Merge branch 'lwpck-586' of...

Merge branch 'lwpck-586' of https://github.com/ROCmSoftwarePlatform/composable_kernel into lwpck-586

docs/source/Makefile

+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SPHINXPROJ    = CK
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/source/Supported_Primitives_Guide.rst

+==========================
+Supported Primitives Guide
+==========================
+
+This document contains details of supported primitives in Composable Kernel (CK). In contrast to the API Reference
+Guide, the Supported Primitives Guide is an introduction to the math which underpins the algorithms implemented in CK.
+
+------------
+Softmax
+------------
+
+For vectors :math:`x^{(1)}, x^{(2)}, \ldots, x^{(T)}` of size :math:`B` we can decompose the softmax of concatenated
+:math:`x = [ x^{(1)}\ | \ \ldots \ | \ x^{(T)} ]` as,
+
+.. math::
+   :nowrap:
+
+   \begin{align}
+      m(x) & = m( [ x^{(1)}\ | \ \ldots \ | \ x^{(T)} ] ) = \max( m(x^{(1)}),\ldots, m(x^{(T)}) )  \\
+      f(x) & = [\exp( m(x^{(1)}) - m(x) ) f( x^{(1)} )\ | \ \ldots \ | \ \exp( m(x^{(T)}) - m(x) ) f( x^{(T)} )] \\
+      z(x) & = \exp( m(x^{(1)}) - m(x) )\ z(x^{(1)}) + \ldots + \exp( m(x^{(T)}) - m(x) )\ z(x^{(1)}) \\
+      \operatorname{softmax}(x) &= f(x)\ / \ z(x)
+   \end{align}
+
+where :math:`f(x^{(j)}) = \exp( x^{(j)} - m(x^{(j)}) )` is of size :math:`B` and
+:math:`z(x^{(j)}) = f(x_1^{(j)})+ \ldots+ f(x_B^{(j)})` is a scalar.
+
+For a matrix :math:`X` composed of :math:`T_r \times T_c` tiles, :math:`X_{ij}`, of size :math:`B_r \times B_c` we can
+compute the row-wise softmax as follows.
+
+For :math:`j` from :math:`1` to :math:`T_c`, and :math:`i` from :math:`1` to :math:`T_r` calculate,
+
+.. math::
+   :nowrap:
+
+   \begin{align}
+      \tilde{m}_{ij}   &= \operatorname{rowmax}( X_{ij} ) \\
+      \tilde{P}_{ij}   &= \exp(X_{ij} - \tilde{m}_{ij} ) \\
+      \tilde{z}_{ij}   &= \operatorname{rowsum}( P_{ij} ) \\
+   \end{align}
+
+If :math:`j=1`, initialize running max, running sum, and the first column block of the output,
+
+.. math::
+   :nowrap:
+
+   \begin{align}
+      m_i            &= \tilde{m}_{i1} \\
+      z_i            &= \tilde{z}_{i1} \\
+      \tilde{Y}_{i1} &= \diag(\tilde{z}_{ij})^{-1} \tilde{P}_{i1}
+   \end{align}
+
+Else if :math:`j>1`,
+
+1. Update running max, running sum and column blocks :math:`k=1` to :math:`k=j-1`
+
+.. math::
+   :nowrap:
+
+   \begin{align}
+      m^{new}_i &= \max(m_i, \tilde{m}_{ij} ) \\
+      z^{new}_i &= \exp(m_i - m^{new}_i)\ z_i + \exp( \tilde{m}_{ij} - m^{new}_i )\ \tilde{z}_{ij}  \\
+      Y_{ik}    &= \diag(z^{new}_{i})^{-1} \diag(z_{i}) \exp(m_i - m^{new}_i)\ Y_{ik}
+   \end{align}
+
+2. Initialize column block :math:`j` of output and reset running max and running sum variables:
+
+.. math::
+   :nowrap:
+
+   \begin{align}
+      \tilde{Y}_{ij} &= \diag(z^{new}_{i})^{-1} \exp(\tilde{m}_{ij} - m^{new}_i ) \tilde{P}_{ij} \\
+      z_i            &= z^{new}_i \\
+      m_i            &= m^{new}_i \\
+   \end{align}
\ No newline at end of file

