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llava_next/lib/python3.10/site-packages/MarkupSafe-2.1.5.dist-info/LICENSE.rst

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+Copyright 2010 Pallets
+
+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 copyright holder 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 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
+HOLDER 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.

llava_next/lib/python3.10/site-packages/MarkupSafe-2.1.5.dist-info/METADATA

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+Metadata-Version: 2.1
+Name: MarkupSafe
+Version: 2.1.5
+Summary: Safely add untrusted strings to HTML/XML markup.
+Home-page: https://palletsprojects.com/p/markupsafe/
+Maintainer: Pallets
+Maintainer-email: contact@palletsprojects.com
+License: BSD-3-Clause
+Project-URL: Donate, https://palletsprojects.com/donate
+Project-URL: Documentation, https://markupsafe.palletsprojects.com/
+Project-URL: Changes, https://markupsafe.palletsprojects.com/changes/
+Project-URL: Source Code, https://github.com/pallets/markupsafe/
+Project-URL: Issue Tracker, https://github.com/pallets/markupsafe/issues/
+Project-URL: Chat, https://discord.gg/pallets
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Web Environment
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
+Classifier: Topic :: Text Processing :: Markup :: HTML
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENSE.rst
+
+MarkupSafe
+==========
+
+MarkupSafe implements a text object that escapes characters so it is
+safe to use in HTML and XML. Characters that have special meanings are
+replaced so that they display as the actual characters. This mitigates
+injection attacks, meaning untrusted user input can safely be displayed
+on a page.
+
+
+Installing
+----------
+
+Install and update using `pip`_:
+
+.. code-block:: text
+
+    pip install -U MarkupSafe
+
+.. _pip: https://pip.pypa.io/en/stable/getting-started/
+
+
+Examples
+--------
+
+.. code-block:: pycon
+
+    >>> from markupsafe import Markup, escape
+
+    >>> # escape replaces special characters and wraps in Markup
+    >>> escape("<script>alert(document.cookie);</script>")
+    Markup('&lt;script&gt;alert(document.cookie);&lt;/script&gt;')
+
+    >>> # wrap in Markup to mark text "safe" and prevent escaping
+    >>> Markup("<strong>Hello</strong>")
+    Markup('<strong>hello</strong>')
+
+    >>> escape(Markup("<strong>Hello</strong>"))
+    Markup('<strong>hello</strong>')
+
+    >>> # Markup is a str subclass
+    >>> # methods and operators escape their arguments
+    >>> template = Markup("Hello <em>{name}</em>")
+    >>> template.format(name='"World"')
+    Markup('Hello <em>&#34;World&#34;</em>')
+
+
+Donate
+------
+
+The Pallets organization develops and supports MarkupSafe and other
+popular packages. In order to grow the community of contributors and
+users, and allow the maintainers to devote more time to the projects,
+`please donate today`_.
+
+.. _please donate today: https://palletsprojects.com/donate
+
+
+Links
+-----
+
+-   Documentation: https://markupsafe.palletsprojects.com/
+-   Changes: https://markupsafe.palletsprojects.com/changes/
+-   PyPI Releases: https://pypi.org/project/MarkupSafe/
+-   Source Code: https://github.com/pallets/markupsafe/
+-   Issue Tracker: https://github.com/pallets/markupsafe/issues/
+-   Chat: https://discord.gg/pallets

llava_next/lib/python3.10/site-packages/MarkupSafe-2.1.5.dist-info/RECORD

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+MarkupSafe-2.1.5.dist-info/LICENSE.rst,sha256=SJqOEQhQntmKN7uYPhHg9-HTHwvY-Zp5yESOf_N9B-o,1475
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llava_next/lib/python3.10/site-packages/MarkupSafe-2.1.5.dist-info/WHEEL

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+Wheel-Version: 1.0
+Generator: bdist_wheel (0.42.0)
+Root-Is-Purelib: false
+Tag: cp310-cp310-manylinux_2_17_x86_64
+Tag: cp310-cp310-manylinux2014_x86_64
+

llava_next/lib/python3.10/site-packages/MarkupSafe-2.1.5.dist-info/top_level.txt

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+markupsafe

llava_next/lib/python3.10/site-packages/jaraco.context-5.3.0.dist-info/METADATA

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+Metadata-Version: 2.1
+Name: jaraco.context
+Version: 5.3.0
+Summary: Useful decorators and context managers
+Home-page: https://github.com/jaraco/jaraco.context
+Author: Jason R. Coombs
+Author-email: jaraco@jaraco.com
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Requires-Python: >=3.8
+License-File: LICENSE
+Requires-Dist: backports.tarfile ; python_version < "3.12"
+Provides-Extra: docs
+Requires-Dist: sphinx >=3.5 ; extra == 'docs'
+Requires-Dist: jaraco.packaging >=9.3 ; extra == 'docs'
+Requires-Dist: rst.linker >=1.9 ; extra == 'docs'
+Requires-Dist: furo ; extra == 'docs'
+Requires-Dist: sphinx-lint ; extra == 'docs'
+Requires-Dist: jaraco.tidelift >=1.4 ; extra == 'docs'
+Provides-Extra: testing
+Requires-Dist: pytest !=8.1.1,>=6 ; extra == 'testing'
+Requires-Dist: pytest-checkdocs >=2.4 ; extra == 'testing'
+Requires-Dist: pytest-cov ; extra == 'testing'
+Requires-Dist: pytest-mypy ; extra == 'testing'
+Requires-Dist: pytest-enabler >=2.2 ; extra == 'testing'
+Requires-Dist: pytest-ruff >=0.2.1 ; extra == 'testing'
+Requires-Dist: portend ; extra == 'testing'
+
+.. image:: https://img.shields.io/pypi/v/jaraco.context.svg
+   :target: https://pypi.org/project/jaraco.context
+
+.. image:: https://img.shields.io/pypi/pyversions/jaraco.context.svg
+
+.. image:: https://github.com/jaraco/jaraco.context/actions/workflows/main.yml/badge.svg
+   :target: https://github.com/jaraco/jaraco.context/actions?query=workflow%3A%22tests%22
+   :alt: tests
+
+.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
+    :target: https://github.com/astral-sh/ruff
+    :alt: Ruff
+
+.. image:: https://readthedocs.org/projects/jaracocontext/badge/?version=latest
+   :target: https://jaracocontext.readthedocs.io/en/latest/?badge=latest
+
+.. image:: https://img.shields.io/badge/skeleton-2024-informational
+   :target: https://blog.jaraco.com/skeleton
+
+.. image:: https://tidelift.com/badges/package/pypi/jaraco.context
+   :target: https://tidelift.com/subscription/pkg/pypi-jaraco.context?utm_source=pypi-jaraco.context&utm_medium=readme
+
+
+Highlights
+==========
+
+See the docs linked from the badge above for the full details, but here are some features that may be of interest.
+
+- ``ExceptionTrap`` provides a general-purpose wrapper for trapping exceptions and then acting on the outcome. Includes ``passes`` and ``raises`` decorators to replace the result of a wrapped function by a boolean indicating the outcome of the exception trap. See `this keyring commit <https://github.com/jaraco/keyring/commit/a85a7cbc6c909f8121660ed1f7b487f99a1c2bf7>`_ for an example of it in production.
+- ``suppress`` simply enables ``contextlib.suppress`` as a decorator.
+- ``on_interrupt`` is a decorator used by CLI entry points to affect the handling of a ``KeyboardInterrupt``. Inspired by `Lucretiel/autocommand#18 <https://github.com/Lucretiel/autocommand/issues/18>`_.
+- ``pushd`` is similar to pytest's ``monkeypatch.chdir`` or path's `default context <https://path.readthedocs.io/en/latest/api.html>`_, changes the current working directory for the duration of the context.
+- ``tarball`` will download a tarball, extract it, change directory, yield, then clean up after. Convenient when working with web assets.
+- ``null`` is there for those times when one code branch needs a context and the other doesn't; this null context provides symmetry across those branches.
+
+
+For Enterprise
+==============
+
+Available as part of the Tidelift Subscription.
+
+This project and the maintainers of thousands of other packages are working with Tidelift to deliver one enterprise subscription that covers all of the open source you use.
+
+`Learn more <https://tidelift.com/subscription/pkg/pypi-jaraco.context?utm_source=pypi-jaraco.context&utm_medium=referral&utm_campaign=github>`_.

llava_next/lib/python3.10/site-packages/jaraco.context-5.3.0.dist-info/top_level.txt

@@ -0,0 +1 @@
+jaraco

llava_next/lib/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_events.h

@@ -0,0 +1,1371 @@
+/*
+ * Copyright 2010-2021 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_EVENTS_H_)
+#define _CUPTI_EVENTS_H_
+
+#include <cuda.h>
+#include <string.h>
+#include <cuda_stdint.h>
+#include <cupti_result.h>
+
+#ifndef CUPTIAPI
+#ifdef _WIN32
+#define CUPTIAPI __stdcall
+#else
+#define CUPTIAPI
+#endif
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \defgroup CUPTI_EVENT_API CUPTI Event API
+ * Functions, types, and enums that implement the CUPTI Event API.
+ *
+ * \note CUPTI event API from the header cupti_events.h are not supported on devices
+ * with compute capability 7.5 and higher (i.e. Turing and later GPU architectures).
+ * These API will be deprecated in a future CUDA release. These are replaced by
+ * Profiling API in the header cupti_profiler_target.h and Perfworks metrics API
+ * in the headers nvperf_host.h and nvperf_target.h which are supported on
+ * devices with compute capability 7.0 and higher (i.e. Volta and later GPU
+ * architectures).
+ *
+ * @{
+ */
+
+/**
+ * \brief ID for an event.
+ *
+ * An event represents a countable activity, action, or occurrence on
+ * the device.
+ */
+typedef uint32_t CUpti_EventID;
+
+/**
+ * \brief ID for an event domain.
+ *
+ * ID for an event domain. An event domain represents a group of
+ * related events. A device may have multiple instances of a domain,
+ * indicating that the device can simultaneously record multiple
+ * instances of each event within that domain.
+ */
+typedef uint32_t CUpti_EventDomainID;
+
+/**
+ * \brief A group of events.
+ *
+ * An event group is a collection of events that are managed
+ * together. All events in an event group must belong to the same
+ * domain.
+ */
+typedef void *CUpti_EventGroup;
+
+/**
+ * \brief Device class.
+ *
+ * Enumeration of device classes for device attribute
+ * CUPTI_DEVICE_ATTR_DEVICE_CLASS.
+ */
+typedef enum {
+  CUPTI_DEVICE_ATTR_DEVICE_CLASS_TESLA              = 0,
+  CUPTI_DEVICE_ATTR_DEVICE_CLASS_QUADRO             = 1,
+  CUPTI_DEVICE_ATTR_DEVICE_CLASS_GEFORCE            = 2,
+  CUPTI_DEVICE_ATTR_DEVICE_CLASS_TEGRA              = 3,
+} CUpti_DeviceAttributeDeviceClass;
+
+/**
+ * \brief Device attributes.
+ *
+ * CUPTI device attributes. These attributes can be read using \ref
+ * cuptiDeviceGetAttribute.
+ */
+typedef enum {
+  /**
+   * Number of event IDs for a device. Value is a uint32_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_EVENT_ID                            = 1,
+  /**
+   * Number of event domain IDs for a device. Value is a uint32_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_EVENT_DOMAIN_ID                     = 2,
+  /**
+   * Get global memory bandwidth in Kbytes/sec. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_GLOBAL_MEMORY_BANDWIDTH                 = 3,
+  /**
+   * Get theoretical maximum number of instructions per cycle. Value
+   * is a uint32_t.
+   */
+  CUPTI_DEVICE_ATTR_INSTRUCTION_PER_CYCLE                   = 4,
+  /**
+   * Get theoretical maximum number of single precision instructions
+   * that can be executed per second. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_INSTRUCTION_THROUGHPUT_SINGLE_PRECISION = 5,
+  /**
+   * Get number of frame buffers for device.  Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_FRAME_BUFFERS                       = 6,
+  /**
+   * Get PCIE link rate in Mega bits/sec for device. Return 0 if bus-type
+   * is non-PCIE. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_PCIE_LINK_RATE                          = 7,
+  /**
+   * Get PCIE link width for device. Return 0 if bus-type
+   * is non-PCIE. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_PCIE_LINK_WIDTH                         = 8,
+  /**
+   * Get PCIE generation for device. Return 0 if bus-type
+   * is non-PCIE. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_PCIE_GEN                                = 9,
+  /**
+   * Get the class for the device. Value is a
+   * CUpti_DeviceAttributeDeviceClass.
+   */
+  CUPTI_DEVICE_ATTR_DEVICE_CLASS                            = 10,
+  /**
+   * Get the peak single precision flop per cycle. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_FLOP_SP_PER_CYCLE                       = 11,
+  /**
+   * Get the peak double precision flop per cycle. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_FLOP_DP_PER_CYCLE                       = 12,
+  /**
+   * Get number of L2 units. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_L2_UNITS                           = 13,
+  /**
+   * Get the maximum shared memory for the CU_FUNC_CACHE_PREFER_SHARED
+   * preference. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_SHARED_MEMORY_CACHE_CONFIG_PREFER_SHARED = 14,
+  /**
+   * Get the maximum shared memory for the CU_FUNC_CACHE_PREFER_L1
+   * preference. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_SHARED_MEMORY_CACHE_CONFIG_PREFER_L1 = 15,
+  /**
+   * Get the maximum shared memory for the CU_FUNC_CACHE_PREFER_EQUAL
+   * preference. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_MAX_SHARED_MEMORY_CACHE_CONFIG_PREFER_EQUAL = 16,
+  /**
+   * Get the peak half precision flop per cycle. Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_FLOP_HP_PER_CYCLE                       = 17,
+  /**
+   * Check if Nvlink is connected to device. Returns 1, if at least one
+   * Nvlink is connected to the device, returns 0 otherwise.
+   * Value is a uint32_t.
+   */
+  CUPTI_DEVICE_ATTR_NVLINK_PRESENT                          = 18,
+    /**
+   * Check if Nvlink is present between GPU and CPU. Returns Bandwidth,
+   * in Bytes/sec, if Nvlink is present, returns 0 otherwise.
+   * Value is a uint64_t.
+   */
+  CUPTI_DEVICE_ATTR_GPU_CPU_NVLINK_BW                       = 19,
+  /**
+   * Check if NVSwitch is present in the underlying topology.
+   * Returns 1, if present, returns 0 otherwise.
+   * Value is a uint32_t.
+   */
+  CUPTI_DEVICE_ATTR_NVSWITCH_PRESENT                        = 20,
+  CUPTI_DEVICE_ATTR_FORCE_INT                               = 0x7fffffff,
+} CUpti_DeviceAttribute;
+
+/**
+ * \brief Event domain attributes.
+ *
+ * Event domain attributes. Except where noted, all the attributes can
+ * be read using either \ref cuptiDeviceGetEventDomainAttribute or
+ * \ref cuptiEventDomainGetAttribute.
+ */
+typedef enum {
+  /**
+   * Event domain name. Value is a null terminated const c-string.
+   */
+  CUPTI_EVENT_DOMAIN_ATTR_NAME                 = 0,
+  /**
+   * Number of instances of the domain for which event counts will be
+   * collected.  The domain may have additional instances that cannot
+   * be profiled (see CUPTI_EVENT_DOMAIN_ATTR_TOTAL_INSTANCE_COUNT).
+   * Can be read only with \ref
+   * cuptiDeviceGetEventDomainAttribute. Value is a uint32_t.
+   */
+  CUPTI_EVENT_DOMAIN_ATTR_INSTANCE_COUNT       = 1,
+  /**
+   * Total number of instances of the domain, including instances that
+   * cannot be profiled.  Use CUPTI_EVENT_DOMAIN_ATTR_INSTANCE_COUNT
+   * to get the number of instances that can be profiled. Can be read
+   * only with \ref cuptiDeviceGetEventDomainAttribute. Value is a
+   * uint32_t.
+   */
+  CUPTI_EVENT_DOMAIN_ATTR_TOTAL_INSTANCE_COUNT = 3,
+  /**
+   * Collection method used for events contained in the event domain.
+   * Value is a \ref CUpti_EventCollectionMethod.
+   */
+  CUPTI_EVENT_DOMAIN_ATTR_COLLECTION_METHOD    = 4,
+
+  CUPTI_EVENT_DOMAIN_ATTR_FORCE_INT      = 0x7fffffff,
+} CUpti_EventDomainAttribute;
+
+/**
+ * \brief The collection method used for an event.
+ *
+ * The collection method indicates how an event is collected.
+ */
+typedef enum {
+  /**
+   * Event is collected using a hardware global performance monitor.
+   */
+  CUPTI_EVENT_COLLECTION_METHOD_PM                  = 0,
+  /**
+   * Event is collected using a hardware SM performance monitor.
+   */
+  CUPTI_EVENT_COLLECTION_METHOD_SM                  = 1,
+  /**
+   * Event is collected using software instrumentation.
+   */
+  CUPTI_EVENT_COLLECTION_METHOD_INSTRUMENTED        = 2,
+  /**
+   * Event is collected using NvLink throughput counter method.
+   */
+  CUPTI_EVENT_COLLECTION_METHOD_NVLINK_TC           = 3,
+  CUPTI_EVENT_COLLECTION_METHOD_FORCE_INT           = 0x7fffffff
+} CUpti_EventCollectionMethod;
+
+/**
+ * \brief Event group attributes.
+ *
+ * Event group attributes. These attributes can be read using \ref
+ * cuptiEventGroupGetAttribute. Attributes marked [rw] can also be
+ * written using \ref cuptiEventGroupSetAttribute.
+ */
+typedef enum {
+  /**
+   * The domain to which the event group is bound. This attribute is
+   * set when the first event is added to the group.  Value is a
+   * CUpti_EventDomainID.
+   */
+  CUPTI_EVENT_GROUP_ATTR_EVENT_DOMAIN_ID              = 0,
+  /**
+   * [rw] Profile all the instances of the domain for this
+   * eventgroup. This feature can be used to get load balancing
+   * across all instances of a domain. Value is an integer.
+   */
+  CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES = 1,
+  /**
+   * [rw] Reserved for user data.
+   */
+  CUPTI_EVENT_GROUP_ATTR_USER_DATA                    = 2,
+  /**
+   * Number of events in the group. Value is a uint32_t.
+   */
+  CUPTI_EVENT_GROUP_ATTR_NUM_EVENTS                   = 3,
+  /**
+   * Enumerates events in the group. Value is a pointer to buffer of
+   * size sizeof(CUpti_EventID) * num_of_events in the eventgroup.
+   * num_of_events can be queried using
+   * CUPTI_EVENT_GROUP_ATTR_NUM_EVENTS.
+   */
+  CUPTI_EVENT_GROUP_ATTR_EVENTS                       = 4,
+  /**
+   * Number of instances of the domain bound to this event group that
+   * will be counted.  Value is a uint32_t.
+   */
+  CUPTI_EVENT_GROUP_ATTR_INSTANCE_COUNT               = 5,
+  /**
+   * Event group scope can be set to CUPTI_EVENT_PROFILING_SCOPE_DEVICE or
+   * CUPTI_EVENT_PROFILING_SCOPE_CONTEXT for an eventGroup, before
+   * adding any event.
+   * Sets the scope of eventgroup as CUPTI_EVENT_PROFILING_SCOPE_DEVICE or
+   * CUPTI_EVENT_PROFILING_SCOPE_CONTEXT when the scope of the events
+   * that will be added is CUPTI_EVENT_PROFILING_SCOPE_BOTH.
+   * If profiling scope of event is either
+   * CUPTI_EVENT_PROFILING_SCOPE_DEVICE or CUPTI_EVENT_PROFILING_SCOPE_CONTEXT
+   * then setting this attribute will not affect the default scope.
+   * It is not allowed to add events of different scope to same eventgroup.
+   * Value is a uint32_t.
+   */
+  CUPTI_EVENT_GROUP_ATTR_PROFILING_SCOPE               = 6,
+  CUPTI_EVENT_GROUP_ATTR_FORCE_INT                     = 0x7fffffff,
+} CUpti_EventGroupAttribute;
+
+/**
+* \brief Profiling scope for event.
+*
+* Profiling scope of event indicates if the event can be collected at context
+* scope or device scope or both i.e. it can be collected at any of context or
+* device scope.
+*/
+typedef enum {
+  /**
+   * Event is collected at context scope.
+   */
+  CUPTI_EVENT_PROFILING_SCOPE_CONTEXT                 = 0,
+  /**
+   * Event is collected at device scope.
+   */
+  CUPTI_EVENT_PROFILING_SCOPE_DEVICE                  = 1,
+  /**
+   * Event can be collected at device or context scope.
+   * The scope can be set using \ref cuptiEventGroupSetAttribute API.
+   */
+  CUPTI_EVENT_PROFILING_SCOPE_BOTH                    = 2,
+  CUPTI_EVENT_PROFILING_SCOPE_FORCE_INT               = 0x7fffffff
+} CUpti_EventProfilingScope;
+
+/**
+ * \brief Event attributes.
+ *
+ * Event attributes. These attributes can be read using \ref
+ * cuptiEventGetAttribute.
+ */
+typedef enum {
+  /**
+   * Event name. Value is a null terminated const c-string.
+   */
+  CUPTI_EVENT_ATTR_NAME              = 0,
+  /**
+   * Short description of event. Value is a null terminated const
+   * c-string.
+   */
+  CUPTI_EVENT_ATTR_SHORT_DESCRIPTION = 1,
+  /**
+   * Long description of event. Value is a null terminated const
+   * c-string.
+   */
+  CUPTI_EVENT_ATTR_LONG_DESCRIPTION  = 2,
+  /**
+   * Category of event. Value is CUpti_EventCategory.
+   */
+  CUPTI_EVENT_ATTR_CATEGORY          = 3,
+  /**
+   * Profiling scope of the events. It can be either device or context or both.
+   * Value is a \ref CUpti_EventProfilingScope.
+   */
+  CUPTI_EVENT_ATTR_PROFILING_SCOPE   = 5,
+
+  CUPTI_EVENT_ATTR_FORCE_INT         = 0x7fffffff,
+} CUpti_EventAttribute;
+
+/**
+ * \brief Event collection modes.
+ *
+ * The event collection mode determines the period over which the
+ * events within the enabled event groups will be collected.
+ */
+typedef enum {
+  /**
+   * Events are collected for the entire duration between the
+   * cuptiEventGroupEnable and cuptiEventGroupDisable calls.
+   * Event values are reset when the events are read.
+   * For CUDA toolkit v6.0 and older this was the default mode.
+   */
+  CUPTI_EVENT_COLLECTION_MODE_CONTINUOUS          = 0,
+  /**
+   * Events are collected only for the durations of kernel executions
+   * that occur between the cuptiEventGroupEnable and
+   * cuptiEventGroupDisable calls. Event collection begins when a
+   * kernel execution begins, and stops when kernel execution
+   * completes. Event values are reset to zero when each kernel
+   * execution begins. If multiple kernel executions occur between the
+   * cuptiEventGroupEnable and cuptiEventGroupDisable calls then the
+   * event values must be read after each kernel launch if those
+   * events need to be associated with the specific kernel launch.
+   * Note that collection in this mode may significantly change the
+   * overall performance characteristics of the application because
+   * kernel executions that occur between the cuptiEventGroupEnable and
+   * cuptiEventGroupDisable calls are serialized on the GPU.
+   * This is the default mode from CUDA toolkit v6.5
+   */
+  CUPTI_EVENT_COLLECTION_MODE_KERNEL              = 1,
+  CUPTI_EVENT_COLLECTION_MODE_FORCE_INT           = 0x7fffffff
+} CUpti_EventCollectionMode;
+
+/**
+ * \brief An event category.
+ *
+ * Each event is assigned to a category that represents the general
+ * type of the event. A event's category is accessed using \ref
+ * cuptiEventGetAttribute and the CUPTI_EVENT_ATTR_CATEGORY attribute.
+ */
+typedef enum {
+  /**
+   * An instruction related event.
+   */
+  CUPTI_EVENT_CATEGORY_INSTRUCTION     = 0,
+  /**
+   * A memory related event.
+   */
+  CUPTI_EVENT_CATEGORY_MEMORY          = 1,
+  /**
+   * A cache related event.
+   */
+  CUPTI_EVENT_CATEGORY_CACHE           = 2,
+  /**
+   * A profile-trigger event.
+   */
+  CUPTI_EVENT_CATEGORY_PROFILE_TRIGGER = 3,
+  /**
+   * A system event.
+   */
+  CUPTI_EVENT_CATEGORY_SYSTEM  = 4,
+  CUPTI_EVENT_CATEGORY_FORCE_INT       = 0x7fffffff
+} CUpti_EventCategory;
+
+/**
+ * \brief The overflow value for a CUPTI event.
+ *
+ * The CUPTI event value that indicates an overflow.
+ */
+#define CUPTI_EVENT_OVERFLOW ((uint64_t)0xFFFFFFFFFFFFFFFFULL)
+
+/**
+ * \brief The value that indicates the event value is invalid
+ */
+#define CUPTI_EVENT_INVALID ((uint64_t)0xFFFFFFFFFFFFFFFEULL)
+
+/**
+ * \brief Flags for cuptiEventGroupReadEvent an
+ * cuptiEventGroupReadAllEvents.
+ *
+ * Flags for \ref cuptiEventGroupReadEvent an \ref
+ * cuptiEventGroupReadAllEvents.
+ */
+typedef enum {
+  /**
+   * No flags.
+   */
+  CUPTI_EVENT_READ_FLAG_NONE          = 0,
+  CUPTI_EVENT_READ_FLAG_FORCE_INT     = 0x7fffffff,
+} CUpti_ReadEventFlags;
+
+
+/**
+ * \brief A set of event groups.
+ *
+ * A set of event groups. When returned by \ref
+ * cuptiEventGroupSetsCreate and \ref cuptiMetricCreateEventGroupSets
+ * a set indicates that event groups that can be enabled at the same
+ * time (i.e. all the events in the set can be collected
+ * simultaneously).
+ */
+typedef struct {
+  /**
+   * The number of event groups in the set.
+   */
+  uint32_t numEventGroups;
+  /**
+   * An array of \p numEventGroups event groups.
+   */
+  CUpti_EventGroup *eventGroups;
+} CUpti_EventGroupSet;
+
+/**
+ * \brief A set of event group sets.
+ *
+ * A set of event group sets. When returned by \ref
+ * cuptiEventGroupSetsCreate and \ref cuptiMetricCreateEventGroupSets
+ * a CUpti_EventGroupSets indicates the number of passes required to
+ * collect all the events, and the event groups that should be
+ * collected during each pass.
+ */
+typedef struct {
+  /**
+   * Number of event group sets.
+   */
+  uint32_t numSets;
+  /**
+   * An array of \p numSets event group sets.
+   */
+  CUpti_EventGroupSet *sets;
+} CUpti_EventGroupSets;
+
+/**
+ * \brief Set the event collection mode.
+ *
+ * Set the event collection mode for a \p context.  The \p mode
+ * controls the event collection behavior of all events in event
+ * groups created in the \p context. This API is invalid in kernel
+ * replay mode.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param context The context
+ * \param mode The event collection mode
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_CONTEXT
+ * \retval CUPTI_ERROR_INVALID_OPERATION if called when replay mode is enabled
+ * \retval CUPTI_ERROR_NOT_SUPPORTED if mode is not supported on the device
+ */
+
+CUptiResult CUPTIAPI cuptiSetEventCollectionMode(CUcontext context,
+                                                 CUpti_EventCollectionMode mode);
+
+/**
+ * \brief Read a device attribute.
+ *
+ * Read a device attribute and return it in \p *value.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param device The CUDA device
+ * \param attrib The attribute to read
+ * \param valueSize Size of buffer pointed by the value, and
+ * returns the number of bytes written to \p value
+ * \param value Returns the value of the attribute
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not a device attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT For non-c-string
+ * attribute values, indicates that the \p value buffer is too small
+ * to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiDeviceGetAttribute(CUdevice device,
+                                             CUpti_DeviceAttribute attrib,
+                                             size_t *valueSize,
+                                             void *value);
+
+/**
+ * \brief Read a device timestamp.
+ *
+ * Returns the device timestamp in \p *timestamp. The timestamp is
+ * reported in nanoseconds and indicates the time since the device was
+ * last reset.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param context A context on the device from which to get the timestamp
+ * \param timestamp Returns the device timestamp
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_CONTEXT
+ * \retval CUPTI_ERROR_INVALID_PARAMETER is \p timestamp is NULL
+
+ * **DEPRECATED** This API is deprecated as of CUDA 11.3
+ */
+CUptiResult CUPTIAPI cuptiDeviceGetTimestamp(CUcontext context,
+                                             uint64_t *timestamp);
+
+/**
+ * \brief Get the number of domains for a device.
+ *
+ * Returns the number of domains in \p numDomains for a device.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param device The CUDA device
+ * \param numDomains Returns the number of domains
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p numDomains is NULL
+ */
+CUptiResult CUPTIAPI cuptiDeviceGetNumEventDomains(CUdevice device,
+                                                   uint32_t *numDomains);
+
+/**
+ * \brief Get the event domains for a device.
+ *
+ * Returns the event domains IDs in \p domainArray for a device.  The
+ * size of the \p domainArray buffer is given by \p
+ * *arraySizeBytes. The size of the \p domainArray buffer must be at
+ * least \p numdomains * sizeof(CUpti_EventDomainID) or else all
+ * domains will not be returned. The value returned in \p
+ * *arraySizeBytes contains the number of bytes returned in \p
+ * domainArray.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param device The CUDA device
+ * \param arraySizeBytes The size of \p domainArray in bytes, and
+ * returns the number of bytes written to \p domainArray
+ * \param domainArray Returns the IDs of the event domains for the device
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p arraySizeBytes or
+ * \p domainArray are NULL
+ */
+CUptiResult CUPTIAPI cuptiDeviceEnumEventDomains(CUdevice device,
+                                                 size_t *arraySizeBytes,
+                                                 CUpti_EventDomainID *domainArray);
+
+/**
+ * \brief Read an event domain attribute.
+ *
+ * Returns an event domain attribute in \p *value. The size of the \p
+ * value buffer is given by \p *valueSize. The value returned in \p
+ * *valueSize contains the number of bytes returned in \p value.
+ *
+ * If the attribute value is a c-string that is longer than \p
+ * *valueSize, then only the first \p *valueSize characters will be
+ * returned and there will be no terminating null byte.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param device The CUDA device
+ * \param eventDomain ID of the event domain
+ * \param attrib The event domain attribute to read
+ * \param valueSize The size of the \p value buffer in bytes, and
+ * returns the number of bytes written to \p value
+ * \param value Returns the attribute's value
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_EVENT_DOMAIN_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not an event domain attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT For non-c-string
+ * attribute values, indicates that the \p value buffer is too small
+ * to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiDeviceGetEventDomainAttribute(CUdevice device,
+                                                        CUpti_EventDomainID eventDomain,
+                                                        CUpti_EventDomainAttribute attrib,
+                                                        size_t *valueSize,
+                                                        void *value);
+
+/**
+ * \brief Get the number of event domains available on any device.
+ *
+ * Returns the total number of event domains available on any
+ * CUDA-capable device.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param numDomains Returns the number of domains
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p numDomains is NULL
+ */
+CUptiResult CUPTIAPI cuptiGetNumEventDomains(uint32_t *numDomains);
+
+/**
+ * \brief Get the event domains available on any device.
+ *
+ * Returns all the event domains available on any CUDA-capable device.
+ * Event domain IDs are returned in \p domainArray. The size of the \p
+ * domainArray buffer is given by \p *arraySizeBytes. The size of the
+ * \p domainArray buffer must be at least \p numDomains *
+ * sizeof(CUpti_EventDomainID) or all domains will not be
+ * returned. The value returned in \p *arraySizeBytes contains the
+ * number of bytes returned in \p domainArray.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param arraySizeBytes The size of \p domainArray in bytes, and
+ * returns the number of bytes written to \p domainArray
+ * \param domainArray Returns all the event domains
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p arraySizeBytes or
+ * \p domainArray are NULL
+ */
+CUptiResult CUPTIAPI cuptiEnumEventDomains(size_t *arraySizeBytes,
+                                           CUpti_EventDomainID *domainArray);
+
+/**
+ * \brief Read an event domain attribute.
+ *
+ * Returns an event domain attribute in \p *value. The size of the \p
+ * value buffer is given by \p *valueSize. The value returned in \p
+ * *valueSize contains the number of bytes returned in \p value.
+ *
+ * If the attribute value is a c-string that is longer than \p
+ * *valueSize, then only the first \p *valueSize characters will be
+ * returned and there will be no terminating null byte.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventDomain ID of the event domain
+ * \param attrib The event domain attribute to read
+ * \param valueSize The size of the \p value buffer in bytes, and
+ * returns the number of bytes written to \p value
+ * \param value Returns the attribute's value
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_DOMAIN_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not an event domain attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT For non-c-string
+ * attribute values, indicates that the \p value buffer is too small
+ * to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiEventDomainGetAttribute(CUpti_EventDomainID eventDomain,
+                                                  CUpti_EventDomainAttribute attrib,
+                                                  size_t *valueSize,
+                                                  void *value);
+
+/**
+ * \brief Get number of events in a domain.
+ *
+ * Returns the number of events in \p numEvents for a domain.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventDomain ID of the event domain
+ * \param numEvents Returns the number of events in the domain
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_DOMAIN_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p numEvents is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventDomainGetNumEvents(CUpti_EventDomainID eventDomain,
+                                                  uint32_t *numEvents);
+
+/**
+ * \brief Get the events in a domain.
+ *
+ * Returns the event IDs in \p eventArray for a domain.  The size of
+ * the \p eventArray buffer is given by \p *arraySizeBytes. The size
+ * of the \p eventArray buffer must be at least \p numdomainevents *
+ * sizeof(CUpti_EventID) or else all events will not be returned. The
+ * value returned in \p *arraySizeBytes contains the number of bytes
+ * returned in \p eventArray.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventDomain ID of the event domain
+ * \param arraySizeBytes The size of \p eventArray in bytes, and
+ * returns the number of bytes written to \p eventArray
+ * \param eventArray Returns the IDs of the events in the domain
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_DOMAIN_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p arraySizeBytes or \p
+ * eventArray are NULL
+ */
+CUptiResult CUPTIAPI cuptiEventDomainEnumEvents(CUpti_EventDomainID eventDomain,
+                                                size_t *arraySizeBytes,
+                                                CUpti_EventID *eventArray);
+
+/**
+ * \brief Get an event attribute.
+ *
+ * Returns an event attribute in \p *value. The size of the \p
+ * value buffer is given by \p *valueSize. The value returned in \p
+ * *valueSize contains the number of bytes returned in \p value.
+ *
+ * If the attribute value is a c-string that is longer than \p
+ * *valueSize, then only the first \p *valueSize characters will be
+ * returned and there will be no terminating null byte.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param event ID of the event
+ * \param attrib The event attribute to read
+ * \param valueSize The size of the \p value buffer in bytes, and
+ * returns the number of bytes written to \p value
+ * \param value Returns the attribute's value
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not an event attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT For non-c-string
+ * attribute values, indicates that the \p value buffer is too small
+ * to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiEventGetAttribute(CUpti_EventID event,
+                                            CUpti_EventAttribute attrib,
+                                            size_t *valueSize,
+                                            void *value);
+
+/**
+ * \brief Find an event by name.
+ *
+ * Find an event by name and return the event ID in \p *event.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param device The CUDA device
+ * \param eventName The name of the event to find
+ * \param event Returns the ID of the found event or undefined if
+ * unable to find the event
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_DEVICE
+ * \retval CUPTI_ERROR_INVALID_EVENT_NAME if unable to find an event
+ * with name \p eventName. In this case \p *event is undefined
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventName or \p event are NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGetIdFromName(CUdevice device,
+                                             const char *eventName,
+                                             CUpti_EventID *event);
+
+/**
+ * \brief Create a new event group for a context.
+ *
+ * Creates a new event group for \p context and returns the new group
+ * in \p *eventGroup.
+ * \note \p flags are reserved for future use and should be set to zero.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param context The context for the event group
+ * \param eventGroup Returns the new event group
+ * \param flags Reserved - must be zero
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_CONTEXT
+ * \retval CUPTI_ERROR_OUT_OF_MEMORY
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupCreate(CUcontext context,
+                                           CUpti_EventGroup *eventGroup,
+                                           uint32_t flags);
+
+/**
+ * \brief Destroy an event group.
+ *
+ * Destroy an \p eventGroup and free its resources. An event group
+ * cannot be destroyed if it is enabled.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group to destroy
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_OPERATION if the event group is enabled
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupDestroy(CUpti_EventGroup eventGroup);
+
+/**
+ * \brief Read an event group attribute.
+ *
+ * Read an event group attribute and return it in \p *value.
+ * \note \b Thread-safety: this function is thread safe but client
+ * must guard against simultaneous destruction or modification of \p
+ * eventGroup (for example, client must guard against simultaneous
+ * calls to \ref cuptiEventGroupDestroy, \ref cuptiEventGroupAddEvent,
+ * etc.), and must guard against simultaneous destruction of the
+ * context in which \p eventGroup was created (for example, client
+ * must guard against simultaneous calls to cudaDeviceReset,
+ * cuCtxDestroy, etc.).
+ *
+ * \param eventGroup The event group
+ * \param attrib The attribute to read
+ * \param valueSize Size of buffer pointed by the value, and
+ * returns the number of bytes written to \p value
+ * \param value Returns the value of the attribute
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not an eventgroup attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT For non-c-string
+ * attribute values, indicates that the \p value buffer is too small
+ * to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiEventGroupGetAttribute(CUpti_EventGroup eventGroup,
+                                                 CUpti_EventGroupAttribute attrib,
+                                                 size_t *valueSize,
+                                                 void *value);
+
+/**
+ * \brief Write an event group attribute.
+ *
+ * Write an event group attribute.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group
+ * \param attrib The attribute to write
+ * \param valueSize The size, in bytes, of the value
+ * \param value The attribute value to write
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p valueSize or \p value
+ * is NULL, or if \p attrib is not an event group attribute, or if
+ * \p attrib is not a writable attribute
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT Indicates that
+ * the \p value buffer is too small to hold the attribute value.
+ */
+CUptiResult CUPTIAPI cuptiEventGroupSetAttribute(CUpti_EventGroup eventGroup,
+                                                 CUpti_EventGroupAttribute attrib,
+                                                 size_t valueSize,
+                                                 void *value);
+
+/**
+ * \brief Add an event to an event group.
+ *
+ * Add an event to an event group. The event add can fail for a number of reasons:
+ * \li The event group is enabled
+ * \li The event does not belong to the same event domain as the
+ * events that are already in the event group
+ * \li Device limitations on the events that can belong to the same group
+ * \li The event group is full
+ *
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group
+ * \param event The event to add to the group
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_ID
+ * \retval CUPTI_ERROR_OUT_OF_MEMORY
+ * \retval CUPTI_ERROR_INVALID_OPERATION if \p eventGroup is enabled
+ * \retval CUPTI_ERROR_NOT_COMPATIBLE if \p event belongs to a
+ * different event domain than the events already in \p eventGroup, or
+ * if a device limitation prevents \p event from being collected at
+ * the same time as the events already in \p eventGroup
+ * \retval CUPTI_ERROR_MAX_LIMIT_REACHED if \p eventGroup is full
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupAddEvent(CUpti_EventGroup eventGroup,
+                                             CUpti_EventID event);
+
+/**
+ * \brief Remove an event from an event group.
+ *
+ * Remove \p event from the an event group. The event cannot be
+ * removed if the event group is enabled.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group
+ * \param event The event to remove from the group
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_ID
+ * \retval CUPTI_ERROR_INVALID_OPERATION if \p eventGroup is enabled
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupRemoveEvent(CUpti_EventGroup eventGroup,
+                                                CUpti_EventID event);
+
+/**
+ * \brief Remove all events from an event group.
+ *
+ * Remove all events from an event group. Events cannot be removed if
+ * the event group is enabled.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_OPERATION if \p eventGroup is enabled
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupRemoveAllEvents(CUpti_EventGroup eventGroup);
+
+/**
+ * \brief Zero all the event counts in an event group.
+ *
+ * Zero all the event counts in an event group.
+ * \note \b Thread-safety: this function is thread safe but client
+ * must guard against simultaneous destruction or modification of \p
+ * eventGroup (for example, client must guard against simultaneous
+ * calls to \ref cuptiEventGroupDestroy, \ref cuptiEventGroupAddEvent,
+ * etc.), and must guard against simultaneous destruction of the
+ * context in which \p eventGroup was created (for example, client
+ * must guard against simultaneous calls to cudaDeviceReset,
+ * cuCtxDestroy, etc.).
+ *
+ * \param eventGroup The event group
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupResetAllEvents(CUpti_EventGroup eventGroup);
+
+/**
+ * \brief Enable an event group.
+ *
+ * Enable an event group. Enabling an event group zeros the value of
+ * all the events in the group and then starts collection of those
+ * events.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_NOT_READY if \p eventGroup does not contain any events
+ * \retval CUPTI_ERROR_NOT_COMPATIBLE if \p eventGroup cannot be
+ * enabled due to other already enabled event groups
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ * \retval CUPTI_ERROR_HARDWARE_BUSY if another client is profiling
+ * and hardware is busy
+ */
+CUptiResult CUPTIAPI cuptiEventGroupEnable(CUpti_EventGroup eventGroup);
+
+/**
+ * \brief Disable an event group.
+ *
+ * Disable an event group. Disabling an event group stops collection
+ * of events contained in the group.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroup The event group
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupDisable(CUpti_EventGroup eventGroup);
+
+/**
+ * \brief Read the value for an event in an event group.
+ *
+ * Read the value for an event in an event group. The event value is
+ * returned in the \p eventValueBuffer buffer. \p
+ * eventValueBufferSizeBytes indicates the size of the \p
+ * eventValueBuffer buffer. The buffer must be at least sizeof(uint64)
+ * if ::CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES is not set
+ * on the group containing the event.  The buffer must be at least
+ * (sizeof(uint64) * number of domain instances) if
+ * ::CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES is set on the
+ * group.
+ *
+ * If any instance of an event counter overflows, the value returned
+ * for that event instance will be ::CUPTI_EVENT_OVERFLOW.
+ *
+ * The only allowed value for \p flags is ::CUPTI_EVENT_READ_FLAG_NONE.
+ *
+ * Reading an event from a disabled event group is not allowed. After
+ * being read, an event's value is reset to zero.
+ * \note \b Thread-safety: this function is thread safe but client
+ * must guard against simultaneous destruction or modification of \p
+ * eventGroup (for example, client must guard against simultaneous
+ * calls to \ref cuptiEventGroupDestroy, \ref cuptiEventGroupAddEvent,
+ * etc.), and must guard against simultaneous destruction of the
+ * context in which \p eventGroup was created (for example, client
+ * must guard against simultaneous calls to cudaDeviceReset,
+ * cuCtxDestroy, etc.). If \ref cuptiEventGroupResetAllEvents is
+ * called simultaneously with this function, then returned event
+ * values are undefined.
+ *
+ * \param eventGroup The event group
+ * \param flags Flags controlling the reading mode
+ * \param event The event to read
+ * \param eventValueBufferSizeBytes The size of \p eventValueBuffer
+ * in bytes, and returns the number of bytes written to \p
+ * eventValueBuffer
+ * \param eventValueBuffer Returns the event value(s)
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_EVENT_ID
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_INVALID_OPERATION if \p eventGroup is disabled
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup, \p
+ * eventValueBufferSizeBytes or \p eventValueBuffer is NULL
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT if size of \p eventValueBuffer
+ * is not sufficient
+ */
+CUptiResult CUPTIAPI cuptiEventGroupReadEvent(CUpti_EventGroup eventGroup,
+                                              CUpti_ReadEventFlags flags,
+                                              CUpti_EventID event,
+                                              size_t *eventValueBufferSizeBytes,
+                                              uint64_t *eventValueBuffer);
+
+/**
+ * \brief Read the values for all the events in an event group.
+ *
+ * Read the values for all the events in an event group. The event
+ * values are returned in the \p eventValueBuffer buffer. \p
+ * eventValueBufferSizeBytes indicates the size of \p
+ * eventValueBuffer.  The buffer must be at least (sizeof(uint64) *
+ * number of events in group) if
+ * ::CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES is not set on
+ * the group containing the events.  The buffer must be at least
+ * (sizeof(uint64) * number of domain instances * number of events in
+ * group) if ::CUPTI_EVENT_GROUP_ATTR_PROFILE_ALL_DOMAIN_INSTANCES is
+ * set on the group.
+ *
+ * The data format returned in \p eventValueBuffer is:
+ *    - domain instance 0: event0 event1 ... eventN
+ *    - domain instance 1: event0 event1 ... eventN
+ *    - ...
+ *    - domain instance M: event0 event1 ... eventN
+ *
+ * The event order in \p eventValueBuffer is returned in \p
+ * eventIdArray. The size of \p eventIdArray is specified in \p
+ * eventIdArraySizeBytes. The size should be at least
+ * (sizeof(CUpti_EventID) * number of events in group).
+ *
+ * If any instance of any event counter overflows, the value returned
+ * for that event instance will be ::CUPTI_EVENT_OVERFLOW.
+ *
+ * The only allowed value for \p flags is ::CUPTI_EVENT_READ_FLAG_NONE.
+ *
+ * Reading events from a disabled event group is not allowed. After
+ * being read, an event's value is reset to zero.
+ * \note \b Thread-safety: this function is thread safe but client
+ * must guard against simultaneous destruction or modification of \p
+ * eventGroup (for example, client must guard against simultaneous
+ * calls to \ref cuptiEventGroupDestroy, \ref cuptiEventGroupAddEvent,
+ * etc.), and must guard against simultaneous destruction of the
+ * context in which \p eventGroup was created (for example, client
+ * must guard against simultaneous calls to cudaDeviceReset,
+ * cuCtxDestroy, etc.). If \ref cuptiEventGroupResetAllEvents is
+ * called simultaneously with this function, then returned event
+ * values are undefined.
+ *
+ * \param eventGroup The event group
+ * \param flags Flags controlling the reading mode
+ * \param eventValueBufferSizeBytes The size of \p eventValueBuffer in
+ * bytes, and returns the number of bytes written to \p
+ * eventValueBuffer
+ * \param eventValueBuffer Returns the event values
+ * \param eventIdArraySizeBytes The size of \p eventIdArray in bytes,
+ * and returns the number of bytes written to \p eventIdArray
+ * \param eventIdArray Returns the IDs of the events in the same order
+ * as the values return in eventValueBuffer.
+ * \param numEventIdsRead Returns the number of event IDs returned
+ * in \p eventIdArray
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_INVALID_OPERATION if \p eventGroup is disabled
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroup, \p
+ * eventValueBufferSizeBytes, \p eventValueBuffer, \p
+ * eventIdArraySizeBytes, \p eventIdArray or \p numEventIdsRead is
+ * NULL
+ * \retval CUPTI_ERROR_PARAMETER_SIZE_NOT_SUFFICIENT if size of \p eventValueBuffer
+ * or \p eventIdArray is not sufficient
+ */
+CUptiResult CUPTIAPI cuptiEventGroupReadAllEvents(CUpti_EventGroup       eventGroup,
+                                                  CUpti_ReadEventFlags   flags,
+                                                  size_t                 *eventValueBufferSizeBytes,
+                                                  uint64_t               *eventValueBuffer,
+                                                  size_t                 *eventIdArraySizeBytes,
+                                                  CUpti_EventID          *eventIdArray,
+                                                  size_t                 *numEventIdsRead);
+
+/**
+ * \brief For a set of events, get the grouping that indicates the
+ * number of passes and the event groups necessary to collect the
+ * events.
+ *
+ * The number of events that can be collected simultaneously varies by
+ * device and by the type of the events. When events can be collected
+ * simultaneously, they may need to be grouped into multiple event
+ * groups because they are from different event domains. This function
+ * takes a set of events and determines how many passes are required
+ * to collect all those events, and which events can be collected
+ * simultaneously in each pass.
+ *
+ * The CUpti_EventGroupSets returned in \p eventGroupPasses indicates
+ * how many passes are required to collect the events with the \p
+ * numSets field. Within each event group set, the \p sets array
+ * indicates the event groups that should be collected on each pass.
+ * \note \b Thread-safety: this function is thread safe, but client
+ * must guard against another thread simultaneously destroying \p
+ * context.
+ *
+ * \param context The context for event collection
+ * \param eventIdArraySizeBytes Size of \p eventIdArray in bytes
+ * \param eventIdArray Array of event IDs that need to be grouped
+ * \param eventGroupPasses Returns a CUpti_EventGroupSets object that
+ * indicates the number of passes required to collect the events and
+ * the events to collect on each pass
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_CONTEXT
+ * \retval CUPTI_ERROR_INVALID_EVENT_ID
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventIdArray or
+ * \p eventGroupPasses is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupSetsCreate(CUcontext context,
+                                               size_t eventIdArraySizeBytes,
+                                               CUpti_EventID *eventIdArray,
+                                               CUpti_EventGroupSets **eventGroupPasses);
+
+/**
+ * \brief Destroy a event group sets object.
+ *
+ * Destroy a CUpti_EventGroupSets object.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroupSets The object to destroy
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_INVALID_OPERATION if any of the event groups
+ * contained in the sets is enabled
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroupSets is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupSetsDestroy(CUpti_EventGroupSets *eventGroupSets);
+
+
+/**
+ * \brief Enable an event group set.
+ *
+ * Enable a set of event groups. Enabling a set of event groups zeros the value of
+ * all the events in all the groups and then starts collection of those events.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param eventGroupSet The pointer to the event group set
+ *
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_NOT_READY if \p eventGroup does not contain any events
+ * \retval CUPTI_ERROR_NOT_COMPATIBLE if \p eventGroup cannot be
+ * enabled due to other already enabled event groups
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroupSet is NULL
+ * \retval CUPTI_ERROR_HARDWARE_BUSY if other client is profiling and hardware is
+ * busy
+ */
+CUptiResult CUPTIAPI cuptiEventGroupSetEnable(CUpti_EventGroupSet *eventGroupSet);
+
+/**
+ * \brief Disable an event group set.
+ *
+ * Disable a set of event groups. Disabling a set of event groups
+ * stops collection of events contained in the groups.
+ * \note \b Thread-safety: this function is thread safe.
+ * \note \b If this call fails, some of the event groups in the set may be disabled
+ * and other event groups may remain enabled.
+ *
+ * \param eventGroupSet The pointer to the event group set
+ * \retval CUPTI_SUCCESS
+ * \retval CUPTI_ERROR_NOT_INITIALIZED
+ * \retval CUPTI_ERROR_HARDWARE
+ * \retval CUPTI_ERROR_INVALID_PARAMETER if \p eventGroupSet is NULL
+ */
+CUptiResult CUPTIAPI cuptiEventGroupSetDisable(CUpti_EventGroupSet *eventGroupSet);
+
+/**
+ * \brief Enable kernel replay mode.
+ *
+ * Set profiling mode for the context to replay mode. In this mode,
+ * any number of events can be collected in one run of the kernel. The
+ * event collection mode will automatically switch to
+ * CUPTI_EVENT_COLLECTION_MODE_KERNEL.  In this mode, \ref
+ * cuptiSetEventCollectionMode will return
+ * CUPTI_ERROR_INVALID_OPERATION.
+ * \note \b Kernels might take longer to run if many events are enabled.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param context The context
+ * \retval CUPTI_SUCCESS
+ */
+CUptiResult CUPTIAPI cuptiEnableKernelReplayMode(CUcontext context);
+
+/**
+ * \brief Disable kernel replay mode.
+ *
+ * Set profiling mode for the context to non-replay (default)
+ * mode. Event collection mode will be set to
+ * CUPTI_EVENT_COLLECTION_MODE_KERNEL.  All previously enabled
+ * event groups and event group sets will be disabled.
+ * \note \b Thread-safety: this function is thread safe.
+ *
+ * \param context The context
+ * \retval CUPTI_SUCCESS
+ */
+CUptiResult CUPTIAPI cuptiDisableKernelReplayMode(CUcontext context);
+
+/**
+ * \brief Function type for getting updates on kernel replay.
+ *
+ * \param kernelName The mangled kernel name
+ * \param numReplaysDone Number of replays done so far
+ * \param customData Pointer of any custom data passed in when subscribing
+ */
+typedef void (CUPTIAPI *CUpti_KernelReplayUpdateFunc)(
+    const char *kernelName,
+    int numReplaysDone,
+    void *customData);
+
+/**
+ * \brief Subscribe to kernel replay updates.
+ *
+ * When subscribed, the function pointer passed in will be called each time a
+ * kernel run is finished during kernel replay. Previously subscribed function
+ * pointer will be replaced. Pass in NULL as the function pointer unsubscribes
+ * the update.
+ *
+ * \param updateFunc The update function pointer
+ * \param customData Pointer to any custom data
+ * \retval CUPTI_SUCCESS
+ */
+CUptiResult CUPTIAPI cuptiKernelReplaySubscribeUpdate(CUpti_KernelReplayUpdateFunc updateFunc, void *customData);
+
+/** @} */ /* END CUPTI_EVENT_API */
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /*_CUPTI_EVENTS_H_*/
+
+

llava_next/lib/python3.10/site-packages/nvidia/cuda_cupti/include/cupti_profiler_target.h

