECON_CAPTURE_FILE_FLUSH', args.file_flush) + set_env_var('GFXRECON_LOG_LEVEL', args.log_level) + set_env_var('GFXRECON_LOG_FILE', args.log_file) + set_env_var('GFXRECON_LOG_OUTPUT_TO_OS_DEBUG_STRING', args.log_debug_view) + set_env_var('GFXRECON_MEMORY_TRACKING_MODE', args.memory_tracking_mode) + + +def print_env_var(env_var): + '''Print the given envrionment variable + + Used for debug. + :param env_var: The envrionment variable printed + ''' + if env_var in os.environ: + print(env_var, os.environ[env_var]) + else: + print(env_var, 'None') + + +def PrintLayerEnv(): + '''Print all GFXReconstruct layer environment variables + + Used for debug. + ''' + print_env_var('GFXRECON_CAPTURE_COMPRESSION_TYPE') + print_env_var('GFXRECON_CAPTURE_FILE') + print_env_var('GFXRECON_CAPTURE_FILE_FLUSH') + print_env_var('GFXRECON_CAPTURE_FILE_TIMESTAMP') + print_env_var('GFXRECON_CAPTURE_FRAMES') + print_env_var('GFXRECON_CAPTURE_TRIGGER') + print_env_var('GFXRECON_LOG_FILE') + print_env_var('GFXRECON_LOG_LEVEL') + print_env_var('GFXRECON_LOG_OUTPUT_TO_OS_DEBUG_STRING') + print_env_var('GFXRECON_MEMORY_TRACKING_MODE') + print_env_var('VK_INSTANCE_LAYERS') + print_env_var('VK_LAYER_PATH') + + +if '__main__' == __name__: + + # We don't support running under Cygwin Python + if sys.platform == 'cygwin': + print_error_and_exit("Cygwin Python not supported") + + # Get and validate args + parser = create_argument_parser() + args = parser.parse_args() + if 'debug' == args.log_level: + print_args(args) # For debugging + validate_args(args) + + # Set up environment + set_env_vars(args) + if 'debug' == args.log_level: + PrintLayerEnv() # For debugging + + # If working_dir was specified, make it the cwd + if args.working_dir is not None: + os.chdir(args.working_dir) + + # Run the program and and exit with the exit status of the program + print('Executing program', get_command_path(args)) + result = subprocess.run(args.program_and_args, capture_output=True) + if 0 != result.returncode: + print('Errors:\n', result.stderr.decode('utf-8')) + print('Output:\n', result.stdout.decode('utf-8')) + sys.exit(result.returncode) diff --git a/vulkan/Bin/gfxrecon-capture.py b/vulkan/Bin/gfxrecon-capture.py new file mode 100644 index 0000000000000000000000000000000000000000..747e6688f4a6609d2a2e912035e09a00f283563d --- /dev/null +++ b/vulkan/Bin/gfxrecon-capture.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +# +# Copyright (c) 2023 LunarG, Inc. +# Copyright (c) 2023 Valve Corporation +# +# 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. +# +# +# Stub for redirecting to new gfxrecon-capture-vulkan command +# Usage: +# +# gfxrecon-capture.py [args] +# +# args is a command-specific argument list + +import argparse +import os +import sys +import subprocess +import platform + +if sys.version_info < (3, 7): + print("Error: Python 3.7 or later is required; you're using:", sys.version) + sys.exit(1) + +argv = sys.argv +argc = len(sys.argv) + +if __name__ == '__main__': + + # We don't support running under Cygwin Python + if sys.platform == 'cygwin': + print(os.path.basename(__file__) + ' error: Cygwin Python not supported') + sys.exit(1) + + print("Warning: the 'gfxrecon-capture.py' script is deprecated and will be removed in a future update. Use 'gfxrecon-capture-vulkan.py' instead.", flush=True) + result = subprocess.run(["gfxrecon-capture-vulkan.py",] + argv[1:], shell = True) + sys.exit(result.returncode) diff --git a/vulkan/Bin/gfxrecon.py b/vulkan/Bin/gfxrecon.py new file mode 100644 index 0000000000000000000000000000000000000000..3a16f15ec30ad718fa9ff4c590f851795bbfa0e2 --- /dev/null +++ b/vulkan/Bin/gfxrecon.py @@ -0,0 +1,169 @@ +#!/usr/bin/env python3 +# +# Copyright (c) 2020-2022 LunarG, Inc. +# Copyright (c) 2022 Valve Corporation +# +# 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. +# +# +# Utility for invoking gfxrecon commands +# Usage: +# +# gfxrecon.py [capture|compress|convert|extract|info|optimize|replay] [] +# +# args is a command-specific argument list + +import argparse +import os +import sys +import subprocess +import platform + +if sys.version_info < (3, 7): + print("Error: Python 3.7 or later is required; you're using:", sys.version) + sys.exit(1) + +argv = sys.argv +argc = len(sys.argv) + +# Supported commands +valid_commands = [ + 'capture-vulkan', + 'compress', + 'convert', + 'extract', + 'info', + 'optimize', + 'replay' +] + +deprecated_commands = [ + 'capture' # alias for `capture-vulkan` +] + +def IsWindows(): + return (sys.platform == 'win32' or sys.platform == 'cygwin') + +# Function to return an executable for the given command. +# Searches for an executable command in: +# current directory +# PATH +# ../cmd (on Linux only, so that this script works when run from a repo build) +# ../cmd/* (on Windows only, so that this script works when run from a repo build) +# If the above search fails, then it tries to find a python script named cmd.py in: +# current directory +# PATH +# ../cmd +# If the a python script is found, the availability of a python interpreter is checked. +# +# On success, returns a list that can be passed to subprocess.run to execute the command, +# possibly including the python interpreter as the first element in the list. +# +# If an executable/python script is not found, or a python interpreter for a python script is +# not found, prints an error message and exits. + +def GetExecutable(cmd): + + def IsExe(fpath): + return os.path.isfile(fpath) and os.access(fpath, os.X_OK) + + # Set the command we are looking for + if IsWindows(): + cmdexe = 'gfxrecon-' + cmd + '.exe' + else: + cmdexe = 'gfxrecon-' + cmd + + # Search for cmdexe in current dir + if IsExe(cmdexe): + return [os.path.join('.', cmdexe)] + + # Search for cmdexe in PATH + for path in os.environ['PATH'].split(os.pathsep): + c = os.path.join(path, cmdexe) + if IsExe(c): + return [c] + + # Windows: Search for cmdexe in /../cmd/* + # ( is the dir that this script is located in.) + scriptdir = os.path.dirname(os.path.realpath(__file__)) + if IsWindows(): + for buildtype in ['Debug', 'Release', 'RelWithDebInfo', 'MinSizeRel']: + c = os.path.join(scriptdir, '..', cmd, buildtype, cmdexe) + if IsExe(c): + return [c] + else: + # Linux: Search for cmdexe in /../cmd + c = os.path.join(scriptdir, '..', cmd, cmdexe) + if IsExe(c): + return [c] + + cmdpy = 'gfxrecon-' + cmd + '.py' + + # Search for cmdpy in current dir + if os.path.isfile(cmdpy): + return [sys.executable, cmdpy] + + # Search for cmdpy in PATH + for path in os.environ['PATH'].split(os.pathsep): + c=os.path.join(path, cmdpy) + if