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"tests/vscode:/vscode.git/clone" did not exist on "88735249da94266a433368d2b899e87dc33446c9"
Commit 185af92b authored by ltqin's avatar ltqin
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Merge branch 'develop' into lib_gemm_softmax_gemm_type

parents 5f4a0f73 8bb2bb4a
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.github/dependabot.yml 0 → 100644
View file @ 185af92b
# To get started with Dependabot version updates, you'll need to specify which
# package ecosystems to update and where the package manifests are located.
# Please see the documentation for all configuration options:
# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
version: 2
updates:
- package-ecosystem: "pip" # See documentation for possible values
directory: "/docs/.sphinx" # Location of package manifests
open-pull-requests-limit: 10
schedule:
interval: "daily"
Dockerfile
View file @ 185af92b
FROM ubuntu:20.04 FROM ubuntu:20.04
ARG ROCMVERSION=5.3 ARG ROCMVERSION=5.6
ARG compiler_version="release" ARG compiler_version=""
ARG compiler_commit="" ARG compiler_commit=""
RUN set -xe RUN set -xe
ARG DEB_ROCM_REPO=http://repo.radeon.com/rocm/apt/.apt_$ROCMVERSION/ ARG DEB_ROCM_REPO=http://repo.radeon.com/rocm/apt/.apt_$ROCMVERSION/
RUN useradd -rm -d /home/jenkins -s /bin/bash -u 1004 jenkins RUN useradd -rm -d /home/jenkins -s /bin/bash -u 1004 jenkins
RUN useradd -rm -d /home/manitera -s /bin/bash -u 1002 manitera
# Add rocm repository # Add rocm repository
RUN apt-get update RUN apt-get update
RUN apt-get install -y wget gnupg RUN apt-get install -y wget gnupg curl
RUN wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add - RUN --mount=type=ssh if [ "$ROCMVERSION" != "5.6"]; then \
RUN sh -c "echo deb [arch=amd64] $DEB_ROCM_REPO ubuntu main > /etc/apt/sources.list.d/rocm.list" wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add - && \
sh -c "echo deb [arch=amd64] $DEB_ROCM_REPO ubuntu main > /etc/apt/sources.list.d/rocm.list"; \
else sh -c "wget http://artifactory-cdn.amd.com/artifactory/list/amdgpu-deb/amd-nonfree-radeon_20.04-1_all.deb" && \
apt update && apt-get install -y ./amd-nonfree-radeon_20.04-1_all.deb && \
amdgpu-repo --amdgpu-build=1567752 --rocm-build=compute-rocm-dkms-no-npi-hipclang/11914 && \
DEBIAN_FRONTEND=noninteractive amdgpu-install -y --usecase=rocm ; \
fi
RUN wget --no-check-certificate -qO - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | apt-key add - RUN wget --no-check-certificate -qO - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | apt-key add -
RUN sh -c "echo deb http://mirrors.kernel.org/ubuntu focal main universe | tee -a /etc/apt/sources.list" RUN sh -c "echo deb http://mirrors.kernel.org/ubuntu focal main universe | tee -a /etc/apt/sources.list"
RUN curl -fsSL https://repo.radeon.com/rocm/rocm.gpg.key | gpg --dearmor -o /etc/apt/trusted.gpg.d/rocm-keyring.gpg
# Install dependencies # Install dependencies
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
apt-utils \ apt-utils \
build-essential \ build-essential \
ccache \ ccache \
cmake-data \
cmake \ cmake \
curl \
git \ git \
hip-rocclr \ hip-rocclr \
jq \ jq \
...@@ -45,6 +49,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow- ...@@ -45,6 +49,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
rocm-device-libs \ rocm-device-libs \
rocm-cmake \ rocm-cmake \
vim \ vim \
nano \
zlib1g-dev \ zlib1g-dev \
openssh-server \ openssh-server \
clang-format-10 \ clang-format-10 \
...@@ -52,6 +57,17 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow- ...@@ -52,6 +57,17 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
apt-get clean && \ apt-get clean && \