@@ -0,0 +1,589 @@
+/*
+ * Copyright 2011-2020   NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#if !defined(_CUPTI_PROFILER_TARGET_H_)
+#define _CUPTI_PROFILER_TARGET_H_
+
+#include <cuda.h>
+#include <cupti_result.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility push(default)
+#endif
+
+/**
+ * \defgroup CUPTI_PROFILER_API CUPTI Profiling API
+ * Functions, types, and enums that implement the CUPTI Profiling API.
+ * @{
+ */
+#ifndef CUPTI_PROFILER_STRUCT_SIZE
+#define CUPTI_PROFILER_STRUCT_SIZE(type_, lastfield_)                     (offsetof(type_, lastfield_) + sizeof(((type_*)0)->lastfield_))
+#endif
+
+/**
+ * \brief Profiler range attribute
+ *
+ * A metric enabled in the session's configuration is collected separately per unique range-stack in the pass.
+ * This is an attribute to collect metrics around each kernel in a profiling session or in an user defined range.
+ */
+typedef enum
+{
+    /**
+     * Invalid value
+     */
+    CUPTI_Range_INVALID,
+    /**
+     * Ranges are auto defined around each kernel in a profiling session
+     */
+    CUPTI_AutoRange,
+    /**
+     * A range in which metric data to be collected is defined by the user
+     */
+    CUPTI_UserRange,
+    /**
+     * Range count
+     */
+    CUPTI_Range_COUNT,
+} CUpti_ProfilerRange;
+
+/**
+ * \brief Profiler replay attribute
+ *
+ * For metrics which require multipass collection, a replay of the GPU kernel(s) is required.
+ * This is an attribute which specify how the replay of the kernel(s) to be measured is done.
+ */
+typedef enum
+{
+    /**
+     * Invalid Value
+     */
+    CUPTI_Replay_INVALID,
+    /**
+     * Replay is done by CUPTI user around the process
+     */
+    CUPTI_ApplicationReplay,
+    /**
+     * Replay is done around kernel implicitly by CUPTI
+     */
+    CUPTI_KernelReplay,
+    /**
+     * Replay is done by CUPTI user within a process
+     */
+    CUPTI_UserReplay,
+    /**
+     * Replay count
+     */
+    CUPTI_Replay_COUNT,
+} CUpti_ProfilerReplayMode;
+
+/**
+ * \brief Default parameter for cuptiProfilerInitialize
+ */
+typedef struct CUpti_Profiler_Initialize_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_Initialize_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+} CUpti_Profiler_Initialize_Params;
+#define CUpti_Profiler_Initialize_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_Initialize_Params, pPriv)
+
+/**
+ * \brief Default parameter for cuptiProfilerDeInitialize
+ */
+typedef struct CUpti_Profiler_DeInitialize_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_DeInitialize_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+} CUpti_Profiler_DeInitialize_Params;
+#define CUpti_Profiler_DeInitialize_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_DeInitialize_Params, pPriv)
+
+/**
+ * \brief Initializes the profiler interface
+ *
+ * Loads the required libraries in the process address space.
+ * Sets up the hooks with the CUDA driver.
+ */
+CUptiResult CUPTIAPI cuptiProfilerInitialize(CUpti_Profiler_Initialize_Params *pParams);
+
+/**
+ * \brief DeInitializes the profiler interface
+ */
+CUptiResult CUPTIAPI cuptiProfilerDeInitialize(CUpti_Profiler_DeInitialize_Params *pParams);
+
+/**
+ * \brief Input parameter to define the counterDataImage
+ */
+typedef struct CUpti_Profiler_CounterDataImageOptions
+{
+    size_t structSize;                                          //!< [in] CUpti_Profiler_CounterDataImageOptions_Params_STRUCT_SIZE
+    void* pPriv;                                                //!< [in] assign to NULL
+
+    const uint8_t* pCounterDataPrefix;                          /**< [in] Address of CounterDataPrefix generated from NVPW_CounterDataBuilder_GetCounterDataPrefix().
+                                                                    Must be align(8).*/
+    size_t counterDataPrefixSize;                               //!< [in] Size of CounterDataPrefix generated from NVPW_CounterDataBuilder_GetCounterDataPrefix().
+    uint32_t maxNumRanges;                                      //!< [in] Maximum number of ranges that can be profiled
+    uint32_t maxNumRangeTreeNodes;                              //!< [in] Maximum number of RangeTree nodes; must be >= maxNumRanges
+    uint32_t maxRangeNameLength;                                //!< [in] Maximum string length of each RangeName, including the trailing NULL character
+} CUpti_Profiler_CounterDataImageOptions;
+#define CUpti_Profiler_CounterDataImageOptions_STRUCT_SIZE                       CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_CounterDataImageOptions, maxRangeNameLength)
+
+/**
+ * \brief Params for cuptiProfilerCounterDataImageCalculateSize
+ */
+typedef struct CUpti_Profiler_CounterDataImage_CalculateSize_Params
+{
+    size_t structSize;                                          //!< [in] CUpti_Profiler_CounterDataImage_CalculateSize_Params_STRUCT_SIZE
+    void* pPriv;                                                //!< [in] assign to NULL
+
+    size_t sizeofCounterDataImageOptions;                       //!< [in] CUpti_Profiler_CounterDataImageOptions_STRUCT_SIZE
+    const CUpti_Profiler_CounterDataImageOptions* pOptions;     //!< [in] Pointer to Counter Data Image Options
+    size_t counterDataImageSize;                                //!< [out]
+} CUpti_Profiler_CounterDataImage_CalculateSize_Params;
+#define CUpti_Profiler_CounterDataImage_CalculateSize_Params_STRUCT_SIZE         CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_CounterDataImage_CalculateSize_Params, counterDataImageSize)
+
+/**
+ * \brief Params for cuptiProfilerCounterDataImageInitialize
+ */
+typedef struct CUpti_Profiler_CounterDataImage_Initialize_Params
+{
+    size_t structSize;                                          //!< [in] CUpti_Profiler_CounterDataImage_Initialize_Params_STRUCT_SIZE
+    void* pPriv;                                                //!< [in] assign to NULL
+
+    size_t sizeofCounterDataImageOptions;                       //!< [in] CUpti_Profiler_CounterDataImageOptions_STRUCT_SIZE
+    const CUpti_Profiler_CounterDataImageOptions* pOptions;     //!< [in] Pointer to Counter Data Image Options
+    size_t counterDataImageSize;                                //!< [in] Size calculated from cuptiProfilerCounterDataImageCalculateSize
+    uint8_t* pCounterDataImage;                                 //!< [in] The buffer to be initialized.
+} CUpti_Profiler_CounterDataImage_Initialize_Params;
+#define CUpti_Profiler_CounterDataImage_Initialize_Params_STRUCT_SIZE            CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_CounterDataImage_Initialize_Params, pCounterDataImage)
+
+/**
+ * \brief A CounterData image allocates space for values for each counter for each range.
+ *
+ * User borne the resposibility of managing the counterDataImage allocations.
+ * CounterDataPrefix contains meta data about the metrics that will be stored in counterDataImage.
+ * Use these APIs to calculate the allocation size and initialize counterData image.
+ */
+CUptiResult cuptiProfilerCounterDataImageCalculateSize(CUpti_Profiler_CounterDataImage_CalculateSize_Params* pParams);
+CUptiResult cuptiProfilerCounterDataImageInitialize(CUpti_Profiler_CounterDataImage_Initialize_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerCounterDataImageCalculateScratchBufferSize
+ */
+typedef struct CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    size_t counterDataImageSize;                            //!< [in] size calculated from cuptiProfilerCounterDataImageCalculateSize
+    uint8_t* pCounterDataImage;                             //!< [in]
+    size_t counterDataScratchBufferSize;                    //!< [out]
+} CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params;
+#define CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params_STRUCT_SIZE    CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params, counterDataScratchBufferSize)
+
+/**
+ * \brief Params for cuptiProfilerCounterDataImageInitializeScratchBuffer
+ */
+typedef struct CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    size_t counterDataImageSize;                            //!< [in] size calculated from cuptiProfilerCounterDataImageCalculateSize
+    uint8_t* pCounterDataImage;                             //!< [in]
+    size_t counterDataScratchBufferSize;                    //!< [in] size calculated using cuptiProfilerCounterDataImageCalculateScratchBufferSize
+    uint8_t* pCounterDataScratchBuffer;                     //!< [in] the scratch buffer to be initialized.
+} CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params;
+#define CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params_STRUCT_SIZE       CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params, pCounterDataScratchBuffer)
+
+/**
+ * \brief A temporary storage for CounterData image needed for internal operations
+ *
+ * Use these APIs to calculate the allocation size and initialize counterData image scratch buffer.
+ */
+CUptiResult cuptiProfilerCounterDataImageCalculateScratchBufferSize(CUpti_Profiler_CounterDataImage_CalculateScratchBufferSize_Params* pParams);
+CUptiResult cuptiProfilerCounterDataImageInitializeScratchBuffer(CUpti_Profiler_CounterDataImage_InitializeScratchBuffer_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerBeginSession
+ */
+typedef struct CUpti_Profiler_BeginSession_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_BeginSession_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+    size_t counterDataImageSize;                            //!< [in] size calculated from cuptiProfilerCounterDataImageCalculateSize
+    uint8_t* pCounterDataImage;                             //!< [in] address of CounterDataImage
+    size_t counterDataScratchBufferSize;                    //!< [in] size calculated from cuptiProfilerCounterDataImageInitializeScratchBuffer
+    uint8_t* pCounterDataScratchBuffer;                     //!< [in] address of CounterDataImage scratch buffer
+    uint8_t bDumpCounterDataInFile;                          //!< [in] [optional]
+    const char* pCounterDataFilePath;                        //!< [in] [optional]
+    CUpti_ProfilerRange range;                               //!< [in] CUpti_ProfilerRange
+    CUpti_ProfilerReplayMode replayMode;                     //!< [in] CUpti_ProfilerReplayMode
+    /* Replay options, required when replay is done by cupti user */
+    size_t maxRangesPerPass;                                //!< [in] Maximum number of ranges that can be recorded in a single pass.
+    size_t maxLaunchesPerPass;                              //!< [in] Maximum number of kernel launches that can be recorded in a single pass; must be >= maxRangesPerPass.
+
+} CUpti_Profiler_BeginSession_Params;
+#define CUpti_Profiler_BeginSession_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_BeginSession_Params, maxLaunchesPerPass)
+/**
+ * \brief Params for cuptiProfilerEndSession
+ */
+typedef struct CUpti_Profiler_EndSession_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_EndSession_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+} CUpti_Profiler_EndSession_Params;
+#define CUpti_Profiler_EndSession_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_EndSession_Params, ctx)
+
+/**
+ * \brief Begin profiling session sets up the profiling on the device
+ *
+ * Although, it doesn't start the profiling but GPU resources needed for profiling are allocated.
+ * Outside of a session, the GPU will return to its normal operating state.
+ */
+CUptiResult CUPTIAPI cuptiProfilerBeginSession(CUpti_Profiler_BeginSession_Params* pParams);
+/**
+ * \brief Ends profiling session
+ *
+ * Frees up the GPU resources acquired for profiling.
+ * Outside of a session, the GPU will return to it's normal operating state.
+ */
+CUptiResult CUPTIAPI cuptiProfilerEndSession(CUpti_Profiler_EndSession_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerSetConfig
+ */
+typedef struct CUpti_Profiler_SetConfig_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_SetConfig_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+    const uint8_t* pConfig;                                 //!< [in] Config created by NVPW_RawMetricsConfig_GetConfigImage(). Must be align(8).
+    size_t configSize;                                      //!< [in] size of config
+    uint16_t minNestingLevel;                               //!< [in] the lowest nesting level to be profiled; must be >= 1
+    uint16_t numNestingLevels;                              //!< [in] the number of nesting levels to profile; must be >= 1
+    size_t passIndex;                                       //!< [in] Set this to zero for in-app replay; set this to the output of EndPass() for application replay
+    uint16_t targetNestingLevel;                            //!< [in] Set this to minNestingLevel for in-app replay; set this to the output of EndPass() for application
+} CUpti_Profiler_SetConfig_Params;
+
+#define CUpti_Profiler_SetConfig_Params_STRUCT_SIZE                    CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_SetConfig_Params, targetNestingLevel)
+
+/**
+ * \brief Params for cuptiProfilerUnsetConfig
+ */
+typedef struct CUpti_Profiler_UnsetConfig_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_UnsetConfig_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+} CUpti_Profiler_UnsetConfig_Params;
+#define CUpti_Profiler_UnsetConfig_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_UnsetConfig_Params, ctx)
+
+/**
+ * \brief Set metrics configuration to be profiled
+ *
+ * Use these APIs to set the config to profile in a session. It can be used for advanced cases such as where multiple
+ * configurations are collected into a single CounterData Image on the need basis, without restarting the session.
+ */
+CUptiResult CUPTIAPI cuptiProfilerSetConfig(CUpti_Profiler_SetConfig_Params* pParams);
+/**
+ * \brief Unset metrics configuration profiled
+ *
+ */
+CUptiResult CUPTIAPI cuptiProfilerUnsetConfig(CUpti_Profiler_UnsetConfig_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerBeginPass
+ */
+typedef struct CUpti_Profiler_BeginPass_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_BeginPass_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+} CUpti_Profiler_BeginPass_Params;
+#define CUpti_Profiler_BeginPass_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_BeginPass_Params, ctx)
+
+/**
+ * \brief Params for cuptiProfilerEndPass
+ */
+typedef struct CUpti_Profiler_EndPass_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_EndPass_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+    uint16_t targetNestingLevel;                            //!  [out] The targetNestingLevel that will be collected by the *next* BeginPass.
+    size_t passIndex;                                       //!< [out] The passIndex that will be collected by the *next* BeginPass
+    uint8_t allPassesSubmitted;                             //!< [out] becomes true when the last pass has been queued to the GPU
+} CUpti_Profiler_EndPass_Params;
+#define CUpti_Profiler_EndPass_Params_STRUCT_SIZE                    CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_EndPass_Params, allPassesSubmitted)
+
+/**
+ * \brief Replay API: used for multipass collection.
+
+ * These APIs are used if user chooses to replay by itself \ref CUPTI_UserReplay or \ref CUPTI_ApplicationReplay
+ * for multipass collection of the metrics configurations.
+ * It's a no-op in case of \ref CUPTI_KernelReplay.
+ */
+CUptiResult cuptiProfilerBeginPass(CUpti_Profiler_BeginPass_Params* pParams);
+
+/**
+ * \brief Replay API: used for multipass collection.
+
+ * These APIs are used if user chooses to replay by itself \ref CUPTI_UserReplay or \ref CUPTI_ApplicationReplay
+ * for multipass collection of the metrics configurations.
+ * Its a no-op in case of \ref CUPTI_KernelReplay.
+ * Returns information for next pass.
+ */
+CUptiResult cuptiProfilerEndPass(CUpti_Profiler_EndPass_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerEnableProfiling
+ */
+typedef struct CUpti_Profiler_EnableProfiling_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_EnableProfiling_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+} CUpti_Profiler_EnableProfiling_Params;
+#define CUpti_Profiler_EnableProfiling_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_EnableProfiling_Params, ctx)
+
+/**
+ * \brief Params for cuptiProfilerDisableProfiling
+ */
+typedef struct CUpti_Profiler_DisableProfiling_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_DisableProfiling_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+} CUpti_Profiler_DisableProfiling_Params;
+#define CUpti_Profiler_DisableProfiling_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_DisableProfiling_Params, ctx)
+
+/**
+ * \brief Enables Profiling
+ *
+ * In \ref CUPTI_AutoRange, these APIs are used to enable/disable profiling for the kernels to be executed in
+ * a profiling session.
+ */
+CUptiResult CUPTIAPI cuptiProfilerEnableProfiling(CUpti_Profiler_EnableProfiling_Params* pParams);
+
+/**
+ * \brief Disable Profiling
+ *
+ * In \ref CUPTI_AutoRange, these APIs are used to enable/disable profiling for the kernels to be executed in
+ * a profiling session.
+ */
+CUptiResult CUPTIAPI cuptiProfilerDisableProfiling(CUpti_Profiler_DisableProfiling_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerIsPassCollected
+ */
+typedef struct CUpti_Profiler_IsPassCollected_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_IsPassCollected_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+    size_t numRangesDropped;                                //!< [out] number of ranges whose data was dropped in the processed pass
+    size_t numTraceBytesDropped;                            //!< [out] number of bytes not written to TraceBuffer due to buffer full
+    uint8_t onePassCollected;                               //!< [out] true if a pass was successfully decoded
+    uint8_t allPassesCollected;                             //!< [out] becomes true when the last pass has been decoded
+} CUpti_Profiler_IsPassCollected_Params;
+#define CUpti_Profiler_IsPassCollected_Params_STRUCT_SIZE            CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_IsPassCollected_Params, allPassesCollected)
+
+/**
+ * \brief Asynchronous call to query if the submitted pass to GPU is collected
+ *
+ */
+CUptiResult CUPTIAPI cuptiProfilerIsPassCollected(CUpti_Profiler_IsPassCollected_Params* pParams);
+
+/**
+ * \brief Params for cuptiProfilerFlushCounterData
+ */
+typedef struct CUpti_Profiler_FlushCounterData_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_FlushCounterData_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+    size_t numRangesDropped;                                //!< [out] number of ranges whose data was dropped in the processed passes
+    size_t numTraceBytesDropped;                            //!< [out] number of bytes not written to TraceBuffer due to buffer full
+} CUpti_Profiler_FlushCounterData_Params;
+#define CUpti_Profiler_FlushCounterData_Params_STRUCT_SIZE           CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_FlushCounterData_Params, numTraceBytesDropped)
+
+/**
+ * \brief Decode all the submitted passes
+ *
+ * Flush Counter data API to ensure every pass is decoded into the counterDataImage passed at beginSession.
+ * This will cause the CPU/GPU sync to collect all the undecoded pass.
+ */
+CUptiResult CUPTIAPI cuptiProfilerFlushCounterData(CUpti_Profiler_FlushCounterData_Params* pParams);
+
+typedef struct CUpti_Profiler_PushRange_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_PushRange_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+    const char* pRangeName;                                 //!< [in] specifies the range for subsequent launches; must not be NULL
+    size_t rangeNameLength;                                 //!< [in] assign to strlen(pRangeName) if known; if set to zero, the library will call strlen()
+} CUpti_Profiler_PushRange_Params;
+#define CUpti_Profiler_PushRange_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_PushRange_Params, rangeNameLength)
+
+typedef struct CUpti_Profiler_PopRange_Params
+{
+    size_t structSize;                                      //!< [in] CUpti_Profiler_PopRange_Params_STRUCT_SIZE
+    void* pPriv;                                            //!< [in] assign to NULL
+
+    CUcontext ctx;                                          //!< [in] if NULL, the current CUcontext is used
+} CUpti_Profiler_PopRange_Params;
+#define CUpti_Profiler_PopRange_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_PopRange_Params, ctx)
+
+
+/**
+ * \brief Range API's : Push user range
+ *
+ * Counter data is collected per unique range-stack. Identified by a string label passsed by the user.
+ * It's an invalid operation in case of \ref CUPTI_AutoRange.
+ */
+CUptiResult CUPTIAPI cuptiProfilerPushRange(CUpti_Profiler_PushRange_Params *pParams);
+
+/**
+ * \brief Range API's : Pop user range
+ *
+ * Counter data is collected per unique range-stack. Identified by a string label passsed by the user.
+ * It's an invalid operation in case of \ref CUPTI_AutoRange.
+ */
+CUptiResult CUPTIAPI cuptiProfilerPopRange(CUpti_Profiler_PopRange_Params *pParams);
+
+/**
+ * \brief Params for cuptiProfilerGetCounterAvailability
+ */
+typedef struct CUpti_Profiler_GetCounterAvailability_Params
+{
+    size_t structSize;                                  //!< [in] CUpti_Profiler_GetCounterAvailability_Params_STRUCT_SIZE
+    void* pPriv;                                        //!< [in] assign to NULL
+    CUcontext ctx;                                      //!< [in] if NULL, the current CUcontext is used
+    size_t counterAvailabilityImageSize;                //!< [in/out] If `pCounterAvailabilityImage` is NULL, then the required size is returned in
+                                                        //!< `counterAvailabilityImageSize`, otherwise `counterAvailabilityImageSize` should be set to the size of
+                                                        //!< `pCounterAvailabilityImage`, and on return it would be overwritten with number of actual bytes copied
+    uint8_t* pCounterAvailabilityImage;                 //!< [in] buffer receiving counter availability image, may be NULL
+} CUpti_Profiler_GetCounterAvailability_Params;
+#define CUpti_Profiler_GetCounterAvailability_Params_STRUCT_SIZE                  CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_GetCounterAvailability_Params, pCounterAvailabilityImage)
+
+/**
+ * \brief Query counter availibility
+ *
+ * Use this API to query counter availability information in a buffer which can be used to filter unavailable raw metrics on host.
+ * Note: This API may fail, if any profiling or sampling session is active on the specified context or its device.
+ */
+CUptiResult CUPTIAPI cuptiProfilerGetCounterAvailability(CUpti_Profiler_GetCounterAvailability_Params *pParams);
+
+/// Generic support level enum for CUPTI
+typedef enum
+{
+    CUPTI_PROFILER_CONFIGURATION_UNKNOWN = 0, //!< Configuration support level unknown - either detection code errored out before setting this value, or unable to determine it
+    CUPTI_PROFILER_CONFIGURATION_UNSUPPORTED, //!< Profiling is unavailable.  For specific feature fields, this means that the current configuration of this feature does not work with profiling.  For instance, SLI-enabled devices do not support profiling, and this value would be returned for SLI on an SLI-enabled device.
+    CUPTI_PROFILER_CONFIGURATION_DISABLED,    //!< Profiling would be available for this configuration, but was disabled by the system
+    CUPTI_PROFILER_CONFIGURATION_SUPPORTED    //!< Profiling is supported.  For specific feature fields, this means that the current configuration of this feature works with profiling.  For instance, SLI-enabled devices do not support profiling, and this value would only be returned for devices which are not SLI-enabled.
+} CUpti_Profiler_Support_Level;
+
+/**
+ * \brief Params for cuptiProfilerDeviceSupported
+ */
+typedef struct
+{
+    size_t structSize;                                //!< [in] Must be CUpti_Profiler_DeviceSupported_Params_STRUCT_SIZE
+    void *pPriv;                                      //!< [in] assign to NULL
+    CUdevice cuDevice;                                //!< [in] if NULL, the current CUcontext is used
+
+    CUpti_Profiler_Support_Level isSupported;         //!< [out] overall SUPPORTED / UNSUPPORTED flag representing whether Profiling and PC Sampling APIs work on the given device and configuration. SUPPORTED if all following flags are SUPPORTED, UNSUPPORTED otherwise.
+
+    CUpti_Profiler_Support_Level architecture;        //!< [out] SUPPORTED if the device architecture level supports the Profiling API (Compute Capability >= 7.0), UNSUPPORTED otherwise
+    CUpti_Profiler_Support_Level sli;                 //!< [out] SUPPORTED if SLI is not enabled, UNSUPPORTED otherwise
+    CUpti_Profiler_Support_Level vGpu;                //!< [out] SUPPORTED if vGPU is supported and profiling is enabled, DISABLED if profiling is supported but not enabled, UNSUPPORTED otherwise
+    CUpti_Profiler_Support_Level confidentialCompute; //!< [out] SUPPORTED if confidential compute is not enabled, UNSUPPORTED otherwise
+    CUpti_Profiler_Support_Level cmp;                 //!< [out] SUPPORTED if not NVIDIA Crypto Mining Processors (CMP), UNSUPPORTED otherwise
+    CUpti_Profiler_Support_Level wsl;                 //!< [out] SUPPORTED if WSL supported, UNSUPPORTED otherwise
+} CUpti_Profiler_DeviceSupported_Params;
+#define CUpti_Profiler_DeviceSupported_Params_STRUCT_SIZE CUPTI_PROFILER_STRUCT_SIZE(CUpti_Profiler_DeviceSupported_Params, confidentialCompute)
+
+/**
+ * \brief Query device compatibility with Profiling API
+ *
+ * Use this call to determine whether a compute device and configuration are compatible with the Profiling API.
+ * If the configuration does not support profiling, one of several flags will indicate why.
+ */
+CUptiResult CUPTIAPI cuptiProfilerDeviceSupported(CUpti_Profiler_DeviceSupported_Params *pParams);
+
+/** @} */ /* END CUPTI_METRIC_API */
+#if defined(__GNUC__) && defined(CUPTI_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#ifdef __cplusplus
+} /* extern "C" */
+#endif
+
+#endif /*_CUPTI_PROFILER_TARGET_H_*/

llava_next/lib/python3.10/site-packages/nvidia/cuda_cupti/include/nvperf_cuda_host.h

@@ -0,0 +1,197 @@
+#ifndef NVPERF_CUDA_HOST_H
+#define NVPERF_CUDA_HOST_H
+
+/*
+ * Copyright 2014-2022  NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO USER:
+ *
+ * This source code is subject to NVIDIA ownership rights under U.S. and
+ * international Copyright laws.
+ *
+ * This software and the information contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+ * of a form of NVIDIA software license agreement.
+ *
+ * NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+ * CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+ * IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+ * REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+ * MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+ * 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 SOURCE CODE.
+ *
+ * U.S. Government End Users.   This source code is a "commercial item" as
+ * that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+ * "commercial computer  software"  and "commercial computer software
+ * documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+ * and is provided to the U.S. Government only as a commercial end item.
+ * Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+ * 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+ * source code with only those rights set forth herein.
+ *
+ * Any use of this source code in individual and commercial software must
+ * include, in the user documentation and internal comments to the code,
+ * the above Disclaimer and U.S. Government End Users Notice.
+ */
+
+#include <stddef.h>
+#include <stdint.h>
+#include "nvperf_common.h"
+#include "nvperf_host.h"
+
+#if defined(__GNUC__) && defined(NVPA_SHARED_LIB)
+    #pragma GCC visibility push(default)
+    #if !defined(NVPW_LOCAL)
+        #define NVPW_LOCAL __attribute__ ((visibility ("hidden")))
+    #endif
+#else
+    #if !defined(NVPW_LOCAL)
+        #define NVPW_LOCAL
+    #endif
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ *  @file   nvperf_cuda_host.h
+ */
+
+    /// 'NVPA_MetricsContext' and its APIs are deprecated, please use 'NVPW_MetricsEvaluator' and its APIs instead.
+    typedef struct NVPA_MetricsContext NVPA_MetricsContext;
+
+    typedef struct NVPW_CUDA_MetricsContext_Create_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        const char* pChipName;
+        /// [out]
+        struct NVPA_MetricsContext* pMetricsContext;
+    } NVPW_CUDA_MetricsContext_Create_Params;
+#define NVPW_CUDA_MetricsContext_Create_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CUDA_MetricsContext_Create_Params, pMetricsContext)
+
+    NVPA_Status NVPW_CUDA_MetricsContext_Create(NVPW_CUDA_MetricsContext_Create_Params* pParams);
+
+    typedef struct NVPW_CUDA_RawMetricsConfig_Create_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        NVPA_ActivityKind activityKind;
+        /// [in]
+        const char* pChipName;
+        /// [out] new NVPA_RawMetricsConfig object
+        struct NVPA_RawMetricsConfig* pRawMetricsConfig;
+    } NVPW_CUDA_RawMetricsConfig_Create_Params;
+#define NVPW_CUDA_RawMetricsConfig_Create_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CUDA_RawMetricsConfig_Create_Params, pRawMetricsConfig)
+
+    NVPA_Status NVPW_CUDA_RawMetricsConfig_Create(NVPW_CUDA_RawMetricsConfig_Create_Params* pParams);
+
+    typedef struct NVPW_CUDA_RawMetricsConfig_Create_V2_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        NVPA_ActivityKind activityKind;
+        /// [in] accepted for chips supported at the time-of-release.
+        const char* pChipName;
+        /// [in] buffer with counter availability image - required for future chip support
+        const uint8_t* pCounterAvailabilityImage;
+        /// [out] new NVPA_RawMetricsConfig object
+        struct NVPA_RawMetricsConfig* pRawMetricsConfig;
+    } NVPW_CUDA_RawMetricsConfig_Create_V2_Params;
+#define NVPW_CUDA_RawMetricsConfig_Create_V2_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CUDA_RawMetricsConfig_Create_V2_Params, pRawMetricsConfig)
+
+    /// Use either 'pChipName' or 'pCounterAvailabilityImage'.
+    NVPA_Status NVPW_CUDA_RawMetricsConfig_Create_V2(NVPW_CUDA_RawMetricsConfig_Create_V2_Params* pParams);
+
+    typedef struct NVPW_CUDA_CounterDataBuilder_Create_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in] accepted for chips supported at the time-of-release.
+        const char* pChipName;
+        /// [in] buffer with counter availability image - required for future chip support
+        const uint8_t* pCounterAvailabilityImage;
+        /// [out] new NVPA_CounterDataBuilder object
+        struct NVPA_CounterDataBuilder* pCounterDataBuilder;
+    } NVPW_CUDA_CounterDataBuilder_Create_Params;
+#define NVPW_CUDA_CounterDataBuilder_Create_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CUDA_CounterDataBuilder_Create_Params, pCounterDataBuilder)
+
+    /// Use either 'pChipName' or 'pCounterAvailabilityImage'.
+    NVPA_Status NVPW_CUDA_CounterDataBuilder_Create(NVPW_CUDA_CounterDataBuilder_Create_Params* pParams);
+
+    typedef struct NVPW_MetricsEvaluator NVPW_MetricsEvaluator;
+
+    typedef struct NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in] accepted for chips supported at the time-of-release.
+        const char* pChipName;
+        /// [in] buffer with counter availability image - required for future chip support
+        const uint8_t* pCounterAvailabilityImage;
+        /// [out]
+        size_t scratchBufferSize;
+    } NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params;
+#define NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params, scratchBufferSize)
+
+    /// Use either 'pChipName' or 'pCounterAvailabilityImage'.
+    NVPA_Status NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize(NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params* pParams);
+
+    typedef struct NVPW_CUDA_MetricsEvaluator_Initialize_Params
+    {
+        /// [in]
+        size_t structSize;
+        /// [in] assign to NULL
+        void* pPriv;
+        /// [in]
+        uint8_t* pScratchBuffer;
+        /// [in] the size of the 'pScratchBuffer' array, should be at least the size of the 'scratchBufferSize' returned
+        /// by 'NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize'
+        size_t scratchBufferSize;
+        /// [in] accepted for chips supported at the time-of-release.
+        const char* pChipName;
+        /// [in] buffer with counter availability image - required for future chip support
+        const uint8_t* pCounterAvailabilityImage;
+        /// [in]
+        const uint8_t* pCounterDataImage;
+        /// [in] must be provided if 'pCounterDataImage' is not NULL
+        size_t counterDataImageSize;
+        /// [out]
+        struct NVPW_MetricsEvaluator* pMetricsEvaluator;
+    } NVPW_CUDA_MetricsEvaluator_Initialize_Params;
+#define NVPW_CUDA_MetricsEvaluator_Initialize_Params_STRUCT_SIZE NVPA_STRUCT_SIZE(NVPW_CUDA_MetricsEvaluator_Initialize_Params, pMetricsEvaluator)
+
+    /// Use one of 'pChipName', 'pCounterAvailabilityImage', or 'pCounterDataImage'. 'pChipName' or
+    /// 'pCounterAvailabilityImage' will create a metrics evaluator based on a virtual device while 'pCounterDataImage'
+    /// will create a metrics evaluator based on the actual device.
+    NVPA_Status NVPW_CUDA_MetricsEvaluator_Initialize(NVPW_CUDA_MetricsEvaluator_Initialize_Params* pParams);
+
+
+
+#ifdef __cplusplus
+} // extern "C"
+#endif
+
+#if defined(__GNUC__) && defined(NVPA_SHARED_LIB)
+    #pragma GCC visibility pop
+#endif
+
+#endif // NVPERF_CUDA_HOST_H

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn.h

@@ -0,0 +1,78 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cudnn : Neural Networks Library
+
+*/
+
+#if !defined(CUDNN_H_)
+#define CUDNN_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+#include "cudnn_ops_train.h"
+#include "cudnn_adv_infer.h"
+#include "cudnn_adv_train.h"
+#include "cudnn_cnn_infer.h"
+#include "cudnn_cnn_train.h"
+
+#include "cudnn_backend.h"
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_adv_infer.h

@@ -0,0 +1,658 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cudnn_adv_infer : cuDNN's advanced and experimental features.
+
+*/
+
+#if !defined(CUDNN_ADV_INFER_H_)
+#define CUDNN_ADV_INFER_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_ADV_INFER_MAJOR 8
+#define CUDNN_ADV_INFER_MINOR 9
+#define CUDNN_ADV_INFER_PATCH 2
+
+#if (CUDNN_ADV_INFER_MAJOR != CUDNN_MAJOR) || (CUDNN_ADV_INFER_MINOR != CUDNN_MINOR) || \
+    (CUDNN_ADV_INFER_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN ADV INFER!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* BASIC RNN API */
+
+typedef enum {
+    CUDNN_FWD_MODE_INFERENCE = 0,
+    CUDNN_FWD_MODE_TRAINING  = 1,
+} cudnnForwardMode_t;
+
+typedef enum {
+    CUDNN_RNN_RELU = 0, /* basic RNN cell type with ReLu activation */
+    CUDNN_RNN_TANH = 1, /* basic RNN cell type with tanh activation */
+    CUDNN_LSTM     = 2, /* LSTM with optional recurrent projection and clipping */
+    CUDNN_GRU      = 3, /* Using h' = tanh(r * Uh(t-1) + Wx) and h = (1 - z) * h' + z * h(t-1); */
+} cudnnRNNMode_t;
+
+typedef enum {
+    CUDNN_RNN_NO_BIAS         = 0, /* rnn cell formulas do not use biases */
+    CUDNN_RNN_SINGLE_INP_BIAS = 1, /* rnn cell formulas use one input bias in input GEMM */
+    CUDNN_RNN_DOUBLE_BIAS     = 2, /* default, rnn cell formulas use two bias vectors */
+    CUDNN_RNN_SINGLE_REC_BIAS = 3  /* rnn cell formulas use one recurrent bias in recurrent GEMM */
+} cudnnRNNBiasMode_t;
+
+typedef enum {
+    CUDNN_UNIDIRECTIONAL = 0, /* single direction network */
+    CUDNN_BIDIRECTIONAL  = 1, /* output concatination at each layer */
+} cudnnDirectionMode_t;
+
+typedef enum {
+    CUDNN_LINEAR_INPUT = 0, /* adjustable weight matrix in first layer input GEMM */
+    CUDNN_SKIP_INPUT   = 1, /* fixed identity matrix in the first layer input GEMM */
+} cudnnRNNInputMode_t;
+
+typedef enum {
+    CUDNN_RNN_CLIP_NONE   = 0, /* disables LSTM cell clipping */
+    CUDNN_RNN_CLIP_MINMAX = 1, /* enables LSTM cell clipping */
+} cudnnRNNClipMode_t;
+
+typedef enum {
+    CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED   = 0, /* padded, outer stride from one time-step to the next */
+    CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_PACKED     = 1, /* sequence length sorted and packed as in basic RNN api */
+    CUDNN_RNN_DATA_LAYOUT_BATCH_MAJOR_UNPACKED = 2, /* padded, outer stride from one batch to the next */
+} cudnnRNNDataLayout_t;
+
+/* Legacy type for backward compatibility */
+typedef unsigned cudnnRNNPaddingMode_t;
+
+/* For auxFlags in cudnnSetRNNDescriptor_v8() and cudnnSetRNNPaddingMode() */
+#define CUDNN_RNN_PADDED_IO_DISABLED 0
+#define CUDNN_RNN_PADDED_IO_ENABLED (1U << 0)
+
+struct cudnnRNNStruct;
+typedef struct cudnnRNNStruct *cudnnRNNDescriptor_t;
+
+struct cudnnPersistentRNNPlan;
+typedef struct cudnnPersistentRNNPlan *cudnnPersistentRNNPlan_t;
+
+struct cudnnRNNDataStruct;
+typedef struct cudnnRNNDataStruct *cudnnRNNDataDescriptor_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateRNNDescriptor(cudnnRNNDescriptor_t *rnnDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyRNNDescriptor(cudnnRNNDescriptor_t rnnDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNDescriptor_v8(cudnnRNNDescriptor_t rnnDesc,
+                         cudnnRNNAlgo_t algo,
+                         cudnnRNNMode_t cellMode,
+                         cudnnRNNBiasMode_t biasMode,
+                         cudnnDirectionMode_t dirMode,
+                         cudnnRNNInputMode_t inputMode,
+                         cudnnDataType_t dataType,
+                         cudnnDataType_t mathPrec,
+                         cudnnMathType_t mathType,
+                         int32_t inputSize,
+                         int32_t hiddenSize,
+                         int32_t projSize,
+                         int32_t numLayers,
+                         cudnnDropoutDescriptor_t dropoutDesc,
+                         uint32_t auxFlags);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNDescriptor_v8(cudnnRNNDescriptor_t rnnDesc,
+                         cudnnRNNAlgo_t *algo,
+                         cudnnRNNMode_t *cellMode,
+                         cudnnRNNBiasMode_t *biasMode,
+                         cudnnDirectionMode_t *dirMode,
+                         cudnnRNNInputMode_t *inputMode,
+                         cudnnDataType_t *dataType,
+                         cudnnDataType_t *mathPrec,
+                         cudnnMathType_t *mathType,
+                         int32_t *inputSize,
+                         int32_t *hiddenSize,
+                         int32_t *projSize,
+                         int32_t *numLayers,
+                         cudnnDropoutDescriptor_t *dropoutDesc,
+                         uint32_t *auxFlags);
+
+/*
+ * mathPrec in cudnnSetRNNDescriptor_v6() specifies compute precision
+ * compute precision is further modified by cudnnSetRNNMatrixMathType()
+ * dataType in cudnnGetRNNParamsSize() and wDesc specify weight storage
+ * dropout is between RNN layers, not between recurrent steps
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNDescriptor_v6(cudnnHandle_t handle,
+                         cudnnRNNDescriptor_t rnnDesc,
+                         const int hiddenSize,
+                         const int numLayers,
+                         cudnnDropoutDescriptor_t dropoutDesc,
+                         cudnnRNNInputMode_t inputMode,
+                         cudnnDirectionMode_t direction,
+                         cudnnRNNMode_t cellMode,
+                         cudnnRNNAlgo_t algo,
+                         cudnnDataType_t mathPrec);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNDescriptor_v6(cudnnHandle_t handle,
+                         cudnnRNNDescriptor_t rnnDesc,
+                         int *hiddenSize,
+                         int *numLayers,
+                         cudnnDropoutDescriptor_t *dropoutDesc,
+                         cudnnRNNInputMode_t *inputMode,
+                         cudnnDirectionMode_t *direction,
+                         cudnnRNNMode_t *cellMode,
+                         cudnnRNNAlgo_t *algo,
+                         cudnnDataType_t *mathPrec);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNMatrixMathType(cudnnRNNDescriptor_t rnnDesc, cudnnMathType_t mType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNMatrixMathType(cudnnRNNDescriptor_t rnnDesc, cudnnMathType_t *mType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNBiasMode(cudnnRNNDescriptor_t rnnDesc, cudnnRNNBiasMode_t biasMode);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNBiasMode(cudnnRNNDescriptor_t rnnDesc, cudnnRNNBiasMode_t *biasMode);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNSetClip_v8(cudnnRNNDescriptor_t rnnDesc,
+                   cudnnRNNClipMode_t clipMode,
+                   cudnnNanPropagation_t clipNanOpt,
+                   double lclip,
+                   double rclip);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNGetClip_v8(cudnnRNNDescriptor_t rnnDesc,
+                   cudnnRNNClipMode_t *clipMode,
+                   cudnnNanPropagation_t *clipNanOpt,
+                   double *lclip,
+                   double *rclip);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNSetClip(cudnnHandle_t handle,
+                cudnnRNNDescriptor_t rnnDesc,
+                cudnnRNNClipMode_t clipMode,
+                cudnnNanPropagation_t clipNanOpt,
+                double lclip,
+                double rclip);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNGetClip(cudnnHandle_t handle,
+                cudnnRNNDescriptor_t rnnDesc,
+                cudnnRNNClipMode_t *clipMode,
+                cudnnNanPropagation_t *clipNanOpt,
+                double *lclip,
+                double *rclip);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNProjectionLayers(cudnnHandle_t handle,
+                            cudnnRNNDescriptor_t rnnDesc,
+                            const int recProjSize,
+                            const int outProjSize);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNProjectionLayers(cudnnHandle_t handle,
+                            const cudnnRNNDescriptor_t rnnDesc,
+                            int *recProjSize,
+                            int *outProjSize);
+
+/* Expensive. Creates the plan for the specific settings. */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreatePersistentRNNPlan(cudnnRNNDescriptor_t rnnDesc,
+                             const int minibatch,
+                             const cudnnDataType_t dataType,
+                             cudnnPersistentRNNPlan_t *plan);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyPersistentRNNPlan(cudnnPersistentRNNPlan_t plan);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetPersistentRNNPlan(cudnnRNNDescriptor_t rnnDesc, cudnnPersistentRNNPlan_t plan);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBuildRNNDynamic(cudnnHandle_t handle, cudnnRNNDescriptor_t rnnDesc, int miniBatch);
+
+/* dataType in weight descriptors and input descriptors is used to describe storage */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNWorkspaceSize(cudnnHandle_t handle,
+                         const cudnnRNNDescriptor_t rnnDesc,
+                         const int seqLength,
+                         const cudnnTensorDescriptor_t *xDesc,
+                         size_t *sizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNTrainingReserveSize(cudnnHandle_t handle,
+                               const cudnnRNNDescriptor_t rnnDesc,
+                               const int seqLength,
+                               const cudnnTensorDescriptor_t *xDesc,
+                               size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNTempSpaceSizes(cudnnHandle_t handle,
+                          cudnnRNNDescriptor_t rnnDesc,
+                          cudnnForwardMode_t fwdMode,
+                          cudnnRNNDataDescriptor_t xDesc,
+                          size_t *workSpaceSize,
+                          size_t *reserveSpaceSize);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNParamsSize(cudnnHandle_t handle,
+                      const cudnnRNNDescriptor_t rnnDesc,
+                      const cudnnTensorDescriptor_t xDesc,
+                      size_t *sizeInBytes,
+                      cudnnDataType_t dataType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNWeightSpaceSize(cudnnHandle_t handle, cudnnRNNDescriptor_t rnnDesc, size_t *weightSpaceSize);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNLinLayerMatrixParams(cudnnHandle_t handle,
+                                const cudnnRNNDescriptor_t rnnDesc,
+                                const int pseudoLayer,
+                                const cudnnTensorDescriptor_t xDesc,
+                                const cudnnFilterDescriptor_t wDesc,
+                                const void *w,
+                                const int linLayerID,
+                                cudnnFilterDescriptor_t linLayerMatDesc,
+                                void **linLayerMat);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNLinLayerBiasParams(cudnnHandle_t handle,
+                              const cudnnRNNDescriptor_t rnnDesc,
+                              const int pseudoLayer,
+                              const cudnnTensorDescriptor_t xDesc,
+                              const cudnnFilterDescriptor_t wDesc,
+                              const void *w,
+                              const int linLayerID,
+                              cudnnFilterDescriptor_t linLayerBiasDesc,
+                              void **linLayerBias);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNWeightParams(cudnnHandle_t handle,
+                        cudnnRNNDescriptor_t rnnDesc,
+                        int32_t pseudoLayer,
+                        size_t weightSpaceSize,
+                        const void *weightSpace,
+                        int32_t linLayerID,
+                        cudnnTensorDescriptor_t mDesc,
+                        void **mAddr,
+                        cudnnTensorDescriptor_t bDesc,
+                        void **bAddr);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNForwardInference(cudnnHandle_t handle,
+                         const cudnnRNNDescriptor_t rnnDesc,
+                         const int seqLength,
+                         const cudnnTensorDescriptor_t *xDesc,
+                         const void *x,
+                         const cudnnTensorDescriptor_t hxDesc,
+                         const void *hx,
+                         const cudnnTensorDescriptor_t cxDesc,
+                         const void *cx,
+                         const cudnnFilterDescriptor_t wDesc,
+                         const void *w,
+                         const cudnnTensorDescriptor_t *yDesc,
+                         void *y,
+                         const cudnnTensorDescriptor_t hyDesc,
+                         void *hy,
+                         const cudnnTensorDescriptor_t cyDesc,
+                         void *cy,
+                         void *workSpace,
+                         size_t workSpaceSizeInBytes);
+
+/* RNN EX API */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNPaddingMode(cudnnRNNDescriptor_t rnnDesc, unsigned paddingMode);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNPaddingMode(cudnnRNNDescriptor_t rnnDesc, unsigned *paddingMode);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateRNNDataDescriptor(cudnnRNNDataDescriptor_t *rnnDataDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc,
+                          cudnnDataType_t dataType,
+                          cudnnRNNDataLayout_t layout,
+                          int maxSeqLength,
+                          int batchSize,
+                          int vectorSize,
+                          const int seqLengthArray[], /* length of each sequence in the batch */
+                          void *paddingFill);         /* symbol for filling padding position in output */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc,
+                          cudnnDataType_t *dataType,
+                          cudnnRNNDataLayout_t *layout,
+                          int *maxSeqLength,
+                          int *batchSize,
+                          int *vectorSize,
+                          int arrayLengthRequested,
+                          int seqLengthArray[],
+                          void *paddingFill);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNForwardInferenceEx(cudnnHandle_t handle,
+                           const cudnnRNNDescriptor_t rnnDesc,
+                           const cudnnRNNDataDescriptor_t xDesc,
+                           const void *x,
+                           const cudnnTensorDescriptor_t hxDesc,
+                           const void *hx,
+                           const cudnnTensorDescriptor_t cxDesc,
+                           const void *cx,
+                           const cudnnFilterDescriptor_t wDesc,
+                           const void *w,
+                           const cudnnRNNDataDescriptor_t yDesc,
+                           void *y,
+                           const cudnnTensorDescriptor_t hyDesc,
+                           void *hy,
+                           const cudnnTensorDescriptor_t cyDesc,
+                           void *cy,
+                           const cudnnRNNDataDescriptor_t kDesc, /* reserved, should pass NULL */
+                           const void *keys,                     /* reserved, should pass NULL */
+                           const cudnnRNNDataDescriptor_t cDesc, /* reserved, should pass NULL */
+                           void *cAttn,                          /* reserved, should pass NULL */
+                           const cudnnRNNDataDescriptor_t iDesc, /* reserved, should pass NULL */
+                           void *iAttn,                          /* reserved, should pass NULL */
+                           const cudnnRNNDataDescriptor_t qDesc, /* reserved, should pass NULL */
+                           void *queries,                        /* reserved, should pass NULL */
+                           void *workSpace,
+                           size_t workSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNForward(cudnnHandle_t handle,
+                cudnnRNNDescriptor_t rnnDesc,
+                cudnnForwardMode_t fwdMode,
+                const int32_t devSeqLengths[],
+                cudnnRNNDataDescriptor_t xDesc,
+                const void *x,
+                cudnnRNNDataDescriptor_t yDesc,
+                void *y,
+                cudnnTensorDescriptor_t hDesc,
+                const void *hx,
+                void *hy,
+                cudnnTensorDescriptor_t cDesc,
+                const void *cx,
+                void *cy,
+                size_t weightSpaceSize,
+                const void *weightSpace,
+                size_t workSpaceSize,
+                void *workSpace,
+                size_t reserveSpaceSize,
+                void *reserveSpace);
+
+/* RNN FIND API */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNAlgorithmDescriptor(cudnnHandle_t handle, cudnnRNNDescriptor_t rnnDesc, cudnnAlgorithmDescriptor_t algoDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNForwardInferenceAlgorithmMaxCount(cudnnHandle_t handle, const cudnnRNNDescriptor_t rnnDesc, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindRNNForwardInferenceAlgorithmEx(cudnnHandle_t handle,
+                                        const cudnnRNNDescriptor_t rnnDesc,
+                                        const int seqLength,
+                                        const cudnnTensorDescriptor_t *xDesc,
+                                        const void *x,
+                                        const cudnnTensorDescriptor_t hxDesc,
+                                        const void *hx,
+                                        const cudnnTensorDescriptor_t cxDesc,
+                                        const void *cx,
+                                        const cudnnFilterDescriptor_t wDesc,
+                                        const void *w,
+                                        const cudnnTensorDescriptor_t *yDesc,
+                                        void *y,
+                                        const cudnnTensorDescriptor_t hyDesc,
+                                        void *hy,
+                                        const cudnnTensorDescriptor_t cyDesc,
+                                        void *cy,
+                                        const float findIntensity,
+                                        const int requestedAlgoCount,
+                                        int *returnedAlgoCount,
+                                        cudnnAlgorithmPerformance_t *perfResults,
+                                        void *workspace,
+                                        size_t workSpaceSizeInBytes);
+
+/* Sequence data descriptor */
+
+typedef enum {
+    CUDNN_SEQDATA_TIME_DIM  = 0, /* index in time */
+    CUDNN_SEQDATA_BATCH_DIM = 1, /* index in batch */
+    CUDNN_SEQDATA_BEAM_DIM  = 2, /* index in beam */
+    CUDNN_SEQDATA_VECT_DIM  = 3  /* index in vector */
+} cudnnSeqDataAxis_t;
+
+struct cudnnSeqDataStruct;
+typedef struct cudnnSeqDataStruct *cudnnSeqDataDescriptor_t;
+
+#define CUDNN_SEQDATA_DIM_COUNT 4 /* dimension count */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateSeqDataDescriptor(cudnnSeqDataDescriptor_t *seqDataDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroySeqDataDescriptor(cudnnSeqDataDescriptor_t seqDataDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetSeqDataDescriptor(cudnnSeqDataDescriptor_t seqDataDesc,
+                          cudnnDataType_t dataType,
+                          int nbDims,
+                          const int dimA[],
+                          const cudnnSeqDataAxis_t axes[],
+                          size_t seqLengthArraySize,
+                          const int seqLengthArray[],
+                          void *paddingFill);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetSeqDataDescriptor(const cudnnSeqDataDescriptor_t seqDataDesc,
+                          cudnnDataType_t *dataType,
+                          int *nbDims,
+                          int nbDimsRequested,
+                          int dimA[],
+                          cudnnSeqDataAxis_t axes[],
+                          size_t *seqLengthArraySize,
+                          size_t seqLengthSizeRequested,
+                          int seqLengthArray[],
+                          void *paddingFill);
+
+/* Multihead Attention */
+
+/* Legacy type for backward compatibility */
+typedef unsigned cudnnAttnQueryMap_t;
+
+/*
+ * Multi-head attention options passed via 'attnMode' in cudnnSetAttnDescriptor().
+ * Use the bitwise OR operator to combine several settings listed below.  Additional
+ * minor options can be added here w/o changing or introducing new API functions.
+ */
+#define CUDNN_ATTN_QUERYMAP_ALL_TO_ONE 0         /* multiple Q-s map to a single (K,V) set when beam size > 1 */
+#define CUDNN_ATTN_QUERYMAP_ONE_TO_ONE (1U << 0) /* multiple Q-s map to multiple (K,V) sets when beam size > 1 */
+#define CUDNN_ATTN_DISABLE_PROJ_BIASES 0         /* no biases in attention input and output projections */
+#define CUDNN_ATTN_ENABLE_PROJ_BIASES (1U << 1)  /* use biases in attention input and output projections */
+
+struct cudnnAttnStruct;
+typedef struct cudnnAttnStruct *cudnnAttnDescriptor_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateAttnDescriptor(cudnnAttnDescriptor_t *attnDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyAttnDescriptor(cudnnAttnDescriptor_t attnDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetAttnDescriptor(cudnnAttnDescriptor_t attnDesc,
+                       unsigned attnMode,
+                       int nHeads,
+                       double smScaler,
+                       cudnnDataType_t dataType,
+                       cudnnDataType_t computePrec,
+                       cudnnMathType_t mathType,
+                       cudnnDropoutDescriptor_t attnDropoutDesc,
+                       cudnnDropoutDescriptor_t postDropoutDesc,
+                       int qSize,
+                       int kSize,
+                       int vSize,
+                       int qProjSize,
+                       int kProjSize,
+                       int vProjSize,
+                       int oProjSize,
+                       int qoMaxSeqLength,
+                       int kvMaxSeqLength,
+                       int maxBatchSize,
+                       int maxBeamSize);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetAttnDescriptor(cudnnAttnDescriptor_t attnDesc,
+                       unsigned *attnMode,
+                       int *nHeads,
+                       double *smScaler,
+                       cudnnDataType_t *dataType,
+                       cudnnDataType_t *computePrec,
+                       cudnnMathType_t *mathType,
+                       cudnnDropoutDescriptor_t *attnDropoutDesc,
+                       cudnnDropoutDescriptor_t *postDropoutDesc,
+                       int *qSize,
+                       int *kSize,
+                       int *vSize,
+                       int *qProjSize,
+                       int *kProjSize,
+                       int *vProjSize,
+                       int *oProjSize,
+                       int *qoMaxSeqLength,
+                       int *kvMaxSeqLength,
+                       int *maxBatchSize,
+                       int *maxBeamSize);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetMultiHeadAttnBuffers(cudnnHandle_t handle,
+                             const cudnnAttnDescriptor_t attnDesc,
+                             size_t *weightSizeInBytes,
+                             size_t *workSpaceSizeInBytes,
+                             size_t *reserveSpaceSizeInBytes);
+
+typedef enum {
+    CUDNN_MH_ATTN_Q_WEIGHTS = 0, /* input projection weights for 'queries' */
+    CUDNN_MH_ATTN_K_WEIGHTS = 1, /* input projection weights for 'keys' */
+    CUDNN_MH_ATTN_V_WEIGHTS = 2, /* input projection weights for 'values' */
+    CUDNN_MH_ATTN_O_WEIGHTS = 3, /* output projection weights */
+    CUDNN_MH_ATTN_Q_BIASES  = 4, /* input projection bias tensor for 'queries' */
+    CUDNN_MH_ATTN_K_BIASES  = 5, /* input projection bias for 'keys' */
+    CUDNN_MH_ATTN_V_BIASES  = 6, /* input projection bias for 'values' */
+    CUDNN_MH_ATTN_O_BIASES  = 7, /* output projection biases */
+} cudnnMultiHeadAttnWeightKind_t;
+
+#define CUDNN_ATTN_WKIND_COUNT 8 /* Number of attention weight/bias tensors */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetMultiHeadAttnWeights(cudnnHandle_t handle,
+                             const cudnnAttnDescriptor_t attnDesc,
+                             cudnnMultiHeadAttnWeightKind_t wKind,
+                             size_t weightSizeInBytes,
+                             const void *weights,
+                             cudnnTensorDescriptor_t wDesc,
+                             void **wAddr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnMultiHeadAttnForward(cudnnHandle_t handle,
+                          const cudnnAttnDescriptor_t attnDesc,
+                          int currIdx,
+                          const int loWinIdx[],
+                          const int hiWinIdx[],
+                          const int devSeqLengthsQO[],
+                          const int devSeqLengthsKV[],
+                          const cudnnSeqDataDescriptor_t qDesc,
+                          const void *queries,
+                          const void *residuals,
+                          const cudnnSeqDataDescriptor_t kDesc,
+                          const void *keys,
+                          const cudnnSeqDataDescriptor_t vDesc,
+                          const void *values,
+                          const cudnnSeqDataDescriptor_t oDesc,
+                          void *out,
+                          size_t weightSizeInBytes,
+                          const void *weights,
+                          size_t workSpaceSizeInBytes,
+                          void *workSpace,
+                          size_t reserveSpaceSizeInBytes,
+                          void *reserveSpace);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnAdvInferVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_ADV_INFER_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_adv_infer_v8.h