docs/source/conf.py

+"""Copyright (C) 2018-2023 Advanced Micro Devices, Inc. All rights reserved.
+
+   Permission is hereby granted, free of charge, to any person obtaining a copy
+   of this software and associated documentation files (the "Software"), to deal
+   in the Software without restriction, including without limitation the rights
+   to use, copy, modify, merge, publish, distribute, sublicense, and/or sell cop-
+   ies of the Software, and to permit persons to whom the Software is furnished
+   to do so, subject to the following conditions:
+
+   The above copyright notice and this permission notice shall be included in all
+   copies or substantial portions of the Software.
+
+   THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IM-
+   PLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+   FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+   COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+   IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNE-
+   CTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+"""
+
+# -*- coding: utf-8 -*-
+#
+# Composable Kernel (CK) docuumentation build configuration file, based on
+# rocBLAS documentation build configuration file, created by
+# sphinx-quickstart on Mon Jan  8 16:34:42 2018.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath('.'))
+
+import os
+import sys
+import subprocess
+
+read_the_docs_build = os.environ.get('READTHEDOCS', None) == 'True'
+
+if read_the_docs_build:
+    subprocess.call('../run_doxygen.sh')
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#
+# needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = ['sphinx.ext.mathjax', 'breathe']
+breathe_projects = { "CK": "../docBin/xml" }
+breathe_default_project = "CK"
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+# source_suffix = ['.rst', '.md']
+source_suffix = '.rst'
+
+# The master toctree document.
+master_doc = 'index'
+
+# General information about the project.
+project = u'Composable Kernel (CK)'
+copyright = u'2018-2023, Advanced Micro Devices'
+author = u'Advanced Micro Devices'
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version.
+#version = u'0.8'
+# The full version, including alpha/beta/rc tags.
+#release = u'0.8'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = 'en'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This patterns also effect to html_static_path and html_extra_path
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'sphinx'
+
+# If true, `todo` and `todoList` produce output, else they produce nothing.
+todo_include_todos = False
+
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+# html_theme = 'alabaster'
+
+#if read_the_docs_build:
+#    html_theme = 'default'
+#else:
+import sphinx_rtd_theme
+html_theme = "sphinx_rtd_theme"
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+html_logo = "rocm_logo.png"
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+html_theme_options = {
+    'logo_only': True,
+    'display_version': True
+}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+#html_static_path = ['_static']
+
+# Custom sidebar templates, must be a dictionary that maps document names
+# to template names.
+#
+# This is required for the alabaster theme
+# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars
+# html_sidebars = {
+#     '**': [
+#         'relations.html',  # needs 'show_related': True theme option to display
+#         'searchbox.html',
+#     ]
+# }
+
+mathjax3_config = {
+'tex': {
+    'macros': {
+        'diag': '\\operatorname{diag}',
+        }
+    }
+}
+
+# -- Options for HTMLHelp output ------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'CKdoc'
+
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    #
+    'papersize': 'letterpaper',
+
+    # The font size ('10pt', '11pt' or '12pt').
+    #
+    'pointsize': '10pt',
+
+    # Additional stuff for the LaTeX preamble.
+    #
+    'preamble': r'''
+\setcounter{tocdepth}{5}
+\newcommand{\diag}{\operatorname{diag}}
+''',
+
+    # Latex figure (float) alignment
+    #
+    # 'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    (master_doc, 'CK.tex', u'Composabl Kernel (CK) Documentation',
+     u'Advanced Micro Devices', 'manual'),
+]
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    (master_doc, 'ck', u'Composable Kernel (CK) Documentation',
+     [author], 1)
+]
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    (master_doc, 'CK', u'Composable Kernel (CK) Documentation',
+     author, 'CK', 'Composable Kernel for AMD ROCm',
+     'Miscellaneous'),
+]