rm -rf /var/lib/apt/lists/* rm -rf /var/lib/apt/lists/*
#Install latest version of cmake
RUN apt purge --auto-remove -y cmake
RUN apt update
RUN apt install -y software-properties-common lsb-release
RUN apt clean all
RUN wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null
RUN apt-add-repository "deb https://apt.kitware.com/ubuntu/ $(lsb_release -cs) main"
RUN apt install -y kitware-archive-keyring
RUN rm /etc/apt/trusted.gpg.d/kitware.gpg
RUN apt install -y cmake
# Setup ubsan environment to printstacktrace # Setup ubsan environment to printstacktrace
RUN ln -s /usr/bin/llvm-symbolizer-3.8 /usr/local/bin/llvm-symbolizer RUN ln -s /usr/bin/llvm-symbolizer-3.8 /usr/local/bin/llvm-symbolizer
ENV UBSAN_OPTIONS=print_stacktrace=1 ENV UBSAN_OPTIONS=print_stacktrace=1
...@@ -87,12 +103,7 @@ ENV compiler_commit=$compiler_commit ...@@ -87,12 +103,7 @@ ENV compiler_commit=$compiler_commit
RUN sh -c "echo compiler version = '$compiler_version'" RUN sh -c "echo compiler version = '$compiler_version'"
RUN sh -c "echo compiler commit = '$compiler_commit'" RUN sh -c "echo compiler commit = '$compiler_commit'"
RUN --mount=type=ssh if [ "$compiler_version" = "amd-stg-open" ]; then \ RUN --mount=type=ssh if [ "$compiler_version" = "amd-stg-open" ] && [ "$compiler_commit" = "" ]; then \
sed -i '/$HIP_CLANG_TARGET = chomp($HIP_CLANG_TARGET);/c\ chomp($HIP_CLANG_TARGET);' /opt/rocm/hip/bin/hipcc.pl && \
sed -i '/$HIP_CLANG_TARGET = chomp($HIP_CLANG_TARGET);/c\ chomp($HIP_CLANG_TARGET);' /opt/rocm/bin/hipcc.pl; \
fi
RUN --mount=type=ssh if [ "$compiler_version" != "release" ] && [ "$compiler_commit" = "" ]; then \
git clone -b "$compiler_version" https://github.com/RadeonOpenCompute/llvm-project.git && \ git clone -b "$compiler_version" https://github.com/RadeonOpenCompute/llvm-project.git && \
cd llvm-project && mkdir build && cd build && \ cd llvm-project && mkdir build && cd build && \
cmake -DCMAKE_INSTALL_PREFIX=/opt/rocm/llvm -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=1 -DLLVM_TARGETS_TO_BUILD="AMDGPU;X86" -DLLVM_ENABLE_PROJECTS="clang;lld;compiler-rt" ../llvm && \ cmake -DCMAKE_INSTALL_PREFIX=/opt/rocm/llvm -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=1 -DLLVM_TARGETS_TO_BUILD="AMDGPU;X86" -DLLVM_ENABLE_PROJECTS="clang;lld;compiler-rt" ../llvm && \
...@@ -100,7 +111,7 @@ RUN --mount=type=ssh if [ "$compiler_version" != "release" ] && [ "$compiler_com ...@@ -100,7 +111,7 @@ RUN --mount=type=ssh if [ "$compiler_version" != "release" ] && [ "$compiler_com
else echo "using the release compiler"; \ else echo "using the release compiler"; \
fi fi
RUN --mount=type=ssh if [ "$compiler_version" != "release" ] && [ "$compiler_commit" != "" ]; then \ RUN --mount=type=ssh if [ "$compiler_version" = "amd-stg-open" ] && [ "$compiler_commit" != "" ]; then \
git clone -b "$compiler_version" https://github.com/RadeonOpenCompute/llvm-project.git && \ git clone -b "$compiler_version" https://github.com/RadeonOpenCompute/llvm-project.git && \
cd llvm-project && git checkout "$compiler_commit" && echo "checking out commit $compiler_commit" && mkdir build && cd build && \ cd llvm-project && git checkout "$compiler_commit" && echo "checking out commit $compiler_commit" && mkdir build && cd build && \
cmake -DCMAKE_INSTALL_PREFIX=/opt/rocm/llvm -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=1 -DLLVM_TARGETS_TO_BUILD="AMDGPU;X86" -DLLVM_ENABLE_PROJECTS="clang;lld;compiler-rt" ../llvm && \ cmake -DCMAKE_INSTALL_PREFIX=/opt/rocm/llvm -DCMAKE_BUILD_TYPE=Release -DLLVM_ENABLE_ASSERTIONS=1 -DLLVM_TARGETS_TO_BUILD="AMDGPU;X86" -DLLVM_ENABLE_PROJECTS="clang;lld;compiler-rt" ../llvm && \
......