@@ -0,0 +1,658 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cudnn_adv_infer : cuDNN's advanced and experimental features.
+
+*/
+
+#if !defined(CUDNN_ADV_INFER_H_)
+#define CUDNN_ADV_INFER_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_ADV_INFER_MAJOR 8
+#define CUDNN_ADV_INFER_MINOR 9
+#define CUDNN_ADV_INFER_PATCH 2
+
+#if (CUDNN_ADV_INFER_MAJOR != CUDNN_MAJOR) || (CUDNN_ADV_INFER_MINOR != CUDNN_MINOR) || \
+    (CUDNN_ADV_INFER_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN ADV INFER!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* BASIC RNN API */
+
+typedef enum {
+    CUDNN_FWD_MODE_INFERENCE = 0,
+    CUDNN_FWD_MODE_TRAINING  = 1,
+} cudnnForwardMode_t;
+
+typedef enum {
+    CUDNN_RNN_RELU = 0, /* basic RNN cell type with ReLu activation */
+    CUDNN_RNN_TANH = 1, /* basic RNN cell type with tanh activation */
+    CUDNN_LSTM     = 2, /* LSTM with optional recurrent projection and clipping */
+    CUDNN_GRU      = 3, /* Using h' = tanh(r * Uh(t-1) + Wx) and h = (1 - z) * h' + z * h(t-1); */
+} cudnnRNNMode_t;
+
+typedef enum {
+    CUDNN_RNN_NO_BIAS         = 0, /* rnn cell formulas do not use biases */
+    CUDNN_RNN_SINGLE_INP_BIAS = 1, /* rnn cell formulas use one input bias in input GEMM */
+    CUDNN_RNN_DOUBLE_BIAS     = 2, /* default, rnn cell formulas use two bias vectors */
+    CUDNN_RNN_SINGLE_REC_BIAS = 3  /* rnn cell formulas use one recurrent bias in recurrent GEMM */
+} cudnnRNNBiasMode_t;
+
+typedef enum {
+    CUDNN_UNIDIRECTIONAL = 0, /* single direction network */
+    CUDNN_BIDIRECTIONAL  = 1, /* output concatination at each layer */
+} cudnnDirectionMode_t;
+
+typedef enum {
+    CUDNN_LINEAR_INPUT = 0, /* adjustable weight matrix in first layer input GEMM */
+    CUDNN_SKIP_INPUT   = 1, /* fixed identity matrix in the first layer input GEMM */
+} cudnnRNNInputMode_t;
+
+typedef enum {
+    CUDNN_RNN_CLIP_NONE   = 0, /* disables LSTM cell clipping */
+    CUDNN_RNN_CLIP_MINMAX = 1, /* enables LSTM cell clipping */
+} cudnnRNNClipMode_t;
+
+typedef enum {
+    CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED   = 0, /* padded, outer stride from one time-step to the next */
+    CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_PACKED     = 1, /* sequence length sorted and packed as in basic RNN api */
+    CUDNN_RNN_DATA_LAYOUT_BATCH_MAJOR_UNPACKED = 2, /* padded, outer stride from one batch to the next */
+} cudnnRNNDataLayout_t;
+
+/* Legacy type for backward compatibility */
+typedef unsigned cudnnRNNPaddingMode_t;
+
+/* For auxFlags in cudnnSetRNNDescriptor_v8() and cudnnSetRNNPaddingMode() */
+#define CUDNN_RNN_PADDED_IO_DISABLED 0
+#define CUDNN_RNN_PADDED_IO_ENABLED (1U << 0)
+
+struct cudnnRNNStruct;
+typedef struct cudnnRNNStruct *cudnnRNNDescriptor_t;
+
+struct cudnnPersistentRNNPlan;
+typedef struct cudnnPersistentRNNPlan *cudnnPersistentRNNPlan_t;
+
+struct cudnnRNNDataStruct;
+typedef struct cudnnRNNDataStruct *cudnnRNNDataDescriptor_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateRNNDescriptor(cudnnRNNDescriptor_t *rnnDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyRNNDescriptor(cudnnRNNDescriptor_t rnnDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNDescriptor_v8(cudnnRNNDescriptor_t rnnDesc,
+                         cudnnRNNAlgo_t algo,
+                         cudnnRNNMode_t cellMode,
+                         cudnnRNNBiasMode_t biasMode,
+                         cudnnDirectionMode_t dirMode,
+                         cudnnRNNInputMode_t inputMode,
+                         cudnnDataType_t dataType,
+                         cudnnDataType_t mathPrec,
+                         cudnnMathType_t mathType,
+                         int32_t inputSize,
+                         int32_t hiddenSize,
+                         int32_t projSize,
+                         int32_t numLayers,
+                         cudnnDropoutDescriptor_t dropoutDesc,
+                         uint32_t auxFlags);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNDescriptor_v8(cudnnRNNDescriptor_t rnnDesc,
+                         cudnnRNNAlgo_t *algo,
+                         cudnnRNNMode_t *cellMode,
+                         cudnnRNNBiasMode_t *biasMode,
+                         cudnnDirectionMode_t *dirMode,
+                         cudnnRNNInputMode_t *inputMode,
+                         cudnnDataType_t *dataType,
+                         cudnnDataType_t *mathPrec,
+                         cudnnMathType_t *mathType,
+                         int32_t *inputSize,
+                         int32_t *hiddenSize,
+                         int32_t *projSize,
+                         int32_t *numLayers,
+                         cudnnDropoutDescriptor_t *dropoutDesc,
+                         uint32_t *auxFlags);
+
+/*
+ * mathPrec in cudnnSetRNNDescriptor_v6() specifies compute precision
+ * compute precision is further modified by cudnnSetRNNMatrixMathType()
+ * dataType in cudnnGetRNNParamsSize() and wDesc specify weight storage
+ * dropout is between RNN layers, not between recurrent steps
+ */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNDescriptor_v6(cudnnHandle_t handle,
+                         cudnnRNNDescriptor_t rnnDesc,
+                         const int hiddenSize,
+                         const int numLayers,
+                         cudnnDropoutDescriptor_t dropoutDesc,
+                         cudnnRNNInputMode_t inputMode,
+                         cudnnDirectionMode_t direction,
+                         cudnnRNNMode_t cellMode,
+                         cudnnRNNAlgo_t algo,
+                         cudnnDataType_t mathPrec);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNDescriptor_v6(cudnnHandle_t handle,
+                         cudnnRNNDescriptor_t rnnDesc,
+                         int *hiddenSize,
+                         int *numLayers,
+                         cudnnDropoutDescriptor_t *dropoutDesc,
+                         cudnnRNNInputMode_t *inputMode,
+                         cudnnDirectionMode_t *direction,
+                         cudnnRNNMode_t *cellMode,
+                         cudnnRNNAlgo_t *algo,
+                         cudnnDataType_t *mathPrec);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNMatrixMathType(cudnnRNNDescriptor_t rnnDesc, cudnnMathType_t mType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNMatrixMathType(cudnnRNNDescriptor_t rnnDesc, cudnnMathType_t *mType);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNBiasMode(cudnnRNNDescriptor_t rnnDesc, cudnnRNNBiasMode_t biasMode);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNBiasMode(cudnnRNNDescriptor_t rnnDesc, cudnnRNNBiasMode_t *biasMode);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNSetClip_v8(cudnnRNNDescriptor_t rnnDesc,
+                   cudnnRNNClipMode_t clipMode,
+                   cudnnNanPropagation_t clipNanOpt,
+                   double lclip,
+                   double rclip);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNGetClip_v8(cudnnRNNDescriptor_t rnnDesc,
+                   cudnnRNNClipMode_t *clipMode,
+                   cudnnNanPropagation_t *clipNanOpt,
+                   double *lclip,
+                   double *rclip);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNSetClip(cudnnHandle_t handle,
+                cudnnRNNDescriptor_t rnnDesc,
+                cudnnRNNClipMode_t clipMode,
+                cudnnNanPropagation_t clipNanOpt,
+                double lclip,
+                double rclip);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNGetClip(cudnnHandle_t handle,
+                cudnnRNNDescriptor_t rnnDesc,
+                cudnnRNNClipMode_t *clipMode,
+                cudnnNanPropagation_t *clipNanOpt,
+                double *lclip,
+                double *rclip);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNProjectionLayers(cudnnHandle_t handle,
+                            cudnnRNNDescriptor_t rnnDesc,
+                            const int recProjSize,
+                            const int outProjSize);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNProjectionLayers(cudnnHandle_t handle,
+                            const cudnnRNNDescriptor_t rnnDesc,
+                            int *recProjSize,
+                            int *outProjSize);
+
+/* Expensive. Creates the plan for the specific settings. */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreatePersistentRNNPlan(cudnnRNNDescriptor_t rnnDesc,
+                             const int minibatch,
+                             const cudnnDataType_t dataType,
+                             cudnnPersistentRNNPlan_t *plan);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyPersistentRNNPlan(cudnnPersistentRNNPlan_t plan);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetPersistentRNNPlan(cudnnRNNDescriptor_t rnnDesc, cudnnPersistentRNNPlan_t plan);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBuildRNNDynamic(cudnnHandle_t handle, cudnnRNNDescriptor_t rnnDesc, int miniBatch);
+
+/* dataType in weight descriptors and input descriptors is used to describe storage */
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNWorkspaceSize(cudnnHandle_t handle,
+                         const cudnnRNNDescriptor_t rnnDesc,
+                         const int seqLength,
+                         const cudnnTensorDescriptor_t *xDesc,
+                         size_t *sizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNTrainingReserveSize(cudnnHandle_t handle,
+                               const cudnnRNNDescriptor_t rnnDesc,
+                               const int seqLength,
+                               const cudnnTensorDescriptor_t *xDesc,
+                               size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNTempSpaceSizes(cudnnHandle_t handle,
+                          cudnnRNNDescriptor_t rnnDesc,
+                          cudnnForwardMode_t fwdMode,
+                          cudnnRNNDataDescriptor_t xDesc,
+                          size_t *workSpaceSize,
+                          size_t *reserveSpaceSize);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNParamsSize(cudnnHandle_t handle,
+                      const cudnnRNNDescriptor_t rnnDesc,
+                      const cudnnTensorDescriptor_t xDesc,
+                      size_t *sizeInBytes,
+                      cudnnDataType_t dataType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNWeightSpaceSize(cudnnHandle_t handle, cudnnRNNDescriptor_t rnnDesc, size_t *weightSpaceSize);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNLinLayerMatrixParams(cudnnHandle_t handle,
+                                const cudnnRNNDescriptor_t rnnDesc,
+                                const int pseudoLayer,
+                                const cudnnTensorDescriptor_t xDesc,
+                                const cudnnFilterDescriptor_t wDesc,
+                                const void *w,
+                                const int linLayerID,
+                                cudnnFilterDescriptor_t linLayerMatDesc,
+                                void **linLayerMat);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNLinLayerBiasParams(cudnnHandle_t handle,
+                              const cudnnRNNDescriptor_t rnnDesc,
+                              const int pseudoLayer,
+                              const cudnnTensorDescriptor_t xDesc,
+                              const cudnnFilterDescriptor_t wDesc,
+                              const void *w,
+                              const int linLayerID,
+                              cudnnFilterDescriptor_t linLayerBiasDesc,
+                              void **linLayerBias);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNWeightParams(cudnnHandle_t handle,
+                        cudnnRNNDescriptor_t rnnDesc,
+                        int32_t pseudoLayer,
+                        size_t weightSpaceSize,
+                        const void *weightSpace,
+                        int32_t linLayerID,
+                        cudnnTensorDescriptor_t mDesc,
+                        void **mAddr,
+                        cudnnTensorDescriptor_t bDesc,
+                        void **bAddr);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNForwardInference(cudnnHandle_t handle,
+                         const cudnnRNNDescriptor_t rnnDesc,
+                         const int seqLength,
+                         const cudnnTensorDescriptor_t *xDesc,
+                         const void *x,
+                         const cudnnTensorDescriptor_t hxDesc,
+                         const void *hx,
+                         const cudnnTensorDescriptor_t cxDesc,
+                         const void *cx,
+                         const cudnnFilterDescriptor_t wDesc,
+                         const void *w,
+                         const cudnnTensorDescriptor_t *yDesc,
+                         void *y,
+                         const cudnnTensorDescriptor_t hyDesc,
+                         void *hy,
+                         const cudnnTensorDescriptor_t cyDesc,
+                         void *cy,
+                         void *workSpace,
+                         size_t workSpaceSizeInBytes);
+
+/* RNN EX API */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNPaddingMode(cudnnRNNDescriptor_t rnnDesc, unsigned paddingMode);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNPaddingMode(cudnnRNNDescriptor_t rnnDesc, unsigned *paddingMode);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateRNNDataDescriptor(cudnnRNNDataDescriptor_t *rnnDataDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc,
+                          cudnnDataType_t dataType,
+                          cudnnRNNDataLayout_t layout,
+                          int maxSeqLength,
+                          int batchSize,
+                          int vectorSize,
+                          const int seqLengthArray[], /* length of each sequence in the batch */
+                          void *paddingFill);         /* symbol for filling padding position in output */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc,
+                          cudnnDataType_t *dataType,
+                          cudnnRNNDataLayout_t *layout,
+                          int *maxSeqLength,
+                          int *batchSize,
+                          int *vectorSize,
+                          int arrayLengthRequested,
+                          int seqLengthArray[],
+                          void *paddingFill);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNForwardInferenceEx(cudnnHandle_t handle,
+                           const cudnnRNNDescriptor_t rnnDesc,
+                           const cudnnRNNDataDescriptor_t xDesc,
+                           const void *x,
+                           const cudnnTensorDescriptor_t hxDesc,
+                           const void *hx,
+                           const cudnnTensorDescriptor_t cxDesc,
+                           const void *cx,
+                           const cudnnFilterDescriptor_t wDesc,
+                           const void *w,
+                           const cudnnRNNDataDescriptor_t yDesc,
+                           void *y,
+                           const cudnnTensorDescriptor_t hyDesc,
+                           void *hy,
+                           const cudnnTensorDescriptor_t cyDesc,
+                           void *cy,
+                           const cudnnRNNDataDescriptor_t kDesc, /* reserved, should pass NULL */
+                           const void *keys,                     /* reserved, should pass NULL */
+                           const cudnnRNNDataDescriptor_t cDesc, /* reserved, should pass NULL */
+                           void *cAttn,                          /* reserved, should pass NULL */
+                           const cudnnRNNDataDescriptor_t iDesc, /* reserved, should pass NULL */
+                           void *iAttn,                          /* reserved, should pass NULL */
+                           const cudnnRNNDataDescriptor_t qDesc, /* reserved, should pass NULL */
+                           void *queries,                        /* reserved, should pass NULL */
+                           void *workSpace,
+                           size_t workSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNForward(cudnnHandle_t handle,
+                cudnnRNNDescriptor_t rnnDesc,
+                cudnnForwardMode_t fwdMode,
+                const int32_t devSeqLengths[],
+                cudnnRNNDataDescriptor_t xDesc,
+                const void *x,
+                cudnnRNNDataDescriptor_t yDesc,
+                void *y,
+                cudnnTensorDescriptor_t hDesc,
+                const void *hx,
+                void *hy,
+                cudnnTensorDescriptor_t cDesc,
+                const void *cx,
+                void *cy,
+                size_t weightSpaceSize,
+                const void *weightSpace,
+                size_t workSpaceSize,
+                void *workSpace,
+                size_t reserveSpaceSize,
+                void *reserveSpace);
+
+/* RNN FIND API */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetRNNAlgorithmDescriptor(cudnnHandle_t handle, cudnnRNNDescriptor_t rnnDesc, cudnnAlgorithmDescriptor_t algoDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNForwardInferenceAlgorithmMaxCount(cudnnHandle_t handle, const cudnnRNNDescriptor_t rnnDesc, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindRNNForwardInferenceAlgorithmEx(cudnnHandle_t handle,
+                                        const cudnnRNNDescriptor_t rnnDesc,
+                                        const int seqLength,
+                                        const cudnnTensorDescriptor_t *xDesc,
+                                        const void *x,
+                                        const cudnnTensorDescriptor_t hxDesc,
+                                        const void *hx,
+                                        const cudnnTensorDescriptor_t cxDesc,
+                                        const void *cx,
+                                        const cudnnFilterDescriptor_t wDesc,
+                                        const void *w,
+                                        const cudnnTensorDescriptor_t *yDesc,
+                                        void *y,
+                                        const cudnnTensorDescriptor_t hyDesc,
+                                        void *hy,
+                                        const cudnnTensorDescriptor_t cyDesc,
+                                        void *cy,
+                                        const float findIntensity,
+                                        const int requestedAlgoCount,
+                                        int *returnedAlgoCount,
+                                        cudnnAlgorithmPerformance_t *perfResults,
+                                        void *workspace,
+                                        size_t workSpaceSizeInBytes);
+
+/* Sequence data descriptor */
+
+typedef enum {
+    CUDNN_SEQDATA_TIME_DIM  = 0, /* index in time */
+    CUDNN_SEQDATA_BATCH_DIM = 1, /* index in batch */
+    CUDNN_SEQDATA_BEAM_DIM  = 2, /* index in beam */
+    CUDNN_SEQDATA_VECT_DIM  = 3  /* index in vector */
+} cudnnSeqDataAxis_t;
+
+struct cudnnSeqDataStruct;
+typedef struct cudnnSeqDataStruct *cudnnSeqDataDescriptor_t;
+
+#define CUDNN_SEQDATA_DIM_COUNT 4 /* dimension count */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateSeqDataDescriptor(cudnnSeqDataDescriptor_t *seqDataDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroySeqDataDescriptor(cudnnSeqDataDescriptor_t seqDataDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetSeqDataDescriptor(cudnnSeqDataDescriptor_t seqDataDesc,
+                          cudnnDataType_t dataType,
+                          int nbDims,
+                          const int dimA[],
+                          const cudnnSeqDataAxis_t axes[],
+                          size_t seqLengthArraySize,
+                          const int seqLengthArray[],
+                          void *paddingFill);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetSeqDataDescriptor(const cudnnSeqDataDescriptor_t seqDataDesc,
+                          cudnnDataType_t *dataType,
+                          int *nbDims,
+                          int nbDimsRequested,
+                          int dimA[],
+                          cudnnSeqDataAxis_t axes[],
+                          size_t *seqLengthArraySize,
+                          size_t seqLengthSizeRequested,
+                          int seqLengthArray[],
+                          void *paddingFill);
+
+/* Multihead Attention */
+
+/* Legacy type for backward compatibility */
+typedef unsigned cudnnAttnQueryMap_t;
+
+/*
+ * Multi-head attention options passed via 'attnMode' in cudnnSetAttnDescriptor().
+ * Use the bitwise OR operator to combine several settings listed below.  Additional
+ * minor options can be added here w/o changing or introducing new API functions.
+ */
+#define CUDNN_ATTN_QUERYMAP_ALL_TO_ONE 0         /* multiple Q-s map to a single (K,V) set when beam size > 1 */
+#define CUDNN_ATTN_QUERYMAP_ONE_TO_ONE (1U << 0) /* multiple Q-s map to multiple (K,V) sets when beam size > 1 */
+#define CUDNN_ATTN_DISABLE_PROJ_BIASES 0         /* no biases in attention input and output projections */
+#define CUDNN_ATTN_ENABLE_PROJ_BIASES (1U << 1)  /* use biases in attention input and output projections */
+
+struct cudnnAttnStruct;
+typedef struct cudnnAttnStruct *cudnnAttnDescriptor_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateAttnDescriptor(cudnnAttnDescriptor_t *attnDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyAttnDescriptor(cudnnAttnDescriptor_t attnDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetAttnDescriptor(cudnnAttnDescriptor_t attnDesc,
+                       unsigned attnMode,
+                       int nHeads,
+                       double smScaler,
+                       cudnnDataType_t dataType,
+                       cudnnDataType_t computePrec,
+                       cudnnMathType_t mathType,
+                       cudnnDropoutDescriptor_t attnDropoutDesc,
+                       cudnnDropoutDescriptor_t postDropoutDesc,
+                       int qSize,
+                       int kSize,
+                       int vSize,
+                       int qProjSize,
+                       int kProjSize,
+                       int vProjSize,
+                       int oProjSize,
+                       int qoMaxSeqLength,
+                       int kvMaxSeqLength,
+                       int maxBatchSize,
+                       int maxBeamSize);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetAttnDescriptor(cudnnAttnDescriptor_t attnDesc,
+                       unsigned *attnMode,
+                       int *nHeads,
+                       double *smScaler,
+                       cudnnDataType_t *dataType,
+                       cudnnDataType_t *computePrec,
+                       cudnnMathType_t *mathType,
+                       cudnnDropoutDescriptor_t *attnDropoutDesc,
+                       cudnnDropoutDescriptor_t *postDropoutDesc,
+                       int *qSize,
+                       int *kSize,
+                       int *vSize,
+                       int *qProjSize,
+                       int *kProjSize,
+                       int *vProjSize,
+                       int *oProjSize,
+                       int *qoMaxSeqLength,
+                       int *kvMaxSeqLength,
+                       int *maxBatchSize,
+                       int *maxBeamSize);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetMultiHeadAttnBuffers(cudnnHandle_t handle,
+                             const cudnnAttnDescriptor_t attnDesc,
+                             size_t *weightSizeInBytes,
+                             size_t *workSpaceSizeInBytes,
+                             size_t *reserveSpaceSizeInBytes);
+
+typedef enum {
+    CUDNN_MH_ATTN_Q_WEIGHTS = 0, /* input projection weights for 'queries' */
+    CUDNN_MH_ATTN_K_WEIGHTS = 1, /* input projection weights for 'keys' */
+    CUDNN_MH_ATTN_V_WEIGHTS = 2, /* input projection weights for 'values' */
+    CUDNN_MH_ATTN_O_WEIGHTS = 3, /* output projection weights */
+    CUDNN_MH_ATTN_Q_BIASES  = 4, /* input projection bias tensor for 'queries' */
+    CUDNN_MH_ATTN_K_BIASES  = 5, /* input projection bias for 'keys' */
+    CUDNN_MH_ATTN_V_BIASES  = 6, /* input projection bias for 'values' */
+    CUDNN_MH_ATTN_O_BIASES  = 7, /* output projection biases */
+} cudnnMultiHeadAttnWeightKind_t;
+
+#define CUDNN_ATTN_WKIND_COUNT 8 /* Number of attention weight/bias tensors */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetMultiHeadAttnWeights(cudnnHandle_t handle,
+                             const cudnnAttnDescriptor_t attnDesc,
+                             cudnnMultiHeadAttnWeightKind_t wKind,
+                             size_t weightSizeInBytes,
+                             const void *weights,
+                             cudnnTensorDescriptor_t wDesc,
+                             void **wAddr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnMultiHeadAttnForward(cudnnHandle_t handle,
+                          const cudnnAttnDescriptor_t attnDesc,
+                          int currIdx,
+                          const int loWinIdx[],
+                          const int hiWinIdx[],
+                          const int devSeqLengthsQO[],
+                          const int devSeqLengthsKV[],
+                          const cudnnSeqDataDescriptor_t qDesc,
+                          const void *queries,
+                          const void *residuals,
+                          const cudnnSeqDataDescriptor_t kDesc,
+                          const void *keys,
+                          const cudnnSeqDataDescriptor_t vDesc,
+                          const void *values,
+                          const cudnnSeqDataDescriptor_t oDesc,
+                          void *out,
+                          size_t weightSizeInBytes,
+                          const void *weights,
+                          size_t workSpaceSizeInBytes,
+                          void *workSpace,
+                          size_t reserveSpaceSizeInBytes,
+                          void *reserveSpace);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnAdvInferVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_ADV_INFER_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_adv_train.h

@@ -0,0 +1,540 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cudnn_adv_train : cuDNN's advanced and experimental features.
+
+*/
+
+#if !defined(CUDNN_ADV_TRAIN_H_)
+#define CUDNN_ADV_TRAIN_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+#include "cudnn_ops_train.h"
+#include "cudnn_adv_infer.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_ADV_TRAIN_MAJOR 8
+#define CUDNN_ADV_TRAIN_MINOR 9
+#define CUDNN_ADV_TRAIN_PATCH 2
+
+#if (CUDNN_ADV_TRAIN_MAJOR != CUDNN_MAJOR) || (CUDNN_ADV_TRAIN_MINOR != CUDNN_MINOR) || \
+    (CUDNN_ADV_TRAIN_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN ADV TRAIN!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+typedef enum {
+    CUDNN_WGRAD_MODE_ADD = 0, /* add partial gradients to wgrad output buffers */
+    CUDNN_WGRAD_MODE_SET = 1, /* write partial gradients to wgrad output buffers */
+} cudnnWgradMode_t;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNForwardTraining(cudnnHandle_t handle,
+                        const cudnnRNNDescriptor_t rnnDesc,
+                        const int seqLength,
+                        const cudnnTensorDescriptor_t *xDesc,
+                        const void *x,
+                        const cudnnTensorDescriptor_t hxDesc,
+                        const void *hx,
+                        const cudnnTensorDescriptor_t cxDesc,
+                        const void *cx,
+                        const cudnnFilterDescriptor_t wDesc,
+                        const void *w,
+                        const cudnnTensorDescriptor_t *yDesc,
+                        void *y,
+                        const cudnnTensorDescriptor_t hyDesc,
+                        void *hy,
+                        const cudnnTensorDescriptor_t cyDesc,
+                        void *cy,
+                        void *workSpace,
+                        size_t workSpaceSizeInBytes,
+                        void *reserveSpace,
+                        size_t reserveSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardData(cudnnHandle_t handle,
+                     const cudnnRNNDescriptor_t rnnDesc,
+                     const int seqLength,
+                     const cudnnTensorDescriptor_t *yDesc,
+                     const void *y,
+                     const cudnnTensorDescriptor_t *dyDesc,
+                     const void *dy,
+                     const cudnnTensorDescriptor_t dhyDesc,
+                     const void *dhy,
+                     const cudnnTensorDescriptor_t dcyDesc,
+                     const void *dcy,
+                     const cudnnFilterDescriptor_t wDesc,
+                     const void *w,
+                     const cudnnTensorDescriptor_t hxDesc,
+                     const void *hx,
+                     const cudnnTensorDescriptor_t cxDesc,
+                     const void *cx,
+                     const cudnnTensorDescriptor_t *dxDesc,
+                     void *dx,
+                     const cudnnTensorDescriptor_t dhxDesc,
+                     void *dhx,
+                     const cudnnTensorDescriptor_t dcxDesc,
+                     void *dcx,
+                     void *workSpace,
+                     size_t workSpaceSizeInBytes,
+                     void *reserveSpace,
+                     size_t reserveSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardData_v8(cudnnHandle_t handle,
+                        cudnnRNNDescriptor_t rnnDesc,
+                        const int32_t devSeqLengths[],
+                        cudnnRNNDataDescriptor_t yDesc,
+                        const void *y,
+                        const void *dy,
+                        cudnnRNNDataDescriptor_t xDesc,
+                        void *dx,
+                        cudnnTensorDescriptor_t hDesc,
+                        const void *hx,
+                        const void *dhy,
+                        void *dhx,
+                        cudnnTensorDescriptor_t cDesc,
+                        const void *cx,
+                        const void *dcy,
+                        void *dcx,
+                        size_t weightSpaceSize,
+                        const void *weightSpace,
+                        size_t workSpaceSize,
+                        void *workSpace,
+                        size_t reserveSpaceSize,
+                        void *reserveSpace);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardWeights(cudnnHandle_t handle,
+                        const cudnnRNNDescriptor_t rnnDesc,
+                        const int seqLength,
+                        const cudnnTensorDescriptor_t *xDesc,
+                        const void *x,
+                        const cudnnTensorDescriptor_t hxDesc,
+                        const void *hx,
+                        const cudnnTensorDescriptor_t *yDesc,
+                        const void *y,
+                        const void *workSpace,
+                        size_t workSpaceSizeInBytes,
+                        const cudnnFilterDescriptor_t dwDesc,
+                        void *dw,
+                        const void *reserveSpace,
+                        size_t reserveSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardWeights_v8(cudnnHandle_t handle,
+                           cudnnRNNDescriptor_t rnnDesc,
+                           cudnnWgradMode_t addGrad,
+                           const int32_t devSeqLengths[],
+                           cudnnRNNDataDescriptor_t xDesc,
+                           const void *x,
+                           cudnnTensorDescriptor_t hDesc,
+                           const void *hx,
+                           cudnnRNNDataDescriptor_t yDesc,
+                           const void *y,
+                           size_t weightSpaceSize,
+                           void *dweightSpace,
+                           size_t workSpaceSize,
+                           void *workSpace,
+                           size_t reserveSpaceSize,
+                           void *reserveSpace);
+
+/* RNN EX API */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNForwardTrainingEx(cudnnHandle_t handle,
+                          const cudnnRNNDescriptor_t rnnDesc,
+                          const cudnnRNNDataDescriptor_t xDesc,
+                          const void *x,
+                          const cudnnTensorDescriptor_t hxDesc,
+                          const void *hx,
+                          const cudnnTensorDescriptor_t cxDesc,
+                          const void *cx,
+                          const cudnnFilterDescriptor_t wDesc,
+                          const void *w,
+                          const cudnnRNNDataDescriptor_t yDesc,
+                          void *y,
+                          const cudnnTensorDescriptor_t hyDesc,
+                          void *hy,
+                          const cudnnTensorDescriptor_t cyDesc,
+                          void *cy,
+                          const cudnnRNNDataDescriptor_t kDesc, /* reserved, should pass NULL */
+                          const void *keys,                     /* reserved, should pass NULL */
+                          const cudnnRNNDataDescriptor_t cDesc, /* reserved, should pass NULL */
+                          void *cAttn,                          /* reserved, should pass NULL */
+                          const cudnnRNNDataDescriptor_t iDesc, /* reserved, should pass NULL */
+                          void *iAttn,                          /* reserved, should pass NULL */
+                          const cudnnRNNDataDescriptor_t qDesc, /* reserved, should pass NULL */
+                          void *queries,                        /* reserved, should pass NULL */
+                          void *workSpace,
+                          size_t workSpaceSizeInBytes,
+                          void *reserveSpace,
+                          size_t reserveSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardDataEx(cudnnHandle_t handle,
+                       const cudnnRNNDescriptor_t rnnDesc,
+                       const cudnnRNNDataDescriptor_t yDesc,
+                       const void *y,
+                       const cudnnRNNDataDescriptor_t dyDesc,
+                       const void *dy,
+                       const cudnnRNNDataDescriptor_t dcDesc, /* reserved, should pass NULL */
+                       const void *dcAttn,                    /* reserved, should pass NULL */
+                       const cudnnTensorDescriptor_t dhyDesc,
+                       const void *dhy,
+                       const cudnnTensorDescriptor_t dcyDesc,
+                       const void *dcy,
+                       const cudnnFilterDescriptor_t wDesc,
+                       const void *w,
+                       const cudnnTensorDescriptor_t hxDesc,
+                       const void *hx,
+                       const cudnnTensorDescriptor_t cxDesc,
+                       const void *cx,
+                       const cudnnRNNDataDescriptor_t dxDesc,
+                       void *dx,
+                       const cudnnTensorDescriptor_t dhxDesc,
+                       void *dhx,
+                       const cudnnTensorDescriptor_t dcxDesc,
+                       void *dcx,
+                       const cudnnRNNDataDescriptor_t dkDesc, /* reserved, should pass NULL */
+                       void *dkeys,                           /* reserved, should pass NULL */
+                       void *workSpace,
+                       size_t workSpaceSizeInBytes,
+                       void *reserveSpace,
+                       size_t reserveSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardWeightsEx(cudnnHandle_t handle,
+                          const cudnnRNNDescriptor_t rnnDesc,
+                          const cudnnRNNDataDescriptor_t xDesc,
+                          const void *x,
+                          const cudnnTensorDescriptor_t hxDesc,
+                          const void *hx,
+                          const cudnnRNNDataDescriptor_t yDesc,
+                          const void *y,
+                          void *workSpace,
+                          size_t workSpaceSizeInBytes,
+                          const cudnnFilterDescriptor_t dwDesc,
+                          void *dw,
+                          void *reserveSpace,
+                          size_t reserveSpaceSizeInBytes);
+
+/* RNN FIND API */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNForwardTrainingAlgorithmMaxCount(cudnnHandle_t handle, const cudnnRNNDescriptor_t rnnDesc, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindRNNForwardTrainingAlgorithmEx(cudnnHandle_t handle,
+                                       const cudnnRNNDescriptor_t rnnDesc,
+                                       const int seqLength,
+                                       const cudnnTensorDescriptor_t *xDesc,
+                                       const void *x,
+                                       const cudnnTensorDescriptor_t hxDesc,
+                                       const void *hx,
+                                       const cudnnTensorDescriptor_t cxDesc,
+                                       const void *cx,
+                                       const cudnnFilterDescriptor_t wDesc,
+                                       const void *w,
+                                       const cudnnTensorDescriptor_t *yDesc,
+                                       void *y,
+                                       const cudnnTensorDescriptor_t hyDesc,
+                                       void *hy,
+                                       const cudnnTensorDescriptor_t cyDesc,
+                                       void *cy,
+                                       const float findIntensity,
+                                       const int requestedAlgoCount,
+                                       int *returnedAlgoCount,
+                                       cudnnAlgorithmPerformance_t *perfResults,
+                                       void *workspace,
+                                       size_t workSpaceSizeInBytes,
+                                       void *reserveSpace,
+                                       size_t reserveSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNBackwardDataAlgorithmMaxCount(cudnnHandle_t handle, const cudnnRNNDescriptor_t rnnDesc, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindRNNBackwardDataAlgorithmEx(cudnnHandle_t handle,
+                                    const cudnnRNNDescriptor_t rnnDesc,
+                                    const int seqLength,
+                                    const cudnnTensorDescriptor_t *yDesc,
+                                    const void *y,
+                                    const cudnnTensorDescriptor_t *dyDesc,
+                                    const void *dy,
+                                    const cudnnTensorDescriptor_t dhyDesc,
+                                    const void *dhy,
+                                    const cudnnTensorDescriptor_t dcyDesc,
+                                    const void *dcy,
+                                    const cudnnFilterDescriptor_t wDesc,
+                                    const void *w,
+                                    const cudnnTensorDescriptor_t hxDesc,
+                                    const void *hx,
+                                    const cudnnTensorDescriptor_t cxDesc,
+                                    const void *cx,
+                                    const cudnnTensorDescriptor_t *dxDesc,
+                                    void *dx,
+                                    const cudnnTensorDescriptor_t dhxDesc,
+                                    void *dhx,
+                                    const cudnnTensorDescriptor_t dcxDesc,
+                                    void *dcx,
+                                    const float findIntensity,
+                                    const int requestedAlgoCount,
+                                    int *returnedAlgoCount,
+                                    cudnnAlgorithmPerformance_t *perfResults,
+                                    void *workspace,
+                                    size_t workSpaceSizeInBytes,
+                                    void *reserveSpace,
+                                    size_t reserveSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNBackwardWeightsAlgorithmMaxCount(cudnnHandle_t handle, const cudnnRNNDescriptor_t rnnDesc, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindRNNBackwardWeightsAlgorithmEx(cudnnHandle_t handle,
+                                       const cudnnRNNDescriptor_t rnnDesc,
+                                       const int seqLength,
+                                       const cudnnTensorDescriptor_t *xDesc,
+                                       const void *x,
+                                       const cudnnTensorDescriptor_t hxDesc,
+                                       const void *hx,
+                                       const cudnnTensorDescriptor_t *yDesc,
+                                       const void *y,
+                                       const float findIntensity,
+                                       const int requestedAlgoCount,
+                                       int *returnedAlgoCount,
+                                       cudnnAlgorithmPerformance_t *perfResults,
+                                       const void *workspace,
+                                       size_t workSpaceSizeInBytes,
+                                       const cudnnFilterDescriptor_t dwDesc,
+                                       void *dw,
+                                       const void *reserveSpace,
+                                       size_t reserveSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnMultiHeadAttnBackwardData(cudnnHandle_t handle,
+                               const cudnnAttnDescriptor_t attnDesc,
+                               const int loWinIdx[],
+                               const int hiWinIdx[],
+                               const int devSeqLengthsDQDO[],
+                               const int devSeqLengthsDKDV[],
+                               const cudnnSeqDataDescriptor_t doDesc,
+                               const void *dout,
+                               const cudnnSeqDataDescriptor_t dqDesc,
+                               void *dqueries,
+                               const void *queries,
+                               const cudnnSeqDataDescriptor_t dkDesc,
+                               void *dkeys,
+                               const void *keys,
+                               const cudnnSeqDataDescriptor_t dvDesc,
+                               void *dvalues,
+                               const void *values,
+                               size_t weightSizeInBytes,
+                               const void *weights,
+                               size_t workSpaceSizeInBytes,
+                               void *workSpace,
+                               size_t reserveSpaceSizeInBytes,
+                               void *reserveSpace);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnMultiHeadAttnBackwardWeights(cudnnHandle_t handle,
+                                  const cudnnAttnDescriptor_t attnDesc,
+                                  cudnnWgradMode_t addGrad,
+                                  const cudnnSeqDataDescriptor_t qDesc,
+                                  const void *queries,
+                                  const cudnnSeqDataDescriptor_t kDesc,
+                                  const void *keys,
+                                  const cudnnSeqDataDescriptor_t vDesc,
+                                  const void *values,
+                                  const cudnnSeqDataDescriptor_t doDesc,
+                                  const void *dout,
+                                  size_t weightSizeInBytes,
+                                  const void *weights,
+                                  void *dweights,
+                                  size_t workSpaceSizeInBytes,
+                                  void *workSpace,
+                                  size_t reserveSpaceSizeInBytes,
+                                  void *reserveSpace);
+
+/*
+* CTC (Connectionist Temporal Classification) loss descriptor create/destory/set/get functions
+*/
+/* Input normalization mode for loss function */
+typedef enum {
+    CUDNN_LOSS_NORMALIZATION_NONE    = 0,
+    CUDNN_LOSS_NORMALIZATION_SOFTMAX = 1,
+} cudnnLossNormalizationMode_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateCTCLossDescriptor(cudnnCTCLossDescriptor_t *ctcLossDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc, cudnnDataType_t compType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptorEx(cudnnCTCLossDescriptor_t ctcLossDesc,
+                            cudnnDataType_t compType,
+                            cudnnLossNormalizationMode_t normMode,
+                            cudnnNanPropagation_t gradMode);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptor_v8(cudnnCTCLossDescriptor_t ctcLossDesc,
+                             cudnnDataType_t compType,
+                             cudnnLossNormalizationMode_t normMode,
+                             cudnnNanPropagation_t gradMode,
+                             int maxLabelLength);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc, cudnnDataType_t *compType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptorEx(cudnnCTCLossDescriptor_t ctcLossDesc,
+                            cudnnDataType_t *compType,
+                            cudnnLossNormalizationMode_t *normMode,
+                            cudnnNanPropagation_t *gradMode);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptor_v8(cudnnCTCLossDescriptor_t ctcLossDesc,
+                             cudnnDataType_t *compType,
+                             cudnnLossNormalizationMode_t *normMode,
+                             cudnnNanPropagation_t *gradMode,
+                             int *maxLabelLength);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc);
+
+/* return the ctc costs and gradients, given the probabilities and labels */
+cudnnStatus_t CUDNNWINAPI
+cudnnCTCLoss(
+    cudnnHandle_t handle,
+    const cudnnTensorDescriptor_t
+        probsDesc,     /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the timing steps, N is the
+                          mini batch size, A is the alphabet size)  */
+    const void *probs, /* probabilities after softmax, in GPU memory */
+    const int hostLabels[],                      /* labels, in CPU memory */
+    const int hostLabelLengths[],                /* the length of each label, in CPU memory */
+    const int hostInputLengths[],                /* the lengths of timing steps in each batch, in CPU memory */
+    void *costs,                                 /* the returned costs of CTC, in GPU memory */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the dimensions are T,N,A */
+    void *gradients,         /* the returned CTC gradients, in GPU memory, to compute costs only, set it to NULL */
+    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    void *workspace,              /* pointer to the workspace, in GPU memory */
+    size_t workSpaceSizeInBytes); /* size of the workspace */
+
+/* return the ctc costs and gradients, given the probabilities and labels */
+cudnnStatus_t CUDNNWINAPI
+cudnnCTCLoss_v8(
+    cudnnHandle_t handle,
+    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    const cudnnTensorDescriptor_t
+        probsDesc,     /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the timing steps, N is the
+                          mini batch size, A is the alphabet size)  */
+    const void *probs, /* probabilities after softmax, in GPU memory */
+    const int labels[],                          /* labels, in GPU memory */
+    const int labelLengths[],                    /* the length of each label, in GPU memory */
+    const int inputLengths[],                    /* the lengths of timing steps in each batch, in GPU memory */
+    void *costs,                                 /* the returned costs of CTC, in GPU memory */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the dimensions are T,N,A */
+    void *gradients,             /* the returned CTC gradients, in GPU memory, to compute costs only, set it to NULL */
+    size_t workSpaceSizeInBytes, /* size of the workspace */
+    void *workspace);            /* pointer to the workspace, in GPU memory */
+
+/* return the workspace size needed for ctc */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossWorkspaceSize(
+    cudnnHandle_t handle,
+    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
+                                                timing steps, N is the mini batch size, A is the alphabet size) */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the
+                                                    dimensions are T,N,A. To compute costs
+                                                    only, set it to NULL */
+    const int *labels,                           /* labels, in CPU memory */
+    const int *labelLengths,                     /* the length of each label, in CPU memory */
+    const int *inputLengths,                     /* the lengths of timing steps in each batch, in CPU memory */
+    cudnnCTCLossAlgo_t algo,                     /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    size_t *sizeInBytes); /* pointer to the returned workspace size */
+
+/* return the workspace size needed for ctc */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossWorkspaceSize_v8(
+    cudnnHandle_t handle,
+    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
+                                                timing steps, N is the mini batch size, A is the alphabet size) */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the
+                                                    dimensions are T,N,A. To compute costs
+                                                    only, set it to NULL */
+    size_t *sizeInBytes);                        /* pointer to the returned workspace size */
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnAdvTrainVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_ADV_TRAIN_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_adv_train_v8.h

@@ -0,0 +1,540 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cudnn_adv_train : cuDNN's advanced and experimental features.
+
+*/
+
+#if !defined(CUDNN_ADV_TRAIN_H_)
+#define CUDNN_ADV_TRAIN_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+#include "cudnn_ops_train.h"
+#include "cudnn_adv_infer.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_ADV_TRAIN_MAJOR 8
+#define CUDNN_ADV_TRAIN_MINOR 9
+#define CUDNN_ADV_TRAIN_PATCH 2
+
+#if (CUDNN_ADV_TRAIN_MAJOR != CUDNN_MAJOR) || (CUDNN_ADV_TRAIN_MINOR != CUDNN_MINOR) || \
+    (CUDNN_ADV_TRAIN_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN ADV TRAIN!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+typedef enum {
+    CUDNN_WGRAD_MODE_ADD = 0, /* add partial gradients to wgrad output buffers */
+    CUDNN_WGRAD_MODE_SET = 1, /* write partial gradients to wgrad output buffers */
+} cudnnWgradMode_t;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNForwardTraining(cudnnHandle_t handle,
+                        const cudnnRNNDescriptor_t rnnDesc,
+                        const int seqLength,
+                        const cudnnTensorDescriptor_t *xDesc,
+                        const void *x,
+                        const cudnnTensorDescriptor_t hxDesc,
+                        const void *hx,
+                        const cudnnTensorDescriptor_t cxDesc,
+                        const void *cx,
+                        const cudnnFilterDescriptor_t wDesc,
+                        const void *w,
+                        const cudnnTensorDescriptor_t *yDesc,
+                        void *y,
+                        const cudnnTensorDescriptor_t hyDesc,
+                        void *hy,
+                        const cudnnTensorDescriptor_t cyDesc,
+                        void *cy,
+                        void *workSpace,
+                        size_t workSpaceSizeInBytes,
+                        void *reserveSpace,
+                        size_t reserveSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardData(cudnnHandle_t handle,
+                     const cudnnRNNDescriptor_t rnnDesc,
+                     const int seqLength,
+                     const cudnnTensorDescriptor_t *yDesc,
+                     const void *y,
+                     const cudnnTensorDescriptor_t *dyDesc,
+                     const void *dy,
+                     const cudnnTensorDescriptor_t dhyDesc,
+                     const void *dhy,
+                     const cudnnTensorDescriptor_t dcyDesc,
+                     const void *dcy,
+                     const cudnnFilterDescriptor_t wDesc,
+                     const void *w,
+                     const cudnnTensorDescriptor_t hxDesc,
+                     const void *hx,
+                     const cudnnTensorDescriptor_t cxDesc,
+                     const void *cx,
+                     const cudnnTensorDescriptor_t *dxDesc,
+                     void *dx,
+                     const cudnnTensorDescriptor_t dhxDesc,
+                     void *dhx,
+                     const cudnnTensorDescriptor_t dcxDesc,
+                     void *dcx,
+                     void *workSpace,
+                     size_t workSpaceSizeInBytes,
+                     void *reserveSpace,
+                     size_t reserveSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardData_v8(cudnnHandle_t handle,
+                        cudnnRNNDescriptor_t rnnDesc,
+                        const int32_t devSeqLengths[],
+                        cudnnRNNDataDescriptor_t yDesc,
+                        const void *y,
+                        const void *dy,
+                        cudnnRNNDataDescriptor_t xDesc,
+                        void *dx,
+                        cudnnTensorDescriptor_t hDesc,
+                        const void *hx,
+                        const void *dhy,
+                        void *dhx,
+                        cudnnTensorDescriptor_t cDesc,
+                        const void *cx,
+                        const void *dcy,
+                        void *dcx,
+                        size_t weightSpaceSize,
+                        const void *weightSpace,
+                        size_t workSpaceSize,
+                        void *workSpace,
+                        size_t reserveSpaceSize,
+                        void *reserveSpace);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardWeights(cudnnHandle_t handle,
+                        const cudnnRNNDescriptor_t rnnDesc,
+                        const int seqLength,
+                        const cudnnTensorDescriptor_t *xDesc,
+                        const void *x,
+                        const cudnnTensorDescriptor_t hxDesc,
+                        const void *hx,
+                        const cudnnTensorDescriptor_t *yDesc,
+                        const void *y,
+                        const void *workSpace,
+                        size_t workSpaceSizeInBytes,
+                        const cudnnFilterDescriptor_t dwDesc,
+                        void *dw,
+                        const void *reserveSpace,
+                        size_t reserveSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardWeights_v8(cudnnHandle_t handle,
+                           cudnnRNNDescriptor_t rnnDesc,
+                           cudnnWgradMode_t addGrad,
+                           const int32_t devSeqLengths[],
+                           cudnnRNNDataDescriptor_t xDesc,
+                           const void *x,
+                           cudnnTensorDescriptor_t hDesc,
+                           const void *hx,
+                           cudnnRNNDataDescriptor_t yDesc,
+                           const void *y,
+                           size_t weightSpaceSize,
+                           void *dweightSpace,
+                           size_t workSpaceSize,
+                           void *workSpace,
+                           size_t reserveSpaceSize,
+                           void *reserveSpace);
+
+/* RNN EX API */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNForwardTrainingEx(cudnnHandle_t handle,
+                          const cudnnRNNDescriptor_t rnnDesc,
+                          const cudnnRNNDataDescriptor_t xDesc,
+                          const void *x,
+                          const cudnnTensorDescriptor_t hxDesc,
+                          const void *hx,
+                          const cudnnTensorDescriptor_t cxDesc,
+                          const void *cx,
+                          const cudnnFilterDescriptor_t wDesc,
+                          const void *w,
+                          const cudnnRNNDataDescriptor_t yDesc,
+                          void *y,
+                          const cudnnTensorDescriptor_t hyDesc,
+                          void *hy,
+                          const cudnnTensorDescriptor_t cyDesc,
+                          void *cy,
+                          const cudnnRNNDataDescriptor_t kDesc, /* reserved, should pass NULL */
+                          const void *keys,                     /* reserved, should pass NULL */
+                          const cudnnRNNDataDescriptor_t cDesc, /* reserved, should pass NULL */
+                          void *cAttn,                          /* reserved, should pass NULL */
+                          const cudnnRNNDataDescriptor_t iDesc, /* reserved, should pass NULL */
+                          void *iAttn,                          /* reserved, should pass NULL */
+                          const cudnnRNNDataDescriptor_t qDesc, /* reserved, should pass NULL */
+                          void *queries,                        /* reserved, should pass NULL */
+                          void *workSpace,
+                          size_t workSpaceSizeInBytes,
+                          void *reserveSpace,
+                          size_t reserveSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardDataEx(cudnnHandle_t handle,
+                       const cudnnRNNDescriptor_t rnnDesc,
+                       const cudnnRNNDataDescriptor_t yDesc,
+                       const void *y,
+                       const cudnnRNNDataDescriptor_t dyDesc,
+                       const void *dy,
+                       const cudnnRNNDataDescriptor_t dcDesc, /* reserved, should pass NULL */
+                       const void *dcAttn,                    /* reserved, should pass NULL */
+                       const cudnnTensorDescriptor_t dhyDesc,
+                       const void *dhy,
+                       const cudnnTensorDescriptor_t dcyDesc,
+                       const void *dcy,
+                       const cudnnFilterDescriptor_t wDesc,
+                       const void *w,
+                       const cudnnTensorDescriptor_t hxDesc,
+                       const void *hx,
+                       const cudnnTensorDescriptor_t cxDesc,
+                       const void *cx,
+                       const cudnnRNNDataDescriptor_t dxDesc,
+                       void *dx,
+                       const cudnnTensorDescriptor_t dhxDesc,
+                       void *dhx,
+                       const cudnnTensorDescriptor_t dcxDesc,
+                       void *dcx,
+                       const cudnnRNNDataDescriptor_t dkDesc, /* reserved, should pass NULL */
+                       void *dkeys,                           /* reserved, should pass NULL */
+                       void *workSpace,
+                       size_t workSpaceSizeInBytes,
+                       void *reserveSpace,
+                       size_t reserveSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRNNBackwardWeightsEx(cudnnHandle_t handle,
+                          const cudnnRNNDescriptor_t rnnDesc,
+                          const cudnnRNNDataDescriptor_t xDesc,
+                          const void *x,
+                          const cudnnTensorDescriptor_t hxDesc,
+                          const void *hx,
+                          const cudnnRNNDataDescriptor_t yDesc,
+                          const void *y,
+                          void *workSpace,
+                          size_t workSpaceSizeInBytes,
+                          const cudnnFilterDescriptor_t dwDesc,
+                          void *dw,
+                          void *reserveSpace,
+                          size_t reserveSpaceSizeInBytes);
+
+/* RNN FIND API */
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNForwardTrainingAlgorithmMaxCount(cudnnHandle_t handle, const cudnnRNNDescriptor_t rnnDesc, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindRNNForwardTrainingAlgorithmEx(cudnnHandle_t handle,
+                                       const cudnnRNNDescriptor_t rnnDesc,
+                                       const int seqLength,
+                                       const cudnnTensorDescriptor_t *xDesc,
+                                       const void *x,
+                                       const cudnnTensorDescriptor_t hxDesc,
+                                       const void *hx,
+                                       const cudnnTensorDescriptor_t cxDesc,
+                                       const void *cx,
+                                       const cudnnFilterDescriptor_t wDesc,
+                                       const void *w,
+                                       const cudnnTensorDescriptor_t *yDesc,
+                                       void *y,
+                                       const cudnnTensorDescriptor_t hyDesc,
+                                       void *hy,
+                                       const cudnnTensorDescriptor_t cyDesc,
+                                       void *cy,
+                                       const float findIntensity,
+                                       const int requestedAlgoCount,
+                                       int *returnedAlgoCount,
+                                       cudnnAlgorithmPerformance_t *perfResults,
+                                       void *workspace,
+                                       size_t workSpaceSizeInBytes,
+                                       void *reserveSpace,
+                                       size_t reserveSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNBackwardDataAlgorithmMaxCount(cudnnHandle_t handle, const cudnnRNNDescriptor_t rnnDesc, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindRNNBackwardDataAlgorithmEx(cudnnHandle_t handle,
+                                    const cudnnRNNDescriptor_t rnnDesc,
+                                    const int seqLength,
+                                    const cudnnTensorDescriptor_t *yDesc,
+                                    const void *y,
+                                    const cudnnTensorDescriptor_t *dyDesc,
+                                    const void *dy,
+                                    const cudnnTensorDescriptor_t dhyDesc,
+                                    const void *dhy,
+                                    const cudnnTensorDescriptor_t dcyDesc,
+                                    const void *dcy,
+                                    const cudnnFilterDescriptor_t wDesc,
+                                    const void *w,
+                                    const cudnnTensorDescriptor_t hxDesc,
+                                    const void *hx,
+                                    const cudnnTensorDescriptor_t cxDesc,
+                                    const void *cx,
+                                    const cudnnTensorDescriptor_t *dxDesc,
+                                    void *dx,
+                                    const cudnnTensorDescriptor_t dhxDesc,
+                                    void *dhx,
+                                    const cudnnTensorDescriptor_t dcxDesc,
+                                    void *dcx,
+                                    const float findIntensity,
+                                    const int requestedAlgoCount,
+                                    int *returnedAlgoCount,
+                                    cudnnAlgorithmPerformance_t *perfResults,
+                                    void *workspace,
+                                    size_t workSpaceSizeInBytes,
+                                    void *reserveSpace,
+                                    size_t reserveSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetRNNBackwardWeightsAlgorithmMaxCount(cudnnHandle_t handle, const cudnnRNNDescriptor_t rnnDesc, int *count);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnFindRNNBackwardWeightsAlgorithmEx(cudnnHandle_t handle,
+                                       const cudnnRNNDescriptor_t rnnDesc,
+                                       const int seqLength,
+                                       const cudnnTensorDescriptor_t *xDesc,
+                                       const void *x,
+                                       const cudnnTensorDescriptor_t hxDesc,
+                                       const void *hx,
+                                       const cudnnTensorDescriptor_t *yDesc,
+                                       const void *y,
+                                       const float findIntensity,
+                                       const int requestedAlgoCount,
+                                       int *returnedAlgoCount,
+                                       cudnnAlgorithmPerformance_t *perfResults,
+                                       const void *workspace,
+                                       size_t workSpaceSizeInBytes,
+                                       const cudnnFilterDescriptor_t dwDesc,
+                                       void *dw,
+                                       const void *reserveSpace,
+                                       size_t reserveSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnMultiHeadAttnBackwardData(cudnnHandle_t handle,
+                               const cudnnAttnDescriptor_t attnDesc,
+                               const int loWinIdx[],
+                               const int hiWinIdx[],
+                               const int devSeqLengthsDQDO[],
+                               const int devSeqLengthsDKDV[],
+                               const cudnnSeqDataDescriptor_t doDesc,
+                               const void *dout,
+                               const cudnnSeqDataDescriptor_t dqDesc,
+                               void *dqueries,
+                               const void *queries,
+                               const cudnnSeqDataDescriptor_t dkDesc,
+                               void *dkeys,
+                               const void *keys,
+                               const cudnnSeqDataDescriptor_t dvDesc,
+                               void *dvalues,
+                               const void *values,
+                               size_t weightSizeInBytes,
+                               const void *weights,
+                               size_t workSpaceSizeInBytes,
+                               void *workSpace,
+                               size_t reserveSpaceSizeInBytes,
+                               void *reserveSpace);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnMultiHeadAttnBackwardWeights(cudnnHandle_t handle,
+                                  const cudnnAttnDescriptor_t attnDesc,
+                                  cudnnWgradMode_t addGrad,
+                                  const cudnnSeqDataDescriptor_t qDesc,
+                                  const void *queries,
+                                  const cudnnSeqDataDescriptor_t kDesc,
+                                  const void *keys,
+                                  const cudnnSeqDataDescriptor_t vDesc,
+                                  const void *values,
+                                  const cudnnSeqDataDescriptor_t doDesc,
+                                  const void *dout,
+                                  size_t weightSizeInBytes,
+                                  const void *weights,
+                                  void *dweights,
+                                  size_t workSpaceSizeInBytes,
+                                  void *workSpace,
+                                  size_t reserveSpaceSizeInBytes,
+                                  void *reserveSpace);
+
+/*
+* CTC (Connectionist Temporal Classification) loss descriptor create/destory/set/get functions
+*/
+/* Input normalization mode for loss function */
+typedef enum {
+    CUDNN_LOSS_NORMALIZATION_NONE    = 0,
+    CUDNN_LOSS_NORMALIZATION_SOFTMAX = 1,
+} cudnnLossNormalizationMode_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateCTCLossDescriptor(cudnnCTCLossDescriptor_t *ctcLossDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc, cudnnDataType_t compType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptorEx(cudnnCTCLossDescriptor_t ctcLossDesc,
+                            cudnnDataType_t compType,
+                            cudnnLossNormalizationMode_t normMode,
+                            cudnnNanPropagation_t gradMode);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCTCLossDescriptor_v8(cudnnCTCLossDescriptor_t ctcLossDesc,
+                             cudnnDataType_t compType,
+                             cudnnLossNormalizationMode_t normMode,
+                             cudnnNanPropagation_t gradMode,
+                             int maxLabelLength);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc, cudnnDataType_t *compType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptorEx(cudnnCTCLossDescriptor_t ctcLossDesc,
+                            cudnnDataType_t *compType,
+                            cudnnLossNormalizationMode_t *normMode,
+                            cudnnNanPropagation_t *gradMode);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossDescriptor_v8(cudnnCTCLossDescriptor_t ctcLossDesc,
+                             cudnnDataType_t *compType,
+                             cudnnLossNormalizationMode_t *normMode,
+                             cudnnNanPropagation_t *gradMode,
+                             int *maxLabelLength);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc);
+
+/* return the ctc costs and gradients, given the probabilities and labels */
+cudnnStatus_t CUDNNWINAPI
+cudnnCTCLoss(
+    cudnnHandle_t handle,
+    const cudnnTensorDescriptor_t
+        probsDesc,     /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the timing steps, N is the
+                          mini batch size, A is the alphabet size)  */
+    const void *probs, /* probabilities after softmax, in GPU memory */
+    const int hostLabels[],                      /* labels, in CPU memory */
+    const int hostLabelLengths[],                /* the length of each label, in CPU memory */
+    const int hostInputLengths[],                /* the lengths of timing steps in each batch, in CPU memory */
+    void *costs,                                 /* the returned costs of CTC, in GPU memory */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the dimensions are T,N,A */
+    void *gradients,         /* the returned CTC gradients, in GPU memory, to compute costs only, set it to NULL */
+    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    void *workspace,              /* pointer to the workspace, in GPU memory */
+    size_t workSpaceSizeInBytes); /* size of the workspace */
+
+/* return the ctc costs and gradients, given the probabilities and labels */
+cudnnStatus_t CUDNNWINAPI
+cudnnCTCLoss_v8(
+    cudnnHandle_t handle,
+    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    const cudnnTensorDescriptor_t
+        probsDesc,     /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the timing steps, N is the
+                          mini batch size, A is the alphabet size)  */
+    const void *probs, /* probabilities after softmax, in GPU memory */
+    const int labels[],                          /* labels, in GPU memory */
+    const int labelLengths[],                    /* the length of each label, in GPU memory */
+    const int inputLengths[],                    /* the lengths of timing steps in each batch, in GPU memory */
+    void *costs,                                 /* the returned costs of CTC, in GPU memory */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the dimensions are T,N,A */
+    void *gradients,             /* the returned CTC gradients, in GPU memory, to compute costs only, set it to NULL */
+    size_t workSpaceSizeInBytes, /* size of the workspace */
+    void *workspace);            /* pointer to the workspace, in GPU memory */
+
+/* return the workspace size needed for ctc */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossWorkspaceSize(
+    cudnnHandle_t handle,
+    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
+                                                timing steps, N is the mini batch size, A is the alphabet size) */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the
+                                                    dimensions are T,N,A. To compute costs
+                                                    only, set it to NULL */
+    const int *labels,                           /* labels, in CPU memory */
+    const int *labelLengths,                     /* the length of each label, in CPU memory */
+    const int *inputLengths,                     /* the lengths of timing steps in each batch, in CPU memory */
+    cudnnCTCLossAlgo_t algo,                     /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    size_t *sizeInBytes); /* pointer to the returned workspace size */
+
+/* return the workspace size needed for ctc */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCTCLossWorkspaceSize_v8(
+    cudnnHandle_t handle,
+    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
+    cudnnCTCLossDescriptor_t ctcLossDesc,
+    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
+                                                timing steps, N is the mini batch size, A is the alphabet size) */
+    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the
+                                                    dimensions are T,N,A. To compute costs
+                                                    only, set it to NULL */
+    size_t *sizeInBytes);                        /* pointer to the returned workspace size */
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnAdvTrainVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_ADV_TRAIN_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_backend.h