docs/source/dockerhub.rst

+===================
+CK docker hub
+===================
+
+`Docker hub <https://hub.docker.com/r/rocm/composable_kernel>`_
+
+-------------------------------------
+Why do I need this?
+-------------------------------------
+
+To make our lives easier and bring Composable Kernel dependencies together, we recommend using docker images.
+
+-------------------------------------
+So what is Composable Kernel?
+-------------------------------------
+
+Composable Kernel (CK) library aims to provide a programming model for writing performance critical kernels for machine learning workloads across multiple architectures including GPUs, CPUs, etc, through general purpose kernel languages, like HIP C++.
+
+To get the CK library::
+
+    git clone https://github.com/ROCmSoftwarePlatform/composable_kernel.git
+
+
+
+run a docker container::
+
+    docker run                                                            \
+    -it                                                                   \
+    --privileged                                                          \
+    --group-add sudo                                                      \
+    -w /root/workspace                                                    \
+    -v ${PATH_TO_LOCAL_WORKSPACE}:/root/workspace                         \
+    rocm/composable_kernel:ck_ub20.04_rocm5.3_release                     \
+    /bin/bash
+
+and build the CK::
+
+    mkdir build && cd build
+    # Need to specify target ID, example below is for gfx908 and gfx90a
+    cmake                                                                                             \
+    -D CMAKE_PREFIX_PATH=/opt/rocm                                                                    \
+    -D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc                                                         \
+    -D CMAKE_CXX_FLAGS="-O3"                                                                          \
+    -D CMAKE_BUILD_TYPE=Release                                                                       \
+    -D GPU_TARGETS="gfx908;gfx90a"                                                                    \
+    ..
+
+and::
+
+    make -j examples tests
+
+To run all the test cases including tests and examples run::
+
+    make test
+
+We can also run specific examples or tests like::
+
+    ./bin/example_gemm_xdl_fp16
+    ./bin/test_gemm_fp16
+
+For more details visit `CK github repo <https://github.com/ROCmSoftwarePlatform/composable_kernel>`_, `CK examples <https://github.com/ROCmSoftwarePlatform/composable_kernel/tree/develop/example)>`_, `even more CK examples <https://github.com/ROCmSoftwarePlatform/composable_kernel/tree/develop/client_example>`_.
+
+-------------------------------------
+And what is inside?
+-------------------------------------
+
+The docker images have everything you need for running CK including:
+
+* `ROCm <https://www.amd.com/en/graphics/servers-solutions-rocm>`_
+* `CMake <https://cmake.org/>`_
+* `Compiler <https://github.com/RadeonOpenCompute/llvm-project>`_
+
+-------------------------------------
+Which image is right for me?
+-------------------------------------
+
+Let's take a look at the image naming, for example "ck_ub20.04_rocm5.4_release". The image specs are:
+
+* "ck" - made for running Composable Kernel
+* "ub20.04" - based on Ubuntu 20.04
+* "rocm5.4" - ROCm platform version 5.4
+* "release" - compiler version is release
+
+So just pick the right image for your project dependencies and you're all set.
+
+-------------------------------------
+DIY starts here
+-------------------------------------
+
+If you need to customize a docker image or just can't stop tinkering, feel free to adjust the `Dockerfile <https://github.com/ROCmSoftwarePlatform/composable_kernel/blob/develop/Dockerfile>`_ for your needs.
+
+-------------------------------------
+License
+-------------------------------------
+
+CK is released under the MIT `license <https://github.com/ROCmSoftwarePlatform/composable_kernel/blob/develop/LICENSE>`_.

docs/source/index.rst

+============================
+Composable Kernel User Guide
+============================
+
+.. toctree::
+   :maxdepth: 5
+   :caption: Contents:
+   :numbered:
+
+   Linux_Install_Guide
+   tutorial_hello_world
+   dockerhub
+   Supported_Primitives_Guide
+   API_Reference_Guide
+   Contributors_Guide
+   Disclaimer
\ No newline at end of file