Jenkinsfile
View file @ 185af92b
...@@ -19,12 +19,33 @@ def runShell(String command){ ...@@ -19,12 +19,33 @@ def runShell(String command){
def getDockerImageName(){ def getDockerImageName(){
def img def img
if (params.COMPILER_COMMIT == ""){ if (params.ROCMVERSION != "5.5" && params.ROCMVERSION != "5.6"){
img = "${env.CK_DOCKERHUB}:ck_ub20.04_rocm${params.ROCMVERSION}_${params.COMPILER_VERSION}" if (params.COMPILER_VERSION == "") {
img = "${env.CK_DOCKERHUB}:ck_ub20.04_rocm${params.ROCMVERSION}"
}
else{
if (params.COMPILER_COMMIT == ""){
img = "${env.CK_DOCKERHUB}:ck_ub20.04_rocm${params.ROCMVERSION}_${params.COMPILER_VERSION}"
}
else{
def commit = "${params.COMPILER_COMMIT}"[0..6]
img = "${env.CK_DOCKERHUB}:ck_ub20.04_rocm${params.ROCMVERSION}_${params.COMPILER_VERSION}_${commit}"
}
}
} }
else{ else{
def commit = "${params.COMPILER_COMMIT}"[0..6] if (params.COMPILER_VERSION == "") {
img = "${env.CK_DOCKERHUB}:ck_ub20.04_rocm${params.ROCMVERSION}_${params.COMPILER_VERSION}_${commit}" img = "${env.CK_DOCKERHUB_PRIVATE}:ck_ub20.04_rocm${params.ROCMVERSION}"
}
else{
if (params.COMPILER_COMMIT == ""){
img = "${env.CK_DOCKERHUB_PRIVATE}:ck_ub20.04_rocm${params.ROCMVERSION}_${params.COMPILER_VERSION}"
}
else{
def commit = "${params.COMPILER_COMMIT}"[0..6]
img = "${env.CK_DOCKERHUB_PRIVATE}:ck_ub20.04_rocm${params.ROCMVERSION}_${params.COMPILER_VERSION}_${commit}"
}
}
} }
return img return img
} }
...@@ -49,11 +70,11 @@ def build_compiler(){ ...@@ -49,11 +70,11 @@ def build_compiler(){
compiler = '/opt/rocm/bin/hipcc' compiler = '/opt/rocm/bin/hipcc'
} }
else{ else{
if (params.COMPILER_VERSION == "release"){ if (params.COMPILER_VERSION == "amd-stg-open" || params.COMPILER_COMMIT != ""){
compiler = "/opt/rocm/llvm/bin/clang++" compiler = "/llvm-project/build/bin/clang++"
} }
else{ else{
compiler = "/llvm-project/build/bin/clang++" compiler = "/opt/rocm/llvm/bin/clang++"
} }
} }
return compiler return compiler
...@@ -232,7 +253,7 @@ def buildHipClangJob(Map conf=[:]){ ...@@ -232,7 +253,7 @@ def buildHipClangJob(Map conf=[:]){
dockerOpts = dockerOpts + " --env HSA_XNACK=1 " dockerOpts = dockerOpts + " --env HSA_XNACK=1 "
} }
def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg compiler_version='${params.COMPILER_VERSION}' --build-arg compiler_commit='${params.COMPILER_COMMIT}' --build-arg ROCMVERSION='${params.ROCMVERSION}' " def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg compiler_version='${params.COMPILER_VERSION}' --build-arg compiler_commit='${params.COMPILER_COMMIT}' --build-arg ROCMVERSION='${params.ROCMVERSION}' "
if (params.COMPILER_VERSION != "release"){ if (params.COMPILER_VERSION == "amd-stg-open" || params.COMPILER_COMMIT != ""){
dockerOpts = dockerOpts + " --env HIP_CLANG_PATH='/llvm-project/build/bin' " dockerOpts = dockerOpts + " --env HIP_CLANG_PATH='/llvm-project/build/bin' "
} }
...@@ -287,7 +308,7 @@ def runCKProfiler(Map conf=[:]){ ...@@ -287,7 +308,7 @@ def runCKProfiler(Map conf=[:]){
dockerOpts = dockerOpts + " --env HSA_XNACK=1 " dockerOpts = dockerOpts + " --env HSA_XNACK=1 "
} }