@@ -0,0 +1,608 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CUDNN_BACKEND_H_
+#define _CUDNN_BACKEND_H_
+
+/*
+ * The content in this header file is under development to be included in cudnn.h in the future
+ * Production code should have all include of this header file remove.
+ */
+
+#include "cudnn_ops_infer.h"
+#include "cudnn_cnn_infer.h"
+
+/* NOTE: definition in extern "C" to be copied later to public header */
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+typedef void *cudnnBackendDescriptor_t;
+
+typedef struct cudnnFractionStruct {
+    int64_t numerator;
+    int64_t denominator;
+} cudnnFraction_t;
+
+typedef enum {
+    CUDNN_POINTWISE_ADD        = 0,
+    CUDNN_POINTWISE_ADD_SQUARE = 5,
+    CUDNN_POINTWISE_DIV        = 6,
+    CUDNN_POINTWISE_MAX        = 3,
+    CUDNN_POINTWISE_MIN        = 2,
+    CUDNN_POINTWISE_MOD        = 7,
+    CUDNN_POINTWISE_MUL        = 1,
+    CUDNN_POINTWISE_POW        = 8,
+    CUDNN_POINTWISE_SUB        = 9,
+
+    CUDNN_POINTWISE_ABS        = 10,
+    CUDNN_POINTWISE_CEIL       = 11,
+    CUDNN_POINTWISE_COS        = 12,
+    CUDNN_POINTWISE_EXP        = 13,
+    CUDNN_POINTWISE_FLOOR      = 14,
+    CUDNN_POINTWISE_LOG        = 15,
+    CUDNN_POINTWISE_NEG        = 16,
+    CUDNN_POINTWISE_RSQRT      = 17,
+    CUDNN_POINTWISE_SIN        = 18,
+    CUDNN_POINTWISE_SQRT       = 4,
+    CUDNN_POINTWISE_TAN        = 19,
+    CUDNN_POINTWISE_ERF        = 20,
+    CUDNN_POINTWISE_IDENTITY   = 21,
+    CUDNN_POINTWISE_RECIPROCAL = 22,
+
+    CUDNN_POINTWISE_RELU_FWD             = 100,
+    CUDNN_POINTWISE_TANH_FWD             = 101,
+    CUDNN_POINTWISE_SIGMOID_FWD          = 102,
+    CUDNN_POINTWISE_ELU_FWD              = 103,
+    CUDNN_POINTWISE_GELU_FWD             = 104,
+    CUDNN_POINTWISE_SOFTPLUS_FWD         = 105,
+    CUDNN_POINTWISE_SWISH_FWD            = 106,
+    CUDNN_POINTWISE_GELU_APPROX_TANH_FWD = 107,
+
+    CUDNN_POINTWISE_RELU_BWD             = 200,
+    CUDNN_POINTWISE_TANH_BWD             = 201,
+    CUDNN_POINTWISE_SIGMOID_BWD          = 202,
+    CUDNN_POINTWISE_ELU_BWD              = 203,
+    CUDNN_POINTWISE_GELU_BWD             = 204,
+    CUDNN_POINTWISE_SOFTPLUS_BWD         = 205,
+    CUDNN_POINTWISE_SWISH_BWD            = 206,
+    CUDNN_POINTWISE_GELU_APPROX_TANH_BWD = 207,
+
+    CUDNN_POINTWISE_CMP_EQ  = 300,
+    CUDNN_POINTWISE_CMP_NEQ = 301,
+    CUDNN_POINTWISE_CMP_GT  = 302,
+    CUDNN_POINTWISE_CMP_GE  = 303,
+    CUDNN_POINTWISE_CMP_LT  = 304,
+    CUDNN_POINTWISE_CMP_LE  = 305,
+
+    CUDNN_POINTWISE_LOGICAL_AND = 400,
+    CUDNN_POINTWISE_LOGICAL_OR  = 401,
+    CUDNN_POINTWISE_LOGICAL_NOT = 402,
+
+    CUDNN_POINTWISE_GEN_INDEX = 501,
+
+    CUDNN_POINTWISE_BINARY_SELECT = 601,
+} cudnnPointwiseMode_t;
+
+typedef enum {
+    CUDNN_RESAMPLE_NEAREST                 = 0,
+    CUDNN_RESAMPLE_BILINEAR                = 1,
+    CUDNN_RESAMPLE_AVGPOOL                 = 2,
+    CUDNN_RESAMPLE_AVGPOOL_INCLUDE_PADDING = 2,
+    CUDNN_RESAMPLE_AVGPOOL_EXCLUDE_PADDING = 4,
+    CUDNN_RESAMPLE_MAXPOOL                 = 3,
+} cudnnResampleMode_t;
+
+typedef enum {
+    CUDNN_SIGNAL_SET  = 0,
+    CUDNN_SIGNAL_WAIT = 1,
+} cudnnSignalMode_t;
+
+typedef enum {
+    CUDNN_GENSTATS_SUM_SQSUM = 0,
+} cudnnGenStatsMode_t;
+
+typedef enum {
+    CUDNN_BN_FINALIZE_STATISTICS_TRAINING  = 0,
+    CUDNN_BN_FINALIZE_STATISTICS_INFERENCE = 1,
+} cudnnBnFinalizeStatsMode_t;
+
+typedef enum {
+    CUDNN_RNG_DISTRIBUTION_BERNOULLI,
+    CUDNN_RNG_DISTRIBUTION_UNIFORM,
+    CUDNN_RNG_DISTRIBUTION_NORMAL,
+} cudnnRngDistribution_t;
+
+typedef enum {
+    CUDNN_ATTR_POINTWISE_MODE                  = 0,
+    CUDNN_ATTR_POINTWISE_MATH_PREC             = 1,
+    CUDNN_ATTR_POINTWISE_NAN_PROPAGATION       = 2,
+    CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP       = 3,
+    CUDNN_ATTR_POINTWISE_RELU_UPPER_CLIP       = 4,
+    CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP_SLOPE = 5,
+    CUDNN_ATTR_POINTWISE_ELU_ALPHA             = 6,
+    CUDNN_ATTR_POINTWISE_SOFTPLUS_BETA         = 7,
+    CUDNN_ATTR_POINTWISE_SWISH_BETA            = 8,
+    CUDNN_ATTR_POINTWISE_AXIS                  = 9,
+
+    CUDNN_ATTR_CONVOLUTION_COMP_TYPE      = 100,
+    CUDNN_ATTR_CONVOLUTION_CONV_MODE      = 101,
+    CUDNN_ATTR_CONVOLUTION_DILATIONS      = 102,
+    CUDNN_ATTR_CONVOLUTION_FILTER_STRIDES = 103,
+    CUDNN_ATTR_CONVOLUTION_POST_PADDINGS  = 104,
+    CUDNN_ATTR_CONVOLUTION_PRE_PADDINGS   = 105,
+    CUDNN_ATTR_CONVOLUTION_SPATIAL_DIMS   = 106,
+
+    CUDNN_ATTR_ENGINEHEUR_MODE            = 200,
+    CUDNN_ATTR_ENGINEHEUR_OPERATION_GRAPH = 201,
+    CUDNN_ATTR_ENGINEHEUR_RESULTS         = 202,
+
+    CUDNN_ATTR_ENGINECFG_ENGINE            = 300,
+    CUDNN_ATTR_ENGINECFG_INTERMEDIATE_INFO = 301,
+    CUDNN_ATTR_ENGINECFG_KNOB_CHOICES      = 302,
+
+    CUDNN_ATTR_EXECUTION_PLAN_HANDLE                     = 400,
+    CUDNN_ATTR_EXECUTION_PLAN_ENGINE_CONFIG              = 401,
+    CUDNN_ATTR_EXECUTION_PLAN_WORKSPACE_SIZE             = 402,
+    CUDNN_ATTR_EXECUTION_PLAN_COMPUTED_INTERMEDIATE_UIDS = 403,
+    CUDNN_ATTR_EXECUTION_PLAN_RUN_ONLY_INTERMEDIATE_UIDS = 404,
+    CUDNN_ATTR_EXECUTION_PLAN_JSON_REPRESENTATION        = 405,
+
+    CUDNN_ATTR_INTERMEDIATE_INFO_UNIQUE_ID            = 500,
+    CUDNN_ATTR_INTERMEDIATE_INFO_SIZE                 = 501,
+    CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_DATA_UIDS  = 502,
+    CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_ATTRIBUTES = 503,
+
+    CUDNN_ATTR_KNOB_CHOICE_KNOB_TYPE  = 600,
+    CUDNN_ATTR_KNOB_CHOICE_KNOB_VALUE = 601,
+
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_ALPHA        = 700,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_BETA         = 701,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_CONV_DESC    = 702,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_W            = 703,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_X            = 704,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_Y            = 705,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_ALPHA       = 706,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_BETA        = 707,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_CONV_DESC   = 708,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_W           = 709,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DX          = 710,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DY          = 711,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_ALPHA     = 712,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_BETA      = 713,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_CONV_DESC = 714,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DW        = 715,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_X         = 716,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DY        = 717,
+
+    CUDNN_ATTR_OPERATION_POINTWISE_PW_DESCRIPTOR = 750,
+    CUDNN_ATTR_OPERATION_POINTWISE_XDESC         = 751,
+    CUDNN_ATTR_OPERATION_POINTWISE_BDESC         = 752,
+    CUDNN_ATTR_OPERATION_POINTWISE_YDESC         = 753,
+    CUDNN_ATTR_OPERATION_POINTWISE_ALPHA1        = 754,
+    CUDNN_ATTR_OPERATION_POINTWISE_ALPHA2        = 755,
+    CUDNN_ATTR_OPERATION_POINTWISE_DXDESC        = 756,
+    CUDNN_ATTR_OPERATION_POINTWISE_DYDESC        = 757,
+    CUDNN_ATTR_OPERATION_POINTWISE_TDESC         = 758,
+
+    CUDNN_ATTR_OPERATION_GENSTATS_MODE      = 770,
+    CUDNN_ATTR_OPERATION_GENSTATS_MATH_PREC = 771,
+    CUDNN_ATTR_OPERATION_GENSTATS_XDESC     = 772,
+    CUDNN_ATTR_OPERATION_GENSTATS_SUMDESC   = 773,
+    CUDNN_ATTR_OPERATION_GENSTATS_SQSUMDESC = 774,
+
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_STATS_MODE                = 780,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_MATH_PREC                 = 781,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SUM_DESC                = 782,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SQ_SUM_DESC             = 783,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SCALE_DESC                = 784,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_BIAS_DESC                 = 785,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_MEAN_DESC    = 786,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_VAR_DESC     = 787,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_MEAN_DESC = 788,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_VAR_DESC  = 789,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_MEAN_DESC           = 790,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_INV_STD_DESC        = 791,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_SCALE_DESC             = 792,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_BIAS_DESC              = 793,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_ACCUM_COUNT_DESC          = 794,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EPSILON_DESC              = 795,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EXP_AVERATE_FACTOR_DESC   = 796,
+
+    CUDNN_ATTR_OPERATIONGRAPH_HANDLE              = 800,
+    CUDNN_ATTR_OPERATIONGRAPH_OPS                 = 801,
+    CUDNN_ATTR_OPERATIONGRAPH_ENGINE_GLOBAL_COUNT = 802,
+
+    CUDNN_ATTR_TENSOR_BYTE_ALIGNMENT       = 900,
+    CUDNN_ATTR_TENSOR_DATA_TYPE            = 901,
+    CUDNN_ATTR_TENSOR_DIMENSIONS           = 902,
+    CUDNN_ATTR_TENSOR_STRIDES              = 903,
+    CUDNN_ATTR_TENSOR_VECTOR_COUNT         = 904,
+    CUDNN_ATTR_TENSOR_VECTORIZED_DIMENSION = 905,
+    CUDNN_ATTR_TENSOR_UNIQUE_ID            = 906,
+    CUDNN_ATTR_TENSOR_IS_VIRTUAL           = 907,
+    CUDNN_ATTR_TENSOR_IS_BY_VALUE          = 908,
+    CUDNN_ATTR_TENSOR_REORDERING_MODE      = 909,
+    CUDNN_ATTR_TENSOR_RAGGED_OFFSET_DESC   = 913,
+
+    CUDNN_ATTR_VARIANT_PACK_UNIQUE_IDS    = 1000,
+    CUDNN_ATTR_VARIANT_PACK_DATA_POINTERS = 1001,
+    CUDNN_ATTR_VARIANT_PACK_INTERMEDIATES = 1002,
+    CUDNN_ATTR_VARIANT_PACK_WORKSPACE     = 1003,
+
+    CUDNN_ATTR_LAYOUT_INFO_TENSOR_UID = 1100,
+    CUDNN_ATTR_LAYOUT_INFO_TYPES      = 1101,
+
+    CUDNN_ATTR_KNOB_INFO_TYPE          = 1200,
+    CUDNN_ATTR_KNOB_INFO_MAXIMUM_VALUE = 1201,
+    CUDNN_ATTR_KNOB_INFO_MINIMUM_VALUE = 1202,
+    CUDNN_ATTR_KNOB_INFO_STRIDE        = 1203,
+
+    CUDNN_ATTR_ENGINE_OPERATION_GRAPH = 1300,
+    CUDNN_ATTR_ENGINE_GLOBAL_INDEX    = 1301,
+    CUDNN_ATTR_ENGINE_KNOB_INFO       = 1302,
+    CUDNN_ATTR_ENGINE_NUMERICAL_NOTE  = 1303,
+    CUDNN_ATTR_ENGINE_LAYOUT_INFO     = 1304,
+    CUDNN_ATTR_ENGINE_BEHAVIOR_NOTE   = 1305,
+
+    CUDNN_ATTR_MATMUL_COMP_TYPE     = 1500,
+    CUDNN_ATTR_MATMUL_PADDING_VALUE = 1503,
+
+    CUDNN_ATTR_OPERATION_MATMUL_ADESC                           = 1520,
+    CUDNN_ATTR_OPERATION_MATMUL_BDESC                           = 1521,
+    CUDNN_ATTR_OPERATION_MATMUL_CDESC                           = 1522,
+    CUDNN_ATTR_OPERATION_MATMUL_DESC                            = 1523,
+    CUDNN_ATTR_OPERATION_MATMUL_IRREGULARLY_STRIDED_BATCH_COUNT = 1524,
+    CUDNN_ATTR_OPERATION_MATMUL_GEMM_M_OVERRIDE_DESC            = 1525,
+    CUDNN_ATTR_OPERATION_MATMUL_GEMM_N_OVERRIDE_DESC            = 1526,
+    CUDNN_ATTR_OPERATION_MATMUL_GEMM_K_OVERRIDE_DESC            = 1527,
+
+    CUDNN_ATTR_REDUCTION_OPERATOR  = 1600,
+    CUDNN_ATTR_REDUCTION_COMP_TYPE = 1601,
+
+    CUDNN_ATTR_OPERATION_REDUCTION_XDESC = 1610,
+    CUDNN_ATTR_OPERATION_REDUCTION_YDESC = 1611,
+    CUDNN_ATTR_OPERATION_REDUCTION_DESC  = 1612,
+
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MATH_PREC        = 1620,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MEAN_DESC        = 1621,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_INVSTD_DESC      = 1622,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_BN_SCALE_DESC    = 1623,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_X_DESC           = 1624,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DY_DESC          = 1625,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_SCALE_DESC   = 1626,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_BIAS_DESC    = 1627,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_DY_SCALE_DESC = 1628,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_X_SCALE_DESC  = 1629,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_BIAS          = 1630,
+
+    CUDNN_ATTR_RESAMPLE_MODE            = 1700,
+    CUDNN_ATTR_RESAMPLE_COMP_TYPE       = 1701,
+    CUDNN_ATTR_RESAMPLE_SPATIAL_DIMS    = 1702,
+    CUDNN_ATTR_RESAMPLE_POST_PADDINGS   = 1703,
+    CUDNN_ATTR_RESAMPLE_PRE_PADDINGS    = 1704,
+    CUDNN_ATTR_RESAMPLE_STRIDES         = 1705,
+    CUDNN_ATTR_RESAMPLE_WINDOW_DIMS     = 1706,
+    CUDNN_ATTR_RESAMPLE_NAN_PROPAGATION = 1707,
+    CUDNN_ATTR_RESAMPLE_PADDING_MODE    = 1708,
+
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_XDESC   = 1710,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_YDESC   = 1711,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_IDXDESC = 1712,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_ALPHA   = 1713,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_BETA    = 1714,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_DESC    = 1716,
+
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DXDESC  = 1720,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DYDESC  = 1721,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_IDXDESC = 1722,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_ALPHA   = 1723,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_BETA    = 1724,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DESC    = 1725,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_XDESC   = 1726,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_YDESC   = 1727,
+
+    CUDNN_ATTR_OPERATION_CONCAT_AXIS          = 1800,
+    CUDNN_ATTR_OPERATION_CONCAT_INPUT_DESCS   = 1801,
+    CUDNN_ATTR_OPERATION_CONCAT_INPLACE_INDEX = 1802,
+    CUDNN_ATTR_OPERATION_CONCAT_OUTPUT_DESC   = 1803,
+
+    CUDNN_ATTR_OPERATION_SIGNAL_MODE     = 1900,
+    CUDNN_ATTR_OPERATION_SIGNAL_FLAGDESC = 1901,
+    CUDNN_ATTR_OPERATION_SIGNAL_VALUE    = 1902,
+    CUDNN_ATTR_OPERATION_SIGNAL_XDESC    = 1903,
+    CUDNN_ATTR_OPERATION_SIGNAL_YDESC    = 1904,
+
+    CUDNN_ATTR_OPERATION_NORM_FWD_MODE                     = 2000,
+    CUDNN_ATTR_OPERATION_NORM_FWD_PHASE                    = 2001,
+    CUDNN_ATTR_OPERATION_NORM_FWD_XDESC                    = 2002,
+    CUDNN_ATTR_OPERATION_NORM_FWD_MEAN_DESC                = 2003,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INV_VARIANCE_DESC        = 2004,
+    CUDNN_ATTR_OPERATION_NORM_FWD_SCALE_DESC               = 2005,
+    CUDNN_ATTR_OPERATION_NORM_FWD_BIAS_DESC                = 2006,
+    CUDNN_ATTR_OPERATION_NORM_FWD_EPSILON_DESC             = 2007,
+    CUDNN_ATTR_OPERATION_NORM_FWD_EXP_AVG_FACTOR_DESC      = 2008,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_MEAN_DESC  = 2009,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_VAR_DESC   = 2010,
+    CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_MEAN_DESC = 2011,
+    CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_VAR_DESC  = 2012,
+    CUDNN_ATTR_OPERATION_NORM_FWD_YDESC                    = 2013,
+    CUDNN_ATTR_OPERATION_NORM_FWD_PEER_STAT_DESCS          = 2014,
+
+    CUDNN_ATTR_OPERATION_NORM_BWD_MODE              = 2100,
+    CUDNN_ATTR_OPERATION_NORM_BWD_XDESC             = 2101,
+    CUDNN_ATTR_OPERATION_NORM_BWD_MEAN_DESC         = 2102,
+    CUDNN_ATTR_OPERATION_NORM_BWD_INV_VARIANCE_DESC = 2103,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DYDESC            = 2104,
+    CUDNN_ATTR_OPERATION_NORM_BWD_SCALE_DESC        = 2105,
+    CUDNN_ATTR_OPERATION_NORM_BWD_EPSILON_DESC      = 2106,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DSCALE_DESC       = 2107,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DBIAS_DESC        = 2108,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DXDESC            = 2109,
+    CUDNN_ATTR_OPERATION_NORM_BWD_PEER_STAT_DESCS   = 2110,
+
+    CUDNN_ATTR_OPERATION_RESHAPE_XDESC = 2200,
+    CUDNN_ATTR_OPERATION_RESHAPE_YDESC = 2201,
+
+    CUDNN_ATTR_RNG_DISTRIBUTION                   = 2300,
+    CUDNN_ATTR_RNG_NORMAL_DIST_MEAN               = 2301,
+    CUDNN_ATTR_RNG_NORMAL_DIST_STANDARD_DEVIATION = 2302,
+    CUDNN_ATTR_RNG_UNIFORM_DIST_MAXIMUM           = 2303,
+    CUDNN_ATTR_RNG_UNIFORM_DIST_MINIMUM           = 2304,
+    CUDNN_ATTR_RNG_BERNOULLI_DIST_PROBABILITY     = 2305,
+
+    CUDNN_ATTR_OPERATION_RNG_YDESC       = 2310,
+    CUDNN_ATTR_OPERATION_RNG_SEED        = 2311,
+    CUDNN_ATTR_OPERATION_RNG_DESC        = 2312,
+    CUDNN_ATTR_OPERATION_RNG_OFFSET_DESC = 2313,
+
+} cudnnBackendAttributeName_t;
+
+typedef enum {
+    CUDNN_TYPE_HANDLE = 0,
+    CUDNN_TYPE_DATA_TYPE,
+    CUDNN_TYPE_BOOLEAN,
+    CUDNN_TYPE_INT64,
+    CUDNN_TYPE_FLOAT,
+    CUDNN_TYPE_DOUBLE,
+    CUDNN_TYPE_VOID_PTR,
+    CUDNN_TYPE_CONVOLUTION_MODE,
+    CUDNN_TYPE_HEUR_MODE,
+    CUDNN_TYPE_KNOB_TYPE,
+    CUDNN_TYPE_NAN_PROPOGATION,
+    CUDNN_TYPE_NUMERICAL_NOTE,
+    CUDNN_TYPE_LAYOUT_TYPE,
+    CUDNN_TYPE_ATTRIB_NAME,
+    CUDNN_TYPE_POINTWISE_MODE,
+    CUDNN_TYPE_BACKEND_DESCRIPTOR,
+    CUDNN_TYPE_GENSTATS_MODE,
+    CUDNN_TYPE_BN_FINALIZE_STATS_MODE,
+    CUDNN_TYPE_REDUCTION_OPERATOR_TYPE,
+    CUDNN_TYPE_BEHAVIOR_NOTE,
+    CUDNN_TYPE_TENSOR_REORDERING_MODE,
+    CUDNN_TYPE_RESAMPLE_MODE,
+    CUDNN_TYPE_PADDING_MODE,
+    CUDNN_TYPE_INT32,
+    CUDNN_TYPE_CHAR,
+    CUDNN_TYPE_SIGNAL_MODE,
+    CUDNN_TYPE_FRACTION,
+    CUDNN_TYPE_NORM_MODE,
+    CUDNN_TYPE_NORM_FWD_PHASE,
+    CUDNN_TYPE_RNG_DISTRIBUTION
+} cudnnBackendAttributeType_t;
+
+typedef enum {
+    CUDNN_BACKEND_POINTWISE_DESCRIPTOR = 0,
+    CUDNN_BACKEND_CONVOLUTION_DESCRIPTOR,
+    CUDNN_BACKEND_ENGINE_DESCRIPTOR,
+    CUDNN_BACKEND_ENGINECFG_DESCRIPTOR,
+    CUDNN_BACKEND_ENGINEHEUR_DESCRIPTOR,
+    CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR,
+    CUDNN_BACKEND_INTERMEDIATE_INFO_DESCRIPTOR,
+    CUDNN_BACKEND_KNOB_CHOICE_DESCRIPTOR,
+    CUDNN_BACKEND_KNOB_INFO_DESCRIPTOR,
+    CUDNN_BACKEND_LAYOUT_INFO_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_GEN_STATS_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATIONGRAPH_DESCRIPTOR,
+    CUDNN_BACKEND_VARIANT_PACK_DESCRIPTOR,
+    CUDNN_BACKEND_TENSOR_DESCRIPTOR,
+    CUDNN_BACKEND_MATMUL_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_MATMUL_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_BN_FINALIZE_STATISTICS_DESCRIPTOR,
+    CUDNN_BACKEND_REDUCTION_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_BN_BWD_WEIGHTS_DESCRIPTOR,
+    CUDNN_BACKEND_RESAMPLE_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_RESAMPLE_FWD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_RESAMPLE_BWD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_CONCAT_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_SIGNAL_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_NORM_FORWARD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_NORM_BACKWARD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_RESHAPE_DESCRIPTOR,
+    CUDNN_BACKEND_RNG_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_RNG_DESCRIPTOR
+} cudnnBackendDescriptorType_t;
+
+typedef enum {
+    CUDNN_NUMERICAL_NOTE_TENSOR_CORE = 0,
+    CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS,
+    CUDNN_NUMERICAL_NOTE_REDUCED_PRECISION_REDUCTION,
+    CUDNN_NUMERICAL_NOTE_FFT,
+    CUDNN_NUMERICAL_NOTE_NONDETERMINISTIC,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_4x4,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_6x6,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_13x13,
+    CUDNN_NUMERICAL_NOTE_TYPE_COUNT,
+} cudnnBackendNumericalNote_t;
+
+typedef enum {
+    CUDNN_BEHAVIOR_NOTE_RUNTIME_COMPILATION             = 0,
+    CUDNN_BEHAVIOR_NOTE_REQUIRES_FILTER_INT8x32_REORDER = 1,
+    CUDNN_BEHAVIOR_NOTE_REQUIRES_BIAS_INT8x32_REORDER   = 2,
+    CUDNN_BEHAVIOR_NOTE_TYPE_COUNT,
+} cudnnBackendBehaviorNote_t;
+
+typedef enum {
+    CUDNN_KNOB_TYPE_SPLIT_K               = 0,
+    CUDNN_KNOB_TYPE_SWIZZLE               = 1,
+    CUDNN_KNOB_TYPE_TILE_SIZE             = 2,
+    CUDNN_KNOB_TYPE_USE_TEX               = 3,
+    CUDNN_KNOB_TYPE_EDGE                  = 4,
+    CUDNN_KNOB_TYPE_KBLOCK                = 5,
+    CUDNN_KNOB_TYPE_LDGA                  = 6,
+    CUDNN_KNOB_TYPE_LDGB                  = 7,
+    CUDNN_KNOB_TYPE_CHUNK_K               = 8,
+    CUDNN_KNOB_TYPE_SPLIT_H               = 9,
+    CUDNN_KNOB_TYPE_WINO_TILE             = 10,
+    CUDNN_KNOB_TYPE_MULTIPLY              = 11,
+    CUDNN_KNOB_TYPE_SPLIT_K_BUF           = 12,
+    CUDNN_KNOB_TYPE_TILEK                 = 13,
+    CUDNN_KNOB_TYPE_STAGES                = 14,
+    CUDNN_KNOB_TYPE_REDUCTION_MODE        = 15,
+    CUDNN_KNOB_TYPE_CTA_SPLIT_K_MODE      = 16,
+    CUDNN_KNOB_TYPE_SPLIT_K_SLC           = 17,
+    CUDNN_KNOB_TYPE_IDX_MODE              = 18,
+    CUDNN_KNOB_TYPE_SLICED                = 19,
+    CUDNN_KNOB_TYPE_SPLIT_RS              = 20,
+    CUDNN_KNOB_TYPE_SINGLEBUFFER          = 21,
+    CUDNN_KNOB_TYPE_LDGC                  = 22,
+    CUDNN_KNOB_TYPE_SPECFILT              = 23,
+    CUDNN_KNOB_TYPE_KERNEL_CFG            = 24,
+    CUDNN_KNOB_TYPE_WORKSPACE             = 25,
+    CUDNN_KNOB_TYPE_TILE_CGA              = 26,
+    CUDNN_KNOB_TYPE_TILE_CGA_M            = 27,
+    CUDNN_KNOB_TYPE_TILE_CGA_N            = 28,
+    CUDNN_KNOB_TYPE_BLOCK_SIZE            = 29,
+    CUDNN_KNOB_TYPE_OCCUPANCY             = 30,
+    CUDNN_KNOB_TYPE_ARRAY_SIZE_PER_THREAD = 31,
+    CUDNN_KNOB_TYPE_NUM_C_PER_BLOCK       = 32,
+    CUDNN_KNOB_TYPE_COUNTS,
+} cudnnBackendKnobType_t;
+
+typedef enum {
+    CUDNN_LAYOUT_TYPE_PREFERRED_NCHW   = 0,
+    CUDNN_LAYOUT_TYPE_PREFERRED_NHWC   = 1,
+    CUDNN_LAYOUT_TYPE_PREFERRED_PAD4CK = 2,
+    CUDNN_LAYOUT_TYPE_PREFERRED_PAD8CK = 3,
+    CUDNN_LAYOUT_TYPE_COUNT            = 4,
+} cudnnBackendLayoutType_t;
+
+typedef enum {
+    CUDNN_HEUR_MODE_INSTANT  = 0,
+    CUDNN_HEUR_MODE_B        = 1,
+    CUDNN_HEUR_MODE_FALLBACK = 2,
+    CUDNN_HEUR_MODE_A        = 3,
+    CUDNN_HEUR_MODES_COUNT   = 4,
+} cudnnBackendHeurMode_t;
+
+typedef enum {
+    CUDNN_TENSOR_REORDERING_NONE    = 0,
+    CUDNN_TENSOR_REORDERING_INT8x32 = 1,
+    CUDNN_TENSOR_REORDERING_F16x16  = 2,
+} cudnnBackendTensorReordering_t;
+
+typedef enum {
+    CUDNN_ZERO_PAD     = 0,
+    CUDNN_NEG_INF_PAD  = 1,
+    CUDNN_EDGE_VAL_PAD = 2,
+} cudnnPaddingMode_t;
+
+typedef enum {
+    CUDNN_LAYER_NORM    = 0,
+    CUDNN_INSTANCE_NORM = 1,
+    CUDNN_BATCH_NORM    = 2,
+    CUDNN_GROUP_NORM    = 3,
+} cudnnBackendNormMode_t;
+
+typedef enum {
+    CUDNN_NORM_FWD_INFERENCE = 0,
+    CUDNN_NORM_FWD_TRAINING  = 1,
+} cudnnBackendNormFwdPhase_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendCreateDescriptor(cudnnBackendDescriptorType_t descriptorType, cudnnBackendDescriptor_t *descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendDestroyDescriptor(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendInitialize(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendFinalize(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendSetAttribute(cudnnBackendDescriptor_t descriptor,
+                         cudnnBackendAttributeName_t attributeName,
+                         cudnnBackendAttributeType_t attributeType,
+                         int64_t elementCount,
+                         const void *arrayOfElements);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendGetAttribute(cudnnBackendDescriptor_t const descriptor,
+                         cudnnBackendAttributeName_t attributeName,
+                         cudnnBackendAttributeType_t attributeType,
+                         int64_t requestedElementCount,
+                         int64_t *elementCount,
+                         void *arrayOfElements);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendExecute(cudnnHandle_t handle, cudnnBackendDescriptor_t executionPlan, cudnnBackendDescriptor_t variantPack);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* _CUDNN_BACKEND_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_backend_v8.h

@@ -0,0 +1,608 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CUDNN_BACKEND_H_
+#define _CUDNN_BACKEND_H_
+
+/*
+ * The content in this header file is under development to be included in cudnn.h in the future
+ * Production code should have all include of this header file remove.
+ */
+
+#include "cudnn_ops_infer.h"
+#include "cudnn_cnn_infer.h"
+
+/* NOTE: definition in extern "C" to be copied later to public header */
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+typedef void *cudnnBackendDescriptor_t;
+
+typedef struct cudnnFractionStruct {
+    int64_t numerator;
+    int64_t denominator;
+} cudnnFraction_t;
+
+typedef enum {
+    CUDNN_POINTWISE_ADD        = 0,
+    CUDNN_POINTWISE_ADD_SQUARE = 5,
+    CUDNN_POINTWISE_DIV        = 6,
+    CUDNN_POINTWISE_MAX        = 3,
+    CUDNN_POINTWISE_MIN        = 2,
+    CUDNN_POINTWISE_MOD        = 7,
+    CUDNN_POINTWISE_MUL        = 1,
+    CUDNN_POINTWISE_POW        = 8,
+    CUDNN_POINTWISE_SUB        = 9,
+
+    CUDNN_POINTWISE_ABS        = 10,
+    CUDNN_POINTWISE_CEIL       = 11,
+    CUDNN_POINTWISE_COS        = 12,
+    CUDNN_POINTWISE_EXP        = 13,
+    CUDNN_POINTWISE_FLOOR      = 14,
+    CUDNN_POINTWISE_LOG        = 15,
+    CUDNN_POINTWISE_NEG        = 16,
+    CUDNN_POINTWISE_RSQRT      = 17,
+    CUDNN_POINTWISE_SIN        = 18,
+    CUDNN_POINTWISE_SQRT       = 4,
+    CUDNN_POINTWISE_TAN        = 19,
+    CUDNN_POINTWISE_ERF        = 20,
+    CUDNN_POINTWISE_IDENTITY   = 21,
+    CUDNN_POINTWISE_RECIPROCAL = 22,
+
+    CUDNN_POINTWISE_RELU_FWD             = 100,
+    CUDNN_POINTWISE_TANH_FWD             = 101,
+    CUDNN_POINTWISE_SIGMOID_FWD          = 102,
+    CUDNN_POINTWISE_ELU_FWD              = 103,
+    CUDNN_POINTWISE_GELU_FWD             = 104,
+    CUDNN_POINTWISE_SOFTPLUS_FWD         = 105,
+    CUDNN_POINTWISE_SWISH_FWD            = 106,
+    CUDNN_POINTWISE_GELU_APPROX_TANH_FWD = 107,
+
+    CUDNN_POINTWISE_RELU_BWD             = 200,
+    CUDNN_POINTWISE_TANH_BWD             = 201,
+    CUDNN_POINTWISE_SIGMOID_BWD          = 202,
+    CUDNN_POINTWISE_ELU_BWD              = 203,
+    CUDNN_POINTWISE_GELU_BWD             = 204,
+    CUDNN_POINTWISE_SOFTPLUS_BWD         = 205,
+    CUDNN_POINTWISE_SWISH_BWD            = 206,
+    CUDNN_POINTWISE_GELU_APPROX_TANH_BWD = 207,
+
+    CUDNN_POINTWISE_CMP_EQ  = 300,
+    CUDNN_POINTWISE_CMP_NEQ = 301,
+    CUDNN_POINTWISE_CMP_GT  = 302,
+    CUDNN_POINTWISE_CMP_GE  = 303,
+    CUDNN_POINTWISE_CMP_LT  = 304,
+    CUDNN_POINTWISE_CMP_LE  = 305,
+
+    CUDNN_POINTWISE_LOGICAL_AND = 400,
+    CUDNN_POINTWISE_LOGICAL_OR  = 401,
+    CUDNN_POINTWISE_LOGICAL_NOT = 402,
+
+    CUDNN_POINTWISE_GEN_INDEX = 501,
+
+    CUDNN_POINTWISE_BINARY_SELECT = 601,
+} cudnnPointwiseMode_t;
+
+typedef enum {
+    CUDNN_RESAMPLE_NEAREST                 = 0,
+    CUDNN_RESAMPLE_BILINEAR                = 1,
+    CUDNN_RESAMPLE_AVGPOOL                 = 2,
+    CUDNN_RESAMPLE_AVGPOOL_INCLUDE_PADDING = 2,
+    CUDNN_RESAMPLE_AVGPOOL_EXCLUDE_PADDING = 4,
+    CUDNN_RESAMPLE_MAXPOOL                 = 3,
+} cudnnResampleMode_t;
+
+typedef enum {
+    CUDNN_SIGNAL_SET  = 0,
+    CUDNN_SIGNAL_WAIT = 1,
+} cudnnSignalMode_t;
+
+typedef enum {
+    CUDNN_GENSTATS_SUM_SQSUM = 0,
+} cudnnGenStatsMode_t;
+
+typedef enum {
+    CUDNN_BN_FINALIZE_STATISTICS_TRAINING  = 0,
+    CUDNN_BN_FINALIZE_STATISTICS_INFERENCE = 1,
+} cudnnBnFinalizeStatsMode_t;
+
+typedef enum {
+    CUDNN_RNG_DISTRIBUTION_BERNOULLI,
+    CUDNN_RNG_DISTRIBUTION_UNIFORM,
+    CUDNN_RNG_DISTRIBUTION_NORMAL,
+} cudnnRngDistribution_t;
+
+typedef enum {
+    CUDNN_ATTR_POINTWISE_MODE                  = 0,
+    CUDNN_ATTR_POINTWISE_MATH_PREC             = 1,
+    CUDNN_ATTR_POINTWISE_NAN_PROPAGATION       = 2,
+    CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP       = 3,
+    CUDNN_ATTR_POINTWISE_RELU_UPPER_CLIP       = 4,
+    CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP_SLOPE = 5,
+    CUDNN_ATTR_POINTWISE_ELU_ALPHA             = 6,
+    CUDNN_ATTR_POINTWISE_SOFTPLUS_BETA         = 7,
+    CUDNN_ATTR_POINTWISE_SWISH_BETA            = 8,
+    CUDNN_ATTR_POINTWISE_AXIS                  = 9,
+
+    CUDNN_ATTR_CONVOLUTION_COMP_TYPE      = 100,
+    CUDNN_ATTR_CONVOLUTION_CONV_MODE      = 101,
+    CUDNN_ATTR_CONVOLUTION_DILATIONS      = 102,
+    CUDNN_ATTR_CONVOLUTION_FILTER_STRIDES = 103,
+    CUDNN_ATTR_CONVOLUTION_POST_PADDINGS  = 104,
+    CUDNN_ATTR_CONVOLUTION_PRE_PADDINGS   = 105,
+    CUDNN_ATTR_CONVOLUTION_SPATIAL_DIMS   = 106,
+
+    CUDNN_ATTR_ENGINEHEUR_MODE            = 200,
+    CUDNN_ATTR_ENGINEHEUR_OPERATION_GRAPH = 201,
+    CUDNN_ATTR_ENGINEHEUR_RESULTS         = 202,
+
+    CUDNN_ATTR_ENGINECFG_ENGINE            = 300,
+    CUDNN_ATTR_ENGINECFG_INTERMEDIATE_INFO = 301,
+    CUDNN_ATTR_ENGINECFG_KNOB_CHOICES      = 302,
+
+    CUDNN_ATTR_EXECUTION_PLAN_HANDLE                     = 400,
+    CUDNN_ATTR_EXECUTION_PLAN_ENGINE_CONFIG              = 401,
+    CUDNN_ATTR_EXECUTION_PLAN_WORKSPACE_SIZE             = 402,
+    CUDNN_ATTR_EXECUTION_PLAN_COMPUTED_INTERMEDIATE_UIDS = 403,
+    CUDNN_ATTR_EXECUTION_PLAN_RUN_ONLY_INTERMEDIATE_UIDS = 404,
+    CUDNN_ATTR_EXECUTION_PLAN_JSON_REPRESENTATION        = 405,
+
+    CUDNN_ATTR_INTERMEDIATE_INFO_UNIQUE_ID            = 500,
+    CUDNN_ATTR_INTERMEDIATE_INFO_SIZE                 = 501,
+    CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_DATA_UIDS  = 502,
+    CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_ATTRIBUTES = 503,
+
+    CUDNN_ATTR_KNOB_CHOICE_KNOB_TYPE  = 600,
+    CUDNN_ATTR_KNOB_CHOICE_KNOB_VALUE = 601,
+
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_ALPHA        = 700,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_BETA         = 701,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_CONV_DESC    = 702,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_W            = 703,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_X            = 704,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_Y            = 705,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_ALPHA       = 706,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_BETA        = 707,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_CONV_DESC   = 708,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_W           = 709,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DX          = 710,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DY          = 711,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_ALPHA     = 712,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_BETA      = 713,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_CONV_DESC = 714,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DW        = 715,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_X         = 716,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DY        = 717,
+
+    CUDNN_ATTR_OPERATION_POINTWISE_PW_DESCRIPTOR = 750,
+    CUDNN_ATTR_OPERATION_POINTWISE_XDESC         = 751,
+    CUDNN_ATTR_OPERATION_POINTWISE_BDESC         = 752,
+    CUDNN_ATTR_OPERATION_POINTWISE_YDESC         = 753,
+    CUDNN_ATTR_OPERATION_POINTWISE_ALPHA1        = 754,
+    CUDNN_ATTR_OPERATION_POINTWISE_ALPHA2        = 755,
+    CUDNN_ATTR_OPERATION_POINTWISE_DXDESC        = 756,
+    CUDNN_ATTR_OPERATION_POINTWISE_DYDESC        = 757,
+    CUDNN_ATTR_OPERATION_POINTWISE_TDESC         = 758,
+
+    CUDNN_ATTR_OPERATION_GENSTATS_MODE      = 770,
+    CUDNN_ATTR_OPERATION_GENSTATS_MATH_PREC = 771,
+    CUDNN_ATTR_OPERATION_GENSTATS_XDESC     = 772,
+    CUDNN_ATTR_OPERATION_GENSTATS_SUMDESC   = 773,
+    CUDNN_ATTR_OPERATION_GENSTATS_SQSUMDESC = 774,
+
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_STATS_MODE                = 780,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_MATH_PREC                 = 781,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SUM_DESC                = 782,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SQ_SUM_DESC             = 783,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SCALE_DESC                = 784,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_BIAS_DESC                 = 785,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_MEAN_DESC    = 786,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_VAR_DESC     = 787,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_MEAN_DESC = 788,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_VAR_DESC  = 789,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_MEAN_DESC           = 790,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_INV_STD_DESC        = 791,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_SCALE_DESC             = 792,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_BIAS_DESC              = 793,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_ACCUM_COUNT_DESC          = 794,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EPSILON_DESC              = 795,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EXP_AVERATE_FACTOR_DESC   = 796,
+
+    CUDNN_ATTR_OPERATIONGRAPH_HANDLE              = 800,
+    CUDNN_ATTR_OPERATIONGRAPH_OPS                 = 801,
+    CUDNN_ATTR_OPERATIONGRAPH_ENGINE_GLOBAL_COUNT = 802,
+
+    CUDNN_ATTR_TENSOR_BYTE_ALIGNMENT       = 900,
+    CUDNN_ATTR_TENSOR_DATA_TYPE            = 901,
+    CUDNN_ATTR_TENSOR_DIMENSIONS           = 902,
+    CUDNN_ATTR_TENSOR_STRIDES              = 903,
+    CUDNN_ATTR_TENSOR_VECTOR_COUNT         = 904,
+    CUDNN_ATTR_TENSOR_VECTORIZED_DIMENSION = 905,
+    CUDNN_ATTR_TENSOR_UNIQUE_ID            = 906,
+    CUDNN_ATTR_TENSOR_IS_VIRTUAL           = 907,
+    CUDNN_ATTR_TENSOR_IS_BY_VALUE          = 908,
+    CUDNN_ATTR_TENSOR_REORDERING_MODE      = 909,
+    CUDNN_ATTR_TENSOR_RAGGED_OFFSET_DESC   = 913,
+
+    CUDNN_ATTR_VARIANT_PACK_UNIQUE_IDS    = 1000,
+    CUDNN_ATTR_VARIANT_PACK_DATA_POINTERS = 1001,
+    CUDNN_ATTR_VARIANT_PACK_INTERMEDIATES = 1002,
+    CUDNN_ATTR_VARIANT_PACK_WORKSPACE     = 1003,
+
+    CUDNN_ATTR_LAYOUT_INFO_TENSOR_UID = 1100,
+    CUDNN_ATTR_LAYOUT_INFO_TYPES      = 1101,
+
+    CUDNN_ATTR_KNOB_INFO_TYPE          = 1200,
+    CUDNN_ATTR_KNOB_INFO_MAXIMUM_VALUE = 1201,
+    CUDNN_ATTR_KNOB_INFO_MINIMUM_VALUE = 1202,
+    CUDNN_ATTR_KNOB_INFO_STRIDE        = 1203,
+
+    CUDNN_ATTR_ENGINE_OPERATION_GRAPH = 1300,
+    CUDNN_ATTR_ENGINE_GLOBAL_INDEX    = 1301,
+    CUDNN_ATTR_ENGINE_KNOB_INFO       = 1302,
+    CUDNN_ATTR_ENGINE_NUMERICAL_NOTE  = 1303,
+    CUDNN_ATTR_ENGINE_LAYOUT_INFO     = 1304,
+    CUDNN_ATTR_ENGINE_BEHAVIOR_NOTE   = 1305,
+
+    CUDNN_ATTR_MATMUL_COMP_TYPE     = 1500,
+    CUDNN_ATTR_MATMUL_PADDING_VALUE = 1503,
+
+    CUDNN_ATTR_OPERATION_MATMUL_ADESC                           = 1520,
+    CUDNN_ATTR_OPERATION_MATMUL_BDESC                           = 1521,
+    CUDNN_ATTR_OPERATION_MATMUL_CDESC                           = 1522,
+    CUDNN_ATTR_OPERATION_MATMUL_DESC                            = 1523,
+    CUDNN_ATTR_OPERATION_MATMUL_IRREGULARLY_STRIDED_BATCH_COUNT = 1524,
+    CUDNN_ATTR_OPERATION_MATMUL_GEMM_M_OVERRIDE_DESC            = 1525,
+    CUDNN_ATTR_OPERATION_MATMUL_GEMM_N_OVERRIDE_DESC            = 1526,
+    CUDNN_ATTR_OPERATION_MATMUL_GEMM_K_OVERRIDE_DESC            = 1527,
+
+    CUDNN_ATTR_REDUCTION_OPERATOR  = 1600,
+    CUDNN_ATTR_REDUCTION_COMP_TYPE = 1601,
+
+    CUDNN_ATTR_OPERATION_REDUCTION_XDESC = 1610,
+    CUDNN_ATTR_OPERATION_REDUCTION_YDESC = 1611,
+    CUDNN_ATTR_OPERATION_REDUCTION_DESC  = 1612,
+
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MATH_PREC        = 1620,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MEAN_DESC        = 1621,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_INVSTD_DESC      = 1622,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_BN_SCALE_DESC    = 1623,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_X_DESC           = 1624,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DY_DESC          = 1625,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_SCALE_DESC   = 1626,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_BIAS_DESC    = 1627,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_DY_SCALE_DESC = 1628,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_X_SCALE_DESC  = 1629,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_BIAS          = 1630,
+
+    CUDNN_ATTR_RESAMPLE_MODE            = 1700,
+    CUDNN_ATTR_RESAMPLE_COMP_TYPE       = 1701,
+    CUDNN_ATTR_RESAMPLE_SPATIAL_DIMS    = 1702,
+    CUDNN_ATTR_RESAMPLE_POST_PADDINGS   = 1703,
+    CUDNN_ATTR_RESAMPLE_PRE_PADDINGS    = 1704,
+    CUDNN_ATTR_RESAMPLE_STRIDES         = 1705,
+    CUDNN_ATTR_RESAMPLE_WINDOW_DIMS     = 1706,
+    CUDNN_ATTR_RESAMPLE_NAN_PROPAGATION = 1707,
+    CUDNN_ATTR_RESAMPLE_PADDING_MODE    = 1708,
+
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_XDESC   = 1710,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_YDESC   = 1711,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_IDXDESC = 1712,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_ALPHA   = 1713,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_BETA    = 1714,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_DESC    = 1716,
+
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DXDESC  = 1720,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DYDESC  = 1721,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_IDXDESC = 1722,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_ALPHA   = 1723,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_BETA    = 1724,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DESC    = 1725,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_XDESC   = 1726,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_YDESC   = 1727,
+
+    CUDNN_ATTR_OPERATION_CONCAT_AXIS          = 1800,
+    CUDNN_ATTR_OPERATION_CONCAT_INPUT_DESCS   = 1801,
+    CUDNN_ATTR_OPERATION_CONCAT_INPLACE_INDEX = 1802,
+    CUDNN_ATTR_OPERATION_CONCAT_OUTPUT_DESC   = 1803,
+
+    CUDNN_ATTR_OPERATION_SIGNAL_MODE     = 1900,
+    CUDNN_ATTR_OPERATION_SIGNAL_FLAGDESC = 1901,
+    CUDNN_ATTR_OPERATION_SIGNAL_VALUE    = 1902,
+    CUDNN_ATTR_OPERATION_SIGNAL_XDESC    = 1903,
+    CUDNN_ATTR_OPERATION_SIGNAL_YDESC    = 1904,
+
+    CUDNN_ATTR_OPERATION_NORM_FWD_MODE                     = 2000,
+    CUDNN_ATTR_OPERATION_NORM_FWD_PHASE                    = 2001,
+    CUDNN_ATTR_OPERATION_NORM_FWD_XDESC                    = 2002,
+    CUDNN_ATTR_OPERATION_NORM_FWD_MEAN_DESC                = 2003,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INV_VARIANCE_DESC        = 2004,
+    CUDNN_ATTR_OPERATION_NORM_FWD_SCALE_DESC               = 2005,
+    CUDNN_ATTR_OPERATION_NORM_FWD_BIAS_DESC                = 2006,
+    CUDNN_ATTR_OPERATION_NORM_FWD_EPSILON_DESC             = 2007,
+    CUDNN_ATTR_OPERATION_NORM_FWD_EXP_AVG_FACTOR_DESC      = 2008,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_MEAN_DESC  = 2009,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_VAR_DESC   = 2010,
+    CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_MEAN_DESC = 2011,
+    CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_VAR_DESC  = 2012,
+    CUDNN_ATTR_OPERATION_NORM_FWD_YDESC                    = 2013,
+    CUDNN_ATTR_OPERATION_NORM_FWD_PEER_STAT_DESCS          = 2014,
+
+    CUDNN_ATTR_OPERATION_NORM_BWD_MODE              = 2100,
+    CUDNN_ATTR_OPERATION_NORM_BWD_XDESC             = 2101,
+    CUDNN_ATTR_OPERATION_NORM_BWD_MEAN_DESC         = 2102,
+    CUDNN_ATTR_OPERATION_NORM_BWD_INV_VARIANCE_DESC = 2103,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DYDESC            = 2104,
+    CUDNN_ATTR_OPERATION_NORM_BWD_SCALE_DESC        = 2105,
+    CUDNN_ATTR_OPERATION_NORM_BWD_EPSILON_DESC      = 2106,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DSCALE_DESC       = 2107,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DBIAS_DESC        = 2108,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DXDESC            = 2109,
+    CUDNN_ATTR_OPERATION_NORM_BWD_PEER_STAT_DESCS   = 2110,
+
+    CUDNN_ATTR_OPERATION_RESHAPE_XDESC = 2200,
+    CUDNN_ATTR_OPERATION_RESHAPE_YDESC = 2201,
+
+    CUDNN_ATTR_RNG_DISTRIBUTION                   = 2300,
+    CUDNN_ATTR_RNG_NORMAL_DIST_MEAN               = 2301,
+    CUDNN_ATTR_RNG_NORMAL_DIST_STANDARD_DEVIATION = 2302,
+    CUDNN_ATTR_RNG_UNIFORM_DIST_MAXIMUM           = 2303,
+    CUDNN_ATTR_RNG_UNIFORM_DIST_MINIMUM           = 2304,
+    CUDNN_ATTR_RNG_BERNOULLI_DIST_PROBABILITY     = 2305,
+
+    CUDNN_ATTR_OPERATION_RNG_YDESC       = 2310,
+    CUDNN_ATTR_OPERATION_RNG_SEED        = 2311,
+    CUDNN_ATTR_OPERATION_RNG_DESC        = 2312,
+    CUDNN_ATTR_OPERATION_RNG_OFFSET_DESC = 2313,
+
+} cudnnBackendAttributeName_t;
+
+typedef enum {
+    CUDNN_TYPE_HANDLE = 0,
+    CUDNN_TYPE_DATA_TYPE,
+    CUDNN_TYPE_BOOLEAN,
+    CUDNN_TYPE_INT64,
+    CUDNN_TYPE_FLOAT,
+    CUDNN_TYPE_DOUBLE,
+    CUDNN_TYPE_VOID_PTR,
+    CUDNN_TYPE_CONVOLUTION_MODE,
+    CUDNN_TYPE_HEUR_MODE,
+    CUDNN_TYPE_KNOB_TYPE,
+    CUDNN_TYPE_NAN_PROPOGATION,
+    CUDNN_TYPE_NUMERICAL_NOTE,
+    CUDNN_TYPE_LAYOUT_TYPE,
+    CUDNN_TYPE_ATTRIB_NAME,
+    CUDNN_TYPE_POINTWISE_MODE,
+    CUDNN_TYPE_BACKEND_DESCRIPTOR,
+    CUDNN_TYPE_GENSTATS_MODE,
+    CUDNN_TYPE_BN_FINALIZE_STATS_MODE,
+    CUDNN_TYPE_REDUCTION_OPERATOR_TYPE,
+    CUDNN_TYPE_BEHAVIOR_NOTE,
+    CUDNN_TYPE_TENSOR_REORDERING_MODE,
+    CUDNN_TYPE_RESAMPLE_MODE,
+    CUDNN_TYPE_PADDING_MODE,
+    CUDNN_TYPE_INT32,
+    CUDNN_TYPE_CHAR,
+    CUDNN_TYPE_SIGNAL_MODE,
+    CUDNN_TYPE_FRACTION,
+    CUDNN_TYPE_NORM_MODE,
+    CUDNN_TYPE_NORM_FWD_PHASE,
+    CUDNN_TYPE_RNG_DISTRIBUTION
+} cudnnBackendAttributeType_t;
+
+typedef enum {
+    CUDNN_BACKEND_POINTWISE_DESCRIPTOR = 0,
+    CUDNN_BACKEND_CONVOLUTION_DESCRIPTOR,
+    CUDNN_BACKEND_ENGINE_DESCRIPTOR,
+    CUDNN_BACKEND_ENGINECFG_DESCRIPTOR,
+    CUDNN_BACKEND_ENGINEHEUR_DESCRIPTOR,
+    CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR,
+    CUDNN_BACKEND_INTERMEDIATE_INFO_DESCRIPTOR,
+    CUDNN_BACKEND_KNOB_CHOICE_DESCRIPTOR,
+    CUDNN_BACKEND_KNOB_INFO_DESCRIPTOR,
+    CUDNN_BACKEND_LAYOUT_INFO_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_GEN_STATS_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATIONGRAPH_DESCRIPTOR,
+    CUDNN_BACKEND_VARIANT_PACK_DESCRIPTOR,
+    CUDNN_BACKEND_TENSOR_DESCRIPTOR,
+    CUDNN_BACKEND_MATMUL_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_MATMUL_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_BN_FINALIZE_STATISTICS_DESCRIPTOR,
+    CUDNN_BACKEND_REDUCTION_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_BN_BWD_WEIGHTS_DESCRIPTOR,
+    CUDNN_BACKEND_RESAMPLE_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_RESAMPLE_FWD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_RESAMPLE_BWD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_CONCAT_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_SIGNAL_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_NORM_FORWARD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_NORM_BACKWARD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_RESHAPE_DESCRIPTOR,
+    CUDNN_BACKEND_RNG_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_RNG_DESCRIPTOR
+} cudnnBackendDescriptorType_t;
+
+typedef enum {
+    CUDNN_NUMERICAL_NOTE_TENSOR_CORE = 0,
+    CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS,
+    CUDNN_NUMERICAL_NOTE_REDUCED_PRECISION_REDUCTION,
+    CUDNN_NUMERICAL_NOTE_FFT,
+    CUDNN_NUMERICAL_NOTE_NONDETERMINISTIC,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_4x4,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_6x6,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_13x13,
+    CUDNN_NUMERICAL_NOTE_TYPE_COUNT,
+} cudnnBackendNumericalNote_t;
+
+typedef enum {
+    CUDNN_BEHAVIOR_NOTE_RUNTIME_COMPILATION             = 0,
+    CUDNN_BEHAVIOR_NOTE_REQUIRES_FILTER_INT8x32_REORDER = 1,
+    CUDNN_BEHAVIOR_NOTE_REQUIRES_BIAS_INT8x32_REORDER   = 2,
+    CUDNN_BEHAVIOR_NOTE_TYPE_COUNT,
+} cudnnBackendBehaviorNote_t;
+
+typedef enum {
+    CUDNN_KNOB_TYPE_SPLIT_K               = 0,
+    CUDNN_KNOB_TYPE_SWIZZLE               = 1,
+    CUDNN_KNOB_TYPE_TILE_SIZE             = 2,
+    CUDNN_KNOB_TYPE_USE_TEX               = 3,
+    CUDNN_KNOB_TYPE_EDGE                  = 4,
+    CUDNN_KNOB_TYPE_KBLOCK                = 5,
+    CUDNN_KNOB_TYPE_LDGA                  = 6,
+    CUDNN_KNOB_TYPE_LDGB                  = 7,
+    CUDNN_KNOB_TYPE_CHUNK_K               = 8,
+    CUDNN_KNOB_TYPE_SPLIT_H               = 9,
+    CUDNN_KNOB_TYPE_WINO_TILE             = 10,
+    CUDNN_KNOB_TYPE_MULTIPLY              = 11,
+    CUDNN_KNOB_TYPE_SPLIT_K_BUF           = 12,
+    CUDNN_KNOB_TYPE_TILEK                 = 13,
+    CUDNN_KNOB_TYPE_STAGES                = 14,
+    CUDNN_KNOB_TYPE_REDUCTION_MODE        = 15,
+    CUDNN_KNOB_TYPE_CTA_SPLIT_K_MODE      = 16,
+    CUDNN_KNOB_TYPE_SPLIT_K_SLC           = 17,
+    CUDNN_KNOB_TYPE_IDX_MODE              = 18,
+    CUDNN_KNOB_TYPE_SLICED                = 19,
+    CUDNN_KNOB_TYPE_SPLIT_RS              = 20,
+    CUDNN_KNOB_TYPE_SINGLEBUFFER          = 21,
+    CUDNN_KNOB_TYPE_LDGC                  = 22,
+    CUDNN_KNOB_TYPE_SPECFILT              = 23,
+    CUDNN_KNOB_TYPE_KERNEL_CFG            = 24,
+    CUDNN_KNOB_TYPE_WORKSPACE             = 25,
+    CUDNN_KNOB_TYPE_TILE_CGA              = 26,
+    CUDNN_KNOB_TYPE_TILE_CGA_M            = 27,
+    CUDNN_KNOB_TYPE_TILE_CGA_N            = 28,
+    CUDNN_KNOB_TYPE_BLOCK_SIZE            = 29,
+    CUDNN_KNOB_TYPE_OCCUPANCY             = 30,
+    CUDNN_KNOB_TYPE_ARRAY_SIZE_PER_THREAD = 31,
+    CUDNN_KNOB_TYPE_NUM_C_PER_BLOCK       = 32,
+    CUDNN_KNOB_TYPE_COUNTS,
+} cudnnBackendKnobType_t;
+
+typedef enum {
+    CUDNN_LAYOUT_TYPE_PREFERRED_NCHW   = 0,
+    CUDNN_LAYOUT_TYPE_PREFERRED_NHWC   = 1,
+    CUDNN_LAYOUT_TYPE_PREFERRED_PAD4CK = 2,
+    CUDNN_LAYOUT_TYPE_PREFERRED_PAD8CK = 3,
+    CUDNN_LAYOUT_TYPE_COUNT            = 4,
+} cudnnBackendLayoutType_t;
+
+typedef enum {
+    CUDNN_HEUR_MODE_INSTANT  = 0,
+    CUDNN_HEUR_MODE_B        = 1,
+    CUDNN_HEUR_MODE_FALLBACK = 2,
+    CUDNN_HEUR_MODE_A        = 3,
+    CUDNN_HEUR_MODES_COUNT   = 4,
+} cudnnBackendHeurMode_t;
+
+typedef enum {
+    CUDNN_TENSOR_REORDERING_NONE    = 0,
+    CUDNN_TENSOR_REORDERING_INT8x32 = 1,
+    CUDNN_TENSOR_REORDERING_F16x16  = 2,
+} cudnnBackendTensorReordering_t;
+
+typedef enum {
+    CUDNN_ZERO_PAD     = 0,
+    CUDNN_NEG_INF_PAD  = 1,
+    CUDNN_EDGE_VAL_PAD = 2,
+} cudnnPaddingMode_t;
+
+typedef enum {
+    CUDNN_LAYER_NORM    = 0,
+    CUDNN_INSTANCE_NORM = 1,
+    CUDNN_BATCH_NORM    = 2,
+    CUDNN_GROUP_NORM    = 3,
+} cudnnBackendNormMode_t;
+
+typedef enum {
+    CUDNN_NORM_FWD_INFERENCE = 0,
+    CUDNN_NORM_FWD_TRAINING  = 1,
+} cudnnBackendNormFwdPhase_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendCreateDescriptor(cudnnBackendDescriptorType_t descriptorType, cudnnBackendDescriptor_t *descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendDestroyDescriptor(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendInitialize(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendFinalize(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendSetAttribute(cudnnBackendDescriptor_t descriptor,
+                         cudnnBackendAttributeName_t attributeName,
+                         cudnnBackendAttributeType_t attributeType,
+                         int64_t elementCount,
+                         const void *arrayOfElements);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendGetAttribute(cudnnBackendDescriptor_t const descriptor,
+                         cudnnBackendAttributeName_t attributeName,
+                         cudnnBackendAttributeType_t attributeType,
+                         int64_t requestedElementCount,
+                         int64_t *elementCount,
+                         void *arrayOfElements);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendExecute(cudnnHandle_t handle, cudnnBackendDescriptor_t executionPlan, cudnnBackendDescriptor_t variantPack);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* _CUDNN_BACKEND_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_cnn_infer.h