os.path.isfile(c): + return [sys.executable, c] + + # Search for cmdpy /../cmd + c = os.path.join(scriptdir, '..', cmd, cmdpy) + if os.path.isfile(c): + return [sys.executable, c] + + # Didn't find the executable or py command, error out + print('Error: Cannot find ' + cmdexe + ' or ' + cmdpy + ' to execute') + sys.exit(1) + +def CreateCommandParser(): + parser = argparse.ArgumentParser(description='GFXReconstruct utility launcher.') + parser.add_argument( + 'command', + choices=(valid_commands + deprecated_commands), + type=str.lower, + metavar='command', + help='Command to execute. Valid options are [{}]'.format( + ', '.join(valid_commands))) + parser.add_argument('args', nargs=argparse.REMAINDER, help='Command-specific argument list. Specify -h after command name for command help.') + return parser + +if __name__ == '__main__': + + # We don't support running under Cygwin Python + if sys.platform == 'cygwin': + print(os.path.basename(__file__) + ' error: Cygwin Python not supported') + sys.exit(1) + + command_parser = CreateCommandParser() + command = command_parser.parse_args() + + # Fixup and warn for any deprecated commands. + if command.command == 'capture': + print( + "Warning: the 'capture' command is deprecated and will be removed in a future update. Use 'capture-vulkan' instead.", + flush=True) + command.command = 'capture-vulkan' + + extras = sys.argv[2::] + cmd = GetExecutable(command.command) + result = subprocess.run(cmd + extras) + sys.exit(result.returncode) diff --git a/vulkan/Bin/slang-standard-module-2026.1/bindless-storage.slang b/vulkan/Bin/slang-standard-module-2026.1/bindless-storage.slang new file mode 100644 index 0000000000000000000000000000000000000000..14d2ea0975292c723e5635a1f136deb54cf1686b --- /dev/null +++ b/vulkan/Bin/slang-standard-module-2026.1/bindless-storage.slang @@ -0,0 +1,149 @@ +implementing neural; + +/** +Bindless address type with pointer-like semantics. +Wraps a buffer handle and base index to provide array-like access. +*/ +public struct BindlessAddress : IPointerLikeAddress + where T : __BuiltinFloatingPointType + where T.Differential == T +{ + public typealias Differential = BindlessAddress; + + internal RWStructuredBuffer.Handle handle; + internal uint baseIndex; + + public __init(RWStructuredBuffer.Handle handle) + { + this.handle = handle; + this.baseIndex = 0; + } + + public __subscript(uint index)->T + { + [nonmutating] + get { return handle[baseIndex + index]; } + + [mutating] + set { handle[baseIndex + index] = newValue; } + } + + [require(cuda_glsl_hlsl_metal_spirv, sm_6_6)] + public void atomicAdd(uint index, T value) + { + __atomic_add(handle[baseIndex + index], value); + } + + public This getOffset(int elements) + { + uint newBaseIndex = baseIndex + elements; + + This address = This(handle); + address.baseIndex = newBaseIndex; + return address; + } +} + +public struct PointerAddress : IPointerLikeAddress + where T : __BuiltinFloatingPointType + where T.Differential == T +{ + public typealias Differential = PointerAddress; + + T* ptr; + + public __init(T* ptr) + { + this.ptr = ptr; + } + + public __subscript(uint index)->T + { + [nonmutating] + get { return ptr[index]; } + + [mutating] + set { ptr[index] = newValue; } + } + + public This getOffset(int elements) + { + return This(ptr + elements); + } + + [require(cuda_glsl_hlsl_metal_spirv, sm_6_6)] + public void atomicAdd(uint index, T value) + { + __atomic_add(ptr[index], value); + } +} + +// We currently don't support UserPointer as an `IDifferentiablePtrType`, the issue is tracked in +// https://github.com/shader-slang/slang/issues/8834. +// So we define an internal extension for now, once we can resolve the issue, we can make it public. +internal extension Ptr : IPointerLikeAddress + where T : __BuiltinFloatingPointType + where T.Differential == T +{ + internal typealias Differential = Ptr; + + internal __init(Ptr ptr) + { + this = ptr; + } + + internal __subscript(uint index)->T + { + [nonmutating] + get { return this[index]; } + + [mutating] + set { this[index] = newValue; } + } + + internal This getOffset(int elements) + { + return This(this + elements); + } + + [require(cuda_glsl_hlsl_metal_spirv, sm_6_6)] + internal void atomicAdd(uint index, T value) {__atomic_add(this[index], value);} +} + +/** +Bindless buffer storage implementation using buffer handles. +Provides pointer-like addressing through buffer handles, enabling more flexible +memory access patterns suitable for dynamic or sparse data structures. +@param T The element type (must be a floating-point type). +@remarks Type constraints: +- `T` must conform to `__BuiltinFloatingPointType` (float, double, half, etc.) +- `T.Differential` must conform to `__BuiltinFloatingPointType` for automatic differentiation +@category neural +*/ +public struct BindlessBufferStorage : IStorage + where T : __BuiltinFloatingPointType + where T.Differential == T +{ + public typealias Address = BindlessAddress; + public typealias Differential = BindlessBufferStorage; + + // Following method will not be needed for bindless storage + public T read(Address address) {return address[0];} + public void atomicAdd(Address address, T value) {address.atomicAdd(0, value);} + public void write(Address address, T value) {address[0] = value;} + public static Address getOffset(Address base, int elements) { return base.getOffset(elements); } +} + +public struct PointerStorage : IStorage + where T : __BuiltinFloatingPointType + where T.Differential == T +{ + public typealias Address = PointerAddress; + public typealias Differential = PointerStorage; + + // Following method will not be needed for pointer storage + public T read(Address address) {return address[0];} + public void atomicAdd(Address address, T value) {address.atomicAdd(0, value);} + public void write(Address address, T value) {address[0] = value;} + public static Address getOffset(Address base, int elements) {return base.getOffset(elements);} +} diff --git a/vulkan/Bin/slang-standard-module-2026.1/buffer-storage.slang b/vulkan/Bin/slang-standard-module-2026.1/buffer-storage.slang new file mode 100644 index 0000000000000000000000000000000000000000..f1d2a7de9f78d306cc72e8f2a349bc50603a28fd --- /dev/null +++ b/vulkan/Bin/slang-standard-module-2026.1/buffer-storage.slang @@ -0,0 +1,129 @@ +implementing neural; + +/** +GPU structured buffer storage implementation. +Implements IStorage using GPU structured buffers (RWStructuredBuffer). +Supports offset-based addressing and atomic operations for gradient accumulation. +@param T The element type (must be a floating-point type). +@remarks