def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg compiler_version='${params.COMPILER_VERSION}' --build-arg compiler_commit='${params.COMPILER_COMMIT}' --build-arg ROCMVERSION='${params.ROCMVERSION}' " def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg compiler_version='${params.COMPILER_VERSION}' --build-arg compiler_commit='${params.COMPILER_COMMIT}' --build-arg ROCMVERSION='${params.ROCMVERSION}' "
if (params.COMPILER_VERSION != "release"){ if (params.COMPILER_VERSION == "amd-stg-open" || params.COMPILER_COMMIT != ""){
dockerOpts = dockerOpts + " --env HIP_CLANG_PATH='/llvm-project/build/bin' " dockerOpts = dockerOpts + " --env HIP_CLANG_PATH='/llvm-project/build/bin' "
} }
...@@ -420,7 +441,7 @@ def Build_CK(Map conf=[:]){ ...@@ -420,7 +441,7 @@ def Build_CK(Map conf=[:]){
dockerOpts = dockerOpts + " --env HSA_XNACK=1 " dockerOpts = dockerOpts + " --env HSA_XNACK=1 "
} }
def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg compiler_version='${params.COMPILER_VERSION}' --build-arg compiler_commit='${params.COMPILER_COMMIT}' --build-arg ROCMVERSION='${params.ROCMVERSION}' " def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg compiler_version='${params.COMPILER_VERSION}' --build-arg compiler_commit='${params.COMPILER_COMMIT}' --build-arg ROCMVERSION='${params.ROCMVERSION}' "
if (params.COMPILER_VERSION != "release"){ if (params.COMPILER_VERSION == "amd-stg-open" || params.COMPILER_COMMIT != ""){
dockerOpts = dockerOpts + " --env HIP_CLANG_PATH='/llvm-project/build/bin' " dockerOpts = dockerOpts + " --env HIP_CLANG_PATH='/llvm-project/build/bin' "
} }
...@@ -576,7 +597,7 @@ def process_results(Map conf=[:]){ ...@@ -576,7 +597,7 @@ def process_results(Map conf=[:]){
//launch develop branch daily at 23:00 UT in FULL_QA mode and at 19:00 UT with latest staging compiler version //launch develop branch daily at 23:00 UT in FULL_QA mode and at 19:00 UT with latest staging compiler version
CRON_SETTINGS = BRANCH_NAME == "develop" ? '''0 23 * * * % RUN_FULL_QA=true CRON_SETTINGS = BRANCH_NAME == "develop" ? '''0 23 * * * % RUN_FULL_QA=true
0 21 * * * % COMPILER_VERSION=release;COMPILER_COMMIT= 0 21 * * * % ROCMVERSION=5.4.3;COMPILER_VERSION=release;COMPILER_COMMIT=
0 19 * * * % BUILD_DOCKER=true;COMPILER_VERSION=amd-stg-open;COMPILER_COMMIT=''' : "" 0 19 * * * % BUILD_DOCKER=true;COMPILER_VERSION=amd-stg-open;COMPILER_COMMIT=''' : ""
pipeline { pipeline {
...@@ -594,16 +615,16 @@ pipeline { ...@@ -594,16 +615,16 @@ pipeline {
description: "Force building docker image (default: false), set to true if docker image needs to be updated.") description: "Force building docker image (default: false), set to true if docker image needs to be updated.")
string( string(
name: 'ROCMVERSION', name: 'ROCMVERSION',
defaultValue: '5.4.3', defaultValue: '5.6',
description: 'Specify which ROCM version to use: 5.4.3 (default).') description: 'Specify which ROCM version to use: 5.6 (default).')
string( string(
name: 'COMPILER_VERSION', name: 'COMPILER_VERSION',
defaultValue: 'amd-stg-open', defaultValue: '',
description: 'Specify which version of compiler to use: ck-9110, release, or amd-stg-open (default).') description: 'Specify which version of compiler to use: release, amd-stg-open, or leave blank (default).')