docs/source/tutorial_hello_world.rst

+===============
+CK Hello world
+===============
+
+-------------------------------------
+Motivation
+-------------------------------------
+
+This tutorial is aimed at engineers dealing with artificial intelligence and machine learning who would like to optimize their pipelines and squeeze every performance drop by adding Composable Kernel (CK) library to their projects. We would like to make the CK library approachable so the tutorial is not based on the latest release and doesn't have all the bleeding edge features, but it will be reproducible now and forever.
+
+During this tutorial we will have an introduction to the CK library, we will build it and run some examples and tests, so to say we will run a "Hello world" example. In future tutorials we will go in depth and breadth and get familiar with other tools and ways to integrate CK into your project.
+
+-------------------------------------
+Description
+-------------------------------------
+
+Modern AI technology solves more and more problems in all imaginable fields, but crafting fast and efficient workflows is still challenging. CK is one of the tools to make AI heavy lifting as fast and efficient as possible. CK is a collection of optimized AI operator kernels and tools to create new ones. The library has components required for majority of modern neural networks architectures including matrix multiplication, convolution, contraction, reduction, attention modules, variety of activation functions, fused operators and many more.
+
+So how do we (almost) reach the speed of light? CK acceleration abilities are based on:
+
+* Layered structure.
+* Tile-based computation model.
+* Tensor coordinate transformation.
+* Hardware acceleration use.
+* Support of low precision data types including fp16, bf16, int8 and int4.
+
+If you are excited and need more technical details and benchmarking results - read this awesome `blog post <https://community.amd.com/t5/instinct-accelerators/amd-composable-kernel-library-efficient-fused-kernels-for-ai/ba-p/553224>`_.
+
+For more details visit our `github repo <https://github.com/ROCmSoftwarePlatform/composable_kernel>`_.
+
+-------------------------------------
+Hardware targets
+-------------------------------------
+
+CK library fully supports "gfx908" and "gfx90a" GPU architectures and only some operators are supported for "gfx1030". Let's check the hardware you have at hand and decide on the target GPU architecture
+
+==========     =========
+GPU Target     AMD GPU
+==========     =========
+gfx908 	       Radeon Instinct MI100
+gfx90a 	       Radeon Instinct MI210, MI250, MI250X
+gfx1030        Radeon PRO V620, W6800, W6800X, W6800X Duo, W6900X, RX 6800, RX 6800 XT, RX 6900 XT, RX 6900 XTX, RX 6950 XT
+==========     =========
+
+There are also `cloud options <https://aws.amazon.com/ec2/instance-types/g4/>`_ you can find if you don't have an AMD GPU at hand.
+
+-------------------------------------
+Build the library
+-------------------------------------
+
+First let's clone the library and rebase to the tested version::
+
+    git clone https://github.com/ROCmSoftwarePlatform/composable_kernel.git
+    cd composable_kernel/
+    git checkout tutorial_hello_world
+
+To make our lives easier we prepared `docker images <https://hub.docker.com/r/rocm/composable_kernel>`_ with all the necessary dependencies. Pick the right image and create a container. In this tutorial we use "rocm/composable_kernel:ck_ub20.04_rocm5.3_release" image, it is based on Ubuntu 20.04, ROCm v5.3, compiler release version.
+
+If your current folder is ${HOME}, start the docker container with::
+
+    docker run  \
+    -it  \
+    --privileged  \
+    --group-add sudo  \
+    -w /root/workspace  \
+    -v ${HOME}:/root/workspace  \
+    rocm/composable_kernel:ck_ub20.04_rocm5.3_release  \
+    /bin/bash
+
+If your current folder is different from ${HOME}, adjust the line `-v ${HOME}:/root/workspace` to fit your folder structure.
+
+Inside the docker container current folder is "~/workspace", library path is "~/workspace/composable_kernel", navigate to the library::
+
+    cd composable_kernel/
+
+Create and go to the "build" directory::
+
+    mkdir build && cd build
+
+In the previous section we talked about target GPU architecture. Once you decide which one is right for you, run cmake using the right GPU_TARGETS flag::
+
+    cmake  \
+    -D CMAKE_PREFIX_PATH=/opt/rocm  \
+    -D CMAKE_CXX_COMPILER=/opt/rocm/bin/hipcc  \
+    -D CMAKE_CXX_FLAGS="-O3"  \
+    -D CMAKE_BUILD_TYPE=Release  \
+    -D BUILD_DEV=OFF  \
+    -D GPU_TARGETS="gfx908;gfx90a;gfx1030" ..
+
+If everything went well the cmake run will end up with::
+
+    -- Configuring done
+    -- Generating done
+    -- Build files have been written to: "/root/workspace/composable_kernel/build"
+
+Finally, we can build examples and tests::
+
+    make -j examples tests
+
+If everything is smooth, you'll see::
+
+    Scanning dependencies of target tests
+    [100%] Built target tests
+
+---------------------------
+Run examples and tests
+---------------------------
+
+Examples are listed as test cases as well, so we can run all examples and tests with::
+
+    ctest
+
+You can check the list of all tests by running::
+
+    ctest -N
+
+We can also run them separately, here is a separate example execution::
+
+    ./bin/example_gemm_xdl_fp16 1 1 1
+
+The arguments "1 1 1" mean that we want to run this example in the mode: verify results with CPU, initialize matrices with integers and benchmark the kernel execution. You can play around with these parameters and see how output and execution results change.
+
+If everything goes well and you have a device based on gfx908 or gfx90a architecture you should see something like::
+
+    a_m_k: dim 2, lengths {3840, 4096}, strides {4096, 1}
+    b_k_n: dim 2, lengths {4096, 4096}, strides {1, 4096}
+    c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
+    launch_and_time_kernel: grid_dim {480, 1, 1}, block_dim {256, 1, 1}
+    Warm up 1 time
+    Start running 10 times...
+    Perf: 1.10017 ms, 117.117 TFlops, 87.6854 GB/s, DeviceGemmXdl<256, 256, 128, 4, 8, 32, 32, 4, 2> NumPrefetch: 1, LoopScheduler: Default, PipelineVersion: v1
+
+Meanwhile, running it on a gfx1030 device should result in::
+
+    a_m_k: dim 2, lengths {3840, 4096}, strides {4096, 1}
+    b_k_n: dim 2, lengths {4096, 4096}, strides {1, 4096}
+    c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
+    DeviceGemmXdl<256, 256, 128, 4, 8, 32, 32, 4, 2> NumPrefetch: 1, LoopScheduler: Default, PipelineVersion: v1 does not support this problem
+
+But don't panic, some of the operators are supported on gfx1030 architecture, so you can run a separate example like::
+
+    ./bin/example_gemm_dl_fp16 1 1 1
+
+and it should result in something nice similar to::
+
+    a_m_k: dim 2, lengths {3840, 4096}, strides {1, 4096}
+    b_k_n: dim 2, lengths {4096, 4096}, strides {4096, 1}
+    c_m_n: dim 2, lengths {3840, 4096}, strides {4096, 1}
+    arg.a_grid_desc_k0_m0_m1_k1_{2048, 3840, 2}
+    arg.b_grid_desc_k0_n0_n1_k1_{2048, 4096, 2}
+    arg.c_grid_desc_m_n_{ 3840, 4096}
+    launch_and_time_kernel: grid_dim {960, 1, 1}, block_dim {256, 1, 1}
+    Warm up 1 time
+    Start running 10 times...
+    Perf: 3.65695 ms, 35.234 TFlops, 26.3797 GB/s, DeviceGemmDl<256, 128, 128, 16, 2, 4, 4, 1>
+
+Or we can run a separate test::
+
+    ctest -R test_gemm_fp16
+
+If everything goes well you should see something like::
+
+    Start 121: test_gemm_fp16
+    1/1 Test #121: test_gemm_fp16 ...................   Passed   51.81 sec
+
+    100% tests passed, 0 tests failed out of 1
+
+-----------
+Summary
+-----------
+
+In this tutorial we took the first look at the Composable Kernel library, built it on your system and ran some examples and tests. Stay tuned, in the next tutorial we will run kernels with different configs to find out the best one for your hardware and task.
+
+P.S.: Don't forget to switch out the cloud instance if you have launched one, you can find better ways to spend your money for sure!