Type constraints: +- `T` must conform to `__BuiltinFloatingPointType` (float, double, half, etc.) +- `T.Differential` must conform to `__BuiltinFloatingPointType` for automatic differentiation +@category neural +*/ +public struct StructuredBufferStorage : IStorage + where T : __BuiltinFloatingPointType + where T.Differential == T +{ + /// Address type is a simple unsigned integer index. + public typealias Address = uint; + + /// The underlying buffer type. + public typealias BufferType = RWStructuredBuffer; + + /// Differential type for automatic differentiation. + public typealias Differential = StructuredBufferStorage; + + /// The underlying GPU buffer. + public BufferType m_buffer; + + /** + Base address offset within the buffer. + @remarks Allows multiple storage instances to share the same buffer at different offsets. + Default is 0 (start of buffer). + */ + public Address m_baseAddress = 0U; + + /** + Constructor. + @param[in] buffer The underlying structured buffer. + @param[in] baseAddress Optional base address offset (default: 0). + */ + public __init(BufferType buffer, Address baseAddress = 0U) + { + this.m_buffer = buffer; + this.m_baseAddress = baseAddress; + } + + /** + Reads a value from the buffer. + @param[in] address The address relative to base address. + @return The value at the specified address. + */ + public T read(Address address) + { + return m_buffer[address + m_baseAddress]; + } + + /** + Atomically adds a value (for gradient accumulation). + @param[in] address The address relative to base address. + @param[in] value The value to add. + */ + [require(cuda_glsl_hlsl_metal_spirv, sm_6_6)] + public void atomicAdd(Address address, T value) + { + __target_switch + { + case hlsl: atomicAddForHLSL(address, value); + default: __atomic_add(m_buffer[address + m_baseAddress], value); + } + } + + /** + Atomic add implementation for HLSL using InterlockedCompareExchangeFloatBitwise. + Uses compare-and-swap loop to atomically add floating-point values. + @param[in] address The address relative to base address. + @param[in] value The value to add. + @remarks This method uses a compare-and-swap loop which may have performance + implications under high contention. Only required for HLSL targets. + Requires Shader Model 6.6 or higher for `InterlockedCompareExchangeFloatBitwise` support. + */ + [require(hlsl, sm_6_6)] + internal void atomicAddForHLSL(Address address, T value) + { + T compareValue; + uint bufferIndex = address + m_baseAddress; + + // Compare-and-swap loop + bool success = false; + do + { + compareValue = m_buffer[bufferIndex]; + T newValue = compareValue + value; + success = __atomic_compare_exchange(m_buffer[bufferIndex], compareValue, newValue) == compareValue; + } while (!success); + } + + /** + Writes a value to the buffer. + @param[in] address The address relative to base address. + @param[in] value The value to write. + */ + public void write(Address address, T value) + { + m_buffer[address + m_baseAddress] = value; + } + + /** + Gets a buffer reference from an address (for advanced use cases). + @param[in] address The address to get buffer from. + @return A buffer reference starting at the specified address. + */ + // [require(cpp_cuda_metal_spirv)] + public BufferType getBufferFromAddress(Address address) + { + let ptr = &m_buffer[address + m_baseAddress]; + return bit_cast(ptr); + } + + /** + Computes an offset address. + @param[in] base The base address. + @param[in] elements Number of elements to offset by. + @return The offset address. + */ + public static Address getOffset(Address base, int elements) + { + return base + elements; + } +} diff --git a/vulkan/Bin/slang-standard-module-2026.1/inline-vector.slang b/vulkan/Bin/slang-standard-module-2026.1/inline-vector.slang new file mode 100644 index 0000000000000000000000000000000000000000..caed6873f9ebcc9eeba07623a0dc8cc43ecf8da6 --- /dev/null +++ b/vulkan/Bin/slang-standard-module-2026.1/inline-vector.slang @@ -0,0 +1,303 @@ +implementing neural; + +/** +Concrete implementation of IVector storing elements inline (on stack/registers). +InlineVector stores all elements in a fixed-size array, making it suitable for +small vectors that can fit in registers or stack memory. Supports automatic differentiation +for gradient computation in neural networks. +@param T The element type +@param N The vector size (compile-time constant). +@remarks Type constraints: +- `T` must conform to `__BuiltinFloatingPointType` (float, double, half, etc.) +- `T.Differential` must conform to `__BuiltinFloatingPointType` for automatic differentiation +@category neural +*/ +public struct InlineVector : IVector + where T : __BuiltinFloatingPointType + where T.Differential == T +{ + /// The differential type for automatic differentiation. + public typealias Differential = InlineVector; + + /// The compile-time size of the vector. + public static const int Size = N; + + /** + Internal storage for vector elements. + @remarks Marked as derivative member to enable automatic differentiation. + */ + [DerivativeMember(Differential.data)] + internal T[N] data; + + /// Default constructor - initializes all elements to zero. + public __init() { data = {}; } + + /** + Scalar broadcast constructor - fills all elements with the same value. + @param[in] value The value to broadcast to all elements. + */ + public __init(T value) { + [ForceUnroll] + for (int i = 0; i < N; i++) + data[i] = value; + } + + /** + Array constructor - initializes from an array. + @param[in] data Array of N elements to initialize the vector. + */ + public __init(T[N] data) { this.data = data; } + + /** + Copy constructor. + @param[in] other The vector to copy from. + */ + public __init(This other) { this.data = other.data; } + + /** + Element access operator. + @param[in] index The element index (0-based). + @return Reference to the element at the given index. + */ + public __subscript(int index) -> T + { + get() { return this.data[index]; } + set() { this.data[index] = newValue; } + } + + // Linear transformation without bias + [BackwardDerivative(linearTransformBwd)] + public OutputVector linearTransform( + Storage weightStorage, + no_diff Storage.Address weightAddress) + where Storage : IStorage + where Storage.Differential : IStorage + where Storage.Address == Storage.Differential.Address + where OutputVector : IVector + { + OutputVector output = OutputVector(); + + [MaxIters(OutputSize)] + for (int row = 0; row < OutputSize; row++) + { + // get the address of each row of the weight matrix + let rowOffset = Storage.getOffset(weightAddress, row * N); + [ForceUnroll] + for (int col = 0; col < N; col++) + { + let elementOffset = Storage.getOffset(rowOffset, col); + output[row] += data[col] * weightStorage.read(elementOffset); + } + } + + return output; + } + + // Linear transformation with bias + [BackwardDerivative(linearTransformBwd)] + public OutputVector linearTransform( + Storage weightStorage, + Storage biasStorage, + no_diff Storage.Address weightAddress, + no_diff Storage.Address biasAddress) + where Storage : IStorage + where Storage.Differential : IStorage + where Storage.Address == Storage.Differential.Address + where OutputVector : IVector + { + // Reuse the unbias matmul method + OutputVector output = this.linearTransform(weightStorage, weightAddress); + + // apply the bias + [ForceUnroll] + for (int i = 0; i < OutputSize; i++) + { + let elementOffset = Storage.getOffset(biasAddress, i); + output[i] = output[i] + biasStorage.read(elementOffset); + } + + return output; + } + + // Backward of linear transformation without bias + static void linearTransformBwd( + inout DifferentialPair dthis, + DifferentialPtrPair dWeightStorage, + no_diff Storage.Address dWeightAddress, + OutputVector.Differential doutput) + where Storage : IStorage + where Storage.Differential : IStorage + where Storage.Address == Storage.Differential.Address + where OutputVector : IVector + { + // Derivative of the input is transposed weight matrix times the output differential + var d = dthis.d; + + [MaxIters(OutputSize)] + for (int j = 0; j < OutputSize; j++) + { + T.Differential dy = doutput[j]; + + [ForceUnroll] + for (int i = 0; i < N; i++) + { + Storage.Address elementOffset = Storage.getOffset(dWeightAddress, i * OutputSize + j); + T.Differential prod = T.Differential.dmul(dWeightStorage.p.read(elementOffset), dy); + d[i] = T.Differential.dadd(d[i], prod); + } + } + + // Derivative of the weights is the outer product of the input and the output differential + // dW = dOutput * dInput^T + [MaxIters(OutputSize)] + for (int row = 0; row < OutputSize; row++) + { + let rowOffset = Storage.getOffset(dWeightAddress, row * N); + T.Differential dy = doutput[row]; + [ForceUnroll] + for (int col = 0; col < N; col++) + { + let x = dthis.p[col]; + let elementOffset = Storage.getOffset(rowOffset, col); + T.Differential prod = T.Differential.dmul(x, dy); + dWeightStorage.d.atomicAdd(elementOffset, prod); + } + } + + dthis = DifferentialPair(dthis.p, d); + } + + // Backward of linear transformation with bias + static void linearTransformBwd( + inout DifferentialPair dthis, + DifferentialPtrPair dWeightStorage, + DifferentialPtrPair dBiasStorage, + no_diff Storage.Address dWeightAddress, + no_diff Storage.Address dBiasAddress, + OutputVector.Differential doutput) + where Storage : IStorage + where Storage.Differential : IStorage + where Storage.Address == Storage.Differential.Address + where OutputVector : IVector + { + // Reuse the unbias backward method + linearTransformBwd(dthis, dWeightStorage, dWeightAddress, doutput); + + // Derivative of the bias is the same as the output differential + [ForceUnroll] + for (int i = 0; i < OutputSize; i++) + { + let biasOffset = Storage.getOffset(dBiasAddress, i); + dBiasStorage.d.atomicAdd(biasOffset, doutput[i]); + } + } + + // Linear transformation without bias (Bindless storage) + [BackwardDerivative(linearTransformBwd)] + public OutputVector linearTransform( + Address weightAddress) + where Address : IPointerLikeAddress + where Address.Differential : IPointerLikeAddress + where OutputVector : IVector + { + var output = OutputVector(); + + // output = W * input + [MaxIters(OutputSize)] + for (int row = 0; row < OutputSize; row++) + { + // get the address of each row of the weight matrix + let rowAddr = weightAddress.getOffset(row * N); + [ForceUnroll] + for (int col = 0; col < N; col++) + { + output[row] += data[col] * rowAddr[col]; + } + } + return output; + } + + // Linear transformation with bias (Bindless storage) + [BackwardDerivative(linearTransformBwd)] + public OutputVector linearTransform( + Address weightAddress, + Address biasAddress) + where Address : IPointerLikeAddress + where Address.Differential : IPointerLikeAddress + where OutputVector : IVector + { + // Reuse the unbias matmul method + OutputVector output = this.linearTransform(weightAddress); + + [ForceUnroll] + for (int i = 0; i < OutputSize; i++) + output[i] = output[i] + biasAddress[i]; + + return output; + } + + // Backward of linear transformation without bias (Bindless storage) + static public void linearTransformBwd( + inout DifferentialPair dthis, + DifferentialPtrPair
dparameters, + OutputVector.Differential doutput) + where Address : IPointerLikeAddress + where Address.Differential : IPointerLikeAddress + where OutputVector : IVector + where OutputVector.Differential : IVector + { + // dInput = dW^T * dOutput + var d = dthis.d; + [MaxIters(OutputSize)] + for (int j = 0; j < OutputSize; j++) + { + let dy = doutput[j]; + [ForceUnroll] + for (int i = 0; i < N; i++) + { + T.Differential prod = T.Differential.dmul(dparameters.p[i * OutputSize + j], dy); + d[i] = T.Differential.dadd(d[i], prod); + } + } + + // Derivative of the weights is the outer product of the input and the output differential + // dW = dOutput * dInput^T + [MaxIters(OutputSize)] + for (int row = 0; row < OutputSize; row++) + { + let rowAddr = dparameters.d.getOffset(row * N); + T.Differential dy = doutput[row]; + [ForceUnroll] + for (int col = 0; col < N; col++) + { + let x = dthis.p[col]; + T.Differential prod = T.Differential.dmul(x, dy); + rowAddr.atomicAdd(col, prod); + } + } + + dthis = DifferentialPair(dthis.p, d); + } + + // Backward of linear transformation with bias (Bindless storage) + static public void linearTransformBwd( + inout DifferentialPair dthis, + DifferentialPtrPair
dWeightAddress, + DifferentialPtrPair