@@ -0,0 +1,571 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_cnn_infer : cuDNN's basic definitions and inference CNN functions.
+ */
+
+#if !defined(CUDNN_CNN_INFER_H_)
+#define CUDNN_CNN_INFER_H_
+
+#pragma once
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_CNN_INFER_MAJOR 8
+#define CUDNN_CNN_INFER_MINOR 9
+#define CUDNN_CNN_INFER_PATCH 2
+
+#if (CUDNN_CNN_INFER_MAJOR != CUDNN_MAJOR) || (CUDNN_CNN_INFER_MINOR != CUDNN_MINOR) || \
+    (CUDNN_CNN_INFER_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN CNN INFER!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+typedef struct cudnnConvolutionStruct *cudnnConvolutionDescriptor_t;
+
+/*
+ *  convolution mode
+ */
+typedef enum { CUDNN_CONVOLUTION = 0, CUDNN_CROSS_CORRELATION = 1 } cudnnConvolutionMode_t;
+
+/*
+ * CUDNN Reorder
+ */
+typedef enum {
+    CUDNN_DEFAULT_REORDER = 0,
+    CUDNN_NO_REORDER      = 1,
+} cudnnReorderType_t;
+
+typedef struct cudnnConvolutionFwdAlgoPerfStruct {
+    cudnnConvolutionFwdAlgo_t algo;
+    cudnnStatus_t status;
+    float time;
+    size_t memory;
+    cudnnDeterminism_t determinism;
+    cudnnMathType_t mathType;
+    int reserved[3];
+} cudnnConvolutionFwdAlgoPerf_t;
+
+/* Create an instance of convolution descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateConvolutionDescriptor(cudnnConvolutionDescriptor_t *convDesc);
+
+/* Destroy an instance of convolution descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyConvolutionDescriptor(cudnnConvolutionDescriptor_t convDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionMathType(cudnnConvolutionDescriptor_t convDesc, cudnnMathType_t mathType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionMathType(cudnnConvolutionDescriptor_t convDesc, cudnnMathType_t *mathType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionGroupCount(cudnnConvolutionDescriptor_t convDesc, int groupCount);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionGroupCount(cudnnConvolutionDescriptor_t convDesc, int *groupCount);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionReorderType(cudnnConvolutionDescriptor_t convDesc, cudnnReorderType_t reorderType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionReorderType(cudnnConvolutionDescriptor_t convDesc, cudnnReorderType_t *reorderType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolution2dDescriptor(cudnnConvolutionDescriptor_t convDesc,
+                                int pad_h,      /* zero-padding height */
+                                int pad_w,      /* zero-padding width */
+                                int u,          /* vertical filter stride */
+                                int v,          /* horizontal filter stride */
+                                int dilation_h, /* filter dilation in the vertical dimension */
+                                int dilation_w, /* filter dilation in the horizontal dimension */
+                                cudnnConvolutionMode_t mode,
+                                cudnnDataType_t computeType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolution2dDescriptor(const cudnnConvolutionDescriptor_t convDesc,
+                                int *pad_h,      /* zero-padding height */
+                                int *pad_w,      /* zero-padding width */
+                                int *u,          /* vertical filter stride */
+                                int *v,          /* horizontal filter stride */
+                                int *dilation_h, /* filter dilation in the vertical dimension */
+                                int *dilation_w, /* filter dilation in the horizontal dimension */
+                                cudnnConvolutionMode_t *mode,
+                                cudnnDataType_t *computeType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionNdDescriptor(cudnnConvolutionDescriptor_t convDesc,
+                                int arrayLength, /* nbDims-2 size */
+                                const int padA[],
+                                const int filterStrideA[],
+                                const int dilationA[],
+                                cudnnConvolutionMode_t mode,
+                                cudnnDataType_t computeType); /* convolution data type */
+
+/* Helper function to return the dimensions of the output tensor given a convolution descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionNdDescriptor(const cudnnConvolutionDescriptor_t convDesc,
+                                int arrayLengthRequested,
+                                int *arrayLength,
+                                int padA[],
+                                int strideA[],
+                                int dilationA[],
+                                cudnnConvolutionMode_t *mode,
+                                cudnnDataType_t *computeType); /* convolution data type */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolution2dForwardOutputDim(const cudnnConvolutionDescriptor_t convDesc,
+                                      const cudnnTensorDescriptor_t inputTensorDesc,
+                                      const cudnnFilterDescriptor_t filterDesc,
+                                      int *n,
+                                      int *c,
+                                      int *h,
+                                      int *w);
+
+/* Helper function to return the dimensions of the output tensor given a convolution descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionNdForwardOutputDim(const cudnnConvolutionDescriptor_t convDesc,
+                                      const cudnnTensorDescriptor_t inputTensorDesc,
+                                      const cudnnFilterDescriptor_t filterDesc,
+                                      int nbDims,
+                                      int tensorOuputDimA[]);
+
+/* helper function to provide the convolution forward algo that fit best the requirement */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionForwardAlgorithmMaxCount(cudnnHandle_t handle, int *count);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionForwardAlgorithm_v7(cudnnHandle_t handle,
+                                       const cudnnTensorDescriptor_t srcDesc,
+                                       const cudnnFilterDescriptor_t filterDesc,
+                                       const cudnnConvolutionDescriptor_t convDesc,
+                                       const cudnnTensorDescriptor_t destDesc,
+                                       const int requestedAlgoCount,
+                                       int *returnedAlgoCount,
+                                       cudnnConvolutionFwdAlgoPerf_t *perfResults);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionForwardAlgorithm(cudnnHandle_t handle,
+                                     const cudnnTensorDescriptor_t xDesc,
+                                     const cudnnFilterDescriptor_t wDesc,
+                                     const cudnnConvolutionDescriptor_t convDesc,
+                                     const cudnnTensorDescriptor_t yDesc,
+                                     const int requestedAlgoCount,
+                                     int *returnedAlgoCount,
+                                     cudnnConvolutionFwdAlgoPerf_t *perfResults);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionForwardAlgorithmEx(cudnnHandle_t handle,
+                                       const cudnnTensorDescriptor_t xDesc,
+                                       const void *x,
+                                       const cudnnFilterDescriptor_t wDesc,
+                                       const void *w,
+                                       const cudnnConvolutionDescriptor_t convDesc,
+                                       const cudnnTensorDescriptor_t yDesc,
+                                       void *y,
+                                       const int requestedAlgoCount,
+                                       int *returnedAlgoCount,
+                                       cudnnConvolutionFwdAlgoPerf_t *perfResults,
+                                       void *workSpace,
+                                       size_t workSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnIm2Col(cudnnHandle_t handle,
+            const cudnnTensorDescriptor_t xDesc,
+            const void *x,
+            const cudnnFilterDescriptor_t wDesc,
+            const cudnnConvolutionDescriptor_t convDesc,
+            void *colBuffer);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnReorderFilterAndBias(cudnnHandle_t handle,
+                          const cudnnFilterDescriptor_t filterDesc,
+                          cudnnReorderType_t reorderType,
+                          const void *filterData,
+                          void *reorderedFilterData,
+                          int reorderBias,
+                          const void *biasData,
+                          void *reorderedBiasData);
+
+/* Helper function to return the minimum size of the workspace to be passed to the convolution given an algo*/
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle_t handle,
+                                        const cudnnTensorDescriptor_t xDesc,
+                                        const cudnnFilterDescriptor_t wDesc,
+                                        const cudnnConvolutionDescriptor_t convDesc,
+                                        const cudnnTensorDescriptor_t yDesc,
+                                        cudnnConvolutionFwdAlgo_t algo,
+                                        size_t *sizeInBytes);
+
+/* Convolution functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform the forward pass for batch convolution */
+cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionForward(cudnnHandle_t handle,
+                        const void *alpha,
+                        const cudnnTensorDescriptor_t xDesc,
+                        const void *x,
+                        const cudnnFilterDescriptor_t wDesc,
+                        const void *w,
+                        const cudnnConvolutionDescriptor_t convDesc,
+                        cudnnConvolutionFwdAlgo_t algo,
+                        void *workSpace,
+                        size_t workSpaceSizeInBytes,
+                        const void *beta,
+                        const cudnnTensorDescriptor_t yDesc,
+                        void *y);
+
+/* Fused conv/bias/activation operation : y = Act( alpha1 * conv(x) + alpha2 * z + bias ) */
+cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBiasActivationForward(cudnnHandle_t handle,
+                                      const void *alpha1,
+                                      const cudnnTensorDescriptor_t xDesc,
+                                      const void *x,
+                                      const cudnnFilterDescriptor_t wDesc,
+                                      const void *w,
+                                      const cudnnConvolutionDescriptor_t convDesc,
+                                      cudnnConvolutionFwdAlgo_t algo,
+                                      void *workSpace,
+                                      size_t workSpaceSizeInBytes,
+                                      const void *alpha2,
+                                      const cudnnTensorDescriptor_t zDesc,
+                                      const void *z,
+                                      const cudnnTensorDescriptor_t biasDesc,
+                                      const void *bias,
+                                      const cudnnActivationDescriptor_t activationDesc,
+                                      const cudnnTensorDescriptor_t yDesc,
+                                      void *y);
+
+/* helper function to provide the convolution backward data algo that fit best the requirement */
+
+typedef struct cudnnConvolutionBwdDataAlgoPerfStruct {
+    cudnnConvolutionBwdDataAlgo_t algo;
+    cudnnStatus_t status;
+    float time;
+    size_t memory;
+    cudnnDeterminism_t determinism;
+    cudnnMathType_t mathType;
+    int reserved[3];
+} cudnnConvolutionBwdDataAlgoPerf_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardDataAlgorithmMaxCount(cudnnHandle_t handle, int *count);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardDataAlgorithm(cudnnHandle_t handle,
+                                          const cudnnFilterDescriptor_t wDesc,
+                                          const cudnnTensorDescriptor_t dyDesc,
+                                          const cudnnConvolutionDescriptor_t convDesc,
+                                          const cudnnTensorDescriptor_t dxDesc,
+                                          const int requestedAlgoCount,
+                                          int *returnedAlgoCount,
+                                          cudnnConvolutionBwdDataAlgoPerf_t *perfResults);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardDataAlgorithmEx(cudnnHandle_t handle,
+                                            const cudnnFilterDescriptor_t wDesc,
+                                            const void *w,
+                                            const cudnnTensorDescriptor_t dyDesc,
+                                            const void *dy,
+                                            const cudnnConvolutionDescriptor_t convDesc,
+                                            const cudnnTensorDescriptor_t dxDesc,
+                                            void *dx,
+                                            const int requestedAlgoCount,
+                                            int *returnedAlgoCount,
+                                            cudnnConvolutionBwdDataAlgoPerf_t *perfResults,
+                                            void *workSpace,
+                                            size_t workSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardDataAlgorithm_v7(cudnnHandle_t handle,
+                                            const cudnnFilterDescriptor_t filterDesc,
+                                            const cudnnTensorDescriptor_t diffDesc,
+                                            const cudnnConvolutionDescriptor_t convDesc,
+                                            const cudnnTensorDescriptor_t gradDesc,
+                                            const int requestedAlgoCount,
+                                            int *returnedAlgoCount,
+                                            cudnnConvolutionBwdDataAlgoPerf_t *perfResults);
+
+/*
+ *  convolution algorithm (which requires potentially some workspace)
+ */
+
+/* Helper function to return the minimum size of the workspace to be passed to the convolution given an algo*/
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardDataWorkspaceSize(cudnnHandle_t handle,
+                                             const cudnnFilterDescriptor_t wDesc,
+                                             const cudnnTensorDescriptor_t dyDesc,
+                                             const cudnnConvolutionDescriptor_t convDesc,
+                                             const cudnnTensorDescriptor_t dxDesc,
+                                             cudnnConvolutionBwdDataAlgo_t algo,
+                                             size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBackwardData(cudnnHandle_t handle,
+                             const void *alpha,
+                             const cudnnFilterDescriptor_t wDesc,
+                             const void *w,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const cudnnConvolutionDescriptor_t convDesc,
+                             cudnnConvolutionBwdDataAlgo_t algo,
+                             void *workSpace,
+                             size_t workSpaceSizeInBytes,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dxDesc,
+                             void *dx);
+
+/* Helper function to calculate folding descriptors for dgrad */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFoldedConvBackwardDataDescriptors(const cudnnHandle_t handle,
+                                          const cudnnFilterDescriptor_t filterDesc,
+                                          const cudnnTensorDescriptor_t diffDesc,
+                                          const cudnnConvolutionDescriptor_t convDesc,
+                                          const cudnnTensorDescriptor_t gradDesc,
+                                          const cudnnTensorFormat_t transformFormat,
+                                          cudnnFilterDescriptor_t foldedFilterDesc,
+                                          cudnnTensorDescriptor_t paddedDiffDesc,
+                                          cudnnConvolutionDescriptor_t foldedConvDesc,
+                                          cudnnTensorDescriptor_t foldedGradDesc,
+                                          cudnnTensorTransformDescriptor_t filterFoldTransDesc,
+                                          cudnnTensorTransformDescriptor_t diffPadTransDesc,
+                                          cudnnTensorTransformDescriptor_t gradFoldTransDesc,
+                                          cudnnTensorTransformDescriptor_t gradUnfoldTransDesc);
+
+/* cudnnFusedOps... */
+struct cudnnFusedOpsConstParamStruct;
+typedef struct cudnnFusedOpsConstParamStruct *cudnnFusedOpsConstParamPack_t;
+
+struct cudnnFusedOpsVariantParamStruct;
+typedef struct cudnnFusedOpsVariantParamStruct *cudnnFusedOpsVariantParamPack_t;
+
+struct cudnnFusedOpsPlanStruct;
+typedef struct cudnnFusedOpsPlanStruct *cudnnFusedOpsPlan_t;
+
+typedef enum {
+    /* each op in [ ] can be disabled by passing NULL ptr */
+    /* [per channel scale], [per channel bias], [activation], convolution, [generate BN stats] */
+    CUDNN_FUSED_SCALE_BIAS_ACTIVATION_CONV_BNSTATS = 0,
+    /* [per channel scale], [per channel bias], [activation], convolutionBackwardWeights */
+    CUDNN_FUSED_SCALE_BIAS_ACTIVATION_WGRAD = 1,
+    /* utility for BN training in BN-conv fusion */
+    /* computes the equivalent scale and bias from ySum ySqSum and learned scale, bias */
+    /* optionally update running stats and generate saved stats */
+    CUDNN_FUSED_BN_FINALIZE_STATISTICS_TRAINING = 2,
+    /* utility for BN inference in BN-conv fusion */
+    /* computes the equivalent scale and bias from learned running stats and learned scale, bias */
+    CUDNN_FUSED_BN_FINALIZE_STATISTICS_INFERENCE = 3,
+    /* reserved for future use: convolution, [per channel scale], [per channel bias], [residual add], [activation] */
+    CUDNN_FUSED_CONV_SCALE_BIAS_ADD_ACTIVATION = 4,
+    /* reserved for future use: [per channel scale], [per channel bias], [residual add],  activation, bitmask */
+    CUDNN_FUSED_SCALE_BIAS_ADD_ACTIVATION_GEN_BITMASK = 5,
+    /* reserved for future use */
+    CUDNN_FUSED_DACTIVATION_FORK_DBATCHNORM = 6,
+} cudnnFusedOps_t;
+
+typedef enum {
+    /* set XDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get XDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_XDESC = 0,
+    /* set/get XDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_XDATA_PLACEHOLDER = 1,
+    /* set/get BN_MODE: pass cudnnBatchNormMode_t* */
+    CUDNN_PARAM_BN_MODE = 2,
+    /* set CUDNN_PARAM_BN_EQSCALEBIAS_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get CUDNN_PARAM_BN_EQSCALEBIAS_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_BN_EQSCALEBIAS_DESC = 3,
+    /* set/get BN_EQSCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_EQSCALE_PLACEHOLDER = 4,
+    /* set/get BN_EQBIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_EQBIAS_PLACEHOLDER = 5,
+    /* set ACTIVATION_DESC: pass previously initialized cudnnActivationDescriptor_t */
+    /* get ACTIVATION_DESC: pass previously created cudnnActivationDescriptor_t */
+    CUDNN_PARAM_ACTIVATION_DESC = 6,
+    /* set CONV_DESC: pass previously initialized cudnnConvolutionDescriptor_t */
+    /* get CONV_DESC: pass previously created cudnnConvolutionDescriptor_t */
+    CUDNN_PARAM_CONV_DESC = 7,
+    /* set WDESC: pass previously initialized cudnnFilterDescriptor_t */
+    /* get WDESC: pass previously created cudnnFilterDescriptor_t */
+    CUDNN_PARAM_WDESC = 8,
+    /* set/get WDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_WDATA_PLACEHOLDER = 9,
+    /* set DWDESC: pass previously initialized cudnnFilterDescriptor_t */
+    /* get DWDESC: pass previously created cudnnFilterDescriptor_t */
+    CUDNN_PARAM_DWDESC = 10,
+    /* set/get DWDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DWDATA_PLACEHOLDER = 11,
+    /* set YDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get YDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_YDESC = 12,
+    /* set/get YDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_YDATA_PLACEHOLDER = 13,
+    /* set DYDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get DYDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_DYDESC = 14,
+    /* set/get DYDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DYDATA_PLACEHOLDER = 15,
+    /* set YSTATS_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get YSTATS_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_YSTATS_DESC = 16,
+    /* set/get YSUM_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_YSUM_PLACEHOLDER = 17,
+    /* set/get YSQSUM_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_YSQSUM_PLACEHOLDER = 18,
+    /* set CUDNN_PARAM_BN_SCALEBIAS_MEANVAR_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get CUDNN_PARAM_BN_SCALEBIAS_MEANVAR_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_BN_SCALEBIAS_MEANVAR_DESC = 19,
+    /* set/get CUDNN_PARAM_BN_SCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_SCALE_PLACEHOLDER = 20,
+    /* set/get CUDNN_PARAM_BN_BIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_BIAS_PLACEHOLDER = 21,
+    /* set/get CUDNN_PARAM_BN_SAVED_MEAN_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_SAVED_MEAN_PLACEHOLDER = 22,
+    /* set/get CUDNN_PARAM_BN_SAVED_INVSTD_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_SAVED_INVSTD_PLACEHOLDER = 23,
+    /* set/get CUDNN_PARAM_BN_RUNNING_MEAN_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_RUNNING_MEAN_PLACEHOLDER = 24,
+    /* set/get CUDNN_PARAM_BN_RUNNING_VAR_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_RUNNING_VAR_PLACEHOLDER = 25,
+
+    /* set ZDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get ZDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_ZDESC = 26,
+    /* set/get ZDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_ZDATA_PLACEHOLDER = 27,
+    /* set BN_Z_EQSCALEBIAS_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get BN_Z_EQSCALEBIAS_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_BN_Z_EQSCALEBIAS_DESC = 28,
+    /* set/get BN_Z_EQSCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_Z_EQSCALE_PLACEHOLDER = 29,
+    /* set/get BN_Z_EQBIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_Z_EQBIAS_PLACEHOLDER = 30,
+
+    /* set ACTIVATION_BITMASK_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get ACTIVATION_BITMASK_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_ACTIVATION_BITMASK_DESC = 31,
+    /* set/get ACTIVATION_BITMASK_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_ACTIVATION_BITMASK_PLACEHOLDER = 32,
+
+    /* set DXDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get DXDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_DXDESC = 33,
+    /* set/get DXDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DXDATA_PLACEHOLDER = 34,
+    /* set DZDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get DZDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_DZDESC = 35,
+    /* set/get DZDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DZDATA_PLACEHOLDER = 36,
+    /* set/get CUDNN_PARAM_BN_DSCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_DSCALE_PLACEHOLDER = 37,
+    /* set/get CUDNN_PARAM_BN_DBIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_DBIAS_PLACEHOLDER = 38,
+} cudnnFusedOpsConstParamLabel_t;
+
+typedef enum {
+    CUDNN_PTR_NULL         = 0,
+    CUDNN_PTR_ELEM_ALIGNED = 1,
+    CUDNN_PTR_16B_ALIGNED  = 2,
+} cudnnFusedOpsPointerPlaceHolder_t;
+
+typedef enum {
+    /* set: pass void* pointing to dev memory */
+    /* get: pass void** pointing to host memory */
+    CUDNN_PTR_XDATA              = 0,
+    CUDNN_PTR_BN_EQSCALE         = 1,
+    CUDNN_PTR_BN_EQBIAS          = 2,
+    CUDNN_PTR_WDATA              = 3,
+    CUDNN_PTR_DWDATA             = 4,
+    CUDNN_PTR_YDATA              = 5,
+    CUDNN_PTR_DYDATA             = 6,
+    CUDNN_PTR_YSUM               = 7,
+    CUDNN_PTR_YSQSUM             = 8,
+    CUDNN_PTR_WORKSPACE          = 9,
+    CUDNN_PTR_BN_SCALE           = 10,
+    CUDNN_PTR_BN_BIAS            = 11,
+    CUDNN_PTR_BN_SAVED_MEAN      = 12,
+    CUDNN_PTR_BN_SAVED_INVSTD    = 13,
+    CUDNN_PTR_BN_RUNNING_MEAN    = 14,
+    CUDNN_PTR_BN_RUNNING_VAR     = 15,
+    CUDNN_PTR_ZDATA              = 16,
+    CUDNN_PTR_BN_Z_EQSCALE       = 17,
+    CUDNN_PTR_BN_Z_EQBIAS        = 18,
+    CUDNN_PTR_ACTIVATION_BITMASK = 19,
+    CUDNN_PTR_DXDATA             = 20,
+    CUDNN_PTR_DZDATA             = 21,
+    CUDNN_PTR_BN_DSCALE          = 22,
+    CUDNN_PTR_BN_DBIAS           = 23,
+
+    /* set/get: pass size_t* pointing to host memory */
+    CUDNN_SCALAR_SIZE_T_WORKSPACE_SIZE_IN_BYTES = 100,
+    /* set/get: pass int64_t* pointing to host memory */
+    CUDNN_SCALAR_INT64_T_BN_ACCUMULATION_COUNT = 101,
+    /* set/get: pass double* pointing to host memory */
+    CUDNN_SCALAR_DOUBLE_BN_EXP_AVG_FACTOR = 102,
+    /* set/get: pass double* pointing to host memory */
+    CUDNN_SCALAR_DOUBLE_BN_EPSILON = 103,
+} cudnnFusedOpsVariantParamLabel_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCnnInferVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_CNN_INFER_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_cnn_infer_v8.h

@@ -0,0 +1,571 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_cnn_infer : cuDNN's basic definitions and inference CNN functions.
+ */
+
+#if !defined(CUDNN_CNN_INFER_H_)
+#define CUDNN_CNN_INFER_H_
+
+#pragma once
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_CNN_INFER_MAJOR 8
+#define CUDNN_CNN_INFER_MINOR 9
+#define CUDNN_CNN_INFER_PATCH 2
+
+#if (CUDNN_CNN_INFER_MAJOR != CUDNN_MAJOR) || (CUDNN_CNN_INFER_MINOR != CUDNN_MINOR) || \
+    (CUDNN_CNN_INFER_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN CNN INFER!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+typedef struct cudnnConvolutionStruct *cudnnConvolutionDescriptor_t;
+
+/*
+ *  convolution mode
+ */
+typedef enum { CUDNN_CONVOLUTION = 0, CUDNN_CROSS_CORRELATION = 1 } cudnnConvolutionMode_t;
+
+/*
+ * CUDNN Reorder
+ */
+typedef enum {
+    CUDNN_DEFAULT_REORDER = 0,
+    CUDNN_NO_REORDER      = 1,
+} cudnnReorderType_t;
+
+typedef struct cudnnConvolutionFwdAlgoPerfStruct {
+    cudnnConvolutionFwdAlgo_t algo;
+    cudnnStatus_t status;
+    float time;
+    size_t memory;
+    cudnnDeterminism_t determinism;
+    cudnnMathType_t mathType;
+    int reserved[3];
+} cudnnConvolutionFwdAlgoPerf_t;
+
+/* Create an instance of convolution descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateConvolutionDescriptor(cudnnConvolutionDescriptor_t *convDesc);
+
+/* Destroy an instance of convolution descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyConvolutionDescriptor(cudnnConvolutionDescriptor_t convDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionMathType(cudnnConvolutionDescriptor_t convDesc, cudnnMathType_t mathType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionMathType(cudnnConvolutionDescriptor_t convDesc, cudnnMathType_t *mathType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionGroupCount(cudnnConvolutionDescriptor_t convDesc, int groupCount);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionGroupCount(cudnnConvolutionDescriptor_t convDesc, int *groupCount);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionReorderType(cudnnConvolutionDescriptor_t convDesc, cudnnReorderType_t reorderType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionReorderType(cudnnConvolutionDescriptor_t convDesc, cudnnReorderType_t *reorderType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolution2dDescriptor(cudnnConvolutionDescriptor_t convDesc,
+                                int pad_h,      /* zero-padding height */
+                                int pad_w,      /* zero-padding width */
+                                int u,          /* vertical filter stride */
+                                int v,          /* horizontal filter stride */
+                                int dilation_h, /* filter dilation in the vertical dimension */
+                                int dilation_w, /* filter dilation in the horizontal dimension */
+                                cudnnConvolutionMode_t mode,
+                                cudnnDataType_t computeType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolution2dDescriptor(const cudnnConvolutionDescriptor_t convDesc,
+                                int *pad_h,      /* zero-padding height */
+                                int *pad_w,      /* zero-padding width */
+                                int *u,          /* vertical filter stride */
+                                int *v,          /* horizontal filter stride */
+                                int *dilation_h, /* filter dilation in the vertical dimension */
+                                int *dilation_w, /* filter dilation in the horizontal dimension */
+                                cudnnConvolutionMode_t *mode,
+                                cudnnDataType_t *computeType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetConvolutionNdDescriptor(cudnnConvolutionDescriptor_t convDesc,
+                                int arrayLength, /* nbDims-2 size */
+                                const int padA[],
+                                const int filterStrideA[],
+                                const int dilationA[],
+                                cudnnConvolutionMode_t mode,
+                                cudnnDataType_t computeType); /* convolution data type */
+
+/* Helper function to return the dimensions of the output tensor given a convolution descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionNdDescriptor(const cudnnConvolutionDescriptor_t convDesc,
+                                int arrayLengthRequested,
+                                int *arrayLength,
+                                int padA[],
+                                int strideA[],
+                                int dilationA[],
+                                cudnnConvolutionMode_t *mode,
+                                cudnnDataType_t *computeType); /* convolution data type */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolution2dForwardOutputDim(const cudnnConvolutionDescriptor_t convDesc,
+                                      const cudnnTensorDescriptor_t inputTensorDesc,
+                                      const cudnnFilterDescriptor_t filterDesc,
+                                      int *n,
+                                      int *c,
+                                      int *h,
+                                      int *w);
+
+/* Helper function to return the dimensions of the output tensor given a convolution descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionNdForwardOutputDim(const cudnnConvolutionDescriptor_t convDesc,
+                                      const cudnnTensorDescriptor_t inputTensorDesc,
+                                      const cudnnFilterDescriptor_t filterDesc,
+                                      int nbDims,
+                                      int tensorOuputDimA[]);
+
+/* helper function to provide the convolution forward algo that fit best the requirement */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionForwardAlgorithmMaxCount(cudnnHandle_t handle, int *count);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionForwardAlgorithm_v7(cudnnHandle_t handle,
+                                       const cudnnTensorDescriptor_t srcDesc,
+                                       const cudnnFilterDescriptor_t filterDesc,
+                                       const cudnnConvolutionDescriptor_t convDesc,
+                                       const cudnnTensorDescriptor_t destDesc,
+                                       const int requestedAlgoCount,
+                                       int *returnedAlgoCount,
+                                       cudnnConvolutionFwdAlgoPerf_t *perfResults);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionForwardAlgorithm(cudnnHandle_t handle,
+                                     const cudnnTensorDescriptor_t xDesc,
+                                     const cudnnFilterDescriptor_t wDesc,
+                                     const cudnnConvolutionDescriptor_t convDesc,
+                                     const cudnnTensorDescriptor_t yDesc,
+                                     const int requestedAlgoCount,
+                                     int *returnedAlgoCount,
+                                     cudnnConvolutionFwdAlgoPerf_t *perfResults);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionForwardAlgorithmEx(cudnnHandle_t handle,
+                                       const cudnnTensorDescriptor_t xDesc,
+                                       const void *x,
+                                       const cudnnFilterDescriptor_t wDesc,
+                                       const void *w,
+                                       const cudnnConvolutionDescriptor_t convDesc,
+                                       const cudnnTensorDescriptor_t yDesc,
+                                       void *y,
+                                       const int requestedAlgoCount,
+                                       int *returnedAlgoCount,
+                                       cudnnConvolutionFwdAlgoPerf_t *perfResults,
+                                       void *workSpace,
+                                       size_t workSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnIm2Col(cudnnHandle_t handle,
+            const cudnnTensorDescriptor_t xDesc,
+            const void *x,
+            const cudnnFilterDescriptor_t wDesc,
+            const cudnnConvolutionDescriptor_t convDesc,
+            void *colBuffer);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnReorderFilterAndBias(cudnnHandle_t handle,
+                          const cudnnFilterDescriptor_t filterDesc,
+                          cudnnReorderType_t reorderType,
+                          const void *filterData,
+                          void *reorderedFilterData,
+                          int reorderBias,
+                          const void *biasData,
+                          void *reorderedBiasData);
+
+/* Helper function to return the minimum size of the workspace to be passed to the convolution given an algo*/
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle_t handle,
+                                        const cudnnTensorDescriptor_t xDesc,
+                                        const cudnnFilterDescriptor_t wDesc,
+                                        const cudnnConvolutionDescriptor_t convDesc,
+                                        const cudnnTensorDescriptor_t yDesc,
+                                        cudnnConvolutionFwdAlgo_t algo,
+                                        size_t *sizeInBytes);
+
+/* Convolution functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform the forward pass for batch convolution */
+cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionForward(cudnnHandle_t handle,
+                        const void *alpha,
+                        const cudnnTensorDescriptor_t xDesc,
+                        const void *x,
+                        const cudnnFilterDescriptor_t wDesc,
+                        const void *w,
+                        const cudnnConvolutionDescriptor_t convDesc,
+                        cudnnConvolutionFwdAlgo_t algo,
+                        void *workSpace,
+                        size_t workSpaceSizeInBytes,
+                        const void *beta,
+                        const cudnnTensorDescriptor_t yDesc,
+                        void *y);
+
+/* Fused conv/bias/activation operation : y = Act( alpha1 * conv(x) + alpha2 * z + bias ) */
+cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBiasActivationForward(cudnnHandle_t handle,
+                                      const void *alpha1,
+                                      const cudnnTensorDescriptor_t xDesc,
+                                      const void *x,
+                                      const cudnnFilterDescriptor_t wDesc,
+                                      const void *w,
+                                      const cudnnConvolutionDescriptor_t convDesc,
+                                      cudnnConvolutionFwdAlgo_t algo,
+                                      void *workSpace,
+                                      size_t workSpaceSizeInBytes,
+                                      const void *alpha2,
+                                      const cudnnTensorDescriptor_t zDesc,
+                                      const void *z,
+                                      const cudnnTensorDescriptor_t biasDesc,
+                                      const void *bias,
+                                      const cudnnActivationDescriptor_t activationDesc,
+                                      const cudnnTensorDescriptor_t yDesc,
+                                      void *y);
+
+/* helper function to provide the convolution backward data algo that fit best the requirement */
+
+typedef struct cudnnConvolutionBwdDataAlgoPerfStruct {
+    cudnnConvolutionBwdDataAlgo_t algo;
+    cudnnStatus_t status;
+    float time;
+    size_t memory;
+    cudnnDeterminism_t determinism;
+    cudnnMathType_t mathType;
+    int reserved[3];
+} cudnnConvolutionBwdDataAlgoPerf_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardDataAlgorithmMaxCount(cudnnHandle_t handle, int *count);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardDataAlgorithm(cudnnHandle_t handle,
+                                          const cudnnFilterDescriptor_t wDesc,
+                                          const cudnnTensorDescriptor_t dyDesc,
+                                          const cudnnConvolutionDescriptor_t convDesc,
+                                          const cudnnTensorDescriptor_t dxDesc,
+                                          const int requestedAlgoCount,
+                                          int *returnedAlgoCount,
+                                          cudnnConvolutionBwdDataAlgoPerf_t *perfResults);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardDataAlgorithmEx(cudnnHandle_t handle,
+                                            const cudnnFilterDescriptor_t wDesc,
+                                            const void *w,
+                                            const cudnnTensorDescriptor_t dyDesc,
+                                            const void *dy,
+                                            const cudnnConvolutionDescriptor_t convDesc,
+                                            const cudnnTensorDescriptor_t dxDesc,
+                                            void *dx,
+                                            const int requestedAlgoCount,
+                                            int *returnedAlgoCount,
+                                            cudnnConvolutionBwdDataAlgoPerf_t *perfResults,
+                                            void *workSpace,
+                                            size_t workSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardDataAlgorithm_v7(cudnnHandle_t handle,
+                                            const cudnnFilterDescriptor_t filterDesc,
+                                            const cudnnTensorDescriptor_t diffDesc,
+                                            const cudnnConvolutionDescriptor_t convDesc,
+                                            const cudnnTensorDescriptor_t gradDesc,
+                                            const int requestedAlgoCount,
+                                            int *returnedAlgoCount,
+                                            cudnnConvolutionBwdDataAlgoPerf_t *perfResults);
+
+/*
+ *  convolution algorithm (which requires potentially some workspace)
+ */
+
+/* Helper function to return the minimum size of the workspace to be passed to the convolution given an algo*/
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardDataWorkspaceSize(cudnnHandle_t handle,
+                                             const cudnnFilterDescriptor_t wDesc,
+                                             const cudnnTensorDescriptor_t dyDesc,
+                                             const cudnnConvolutionDescriptor_t convDesc,
+                                             const cudnnTensorDescriptor_t dxDesc,
+                                             cudnnConvolutionBwdDataAlgo_t algo,
+                                             size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBackwardData(cudnnHandle_t handle,
+                             const void *alpha,
+                             const cudnnFilterDescriptor_t wDesc,
+                             const void *w,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const cudnnConvolutionDescriptor_t convDesc,
+                             cudnnConvolutionBwdDataAlgo_t algo,
+                             void *workSpace,
+                             size_t workSpaceSizeInBytes,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dxDesc,
+                             void *dx);
+
+/* Helper function to calculate folding descriptors for dgrad */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFoldedConvBackwardDataDescriptors(const cudnnHandle_t handle,
+                                          const cudnnFilterDescriptor_t filterDesc,
+                                          const cudnnTensorDescriptor_t diffDesc,
+                                          const cudnnConvolutionDescriptor_t convDesc,
+                                          const cudnnTensorDescriptor_t gradDesc,
+                                          const cudnnTensorFormat_t transformFormat,
+                                          cudnnFilterDescriptor_t foldedFilterDesc,
+                                          cudnnTensorDescriptor_t paddedDiffDesc,
+                                          cudnnConvolutionDescriptor_t foldedConvDesc,
+                                          cudnnTensorDescriptor_t foldedGradDesc,
+                                          cudnnTensorTransformDescriptor_t filterFoldTransDesc,
+                                          cudnnTensorTransformDescriptor_t diffPadTransDesc,
+                                          cudnnTensorTransformDescriptor_t gradFoldTransDesc,
+                                          cudnnTensorTransformDescriptor_t gradUnfoldTransDesc);
+
+/* cudnnFusedOps... */
+struct cudnnFusedOpsConstParamStruct;
+typedef struct cudnnFusedOpsConstParamStruct *cudnnFusedOpsConstParamPack_t;
+
+struct cudnnFusedOpsVariantParamStruct;
+typedef struct cudnnFusedOpsVariantParamStruct *cudnnFusedOpsVariantParamPack_t;
+
+struct cudnnFusedOpsPlanStruct;
+typedef struct cudnnFusedOpsPlanStruct *cudnnFusedOpsPlan_t;
+
+typedef enum {
+    /* each op in [ ] can be disabled by passing NULL ptr */
+    /* [per channel scale], [per channel bias], [activation], convolution, [generate BN stats] */
+    CUDNN_FUSED_SCALE_BIAS_ACTIVATION_CONV_BNSTATS = 0,
+    /* [per channel scale], [per channel bias], [activation], convolutionBackwardWeights */
+    CUDNN_FUSED_SCALE_BIAS_ACTIVATION_WGRAD = 1,
+    /* utility for BN training in BN-conv fusion */
+    /* computes the equivalent scale and bias from ySum ySqSum and learned scale, bias */
+    /* optionally update running stats and generate saved stats */
+    CUDNN_FUSED_BN_FINALIZE_STATISTICS_TRAINING = 2,
+    /* utility for BN inference in BN-conv fusion */
+    /* computes the equivalent scale and bias from learned running stats and learned scale, bias */
+    CUDNN_FUSED_BN_FINALIZE_STATISTICS_INFERENCE = 3,
+    /* reserved for future use: convolution, [per channel scale], [per channel bias], [residual add], [activation] */
+    CUDNN_FUSED_CONV_SCALE_BIAS_ADD_ACTIVATION = 4,
+    /* reserved for future use: [per channel scale], [per channel bias], [residual add],  activation, bitmask */
+    CUDNN_FUSED_SCALE_BIAS_ADD_ACTIVATION_GEN_BITMASK = 5,
+    /* reserved for future use */
+    CUDNN_FUSED_DACTIVATION_FORK_DBATCHNORM = 6,
+} cudnnFusedOps_t;
+
+typedef enum {
+    /* set XDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get XDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_XDESC = 0,
+    /* set/get XDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_XDATA_PLACEHOLDER = 1,
+    /* set/get BN_MODE: pass cudnnBatchNormMode_t* */
+    CUDNN_PARAM_BN_MODE = 2,
+    /* set CUDNN_PARAM_BN_EQSCALEBIAS_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get CUDNN_PARAM_BN_EQSCALEBIAS_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_BN_EQSCALEBIAS_DESC = 3,
+    /* set/get BN_EQSCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_EQSCALE_PLACEHOLDER = 4,
+    /* set/get BN_EQBIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_EQBIAS_PLACEHOLDER = 5,
+    /* set ACTIVATION_DESC: pass previously initialized cudnnActivationDescriptor_t */
+    /* get ACTIVATION_DESC: pass previously created cudnnActivationDescriptor_t */
+    CUDNN_PARAM_ACTIVATION_DESC = 6,
+    /* set CONV_DESC: pass previously initialized cudnnConvolutionDescriptor_t */
+    /* get CONV_DESC: pass previously created cudnnConvolutionDescriptor_t */
+    CUDNN_PARAM_CONV_DESC = 7,
+    /* set WDESC: pass previously initialized cudnnFilterDescriptor_t */
+    /* get WDESC: pass previously created cudnnFilterDescriptor_t */
+    CUDNN_PARAM_WDESC = 8,
+    /* set/get WDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_WDATA_PLACEHOLDER = 9,
+    /* set DWDESC: pass previously initialized cudnnFilterDescriptor_t */
+    /* get DWDESC: pass previously created cudnnFilterDescriptor_t */
+    CUDNN_PARAM_DWDESC = 10,
+    /* set/get DWDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DWDATA_PLACEHOLDER = 11,
+    /* set YDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get YDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_YDESC = 12,
+    /* set/get YDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_YDATA_PLACEHOLDER = 13,
+    /* set DYDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get DYDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_DYDESC = 14,
+    /* set/get DYDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DYDATA_PLACEHOLDER = 15,
+    /* set YSTATS_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get YSTATS_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_YSTATS_DESC = 16,
+    /* set/get YSUM_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_YSUM_PLACEHOLDER = 17,
+    /* set/get YSQSUM_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_YSQSUM_PLACEHOLDER = 18,
+    /* set CUDNN_PARAM_BN_SCALEBIAS_MEANVAR_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get CUDNN_PARAM_BN_SCALEBIAS_MEANVAR_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_BN_SCALEBIAS_MEANVAR_DESC = 19,
+    /* set/get CUDNN_PARAM_BN_SCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_SCALE_PLACEHOLDER = 20,
+    /* set/get CUDNN_PARAM_BN_BIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_BIAS_PLACEHOLDER = 21,
+    /* set/get CUDNN_PARAM_BN_SAVED_MEAN_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_SAVED_MEAN_PLACEHOLDER = 22,
+    /* set/get CUDNN_PARAM_BN_SAVED_INVSTD_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_SAVED_INVSTD_PLACEHOLDER = 23,
+    /* set/get CUDNN_PARAM_BN_RUNNING_MEAN_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_RUNNING_MEAN_PLACEHOLDER = 24,
+    /* set/get CUDNN_PARAM_BN_RUNNING_VAR_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_RUNNING_VAR_PLACEHOLDER = 25,
+
+    /* set ZDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get ZDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_ZDESC = 26,
+    /* set/get ZDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_ZDATA_PLACEHOLDER = 27,
+    /* set BN_Z_EQSCALEBIAS_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get BN_Z_EQSCALEBIAS_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_BN_Z_EQSCALEBIAS_DESC = 28,
+    /* set/get BN_Z_EQSCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_Z_EQSCALE_PLACEHOLDER = 29,
+    /* set/get BN_Z_EQBIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_Z_EQBIAS_PLACEHOLDER = 30,
+
+    /* set ACTIVATION_BITMASK_DESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get ACTIVATION_BITMASK_DESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_ACTIVATION_BITMASK_DESC = 31,
+    /* set/get ACTIVATION_BITMASK_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_ACTIVATION_BITMASK_PLACEHOLDER = 32,
+
+    /* set DXDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get DXDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_DXDESC = 33,
+    /* set/get DXDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DXDATA_PLACEHOLDER = 34,
+    /* set DZDESC: pass previously initialized cudnnTensorDescriptor_t */
+    /* get DZDESC: pass previously created cudnnTensorDescriptor_t */
+    CUDNN_PARAM_DZDESC = 35,
+    /* set/get DZDATA_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_DZDATA_PLACEHOLDER = 36,
+    /* set/get CUDNN_PARAM_BN_DSCALE_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_DSCALE_PLACEHOLDER = 37,
+    /* set/get CUDNN_PARAM_BN_DBIAS_PLACEHOLDER: pass cudnnFusedOpsPointerPlaceHolder_t* */
+    CUDNN_PARAM_BN_DBIAS_PLACEHOLDER = 38,
+} cudnnFusedOpsConstParamLabel_t;
+
+typedef enum {
+    CUDNN_PTR_NULL         = 0,
+    CUDNN_PTR_ELEM_ALIGNED = 1,
+    CUDNN_PTR_16B_ALIGNED  = 2,
+} cudnnFusedOpsPointerPlaceHolder_t;
+
+typedef enum {
+    /* set: pass void* pointing to dev memory */
+    /* get: pass void** pointing to host memory */
+    CUDNN_PTR_XDATA              = 0,
+    CUDNN_PTR_BN_EQSCALE         = 1,
+    CUDNN_PTR_BN_EQBIAS          = 2,
+    CUDNN_PTR_WDATA              = 3,
+    CUDNN_PTR_DWDATA             = 4,
+    CUDNN_PTR_YDATA              = 5,
+    CUDNN_PTR_DYDATA             = 6,
+    CUDNN_PTR_YSUM               = 7,
+    CUDNN_PTR_YSQSUM             = 8,
+    CUDNN_PTR_WORKSPACE          = 9,
+    CUDNN_PTR_BN_SCALE           = 10,
+    CUDNN_PTR_BN_BIAS            = 11,
+    CUDNN_PTR_BN_SAVED_MEAN      = 12,
+    CUDNN_PTR_BN_SAVED_INVSTD    = 13,
+    CUDNN_PTR_BN_RUNNING_MEAN    = 14,
+    CUDNN_PTR_BN_RUNNING_VAR     = 15,
+    CUDNN_PTR_ZDATA              = 16,
+    CUDNN_PTR_BN_Z_EQSCALE       = 17,
+    CUDNN_PTR_BN_Z_EQBIAS        = 18,
+    CUDNN_PTR_ACTIVATION_BITMASK = 19,
+    CUDNN_PTR_DXDATA             = 20,
+    CUDNN_PTR_DZDATA             = 21,
+    CUDNN_PTR_BN_DSCALE          = 22,
+    CUDNN_PTR_BN_DBIAS           = 23,
+
+    /* set/get: pass size_t* pointing to host memory */
+    CUDNN_SCALAR_SIZE_T_WORKSPACE_SIZE_IN_BYTES = 100,
+    /* set/get: pass int64_t* pointing to host memory */
+    CUDNN_SCALAR_INT64_T_BN_ACCUMULATION_COUNT = 101,
+    /* set/get: pass double* pointing to host memory */
+    CUDNN_SCALAR_DOUBLE_BN_EXP_AVG_FACTOR = 102,
+    /* set/get: pass double* pointing to host memory */
+    CUDNN_SCALAR_DOUBLE_BN_EPSILON = 103,
+} cudnnFusedOpsVariantParamLabel_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCnnInferVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_CNN_INFER_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_cnn_train.h

@@ -0,0 +1,219 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_cnn_train : cuDNN's basic definitions and inference CNN functions.
+ */
+
+#pragma once
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+#include "cudnn_ops_train.h"
+#include "cudnn_cnn_infer.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_CNN_TRAIN_MAJOR 8
+#define CUDNN_CNN_TRAIN_MINOR 9
+#define CUDNN_CNN_TRAIN_PATCH 2
+
+#if (CUDNN_CNN_TRAIN_MAJOR != CUDNN_MAJOR) || (CUDNN_CNN_TRAIN_MINOR != CUDNN_MINOR) || \
+    (CUDNN_CNN_TRAIN_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN CNN INFER!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* helper function to provide the convolution backward filter algo that fit best the requirement */
+
+typedef struct cudnnConvolutionBwdFilterAlgoPerfStruct {
+    cudnnConvolutionBwdFilterAlgo_t algo;
+    cudnnStatus_t status;
+    float time;
+    size_t memory;
+    cudnnDeterminism_t determinism;
+    cudnnMathType_t mathType;
+    int reserved[3];
+} cudnnConvolutionBwdFilterAlgoPerf_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(cudnnHandle_t handle, int *count);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardFilterAlgorithm(cudnnHandle_t handle,
+                                            const cudnnTensorDescriptor_t xDesc,
+                                            const cudnnTensorDescriptor_t dyDesc,
+                                            const cudnnConvolutionDescriptor_t convDesc,
+                                            const cudnnFilterDescriptor_t dwDesc,
+                                            const int requestedAlgoCount,
+                                            int *returnedAlgoCount,
+                                            cudnnConvolutionBwdFilterAlgoPerf_t *perfResults);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardFilterAlgorithmEx(cudnnHandle_t handle,
+                                              const cudnnTensorDescriptor_t xDesc,
+                                              const void *x,
+                                              const cudnnTensorDescriptor_t dyDesc,
+                                              const void *y,
+                                              const cudnnConvolutionDescriptor_t convDesc,
+                                              const cudnnFilterDescriptor_t dwDesc,
+                                              void *dw,
+                                              const int requestedAlgoCount,
+                                              int *returnedAlgoCount,
+                                              cudnnConvolutionBwdFilterAlgoPerf_t *perfResults,
+                                              void *workSpace,
+                                              size_t workSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardFilterAlgorithm_v7(cudnnHandle_t handle,
+                                              const cudnnTensorDescriptor_t srcDesc,
+                                              const cudnnTensorDescriptor_t diffDesc,
+                                              const cudnnConvolutionDescriptor_t convDesc,
+                                              const cudnnFilterDescriptor_t gradDesc,
+                                              const int requestedAlgoCount,
+                                              int *returnedAlgoCount,
+                                              cudnnConvolutionBwdFilterAlgoPerf_t *perfResults);
+
+/*
+ *  convolution algorithm (which requires potentially some workspace)
+ */
+
+/* Helper function to return the minimum size of the workspace to be passed to the convolution given an algo*/
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnnHandle_t handle,
+                                               const cudnnTensorDescriptor_t xDesc,
+                                               const cudnnTensorDescriptor_t dyDesc,
+                                               const cudnnConvolutionDescriptor_t convDesc,
+                                               const cudnnFilterDescriptor_t gradDesc,
+                                               cudnnConvolutionBwdFilterAlgo_t algo,
+                                               size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBackwardFilter(cudnnHandle_t handle,
+                               const void *alpha,
+                               const cudnnTensorDescriptor_t xDesc,
+                               const void *x,
+                               const cudnnTensorDescriptor_t dyDesc,
+                               const void *dy,
+                               const cudnnConvolutionDescriptor_t convDesc,
+                               cudnnConvolutionBwdFilterAlgo_t algo,
+                               void *workSpace,
+                               size_t workSpaceSizeInBytes,
+                               const void *beta,
+                               const cudnnFilterDescriptor_t dwDesc,
+                               void *dw);
+
+/* Function to compute the bias gradient for batch convolution */
+cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBackwardBias(cudnnHandle_t handle,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dbDesc,
+                             void *db);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateFusedOpsConstParamPack(cudnnFusedOpsConstParamPack_t *constPack, cudnnFusedOps_t ops);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFusedOpsConstParamPack(cudnnFusedOpsConstParamPack_t constPack);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetFusedOpsConstParamPackAttribute(cudnnFusedOpsConstParamPack_t constPack,
+                                        cudnnFusedOpsConstParamLabel_t paramLabel,
+                                        const void *param);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFusedOpsConstParamPackAttribute(const cudnnFusedOpsConstParamPack_t constPack,
+                                        cudnnFusedOpsConstParamLabel_t paramLabel,
+                                        void *param,
+                                        int *isNULL);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateFusedOpsVariantParamPack(cudnnFusedOpsVariantParamPack_t *varPack, cudnnFusedOps_t ops);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFusedOpsVariantParamPack(cudnnFusedOpsVariantParamPack_t varPack);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetFusedOpsVariantParamPackAttribute(cudnnFusedOpsVariantParamPack_t varPack,
+                                          cudnnFusedOpsVariantParamLabel_t paramLabel,
+                                          void *ptr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFusedOpsVariantParamPackAttribute(const cudnnFusedOpsVariantParamPack_t varPack,
+                                          cudnnFusedOpsVariantParamLabel_t paramLabel,
+                                          void *ptr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateFusedOpsPlan(cudnnFusedOpsPlan_t *plan, cudnnFusedOps_t ops);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFusedOpsPlan(cudnnFusedOpsPlan_t plan);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnMakeFusedOpsPlan(cudnnHandle_t handle,
+                      cudnnFusedOpsPlan_t plan,
+                      const cudnnFusedOpsConstParamPack_t constPack,
+                      size_t *workspaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFusedOpsExecute(cudnnHandle_t handle, const cudnnFusedOpsPlan_t plan, cudnnFusedOpsVariantParamPack_t varPack);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCnnTrainVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_cnn_train_v8.h

@@ -0,0 +1,219 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_cnn_train : cuDNN's basic definitions and inference CNN functions.
+ */
+
+#pragma once
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+#include "cudnn_ops_train.h"
+#include "cudnn_cnn_infer.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_CNN_TRAIN_MAJOR 8
+#define CUDNN_CNN_TRAIN_MINOR 9
+#define CUDNN_CNN_TRAIN_PATCH 2
+
+#if (CUDNN_CNN_TRAIN_MAJOR != CUDNN_MAJOR) || (CUDNN_CNN_TRAIN_MINOR != CUDNN_MINOR) || \
+    (CUDNN_CNN_TRAIN_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN CNN INFER!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* helper function to provide the convolution backward filter algo that fit best the requirement */
+
+typedef struct cudnnConvolutionBwdFilterAlgoPerfStruct {
+    cudnnConvolutionBwdFilterAlgo_t algo;
+    cudnnStatus_t status;
+    float time;
+    size_t memory;
+    cudnnDeterminism_t determinism;
+    cudnnMathType_t mathType;
+    int reserved[3];
+} cudnnConvolutionBwdFilterAlgoPerf_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(cudnnHandle_t handle, int *count);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardFilterAlgorithm(cudnnHandle_t handle,
+                                            const cudnnTensorDescriptor_t xDesc,
+                                            const cudnnTensorDescriptor_t dyDesc,
+                                            const cudnnConvolutionDescriptor_t convDesc,
+                                            const cudnnFilterDescriptor_t dwDesc,
+                                            const int requestedAlgoCount,
+                                            int *returnedAlgoCount,
+                                            cudnnConvolutionBwdFilterAlgoPerf_t *perfResults);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFindConvolutionBackwardFilterAlgorithmEx(cudnnHandle_t handle,
+                                              const cudnnTensorDescriptor_t xDesc,
+                                              const void *x,
+                                              const cudnnTensorDescriptor_t dyDesc,
+                                              const void *y,
+                                              const cudnnConvolutionDescriptor_t convDesc,
+                                              const cudnnFilterDescriptor_t dwDesc,
+                                              void *dw,
+                                              const int requestedAlgoCount,
+                                              int *returnedAlgoCount,
+                                              cudnnConvolutionBwdFilterAlgoPerf_t *perfResults,
+                                              void *workSpace,
+                                              size_t workSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardFilterAlgorithm_v7(cudnnHandle_t handle,
+                                              const cudnnTensorDescriptor_t srcDesc,
+                                              const cudnnTensorDescriptor_t diffDesc,
+                                              const cudnnConvolutionDescriptor_t convDesc,
+                                              const cudnnFilterDescriptor_t gradDesc,
+                                              const int requestedAlgoCount,
+                                              int *returnedAlgoCount,
+                                              cudnnConvolutionBwdFilterAlgoPerf_t *perfResults);
+
+/*
+ *  convolution algorithm (which requires potentially some workspace)
+ */
+
+/* Helper function to return the minimum size of the workspace to be passed to the convolution given an algo*/
+cudnnStatus_t CUDNNWINAPI
+cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnnHandle_t handle,
+                                               const cudnnTensorDescriptor_t xDesc,
+                                               const cudnnTensorDescriptor_t dyDesc,
+                                               const cudnnConvolutionDescriptor_t convDesc,
+                                               const cudnnFilterDescriptor_t gradDesc,
+                                               cudnnConvolutionBwdFilterAlgo_t algo,
+                                               size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBackwardFilter(cudnnHandle_t handle,
+                               const void *alpha,
+                               const cudnnTensorDescriptor_t xDesc,
+                               const void *x,
+                               const cudnnTensorDescriptor_t dyDesc,
+                               const void *dy,
+                               const cudnnConvolutionDescriptor_t convDesc,
+                               cudnnConvolutionBwdFilterAlgo_t algo,
+                               void *workSpace,
+                               size_t workSpaceSizeInBytes,
+                               const void *beta,
+                               const cudnnFilterDescriptor_t dwDesc,
+                               void *dw);
+
+/* Function to compute the bias gradient for batch convolution */
+cudnnStatus_t CUDNNWINAPI
+cudnnConvolutionBackwardBias(cudnnHandle_t handle,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dbDesc,
+                             void *db);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateFusedOpsConstParamPack(cudnnFusedOpsConstParamPack_t *constPack, cudnnFusedOps_t ops);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFusedOpsConstParamPack(cudnnFusedOpsConstParamPack_t constPack);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetFusedOpsConstParamPackAttribute(cudnnFusedOpsConstParamPack_t constPack,
+                                        cudnnFusedOpsConstParamLabel_t paramLabel,
+                                        const void *param);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFusedOpsConstParamPackAttribute(const cudnnFusedOpsConstParamPack_t constPack,
+                                        cudnnFusedOpsConstParamLabel_t paramLabel,
+                                        void *param,
+                                        int *isNULL);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateFusedOpsVariantParamPack(cudnnFusedOpsVariantParamPack_t *varPack, cudnnFusedOps_t ops);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFusedOpsVariantParamPack(cudnnFusedOpsVariantParamPack_t varPack);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetFusedOpsVariantParamPackAttribute(cudnnFusedOpsVariantParamPack_t varPack,
+                                          cudnnFusedOpsVariantParamLabel_t paramLabel,
+                                          void *ptr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFusedOpsVariantParamPackAttribute(const cudnnFusedOpsVariantParamPack_t varPack,
+                                          cudnnFusedOpsVariantParamLabel_t paramLabel,
+                                          void *ptr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateFusedOpsPlan(cudnnFusedOpsPlan_t *plan, cudnnFusedOps_t ops);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFusedOpsPlan(cudnnFusedOpsPlan_t plan);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnMakeFusedOpsPlan(cudnnHandle_t handle,
+                      cudnnFusedOpsPlan_t plan,
+                      const cudnnFusedOpsConstParamPack_t constPack,
+                      size_t *workspaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnFusedOpsExecute(cudnnHandle_t handle, const cudnnFusedOpsPlan_t plan, cudnnFusedOpsVariantParamPack_t varPack);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCnnTrainVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops_infer.h