example/01_gemm/CMakeLists.txt

@@ -38,7 +38,9 @@ add_example_executable_no_testing(example_gemm_xdl_fp64 gemm_xdl_fp64.cpp)
 add_dependencies(example_gemm_xdl example_gemm_xdl_skip_b_lds_fp16)
 add_dependencies(example_gemm_xdl example_gemm_xdl_fp64)
 
-add_custom_target(example_gemm_wmma)
-add_example_executable(example_gemm_wmma_fp16 gemm_wmma_fp16.cpp)
-add_dependencies(example_gemm_wmma example_gemm_wmma_fp16)
+if(GPU_TARGETS MATCHES "gfx1100")
+  add_custom_target(example_gemm_wmma)
+  add_example_executable(example_gemm_wmma_fp16 gemm_wmma_fp16.cpp)
+  add_dependencies(example_gemm_wmma example_gemm_wmma_fp16)
+endif()
 

example/01_gemm/gemm_wmma_fp16.cpp

@@ -19,15 +19,15 @@ using AElementOp = PassThrough;
 using BElementOp = PassThrough;
 using CElementOp = PassThrough;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
 
 // clang-format off
 using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmWmma_CShuffle
-// ######| ALayout| BLayout| CLayout|     AData|     BData|     CData|     AccData|         CShuffle|           A|           B|           C|           GEMM| Block|  MPer|  NPer| K0Per|  K1| MPer| NPer|MRepeat|NRepeat|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
-// ######|        |        |        |      Type|      Type|      Type|        Type|         DataType| Elementwise| Elementwise| Elementwise| Spacialization|  Size| Block| Block| Block|    | WMMA| WMMA|       |       |   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN|MWmmaPerWave|NWmmaPerWave|        _MBlock_MWaveMPerWmma| ScalarPerVector|
-// ######|        |        |        |          |          |          |            |                 |   Operation|   Operation|   Operation|               |      |      |      |      |    |     |     |       |       | Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|        _NBlock_NWaveNPerWmma|  _NWaveNPerWmma|
-// ######|        |        |        |          |          |          |            |                 |            |            |            |               |      |      |      |      |    |     |     |       |       |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
-         < ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp,  CElementOp,    GemmDefault,   256,   128,   256,     8,   8,   16,   16,      4,      4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,      true,           1,           1,              S<1, 32, 1,  8>,               8, 1>;
+// ######| ALayout| BLayout| CLayout|     AData|     BData|     CData|     AccData|         CShuffle|           A|           B|           C|              GEMM| Block|  MPer|  NPer| K0Per|  K1| MPer| NPer|MRepeat|NRepeat|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
+// ######|        |        |        |      Type|      Type|      Type|        Type|         DataType| Elementwise| Elementwise| Elementwise|    Spacialization|  Size| Block| Block| Block|    | WMMA| WMMA|       |       |   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN|MWmmaPerWave|NWmmaPerWave|        _MBlock_MWaveMPerWmma| ScalarPerVector|
+// ######|        |        |        |          |          |          |            |                 |   Operation|   Operation|   Operation|                  |      |      |      |      |    |     |     |       |       | Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|        _NBlock_NWaveNPerWmma|  _NWaveNPerWmma|
+// ######|        |        |        |          |          |          |            |                 |            |            |            |                  |      |      |      |      |    |     |     |       |       |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
+         < ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp,  CElementOp,    GemmMNKPadding,   256,   128,   256,     8,   8,   16,   16,      4,      4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,      true,           1,           1,              S<1, 32, 1,  8>,               8, 1>;
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::