dBiasAddress, + OutputVector.Differential doutput) + where Address : IPointerLikeAddress + where Address.Differential : IPointerLikeAddress + where OutputVector : IVector + { + // Reuse the unbias backward method + linearTransformBwd(dthis, dWeightAddress, doutput); + + let biasOffset = dBiasAddress.d.getOffset(0); + // dBias = dOutput + [ForceUnroll] + for (int i = 0; i < OutputSize; i++) + { + biasOffset.atomicAdd(i, doutput[i]); + } + } +} diff --git a/vulkan/Bin/slang-standard-module-2026.1/istorages.slang b/vulkan/Bin/slang-standard-module-2026.1/istorages.slang new file mode 100644 index 0000000000000000000000000000000000000000..cc55fe3ed129b7a7cb948922b8b16d9d6c98b674 --- /dev/null +++ b/vulkan/Bin/slang-standard-module-2026.1/istorages.slang @@ -0,0 +1,90 @@ +implementing neural; + +/** +Storage interface for accessing neural network parameters. +Provides an abstraction for reading/writing parameters from various storage backends +(GPU buffers, shared memory, etc.). Supports automatic differentiation for gradient accumulation. +@param T The element type (must be a floating-point type). +@remarks Type constraints: +- `T` must conform to `__BuiltinFloatingPointType` (float, double, half, etc.) +- `T.Differential` must conform to `__BuiltinFloatingPointType` for automatic differentiation +@see `StructuredBufferStorage`, `BindlessBufferStorage` +@category neural +*/ +public interface IStorage : IDifferentiablePtrType + where T : __BuiltinFloatingPointType + where T.Differential == T +{ + /** + Address type for this storage backend. + @remarks Different storage types may use different addressing schemes (indices, pointers, etc.). + */ + public associatedtype Address; + + /** + Reads a value from storage. + @param[in] address The address to read from. + @return The value at the specified address. + */ + public T read(Address address); + + /** + Atomically adds a value to storage (for gradient accumulation). + @param[in] address The address to add to. + @param[in] value The value to add. + */ + [require(cuda_glsl_hlsl_metal_spirv, sm_6_6)] + public void atomicAdd(Address address, T value); + + /** + Writes a value to storage. + @param[in] address The address to write to. + @param[in] value The value to write. + */ + public void write(Address address, T value); + + /** + Computes an offset address. + @param[in] base The base address. + @param[in] elements Number of elements to offset by. + @return The offset address. + */ + public static Address getOffset(Address base, int elements); +} + +/** +Interface for pointer-like addressing with direct subscript access. +Provides array-like access patterns for storage backends that support pointer arithmetic. +@param T The element type. +@remarks Type constraints: +- `T` must conform to `__BuiltinFloatingPointType` (float, double, half, etc.) +- `T.Differential` must conform to `__BuiltinFloatingPointType` for automatic differentiation +@see `BindlessBufferStorage.BindlessAddress` +@category neural +*/ +public interface IPointerLikeAddress : IDifferentiablePtrType + where T : __BuiltinFloatingPointType + where T.Differential == T +{ + /** + Array-style element access. + @param[in] index The element index. + @return Reference to the element at the given index. + */ + public __subscript(uint index) -> T { get; set; } + + /** + Computes an offset pointer. + @param[in] elements Number of elements to offset by. + @return The offset pointer. + */ + public This getOffset(int elements); + + /** + Atomically adds a value at an index. + @param[in] index The element index. + @param[in] value The value to add. + */ + [require(cuda_glsl_hlsl_metal_spirv, sm_6_6)] + public void atomicAdd(uint index, T value); +} diff --git a/vulkan/Bin/slang-standard-module-2026.1/ivector.slang b/vulkan/Bin/slang-standard-module-2026.1/ivector.slang new file mode 100644 index 0000000000000000000000000000000000000000..6b3547fec32fd98aef0f1361b993410cbc0ed867 --- /dev/null +++ b/vulkan/Bin/slang-standard-module-2026.1/ivector.slang @@ -0,0 +1,171 @@ +implementing neural; + +/** +Generic vector interface for neural network operations. +Provides a differentiable vector abstraction supporting automatic differentiation +and linear algebra operations for neural network computations. +@param T The element type (must be a floating-point type). +@param N The vector size (compile-time constant). +@remarks Type constraints: +- `T` must conform to `__BuiltinFloatingPointType` (float, double, half, etc.) +- `T.Differential` must conform to `__BuiltinFloatingPointType` for automatic differentiation +@see `InlineVector` +@category neural +*/ +public interface IVector : IDifferentiable + where T : __BuiltinFloatingPointType + where T.Differential == T +{ + /// The compile-time size of the vector. + public static const int Size; + + /// The differential type for automatic differentiation. + /// @remarks Ensures the differential is also a vector with the same structure. + public associatedtype Differential : IVector; + + /// Default constructor - initializes vector to zero. + public __init(); + + /** + Scalar broadcast constructor - fills all elements with the same value. + @param[in] value The value to broadcast to all elements. + */ + public __init(T value); + + /** + Array constructor - initializes from an array. + @param[in] data Array of N elements to initialize the vector. + */ + public __init(T[N] data); + + /** + Copy constructor. + @param[in] other The vector to copy from. + */ + public __init(This other); + + /** + Element access operator. + @param[in] index The element index (0-based). + @return Reference to the element at the given index. + */ + public __subscript(int index)->T + { + get; + set; + } + + /** + Evaluates a linear transformation: output = W * input. + Performs matrix-vector multiplication. + Supports automatic differentiation for backpropagation. + @param OutputSize The size of the output vector. + @param Bias Whether to include bias in the computation. + @param Storage The storage type for parameters (must implement IStorage). + @param OutputVector The output vector type. + @param[in] weightStorage The storage object containing weight matrix. + The weight matrix is stored in a contiguous block of memory, the size + of the weight matrix is in `OutputSize` rows, and `N` columns. + @param[in] weightAddress The starting address in weightStorage for the weight matrix. + @return The result of the linear transformation, `result = W * this`, whose size is `OutputSize`. + @remarks Type constraints: + - `Storage` must conform to `IStorage` + - `Storage.Differential` must conform to `IStorage` + - `Storage.Address` must equal `Storage.Differential.Address` (same address type) + - `OutputVector` must conform to `IVector` + */ + [Differentiable] + public OutputVector linearTransform( + Storage weightStorage, + no_diff Storage.Address weightAddress) + where Storage : IStorage + where Storage.Differential : IStorage + where Storage.Address == Storage.Differential.Address + where OutputVector : IVector; + + /** + Evaluates a linear transformation: output = W * this + bias. + Performs matrix-vector multiplication with bias addition. + Supports automatic differentiation for backpropagation. + @param OutputSize The size of the output vector. + @param Storage The storage type for parameters (must implement IStorage). + @param OutputVector The output vector type. + @param[in] weightStorage The storage object containing weight matrix. + The weight matrix is stored in a contiguous block of memory, the size + of the weight matrix is in `OutputSize` rows, and `N` columns. + @param[in] weightAddress The starting address in weightStorage for the weight matrix. + @param[in] biasStorage The storage object containing bias vector. + The bias vector is stored in a contiguous block of memory, the size + of the bias vector is `OutputSize`. + @param[in] weightAddress The starting address in `weightStorage` for the weight matrix. + @param[in] biasAddress The starting address in `biasStorage` for the bias vector. + @return The result of the linear transformation, `result = W * this + bias`, whose size is `OutputSize`. + @remarks Type constraints: + - `Storage` must conform to `IStorage` + - `Storage.Differential` must conform to `IStorage` + - `Storage.Address` must equal `Storage.Differential.Address` (same address type) + - `OutputVector` must conform to `IVector` + */ + [Differentiable] + public OutputVector linearTransform( + Storage weightStorage, + Storage biasStorage, + no_diff Storage.Address weightAddress, + no_diff Storage.Address biasAddress) + where Storage : IStorage + where Storage.Differential : IStorage + where Storage.Address == Storage.Differential.Address + where OutputVector : IVector; + + /** + Evaluates a linear transformation: output = W * this. + This is a overload method but using bindless storage (pointer-like addressing). + @param OutputSize The size of the output vector. + @param BindlessStorage The bindless storage type with pointer-like addresses. + @param OutputVector The output vector type. + @param[in] weightAddress The address of weight matrix in bindless storage. + The weight matrix is stored in a contiguous block of memory, the size + of the weight matrix is in `OutputSize` rows, and `N` columns. As this method take the + bindless storage buffer, we don't need to provide the storage object. + @return The result of the linear transformation, `result = W * this`, whose size is `OutputSize`. + @remarks Type constraints: + - `BindlessStorage` must conform to `IStorage` + - `BindlessStorage.Address` must conform to `IPointerLikeAddress` + - `BindlessStorage.Address.Differential` must conform to `IPointerLikeAddress` + - `OutputVector` must conform to `IVector` + */ + [Differentiable] + public OutputVector linearTransform(Address weightAddress) + where Address : IPointerLikeAddress + where Address.Differential : IPointerLikeAddress + where OutputVector : IVector; + + /** + Evaluates a linear transformation: output = W * this + bias. + Performs matrix-vector multiplication with bias addition. + Supports automatic differentiation for backpropagation. + @param OutputSize The size of the output vector. + @param BindlessStorage The bindless storage type with pointer-like addresses. + @param OutputVector The output vector type. + @param[in] weightAddress The address of weight matrix in bindless storage. + The weight matrix is stored in a contiguous block of memory, the size + of the weight matrix is in `OutputSize` rows, and `N` columns. As this method take the + bindless storage buffer, we don't need to provide the storage object. + @param[in] biasAddress The address of bias vector in bindless storage. + The bias vector is stored in a contiguous block of memory, the size + of the bias vector is `OutputSize`. As this method take the + bindless storage buffer, we don't need to provide the storage object. + @return The result of the linear transformation, `result = W * this + bias`, whose size is `OutputSize`. + @remarks Type constraints: + - `BindlessStorage` must conform to `IStorage` + - `BindlessStorage.Address` must conform to `IPointerLikeAddress` + - `BindlessStorage.Address.Differential` must conform to `IPointerLikeAddress` + - `OutputVector` must conform to `IVector` + */ + [Differentiable] + public OutputVector linearTransform( + Address weightAddress, Address biasAddress) + where Address : IPointerLikeAddress + where Address.Differential : IPointerLikeAddress + where OutputVector : IVector; +} diff --git a/vulkan/Bin/slang-standard-module-2026.1/neural.slang b/vulkan/Bin/slang-standard-module-2026.1/neural.slang new file mode 100644 index 0000000000000000000000000000000000000000..3f772de2e3b681b71bb8cb3df611903d8683925e --- /dev/null +++ b/vulkan/Bin/slang-standard-module-2026.1/neural.slang @@ -0,0 +1,39 @@ +/** +Neural network primitives for Slang. +This module provides differentiable primitive data structures and operations for implementing +inline MLP (Multilayer Perceptron) in Slang shaders. It includes vector types, storage abstractions, +activation functions, optimizers, and automatic differentiation support for training small inline +neural networks on the GPU. + +@remarks EXPERIMENTAL: This module is under active design and may change significantly +or be removed in future versions. DO NOT USE IN PRODUCTION. + +@category neural + +Features: +- Differentiable vector types (IVector, InlineVector) +- Storage abstractions for GPU buffers (IStorage, StructuredBufferStorage) +- Automatic differentiation support +- Linear transformations with optional bias +- Atomic operations for gradient accumulation + +Example Usage: +``` +// Create an input vector +InlineVector input = InlineVector({1.0, 2.0, 3.0, 4.0}); + +// Set up storage for weights +StructuredBufferStorage storage = StructuredBufferStorage(weightsBuffer); + +// Perform linear transformation: output = W * input +InlineVector output = input.eval<2, false>(storage, 0); +``` +*/ +[ExperimentalModule] +module neural; + +__include "ivector"; +__include "inline-vector"; +__include "istorages"; +__include "bindless-storage"; +__include "buffer-storage"; diff --git a/vulkan/Bin/slang.dll b/vulkan/Bin/slang.dll new file mode 100644 index 0000000000000000000000000000000000000000..57dd1d5bed7af094c8224f026d87b2e37644f405 Binary files /dev/null and b/vulkan/Bin/slang.dll differ diff --git a/vulkan/Bin/slang.slang b/vulkan/Bin/slang.slang new file mode 100644 index 0000000000000000000000000000000000000000..75fa8a01fddba72300c95da5bf8a8d8d22afaa80 --- /dev/null +++ b/vulkan/Bin/slang.slang @@ -0,0 +1,446 @@ +public namespace slang +{ + +public typedef int32_t Result; +public typedef uint64_t Size; +public typedef int64_t Int; +public typedef uint64_t UInt; + +/*! +@brief Severity of a diagnostic generated by the compiler. +Values come from the enum below, with higher values representing more