@@ -0,0 +1,1183 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_ops_infer : cuDNN's basic definitions and inference operations.
+ */
+
+#if !defined(CUDNN_OPS_INFER_H_)
+#define CUDNN_OPS_INFER_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_OPS_INFER_MAJOR 8
+#define CUDNN_OPS_INFER_MINOR 9
+#define CUDNN_OPS_INFER_PATCH 2
+
+#if (CUDNN_OPS_INFER_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_INFER_MINOR != CUDNN_MINOR) || \
+    (CUDNN_OPS_INFER_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN OPS INFER!!!
+#endif
+
+#ifndef CUDNNWINAPI
+#ifdef _WIN32
+#define CUDNNWINAPI __stdcall
+#else
+#define CUDNNWINAPI
+#endif
+#endif
+
+/* Warnings for deprecated API-s are enabled using the CUDNN_WARN_DEPRECATED macro */
+#if defined(CUDNN_WARN_DEPRECATED) && (defined(__GNUC__) || defined(__clang__))
+/* GCC, Intel C/C++, Cray C/C++, CLANG, IBM XL C/C++ little endian */
+#define CUDNN_DEPRECATED __attribute__((deprecated))
+#elif defined(CUDNN_WARN_DEPRECATED) && defined(_MSC_VER)
+/* Microsoft Visual C++ */
+#define CUDNN_DEPRECATED __declspec(deprecated)
+#elif defined(CUDNN_WARN_DEPRECATED) && (__cplusplus >= 201402L)
+/* C++14 compilers */
+#define CUDNN_DEPRECATED [[deprecated]]
+#else
+/* No support for the deprecated attribute */
+#define CUDNN_DEPRECATED
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+struct cudnnContext;
+typedef struct cudnnContext *cudnnHandle_t;
+
+size_t CUDNNWINAPI
+cudnnGetVersion(void);
+
+size_t CUDNNWINAPI
+cudnnGetMaxDeviceVersion(void);
+
+/* Returns CUDA Runtime version statically linked against cudnn */
+size_t CUDNNWINAPI
+cudnnGetCudartVersion(void);
+
+/*
+ * CUDNN return codes
+ */
+typedef enum {
+    CUDNN_STATUS_SUCCESS                      = 0,
+    CUDNN_STATUS_NOT_INITIALIZED              = 1,
+    CUDNN_STATUS_ALLOC_FAILED                 = 2,
+    CUDNN_STATUS_BAD_PARAM                    = 3,
+    CUDNN_STATUS_INTERNAL_ERROR               = 4,
+    CUDNN_STATUS_INVALID_VALUE                = 5,
+    CUDNN_STATUS_ARCH_MISMATCH                = 6,
+    CUDNN_STATUS_MAPPING_ERROR                = 7,
+    CUDNN_STATUS_EXECUTION_FAILED             = 8,
+    CUDNN_STATUS_NOT_SUPPORTED                = 9,
+    CUDNN_STATUS_LICENSE_ERROR                = 10,
+    CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING = 11,
+    CUDNN_STATUS_RUNTIME_IN_PROGRESS          = 12,
+    CUDNN_STATUS_RUNTIME_FP_OVERFLOW          = 13,
+    CUDNN_STATUS_VERSION_MISMATCH             = 14,
+} cudnnStatus_t;
+
+/* human-readable error messages */
+const char *CUDNNWINAPI
+cudnnGetErrorString(cudnnStatus_t status);
+
+/* Forward definition in this version only */
+typedef struct cudnnRuntimeTag_t cudnnRuntimeTag_t;
+
+typedef enum {
+    CUDNN_ERRQUERY_RAWCODE     = 0,
+    CUDNN_ERRQUERY_NONBLOCKING = 1,
+    CUDNN_ERRQUERY_BLOCKING    = 2,
+} cudnnErrQueryMode_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnQueryRuntimeError(cudnnHandle_t handle, cudnnStatus_t *rstatus, cudnnErrQueryMode_t mode, cudnnRuntimeTag_t *tag);
+
+#ifndef __LIBRARY_TYPES_H__
+
+typedef enum libraryPropertyType_t { MAJOR_VERSION, MINOR_VERSION, PATCH_LEVEL } libraryPropertyType;
+
+#endif
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetProperty(libraryPropertyType type, int *value);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreate(cudnnHandle_t *handle);
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroy(cudnnHandle_t handle);
+cudnnStatus_t CUDNNWINAPI
+cudnnSetStream(cudnnHandle_t handle, cudaStream_t streamId);
+cudnnStatus_t CUDNNWINAPI
+cudnnGetStream(cudnnHandle_t handle, cudaStream_t *streamId);
+
+/* Data structures to represent Image/Filter and the Neural Network Layer */
+typedef struct cudnnTensorStruct *cudnnTensorDescriptor_t;
+typedef struct cudnnPoolingStruct *cudnnPoolingDescriptor_t;
+typedef struct cudnnFilterStruct *cudnnFilterDescriptor_t;
+typedef struct cudnnLRNStruct *cudnnLRNDescriptor_t;
+typedef struct cudnnActivationStruct *cudnnActivationDescriptor_t;
+typedef struct cudnnSpatialTransformerStruct *cudnnSpatialTransformerDescriptor_t;
+typedef struct cudnnOpTensorStruct *cudnnOpTensorDescriptor_t;
+typedef struct cudnnReduceTensorStruct *cudnnReduceTensorDescriptor_t;
+typedef struct cudnnCTCLossStruct *cudnnCTCLossDescriptor_t;
+typedef struct cudnnTensorTransformStruct *cudnnTensorTransformDescriptor_t;
+/*
+ * CUDNN data type
+ */
+typedef enum {
+    CUDNN_DATA_FLOAT              = 0,
+    CUDNN_DATA_DOUBLE             = 1,
+    CUDNN_DATA_HALF               = 2,
+    CUDNN_DATA_INT8               = 3,
+    CUDNN_DATA_INT32              = 4,
+    CUDNN_DATA_INT8x4             = 5,
+    CUDNN_DATA_UINT8              = 6,
+    CUDNN_DATA_UINT8x4            = 7,
+    CUDNN_DATA_INT8x32            = 8,
+    CUDNN_DATA_BFLOAT16           = 9,
+    CUDNN_DATA_INT64              = 10,
+    CUDNN_DATA_BOOLEAN            = 11,
+    CUDNN_DATA_FP8_E4M3           = 12,
+    CUDNN_DATA_FP8_E5M2           = 13,
+    CUDNN_DATA_FAST_FLOAT_FOR_FP8 = 14,
+} cudnnDataType_t;
+
+/*
+ * CUDNN math type
+ */
+typedef enum {
+    CUDNN_DEFAULT_MATH                    = 0,
+    CUDNN_TENSOR_OP_MATH                  = 1,
+    CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION = 2,
+    CUDNN_FMA_MATH                        = 3,
+} cudnnMathType_t;
+
+/*
+ * CUDNN propagate Nan
+ */
+typedef enum {
+    CUDNN_NOT_PROPAGATE_NAN = 0,
+    CUDNN_PROPAGATE_NAN     = 1,
+} cudnnNanPropagation_t;
+
+/*
+ * CUDNN Determinism
+ */
+typedef enum {
+    CUDNN_NON_DETERMINISTIC = 0,
+    CUDNN_DETERMINISTIC     = 1,
+} cudnnDeterminism_t;
+
+/* Maximum supported number of tensor dimensions */
+#define CUDNN_DIM_MAX 8
+
+/* Create an instance of a generic Tensor descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateTensorDescriptor(cudnnTensorDescriptor_t *tensorDesc);
+
+typedef enum {
+    CUDNN_TENSOR_NCHW        = 0, /* row major (wStride = 1, hStride = w) */
+    CUDNN_TENSOR_NHWC        = 1, /* feature maps interleaved ( cStride = 1 )*/
+    CUDNN_TENSOR_NCHW_VECT_C = 2, /* each image point is vector of element of C, vector length in data type */
+} cudnnTensorFormat_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor4dDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                           cudnnTensorFormat_t format,
+                           cudnnDataType_t dataType, /* image data type */
+                           int n,                    /* number of inputs (batch size) */
+                           int c,                    /* number of input feature maps */
+                           int h,                    /* height of input section */
+                           int w);                   /* width of input section */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor4dDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
+                             cudnnDataType_t dataType, /* image data type */
+                             int n,                    /* number of inputs (batch size) */
+                             int c,                    /* number of input feature maps */
+                             int h,                    /* height of input section */
+                             int w,                    /* width of input section */
+                             int nStride,
+                             int cStride,
+                             int hStride,
+                             int wStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensor4dDescriptor(const cudnnTensorDescriptor_t tensorDesc,
+                           cudnnDataType_t *dataType, /* image data type */
+                           int *n,                    /* number of inputs (batch size) */
+                           int *c,                    /* number of input feature maps  */
+                           int *h,                    /* height of input section */
+                           int *w,                    /* width of input section */
+                           int *nStride,
+                           int *cStride,
+                           int *hStride,
+                           int *wStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorNdDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                           cudnnDataType_t dataType,
+                           int nbDims,
+                           const int dimA[],
+                           const int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorNdDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
+                             cudnnTensorFormat_t format,
+                             cudnnDataType_t dataType,
+                             int nbDims,
+                             const int dimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorNdDescriptor(const cudnnTensorDescriptor_t tensorDesc,
+                           int nbDimsRequested,
+                           cudnnDataType_t *dataType,
+                           int *nbDims,
+                           int dimA[],
+                           int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorSizeInBytes(const cudnnTensorDescriptor_t tensorDesc, size_t *size);
+
+/* PixelOffset( n, c, h, w ) = n *input_stride + c * feature_stride + h * h_stride + w * w_stride
+
+   1)Example of all images in row major order one batch of features after the other (with an optional padding on row)
+   input_stride :  c x h x h_stride
+   feature_stride : h x h_stride
+   h_stride  :  >= w  ( h_stride = w if no padding)
+   w_stride  : 1
+
+
+   2)Example of all images in row major with features maps interleaved
+   input_stride :  c x h x h_stride
+   feature_stride : 1
+   h_stride  :  w x c
+   w_stride  : c
+
+   3)Example of all images in column major order one batch of features after the other (with optional padding on column)
+   input_stride :  c x w x w_stride
+   feature_stride : w x w_stride
+   h_stride  :  1
+   w_stride  :  >= h
+
+*/
+
+/* Destroy an instance of Tensor4d descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyTensorDescriptor(cudnnTensorDescriptor_t tensorDesc);
+
+/* Fold/unfold transforms */
+typedef enum {
+    CUDNN_TRANSFORM_FOLD   = 0U,
+    CUDNN_TRANSFORM_UNFOLD = 1U,
+} cudnnFoldingDirection_t;
+
+/** Create a destination descriptor for cudnnTransformTensor */
+cudnnStatus_t CUDNNWINAPI
+cudnnInitTransformDest(const cudnnTensorTransformDescriptor_t transformDesc,
+                       const cudnnTensorDescriptor_t srcDesc,
+                       cudnnTensorDescriptor_t destDesc,
+                       size_t *destSizeInBytes);
+
+/** Create an empty tensor transform descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateTensorTransformDescriptor(cudnnTensorTransformDescriptor_t *transformDesc);
+
+/** Initialize a previously created tensor transform descriptor. */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
+                                  const uint32_t nbDims,
+                                  const cudnnTensorFormat_t destFormat,
+                                  const int32_t padBeforeA[],
+                                  const int32_t padAfterA[],
+                                  const uint32_t foldA[],
+                                  const cudnnFoldingDirection_t direction);
+
+/**
+ * Retrieves the values stored in a previously initialized tensor transform
+ * descriptor.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
+                                  uint32_t nbDimsRequested,
+                                  cudnnTensorFormat_t *destFormat,
+                                  int32_t padBeforeA[],
+                                  int32_t padAfterA[],
+                                  uint32_t foldA[],
+                                  cudnnFoldingDirection_t *direction);
+
+/**
+ * Destroys a previously created tensor transform descriptor.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc);
+
+/* Tensor layout conversion helper (y = alpha * x + beta * y) */
+cudnnStatus_t CUDNNWINAPI
+cudnnTransformTensor(cudnnHandle_t handle,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t xDesc,
+                     const void *x,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t yDesc,
+                     void *y);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnTransformTensorEx(cudnnHandle_t handle,
+                       const cudnnTensorTransformDescriptor_t transDesc,
+                       const void *alpha,
+                       const cudnnTensorDescriptor_t srcDesc,
+                       const void *srcData,
+                       const void *beta,
+                       const cudnnTensorDescriptor_t destDesc,
+                       void *destData);
+
+/* Tensor Bias addition : C = alpha * A + beta * C  */
+cudnnStatus_t CUDNNWINAPI
+cudnnAddTensor(cudnnHandle_t handle,
+               const void *alpha,
+               const cudnnTensorDescriptor_t aDesc,
+               const void *A,
+               const void *beta,
+               const cudnnTensorDescriptor_t cDesc,
+               void *C);
+
+/*
+ * CUDNN OpTensor op type
+ */
+typedef enum {
+    CUDNN_OP_TENSOR_ADD  = 0,
+    CUDNN_OP_TENSOR_MUL  = 1,
+    CUDNN_OP_TENSOR_MIN  = 2,
+    CUDNN_OP_TENSOR_MAX  = 3,
+    CUDNN_OP_TENSOR_SQRT = 4,
+    CUDNN_OP_TENSOR_NOT  = 5,
+} cudnnOpTensorOp_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateOpTensorDescriptor(cudnnOpTensorDescriptor_t *opTensorDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc,
+                           cudnnOpTensorOp_t opTensorOp,
+                           cudnnDataType_t opTensorCompType,
+                           cudnnNanPropagation_t opTensorNanOpt);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetOpTensorDescriptor(const cudnnOpTensorDescriptor_t opTensorDesc,
+                           cudnnOpTensorOp_t *opTensorOp,
+                           cudnnDataType_t *opTensorCompType,
+                           cudnnNanPropagation_t *opTensorNanOpt);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc);
+
+/* Tensor operation : C = op( alpha1 * A, alpha2 * B ) + beta * C */
+/* B tensor is ignored for CUDNN_OP_TENSOR_SQRT, CUDNN_OP_TENSOR_NOT. */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpTensor(cudnnHandle_t handle,
+              const cudnnOpTensorDescriptor_t opTensorDesc,
+              const void *alpha1,
+              const cudnnTensorDescriptor_t aDesc,
+              const void *A,
+              const void *alpha2,
+              const cudnnTensorDescriptor_t bDesc,
+              const void *B,
+              const void *beta,
+              const cudnnTensorDescriptor_t cDesc,
+              void *C);
+
+/*
+ * CUDNN ReduceTensor op type
+ */
+typedef enum {
+    CUDNN_REDUCE_TENSOR_ADD          = 0,
+    CUDNN_REDUCE_TENSOR_MUL          = 1,
+    CUDNN_REDUCE_TENSOR_MIN          = 2,
+    CUDNN_REDUCE_TENSOR_MAX          = 3,
+    CUDNN_REDUCE_TENSOR_AMAX         = 4,
+    CUDNN_REDUCE_TENSOR_AVG          = 5,
+    CUDNN_REDUCE_TENSOR_NORM1        = 6,
+    CUDNN_REDUCE_TENSOR_NORM2        = 7,
+    CUDNN_REDUCE_TENSOR_MUL_NO_ZEROS = 8,
+} cudnnReduceTensorOp_t;
+
+/*
+ * CUDNN ReduceTensor indices type
+ */
+typedef enum {
+    CUDNN_REDUCE_TENSOR_NO_INDICES        = 0,
+    CUDNN_REDUCE_TENSOR_FLATTENED_INDICES = 1,
+} cudnnReduceTensorIndices_t;
+
+/*
+ * CUDNN tensor indices type size (all unsigned)
+ * Currently not supported, default is 32 bit unsigned.
+ */
+typedef enum {
+    CUDNN_32BIT_INDICES = 0,
+    CUDNN_64BIT_INDICES = 1,
+    CUDNN_16BIT_INDICES = 2,
+    CUDNN_8BIT_INDICES  = 3,
+} cudnnIndicesType_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateReduceTensorDescriptor(cudnnReduceTensorDescriptor_t *reduceTensorDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               cudnnReduceTensorOp_t reduceTensorOp,
+                               cudnnDataType_t reduceTensorCompType,
+                               cudnnNanPropagation_t reduceTensorNanOpt,
+                               cudnnReduceTensorIndices_t reduceTensorIndices,
+                               cudnnIndicesType_t reduceTensorIndicesType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetReduceTensorDescriptor(const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               cudnnReduceTensorOp_t *reduceTensorOp,
+                               cudnnDataType_t *reduceTensorCompType,
+                               cudnnNanPropagation_t *reduceTensorNanOpt,
+                               cudnnReduceTensorIndices_t *reduceTensorIndices,
+                               cudnnIndicesType_t *reduceTensorIndicesType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc);
+
+/* Helper function to return the minimum size of the index space to be passed to the reduction given the input and
+ * output tensors */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetReductionIndicesSize(cudnnHandle_t handle,
+                             const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                             const cudnnTensorDescriptor_t aDesc,
+                             const cudnnTensorDescriptor_t cDesc,
+                             size_t *sizeInBytes);
+
+/* Helper function to return the minimum size of the workspace to be passed to the reduction given the input and output
+ * tensors */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetReductionWorkspaceSize(cudnnHandle_t handle,
+                               const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               const cudnnTensorDescriptor_t aDesc,
+                               const cudnnTensorDescriptor_t cDesc,
+                               size_t *sizeInBytes);
+
+/* Tensor operation : C = reduce op( alpha * A ) + beta * C */
+/* The NaN propagation enum applies to only the min and max reduce ops; the other reduce ops propagate NaN as usual. */
+/* The indices space is ignored for reduce ops other than min or max. */
+cudnnStatus_t CUDNNWINAPI
+cudnnReduceTensor(cudnnHandle_t handle,
+                  const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                  void *indices,
+                  size_t indicesSizeInBytes,
+                  void *workspace,
+                  size_t workspaceSizeInBytes,
+                  const void *alpha,
+                  const cudnnTensorDescriptor_t aDesc,
+                  const void *A,
+                  const void *beta,
+                  const cudnnTensorDescriptor_t cDesc,
+                  void *C);
+
+/* Set all values of a tensor to a given value : y[i] = value[0] */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *valuePtr);
+
+/* Scale all values of a tensor by a given factor : y[i] = alpha * y[i] */
+cudnnStatus_t CUDNNWINAPI
+cudnnScaleTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *alpha);
+
+/* Create an instance of FilterStruct */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateFilterDescriptor(cudnnFilterDescriptor_t *filterDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetFilter4dDescriptor(cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t dataType, /* image data type */
+                           cudnnTensorFormat_t format,
+                           int k,  /* number of output feature maps */
+                           int c,  /* number of input feature maps */
+                           int h,  /* height of each input filter */
+                           int w); /* width of  each input filter */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFilter4dDescriptor(const cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t *dataType, /* image data type */
+                           cudnnTensorFormat_t *format,
+                           int *k,  /* number of output feature maps */
+                           int *c,  /* number of input feature maps */
+                           int *h,  /* height of each input filter */
+                           int *w); /* width of  each input filter */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetFilterNdDescriptor(cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t dataType, /* image data type */
+                           cudnnTensorFormat_t format,
+                           int nbDims,
+                           const int filterDimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFilterNdDescriptor(const cudnnFilterDescriptor_t filterDesc,
+                           int nbDimsRequested,
+                           cudnnDataType_t *dataType, /* image data type */
+                           cudnnTensorFormat_t *format,
+                           int *nbDims,
+                           int filterDimA[]);
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFilterSizeInBytes(const cudnnFilterDescriptor_t filterDesc, size_t *size);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnTransformFilter(cudnnHandle_t handle,
+                     const cudnnTensorTransformDescriptor_t transDesc,
+                     const void *alpha,
+                     const cudnnFilterDescriptor_t srcDesc,
+                     const void *srcData,
+                     const void *beta,
+                     const cudnnFilterDescriptor_t destDesc,
+                     void *destData);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFilterDescriptor(cudnnFilterDescriptor_t filterDesc);
+
+/*
+ *  softmax algorithm
+ */
+typedef enum {
+    CUDNN_SOFTMAX_FAST     = 0, /* straightforward implementation */
+    CUDNN_SOFTMAX_ACCURATE = 1, /* subtract max from every point to avoid overflow */
+    CUDNN_SOFTMAX_LOG      = 2
+} cudnnSoftmaxAlgorithm_t;
+
+typedef enum {
+    CUDNN_SOFTMAX_MODE_INSTANCE = 0, /* compute the softmax over all C, H, W for each N */
+    CUDNN_SOFTMAX_MODE_CHANNEL  = 1  /* compute the softmax over all C for each H, W, N */
+} cudnnSoftmaxMode_t;
+
+/* Softmax functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform forward softmax */
+cudnnStatus_t CUDNNWINAPI
+cudnnSoftmaxForward(cudnnHandle_t handle,
+                    cudnnSoftmaxAlgorithm_t algo,
+                    cudnnSoftmaxMode_t mode,
+                    const void *alpha,
+                    const cudnnTensorDescriptor_t xDesc,
+                    const void *x,
+                    const void *beta,
+                    const cudnnTensorDescriptor_t yDesc,
+                    void *y);
+
+/*
+ *  pooling mode
+ */
+typedef enum {
+    CUDNN_POOLING_MAX                           = 0,
+    CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING = 1, /* count for average includes padded values */
+    CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING = 2, /* count for average does not include padded values */
+    CUDNN_POOLING_MAX_DETERMINISTIC             = 3
+} cudnnPoolingMode_t;
+
+/* Create an instance of pooling descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreatePoolingDescriptor(cudnnPoolingDescriptor_t *poolingDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t poolingDesc,
+                            cudnnPoolingMode_t mode,
+                            cudnnNanPropagation_t maxpoolingNanOpt,
+                            int windowHeight,
+                            int windowWidth,
+                            int verticalPadding,
+                            int horizontalPadding,
+                            int verticalStride,
+                            int horizontalStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPooling2dDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
+                            cudnnPoolingMode_t *mode,
+                            cudnnNanPropagation_t *maxpoolingNanOpt,
+                            int *windowHeight,
+                            int *windowWidth,
+                            int *verticalPadding,
+                            int *horizontalPadding,
+                            int *verticalStride,
+                            int *horizontalStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetPoolingNdDescriptor(cudnnPoolingDescriptor_t poolingDesc,
+                            const cudnnPoolingMode_t mode,
+                            const cudnnNanPropagation_t maxpoolingNanOpt,
+                            int nbDims,
+                            const int windowDimA[],
+                            const int paddingA[],
+                            const int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPoolingNdDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
+                            int nbDimsRequested,
+                            cudnnPoolingMode_t *mode,
+                            cudnnNanPropagation_t *maxpoolingNanOpt,
+                            int *nbDims,
+                            int windowDimA[],
+                            int paddingA[],
+                            int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPoolingNdForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
+                                  const cudnnTensorDescriptor_t inputTensorDesc,
+                                  int nbDims,
+                                  int outputTensorDimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPooling2dForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
+                                  const cudnnTensorDescriptor_t inputTensorDesc,
+                                  int *n,
+                                  int *c,
+                                  int *h,
+                                  int *w);
+
+/* Destroy an instance of pooling descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyPoolingDescriptor(cudnnPoolingDescriptor_t poolingDesc);
+
+/* Pooling functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform forward pooling */
+cudnnStatus_t CUDNNWINAPI
+cudnnPoolingForward(cudnnHandle_t handle,
+                    const cudnnPoolingDescriptor_t poolingDesc,
+                    const void *alpha,
+                    const cudnnTensorDescriptor_t xDesc,
+                    const void *x,
+                    const void *beta,
+                    const cudnnTensorDescriptor_t yDesc,
+                    void *y);
+
+/*
+ * activation mode
+ */
+typedef enum {
+    CUDNN_ACTIVATION_SIGMOID      = 0,
+    CUDNN_ACTIVATION_RELU         = 1,
+    CUDNN_ACTIVATION_TANH         = 2,
+    CUDNN_ACTIVATION_CLIPPED_RELU = 3,
+    CUDNN_ACTIVATION_ELU          = 4,
+    CUDNN_ACTIVATION_IDENTITY     = 5,
+    CUDNN_ACTIVATION_SWISH        = 6
+} cudnnActivationMode_t;
+
+/* Activation functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateActivationDescriptor(cudnnActivationDescriptor_t *activationDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetActivationDescriptor(cudnnActivationDescriptor_t activationDesc,
+                             cudnnActivationMode_t mode,
+                             cudnnNanPropagation_t reluNanOpt,
+                             double coef); /* ceiling for clipped RELU, alpha for ELU */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetActivationDescriptor(const cudnnActivationDescriptor_t activationDesc,
+                             cudnnActivationMode_t *mode,
+                             cudnnNanPropagation_t *reluNanOpt,
+                             double *coef); /* ceiling for clipped RELU, alpha for ELU */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double swish_beta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double *swish_beta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyActivationDescriptor(cudnnActivationDescriptor_t activationDesc);
+
+/* Function to perform forward activation  */
+cudnnStatus_t CUDNNWINAPI
+cudnnActivationForward(cudnnHandle_t handle,
+                       cudnnActivationDescriptor_t activationDesc,
+                       const void *alpha,
+                       const cudnnTensorDescriptor_t xDesc,
+                       const void *x,
+                       const void *beta,
+                       const cudnnTensorDescriptor_t yDesc,
+                       void *y);
+
+/*
+ * Create an instance of LRN (Local Response Normalization) descriptor
+ * Uses lrnN=5, lrnAlpha=1e-4, lrnBeta=0.75, lrnK=2.0 as defaults from Krizhevsky'12 ImageNet paper
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateLRNDescriptor(cudnnLRNDescriptor_t *normDesc);
+
+#define CUDNN_LRN_MIN_N 1       /* minimum allowed lrnN */
+#define CUDNN_LRN_MAX_N 16      /* maximum allowed lrnN */
+#define CUDNN_LRN_MIN_K 1e-5    /* minimum allowed lrnK */
+#define CUDNN_LRN_MIN_BETA 0.01 /* minimum allowed lrnBeta */
+
+/* LRN layer mode */
+typedef enum {
+    CUDNN_LRN_CROSS_CHANNEL_DIM1 = 0, /* Normalize across tensor's dimA[1] dimension */
+} cudnnLRNMode_t;
+
+/*
+ * Uses a window [center-lookBehind, center+lookAhead], where
+ * lookBehind = floor( (lrnN-1)/2 ), lookAhead = lrnN-lookBehind-1.
+ * Values of double parameters cast to tensor data type.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned lrnN, double lrnAlpha, double lrnBeta, double lrnK);
+/*
+ * Retrieve the settings currently stored in an LRN layer descriptor
+ * Any of the provided pointers can be NULL (no corresponding value will be returned)
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned *lrnN, double *lrnAlpha, double *lrnBeta, double *lrnK);
+
+/* Destroy an instance of LRN descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyLRNDescriptor(cudnnLRNDescriptor_t lrnDesc);
+
+/* LRN functions: output = alpha * normalize(x) + beta * old_y */
+
+/* LRN cross-channel forward computation. Double parameters cast to tensor data type */
+cudnnStatus_t CUDNNWINAPI
+cudnnLRNCrossChannelForward(cudnnHandle_t handle,
+                            cudnnLRNDescriptor_t normDesc,
+                            cudnnLRNMode_t lrnMode,
+                            const void *alpha,
+                            const cudnnTensorDescriptor_t xDesc,
+                            const void *x,
+                            const void *beta,
+                            const cudnnTensorDescriptor_t yDesc,
+                            void *y);
+
+typedef enum {
+    CUDNN_DIVNORM_PRECOMPUTED_MEANS = 0,
+} cudnnDivNormMode_t;
+
+/* LCN/divisive normalization functions: y = alpha * normalize(x) + beta * y */
+cudnnStatus_t CUDNNWINAPI
+cudnnDivisiveNormalizationForward(cudnnHandle_t handle,
+                                  cudnnLRNDescriptor_t normDesc,
+                                  cudnnDivNormMode_t mode,
+                                  const void *alpha,
+                                  const cudnnTensorDescriptor_t xDesc, /* same desc for means, temp, temp2 */
+                                  const void *x,
+                                  const void *means, /* if NULL, means are assumed to be zero */
+                                  void *temp,
+                                  void *temp2,
+                                  const void *beta,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  void *y);
+
+typedef enum {
+    /* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
+    CUDNN_BATCHNORM_PER_ACTIVATION = 0,
+
+    /* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
+    CUDNN_BATCHNORM_SPATIAL = 1,
+
+    /*
+     * bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors).
+     * May be faster than CUDNN_BATCHNORM_SPATIAL but imposes some limits on the range of values
+     */
+    CUDNN_BATCHNORM_SPATIAL_PERSISTENT = 2,
+} cudnnBatchNormMode_t;
+
+#define CUDNN_BN_MIN_EPSILON 0.0 /* Minimum epsilon allowed to be used in the Batch Normalization formula */
+
+/*
+ * Derives a tensor descriptor from layer data descriptor for BatchNormalization
+ * scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
+ * bnScaleBiasMeanVarDesc and bnScaleBiasDiffDesc in Batch Normalization forward and backward functions.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnDeriveBNTensorDescriptor(cudnnTensorDescriptor_t derivedBnDesc,
+                              const cudnnTensorDescriptor_t xDesc,
+                              cudnnBatchNormMode_t mode);
+
+typedef enum {
+    CUDNN_BATCHNORM_OPS_BN                = 0, /* do batch normalization only */
+    CUDNN_BATCHNORM_OPS_BN_ACTIVATION     = 1, /* do batchNorm, then activation */
+    CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION = 2, /* do batchNorm, then elemWiseAdd, then activation */
+} cudnnBatchNormOps_t;
+
+/*
+ * Performs Batch Normalization during Inference:
+ * y[i] = bnScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + bnBias[k]
+ * with bnScale, bnBias, runningMean, runningInvVariance tensors indexed
+ * according to spatial or per-activation mode. Refer to cudnnBatchNormalizationForwardTraining
+ * above for notes on function arguments.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardInference(cudnnHandle_t handle,
+                                        cudnnBatchNormMode_t mode,
+                                        const void *alpha, /* alpha[0] = result blend factor */
+                                        const void *beta,  /* beta[0] = dest layer blend factor */
+                                        const cudnnTensorDescriptor_t xDesc,
+                                        const void *x, /* NxCxHxW */
+                                        const cudnnTensorDescriptor_t yDesc,
+                                        void *y, /* NxCxHxW */
+                                        const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+                                        const void *bnScale,
+                                        const void *bnBias,
+                                        const void *estimatedMean,
+                                        const void *estimatedVariance,
+                                        double epsilon);
+
+typedef enum {
+    /* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
+    CUDNN_NORM_PER_ACTIVATION = 0,
+
+    /* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
+    CUDNN_NORM_PER_CHANNEL = 1,
+} cudnnNormMode_t;
+
+typedef enum { CUDNN_NORM_ALGO_STANDARD = 0, CUDNN_NORM_ALGO_PERSIST = 1 } cudnnNormAlgo_t;
+
+/*
+ * Derives a tensor descriptor from layer data descriptor for Normalization
+ * scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
+ * normScaleBiasMeanVarDesc and normScaleBiasDiffDesc in Normalization forward and backward functions.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnDeriveNormTensorDescriptor(cudnnTensorDescriptor_t derivedNormScaleBiasDesc,
+                                cudnnTensorDescriptor_t derivedNormMeanVarDesc,
+                                const cudnnTensorDescriptor_t xDesc,
+                                cudnnNormMode_t mode,
+                                int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+typedef enum {
+    CUDNN_NORM_OPS_NORM                = 0, /* do normalization only */
+    CUDNN_NORM_OPS_NORM_ACTIVATION     = 1, /* do Norm, then activation */
+    CUDNN_NORM_OPS_NORM_ADD_ACTIVATION = 2, /* do Norm, then elemWiseAdd, then activation */
+} cudnnNormOps_t;
+
+/*
+ * Performs Normalization during Inference:
+ * y[i] = normScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + normBias[k]
+ * with normScale, normBias, runningMean, runningInvVariance tensors indexed
+ * according to per-channel or per-activation mode. Refer to cudnnNormalizationForwardTraining
+ * above for notes on function arguments.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationForwardInference(cudnnHandle_t handle,
+                                   cudnnNormMode_t mode,
+                                   cudnnNormOps_t normOps,
+                                   cudnnNormAlgo_t algo,
+                                   const void *alpha, /* alpha[0] = result blend factor */
+                                   const void *beta,  /* beta[0] = dest layer blend factor */
+                                   const cudnnTensorDescriptor_t xDesc,
+                                   const void *x, /* NxCxHxW */
+                                   const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                   const void *normScale,
+                                   const void *normBias,
+                                   const cudnnTensorDescriptor_t normMeanVarDesc,
+                                   const void *estimatedMean,
+                                   const void *estimatedVariance,
+                                   const cudnnTensorDescriptor_t zDesc,
+                                   const void *z,
+                                   cudnnActivationDescriptor_t activationDesc,
+                                   const cudnnTensorDescriptor_t yDesc,
+                                   void *y, /* NxCxHxW */
+                                   double epsilon,
+                                   int groupCnt); /* Place hold for future work*/
+
+/* APIs for spatial transformer network*/
+typedef enum {
+    CUDNN_SAMPLER_BILINEAR = 0,
+} cudnnSamplerType_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateSpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t *stDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetSpatialTransformerNdDescriptor(cudnnSpatialTransformerDescriptor_t stDesc,
+                                       cudnnSamplerType_t samplerType,
+                                       cudnnDataType_t dataType,
+                                       const int nbDims,
+                                       const int dimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroySpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t stDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfGridGeneratorForward(cudnnHandle_t handle,
+                                   const cudnnSpatialTransformerDescriptor_t stDesc,
+                                   const void *theta,
+                                   void *grid);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfSamplerForward(cudnnHandle_t handle,
+                             cudnnSpatialTransformerDescriptor_t stDesc,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t xDesc,
+                             const void *x,
+                             const void *grid,
+                             const void *beta,
+                             cudnnTensorDescriptor_t yDesc,
+                             void *y);
+
+typedef struct cudnnDropoutStruct *cudnnDropoutDescriptor_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateDropoutDescriptor(cudnnDropoutDescriptor_t *dropoutDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc);
+
+/*helper function to determine size of the states to be passed to cudnnSetDropoutDescriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutGetStatesSize(cudnnHandle_t handle, size_t *sizeInBytes);
+
+/*helper function to determine size of the reserve space to be passed to dropout forward/backward calls */
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutGetReserveSpaceSize(cudnnTensorDescriptor_t xdesc, size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                          cudnnHandle_t handle,
+                          float dropout,
+                          void *states,
+                          size_t stateSizeInBytes,
+                          unsigned long long seed);
+
+/* Restores the dropout descriptor to a previously saved-off state */
+cudnnStatus_t CUDNNWINAPI
+cudnnRestoreDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                              cudnnHandle_t handle,
+                              float dropout,
+                              void *states,
+                              size_t stateSizeInBytes,
+                              unsigned long long seed);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                          cudnnHandle_t handle,
+                          float *dropout,
+                          void **states,
+                          unsigned long long *seed);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutForward(cudnnHandle_t handle,
+                    const cudnnDropoutDescriptor_t dropoutDesc,
+                    const cudnnTensorDescriptor_t xdesc,
+                    const void *x,
+                    const cudnnTensorDescriptor_t ydesc,
+                    void *y,
+                    void *reserveSpace,
+                    size_t reserveSpaceSizeInBytes);
+
+/* TODO: remove */
+
+typedef struct cudnnAlgorithmStruct *cudnnAlgorithmDescriptor_t;
+typedef struct cudnnAlgorithmPerformanceStruct *cudnnAlgorithmPerformance_t;
+
+/* TODO: move these enums out to the appropriate submodule */
+typedef enum {
+    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM         = 0,
+    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM = 1,
+    CUDNN_CONVOLUTION_FWD_ALGO_GEMM                  = 2,
+    CUDNN_CONVOLUTION_FWD_ALGO_DIRECT                = 3,
+    CUDNN_CONVOLUTION_FWD_ALGO_FFT                   = 4,
+    CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING            = 5,
+    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD              = 6,
+    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED     = 7,
+    CUDNN_CONVOLUTION_FWD_ALGO_COUNT                 = 8
+} cudnnConvolutionFwdAlgo_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0                 = 0, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1                 = 1,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT               = 2,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3                 = 3, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD          = 4, /* not implemented */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED = 5,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING        = 6,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT             = 7
+} cudnnConvolutionBwdFilterAlgo_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_0                 = 0, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_1                 = 1,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT               = 2,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING        = 3,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD          = 4,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED = 5,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT             = 6
+} cudnnConvolutionBwdDataAlgo_t;
+
+typedef enum {
+    CUDNN_RNN_ALGO_STANDARD               = 0,
+    CUDNN_RNN_ALGO_PERSIST_STATIC         = 1,
+    CUDNN_RNN_ALGO_PERSIST_DYNAMIC        = 2,
+    CUDNN_RNN_ALGO_PERSIST_STATIC_SMALL_H = 3,
+    CUDNN_RNN_ALGO_COUNT                  = 4,
+} cudnnRNNAlgo_t;
+
+typedef enum { CUDNN_CTC_LOSS_ALGO_DETERMINISTIC = 0, CUDNN_CTC_LOSS_ALGO_NON_DETERMINISTIC = 1 } cudnnCTCLossAlgo_t;
+
+/* TODO: remove */
+typedef struct cudnnAlgorithmUnionStruct {
+    union Algorithm {
+        cudnnConvolutionFwdAlgo_t convFwdAlgo;
+        cudnnConvolutionBwdFilterAlgo_t convBwdFilterAlgo;
+        cudnnConvolutionBwdDataAlgo_t convBwdDataAlgo;
+        cudnnRNNAlgo_t RNNAlgo;
+        cudnnCTCLossAlgo_t CTCLossAlgo;
+    } algo;
+} cudnnAlgorithm_t;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateAlgorithmDescriptor(cudnnAlgorithmDescriptor_t *algoDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetAlgorithmDescriptor(cudnnAlgorithmDescriptor_t algoDesc, cudnnAlgorithm_t algorithm);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAlgorithmDescriptor(const cudnnAlgorithmDescriptor_t algoDesc, cudnnAlgorithm_t *algorithm);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCopyAlgorithmDescriptor(const cudnnAlgorithmDescriptor_t src, cudnnAlgorithmDescriptor_t dest);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyAlgorithmDescriptor(cudnnAlgorithmDescriptor_t algoDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateAlgorithmPerformance(cudnnAlgorithmPerformance_t *algoPerf, int numberToCreate);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetAlgorithmPerformance(cudnnAlgorithmPerformance_t algoPerf,
+                             cudnnAlgorithmDescriptor_t algoDesc,
+                             cudnnStatus_t status,
+                             float time,
+                             size_t memory);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAlgorithmPerformance(const cudnnAlgorithmPerformance_t algoPerf,
+                             cudnnAlgorithmDescriptor_t *algoDesc,
+                             cudnnStatus_t *status,
+                             float *time,
+                             size_t *memory);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyAlgorithmPerformance(cudnnAlgorithmPerformance_t *algoPerf, int numberToDestroy);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAlgorithmSpaceSize(cudnnHandle_t handle, cudnnAlgorithmDescriptor_t algoDesc, size_t *algoSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSaveAlgorithm(cudnnHandle_t handle,
+                   cudnnAlgorithmDescriptor_t algoDesc,
+                   void *algoSpace,
+                   size_t algoSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRestoreAlgorithm(cudnnHandle_t handle,
+                      void *algoSpace,
+                      size_t algoSpaceSizeInBytes,
+                      cudnnAlgorithmDescriptor_t algoDesc);
+
+typedef enum {
+    CUDNN_SEV_FATAL   = 0,
+    CUDNN_SEV_ERROR   = 1,
+    CUDNN_SEV_WARNING = 2,
+    CUDNN_SEV_INFO    = 3,
+} cudnnSeverity_t;
+
+/* Message masks to be used with cudnnSetCallback() */
+#define CUDNN_SEV_ERROR_EN (1U << CUDNN_SEV_ERROR)
+#define CUDNN_SEV_WARNING_EN (1U << CUDNN_SEV_WARNING)
+#define CUDNN_SEV_INFO_EN (1U << CUDNN_SEV_INFO)
+
+/* struct containing useful informaiton for each API call */
+typedef struct cudnnDebugStruct {
+    unsigned cudnn_version;
+    cudnnStatus_t cudnnStatus;
+    unsigned time_sec;      /* epoch time in seconds */
+    unsigned time_usec;     /* microseconds part of epoch time */
+    unsigned time_delta;    /* time since start in seconds */
+    cudnnHandle_t handle;   /* cudnn handle */
+    cudaStream_t stream;    /* cuda stream ID */
+    unsigned long long pid; /* process ID */
+    unsigned long long tid; /* thread ID */
+    int cudaDeviceId;       /* CUDA device ID */
+    int reserved[15];       /* reserved for future use */
+} cudnnDebug_t;
+
+typedef void (*cudnnCallback_t)(cudnnSeverity_t sev, void *udata, const cudnnDebug_t *dbg, const char *msg);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCallback(unsigned mask, void *udata, cudnnCallback_t fptr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCallback(unsigned *mask, void **udata, cudnnCallback_t *fptr);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpsInferVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_OPS_INFER_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops_infer_v8.h

@@ -0,0 +1,1183 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_ops_infer : cuDNN's basic definitions and inference operations.
+ */
+
+#if !defined(CUDNN_OPS_INFER_H_)
+#define CUDNN_OPS_INFER_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_OPS_INFER_MAJOR 8
+#define CUDNN_OPS_INFER_MINOR 9
+#define CUDNN_OPS_INFER_PATCH 2
+
+#if (CUDNN_OPS_INFER_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_INFER_MINOR != CUDNN_MINOR) || \
+    (CUDNN_OPS_INFER_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN OPS INFER!!!
+#endif
+
+#ifndef CUDNNWINAPI
+#ifdef _WIN32
+#define CUDNNWINAPI __stdcall
+#else
+#define CUDNNWINAPI
+#endif
+#endif
+
+/* Warnings for deprecated API-s are enabled using the CUDNN_WARN_DEPRECATED macro */
+#if defined(CUDNN_WARN_DEPRECATED) && (defined(__GNUC__) || defined(__clang__))
+/* GCC, Intel C/C++, Cray C/C++, CLANG, IBM XL C/C++ little endian */
+#define CUDNN_DEPRECATED __attribute__((deprecated))
+#elif defined(CUDNN_WARN_DEPRECATED) && defined(_MSC_VER)
+/* Microsoft Visual C++ */
+#define CUDNN_DEPRECATED __declspec(deprecated)
+#elif defined(CUDNN_WARN_DEPRECATED) && (__cplusplus >= 201402L)
+/* C++14 compilers */
+#define CUDNN_DEPRECATED [[deprecated]]
+#else
+/* No support for the deprecated attribute */
+#define CUDNN_DEPRECATED
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+struct cudnnContext;
+typedef struct cudnnContext *cudnnHandle_t;
+
+size_t CUDNNWINAPI
+cudnnGetVersion(void);
+
+size_t CUDNNWINAPI
+cudnnGetMaxDeviceVersion(void);
+
+/* Returns CUDA Runtime version statically linked against cudnn */
+size_t CUDNNWINAPI
+cudnnGetCudartVersion(void);
+
+/*
+ * CUDNN return codes
+ */
+typedef enum {
+    CUDNN_STATUS_SUCCESS                      = 0,
+    CUDNN_STATUS_NOT_INITIALIZED              = 1,
+    CUDNN_STATUS_ALLOC_FAILED                 = 2,
+    CUDNN_STATUS_BAD_PARAM                    = 3,
+    CUDNN_STATUS_INTERNAL_ERROR               = 4,
+    CUDNN_STATUS_INVALID_VALUE                = 5,
+    CUDNN_STATUS_ARCH_MISMATCH                = 6,
+    CUDNN_STATUS_MAPPING_ERROR                = 7,
+    CUDNN_STATUS_EXECUTION_FAILED             = 8,
+    CUDNN_STATUS_NOT_SUPPORTED                = 9,
+    CUDNN_STATUS_LICENSE_ERROR                = 10,
+    CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING = 11,
+    CUDNN_STATUS_RUNTIME_IN_PROGRESS          = 12,
+    CUDNN_STATUS_RUNTIME_FP_OVERFLOW          = 13,
+    CUDNN_STATUS_VERSION_MISMATCH             = 14,
+} cudnnStatus_t;
+
+/* human-readable error messages */
+const char *CUDNNWINAPI
+cudnnGetErrorString(cudnnStatus_t status);
+
+/* Forward definition in this version only */
+typedef struct cudnnRuntimeTag_t cudnnRuntimeTag_t;
+
+typedef enum {
+    CUDNN_ERRQUERY_RAWCODE     = 0,
+    CUDNN_ERRQUERY_NONBLOCKING = 1,
+    CUDNN_ERRQUERY_BLOCKING    = 2,
+} cudnnErrQueryMode_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnQueryRuntimeError(cudnnHandle_t handle, cudnnStatus_t *rstatus, cudnnErrQueryMode_t mode, cudnnRuntimeTag_t *tag);
+
+#ifndef __LIBRARY_TYPES_H__
+
+typedef enum libraryPropertyType_t { MAJOR_VERSION, MINOR_VERSION, PATCH_LEVEL } libraryPropertyType;
+
+#endif
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetProperty(libraryPropertyType type, int *value);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreate(cudnnHandle_t *handle);
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroy(cudnnHandle_t handle);
+cudnnStatus_t CUDNNWINAPI
+cudnnSetStream(cudnnHandle_t handle, cudaStream_t streamId);
+cudnnStatus_t CUDNNWINAPI
+cudnnGetStream(cudnnHandle_t handle, cudaStream_t *streamId);
+
+/* Data structures to represent Image/Filter and the Neural Network Layer */
+typedef struct cudnnTensorStruct *cudnnTensorDescriptor_t;
+typedef struct cudnnPoolingStruct *cudnnPoolingDescriptor_t;
+typedef struct cudnnFilterStruct *cudnnFilterDescriptor_t;
+typedef struct cudnnLRNStruct *cudnnLRNDescriptor_t;
+typedef struct cudnnActivationStruct *cudnnActivationDescriptor_t;
+typedef struct cudnnSpatialTransformerStruct *cudnnSpatialTransformerDescriptor_t;
+typedef struct cudnnOpTensorStruct *cudnnOpTensorDescriptor_t;
+typedef struct cudnnReduceTensorStruct *cudnnReduceTensorDescriptor_t;
+typedef struct cudnnCTCLossStruct *cudnnCTCLossDescriptor_t;
+typedef struct cudnnTensorTransformStruct *cudnnTensorTransformDescriptor_t;
+/*
+ * CUDNN data type
+ */
+typedef enum {
+    CUDNN_DATA_FLOAT              = 0,
+    CUDNN_DATA_DOUBLE             = 1,
+    CUDNN_DATA_HALF               = 2,
+    CUDNN_DATA_INT8               = 3,
+    CUDNN_DATA_INT32              = 4,
+    CUDNN_DATA_INT8x4             = 5,
+    CUDNN_DATA_UINT8              = 6,
+    CUDNN_DATA_UINT8x4            = 7,
+    CUDNN_DATA_INT8x32            = 8,
+    CUDNN_DATA_BFLOAT16           = 9,
+    CUDNN_DATA_INT64              = 10,
+    CUDNN_DATA_BOOLEAN            = 11,
+    CUDNN_DATA_FP8_E4M3           = 12,
+    CUDNN_DATA_FP8_E5M2           = 13,
+    CUDNN_DATA_FAST_FLOAT_FOR_FP8 = 14,
+} cudnnDataType_t;
+
+/*
+ * CUDNN math type
+ */
+typedef enum {
+    CUDNN_DEFAULT_MATH                    = 0,
+    CUDNN_TENSOR_OP_MATH                  = 1,
+    CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION = 2,
+    CUDNN_FMA_MATH                        = 3,
+} cudnnMathType_t;
+
+/*
+ * CUDNN propagate Nan
+ */
+typedef enum {
+    CUDNN_NOT_PROPAGATE_NAN = 0,
+    CUDNN_PROPAGATE_NAN     = 1,
+} cudnnNanPropagation_t;
+
+/*
+ * CUDNN Determinism
+ */
+typedef enum {
+    CUDNN_NON_DETERMINISTIC = 0,
+    CUDNN_DETERMINISTIC     = 1,
+} cudnnDeterminism_t;
+
+/* Maximum supported number of tensor dimensions */
+#define CUDNN_DIM_MAX 8
+
+/* Create an instance of a generic Tensor descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateTensorDescriptor(cudnnTensorDescriptor_t *tensorDesc);
+
+typedef enum {
+    CUDNN_TENSOR_NCHW        = 0, /* row major (wStride = 1, hStride = w) */
+    CUDNN_TENSOR_NHWC        = 1, /* feature maps interleaved ( cStride = 1 )*/
+    CUDNN_TENSOR_NCHW_VECT_C = 2, /* each image point is vector of element of C, vector length in data type */
+} cudnnTensorFormat_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor4dDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                           cudnnTensorFormat_t format,
+                           cudnnDataType_t dataType, /* image data type */
+                           int n,                    /* number of inputs (batch size) */
+                           int c,                    /* number of input feature maps */
+                           int h,                    /* height of input section */
+                           int w);                   /* width of input section */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor4dDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
+                             cudnnDataType_t dataType, /* image data type */
+                             int n,                    /* number of inputs (batch size) */
+                             int c,                    /* number of input feature maps */
+                             int h,                    /* height of input section */
+                             int w,                    /* width of input section */
+                             int nStride,
+                             int cStride,
+                             int hStride,
+                             int wStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensor4dDescriptor(const cudnnTensorDescriptor_t tensorDesc,
+                           cudnnDataType_t *dataType, /* image data type */
+                           int *n,                    /* number of inputs (batch size) */
+                           int *c,                    /* number of input feature maps  */
+                           int *h,                    /* height of input section */
+                           int *w,                    /* width of input section */
+                           int *nStride,
+                           int *cStride,
+                           int *hStride,
+                           int *wStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorNdDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                           cudnnDataType_t dataType,
+                           int nbDims,
+                           const int dimA[],
+                           const int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorNdDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
+                             cudnnTensorFormat_t format,
+                             cudnnDataType_t dataType,
+                             int nbDims,
+                             const int dimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorNdDescriptor(const cudnnTensorDescriptor_t tensorDesc,
+                           int nbDimsRequested,
+                           cudnnDataType_t *dataType,
+                           int *nbDims,
+                           int dimA[],
+                           int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorSizeInBytes(const cudnnTensorDescriptor_t tensorDesc, size_t *size);
+
+/* PixelOffset( n, c, h, w ) = n *input_stride + c * feature_stride + h * h_stride + w * w_stride
+
+   1)Example of all images in row major order one batch of features after the other (with an optional padding on row)
+   input_stride :  c x h x h_stride
+   feature_stride : h x h_stride
+   h_stride  :  >= w  ( h_stride = w if no padding)
+   w_stride  : 1
+
+
+   2)Example of all images in row major with features maps interleaved
+   input_stride :  c x h x h_stride
+   feature_stride : 1
+   h_stride  :  w x c
+   w_stride  : c
+
+   3)Example of all images in column major order one batch of features after the other (with optional padding on column)
+   input_stride :  c x w x w_stride
+   feature_stride : w x w_stride
+   h_stride  :  1
+   w_stride  :  >= h
+
+*/
+
+/* Destroy an instance of Tensor4d descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyTensorDescriptor(cudnnTensorDescriptor_t tensorDesc);
+
+/* Fold/unfold transforms */
+typedef enum {
+    CUDNN_TRANSFORM_FOLD   = 0U,
+    CUDNN_TRANSFORM_UNFOLD = 1U,
+} cudnnFoldingDirection_t;
+
+/** Create a destination descriptor for cudnnTransformTensor */
+cudnnStatus_t CUDNNWINAPI
+cudnnInitTransformDest(const cudnnTensorTransformDescriptor_t transformDesc,
+                       const cudnnTensorDescriptor_t srcDesc,
+                       cudnnTensorDescriptor_t destDesc,
+                       size_t *destSizeInBytes);
+
+/** Create an empty tensor transform descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateTensorTransformDescriptor(cudnnTensorTransformDescriptor_t *transformDesc);
+
+/** Initialize a previously created tensor transform descriptor. */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
+                                  const uint32_t nbDims,
+                                  const cudnnTensorFormat_t destFormat,
+                                  const int32_t padBeforeA[],
+                                  const int32_t padAfterA[],
+                                  const uint32_t foldA[],
+                                  const cudnnFoldingDirection_t direction);
+
+/**
+ * Retrieves the values stored in a previously initialized tensor transform
+ * descriptor.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
+                                  uint32_t nbDimsRequested,
+                                  cudnnTensorFormat_t *destFormat,
+                                  int32_t padBeforeA[],
+                                  int32_t padAfterA[],
+                                  uint32_t foldA[],
+                                  cudnnFoldingDirection_t *direction);
+
+/**
+ * Destroys a previously created tensor transform descriptor.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc);
+
+/* Tensor layout conversion helper (y = alpha * x + beta * y) */
+cudnnStatus_t CUDNNWINAPI
+cudnnTransformTensor(cudnnHandle_t handle,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t xDesc,
+                     const void *x,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t yDesc,
+                     void *y);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnTransformTensorEx(cudnnHandle_t handle,
+                       const cudnnTensorTransformDescriptor_t transDesc,
+                       const void *alpha,
+                       const cudnnTensorDescriptor_t srcDesc,
+                       const void *srcData,
+                       const void *beta,
+                       const cudnnTensorDescriptor_t destDesc,
+                       void *destData);
+
+/* Tensor Bias addition : C = alpha * A + beta * C  */
+cudnnStatus_t CUDNNWINAPI
+cudnnAddTensor(cudnnHandle_t handle,
+               const void *alpha,
+               const cudnnTensorDescriptor_t aDesc,
+               const void *A,
+               const void *beta,
+               const cudnnTensorDescriptor_t cDesc,
+               void *C);
+
+/*
+ * CUDNN OpTensor op type
+ */
+typedef enum {
+    CUDNN_OP_TENSOR_ADD  = 0,
+    CUDNN_OP_TENSOR_MUL  = 1,
+    CUDNN_OP_TENSOR_MIN  = 2,
+    CUDNN_OP_TENSOR_MAX  = 3,
+    CUDNN_OP_TENSOR_SQRT = 4,
+    CUDNN_OP_TENSOR_NOT  = 5,
+} cudnnOpTensorOp_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateOpTensorDescriptor(cudnnOpTensorDescriptor_t *opTensorDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc,
+                           cudnnOpTensorOp_t opTensorOp,
+                           cudnnDataType_t opTensorCompType,
+                           cudnnNanPropagation_t opTensorNanOpt);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetOpTensorDescriptor(const cudnnOpTensorDescriptor_t opTensorDesc,
+                           cudnnOpTensorOp_t *opTensorOp,
+                           cudnnDataType_t *opTensorCompType,
+                           cudnnNanPropagation_t *opTensorNanOpt);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc);
+
+/* Tensor operation : C = op( alpha1 * A, alpha2 * B ) + beta * C */
+/* B tensor is ignored for CUDNN_OP_TENSOR_SQRT, CUDNN_OP_TENSOR_NOT. */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpTensor(cudnnHandle_t handle,
+              const cudnnOpTensorDescriptor_t opTensorDesc,
+              const void *alpha1,
+              const cudnnTensorDescriptor_t aDesc,
+              const void *A,
+              const void *alpha2,
+              const cudnnTensorDescriptor_t bDesc,
+              const void *B,
+              const void *beta,
+              const cudnnTensorDescriptor_t cDesc,
+              void *C);
+
+/*
+ * CUDNN ReduceTensor op type
+ */
+typedef enum {
+    CUDNN_REDUCE_TENSOR_ADD          = 0,
+    CUDNN_REDUCE_TENSOR_MUL          = 1,
+    CUDNN_REDUCE_TENSOR_MIN          = 2,
+    CUDNN_REDUCE_TENSOR_MAX          = 3,
+    CUDNN_REDUCE_TENSOR_AMAX         = 4,
+    CUDNN_REDUCE_TENSOR_AVG          = 5,
+    CUDNN_REDUCE_TENSOR_NORM1        = 6,
+    CUDNN_REDUCE_TENSOR_NORM2        = 7,
+    CUDNN_REDUCE_TENSOR_MUL_NO_ZEROS = 8,
+} cudnnReduceTensorOp_t;
+
+/*
+ * CUDNN ReduceTensor indices type
+ */
+typedef enum {
+    CUDNN_REDUCE_TENSOR_NO_INDICES        = 0,
+    CUDNN_REDUCE_TENSOR_FLATTENED_INDICES = 1,
+} cudnnReduceTensorIndices_t;
+
+/*
+ * CUDNN tensor indices type size (all unsigned)
+ * Currently not supported, default is 32 bit unsigned.
+ */
+typedef enum {
+    CUDNN_32BIT_INDICES = 0,
+    CUDNN_64BIT_INDICES = 1,
+    CUDNN_16BIT_INDICES = 2,
+    CUDNN_8BIT_INDICES  = 3,
+} cudnnIndicesType_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateReduceTensorDescriptor(cudnnReduceTensorDescriptor_t *reduceTensorDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               cudnnReduceTensorOp_t reduceTensorOp,
+                               cudnnDataType_t reduceTensorCompType,
+                               cudnnNanPropagation_t reduceTensorNanOpt,
+                               cudnnReduceTensorIndices_t reduceTensorIndices,
+                               cudnnIndicesType_t reduceTensorIndicesType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetReduceTensorDescriptor(const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               cudnnReduceTensorOp_t *reduceTensorOp,
+                               cudnnDataType_t *reduceTensorCompType,
+                               cudnnNanPropagation_t *reduceTensorNanOpt,
+                               cudnnReduceTensorIndices_t *reduceTensorIndices,
+                               cudnnIndicesType_t *reduceTensorIndicesType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc);
+
+/* Helper function to return the minimum size of the index space to be passed to the reduction given the input and
+ * output tensors */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetReductionIndicesSize(cudnnHandle_t handle,
+                             const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                             const cudnnTensorDescriptor_t aDesc,
+                             const cudnnTensorDescriptor_t cDesc,
+                             size_t *sizeInBytes);
+
+/* Helper function to return the minimum size of the workspace to be passed to the reduction given the input and output
+ * tensors */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetReductionWorkspaceSize(cudnnHandle_t handle,
+                               const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               const cudnnTensorDescriptor_t aDesc,
+                               const cudnnTensorDescriptor_t cDesc,
+                               size_t *sizeInBytes);
+
+/* Tensor operation : C = reduce op( alpha * A ) + beta * C */
+/* The NaN propagation enum applies to only the min and max reduce ops; the other reduce ops propagate NaN as usual. */
+/* The indices space is ignored for reduce ops other than min or max. */
+cudnnStatus_t CUDNNWINAPI
+cudnnReduceTensor(cudnnHandle_t handle,
+                  const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                  void *indices,
+                  size_t indicesSizeInBytes,
+                  void *workspace,
+                  size_t workspaceSizeInBytes,
+                  const void *alpha,
+                  const cudnnTensorDescriptor_t aDesc,
+                  const void *A,
+                  const void *beta,
+                  const cudnnTensorDescriptor_t cDesc,
+                  void *C);
+
+/* Set all values of a tensor to a given value : y[i] = value[0] */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *valuePtr);
+
+/* Scale all values of a tensor by a given factor : y[i] = alpha * y[i] */
+cudnnStatus_t CUDNNWINAPI
+cudnnScaleTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *alpha);
+
+/* Create an instance of FilterStruct */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateFilterDescriptor(cudnnFilterDescriptor_t *filterDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetFilter4dDescriptor(cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t dataType, /* image data type */
+                           cudnnTensorFormat_t format,
+                           int k,  /* number of output feature maps */
+                           int c,  /* number of input feature maps */
+                           int h,  /* height of each input filter */
+                           int w); /* width of  each input filter */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFilter4dDescriptor(const cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t *dataType, /* image data type */
+                           cudnnTensorFormat_t *format,
+                           int *k,  /* number of output feature maps */
+                           int *c,  /* number of input feature maps */
+                           int *h,  /* height of each input filter */
+                           int *w); /* width of  each input filter */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetFilterNdDescriptor(cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t dataType, /* image data type */
+                           cudnnTensorFormat_t format,
+                           int nbDims,
+                           const int filterDimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFilterNdDescriptor(const cudnnFilterDescriptor_t filterDesc,
+                           int nbDimsRequested,
+                           cudnnDataType_t *dataType, /* image data type */
+                           cudnnTensorFormat_t *format,
+                           int *nbDims,
+                           int filterDimA[]);
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFilterSizeInBytes(const cudnnFilterDescriptor_t filterDesc, size_t *size);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnTransformFilter(cudnnHandle_t handle,
+                     const cudnnTensorTransformDescriptor_t transDesc,
+                     const void *alpha,
+                     const cudnnFilterDescriptor_t srcDesc,
+                     const void *srcData,
+                     const void *beta,
+                     const cudnnFilterDescriptor_t destDesc,
+                     void *destData);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFilterDescriptor(cudnnFilterDescriptor_t filterDesc);
+
+/*
+ *  softmax algorithm
+ */
+typedef enum {
+    CUDNN_SOFTMAX_FAST     = 0, /* straightforward implementation */
+    CUDNN_SOFTMAX_ACCURATE = 1, /* subtract max from every point to avoid overflow */
+    CUDNN_SOFTMAX_LOG      = 2
+} cudnnSoftmaxAlgorithm_t;
+
+typedef enum {
+    CUDNN_SOFTMAX_MODE_INSTANCE = 0, /* compute the softmax over all C, H, W for each N */
+    CUDNN_SOFTMAX_MODE_CHANNEL  = 1  /* compute the softmax over all C for each H, W, N */
+} cudnnSoftmaxMode_t;
+
+/* Softmax functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform forward softmax */
+cudnnStatus_t CUDNNWINAPI
+cudnnSoftmaxForward(cudnnHandle_t handle,
+                    cudnnSoftmaxAlgorithm_t algo,
+                    cudnnSoftmaxMode_t mode,
+                    const void *alpha,
+                    const cudnnTensorDescriptor_t xDesc,
+                    const void *x,
+                    const void *beta,
+                    const cudnnTensorDescriptor_t yDesc,
+                    void *y);
+
+/*
+ *  pooling mode
+ */
+typedef enum {
+    CUDNN_POOLING_MAX                           = 0,
+    CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING = 1, /* count for average includes padded values */
+    CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING = 2, /* count for average does not include padded values */
+    CUDNN_POOLING_MAX_DETERMINISTIC             = 3
+} cudnnPoolingMode_t;
+
+/* Create an instance of pooling descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreatePoolingDescriptor(cudnnPoolingDescriptor_t *poolingDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t poolingDesc,
+                            cudnnPoolingMode_t mode,
+                            cudnnNanPropagation_t maxpoolingNanOpt,
+                            int windowHeight,
+                            int windowWidth,
+                            int verticalPadding,
+                            int horizontalPadding,
+                            int verticalStride,
+                            int horizontalStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPooling2dDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
+                            cudnnPoolingMode_t *mode,
+                            cudnnNanPropagation_t *maxpoolingNanOpt,
+                            int *windowHeight,
+                            int *windowWidth,
+                            int *verticalPadding,
+                            int *horizontalPadding,
+                            int *verticalStride,
+                            int *horizontalStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetPoolingNdDescriptor(cudnnPoolingDescriptor_t poolingDesc,
+                            const cudnnPoolingMode_t mode,
+                            const cudnnNanPropagation_t maxpoolingNanOpt,
+                            int nbDims,
+                            const int windowDimA[],
+                            const int paddingA[],
+                            const int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPoolingNdDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
+                            int nbDimsRequested,
+                            cudnnPoolingMode_t *mode,
+                            cudnnNanPropagation_t *maxpoolingNanOpt,
+                            int *nbDims,
+                            int windowDimA[],
+                            int paddingA[],
+                            int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPoolingNdForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
+                                  const cudnnTensorDescriptor_t inputTensorDesc,
+                                  int nbDims,
+                                  int outputTensorDimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPooling2dForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
+                                  const cudnnTensorDescriptor_t inputTensorDesc,
+                                  int *n,
+                                  int *c,
+                                  int *h,
+                                  int *w);
+
+/* Destroy an instance of pooling descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyPoolingDescriptor(cudnnPoolingDescriptor_t poolingDesc);
+
+/* Pooling functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform forward pooling */
+cudnnStatus_t CUDNNWINAPI
+cudnnPoolingForward(cudnnHandle_t handle,
+                    const cudnnPoolingDescriptor_t poolingDesc,
+                    const void *alpha,
+                    const cudnnTensorDescriptor_t xDesc,
+                    const void *x,
+                    const void *beta,
+                    const cudnnTensorDescriptor_t yDesc,
+                    void *y);
+
+/*
+ * activation mode
+ */
+typedef enum {
+    CUDNN_ACTIVATION_SIGMOID      = 0,
+    CUDNN_ACTIVATION_RELU         = 1,
+    CUDNN_ACTIVATION_TANH         = 2,
+    CUDNN_ACTIVATION_CLIPPED_RELU = 3,
+    CUDNN_ACTIVATION_ELU          = 4,
+    CUDNN_ACTIVATION_IDENTITY     = 5,
+    CUDNN_ACTIVATION_SWISH        = 6
+} cudnnActivationMode_t;
+
+/* Activation functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateActivationDescriptor(cudnnActivationDescriptor_t *activationDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetActivationDescriptor(cudnnActivationDescriptor_t activationDesc,
+                             cudnnActivationMode_t mode,
+                             cudnnNanPropagation_t reluNanOpt,
+                             double coef); /* ceiling for clipped RELU, alpha for ELU */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetActivationDescriptor(const cudnnActivationDescriptor_t activationDesc,
+                             cudnnActivationMode_t *mode,
+                             cudnnNanPropagation_t *reluNanOpt,
+                             double *coef); /* ceiling for clipped RELU, alpha for ELU */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double swish_beta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double *swish_beta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyActivationDescriptor(cudnnActivationDescriptor_t activationDesc);
+
+/* Function to perform forward activation  */
+cudnnStatus_t CUDNNWINAPI
+cudnnActivationForward(cudnnHandle_t handle,
+                       cudnnActivationDescriptor_t activationDesc,
+                       const void *alpha,
+                       const cudnnTensorDescriptor_t xDesc,
+                       const void *x,
+                       const void *beta,
+                       const cudnnTensorDescriptor_t yDesc,
+                       void *y);
+
+/*
+ * Create an instance of LRN (Local Response Normalization) descriptor
+ * Uses lrnN=5, lrnAlpha=1e-4, lrnBeta=0.75, lrnK=2.0 as defaults from Krizhevsky'12 ImageNet paper
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateLRNDescriptor(cudnnLRNDescriptor_t *normDesc);
+
+#define CUDNN_LRN_MIN_N 1       /* minimum allowed lrnN */
+#define CUDNN_LRN_MAX_N 16      /* maximum allowed lrnN */
+#define CUDNN_LRN_MIN_K 1e-5    /* minimum allowed lrnK */
+#define CUDNN_LRN_MIN_BETA 0.01 /* minimum allowed lrnBeta */
+
+/* LRN layer mode */
+typedef enum {
+    CUDNN_LRN_CROSS_CHANNEL_DIM1 = 0, /* Normalize across tensor's dimA[1] dimension */
+} cudnnLRNMode_t;
+
+/*
+ * Uses a window [center-lookBehind, center+lookAhead], where
+ * lookBehind = floor( (lrnN-1)/2 ), lookAhead = lrnN-lookBehind-1.
+ * Values of double parameters cast to tensor data type.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned lrnN, double lrnAlpha, double lrnBeta, double lrnK);
+/*
+ * Retrieve the settings currently stored in an LRN layer descriptor
+ * Any of the provided pointers can be NULL (no corresponding value will be returned)
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned *lrnN, double *lrnAlpha, double *lrnBeta, double *lrnK);
+
+/* Destroy an instance of LRN descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyLRNDescriptor(cudnnLRNDescriptor_t lrnDesc);
+
+/* LRN functions: output = alpha * normalize(x) + beta * old_y */
+
+/* LRN cross-channel forward computation. Double parameters cast to tensor data type */
+cudnnStatus_t CUDNNWINAPI
+cudnnLRNCrossChannelForward(cudnnHandle_t handle,
+                            cudnnLRNDescriptor_t normDesc,
+                            cudnnLRNMode_t lrnMode,
+                            const void *alpha,
+                            const cudnnTensorDescriptor_t xDesc,
+                            const void *x,
+                            const void *beta,
+                            const cudnnTensorDescriptor_t yDesc,
+                            void *y);
+
+typedef enum {
+    CUDNN_DIVNORM_PRECOMPUTED_MEANS = 0,
+} cudnnDivNormMode_t;
+
+/* LCN/divisive normalization functions: y = alpha * normalize(x) + beta * y */
+cudnnStatus_t CUDNNWINAPI
+cudnnDivisiveNormalizationForward(cudnnHandle_t handle,
+                                  cudnnLRNDescriptor_t normDesc,
+                                  cudnnDivNormMode_t mode,
+                                  const void *alpha,
+                                  const cudnnTensorDescriptor_t xDesc, /* same desc for means, temp, temp2 */
+                                  const void *x,
+                                  const void *means, /* if NULL, means are assumed to be zero */
+                                  void *temp,
+                                  void *temp2,
+                                  const void *beta,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  void *y);
+
+typedef enum {
+    /* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
+    CUDNN_BATCHNORM_PER_ACTIVATION = 0,
+
+    /* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
+    CUDNN_BATCHNORM_SPATIAL = 1,
+
+    /*
+     * bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors).
+     * May be faster than CUDNN_BATCHNORM_SPATIAL but imposes some limits on the range of values
+     */
+    CUDNN_BATCHNORM_SPATIAL_PERSISTENT = 2,
+} cudnnBatchNormMode_t;
+
+#define CUDNN_BN_MIN_EPSILON 0.0 /* Minimum epsilon allowed to be used in the Batch Normalization formula */
+
+/*
+ * Derives a tensor descriptor from layer data descriptor for BatchNormalization
+ * scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
+ * bnScaleBiasMeanVarDesc and bnScaleBiasDiffDesc in Batch Normalization forward and backward functions.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnDeriveBNTensorDescriptor(cudnnTensorDescriptor_t derivedBnDesc,
+                              const cudnnTensorDescriptor_t xDesc,
+                              cudnnBatchNormMode_t mode);
+
+typedef enum {
+    CUDNN_BATCHNORM_OPS_BN                = 0, /* do batch normalization only */
+    CUDNN_BATCHNORM_OPS_BN_ACTIVATION     = 1, /* do batchNorm, then activation */
+    CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION = 2, /* do batchNorm, then elemWiseAdd, then activation */
+} cudnnBatchNormOps_t;
+
+/*
+ * Performs Batch Normalization during Inference:
+ * y[i] = bnScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + bnBias[k]
+ * with bnScale, bnBias, runningMean, runningInvVariance tensors indexed
+ * according to spatial or per-activation mode. Refer to cudnnBatchNormalizationForwardTraining
+ * above for notes on function arguments.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardInference(cudnnHandle_t handle,
+                                        cudnnBatchNormMode_t mode,
+                                        const void *alpha, /* alpha[0] = result blend factor */
+                                        const void *beta,  /* beta[0] = dest layer blend factor */
+                                        const cudnnTensorDescriptor_t xDesc,
+                                        const void *x, /* NxCxHxW */
+                                        const cudnnTensorDescriptor_t yDesc,
+                                        void *y, /* NxCxHxW */
+                                        const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+                                        const void *bnScale,
+                                        const void *bnBias,
+                                        const void *estimatedMean,
+                                        const void *estimatedVariance,
+                                        double epsilon);
+
+typedef enum {
+    /* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
+    CUDNN_NORM_PER_ACTIVATION = 0,
+
+    /* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
+    CUDNN_NORM_PER_CHANNEL = 1,
+} cudnnNormMode_t;
+
+typedef enum { CUDNN_NORM_ALGO_STANDARD = 0, CUDNN_NORM_ALGO_PERSIST = 1 } cudnnNormAlgo_t;
+
+/*
+ * Derives a tensor descriptor from layer data descriptor for Normalization
+ * scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
+ * normScaleBiasMeanVarDesc and normScaleBiasDiffDesc in Normalization forward and backward functions.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnDeriveNormTensorDescriptor(cudnnTensorDescriptor_t derivedNormScaleBiasDesc,
+                                cudnnTensorDescriptor_t derivedNormMeanVarDesc,
+                                const cudnnTensorDescriptor_t xDesc,
+                                cudnnNormMode_t mode,
+                                int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+typedef enum {
+    CUDNN_NORM_OPS_NORM                = 0, /* do normalization only */
+    CUDNN_NORM_OPS_NORM_ACTIVATION     = 1, /* do Norm, then activation */
+    CUDNN_NORM_OPS_NORM_ADD_ACTIVATION = 2, /* do Norm, then elemWiseAdd, then activation */
+} cudnnNormOps_t;
+
+/*
+ * Performs Normalization during Inference:
+ * y[i] = normScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + normBias[k]
+ * with normScale, normBias, runningMean, runningInvVariance tensors indexed
+ * according to per-channel or per-activation mode. Refer to cudnnNormalizationForwardTraining
+ * above for notes on function arguments.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationForwardInference(cudnnHandle_t handle,
+                                   cudnnNormMode_t mode,
+                                   cudnnNormOps_t normOps,
+                                   cudnnNormAlgo_t algo,
+                                   const void *alpha, /* alpha[0] = result blend factor */
+                                   const void *beta,  /* beta[0] = dest layer blend factor */
+                                   const cudnnTensorDescriptor_t xDesc,
+                                   const void *x, /* NxCxHxW */
+                                   const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                   const void *normScale,
+                                   const void *normBias,
+                                   const cudnnTensorDescriptor_t normMeanVarDesc,
+                                   const void *estimatedMean,
+                                   const void *estimatedVariance,
+                                   const cudnnTensorDescriptor_t zDesc,
+                                   const void *z,
+                                   cudnnActivationDescriptor_t activationDesc,
+                                   const cudnnTensorDescriptor_t yDesc,
+                                   void *y, /* NxCxHxW */
+                                   double epsilon,
+                                   int groupCnt); /* Place hold for future work*/
+
+/* APIs for spatial transformer network*/
+typedef enum {
+    CUDNN_SAMPLER_BILINEAR = 0,
+} cudnnSamplerType_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateSpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t *stDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetSpatialTransformerNdDescriptor(cudnnSpatialTransformerDescriptor_t stDesc,
+                                       cudnnSamplerType_t samplerType,
+                                       cudnnDataType_t dataType,
+                                       const int nbDims,
+                                       const int dimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroySpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t stDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfGridGeneratorForward(cudnnHandle_t handle,
+                                   const cudnnSpatialTransformerDescriptor_t stDesc,
+                                   const void *theta,
+                                   void *grid);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfSamplerForward(cudnnHandle_t handle,
+                             cudnnSpatialTransformerDescriptor_t stDesc,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t xDesc,
+                             const void *x,
+                             const void *grid,
+                             const void *beta,
+                             cudnnTensorDescriptor_t yDesc,
+                             void *y);
+
+typedef struct cudnnDropoutStruct *cudnnDropoutDescriptor_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateDropoutDescriptor(cudnnDropoutDescriptor_t *dropoutDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc);
+
+/*helper function to determine size of the states to be passed to cudnnSetDropoutDescriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutGetStatesSize(cudnnHandle_t handle, size_t *sizeInBytes);
+
+/*helper function to determine size of the reserve space to be passed to dropout forward/backward calls */
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutGetReserveSpaceSize(cudnnTensorDescriptor_t xdesc, size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                          cudnnHandle_t handle,
+                          float dropout,
+                          void *states,
+                          size_t stateSizeInBytes,
+                          unsigned long long seed);
+
+/* Restores the dropout descriptor to a previously saved-off state */
+cudnnStatus_t CUDNNWINAPI
+cudnnRestoreDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                              cudnnHandle_t handle,
+                              float dropout,
+                              void *states,
+                              size_t stateSizeInBytes,
+                              unsigned long long seed);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                          cudnnHandle_t handle,
+                          float *dropout,
+                          void **states,
+                          unsigned long long *seed);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutForward(cudnnHandle_t handle,
+                    const cudnnDropoutDescriptor_t dropoutDesc,
+                    const cudnnTensorDescriptor_t xdesc,
+                    const void *x,
+                    const cudnnTensorDescriptor_t ydesc,
+                    void *y,
+                    void *reserveSpace,
+                    size_t reserveSpaceSizeInBytes);
+
+/* TODO: remove */
+
+typedef struct cudnnAlgorithmStruct *cudnnAlgorithmDescriptor_t;
+typedef struct cudnnAlgorithmPerformanceStruct *cudnnAlgorithmPerformance_t;
+
+/* TODO: move these enums out to the appropriate submodule */
+typedef enum {
+    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM         = 0,
+    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM = 1,
+    CUDNN_CONVOLUTION_FWD_ALGO_GEMM                  = 2,
+    CUDNN_CONVOLUTION_FWD_ALGO_DIRECT                = 3,
+    CUDNN_CONVOLUTION_FWD_ALGO_FFT                   = 4,
+    CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING            = 5,
+    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD              = 6,
+    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED     = 7,
+    CUDNN_CONVOLUTION_FWD_ALGO_COUNT                 = 8
+} cudnnConvolutionFwdAlgo_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0                 = 0, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1                 = 1,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT               = 2,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3                 = 3, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD          = 4, /* not implemented */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED = 5,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING        = 6,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT             = 7
+} cudnnConvolutionBwdFilterAlgo_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_0                 = 0, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_1                 = 1,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT               = 2,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING        = 3,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD          = 4,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED = 5,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT             = 6
+} cudnnConvolutionBwdDataAlgo_t;
+
+typedef enum {
+    CUDNN_RNN_ALGO_STANDARD               = 0,
+    CUDNN_RNN_ALGO_PERSIST_STATIC         = 1,
+    CUDNN_RNN_ALGO_PERSIST_DYNAMIC        = 2,
+    CUDNN_RNN_ALGO_PERSIST_STATIC_SMALL_H = 3,
+    CUDNN_RNN_ALGO_COUNT                  = 4,
+} cudnnRNNAlgo_t;
+
+typedef enum { CUDNN_CTC_LOSS_ALGO_DETERMINISTIC = 0, CUDNN_CTC_LOSS_ALGO_NON_DETERMINISTIC = 1 } cudnnCTCLossAlgo_t;
+
+/* TODO: remove */
+typedef struct cudnnAlgorithmUnionStruct {
+    union Algorithm {
+        cudnnConvolutionFwdAlgo_t convFwdAlgo;
+        cudnnConvolutionBwdFilterAlgo_t convBwdFilterAlgo;
+        cudnnConvolutionBwdDataAlgo_t convBwdDataAlgo;
+        cudnnRNNAlgo_t RNNAlgo;
+        cudnnCTCLossAlgo_t CTCLossAlgo;
+    } algo;
+} cudnnAlgorithm_t;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateAlgorithmDescriptor(cudnnAlgorithmDescriptor_t *algoDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetAlgorithmDescriptor(cudnnAlgorithmDescriptor_t algoDesc, cudnnAlgorithm_t algorithm);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAlgorithmDescriptor(const cudnnAlgorithmDescriptor_t algoDesc, cudnnAlgorithm_t *algorithm);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCopyAlgorithmDescriptor(const cudnnAlgorithmDescriptor_t src, cudnnAlgorithmDescriptor_t dest);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyAlgorithmDescriptor(cudnnAlgorithmDescriptor_t algoDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateAlgorithmPerformance(cudnnAlgorithmPerformance_t *algoPerf, int numberToCreate);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetAlgorithmPerformance(cudnnAlgorithmPerformance_t algoPerf,
+                             cudnnAlgorithmDescriptor_t algoDesc,
+                             cudnnStatus_t status,
+                             float time,
+                             size_t memory);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAlgorithmPerformance(const cudnnAlgorithmPerformance_t algoPerf,
+                             cudnnAlgorithmDescriptor_t *algoDesc,
+                             cudnnStatus_t *status,
+                             float *time,
+                             size_t *memory);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyAlgorithmPerformance(cudnnAlgorithmPerformance_t *algoPerf, int numberToDestroy);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAlgorithmSpaceSize(cudnnHandle_t handle, cudnnAlgorithmDescriptor_t algoDesc, size_t *algoSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSaveAlgorithm(cudnnHandle_t handle,
+                   cudnnAlgorithmDescriptor_t algoDesc,
+                   void *algoSpace,
+                   size_t algoSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRestoreAlgorithm(cudnnHandle_t handle,
+                      void *algoSpace,
+                      size_t algoSpaceSizeInBytes,
+                      cudnnAlgorithmDescriptor_t algoDesc);
+
+typedef enum {
+    CUDNN_SEV_FATAL   = 0,
+    CUDNN_SEV_ERROR   = 1,
+    CUDNN_SEV_WARNING = 2,
+    CUDNN_SEV_INFO    = 3,
+} cudnnSeverity_t;
+
+/* Message masks to be used with cudnnSetCallback() */
+#define CUDNN_SEV_ERROR_EN (1U << CUDNN_SEV_ERROR)
+#define CUDNN_SEV_WARNING_EN (1U << CUDNN_SEV_WARNING)
+#define CUDNN_SEV_INFO_EN (1U << CUDNN_SEV_INFO)
+
+/* struct containing useful informaiton for each API call */
+typedef struct cudnnDebugStruct {
+    unsigned cudnn_version;
+    cudnnStatus_t cudnnStatus;
+    unsigned time_sec;      /* epoch time in seconds */
+    unsigned time_usec;     /* microseconds part of epoch time */
+    unsigned time_delta;    /* time since start in seconds */
+    cudnnHandle_t handle;   /* cudnn handle */
+    cudaStream_t stream;    /* cuda stream ID */
+    unsigned long long pid; /* process ID */
+    unsigned long long tid; /* thread ID */
+    int cudaDeviceId;       /* CUDA device ID */
+    int reserved[15];       /* reserved for future use */
+} cudnnDebug_t;
+
+typedef void (*cudnnCallback_t)(cudnnSeverity_t sev, void *udata, const cudnnDebug_t *dbg, const char *msg);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCallback(unsigned mask, void *udata, cudnnCallback_t fptr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCallback(unsigned *mask, void **udata, cudnnCallback_t *fptr);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpsInferVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_OPS_INFER_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops_train.h