example/02_gemm_bilinear/CMakeLists.txt

 add_example_executable(example_gemm_bilinear_xdl_fp16 gemm_bilinear_xdl_fp16.cpp)
+if(GPU_TARGETS MATCHES "gfx1100")
+    add_example_executable(example_gemm_bilinear_wmma_fp16 gemm_bilinear_wmma_fp16.cpp)
+endif()

example/02_gemm_bilinear/gemm_bilinear_wmma_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_wmma_cshuffle.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+#include "ck/library/utility/check_err.hpp"
+
+struct AlphaBetaAdd
+{
+    AlphaBetaAdd(float alpha, float beta) : alpha_(alpha), beta_(beta){};
+
+    template <typename E, typename C, typename D>
+    __host__ __device__ constexpr void operator()(E& e, const C& c, const D& d) const;
+
+    template <>
+    __host__ __device__ constexpr void operator()<ck::half_t, float, ck::half_t>(
+        ck::half_t& e, const float& c, const ck::half_t& d) const
+    {
+        e = ck::type_convert<ck::half_t>(alpha_ * c + beta_ * ck::type_convert<float>(d));
+    };
+
+    float alpha_;
+    float beta_;
+};
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ADataType        = F16;
+using BDataType        = F16;
+using AccDataType      = F32;
+using CShuffleDataType = F32;
+using DDataType        = F16;
+using EDataType        = F16;
+
+using ALayout = Row;
+using BLayout = Col;
+using DLayout = Row;
+using ELayout = Row;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = AlphaBetaAdd;
+
+static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
+
+using DeviceOpInstance =
+    ck::tensor_operation::device::DeviceGemmMultipleD_Wmma_CShuffle<ALayout,
+                                                                    BLayout,
+                                                                    ck::Tuple<DLayout>,
+                                                                    ELayout,
+                                                                    ADataType,
+                                                                    BDataType,
+                                                                    ck::Tuple<DDataType>,
+                                                                    EDataType,
+                                                                    AccDataType,
+                                                                    CShuffleDataType,
+                                                                    AElementOp,
+                                                                    BElementOp,
+                                                                    CDEElementOp,
+                                                                    GemmSpec,
+                                                                    256,
+                                                                    128,
+                                                                    256,
+                                                                    8,
+                                                                    8,
+                                                                    16,
+                                                                    16,
+                                                                    4,
+                                                                    4,
+                                                                    S<4, 64, 1>,
+                                                                    S<1, 0, 2>,
+                                                                    S<1, 0, 2>,
+                                                                    2,
+                                                                    8,
+                                                                    8,
+                                                                    true,
+                                                                    S<4, 64, 1>,
+                                                                    S<1, 0, 2>,
+                                                                    S<1, 0, 2>,
+                                                                    2,
+                                                                    8,
+                                                                    8,
+                                                                    true,
+                                                                    1,
+                                                                    1,
+                                                                    S<1, 32, 1, 8>,
+                                                                    8>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = true;
+
+    // GEMM shape
+    ck::index_t M = 3840;
+    ck::index_t N = 4096;
+    ck::index_t K = 4096;
+
+    ck::index_t StrideA = 4096;
+    ck::index_t StrideB = 4096;
+    ck::index_t StrideD = 4096;
+    ck::index_t StrideE = 4096;
+
+    float alpha = 1.0f;
+    float beta  = 1.0f;
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else if(argc == 6)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        alpha = std::stof(argv[4]);
+        beta  = std::stof(argv[5]);
+    }
+    else if(argc == 13)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        M = std::stoi(argv[4]);
+        N = std::stoi(argv[5]);
+        K = std::stoi(argv[6]);
+
+        StrideA = std::stoi(argv[7]);
+        StrideB = std::stoi(argv[8]);
+        StrideD = std::stoi(argv[9]);
+        StrideE = std::stoi(argv[10]);
+
+        alpha = std::stof(argv[11]);
+        beta  = std::stof(argv[12]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=no, 1=yes)\n");
+        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD, StrideE, alpha, "
+               "beta\n");
+        exit(0);
+    }
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+    Tensor<DDataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, DLayout{}));
+    Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
+
+    std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
+    std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
+    std::cout << "d_m_n: " << d_m_n.mDesc << std::endl;
+    std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        d_m_n.GenerateTensorValue(GeneratorTensor_2<DDataType>{-5, 5});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        d_m_n.GenerateTensorValue(GeneratorTensor_3<DDataType>{-0.5, 0.5});
+    }
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem d_device_buf(sizeof(DDataType) * d_m_n.mDesc.GetElementSpaceSize());
+    DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a_m_k.mData.data());
+    b_device_buf.ToDevice(b_k_n.mData.data());
+    d_device_buf.ToDevice(d_m_n.mData.data());
+    e_device_buf.ToDevice(e_m_n_device_result.mData.data());
+
+    auto a_element_op   = AElementOp{};
+    auto b_element_op   = BElementOp{};
+    auto cde_element_op = CDEElementOp{alpha, beta};
+
+    // do GEMM
+    auto device_op = DeviceOpInstance{};
+    auto invoker   = device_op.MakeInvoker();
+    auto argument =
+        device_op.MakeArgument(a_device_buf.GetDeviceBuffer(),
+                               b_device_buf.GetDeviceBuffer(),
+                               std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
+                               e_device_buf.GetDeviceBuffer(),
+                               M,
+                               N,
+                               K,
+                               StrideA,
+                               StrideB,
+                               std::array<ck::index_t, 1>{StrideD},
+                               StrideE,
+                               a_element_op,
+                               b_element_op,
+                               cde_element_op);
+
+    if(!device_op.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_gemm with the specified compilation parameters does "
+            "not support this GEMM problem");
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop = std::size_t(2) * M * N * K;
+    std::size_t num_btype =
+        sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;
+
+    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+    float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
+              << std::endl;
+
+    e_device_buf.FromDevice(e_m_n_device_result.mData.data());
+
+    if(do_verification)
+    {
+        Tensor<CShuffleDataType> c_m_n({M, N});
+
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                                BDataType,
+                                                                                CShuffleDataType,
+                                                                                AccDataType,
+                                                                                AElementOp,
+                                                                                BElementOp,
+                                                                                PassThrough>;
+        auto ref_gemm               = ReferenceGemmInstance{};
+        auto ref_invoker            = ref_gemm.MakeInvoker();
+
+        auto ref_argument =
+            ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        for(int m = 0; m < M; ++m)
+        {
+            for(int n = 0; n < N; ++n)
+            {
+                cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d_m_n(m, n));
+            }
+        }
+
+        e_device_buf.FromDevice(e_m_n_device_result.mData.data());
+
+        return ck::utils::check_err(e_m_n_device_result, e_m_n_host_result) ? 0 : 1;
+    }
+
+    return 0;
+}