severe +conditions, and all values >= SLANG_SEVERITY_ERROR indicating compilation +failure. +*/ +public enum SlangSeverity +{ + SLANG_SEVERITY_DISABLED = 0, /**< A message that is disabled, filtered out. */ + SLANG_SEVERITY_NOTE, /**< An informative message. */ + SLANG_SEVERITY_WARNING, /**< A warning, which indicates a possible proble. */ + SLANG_SEVERITY_ERROR, /**< An error, indicating that compilation failed. */ + SLANG_SEVERITY_FATAL, /**< An unrecoverable error, which forced compilation to abort. */ + SLANG_SEVERITY_INTERNAL, /**< An internal error, indicating a logic error in the compiler. */ +}; + +public enum SlangDiagnosticFlags +{ + SLANG_DIAGNOSTIC_FLAG_VERBOSE_PATHS = 0x01, + SLANG_DIAGNOSTIC_FLAG_TREAT_WARNINGS_AS_ERRORS = 0x02 +}; + +public enum SlangBindableResourceType +{ + SLANG_NON_BINDABLE = 0, + SLANG_TEXTURE, + SLANG_SAMPLER, + SLANG_UNIFORM_BUFFER, + SLANG_STORAGE_BUFFER, +}; + +public enum SlangCompileTarget +{ + SLANG_TARGET_UNKNOWN, + SLANG_TARGET_NONE, + SLANG_GLSL, + SLANG_GLSL_VULKAN, //< deprecated: just use `SLANG_GLSL` + SLANG_GLSL_VULKAN_ONE_DESC, //< deprecated + SLANG_HLSL, + SLANG_SPIRV, + SLANG_SPIRV_ASM, + SLANG_DXBC, + SLANG_DXBC_ASM, + SLANG_DXIL, + SLANG_DXIL_ASM, + SLANG_C_SOURCE, ///< The C language + SLANG_CPP_SOURCE, ///< C++ code for shader kernels. + SLANG_CPP_PYTORCH_BINDING, + SLANG_HOST_EXECUTABLE, ///< Standalone binary executable (for hosting CPU/OS) + SLANG_SHADER_SHARED_LIBRARY, ///< A shared library/Dll for shader kernels (for hosting CPU/OS) + SLANG_SHADER_HOST_CALLABLE, ///< A CPU target that makes the compiled shader code available to be run immediately + SLANG_CUDA_SOURCE, ///< Cuda source + SLANG_PTX, ///< PTX + SLANG_OBJECT_CODE, ///< Object code that can be used for later linking + SLANG_HOST_CPP_SOURCE, ///< C++ code for host library or executable. + SLANG_HOST_HOST_CALLABLE, ///< + SLANG_CPP_HEADER, ///< C++ header for shader kernels. + SLANG_CUDA_HEADER, ///< Cuda header + SLANG_TARGET_COUNT_OF, +}; + +/* A "container format" describes the way that the outputs +for multiple files, entry points, targets, etc. should be +combined into a single artifact for output. */ +public enum SlangContainerFormat +{ + /* Don't generate a container. */ + SLANG_CONTAINER_FORMAT_NONE, + + /* Generate a container in the `.slang-module` format, + which includes reflection information, compiled kernels, etc. */ + SLANG_CONTAINER_FORMAT_SLANG_MODULE, +}; + +public enum SlangPassThrough : int +{ + SLANG_PASS_THROUGH_NONE, + SLANG_PASS_THROUGH_FXC, + SLANG_PASS_THROUGH_DXC, + SLANG_PASS_THROUGH_GLSLANG, + SLANG_PASS_THROUGH_SPIRV_DIS, + SLANG_PASS_THROUGH_CLANG, ///< Clang C/C++ compiler + SLANG_PASS_THROUGH_VISUAL_STUDIO, ///< Visual studio C/C++ compiler + SLANG_PASS_THROUGH_GCC, ///< GCC C/C++ compiler + SLANG_PASS_THROUGH_GENERIC_C_CPP, ///< Generic C or C++ compiler, which is decided by the source type + SLANG_PASS_THROUGH_NVRTC, ///< NVRTC Cuda compiler + SLANG_PASS_THROUGH_LLVM, ///< LLVM 'compiler' - includes LLVM and Clang + SLANG_PASS_THROUGH_SPIRV_OPT, + SLANG_PASS_THROUGH_COUNT_OF, +}; + +/* Defines an archive type used to holds a 'file system' type structure. */ +public enum SlangArchiveType : int +{ + SLANG_ARCHIVE_TYPE_UNDEFINED, + SLANG_ARCHIVE_TYPE_ZIP, + SLANG_ARCHIVE_TYPE_RIFF, ///< Riff container with no compression + SLANG_ARCHIVE_TYPE_RIFF_DEFLATE, + SLANG_ARCHIVE_TYPE_RIFF_LZ4, + SLANG_ARCHIVE_TYPE_COUNT_OF, +}; + +/*! +Flags to control compilation behavior. +*/ +public enum SlangCompileFlags +{ + /* Do as little mangling of names as possible, to try to preserve original names */ + SLANG_COMPILE_FLAG_NO_MANGLING = 1 << 3, + + /* Skip code generation step, just check the code and generate layout */ + SLANG_COMPILE_FLAG_NO_CODEGEN = 1 << 4, + + /* Obfuscate shader names on release products */ + SLANG_COMPILE_FLAG_OBFUSCATE = 1 << 5, + + /* Deprecated flags: kept around to allow existing applications to + compile. Note that the relevant features will still be left in + their default state. */ + SLANG_COMPILE_FLAG_NO_CHECKING = 0, + SLANG_COMPILE_FLAG_SPLIT_MIXED_TYPES = 0, +}; + +/*! +@brief Flags to control code generation behavior of a compilation target */ +public enum SlangTargetFlags +{ + None = 0, + + /* When compiling for a D3D Shader Model 5.1 or higher target, allocate + distinct register spaces for parameter blocks. + + @deprecated This behavior is now enabled unconditionally. + */ + SLANG_TARGET_FLAG_PARAMETER_BLOCKS_USE_REGISTER_SPACES = 1 << 4, + + /* When set, will generate target code that contains all entrypoints defined + in the input source or specified via the `spAddEntryPoint` function in a + single output module (library/source file). + */ + SLANG_TARGET_FLAG_GENERATE_WHOLE_PROGRAM = 1 << 8, + + /* When set, will dump out the IR between intermediate compilation steps.*/ + SLANG_TARGET_FLAG_DUMP_IR = 1 << 9, + + /* When set, will generate SPIRV directly instead of going through glslang. */ + SLANG_TARGET_FLAG_GENERATE_SPIRV_DIRECTLY = 1 << 10, +}; + +/*! +@brief Options to control floating-point precision guarantees for a target. +*/ +public enum SlangFloatingPointMode +{ + SLANG_FLOATING_POINT_MODE_DEFAULT = 0, + SLANG_FLOATING_POINT_MODE_FAST, + SLANG_FLOATING_POINT_MODE_PRECISE, +}; + +/*! +@brief Options to control emission of `#line` directives +*/ +public enum SlangLineDirectiveMode +{ + SLANG_LINE_DIRECTIVE_MODE_DEFAULT = 0, /**< Default behavior: pick behavior base on target. */ + SLANG_LINE_DIRECTIVE_MODE_NONE, /**< Don't emit line directives at all. */ + SLANG_LINE_DIRECTIVE_MODE_STANDARD, /**< Emit standard C-style `#line` directives. */ + SLANG_LINE_DIRECTIVE_MODE_GLSL, /**< Emit GLSL-style directives with file *number* instead of name */ +}; + +public enum SlangSourceLanguage : int +{ + SLANG_SOURCE_LANGUAGE_UNKNOWN, + SLANG_SOURCE_LANGUAGE_SLANG, + SLANG_SOURCE_LANGUAGE_HLSL, + SLANG_SOURCE_LANGUAGE_GLSL, + SLANG_SOURCE_LANGUAGE_C, + SLANG_SOURCE_LANGUAGE_CPP, + SLANG_SOURCE_LANGUAGE_CUDA, + SLANG_SOURCE_LANGUAGE_COUNT_OF, +}; + +public enum SlangProfileID +{ + SLANG_PROFILE_UNKNOWN, +}; + +public enum SlangCapabilityID +{ + SLANG_CAPABILITY_UNKNOWN = 0, +}; + +public enum SlangMatrixLayoutMode +{ + SLANG_MATRIX_LAYOUT_MODE_UNKNOWN = 0, + SLANG_MATRIX_LAYOUT_ROW_MAJOR, + SLANG_MATRIX_LAYOUT_COLUMN_MAJOR, +}; + +public enum SlangStage +{ + SLANG_STAGE_NONE, + SLANG_STAGE_VERTEX, + SLANG_STAGE_HULL, + SLANG_STAGE_DOMAIN, + SLANG_STAGE_GEOMETRY, + SLANG_STAGE_FRAGMENT, + SLANG_STAGE_COMPUTE, + SLANG_STAGE_RAY_GENERATION, + SLANG_STAGE_INTERSECTION, + SLANG_STAGE_ANY_HIT, + SLANG_STAGE_CLOSEST_HIT, + SLANG_STAGE_MISS, + SLANG_STAGE_CALLABLE, + SLANG_STAGE_MESH, + SLANG_STAGE_AMPLIFICATION, +}; + +public