@@ -0,0 +1,501 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_ops_train : cuDNN's basic training operations and algorithms.
+ */
+
+#if !defined(CUDNN_OPS_TRAIN_H_)
+#define CUDNN_OPS_TRAIN_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_OPS_TRAIN_MAJOR 8
+#define CUDNN_OPS_TRAIN_MINOR 9
+#define CUDNN_OPS_TRAIN_PATCH 2
+
+#if (CUDNN_OPS_TRAIN_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_TRAIN_MINOR != CUDNN_MINOR) || \
+    (CUDNN_OPS_TRAIN_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN OPS TRAIN!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* Function to perform backward softmax */
+cudnnStatus_t CUDNNWINAPI
+cudnnSoftmaxBackward(cudnnHandle_t handle,
+                     cudnnSoftmaxAlgorithm_t algo,
+                     cudnnSoftmaxMode_t mode,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t yDesc,
+                     const void *y,
+                     const cudnnTensorDescriptor_t dyDesc,
+                     const void *dy,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t dxDesc,
+                     void *dx);
+
+/* Function to perform backward pooling */
+cudnnStatus_t CUDNNWINAPI
+cudnnPoolingBackward(cudnnHandle_t handle,
+                     const cudnnPoolingDescriptor_t poolingDesc,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t yDesc,
+                     const void *y,
+                     const cudnnTensorDescriptor_t dyDesc,
+                     const void *dy,
+                     const cudnnTensorDescriptor_t xDesc,
+                     const void *x,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t dxDesc,
+                     void *dx);
+
+/* Function to perform backward activation  */
+cudnnStatus_t CUDNNWINAPI
+cudnnActivationBackward(cudnnHandle_t handle,
+                        cudnnActivationDescriptor_t activationDesc,
+                        const void *alpha,
+                        const cudnnTensorDescriptor_t yDesc,
+                        const void *y,
+                        const cudnnTensorDescriptor_t dyDesc,
+                        const void *dy,
+                        const cudnnTensorDescriptor_t xDesc,
+                        const void *x,
+                        const void *beta,
+                        const cudnnTensorDescriptor_t dxDesc,
+                        void *dx);
+
+/* LRN cross-channel backward computation. Double parameters cast to tensor data type */
+cudnnStatus_t CUDNNWINAPI
+cudnnLRNCrossChannelBackward(cudnnHandle_t handle,
+                             cudnnLRNDescriptor_t normDesc,
+                             cudnnLRNMode_t lrnMode,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t yDesc,
+                             const void *y,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const cudnnTensorDescriptor_t xDesc,
+                             const void *x,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dxDesc,
+                             void *dx);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDivisiveNormalizationBackward(cudnnHandle_t handle,
+                                   cudnnLRNDescriptor_t normDesc,
+                                   cudnnDivNormMode_t mode,
+                                   const void *alpha,
+                                   const cudnnTensorDescriptor_t xDesc, /* same desc for x, means, dy, temp, temp2 */
+                                   const void *x,
+                                   const void *means, /* if NULL, means are assumed to be zero */
+                                   const void *dy,
+                                   void *temp,
+                                   void *temp2,
+                                   const void *beta,
+                                   const cudnnTensorDescriptor_t dXdMeansDesc, /* same desc for dx, dMeans */
+                                   void *dx,                                   /* output x differential */
+                                   void *dMeans); /* output means differential, can be NULL */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(cudnnHandle_t handle,
+                                                         cudnnBatchNormMode_t mode,
+                                                         cudnnBatchNormOps_t bnOps,
+                                                         const cudnnTensorDescriptor_t xDesc,
+                                                         const cudnnTensorDescriptor_t zDesc,
+                                                         const cudnnTensorDescriptor_t yDesc,
+                                                         const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+                                                         const cudnnActivationDescriptor_t activationDesc,
+                                                         size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationBackwardExWorkspaceSize(cudnnHandle_t handle,
+                                                  cudnnBatchNormMode_t mode,
+                                                  cudnnBatchNormOps_t bnOps,
+                                                  const cudnnTensorDescriptor_t xDesc,
+                                                  const cudnnTensorDescriptor_t yDesc,
+                                                  const cudnnTensorDescriptor_t dyDesc,
+                                                  const cudnnTensorDescriptor_t dzDesc,
+                                                  const cudnnTensorDescriptor_t dxDesc,
+                                                  const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                                  const cudnnActivationDescriptor_t activationDesc,
+                                                  size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationTrainingExReserveSpaceSize(cudnnHandle_t handle,
+                                                     cudnnBatchNormMode_t mode,
+                                                     cudnnBatchNormOps_t bnOps,
+                                                     const cudnnActivationDescriptor_t activationDesc,
+                                                     const cudnnTensorDescriptor_t xDesc,
+                                                     size_t *sizeInBytes);
+
+/* Computes y = BN(x). Also accumulates moving averages of mean and inverse variances */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardTraining(
+    cudnnHandle_t handle,
+    cudnnBatchNormMode_t mode,
+
+    const void *alpha, /* alpha[0] = result blend factor */
+    const void *beta,  /* beta[0] = dest layer blend factor */
+
+    const cudnnTensorDescriptor_t xDesc,
+    const void *x, /* NxCxHxW */
+    const cudnnTensorDescriptor_t yDesc,
+    void *y, /* NxCxHxW */
+
+    /* Shared desc for the next 6 tensors in the argument list.
+       Data type to be set as follows:
+       type = (typeOf(x) == double) ? double : float
+       Dimensions for this descriptor depend on normalization mode
+       - Spatial Normalization : tensors are expected to have dims 1xCx1x1
+        (normalization is performed across NxHxW)
+       - Per-Activation Normalization : tensors are expected to have dims of 1xCxHxW
+        (normalization is performed across N) */
+    const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+
+    /* 'Gamma' and 'Beta' respectively in Ioffe and Szegedy's paper's notation */
+    const void *bnScale,
+    const void *bnBias,
+
+    /* MUST use factor=1 in the very first call of a complete training cycle.
+       Use a factor=1/(1+n) at N-th call to the function to get
+       Cumulative Moving Average (CMA) behavior
+       CMA[n] = (x[1]+...+x[n])/n
+       Since CMA[n+1] = (n*CMA[n]+x[n+1])/(n+1) =
+       ((n+1)*CMA[n]-CMA[n])/(n+1) + x[n+1]/(n+1) =
+       CMA[n]*(1-1/(n+1)) + x[n+1]*1/(n+1) */
+    double exponentialAverageFactor,
+
+    /* Used in Training phase only.
+       runningMean = newMean*factor + runningMean*(1-factor) */
+    void *resultRunningMean,
+    /* Output in training mode, input in inference. Is the moving average
+       of  variance[x] (factor is applied in the same way as for runningMean) */
+    void *resultRunningVariance,
+
+    /* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
+    double epsilon,
+
+    /* Optionally save intermediate results from the forward pass here
+       - can be reused to speed up backward pass. NULL if unused */
+    void *resultSaveMean,
+    void *resultSaveInvVariance);
+
+/* Computes y = relu(BN(x) + z). Also accumulates moving averages of mean and inverse variances */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardTrainingEx(
+    cudnnHandle_t handle,
+    cudnnBatchNormMode_t mode,
+    cudnnBatchNormOps_t bnOps,
+
+    const void *alpha, /* alpha[0] = result blend factor */
+    const void *beta,  /* beta[0] = dest layer blend factor */
+
+    const cudnnTensorDescriptor_t xDesc,
+    const void *xData,
+    const cudnnTensorDescriptor_t zDesc,
+    const void *zData,
+    const cudnnTensorDescriptor_t yDesc,
+    void *yData,
+
+    const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+    const void *bnScale,
+    const void *bnBias,
+
+    double exponentialAverageFactor,
+    void *resultRunningMean,
+    void *resultRunningVariance,
+
+    /* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
+    double epsilon,
+
+    /* Optionally save intermediate results from the forward pass here
+       - can be reused to speed up backward pass. NULL if unused */
+    void *resultSaveMean,
+    void *resultSaveInvVariance,
+
+    cudnnActivationDescriptor_t activationDesc,
+    void *workspace,
+    size_t workSpaceSizeInBytes,
+    void *reserveSpace,
+    size_t reserveSpaceSizeInBytes);
+
+/* Performs backward pass of Batch Normalization layer. Returns x gradient,
+* bnScale gradient and bnBias gradient */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationBackward(cudnnHandle_t handle,
+                                cudnnBatchNormMode_t mode,
+                                const void *alphaDataDiff,
+                                const void *betaDataDiff,
+                                const void *alphaParamDiff,
+                                const void *betaParamDiff,
+                                const cudnnTensorDescriptor_t xDesc, /* same desc for x, dx, dy */
+                                const void *x,
+                                const cudnnTensorDescriptor_t dyDesc,
+                                const void *dy,
+                                const cudnnTensorDescriptor_t dxDesc,
+                                void *dx,
+                                /* Shared tensor desc for the 4 tensors below */
+                                const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                const void *bnScale, /* bnBias doesn't affect backpropagation */
+                                /* scale and bias diff are not backpropagated below this layer */
+                                void *dBnScaleResult,
+                                void *dBnBiasResult,
+                                /* Same epsilon as forward pass */
+                                double epsilon,
+
+                                /* Optionally cached intermediate results from
+                                   forward pass */
+                                const void *savedMean,
+                                const void *savedInvVariance);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationBackwardEx(cudnnHandle_t handle,
+                                  cudnnBatchNormMode_t mode,
+                                  cudnnBatchNormOps_t bnOps,
+
+                                  const void *alphaDataDiff,
+                                  const void *betaDataDiff,
+                                  const void *alphaParamDiff,
+                                  const void *betaParamDiff,
+                                  const cudnnTensorDescriptor_t xDesc,
+                                  const void *xData,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  const void *yData,
+                                  const cudnnTensorDescriptor_t dyDesc,
+                                  const void *dyData,
+                                  const cudnnTensorDescriptor_t dzDesc,
+                                  void *dzData,
+                                  const cudnnTensorDescriptor_t dxDesc,
+                                  void *dxData,
+
+                                  /* Shared tensor desc for the 4 tensors below */
+                                  const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                  const void *bnScaleData,
+                                  const void *bnBiasData, /* needed if there is activation */
+                                  void *dBnScaleData,
+                                  void *dBnBiasData,
+                                  double epsilon, /* Same epsilon as forward pass */
+
+                                  /* Optionally cached intermediate results from
+                                     forward pass */
+                                  const void *savedMean,
+                                  const void *savedInvVariance,
+                                  cudnnActivationDescriptor_t activationDesc,
+                                  void *workSpace,
+                                  size_t workSpaceSizeInBytes,
+                                  void *reserveSpace,
+                                  size_t reserveSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationForwardTrainingWorkspaceSize(cudnnHandle_t handle,
+                                                  cudnnNormMode_t mode,
+                                                  cudnnNormOps_t normOps,
+                                                  cudnnNormAlgo_t algo,
+                                                  const cudnnTensorDescriptor_t xDesc,
+                                                  const cudnnTensorDescriptor_t zDesc,
+                                                  const cudnnTensorDescriptor_t yDesc,
+                                                  const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                                  const cudnnActivationDescriptor_t activationDesc,
+                                                  const cudnnTensorDescriptor_t normMeanVarDesc,
+                                                  size_t *sizeInBytes,
+                                                  int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationBackwardWorkspaceSize(cudnnHandle_t handle,
+                                           cudnnNormMode_t mode,
+                                           cudnnNormOps_t normOps,
+                                           cudnnNormAlgo_t algo,
+                                           const cudnnTensorDescriptor_t xDesc,
+                                           const cudnnTensorDescriptor_t yDesc,
+                                           const cudnnTensorDescriptor_t dyDesc,
+                                           const cudnnTensorDescriptor_t dzDesc,
+                                           const cudnnTensorDescriptor_t dxDesc,
+                                           const cudnnTensorDescriptor_t dNormScaleBiasDesc,
+                                           const cudnnActivationDescriptor_t activationDesc,
+                                           const cudnnTensorDescriptor_t normMeanVarDesc,
+                                           size_t *sizeInBytes,
+                                           int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationTrainingReserveSpaceSize(cudnnHandle_t handle,
+                                              cudnnNormMode_t mode,
+                                              cudnnNormOps_t normOps,
+                                              cudnnNormAlgo_t algo,
+                                              const cudnnActivationDescriptor_t activationDesc,
+                                              const cudnnTensorDescriptor_t xDesc,
+                                              size_t *sizeInBytes,
+                                              int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+/* Computes y = relu(Norm(x) + z). Also accumulates moving averages of mean and inverse variances */
+cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationForwardTraining(cudnnHandle_t handle,
+                                  cudnnNormMode_t mode,
+                                  cudnnNormOps_t normOps,
+                                  cudnnNormAlgo_t algo,
+                                  const void *alpha, /* alpha[0] = result blend factor */
+                                  const void *beta,  /* beta[0] = dest layer blend factor */
+                                  const cudnnTensorDescriptor_t xDesc,
+                                  const void *xData,
+                                  const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                  const void *normScale,
+                                  const void *normBias,
+                                  double exponentialAverageFactor,
+                                  const cudnnTensorDescriptor_t normMeanVarDesc,
+                                  void *resultRunningMean,
+                                  void *resultRunningVariance,
+                                  /* Has to be >= 0. Should be the same in forward and backward functions. */
+                                  double epsilon,
+                                  /* Optionally save intermediate results from the forward pass here
+                                     - can be reused to speed up backward pass. NULL if unused */
+                                  void *resultSaveMean,
+                                  void *resultSaveInvVariance,
+                                  cudnnActivationDescriptor_t activationDesc,
+                                  const cudnnTensorDescriptor_t zDesc,
+                                  const void *zData,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  void *yData,
+                                  void *workspace,
+                                  size_t workSpaceSizeInBytes,
+                                  void *reserveSpace,
+                                  size_t reserveSpaceSizeInBytes,
+                                  int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationBackward(cudnnHandle_t handle,
+                           cudnnNormMode_t mode,
+                           cudnnNormOps_t normOps,
+                           cudnnNormAlgo_t algo,
+                           const void *alphaDataDiff,
+                           const void *betaDataDiff,
+                           const void *alphaParamDiff,
+                           const void *betaParamDiff,
+                           const cudnnTensorDescriptor_t xDesc,
+                           const void *xData,
+                           const cudnnTensorDescriptor_t yDesc,
+                           const void *yData,
+                           const cudnnTensorDescriptor_t dyDesc,
+                           const void *dyData,
+                           const cudnnTensorDescriptor_t dzDesc,
+                           void *dzData,
+                           const cudnnTensorDescriptor_t dxDesc,
+                           void *dxData,
+                           /* Shared tensor desc for the 4 tensors below */
+                           const cudnnTensorDescriptor_t dNormScaleBiasDesc,
+                           const void *normScaleData,
+                           const void *normBiasData, /* needed if there is activation */
+                           void *dNormScaleData,
+                           void *dNormBiasData,
+                           double epsilon, /* Same epsilon as forward pass */
+                           const cudnnTensorDescriptor_t normMeanVarDesc,
+                           /* Optionally cached intermediate results from
+                              forward pass */
+                           const void *savedMean,
+                           const void *savedInvVariance,
+                           cudnnActivationDescriptor_t activationDesc,
+                           void *workSpace,
+                           size_t workSpaceSizeInBytes,
+                           void *reserveSpace,
+                           size_t reserveSpaceSizeInBytes,
+                           int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfGridGeneratorBackward(cudnnHandle_t handle,
+                                    const cudnnSpatialTransformerDescriptor_t stDesc,
+                                    const void *dgrid,
+                                    void *dtheta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfSamplerBackward(cudnnHandle_t handle,
+                              cudnnSpatialTransformerDescriptor_t stDesc,
+                              const void *alpha,
+                              const cudnnTensorDescriptor_t xDesc,
+                              const void *x,
+                              const void *beta,
+                              const cudnnTensorDescriptor_t dxDesc,
+                              void *dx,
+                              const void *alphaDgrid,
+                              const cudnnTensorDescriptor_t dyDesc,
+                              const void *dy,
+                              const void *grid,
+                              const void *betaDgrid,
+                              void *dgrid);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutBackward(cudnnHandle_t handle,
+                     const cudnnDropoutDescriptor_t dropoutDesc,
+                     const cudnnTensorDescriptor_t dydesc,
+                     const void *dy,
+                     const cudnnTensorDescriptor_t dxdesc,
+                     void *dx,
+                     void *reserveSpace,
+                     size_t reserveSpaceSizeInBytes);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpsTrainVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_OPS_TRAIN_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops_train_v8.h

@@ -0,0 +1,501 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_ops_train : cuDNN's basic training operations and algorithms.
+ */
+
+#if !defined(CUDNN_OPS_TRAIN_H_)
+#define CUDNN_OPS_TRAIN_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_OPS_TRAIN_MAJOR 8
+#define CUDNN_OPS_TRAIN_MINOR 9
+#define CUDNN_OPS_TRAIN_PATCH 2
+
+#if (CUDNN_OPS_TRAIN_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_TRAIN_MINOR != CUDNN_MINOR) || \
+    (CUDNN_OPS_TRAIN_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN OPS TRAIN!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* Function to perform backward softmax */
+cudnnStatus_t CUDNNWINAPI
+cudnnSoftmaxBackward(cudnnHandle_t handle,
+                     cudnnSoftmaxAlgorithm_t algo,
+                     cudnnSoftmaxMode_t mode,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t yDesc,
+                     const void *y,
+                     const cudnnTensorDescriptor_t dyDesc,
+                     const void *dy,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t dxDesc,
+                     void *dx);
+
+/* Function to perform backward pooling */
+cudnnStatus_t CUDNNWINAPI
+cudnnPoolingBackward(cudnnHandle_t handle,
+                     const cudnnPoolingDescriptor_t poolingDesc,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t yDesc,
+                     const void *y,
+                     const cudnnTensorDescriptor_t dyDesc,
+                     const void *dy,
+                     const cudnnTensorDescriptor_t xDesc,
+                     const void *x,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t dxDesc,
+                     void *dx);
+
+/* Function to perform backward activation  */
+cudnnStatus_t CUDNNWINAPI
+cudnnActivationBackward(cudnnHandle_t handle,
+                        cudnnActivationDescriptor_t activationDesc,
+                        const void *alpha,
+                        const cudnnTensorDescriptor_t yDesc,
+                        const void *y,
+                        const cudnnTensorDescriptor_t dyDesc,
+                        const void *dy,
+                        const cudnnTensorDescriptor_t xDesc,
+                        const void *x,
+                        const void *beta,
+                        const cudnnTensorDescriptor_t dxDesc,
+                        void *dx);
+
+/* LRN cross-channel backward computation. Double parameters cast to tensor data type */
+cudnnStatus_t CUDNNWINAPI
+cudnnLRNCrossChannelBackward(cudnnHandle_t handle,
+                             cudnnLRNDescriptor_t normDesc,
+                             cudnnLRNMode_t lrnMode,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t yDesc,
+                             const void *y,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const cudnnTensorDescriptor_t xDesc,
+                             const void *x,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dxDesc,
+                             void *dx);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDivisiveNormalizationBackward(cudnnHandle_t handle,
+                                   cudnnLRNDescriptor_t normDesc,
+                                   cudnnDivNormMode_t mode,
+                                   const void *alpha,
+                                   const cudnnTensorDescriptor_t xDesc, /* same desc for x, means, dy, temp, temp2 */
+                                   const void *x,
+                                   const void *means, /* if NULL, means are assumed to be zero */
+                                   const void *dy,
+                                   void *temp,
+                                   void *temp2,
+                                   const void *beta,
+                                   const cudnnTensorDescriptor_t dXdMeansDesc, /* same desc for dx, dMeans */
+                                   void *dx,                                   /* output x differential */
+                                   void *dMeans); /* output means differential, can be NULL */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(cudnnHandle_t handle,
+                                                         cudnnBatchNormMode_t mode,
+                                                         cudnnBatchNormOps_t bnOps,
+                                                         const cudnnTensorDescriptor_t xDesc,
+                                                         const cudnnTensorDescriptor_t zDesc,
+                                                         const cudnnTensorDescriptor_t yDesc,
+                                                         const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+                                                         const cudnnActivationDescriptor_t activationDesc,
+                                                         size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationBackwardExWorkspaceSize(cudnnHandle_t handle,
+                                                  cudnnBatchNormMode_t mode,
+                                                  cudnnBatchNormOps_t bnOps,
+                                                  const cudnnTensorDescriptor_t xDesc,
+                                                  const cudnnTensorDescriptor_t yDesc,
+                                                  const cudnnTensorDescriptor_t dyDesc,
+                                                  const cudnnTensorDescriptor_t dzDesc,
+                                                  const cudnnTensorDescriptor_t dxDesc,
+                                                  const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                                  const cudnnActivationDescriptor_t activationDesc,
+                                                  size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationTrainingExReserveSpaceSize(cudnnHandle_t handle,
+                                                     cudnnBatchNormMode_t mode,
+                                                     cudnnBatchNormOps_t bnOps,
+                                                     const cudnnActivationDescriptor_t activationDesc,
+                                                     const cudnnTensorDescriptor_t xDesc,
+                                                     size_t *sizeInBytes);
+
+/* Computes y = BN(x). Also accumulates moving averages of mean and inverse variances */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardTraining(
+    cudnnHandle_t handle,
+    cudnnBatchNormMode_t mode,
+
+    const void *alpha, /* alpha[0] = result blend factor */
+    const void *beta,  /* beta[0] = dest layer blend factor */
+
+    const cudnnTensorDescriptor_t xDesc,
+    const void *x, /* NxCxHxW */
+    const cudnnTensorDescriptor_t yDesc,
+    void *y, /* NxCxHxW */
+
+    /* Shared desc for the next 6 tensors in the argument list.
+       Data type to be set as follows:
+       type = (typeOf(x) == double) ? double : float
+       Dimensions for this descriptor depend on normalization mode
+       - Spatial Normalization : tensors are expected to have dims 1xCx1x1
+        (normalization is performed across NxHxW)
+       - Per-Activation Normalization : tensors are expected to have dims of 1xCxHxW
+        (normalization is performed across N) */
+    const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+
+    /* 'Gamma' and 'Beta' respectively in Ioffe and Szegedy's paper's notation */
+    const void *bnScale,
+    const void *bnBias,
+
+    /* MUST use factor=1 in the very first call of a complete training cycle.
+       Use a factor=1/(1+n) at N-th call to the function to get
+       Cumulative Moving Average (CMA) behavior
+       CMA[n] = (x[1]+...+x[n])/n
+       Since CMA[n+1] = (n*CMA[n]+x[n+1])/(n+1) =
+       ((n+1)*CMA[n]-CMA[n])/(n+1) + x[n+1]/(n+1) =
+       CMA[n]*(1-1/(n+1)) + x[n+1]*1/(n+1) */
+    double exponentialAverageFactor,
+
+    /* Used in Training phase only.
+       runningMean = newMean*factor + runningMean*(1-factor) */
+    void *resultRunningMean,
+    /* Output in training mode, input in inference. Is the moving average
+       of  variance[x] (factor is applied in the same way as for runningMean) */
+    void *resultRunningVariance,
+
+    /* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
+    double epsilon,
+
+    /* Optionally save intermediate results from the forward pass here
+       - can be reused to speed up backward pass. NULL if unused */
+    void *resultSaveMean,
+    void *resultSaveInvVariance);
+
+/* Computes y = relu(BN(x) + z). Also accumulates moving averages of mean and inverse variances */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardTrainingEx(
+    cudnnHandle_t handle,
+    cudnnBatchNormMode_t mode,
+    cudnnBatchNormOps_t bnOps,
+
+    const void *alpha, /* alpha[0] = result blend factor */
+    const void *beta,  /* beta[0] = dest layer blend factor */
+
+    const cudnnTensorDescriptor_t xDesc,
+    const void *xData,
+    const cudnnTensorDescriptor_t zDesc,
+    const void *zData,
+    const cudnnTensorDescriptor_t yDesc,
+    void *yData,
+
+    const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+    const void *bnScale,
+    const void *bnBias,
+
+    double exponentialAverageFactor,
+    void *resultRunningMean,
+    void *resultRunningVariance,
+
+    /* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
+    double epsilon,
+
+    /* Optionally save intermediate results from the forward pass here
+       - can be reused to speed up backward pass. NULL if unused */
+    void *resultSaveMean,
+    void *resultSaveInvVariance,
+
+    cudnnActivationDescriptor_t activationDesc,
+    void *workspace,
+    size_t workSpaceSizeInBytes,
+    void *reserveSpace,
+    size_t reserveSpaceSizeInBytes);
+
+/* Performs backward pass of Batch Normalization layer. Returns x gradient,
+* bnScale gradient and bnBias gradient */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationBackward(cudnnHandle_t handle,
+                                cudnnBatchNormMode_t mode,
+                                const void *alphaDataDiff,
+                                const void *betaDataDiff,
+                                const void *alphaParamDiff,
+                                const void *betaParamDiff,
+                                const cudnnTensorDescriptor_t xDesc, /* same desc for x, dx, dy */
+                                const void *x,
+                                const cudnnTensorDescriptor_t dyDesc,
+                                const void *dy,
+                                const cudnnTensorDescriptor_t dxDesc,
+                                void *dx,
+                                /* Shared tensor desc for the 4 tensors below */
+                                const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                const void *bnScale, /* bnBias doesn't affect backpropagation */
+                                /* scale and bias diff are not backpropagated below this layer */
+                                void *dBnScaleResult,
+                                void *dBnBiasResult,
+                                /* Same epsilon as forward pass */
+                                double epsilon,
+
+                                /* Optionally cached intermediate results from
+                                   forward pass */
+                                const void *savedMean,
+                                const void *savedInvVariance);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationBackwardEx(cudnnHandle_t handle,
+                                  cudnnBatchNormMode_t mode,
+                                  cudnnBatchNormOps_t bnOps,
+
+                                  const void *alphaDataDiff,
+                                  const void *betaDataDiff,
+                                  const void *alphaParamDiff,
+                                  const void *betaParamDiff,
+                                  const cudnnTensorDescriptor_t xDesc,
+                                  const void *xData,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  const void *yData,
+                                  const cudnnTensorDescriptor_t dyDesc,
+                                  const void *dyData,
+                                  const cudnnTensorDescriptor_t dzDesc,
+                                  void *dzData,
+                                  const cudnnTensorDescriptor_t dxDesc,
+                                  void *dxData,
+
+                                  /* Shared tensor desc for the 4 tensors below */
+                                  const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                  const void *bnScaleData,
+                                  const void *bnBiasData, /* needed if there is activation */
+                                  void *dBnScaleData,
+                                  void *dBnBiasData,
+                                  double epsilon, /* Same epsilon as forward pass */
+
+                                  /* Optionally cached intermediate results from
+                                     forward pass */
+                                  const void *savedMean,
+                                  const void *savedInvVariance,
+                                  cudnnActivationDescriptor_t activationDesc,
+                                  void *workSpace,
+                                  size_t workSpaceSizeInBytes,
+                                  void *reserveSpace,
+                                  size_t reserveSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationForwardTrainingWorkspaceSize(cudnnHandle_t handle,
+                                                  cudnnNormMode_t mode,
+                                                  cudnnNormOps_t normOps,
+                                                  cudnnNormAlgo_t algo,
+                                                  const cudnnTensorDescriptor_t xDesc,
+                                                  const cudnnTensorDescriptor_t zDesc,
+                                                  const cudnnTensorDescriptor_t yDesc,
+                                                  const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                                  const cudnnActivationDescriptor_t activationDesc,
+                                                  const cudnnTensorDescriptor_t normMeanVarDesc,
+                                                  size_t *sizeInBytes,
+                                                  int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationBackwardWorkspaceSize(cudnnHandle_t handle,
+                                           cudnnNormMode_t mode,
+                                           cudnnNormOps_t normOps,
+                                           cudnnNormAlgo_t algo,
+                                           const cudnnTensorDescriptor_t xDesc,
+                                           const cudnnTensorDescriptor_t yDesc,
+                                           const cudnnTensorDescriptor_t dyDesc,
+                                           const cudnnTensorDescriptor_t dzDesc,
+                                           const cudnnTensorDescriptor_t dxDesc,
+                                           const cudnnTensorDescriptor_t dNormScaleBiasDesc,
+                                           const cudnnActivationDescriptor_t activationDesc,
+                                           const cudnnTensorDescriptor_t normMeanVarDesc,
+                                           size_t *sizeInBytes,
+                                           int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationTrainingReserveSpaceSize(cudnnHandle_t handle,
+                                              cudnnNormMode_t mode,
+                                              cudnnNormOps_t normOps,
+                                              cudnnNormAlgo_t algo,
+                                              const cudnnActivationDescriptor_t activationDesc,
+                                              const cudnnTensorDescriptor_t xDesc,
+                                              size_t *sizeInBytes,
+                                              int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+/* Computes y = relu(Norm(x) + z). Also accumulates moving averages of mean and inverse variances */
+cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationForwardTraining(cudnnHandle_t handle,
+                                  cudnnNormMode_t mode,
+                                  cudnnNormOps_t normOps,
+                                  cudnnNormAlgo_t algo,
+                                  const void *alpha, /* alpha[0] = result blend factor */
+                                  const void *beta,  /* beta[0] = dest layer blend factor */
+                                  const cudnnTensorDescriptor_t xDesc,
+                                  const void *xData,
+                                  const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                  const void *normScale,
+                                  const void *normBias,
+                                  double exponentialAverageFactor,
+                                  const cudnnTensorDescriptor_t normMeanVarDesc,
+                                  void *resultRunningMean,
+                                  void *resultRunningVariance,
+                                  /* Has to be >= 0. Should be the same in forward and backward functions. */
+                                  double epsilon,
+                                  /* Optionally save intermediate results from the forward pass here
+                                     - can be reused to speed up backward pass. NULL if unused */
+                                  void *resultSaveMean,
+                                  void *resultSaveInvVariance,
+                                  cudnnActivationDescriptor_t activationDesc,
+                                  const cudnnTensorDescriptor_t zDesc,
+                                  const void *zData,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  void *yData,
+                                  void *workspace,
+                                  size_t workSpaceSizeInBytes,
+                                  void *reserveSpace,
+                                  size_t reserveSpaceSizeInBytes,
+                                  int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationBackward(cudnnHandle_t handle,
+                           cudnnNormMode_t mode,
+                           cudnnNormOps_t normOps,
+                           cudnnNormAlgo_t algo,
+                           const void *alphaDataDiff,
+                           const void *betaDataDiff,
+                           const void *alphaParamDiff,
+                           const void *betaParamDiff,
+                           const cudnnTensorDescriptor_t xDesc,
+                           const void *xData,
+                           const cudnnTensorDescriptor_t yDesc,
+                           const void *yData,
+                           const cudnnTensorDescriptor_t dyDesc,
+                           const void *dyData,
+                           const cudnnTensorDescriptor_t dzDesc,
+                           void *dzData,
+                           const cudnnTensorDescriptor_t dxDesc,
+                           void *dxData,
+                           /* Shared tensor desc for the 4 tensors below */
+                           const cudnnTensorDescriptor_t dNormScaleBiasDesc,
+                           const void *normScaleData,
+                           const void *normBiasData, /* needed if there is activation */
+                           void *dNormScaleData,
+                           void *dNormBiasData,
+                           double epsilon, /* Same epsilon as forward pass */
+                           const cudnnTensorDescriptor_t normMeanVarDesc,
+                           /* Optionally cached intermediate results from
+                              forward pass */
+                           const void *savedMean,
+                           const void *savedInvVariance,
+                           cudnnActivationDescriptor_t activationDesc,
+                           void *workSpace,
+                           size_t workSpaceSizeInBytes,
+                           void *reserveSpace,
+                           size_t reserveSpaceSizeInBytes,
+                           int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfGridGeneratorBackward(cudnnHandle_t handle,
+                                    const cudnnSpatialTransformerDescriptor_t stDesc,
+                                    const void *dgrid,
+                                    void *dtheta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfSamplerBackward(cudnnHandle_t handle,
+                              cudnnSpatialTransformerDescriptor_t stDesc,
+                              const void *alpha,
+                              const cudnnTensorDescriptor_t xDesc,
+                              const void *x,
+                              const void *beta,
+                              const cudnnTensorDescriptor_t dxDesc,
+                              void *dx,
+                              const void *alphaDgrid,
+                              const cudnnTensorDescriptor_t dyDesc,
+                              const void *dy,
+                              const void *grid,
+                              const void *betaDgrid,
+                              void *dgrid);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutBackward(cudnnHandle_t handle,
+                     const cudnnDropoutDescriptor_t dropoutDesc,
+                     const cudnnTensorDescriptor_t dydesc,
+                     const void *dy,
+                     const cudnnTensorDescriptor_t dxdesc,
+                     void *dx,
+                     void *reserveSpace,
+                     size_t reserveSpaceSizeInBytes);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpsTrainVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_OPS_TRAIN_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_v8.h

@@ -0,0 +1,78 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cudnn : Neural Networks Library
+
+*/
+
+#if !defined(CUDNN_H_)
+#define CUDNN_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+#include "cudnn_ops_train.h"
+#include "cudnn_adv_infer.h"
+#include "cudnn_adv_train.h"
+#include "cudnn_cnn_infer.h"
+#include "cudnn_cnn_train.h"
+
+#include "cudnn_backend.h"
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_H_ */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_version.h

@@ -0,0 +1,109 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/**
+ * \file: The master cuDNN version file.
+ */
+
+#ifndef CUDNN_VERSION_H_
+#define CUDNN_VERSION_H_
+
+#define CUDNN_MAJOR 8
+#define CUDNN_MINOR 9
+#define CUDNN_PATCHLEVEL 2
+
+#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
+
+/* cannot use constexpr here since this is a C-only file */
+/* Below is the max SM version this cuDNN library is aware of and supports natively */
+
+#define CUDNN_MAX_SM_MAJOR_NUMBER 9
+#define CUDNN_MAX_SM_MINOR_NUMBER 0
+#define CUDNN_MAX_DEVICE_VERSION (CUDNN_MAX_SM_MAJOR_NUMBER * 100 + CUDNN_MAX_SM_MINOR_NUMBER * 10)
+
+/* Here are constants for each of the SM Architectures we support to use in code where device version checks must be
+ * made */
+
+/* MAXWELL SM 50 52 53 */
+#define CUDNN_SM_50 500
+#define CUDNN_SM_52 520
+#define CUDNN_SM_53 530
+
+/* PASCAL SM 60 61 62 */
+#define CUDNN_SM_60 600
+#define CUDNN_SM_61 610
+#define CUDNN_SM_62 620
+
+/* VOLTA SM 70 72 */
+#define CUDNN_SM_70 700
+#define CUDNN_SM_72 720
+
+/* TURING SM 75 */
+#define CUDNN_SM_75 750
+
+/* AMPERE SM 80 86 87 */
+#define CUDNN_SM_80 800
+#define CUDNN_SM_86 860
+#define CUDNN_SM_87 870
+
+/* ADA LOVELACE SM 89 */
+#define CUDNN_SM_89 890
+
+/* HOPPER SM 90 */
+#define CUDNN_SM_90 900
+
+/* END MARKER for last known version.
+ * This can be replaced after support for 1000 is added
+ */
+#define CUDNN_SM_9X_END 999
+
+/* This is the minimum version we support devices below this will return CUDNN_STATUS_ARCH_MISMATCH */
+#define CUDNN_MIN_DEVICE_VERSION CUDNN_SM_50
+
+#endif /* CUDNN_VERSION_H */

llava_next/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_version_v8.h

@@ -0,0 +1,109 @@
+/*
+ * Copyright 2014-2023 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, 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 THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/**
+ * \file: The master cuDNN version file.
+ */
+
+#ifndef CUDNN_VERSION_H_
+#define CUDNN_VERSION_H_
+
+#define CUDNN_MAJOR 8
+#define CUDNN_MINOR 9
+#define CUDNN_PATCHLEVEL 2
+
+#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
+
+/* cannot use constexpr here since this is a C-only file */
+/* Below is the max SM version this cuDNN library is aware of and supports natively */
+
+#define CUDNN_MAX_SM_MAJOR_NUMBER 9
+#define CUDNN_MAX_SM_MINOR_NUMBER 0
+#define CUDNN_MAX_DEVICE_VERSION (CUDNN_MAX_SM_MAJOR_NUMBER * 100 + CUDNN_MAX_SM_MINOR_NUMBER * 10)
+
+/* Here are constants for each of the SM Architectures we support to use in code where device version checks must be
+ * made */
+
+/* MAXWELL SM 50 52 53 */
+#define CUDNN_SM_50 500
+#define CUDNN_SM_52 520
+#define CUDNN_SM_53 530
+
+/* PASCAL SM 60 61 62 */
+#define CUDNN_SM_60 600
+#define CUDNN_SM_61 610
+#define CUDNN_SM_62 620
+
+/* VOLTA SM 70 72 */
+#define CUDNN_SM_70 700
+#define CUDNN_SM_72 720
+
+/* TURING SM 75 */
+#define CUDNN_SM_75 750
+
+/* AMPERE SM 80 86 87 */
+#define CUDNN_SM_80 800
+#define CUDNN_SM_86 860
+#define CUDNN_SM_87 870
+
+/* ADA LOVELACE SM 89 */
+#define CUDNN_SM_89 890
+
+/* HOPPER SM 90 */
+#define CUDNN_SM_90 900
+
+/* END MARKER for last known version.
+ * This can be replaced after support for 1000 is added
+ */
+#define CUDNN_SM_9X_END 999
+
+/* This is the minimum version we support devices below this will return CUDNN_STATUS_ARCH_MISMATCH */
+#define CUDNN_MIN_DEVICE_VERSION CUDNN_SM_50
+
+#endif /* CUDNN_VERSION_H */

llava_next/lib/python3.10/site-packages/nvidia/nvjitlink/include/nvJitLink.h

@@ -0,0 +1,531 @@
+/*
+ * NVIDIA_COPYRIGHT_BEGIN
+ *
+ * Copyright (c) 2022-2024, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * NVIDIA CORPORATION and its licensors retain all intellectual property
+ * and proprietary rights in and to this software, related documentation
+ * and any modifications thereto.  Any use, reproduction, disclosure or
+ * distribution of this software and related documentation without an express
+ * license agreement from NVIDIA CORPORATION is strictly prohibited.
+ *
+ * NVIDIA_COPYRIGHT_END
+ */
+
+#ifndef nvJitLink_INCLUDED
+#define nvJitLink_INCLUDED
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include <stdint.h>
+#include <stdlib.h>
+
+/**
+ *
+ * \defgroup error Error codes
+ *
+ */
+
+/** \ingroup error
+ *
+ * \brief    The enumerated type nvJitLinkResult defines API call result codes.
+ *           nvJitLink APIs return nvJitLinkResult codes to indicate the result.
+ */
+
+typedef enum {
+  NVJITLINK_SUCCESS = 0,
+  NVJITLINK_ERROR_UNRECOGNIZED_OPTION,
+  NVJITLINK_ERROR_MISSING_ARCH, // -arch=sm_NN option not specified
+  NVJITLINK_ERROR_INVALID_INPUT,
+  NVJITLINK_ERROR_PTX_COMPILE,
+  NVJITLINK_ERROR_NVVM_COMPILE,
+  NVJITLINK_ERROR_INTERNAL,
+  NVJITLINK_ERROR_THREADPOOL,
+  NVJITLINK_ERROR_UNRECOGNIZED_INPUT,
+  NVJITLINK_ERROR_FINALIZE,
+#ifdef NEW_ERROR_CODES // These error codes will appear in a future CUDA release.
+  NVJITLINK_ERROR_NULL_INPUT,
+  NVJITLINK_ERROR_INCOMPATIBLE_OPTIONS,
+  NVJITLINK_ERROR_INCORRECT_INPUT_TYPE,
+  NVJITLINK_ERROR_ARCH_MISMATCH,
+  NVJITLINK_ERROR_OUTDATED_LIBRARY,
+  NVJITLINK_ERROR_MISSING_FATBIN
+#endif
+} nvJitLinkResult;
+
+#ifndef NEW_ERROR_CODES // To avoid breaking compatibility, we map them to existing error codes for now.
+#define NVJITLINK_ERROR_NULL_INPUT NVJITLINK_ERROR_INVALID_INPUT
+#define NVJITLINK_ERROR_INCOMPATIBLE_OPTIONS NVJITLINK_ERROR_INVALID_INPUT
+#define NVJITLINK_ERROR_INCORRECT_INPUT_TYPE NVJITLINK_ERROR_INVALID_INPUT
+#define NVJITLINK_ERROR_ARCH_MISMATCH NVJITLINK_ERROR_INTERNAL
+#define NVJITLINK_ERROR_OUTDATED_LIBRARY NVJITLINK_ERROR_INTERNAL
+#define NVJITLINK_ERROR_MISSING_FATBIN NVJITLINK_ERROR_INVALID_INPUT
+#endif  
+
+/**
+ *
+ * \defgroup linking Linking
+ *
+ */
+
+/** \ingroup linking
+ *
+ * \brief    The enumerated type nvJitLinkInputType defines the kind of inputs
+ *           that can be passed to nvJitLinkAdd* APIs.
+ */
+
+typedef enum {
+  NVJITLINK_INPUT_NONE = 0, // error
+  NVJITLINK_INPUT_CUBIN = 1,
+  NVJITLINK_INPUT_PTX,
+  NVJITLINK_INPUT_LTOIR,
+  NVJITLINK_INPUT_FATBIN,
+  NVJITLINK_INPUT_OBJECT,
+  NVJITLINK_INPUT_LIBRARY,
+  NVJITLINK_INPUT_INDEX,
+  NVJITLINK_INPUT_ANY = 10 // will dynamically determine one of above types
+} nvJitLinkInputType;
+
+/**
+ * \defgroup options Supported Link Options
+ *
+ * nvJitLink supports the link options below.
+ * Option names are prefixed with a single dash (\c -).
+ * Options that take a value have an assignment operator (\c =)
+ * followed by the option value, with no spaces, e.g. \c "-arch=sm_90".
+ *
+ * The supported options are:
+ * - \c -arch=sm_<N\> \n
+ *   Pass SM architecture value.  See nvcc for valid values of <N\>.
+ *   Can use compute_<N\> value instead if only generating PTX.
+ *   This is a required option.  
+ * - \c -maxrregcount=<N\> \n
+ *   Maximum register count.
+ * - \c -time \n
+ *   Print timing information to InfoLog.
+ * - \c -verbose \n
+ *   Print verbose messages to InfoLog.
+ * - \c -lto \n
+ *   Do link time optimization.
+ * - \c -ptx \n
+ *   Emit ptx after linking instead of cubin; only supported with \c -lto
+ * - \c -O<N\> \n
+ *   Optimization level. Only 0 and 3 are accepted.
+ * - \c -g \n
+ *   Generate debug information.
+ * - \c -lineinfo \n
+ *   Generate line information.
+ * - \c -ftz=<n\> \n
+ *   Flush to zero.
+ * - \c -prec-div=<n\> \n
+ *   Precise divide.
+ * - \c -prec-sqrt=<n\> \n
+ *   Precise square root.
+ * - \c -fma=<n\> \n
+ *   Fast multiply add.
+ * - \c -kernels-used=<name\> \n
+ *   Pass list of kernels that are used; any not in the list can be removed.
+ *   This option can be specified multiple times.
+ * - \c -variables-used=<name\> \n
+ *   Pass list of variables that are used; any not in the list can be removed.
+ *   This option can be specified multiple times.
+ * - \c -optimize-unused-variables \n
+ *   Normally device code optimization is limited by not knowing what the
+ *   host code references.  With this option it can assume that if a variable
+ *   is not referenced in device code then it can be removed.
+ * - \c -Xptxas=<opt\> \n
+ *   Pass <opt\> to ptxas.  This option can be called multiple times.
+ * - \c -split-compile=<N\> \n
+ *   Split compilation maximum thread count. Use 0 to use all available processors.
+ *   Value of 1 disables split compilation (default).
+ * - \c -split-compile-extended=<N\> \n
+ *   A more aggressive form of split compilation available in LTO mode only.
+ *   Accepts a maximum thread count value. Use 0 to use all available processors.
+ *   Value of 1 disables extended split compilation (default).
+ *   Note: This option can potentially impact performance of the compiled binary.
+ * - \c -jump-table-density=<N\> \n
+ *   When doing LTO, specify the case density percentage in switch statements,
+ *   and use it as a minimal threshold to determine whether jump table(brx.idx 
+ *   instruction) will be used to implement a switch statement. Default
+ *   value is 101. The percentage ranges from 0 to 101 inclusively.
+ * - \c -no-cache \n
+ *   Don't cache the intermediate steps of nvJitLink.
+ * - \c -device-stack-protector \n
+ *   Enable stack canaries in device code.
+ *   Stack canaries make it more difficult to exploit certain types of memory safety bugs involving stack-local variables.
+ *   The compiler uses heuristics to assess the risk of such a bug in each function.  Only those functions which are deemed high-risk make use of a stack canary.
+ */
+
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkHandle is the unit of linking, and an opaque handle for
+ *          a program.
+ *
+ * To link inputs, an instance of nvJitLinkHandle must be created first with
+ * nvJitLinkCreate().
+ */
+
+typedef struct nvJitLink* nvJitLinkHandle; // opaque handle
+
+// For versioning we will have separate API version for each library version
+
+extern nvJitLinkResult __nvJitLinkCreate_12_6(
+  nvJitLinkHandle *handle,
+  uint32_t numOptions,
+  const char **options);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkCreate creates an instance of nvJitLinkHandle with the
+ *          given input options, and sets the output parameter \p handle.
+ *
+ * \param   [out] handle       Address of nvJitLink handle.
+ * \param   [in]  numOptions   Number of options passed.
+ * \param   [in]  options      Array of size \p numOptions of option strings.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_UNRECOGNIZED_OPTION\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_MISSING_ARCH\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * It supports options listed in \ref options.
+ *
+ * \see nvJitLinkDestroy
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkCreate(
+  nvJitLinkHandle *handle,
+  uint32_t numOptions,
+  const char **options)
+{
+  return __nvJitLinkCreate_12_6 (handle, numOptions, options);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkDestroy_12_6 (nvJitLinkHandle *handle);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkDestroy frees the memory associated with the given handle
+ *          and sets it to NULL.
+ *
+ * \param    [in] handle      Address of nvJitLink handle.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * \see nvJitLinkCreate
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkDestroy (nvJitLinkHandle *handle)
+{
+  return __nvJitLinkDestroy_12_6 (handle);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkAddData_12_6(
+  nvJitLinkHandle handle,
+  nvJitLinkInputType inputType,
+  const void *data,
+  size_t size,
+  const char *name); // name can be null
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkAddData adds data image to the link. 
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [in] inputType   kind of input.
+ * \param    [in] data        pointer to data image in memory.
+ * \param    [in] size        size of the data.
+ * \param    [in] name        name of input object.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkAddData(
+  nvJitLinkHandle handle,
+  nvJitLinkInputType inputType,
+  const void *data,
+  size_t size,
+  const char *name) // name can be null
+{
+  return __nvJitLinkAddData_12_6 (handle, inputType, data, size, name);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkAddFile_12_6(
+  nvJitLinkHandle handle,
+  nvJitLinkInputType inputType,
+  const char *fileName); // includes path to file
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkAddFile reads data from file and links it in. 
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [in] inputType   kind of input.
+ * \param    [in] fileName    name of file.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkAddFile(
+  nvJitLinkHandle handle,
+  nvJitLinkInputType inputType,
+  const char *fileName) // includes path to file
+{
+  return __nvJitLinkAddFile_12_6 (handle, inputType, fileName);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkComplete_12_6 (nvJitLinkHandle handle);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkComplete does the actual link. 
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkComplete (nvJitLinkHandle handle)
+{
+  return __nvJitLinkComplete_12_6 (handle);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkGetLinkedCubinSize_12_6(
+  nvJitLinkHandle handle,
+  size_t *size);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetLinkedCubinSize gets the size of the linked cubin.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] size       Size of the linked cubin.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * \see nvJitLinkGetLinkedCubin
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetLinkedCubinSize(
+  nvJitLinkHandle handle,
+  size_t *size)
+{
+  return __nvJitLinkGetLinkedCubinSize_12_6 (handle, size);
+}
+#endif
+
+extern nvJitLinkResult __nvJitLinkGetLinkedCubin_12_6(
+  nvJitLinkHandle handle,
+  void *cubin);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetLinkedCubin gets the linked cubin.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] cubin      The linked cubin.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * User is responsible for allocating enough space to hold the \p cubin.
+ * \see nvJitLinkGetLinkedCubinSize
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetLinkedCubin(
+  nvJitLinkHandle handle,
+  void *cubin)
+{
+  return __nvJitLinkGetLinkedCubin_12_6 (handle, cubin);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkGetLinkedPtxSize_12_6(
+  nvJitLinkHandle handle,
+  size_t *size);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetLinkedPtxSize gets the size of the linked ptx.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] size       Size of the linked PTX.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * Linked PTX is only available when using the \c -lto option.
+ * \see nvJitLinkGetLinkedPtx
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetLinkedPtxSize(
+  nvJitLinkHandle handle,
+  size_t *size)
+{
+  return __nvJitLinkGetLinkedPtxSize_12_6 (handle, size);
+}
+#endif
+
+extern nvJitLinkResult __nvJitLinkGetLinkedPtx_12_6(
+  nvJitLinkHandle handle,
+  char *ptx);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetLinkedPtx gets the linked ptx.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] ptx        The linked PTX.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * Linked PTX is only available when using the \c -lto option.
+ * User is responsible for allocating enough space to hold the \p ptx.
+ * \see nvJitLinkGetLinkedPtxSize
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetLinkedPtx(
+  nvJitLinkHandle handle,
+  char *ptx)
+{
+  return __nvJitLinkGetLinkedPtx_12_6 (handle, ptx);
+}
+#endif
+ 
+extern nvJitLinkResult __nvJitLinkGetErrorLogSize_12_6(
+  nvJitLinkHandle handle,
+  size_t *size);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetErrorLogSize gets the size of the error log.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] size       Size of the error log.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * \see nvJitLinkGetErrorLog
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetErrorLogSize(
+  nvJitLinkHandle handle,
+  size_t *size)
+{
+  return __nvJitLinkGetErrorLogSize_12_6 (handle, size);
+}
+#endif
+
+extern nvJitLinkResult __nvJitLinkGetErrorLog_12_6(
+  nvJitLinkHandle handle,
+  char *log);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetErrorLog puts any error messages in the log.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] log        The error log.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * User is responsible for allocating enough space to hold the \p log.
+ * \see nvJitLinkGetErrorLogSize
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetErrorLog(
+  nvJitLinkHandle handle,
+  char *log)
+{
+  return __nvJitLinkGetErrorLog_12_6 (handle, log);
+}
+#endif
+
+extern nvJitLinkResult __nvJitLinkGetInfoLogSize_12_6(
+  nvJitLinkHandle handle,
+  size_t *size);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetInfoLogSize gets the size of the info log.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] size       Size of the info log.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * \see nvJitLinkGetInfoLog
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetInfoLogSize(
+  nvJitLinkHandle handle,
+  size_t *size)
+{
+  return __nvJitLinkGetInfoLogSize_12_6 (handle, size);
+}
+#endif
+
+extern nvJitLinkResult __nvJitLinkGetInfoLog_12_6(
+  nvJitLinkHandle handle,
+  char *log);
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkGetInfoLog puts any info messages in the log.
+ *
+ * \param    [in] handle      nvJitLink handle.
+ * \param    [out] log        The info log.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ * User is responsible for allocating enough space to hold the \p log.
+ * \see nvJitLinkGetInfoLogSize
+ */
+#ifndef NVJITLINK_NO_INLINE
+static inline nvJitLinkResult nvJitLinkGetInfoLog(
+  nvJitLinkHandle handle,
+  char *log)
+{
+  return __nvJitLinkGetInfoLog_12_6 (handle, log);
+}
+#endif
+
+/**
+ * \ingroup linking
+ * \brief   nvJitLinkVersion returns the current version of nvJitLink.
+ *
+ * \param    [out] major        The major version.
+ * \param    [out] minor        The minor version.
+ * \return
+ *   - \link #nvJitLinkResult NVJITLINK_SUCCESS \endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INVALID_INPUT\endlink
+ *   - \link #nvJitLinkResult NVJITLINK_ERROR_INTERNAL\endlink
+ *
+ */
+extern nvJitLinkResult nvJitLinkVersion(
+  unsigned int *major,
+  unsigned int *minor);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // nvJitLink_INCLUDED
+