example/04_gemm_add_add_fastgelu/common.hpp

@@ -62,7 +62,7 @@ struct ExecutionConfig final
 };
 
 inline bool
-parse_cmd_args(int argc, char* argv[], ProblemSize& problem_size, ExecutionConfig config)
+parse_cmd_args(int argc, char* argv[], ProblemSize& problem_size, ExecutionConfig& config)
 {
     if(argc == 1)
     {

example/04_gemm_add_add_fastgelu/gemm_add_add_fastgelu_xdl_bf16.cpp

@@ -7,10 +7,11 @@ using ADataType        = BF16;
 using BDataType        = BF16;
 using AccDataType      = F32;
 using CShuffleDataType = F32;
-using D0DataType       = BF16;
-using D1DataType       = BF16;
-using DsDataType       = ck::Tuple<D0DataType, D1DataType>;
-using EDataType        = BF16;
+using CDataType  = F32; // C matrix doesn't exsit in GPU memory, this is used for host verification
+using D0DataType = BF16;
+using D1DataType = BF16;
+using DsDataType = ck::Tuple<D0DataType, D1DataType>;
+using EDataType  = BF16;
 
 using ALayout  = Row;
 using BLayout  = Col;
@@ -36,7 +37,7 @@ using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_C
 
 using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
                                                                         BDataType,
-                                                                        AccDataType,
+                                                                        CDataType,
                                                                         AccDataType,
                                                                         AElementOp,
                                                                         BElementOp,

example/04_gemm_add_add_fastgelu/gemm_add_add_fastgelu_xdl_fp16.cpp

@@ -7,10 +7,11 @@ using ADataType        = F16;
 using BDataType        = F16;
 using AccDataType      = F32;
 using CShuffleDataType = F32;
-using D0DataType       = F16;
-using D1DataType       = F16;
-using DsDataType       = ck::Tuple<D0DataType, D1DataType>;
-using EDataType        = F16;
+using CDataType  = F32; // C matrix doesn't exsit in GPU memory, this is used for host verification
+using D0DataType = F16;
+using D1DataType = F16;
+using DsDataType = ck::Tuple<D0DataType, D1DataType>;
+using EDataType  = F16;
 
 using ALayout  = Row;
 using BLayout  = Col;
@@ -36,7 +37,7 @@ using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_C
 
 using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
                                                                         BDataType,
-                                                                        AccDataType,
+                                                                        CDataType,
                                                                         AccDataType,
                                                                         AElementOp,
                                                                         BElementOp,