enum SlangDebugInfoLevel +{ + SLANG_DEBUG_INFO_LEVEL_NONE = 0, /**< Don't emit debug information at all. */ + SLANG_DEBUG_INFO_LEVEL_MINIMAL, /**< Emit as little debug information as possible, while still supporting stack trackes. */ + SLANG_DEBUG_INFO_LEVEL_STANDARD, /**< Emit whatever is the standard level of debug information for each target. */ + SLANG_DEBUG_INFO_LEVEL_MAXIMAL, /**< Emit as much debug infromation as possible for each target. */ +}; + +public enum SlangOptimizationLevel +{ + SLANG_OPTIMIZATION_LEVEL_NONE = 0, /**< Don't optimize at all. */ + SLANG_OPTIMIZATION_LEVEL_DEFAULT, /**< Default optimization level: balance code quality and compilation time. */ + SLANG_OPTIMIZATION_LEVEL_HIGH, /**< Optimize aggressively. */ + SLANG_OPTIMIZATION_LEVEL_MAXIMAL, /**< Include optimizations that may take a very long time, or may involve severe space-vs-speed tradeoffs */ +}; +public enum SlangTypeKind +{ + NONE, + STRUCT, + ARRAY, + MATRIX, + VECTOR, + SCALAR, + CONSTANT_BUFFER, + RESOURCE, + SAMPLER_STATE, + TEXTURE_BUFFER, + SHADER_STORAGE_BUFFER, + PARAMETER_BLOCK, + GENERIC_TYPE_PARAMETER, + INTERFACE, + OUTPUT_STREAM, + SPECIALIZED, + FEEDBACK, + COUNT, +}; + +public enum SlangScalarType +{ + NONE, + VOID, + BOOL, + INT32, + UINT32, + INT64, + UINT64, + FLOAT16, + FLOAT32, + FLOAT64, + INT8, + UINT8, + INT16, + UINT16, +}; + +public struct TypeReflection +{ +}; + +public enum CompileStdLibFlags +{ + WriteDocumentation = 0x1, +}; + +[COM("8BA5FB08-5195-40e2-AC58-0D-98-9C-3A-01-02")] +public interface ISlangBlob +{ + public void *getBufferPointer(); + public Size getBufferSize(); +}; + +/** Description of a code generation target. + */ +public struct TargetDesc +{ + /** The size of this structure, in bytes. + */ + public Size structureSize = 40; + + /** The target format to generate code for (e.g., SPIR-V, DXIL, etc.) + */ + public SlangCompileTarget format = SlangCompileTarget.SLANG_TARGET_UNKNOWN; + + /** The compilation profile supported by the target (e.g., "Shader Model 5.1") + */ + public SlangProfileID profile = SlangProfileID.SLANG_PROFILE_UNKNOWN; + + /** Flags for the code generation target. Currently unused. */ + public SlangTargetFlags flags = SlangTargetFlags.None; + + /** Default mode to use for floating-point operations on the target. + */ + public SlangFloatingPointMode floatingPointMode = SlangFloatingPointMode.SLANG_FLOATING_POINT_MODE_DEFAULT; + + /** Optimization level to use for the target. + */ + public SlangOptimizationLevel optimizationLevel = SlangOptimizationLevel.SLANG_OPTIMIZATION_LEVEL_DEFAULT; + + /** The line directive mode for output source code. + */ + public SlangLineDirectiveMode lineDirectiveMode = SlangLineDirectiveMode.SLANG_LINE_DIRECTIVE_MODE_DEFAULT; + + /** Whether to force `scalar` layout for glsl shader storage buffers. + */ + public bool forceGLSLScalarBufferLayout = false; +}; + +public enum SessionFlags +{ + kSessionFlags_None = 0 +}; + +public struct PreprocessorMacroDesc +{ + public NativeString name; + public NativeString value; +}; + +public struct SessionDesc +{ + /** The size of this structure, in bytes. + */ + public Size structureSize = 72; + + /** Code generation targets to include in the session. + */ + public TargetDesc *targets = nullptr; + public Int targetCount = 0; + + /** Flags to configure the session. + */ + public SessionFlags flags = SessionFlags.kSessionFlags_None; + + /** Default layout to assume for variables with matrix types. + */ + public SlangMatrixLayoutMode defaultMatrixLayoutMode = SlangMatrixLayoutMode.SLANG_MATRIX_LAYOUT_ROW_MAJOR; + + /** Paths to use when searching for `#include`d or `import`ed files. + */ + public NativeString *searchPaths = nullptr; + public Int searchPathCount = 0; + + public PreprocessorMacroDesc *preprocessorMacros = nullptr; + public Int preprocessorMacroCount = 0; + + public void *fileSystem = nullptr; +}; + +/** A global session for interaction with the Slang library. + +An application may create and re-use a single global session across +multiple sessions, in order to amortize startups costs (in current +Slang this is mostly the cost of loading the Slang standard library). + +The global session is currently *not* thread-safe and objects created from +a single global session should only be used from a single thread at +a time. +*/ +[COM("c140b5fd-0c78-452e-ba7c-1a-1e-70-c7-f7-1c")] +public interface IGlobalSession +{ +}; + +public enum class ContainerType +{ + None, UnsizedArray, StructuredBuffer, ConstantBuffer, ParameterBlock +}; + +/** A session provides a scope for code that is loaded. + +A session can be used to load modules of Slang source code, +and to request target-specific compiled binaries and layout +information. + +In order to be able to load code, the session owns a set +of active "search paths" for resolving `#include` directives +and `import` declrations, as well as a set of global +preprocessor definitions that will be used for all code +that gets `import`ed in the session. + +If multiple user shaders are loaded in the same session, +and import the same module (e.g., two source files do `import X`) +then there will only be one copy of `X` loaded within the session. + +In order to be able to generate target code, the session +owns a list of available compilation targets, which specify +code generation options. + +Code loaded and compiled within a session is owned by the session +and will remain resident in memory until the session is released. +Applications wishing to control the memory usage for compiled +and loaded code should use multiple sessions. +*/ +[COM("67618701-d116-468f-ab3b-47-4b-ed-ce-0e-3d")] +public interface ISession +{ +}; + +[COM("5bc42be8-5c50-4929-9e5e-d15e7c24015f")] +public interface IComponentType +{ +} + +public struct TypeLayoutReflection { } + +/** The kind of specialization argument. */ +public enum class SpecializationArgKind : int32_t +{ + Unknown, /**< An invalid specialization argument. */ + Type, /**< Specialize to a type. */ +}; + +public struct SpecializationArg +{ + public SpecializationArgKind kind; + /** A type specialization argument, used for `Kind::Type`. */ + public TypeReflection *type; +} + +} diff --git a/vulkan/Bin/slangd.exe b/vulkan/Bin/slangd.exe new file mode 100644 index 0000000000000000000000000000000000000000..11a6e068255cabb9357695e6e8f425b244b8d896 Binary files /dev/null and b/vulkan/Bin/slangd.exe differ

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