llava_next/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExt.h

@@ -0,0 +1,1561 @@
+/*
+* Copyright 2009-2017  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* 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 SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+/** \file nvToolsExt.h
+ */
+
+/* ========================================================================= */
+/** \mainpage
+ * \tableofcontents
+ * \section INTRODUCTION Introduction
+ *
+ * The NVIDIA Tools Extension library is a set of functions that a
+ * developer can use to provide additional information to tools.
+ * The additional information is used by the tool to improve
+ * analysis and visualization of data.
+ *
+ * The library introduces close to zero overhead if no tool is
+ * attached to the application.  The overhead when a tool is
+ * attached is specific to the tool.
+ *
+ * \section INITIALIZATION_SECTION Initialization
+ *
+ * Typically the tool's library that plugs into NVTX is indirectly 
+ * loaded via enviromental properties that are platform specific. 
+ * For some platform or special cases, the user may be required 
+ * to instead explicity initialize instead though.   This can also
+ * be helpful to control when the API loads a tool's library instead
+ * of what would typically be the first function call to emit info.
+ * For these rare case, see \ref INITIALIZATION for additional information.
+ *
+ * \section MARKERS_AND_RANGES Markers and Ranges
+ *
+ * Markers and ranges are used to describe events at a specific time (markers)
+ * or over a time span (ranges) during the execution of the application
+ * respectively. 
+ *
+ * \subsection MARKERS Markers
+ * 
+ * Markers denote specific moments in time.
+ * 
+ * 
+ * See \ref DOMAINS and \ref EVENT_ATTRIBUTES for additional information on
+ * how to specify the domain.
+ * 
+ * \subsection THREAD_RANGES Thread Ranges
+ *
+ * Thread ranges denote nested time ranges. Nesting is maintained per thread
+ * per domain and does not require any additional correlation mechanism. The
+ * duration of a thread range is defined by the corresponding pair of
+ * nvtxRangePush* to nvtxRangePop API calls.
+ *
+ * See \ref DOMAINS and \ref EVENT_ATTRIBUTES for additional information on
+ * how to specify the domain.
+ *
+ * \subsection PROCESS_RANGES Process Ranges
+ *
+ * Process ranges denote a time span that can expose arbitrary concurrency, as 
+ * opposed to thread ranges that only support nesting. In addition the range
+ * start event can happen on a different thread than the end marker. For the 
+ * correlation of a start/end pair an unique correlation ID is used that is
+ * returned from the start API call and needs to be passed into the end API
+ * call.
+ *
+ * \subsection EVENT_ATTRIBUTES Event Attributes
+ *
+ * \ref MARKERS_AND_RANGES can be annotated with various attributes to provide
+ * additional information for an event or to guide the tool's visualization of
+ * the data. Each of the attributes is optional and if left unused the
+ * attributes fall back to a default value. The attributes include:
+ * - color
+ * - category
+ *
+ * To specify any attribute other than the text message, the \ref
+ * EVENT_ATTRIBUTE_STRUCTURE "Event Attribute Structure" must be used.
+ *
+ * \section DOMAINS Domains
+ *
+ * Domains enable developers to scope annotations. By default all events and
+ * annotations are in the default domain. Additional domains can be registered.
+ * This allows developers to scope markers, ranges, and resources names to
+ * avoid conflicts.
+ *
+ * The function ::nvtxDomainCreateA or ::nvtxDomainCreateW is used to create
+ * a named domain.
+ * 
+ * Each domain maintains its own
+ * - categories
+ * - thread range stacks
+ * - registered strings
+ *
+ * The function ::nvtxDomainDestroy marks the end of the domain. Destroying 
+ * a domain unregisters and destroys all objects associated with it such as 
+ * registered strings, resource objects, named categories, and started ranges. 
+ *
+ * \section RESOURCE_NAMING Resource Naming
+ *
+ * This section covers calls that allow to annotate objects with user-provided
+ * names in order to allow for a better analysis of complex trace data. All of
+ * the functions take the handle or the ID of the object to name and the name.
+ * The functions can be called multiple times during the execution of an
+ * application, however, in that case it is implementation dependent which
+ * name will be reported by the tool.
+ * 
+ * \subsection CATEGORY_NAMING Category Naming
+ *
+ * Some function in this library support associating an integer category 
+ * to enable filtering and sorting.  The category naming functions allow 
+ * the application to associate a user friendly name with the integer 
+ * category.  Support for domains have been added in NVTX_VERSION_2 to 
+ * avoid collisions when domains are developed independantly. 
+ *
+ * \subsection RESOURCE_OBJECTS Resource Objects
+ *
+ * Resource objects are a generic mechanism for attaching data to an application 
+ * resource.  The identifier field makes the association to a pointer or handle, 
+ * while the type field helps provide deeper understanding of the identifier as 
+ * well as enabling differentiation in cases where handles generated by different
+ * APIs may collide.  The resource object may also have an associated message to
+ * associate with the application resource, enabling further annotation of this 
+ * object and how it is used.
+ * 
+ * The resource object was introduced in NVTX_VERSION_2 to supersede existing naming
+ * functions and allow the application resource identified by those functions to be
+ * associated to a domain.  The other naming functions are still supported for backward
+ * compatibility but will be associated only to the default domain.
+ *
+ * \subsection RESOURCE_NAMING_OS Resource Naming
+ * 
+ * Some operating system resources creation APIs do not support providing a user friendly 
+ * name, such as some OS thread creation APIs.  This API support resource naming though 
+ * both through resource objects and functions following the pattern 
+ * nvtxName[RESOURCE_TYPE][A|W](identifier, name).  Resource objects introduced in NVTX_VERSION 2 
+ * supersede the other functions with a a more general method of assigning names to OS resources,
+ * along with associating them to domains too.  The older nvtxName* functions are only associated 
+ * with the default domain.
+ * \section EXTENSIONS Optional Extensions
+ * Optional extensions will either appear within the existing sections the extend or appear 
+ * in the "Related Pages" when they introduce new concepts.
+ */
+
+#ifndef NVTOOLSEXT_H_
+#define NVTOOLSEXT_H_
+
+#if defined(_MSC_VER)
+    #ifdef NVTX_EXPORTS
+        #define NVTX_DECLSPEC
+    #else
+        #define NVTX_DECLSPEC __declspec(dllimport)
+    #endif /* NVTX_EXPORTS */
+    #define NVTX_API __stdcall
+    #define NVTX_INLINE_STATIC __inline static
+#else /*defined(__GNUC__)*/
+    #define NVTX_DECLSPEC
+    #define NVTX_API
+    #define NVTX_INLINE_STATIC inline static
+#endif /* Platform */
+
+/**
+ * The nvToolsExt library depends on stdint.h.  If the build tool chain in use
+ * does not include stdint.h then define NVTX_STDINT_TYPES_ALREADY_DEFINED
+ * and define the following types:
+ * <ul>
+ *   <li>uint8_t
+ *   <li>int8_t
+ *   <li>uint16_t
+ *   <li>int16_t
+ *   <li>uint32_t
+ *   <li>int32_t
+ *   <li>uint64_t
+ *   <li>int64_t
+ *   <li>uintptr_t
+ *   <li>intptr_t
+ * </ul>
+ #define NVTX_STDINT_TYPES_ALREADY_DEFINED if you are using your own header file.
+ */
+#ifndef NVTX_STDINT_TYPES_ALREADY_DEFINED
+#include <stdint.h>
+#endif
+
+#include <stddef.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/**
+ * Tools Extension API version
+ */
+#define NVTX_VERSION 2
+
+/**
+ * Size of the nvtxEventAttributes_t structure.
+ */
+#define NVTX_EVENT_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxEventAttributes_t) ) )
+
+/**
+ * Size of the nvtxInitializationAttributes_t structure.
+ */
+#define NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxInitializationAttributes_t) ) )
+
+#define NVTX_NO_PUSH_POP_TRACKING ((int)-2)
+
+typedef uint64_t nvtxRangeId_t;
+
+
+/* \brief String Handle Structure.
+* \anchor STRING_HANDLE_STRUCTURE
+*
+* This structure is opaque to the user and is used as a handle to reference
+* a string.  The tools will return a pointer through the API for the application
+* to hold on it's behalf to reference the string in the future.
+*
+*/
+typedef struct nvtxStringHandle* nvtxStringHandle_t;
+
+/* \brief Domain Handle Structure.
+* \anchor DOMAIN_HANDLE_STRUCTURE
+*
+* This structure is opaque to the user and is used as a handle to reference
+* a domain.  The tools will return a pointer through the API for the application
+* to hold on its behalf to reference the domain in the future.
+*
+*/
+typedef struct nvtxDomainHandle* nvtxDomainHandle_t;
+
+
+
+
+
+
+/* ========================================================================= */
+/** \defgroup GENERAL General
+ * @{
+ */
+
+/** ---------------------------------------------------------------------------
+ * Color Types
+ * ------------------------------------------------------------------------- */
+typedef enum nvtxColorType_t
+{
+    NVTX_COLOR_UNKNOWN  = 0,                 /**< Color attribute is unused. */
+    NVTX_COLOR_ARGB     = 1                  /**< An ARGB color is provided. */
+} nvtxColorType_t;
+
+/** ---------------------------------------------------------------------------
+ * Message Types
+ * ------------------------------------------------------------------------- */
+typedef enum nvtxMessageType_t
+{
+    NVTX_MESSAGE_UNKNOWN          = 0,    /**< Message payload is unused. */
+    NVTX_MESSAGE_TYPE_ASCII       = 1,    /**< A character sequence is used as payload. */
+    NVTX_MESSAGE_TYPE_UNICODE     = 2,     /**< A wide character sequence is used as payload. */
+    /* NVTX_VERSION_2 */
+    NVTX_MESSAGE_TYPE_REGISTERED  = 3     /**< A unique string handle that was registered
+                                                with \ref nvtxDomainRegisterStringA() or 
+                                                \ref nvtxDomainRegisterStringW(). */
+} nvtxMessageType_t;
+
+typedef union nvtxMessageValue_t
+{
+    const char* ascii;
+    const wchar_t* unicode;
+    /* NVTX_VERSION_2 */
+    nvtxStringHandle_t registered;
+} nvtxMessageValue_t;
+
+
+/** @} */ /*END defgroup*/
+
+/* ========================================================================= */
+/** \defgroup INITIALIZATION Initialization
+* @{
+* Typically the tool's library that plugs into NVTX is indirectly
+* loaded via enviromental properties that are platform specific.
+* For some platform or special cases, the user may be required
+* to instead explicity initialize instead though.  This can also
+* be helpful to control when the API loads a tool's library instead
+* of what would typically be the first function call to emit info.
+*/
+
+/** ---------------------------------------------------------------------------
+* Initialization Modes
+* ------------------------------------------------------------------------- */
+typedef enum nvtxInitializationMode_t
+{
+    NVTX_INITIALIZATION_MODE_UNKNOWN = 0,   /**< A platform that supports indirect initialization will attempt this style, otherwise expect failure. */
+    NVTX_INITIALIZATION_MODE_CALLBACK_V1 = 1,   /**< A function pointer conforming to NVTX_VERSION=1 will be used. */
+    NVTX_INITIALIZATION_MODE_CALLBACK_V2 = 2,   /**< A function pointer conforming to NVTX_VERSION=2 will be used. */
+    NVTX_INITIALIZATION_MODE_SIZE
+} nvtxInitializationMode_t;
+
+
+/** \brief Initialization Attribute Structure.
+* \anchor INITIALIZATION_ATTRIBUTE_STRUCTURE
+*
+* This structure is used to describe the attributes used for initialization
+* of the NVTX API.
+*
+* \par Initializing the Attributes
+*
+* The caller should always perform the following three tasks when using
+* attributes:
+* <ul>
+*    <li>Zero the structure
+*    <li>Set the version field
+*    <li>Set the size field
+* </ul>
+*
+* Zeroing the structure sets all the event attributes types and values
+* to the default value.
+*
+* The version and size field are used by the Tools Extension
+* implementation to handle multiple versions of the attributes structure.
+* NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE may be used for the size.
+*
+* It is recommended that the caller use one of the following to methods
+* to initialize the event attributes structure:
+*
+* \par Method 1: Initializing nvtxInitializationAttributes_t for future compatibility
+* \code
+* nvtxInitializationAttributes_t initAttribs = {0};
+* initAttribs.version = NVTX_VERSION;
+* initAttribs.size = NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE;
+* \endcode
+*
+* \par Method 2: Initializing nvtxInitializationAttributes_t for a specific version
+* \code
+* nvtxInitializationAttributes_t initAttribs = {0};
+* initAttribs.version =2;
+* initAttribs.size = (uint16_t)(sizeof(nvtxInitializationAttributes_v2));
+* \endcode
+*
+* If the caller uses Method 1 it is critical that the entire binary
+* layout of the structure be configured to 0 so that all fields
+* are initialized to the default value.
+*
+* The caller should either use both NVTX_VERSION and
+* NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values
+* and a versioned type (Method 2).  Using a mix of the two methods
+* will likely cause either source level incompatibility or binary
+* incompatibility in the future.
+*
+* \par Settings Attribute Types and Values
+*
+*
+* \par Example:
+* \code
+* // Initialize
+* nvtxInitializationAttributes_t initAttribs = {0};
+* initAttribs.version = NVTX_VERSION;
+* initAttribs.size = NVTX_INITIALIZATION_ATTRIB_STRUCT_SIZE;
+*
+* // Configure the Attributes
+* initAttribs.mode = NVTX_INITIALIZATION_MODE_CALLBACK_V2;
+* initAttribs.fnptr = InitializeInjectionNvtx2;
+* \endcode
+
+* \sa
+* ::nvtxInitializationMode_t
+* ::nvtxInitialize
+*/
+typedef struct nvtxInitializationAttributes_v2
+{
+    /**
+    * \brief Version flag of the structure.
+    *
+    * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs
+    * supported in this header file. This can optionally be overridden to
+    * another version of the tools extension library.
+    */
+    uint16_t version;
+
+    /**
+    * \brief Size of the structure.
+    *
+    * Needs to be set to the size in bytes of the event attribute
+    * structure used to specify the event.
+    */
+    uint16_t size;
+
+    /**
+    * \brief Mode of initialization.
+    *
+    * The mode of initialization dictates the overall behavior and which
+    * attributes in this struct will be used.
+    *
+    * Default Value is NVTX_INITIALIZATION_MODE_UNKNOWN = 0
+    * \sa
+    * ::nvtxInitializationMode_t
+    */
+    uint32_t mode;
+
+    /**
+    * \brief Function pointer used for initialization if the mode requires
+    *
+    * The user has retrieved this function pointer from the tool library
+    * and would like to use it to initialize.  The mode must be set to a
+    * NVTX_INITIALIZATION_MODE_CALLBACK_V# for this to be used.  The mode
+    * will dictate the expectations for this member.  The function signature
+    * will be cast from void(*)() to the appropriate signature for the mode.
+    * the expected behavior of the function will also depend on the mode
+    * beyond the simple function signature.
+    *
+    * Default Value is NVTX_INITIALIZATION_MODE_UNKNOWN which will either
+    * initialize based on external properties or fail if not supported on
+    * the given platform.
+
+    * \sa
+    * ::nvtxInitializationMode_t
+    */
+    void(*fnptr)(void);
+
+} nvtxInitializationAttributes_v2;
+
+typedef struct nvtxInitializationAttributes_v2 nvtxInitializationAttributes_t;
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Force initialization (optional on most platforms)
+*
+* Force NVTX library to initialize.  On some platform NVTX will implicit initialize
+* upon the first function call into an NVTX API.
+*
+* \return Result codes are simplest to assume NVTX_SUCCESS or !NVTX_SUCCESS
+*
+* \param initAttrib - The initialization attribute structure
+*
+* \sa
+* ::nvtxInitializationAttributes_t
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC int NVTX_API nvtxInitialize(const nvtxInitializationAttributes_t* initAttrib);
+/** @} */
+
+
+/** @} */ /*END defgroup*/
+
+/* ========================================================================= */
+/** \defgroup EVENT_ATTRIBUTES Event Attributes
+* @{
+*/
+
+/** ---------------------------------------------------------------------------
+* Payload Types
+* ------------------------------------------------------------------------- */
+typedef enum nvtxPayloadType_t
+{
+    NVTX_PAYLOAD_UNKNOWN = 0,   /**< Color payload is unused. */
+    NVTX_PAYLOAD_TYPE_UNSIGNED_INT64 = 1,   /**< A 64 bit unsigned integer value is used as payload. */
+    NVTX_PAYLOAD_TYPE_INT64 = 2,   /**< A 64 bit signed integer value is used as payload. */
+    NVTX_PAYLOAD_TYPE_DOUBLE = 3,   /**< A 64 bit floating point value is used as payload. */
+    /* NVTX_VERSION_2 */
+    NVTX_PAYLOAD_TYPE_UNSIGNED_INT32 = 4,   /**< A 32 bit floating point value is used as payload. */
+    NVTX_PAYLOAD_TYPE_INT32 = 5,   /**< A 32 bit floating point value is used as payload. */
+    NVTX_PAYLOAD_TYPE_FLOAT = 6    /**< A 32 bit floating point value is used as payload. */
+} nvtxPayloadType_t;
+
+/** \brief Event Attribute Structure.
+ * \anchor EVENT_ATTRIBUTE_STRUCTURE
+ *
+ * This structure is used to describe the attributes of an event. The layout of
+ * the structure is defined by a specific version of the tools extension
+ * library and can change between different versions of the Tools Extension
+ * library.
+ *
+ * \par Initializing the Attributes
+ *
+ * The caller should always perform the following three tasks when using
+ * attributes:
+ * <ul>
+ *    <li>Zero the structure
+ *    <li>Set the version field
+ *    <li>Set the size field
+ * </ul>
+ *
+ * Zeroing the structure sets all the event attributes types and values
+ * to the default value.
+ *
+ * The version and size field are used by the Tools Extension
+ * implementation to handle multiple versions of the attributes structure.
+ *
+ * It is recommended that the caller use one of the following to methods
+ * to initialize the event attributes structure:
+ *
+ * \par Method 1: Initializing nvtxEventAttributes for future compatibility
+ * \code
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ * \endcode
+ *
+ * \par Method 2: Initializing nvtxEventAttributes for a specific version
+ * \code
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = 1;
+ * eventAttrib.size = (uint16_t)(sizeof(nvtxEventAttributes_v1));
+ * \endcode
+ *
+ * If the caller uses Method 1 it is critical that the entire binary
+ * layout of the structure be configured to 0 so that all fields
+ * are initialized to the default value.
+ *
+ * The caller should either use both NVTX_VERSION and
+ * NVTX_EVENT_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values
+ * and a versioned type (Method 2).  Using a mix of the two methods
+ * will likely cause either source level incompatibility or binary
+ * incompatibility in the future.
+ *
+ * \par Settings Attribute Types and Values
+ *
+ *
+ * \par Example:
+ * \code
+ * // Initialize
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ *
+ * // Configure the Attributes
+ * eventAttrib.colorType = NVTX_COLOR_ARGB;
+ * eventAttrib.color = 0xFF880000;
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+ * eventAttrib.message.ascii = "Example";
+ * \endcode
+ *
+ * In the example the caller does not have to set the value of
+ * \ref ::nvtxEventAttributes_v2::category or
+ * \ref ::nvtxEventAttributes_v2::payload as these fields were set to
+ * the default value by {0}.
+ * \sa
+ * ::nvtxDomainMarkEx
+ * ::nvtxDomainRangeStartEx
+ * ::nvtxDomainRangePushEx
+ */
+typedef struct nvtxEventAttributes_v2
+{
+    /**
+    * \brief Version flag of the structure.
+    *
+    * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs
+    * supported in this header file. This can optionally be overridden to
+    * another version of the tools extension library.
+    */
+    uint16_t version;
+
+    /**
+    * \brief Size of the structure.
+    *
+    * Needs to be set to the size in bytes of the event attribute
+    * structure used to specify the event.
+    */
+    uint16_t size;
+
+    /**
+     * \brief ID of the category the event is assigned to.
+     *
+     * A category is a user-controlled ID that can be used to group
+     * events.  The tool may use category IDs to improve filtering or
+     * enable grouping of events in the same category. The functions
+     * \ref ::nvtxNameCategoryA or \ref ::nvtxNameCategoryW can be used
+     * to name a category.
+     *
+     * Default Value is 0
+     */
+    uint32_t category;
+
+    /** \brief Color type specified in this attribute structure.
+     *
+     * Defines the color format of the attribute structure's \ref COLOR_FIELD
+     * "color" field.
+     *
+     * Default Value is NVTX_COLOR_UNKNOWN
+     */
+    int32_t colorType;              /* nvtxColorType_t */
+
+    /** \brief Color assigned to this event. \anchor COLOR_FIELD
+     *
+     * The color that the tool should use to visualize the event.
+     */
+    uint32_t color;
+
+    /**
+     * \brief Payload type specified in this attribute structure.
+     *
+     * Defines the payload format of the attribute structure's \ref PAYLOAD_FIELD
+     * "payload" field.
+     *
+     * Default Value is NVTX_PAYLOAD_UNKNOWN
+     */
+    int32_t payloadType;            /* nvtxPayloadType_t */
+
+    int32_t reserved0;
+
+    /**
+     * \brief Payload assigned to this event. \anchor PAYLOAD_FIELD
+     *
+     * A numerical value that can be used to annotate an event. The tool could
+     * use the payload data to reconstruct graphs and diagrams.
+     */
+    union payload_t
+    {
+        uint64_t ullValue;
+        int64_t llValue;
+        double dValue;
+        /* NVTX_VERSION_2 */
+        uint32_t uiValue;
+        int32_t iValue;
+        float fValue;
+    } payload;
+
+    /** \brief Message type specified in this attribute structure.
+     *
+     * Defines the message format of the attribute structure's \ref MESSAGE_FIELD
+     * "message" field.
+     *
+     * Default Value is NVTX_MESSAGE_UNKNOWN
+     */
+    int32_t messageType;            /* nvtxMessageType_t */
+
+    /** \brief Message assigned to this attribute structure. \anchor MESSAGE_FIELD
+     *
+     * The text message that is attached to an event.
+     */
+    nvtxMessageValue_t message;
+
+} nvtxEventAttributes_v2;
+
+typedef struct nvtxEventAttributes_v2 nvtxEventAttributes_t;
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \defgroup MARKERS_AND_RANGES Markers and Ranges
+ *
+ * See \ref MARKERS_AND_RANGES for more details
+ *
+ * @{
+ */
+
+/** \name Marker */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Marks an instantaneous event in the application.
+*
+* A marker can contain a text message or specify additional information
+* using the event attributes structure.  These attributes include a text
+* message, color, category, and a payload. Each of the attributes is optional
+* and can only be sent out using the \ref nvtxDomainMarkEx function.
+*
+* nvtxDomainMarkEx(NULL, event) is equivalent to calling
+* nvtxMarkEx(event).
+*
+* \param domain    - The domain of scoping the category.
+* \param eventAttrib - The event attribute structure defining the marker's
+* attribute types and attribute values.
+*
+* \sa
+* ::nvtxMarkEx
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainMarkEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Marks an instantaneous event in the application.
+ *
+ * A marker can contain a text message or specify additional information
+ * using the event attributes structure.  These attributes include a text
+ * message, color, category, and a payload. Each of the attributes is optional
+ * and can only be sent out using the \ref nvtxMarkEx function.
+ * If \ref nvtxMarkA or \ref nvtxMarkW are used to specify the marker
+ * or if an attribute is unspecified then a default value will be used.
+ *
+ * \param eventAttrib - The event attribute structure defining the marker's
+ * attribute types and attribute values.
+ *
+ * \par Example:
+ * \code
+ * // zero the structure
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * // set the version and the size information
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ * // configure the attributes.  0 is the default for all attributes.
+ * eventAttrib.colorType = NVTX_COLOR_ARGB;
+ * eventAttrib.color = 0xFF880000;
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+ * eventAttrib.message.ascii = "Example nvtxMarkEx";
+ * nvtxMarkEx(&eventAttrib);
+ * \endcode
+ *
+ * \sa
+ * ::nvtxDomainMarkEx
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxMarkEx(const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Marks an instantaneous event in the application.
+ *
+ * A marker created using \ref nvtxMarkA or \ref nvtxMarkW contains only a
+ * text message.
+ *
+ * \param message     - The message associated to this marker event.
+ *
+ * \par Example:
+ * \code
+ * nvtxMarkA("Example nvtxMarkA");
+ * nvtxMarkW(L"Example nvtxMarkW");
+ * \endcode
+ *
+ * \sa
+ * ::nvtxDomainMarkEx
+ * ::nvtxMarkEx
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxMarkA(const char* message);
+NVTX_DECLSPEC void NVTX_API nvtxMarkW(const wchar_t* message);
+/** @} */
+
+
+/** \name Process Ranges */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a process range in a domain.
+*
+* \param domain    - The domain of scoping the category.
+* \param eventAttrib - The event attribute structure defining the range's
+* attribute types and attribute values.
+*
+* \return The unique ID used to correlate a pair of Start and End events.
+*
+* \remarks Ranges defined by Start/End can overlap.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("my domain");
+* nvtxEventAttributes_t eventAttrib = {0};
+* eventAttrib.version = NVTX_VERSION;
+* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* eventAttrib.message.ascii = "my range";
+* nvtxRangeId_t rangeId = nvtxDomainRangeStartEx(&eventAttrib);
+* // ...
+* nvtxDomainRangeEnd(rangeId);
+* \endcode
+*
+* \sa
+* ::nvtxDomainRangeEnd
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxDomainRangeStartEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a process range.
+ *
+ * \param eventAttrib - The event attribute structure defining the range's
+ * attribute types and attribute values.
+ *
+ * \return The unique ID used to correlate a pair of Start and End events.
+ *
+ * \remarks Ranges defined by Start/End can overlap.
+ *
+ * \par Example:
+ * \code
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ * eventAttrib.category = 3;
+ * eventAttrib.colorType = NVTX_COLOR_ARGB;
+ * eventAttrib.color = 0xFF0088FF;
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+ * eventAttrib.message.ascii = "Example Range";
+ * nvtxRangeId_t rangeId = nvtxRangeStartEx(&eventAttrib);
+ * // ...
+ * nvtxRangeEnd(rangeId);
+ * \endcode
+ *
+ * \sa
+ * ::nvtxRangeEnd
+ * ::nvtxDomainRangeStartEx
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartEx(const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a process range.
+ *
+ * \param message     - The event message associated to this range event.
+ *
+ * \return The unique ID used to correlate a pair of Start and End events.
+ *
+ * \remarks Ranges defined by Start/End can overlap.
+ *
+ * \par Example:
+ * \code
+ * nvtxRangeId_t r1 = nvtxRangeStartA("Range 1");
+ * nvtxRangeId_t r2 = nvtxRangeStartW(L"Range 2");
+ * nvtxRangeEnd(r1);
+ * nvtxRangeEnd(r2);
+ * \endcode
+ *
+ * \sa
+ * ::nvtxRangeEnd
+ * ::nvtxRangeStartEx
+ * ::nvtxDomainRangeStartEx
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartA(const char* message);
+NVTX_DECLSPEC nvtxRangeId_t NVTX_API nvtxRangeStartW(const wchar_t* message);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Ends a process range.
+*
+* \param domain - The domain 
+* \param id - The correlation ID returned from a nvtxRangeStart call.
+*
+* \remarks This function is offered completeness but is an alias for ::nvtxRangeEnd. 
+* It does not need a domain param since that is associated iwth the range ID at ::nvtxDomainRangeStartEx
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("my domain");
+* nvtxEventAttributes_t eventAttrib = {0};
+* eventAttrib.version = NVTX_VERSION;
+* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* eventAttrib.message.ascii = "my range";
+* nvtxRangeId_t rangeId = nvtxDomainRangeStartEx(&eventAttrib);
+* // ...
+* nvtxDomainRangeEnd(rangeId);
+* \endcode
+*
+* \sa
+* ::nvtxDomainRangeStartEx
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainRangeEnd(nvtxDomainHandle_t domain, nvtxRangeId_t id);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Ends a process range.
+ *
+ * \param id - The correlation ID returned from an nvtxRangeStart call.
+ *
+ * \sa
+ * ::nvtxDomainRangeStartEx
+ * ::nvtxRangeStartEx
+ * ::nvtxRangeStartA
+ * ::nvtxRangeStartW
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxRangeEnd(nvtxRangeId_t id);
+/** @} */
+
+/** \name Thread Ranges */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a nested thread range.
+*
+* \param domain    - The domain of scoping.
+* \param eventAttrib - The event attribute structure defining the range's
+* attribute types and attribute values.
+*
+* \return The 0 based level of range being started. This value is scoped to the domain.
+* If an error occurs, a negative value is returned.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain");
+* nvtxEventAttributes_t eventAttrib = {0};
+* eventAttrib.version = NVTX_VERSION;
+* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib.colorType = NVTX_COLOR_ARGB;
+* eventAttrib.color = 0xFFFF0000;
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* eventAttrib.message.ascii = "Level 0";
+* nvtxDomainRangePushEx(domain, &eventAttrib);
+*
+* // Re-use eventAttrib
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_UNICODE;
+* eventAttrib.message.unicode = L"Level 1";
+* nvtxDomainRangePushEx(domain, &eventAttrib);
+*
+* nvtxDomainRangePop(domain); //level 1
+* nvtxDomainRangePop(domain); //level 0
+* \endcode
+*
+* \sa
+* ::nvtxDomainRangePop
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC int NVTX_API nvtxDomainRangePushEx(nvtxDomainHandle_t domain, const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a nested thread range.
+ *
+ * \param eventAttrib - The event attribute structure defining the range's
+ * attribute types and attribute values.
+ *
+ * \return The 0 based level of range being started. This level is per domain.
+ * If an error occurs a negative value is returned.
+ *
+ * \par Example:
+ * \code
+ * nvtxEventAttributes_t eventAttrib = {0};
+ * eventAttrib.version = NVTX_VERSION;
+ * eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+ * eventAttrib.colorType = NVTX_COLOR_ARGB;
+ * eventAttrib.color = 0xFFFF0000;
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_ASCII;
+ * eventAttrib.message.ascii = "Level 0";
+ * nvtxRangePushEx(&eventAttrib);
+ *
+ * // Re-use eventAttrib
+ * eventAttrib.messageType = NVTX_MESSAGE_TYPE_UNICODE;
+ * eventAttrib.message.unicode = L"Level 1";
+ * nvtxRangePushEx(&eventAttrib);
+ *
+ * nvtxRangePop();
+ * nvtxRangePop();
+ * \endcode
+ *
+ * \sa
+ * ::nvtxDomainRangePushEx
+ * ::nvtxRangePop
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC int NVTX_API nvtxRangePushEx(const nvtxEventAttributes_t* eventAttrib);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Starts a nested thread range.
+ *
+ * \param message     - The event message associated to this range event.
+ *
+ * \return The 0 based level of range being started.  If an error occurs a
+ * negative value is returned.
+ *
+ * \par Example:
+ * \code
+ * nvtxRangePushA("Level 0");
+ * nvtxRangePushW(L"Level 1");
+ * nvtxRangePop();
+ * nvtxRangePop();
+ * \endcode
+ *
+ * \sa
+ * ::nvtxDomainRangePushEx
+ * ::nvtxRangePop
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC int NVTX_API nvtxRangePushA(const char* message);
+NVTX_DECLSPEC int NVTX_API nvtxRangePushW(const wchar_t* message);
+/** @} */
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Ends a nested thread range.
+*
+* \return The level of the range being ended. If an error occurs a negative
+* value is returned on the current thread.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreate("example library");
+* nvtxDomainRangePushA(domain, "Level 0");
+* nvtxDomainRangePushW(domain, L"Level 1");
+* nvtxDomainRangePop(domain);
+* nvtxDomainRangePop(domain);
+* \endcode
+*
+* \sa
+* ::nvtxRangePushEx
+* ::nvtxRangePushA
+* ::nvtxRangePushW
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC int NVTX_API nvtxDomainRangePop(nvtxDomainHandle_t domain);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Ends a nested thread range.
+ *
+ * \return The level of the range being ended. If an error occurs a negative
+ * value is returned on the current thread.
+ *
+ * \par Example:
+ * \code
+ * nvtxRangePushA("Level 0");
+ * nvtxRangePushW(L"Level 1");
+ * nvtxRangePop();
+ * nvtxRangePop();
+ * \endcode
+ *
+ * \sa
+ * ::nvtxRangePushEx
+ * ::nvtxRangePushA
+ * ::nvtxRangePushW
+ *
+ * \version \NVTX_VERSION_0
+ * @{ */
+NVTX_DECLSPEC int NVTX_API nvtxRangePop(void);
+/** @} */
+
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \defgroup RESOURCE_NAMING Resource Naming
+ *
+ * See \ref RESOURCE_NAMING for more details
+ *
+ * @{
+ */
+
+
+/*  ------------------------------------------------------------------------- */
+/** \name Functions for Generic Resource Naming*/
+/*  ------------------------------------------------------------------------- */
+
+/*  ------------------------------------------------------------------------- */
+/** \cond SHOW_HIDDEN
+* \brief Resource typing helpers.  
+*
+* Classes are used to make it easy to create a series of resource types 
+* per API without collisions 
+*/
+#define NVTX_RESOURCE_MAKE_TYPE(CLASS, INDEX) ((((uint32_t)(NVTX_RESOURCE_CLASS_ ## CLASS))<<16)|((uint32_t)(INDEX)))
+#define NVTX_RESOURCE_CLASS_GENERIC 1
+/** \endcond */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Generic resource type for when a resource class is not available.
+*
+* \sa
+* ::nvtxDomainResourceCreate
+*
+* \version \NVTX_VERSION_2
+*/
+typedef enum nvtxResourceGenericType_t
+{
+    NVTX_RESOURCE_TYPE_UNKNOWN = 0,
+    NVTX_RESOURCE_TYPE_GENERIC_POINTER = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 1), /**< Generic pointer assumed to have no collisions with other pointers. */
+    NVTX_RESOURCE_TYPE_GENERIC_HANDLE = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 2), /**< Generic handle assumed to have no collisions with other handles. */
+    NVTX_RESOURCE_TYPE_GENERIC_THREAD_NATIVE = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 3), /**< OS native thread identifier. */
+    NVTX_RESOURCE_TYPE_GENERIC_THREAD_POSIX = NVTX_RESOURCE_MAKE_TYPE(GENERIC, 4) /**< POSIX pthread identifier. */
+} nvtxResourceGenericType_t;
+
+
+
+/** \brief Resource Attribute Structure.
+* \anchor RESOURCE_ATTRIBUTE_STRUCTURE
+*
+* This structure is used to describe the attributes of a resource. The layout of
+* the structure is defined by a specific version of the tools extension
+* library and can change between different versions of the Tools Extension
+* library.
+*
+* \par Initializing the Attributes
+*
+* The caller should always perform the following three tasks when using
+* attributes:
+* <ul>
+*    <li>Zero the structure
+*    <li>Set the version field
+*    <li>Set the size field
+* </ul>
+*
+* Zeroing the structure sets all the resource attributes types and values
+* to the default value.
+*
+* The version and size field are used by the Tools Extension
+* implementation to handle multiple versions of the attributes structure.
+*
+* It is recommended that the caller use one of the following to methods
+* to initialize the event attributes structure:
+*
+* \par Method 1: Initializing nvtxEventAttributes for future compatibility
+* \code
+* nvtxResourceAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE;
+* \endcode
+*
+* \par Method 2: Initializing nvtxEventAttributes for a specific version
+* \code
+* nvtxResourceAttributes_v0 attribs = {0};
+* attribs.version = 2;
+* attribs.size = (uint16_t)(sizeof(nvtxResourceAttributes_v0));
+* \endcode
+*
+* If the caller uses Method 1 it is critical that the entire binary
+* layout of the structure be configured to 0 so that all fields
+* are initialized to the default value.
+*
+* The caller should either use both NVTX_VERSION and
+* NVTX_RESOURCE_ATTRIB_STRUCT_SIZE (Method 1) or use explicit values
+* and a versioned type (Method 2).  Using a mix of the two methods
+* will likely cause either source level incompatibility or binary
+* incompatibility in the future.
+*
+* \par Settings Attribute Types and Values
+*
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain");
+*
+* // Initialize
+* nvtxResourceAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE;
+*
+* // Configure the Attributes
+* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER;
+* attribs.identifier.pValue = (const void*)pMutex;
+* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* attribs.message.ascii = "Single thread access to database.";
+*
+* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs);
+* \endcode
+*
+* \sa
+* ::nvtxDomainResourceCreate
+*/
+typedef struct nvtxResourceAttributes_v0
+{
+    /**
+    * \brief Version flag of the structure.
+    *
+    * Needs to be set to NVTX_VERSION to indicate the version of NVTX APIs
+    * supported in this header file. This can optionally be overridden to
+    * another version of the tools extension library.
+    */
+    uint16_t version;
+
+    /**
+    * \brief Size of the structure.
+    *
+    * Needs to be set to the size in bytes of this attribute
+    * structure.
+    */
+    uint16_t size;
+
+    /**
+    * \brief Identifier type specifies how to interpret the identifier field
+    *
+    * Defines the identifier format of the attribute structure's \ref RESOURCE_IDENTIFIER_FIELD
+    * "identifier" field.
+    *
+    * Default Value is NVTX_RESOURCE_TYPE_UNKNOWN
+    */
+    int32_t identifierType;            /* values from enums following the pattern nvtxResource[name]Type_t */
+
+    /**
+    * \brief Identifier for the resource. 
+    * \anchor RESOURCE_IDENTIFIER_FIELD
+    *
+    * An identifier may be a pointer or a handle to an OS or middleware API object.
+    * The resource type will assist in avoiding collisions where handles values may collide.
+    */
+    union identifier_t
+    {
+        const void* pValue;
+        uint64_t ullValue;
+    } identifier;
+
+    /** \brief Message type specified in this attribute structure.
+    *
+    * Defines the message format of the attribute structure's \ref RESOURCE_MESSAGE_FIELD
+    * "message" field.
+    *
+    * Default Value is NVTX_MESSAGE_UNKNOWN
+    */
+    int32_t messageType;            /* nvtxMessageType_t */
+
+    /** \brief Message assigned to this attribute structure. \anchor RESOURCE_MESSAGE_FIELD
+    *
+    * The text message that is attached to a resource.
+    */
+    nvtxMessageValue_t message;
+
+} nvtxResourceAttributes_v0;
+
+typedef struct nvtxResourceAttributes_v0 nvtxResourceAttributes_t;
+
+/* \cond SHOW_HIDDEN 
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_RESOURCE_ATTRIB_STRUCT_SIZE ( (uint16_t)( sizeof(nvtxResourceAttributes_v0) ) )
+typedef struct nvtxResourceHandle* nvtxResourceHandle_t;
+/** \endcond */
+
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Create a resource object to track and associate data with OS and middleware objects
+*
+* Allows users to associate an API handle or pointer with a user-provided name.
+* 
+*
+* \param domain - Domain to own the resource object
+* \param attribs - Attributes to be associated with the resource
+*
+* \return A handle that represents the newly created resource object.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain");
+* nvtxResourceAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE;
+* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER;
+* attribs.identifier.pValue = (const void*)pMutex;
+* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* attribs.message.ascii = "Single thread access to database.";
+* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs);
+* \endcode
+*
+* \sa
+* ::nvtxResourceAttributes_t
+* ::nvtxDomainResourceDestroy
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC nvtxResourceHandle_t NVTX_API nvtxDomainResourceCreate(nvtxDomainHandle_t domain, nvtxResourceAttributes_t* attribs);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Destroy a resource object to track and associate data with OS and middleware objects
+*
+* Allows users to associate an API handle or pointer with a user-provided name.
+*
+* \param resource - Handle to the resource in which to operate.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example domain");
+* nvtxResourceAttributes_t attribs = {0};
+* attribs.version = NVTX_VERSION;
+* attribs.size = NVTX_RESOURCE_ATTRIB_STRUCT_SIZE;
+* attribs.identifierType = NVTX_RESOURCE_TYPE_GENERIC_POINTER;
+* attribs.identifier.pValue = (const void*)pMutex;
+* attribs.messageType = NVTX_MESSAGE_TYPE_ASCII;
+* attribs.message.ascii = "Single thread access to database.";
+* nvtxResourceHandle_t handle = nvtxDomainResourceCreate(domain, attribs);
+* nvtxDomainResourceDestroy(handle);
+* \endcode
+*
+* \sa
+* ::nvtxDomainResourceCreate
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainResourceDestroy(nvtxResourceHandle_t resource);
+/** @} */
+
+
+/** \name Functions for NVTX Category Naming*/
+
+/* ------------------------------------------------------------------------- */
+/**
+* \brief Annotate an NVTX category used within a domain.
+*
+* Categories are used to group sets of events. Each category is identified
+* through a unique ID and that ID is passed into any of the marker/range
+* events to assign that event to a specific category. The nvtxDomainNameCategory
+* function calls allow the user to assign a name to a category ID that is
+* specific to the domain.
+*
+* nvtxDomainNameCategory(NULL, category, name) is equivalent to calling
+* nvtxNameCategory(category, name).
+*
+* \param domain    - The domain of scoping the category.
+* \param category  - The category ID to name.
+* \param name      - The name of the category.
+*
+* \remarks The category names are tracked per domain.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("example");
+* nvtxDomainNameCategoryA(domain, 1, "Memory Allocation");
+* nvtxDomainNameCategoryW(domain, 2, L"Memory Transfer");
+* \endcode
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryA(nvtxDomainHandle_t domain, uint32_t category, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxDomainNameCategoryW(nvtxDomainHandle_t domain, uint32_t category, const wchar_t* name);
+/** @} */
+
+/** \brief Annotate an NVTX category.
+ *
+ * Categories are used to group sets of events. Each category is identified
+ * through a unique ID and that ID is passed into any of the marker/range
+ * events to assign that event to a specific category. The nvtxNameCategory
+ * function calls allow the user to assign a name to a category ID.
+ *
+ * \param category - The category ID to name.
+ * \param name     - The name of the category.
+ *
+ * \remarks The category names are tracked per process.
+ *
+ * \par Example:
+ * \code
+ * nvtxNameCategory(1, "Memory Allocation");
+ * nvtxNameCategory(2, "Memory Transfer");
+ * nvtxNameCategory(3, "Memory Object Lifetime");
+ * \endcode
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCategoryA(uint32_t category, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCategoryW(uint32_t category, const wchar_t* name);
+/** @} */
+
+/** \name Functions for OS Threads Naming*/
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotate an OS thread.
+ *
+ * Allows the user to name an active thread of the current process. If an
+ * invalid thread ID is provided or a thread ID from a different process is
+ * used the behavior of the tool is implementation dependent.
+ *
+ * The thread name is associated to the default domain.  To support domains 
+ * use resource objects via ::nvtxDomainResourceCreate.
+ *
+ * \param threadId - The ID of the thread to name.
+ * \param name     - The name of the thread.
+ *
+ * \par Example:
+ * \code
+ * nvtxNameOsThread(GetCurrentThreadId(), "MAIN_THREAD");
+ * \endcode
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadA(uint32_t threadId, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameOsThreadW(uint32_t threadId, const wchar_t* name);
+/** @} */
+
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \defgroup STRING_REGISTRATION String Registration
+*
+* Registered strings are intended to increase performance by lowering instrumentation
+* overhead.  String may be registered once and the handle may be passed in place of
+* a string where an the APIs may allow.
+*
+* See \ref STRING_REGISTRATION for more details
+*
+* @{
+*/
+
+/* ------------------------------------------------------------------------- */
+/** \brief Register a string.
+
+* Registers an immutable string with NVTX. Once registered the pointer used
+* to register the domain name can be used in nvtxEventAttributes_t
+* \ref MESSAGE_FIELD. This allows NVTX implementation to skip copying the
+* contents of the message on each event invocation.
+*
+* String registration is an optimization. It is recommended to use string
+* registration if the string will be passed to an event many times.
+*
+* String are not unregistered, except that by unregistering the entire domain
+*
+* \param domain  - Domain handle. If NULL then the global domain is used.
+* \param string    - A unique pointer to a sequence of characters.
+*
+* \return A handle representing the registered string.
+*
+* \par Example:
+* \code
+* nvtxDomainCreateA("com.nvidia.nvtx.example");
+* nvtxStringHandle_t message = nvtxDomainRegisterStringA(domain, "registered string");
+* nvtxEventAttributes_t eventAttrib = {0};
+* eventAttrib.version = NVTX_VERSION;
+* eventAttrib.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib.messageType = NVTX_MESSAGE_TYPE_REGISTERED;
+* eventAttrib.message.registered = message;
+* \endcode
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringA(nvtxDomainHandle_t domain, const char* string);
+NVTX_DECLSPEC nvtxStringHandle_t NVTX_API nvtxDomainRegisterStringW(nvtxDomainHandle_t domain, const wchar_t* string);
+/** @} */
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \defgroup DOMAINS Domains
+*
+* Domains are used to group events to a developer defined scope. Middleware
+* vendors may also scope their own events to avoid collisions with the
+* the application developer's events, so that the application developer may
+* inspect both parts and easily differentiate or filter them.  By default
+* all events are scoped to a global domain where NULL is provided or when
+* using APIs provided b versions of NVTX below v2
+*
+* Domains are intended to be typically long lived objects with the intention
+* of logically separating events of large modules from each other such as
+* middleware libraries from each other and the main application.
+*
+* See \ref DOMAINS for more details
+*
+* @{
+*/
+
+/* ------------------------------------------------------------------------- */
+/** \brief Register a NVTX domain.
+*
+* Domains are used to scope annotations. All NVTX_VERSION_0 and NVTX_VERSION_1
+* annotations are scoped to the global domain. The function nvtxDomainCreate
+* creates a new named domain.
+*
+* Each domain maintains its own nvtxRangePush and nvtxRangePop stack.
+*
+* \param name - A unique string representing the domain.
+*
+* \return A handle representing the domain.
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("com.nvidia.nvtx.example");
+*
+* nvtxMarkA("nvtxMarkA to global domain");
+*
+* nvtxEventAttributes_t eventAttrib1 = {0};
+* eventAttrib1.version = NVTX_VERSION;
+* eventAttrib1.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib1.message.ascii = "nvtxDomainMarkEx to global domain";
+* nvtxDomainMarkEx(NULL, &eventAttrib1);
+*
+* nvtxEventAttributes_t eventAttrib2 = {0};
+* eventAttrib2.version = NVTX_VERSION;
+* eventAttrib2.size = NVTX_EVENT_ATTRIB_STRUCT_SIZE;
+* eventAttrib2.message.ascii = "nvtxDomainMarkEx to com.nvidia.nvtx.example";
+* nvtxDomainMarkEx(domain, &eventAttrib2);
+* nvtxDomainDestroy(domain);
+* \endcode
+*
+* \sa
+* ::nvtxDomainDestroy
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateA(const char* name);
+NVTX_DECLSPEC nvtxDomainHandle_t NVTX_API nvtxDomainCreateW(const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Unregister a NVTX domain.
+*
+* Unregisters the domain handle and frees all domain specific resources.
+*
+* \param domain    - the domain handle
+*
+* \par Example:
+* \code
+* nvtxDomainHandle_t domain = nvtxDomainCreateA("com.nvidia.nvtx.example");
+* nvtxDomainDestroy(domain);
+* \endcode
+*
+* \sa
+* ::nvtxDomainCreateA
+* ::nvtxDomainCreateW
+*
+* \version \NVTX_VERSION_2
+* @{ */
+NVTX_DECLSPEC void NVTX_API nvtxDomainDestroy(nvtxDomainHandle_t domain);
+/** @} */
+
+
+/** @} */ /*END defgroup*/
+/* ========================================================================= */
+/** \cond SHOW_HIDDEN */
+
+#ifdef UNICODE
+    #define nvtxMark            nvtxMarkW
+    #define nvtxRangeStart      nvtxRangeStartW
+    #define nvtxRangePush       nvtxRangePushW
+    #define nvtxNameCategory    nvtxNameCategoryW
+    #define nvtxNameOsThread    nvtxNameOsThreadW
+    /* NVTX_VERSION_2 */
+    #define nvtxDomainCreate         nvtxDomainCreateW
+    #define nvtxDomainRegisterString nvtxDomainRegisterStringW
+    #define nvtxDomainNameCategory   nvtxDomainNameCategoryW
+#else
+    #define nvtxMark            nvtxMarkA
+    #define nvtxRangeStart      nvtxRangeStartA
+    #define nvtxRangePush       nvtxRangePushA
+    #define nvtxNameCategory    nvtxNameCategoryA
+    #define nvtxNameOsThread    nvtxNameOsThreadA
+    /* NVTX_VERSION_2 */
+    #define nvtxDomainCreate         nvtxDomainCreateA
+    #define nvtxDomainRegisterString nvtxDomainRegisterStringA
+    #define nvtxDomainNameCategory   nvtxDomainNameCategoryA    
+#endif
+
+/** \endcond */
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#endif /* NVTOOLSEXT_H_ */

llava_next/lib/python3.10/site-packages/nvidia/nvtx/include/nvToolsExtCuda.h

@@ -0,0 +1,164 @@
+/*
+* Copyright 2009-2017  NVIDIA Corporation.  All rights reserved.
+*
+* NOTICE TO USER:
+*
+* This source code is subject to NVIDIA ownership rights under U.S. and
+* international Copyright laws.
+*
+* This software and the information contained herein is PROPRIETARY and
+* CONFIDENTIAL to NVIDIA and is being provided under the terms and conditions
+* of a form of NVIDIA software license agreement.
+*
+* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
+* CODE FOR ANY PURPOSE.  IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
+* IMPLIED WARRANTY OF ANY KIND.  NVIDIA DISCLAIMS ALL WARRANTIES WITH
+* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
+* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
+* 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 SOURCE CODE.
+*
+* U.S. Government End Users.   This source code is a "commercial item" as
+* that term is defined at  48 C.F.R. 2.101 (OCT 1995), consisting  of
+* "commercial computer  software"  and "commercial computer software
+* documentation" as such terms are  used in 48 C.F.R. 12.212 (SEPT 1995)
+* and is provided to the U.S. Government only as a commercial end item.
+* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
+* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
+* source code with only those rights set forth herein.
+*
+* Any use of this source code in individual and commercial software must
+* include, in the user documentation and internal comments to the code,
+* the above Disclaimer and U.S. Government End Users Notice.
+*/
+
+#ifndef NVTOOLSEXT_CUDA_H_
+#define NVTOOLSEXT_CUDA_H_
+
+#include "cuda.h"
+
+#include "nvToolsExt.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+/* ========================================================================= */
+/** \name Functions for CUDA Resource Naming
+*/
+/** \addtogroup RESOURCE_NAMING
+ * \section RESOURCE_NAMING_CUDA CUDA Resource Naming
+ *
+ * This section covers the API functions that allow to annotate CUDA resources
+ * with user-provided names.
+ *
+ * @{
+ */
+
+/*  ------------------------------------------------------------------------- */
+/* \cond SHOW_HIDDEN 
+* \brief Used to build a non-colliding value for resource types separated class
+* \version \NVTX_VERSION_2
+*/
+#define NVTX_RESOURCE_CLASS_CUDA  4
+/** \endcond */
+
+/*  ------------------------------------------------------------------------- */
+/** \brief Resource types for CUDA
+*/
+typedef enum nvtxResourceCUDAType_t
+{
+    NVTX_RESOURCE_TYPE_CUDA_DEVICE = NVTX_RESOURCE_MAKE_TYPE(CUDA, 1), /* CUdevice */
+    NVTX_RESOURCE_TYPE_CUDA_CONTEXT = NVTX_RESOURCE_MAKE_TYPE(CUDA, 2), /* CUcontext */
+    NVTX_RESOURCE_TYPE_CUDA_STREAM = NVTX_RESOURCE_MAKE_TYPE(CUDA, 3), /* CUstream */
+    NVTX_RESOURCE_TYPE_CUDA_EVENT = NVTX_RESOURCE_MAKE_TYPE(CUDA, 4)  /* CUevent */
+} nvtxResourceCUDAType_t;
+
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA device.
+ *
+ * Allows the user to associate a CUDA device with a user-provided name.
+ *
+ * \param device - The handle of the CUDA device to name.
+ * \param name   - The name of the CUDA device.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceA(CUdevice device, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCuDeviceW(CUdevice device, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA context.
+ *
+ * Allows the user to associate a CUDA context with a user-provided name.
+ *
+ * \param context - The handle of the CUDA context to name.
+ * \param name    - The name of the CUDA context.
+ *
+ * \par Example:
+ * \code
+ * CUresult status = cuCtxCreate( &cuContext, 0, cuDevice );
+ * if ( CUDA_SUCCESS != status )
+ *     goto Error;
+ * nvtxNameCuContext(cuContext, "CTX_NAME");
+ * \endcode
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCuContextA(CUcontext context, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCuContextW(CUcontext context, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA stream.
+ *
+ * Allows the user to associate a CUDA stream with a user-provided name.
+ *
+ * \param stream - The handle of the CUDA stream to name.
+ * \param name   - The name of the CUDA stream.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamA(CUstream stream, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCuStreamW(CUstream stream, const wchar_t* name);
+/** @} */
+
+/* ------------------------------------------------------------------------- */
+/** \brief Annotates a CUDA event.
+ *
+ * Allows the user to associate a CUDA event with a user-provided name.
+ *
+ * \param event - The handle of the CUDA event to name.
+ * \param name  - The name of the CUDA event.
+ *
+ * \version \NVTX_VERSION_1
+ * @{ */
+NVTX_DECLSPEC void NVTX_API nvtxNameCuEventA(CUevent event, const char* name);
+NVTX_DECLSPEC void NVTX_API nvtxNameCuEventW(CUevent event, const wchar_t* name);
+/** @} */
+
+/** @} */ /* END RESOURCE_NAMING */
+
+/* ========================================================================= */
+#ifdef UNICODE
+  #define nvtxNameCuDevice   nvtxNameCuDeviceW
+  #define nvtxNameCuContext  nvtxNameCuContextW
+  #define nvtxNameCuStream   nvtxNameCuStreamW
+  #define nvtxNameCuEvent    nvtxNameCuEventW
+#else
+  #define nvtxNameCuDevice   nvtxNameCuDeviceA
+  #define nvtxNameCuContext  nvtxNameCuContextA
+  #define nvtxNameCuStream   nvtxNameCuStreamA
+  #define nvtxNameCuEvent    nvtxNameCuEventA
+#endif
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#endif /* NVTOOLSEXT_CUDA_H_ */