example/04_gemm_add_add_fastgelu/gemm_add_add_fastgelu_xdl_fp32.cpp

-// SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "common.hpp"
@@ -7,10 +6,11 @@ using ADataType        = F32;
 using BDataType        = F32;
 using AccDataType      = F32;
 using CShuffleDataType = F32;
-using D0DataType       = F32;
-using D1DataType       = F32;
-using DsDataType       = ck::Tuple<D0DataType, D1DataType>;
-using EDataType        = F32;
+using CDataType  = F32; // C matrix doesn't exsit in GPU memory, this is used for host verification
+using D0DataType = F32;
+using D1DataType = F32;
+using DsDataType = ck::Tuple<D0DataType, D1DataType>;
+using EDataType  = F32;
 
 using ALayout  = Row;
 using BLayout  = Col;
@@ -36,7 +36,7 @@ using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_C
 
 using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
                                                                         BDataType,
-                                                                        AccDataType,
+                                                                        CDataType,
                                                                         AccDataType,
                                                                         AElementOp,
                                                                         BElementOp,

example/04_gemm_add_add_fastgelu/gemm_add_add_fastgelu_xdl_int4.cpp

@@ -11,10 +11,11 @@ using ADataType        = I4;
 using BDataType        = I4;
 using AccDataType      = I32;
 using CShuffleDataType = I32;
-using D0DataType       = I4;
-using D1DataType       = I4;
-using DsDataType       = ck::Tuple<D0DataType, D1DataType>;
-using EDataType        = I4;
+using CDataType  = I32; // C matrix doesn't exsit in GPU memory, this is used for host verification
+using D0DataType = I4;
+using D1DataType = I4;
+using DsDataType = ck::Tuple<D0DataType, D1DataType>;
+using EDataType  = I4;
 
 using KernelADataType  = I8;
 using KernelBDataType  = I8;
@@ -47,7 +48,7 @@ using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_C
 
 using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
                                                                         BDataType,
-                                                                        AccDataType,
+                                                                        CDataType,
                                                                         AccDataType,
                                                                         AElementOp,
                                                                         BElementOp,

example/04_gemm_add_add_fastgelu/gemm_add_add_fastgelu_xdl_int8.cpp

@@ -7,10 +7,11 @@ using ADataType        = I8;
 using BDataType        = I8;
 using AccDataType      = I32;
 using CShuffleDataType = I32;
-using D0DataType       = I8;
-using D1DataType       = I8;
-using DsDataType       = ck::Tuple<D0DataType, D1DataType>;
-using EDataType        = I8;
+using CDataType  = I32; // C matrix doesn't exsit in GPU memory, this is used for host verification
+using D0DataType = I8;
+using D1DataType = I8;
+using DsDataType = ck::Tuple<D0DataType, D1DataType>;
+using EDataType  = I8;
 
 using ALayout  = Row;
 using BLayout  = Col;
@@ -36,7 +37,7 @@ using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_C
 
 using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
                                                                         BDataType,
-                                                                        AccDataType,
+                                                                        CDataType,
                                                                         AccDataType,
                                                                         AElementOp,
                                                                         BElementOp,

example/04_gemm_add_add_fastgelu/run_gemm_add_add_fastgelu_example.inc

@@ -124,7 +124,7 @@ bool run_gemm_add_add_fastgelu(const ProblemSize& problem_size, const ExecutionC
 
     if(config.do_verification)
     {
-        Tensor<AccDataType> c_m_n({M, N});
+        Tensor<CDataType> c_m_n({M, N});
 
         auto ref_gemm    = ReferenceGemmInstance{};
         auto ref_invoker = ref_gemm.MakeInvoker();

example/20_grouped_conv_bwd_weight/CMakeLists.txt

@@ -6,3 +6,9 @@ add_example_executable(example_grouped_conv_bwd_weight_xdl_bf16 grouped_conv_bwd
 
 add_dependencies(example_grouped_conv_bwd_weight example_grouped_conv_bwd_weight_xdl_fp16
                                                  example_grouped_conv_bwd_weight_xdl_bf16)
+
+add_custom_target(example_grouped_conv_bwd_weight_dl)
+
+add_example_executable(example_grouped_conv_bwd_weight_dl_fp16 grouped_conv_bwd_weight_dl_fp16.cpp)
+
+add_dependencies(example_grouped_conv_bwd_weight_dl example_grouped_conv_bwd_weight_dl_fp16)

example/20_grouped_conv_bwd_weight/common.hpp

@@ -9,7 +9,6 @@
 
 #include "ck/ck.hpp"
 #include "ck/tensor_operation/gpu/device/convolution_backward_weight_specialization.hpp"
-#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_xdl_cshuffle.hpp"
 #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"