string( string(
name: 'COMPILER_COMMIT', name: 'COMPILER_COMMIT',
defaultValue: '5541927df00eabd6a110180170eca7785d436ee3', defaultValue: '',
description: 'Specify which commit of compiler branch to use: leave empty to use the latest commit, or use 5541927df00eabd6a110180170eca7785d436ee3 (default) commit of amd-stg-open branch.') description: 'Specify which commit of compiler branch to use: leave blank to use the latest commit, or use 5541927df00eabd6a110180170eca7785d436ee3 (default) commit of amd-stg-open branch.')
string( string(
name: 'BUILD_COMPILER', name: 'BUILD_COMPILER',
defaultValue: 'hipcc', defaultValue: 'hipcc',
......
client_example/07_grouped_convnd_fwd/grouped_conv2d_fwd.cpp
View file @ 185af92b
...@@ -17,22 +17,22 @@ using InDataType = ck::half_t; ...@@ -17,22 +17,22 @@ using InDataType = ck::half_t;
using WeiDataType = ck::half_t; using WeiDataType = ck::half_t;
using OutDataType = ck::half_t; using OutDataType = ck::half_t;
using InLayout = ck::tensor_layout::convolution::GNHWC; using InLayout = ck::tensor_layout::convolution::NHWGC;
using WeiLayout = ck::tensor_layout::convolution::GKYXC; using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using OutLayout = ck::tensor_layout::convolution::GNHWK; using OutLayout = ck::tensor_layout::convolution::NHWGK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr ck::index_t NumDimSpatial = 2; static constexpr ck::index_t NumDimSpatial = 2;
static constexpr ck::index_t G = 32; static constexpr ck::index_t G = 32;
static constexpr ck::index_t N = 256; static constexpr ck::index_t N = 256; // batch size
static constexpr ck::index_t K = 192; static constexpr ck::index_t K = 64; // output channel
static constexpr ck::index_t C = 192; static constexpr ck::index_t C = 32; // input channel (per group)
static constexpr ck::index_t Y = 3; static constexpr ck::index_t Y = 3; // filter H
static constexpr ck::index_t X = 3; static constexpr ck::index_t X = 3; // filter W
static constexpr ck::index_t Hi = 28; static constexpr ck::index_t Hi = 28; // input H
static constexpr ck::index_t Wi = 28; static constexpr ck::index_t Wi = 28; // input W
static constexpr ck::index_t Ho = 28; static constexpr ck::index_t Ho = 28; // output H
static constexpr ck::index_t Wo = 28; static constexpr ck::index_t Wo = 28; // output W
struct SimpleDeviceMem struct SimpleDeviceMem
{ {
...@@ -52,50 +52,24 @@ struct SimpleDeviceMem ...@@ -52,50 +52,24 @@ struct SimpleDeviceMem
int main() int main()
{ {
std::array<ck::index_t, NumDimSpatial + 3> in_lengths{G, N, Hi, Wi, C}; // We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space
std::array<ck::index_t, NumDimSpatial + 3> in_strides{0, 0, 0, 0, 1}; // However, CK's API only accept length and stride with order of GNCHW/GKCYX/GNCHW
// Hence, we need to adjust the order of stride
std::array<ck::index_t, NumDimSpatial + 3> wei_lengths{G, K, Y, X, C}; std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
std::array<ck::index_t, NumDimSpatial + 3> wei_strides{0, 0, 0, 0, 1}; std::array<ck::index_t, 5> in_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
std::array<ck::index_t, 5> wei_lengths{G, K, C, Y, X};
std::array<ck::index_t, NumDimSpatial + 3> out_lengths{G, N, Ho, Wo, K}; std::array<ck::index_t, 5> wei_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
std::array<ck::index_t, NumDimSpatial + 3> out_strides{0, 0, 0, 0, 1}; std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> out_strides{C, Ho * Wo * G * C, 1, Wo * G * C, G * C};
std::partial_sum(rbegin(in_lengths),
std::prev(rend(in_lengths)),
std::next(rbegin(in_strides)),
std::multiplies<>{});
std::partial_sum(rbegin(wei_lengths),
std::prev(rend(wei_lengths)),
std::next(rbegin(wei_strides)),
std::multiplies<>{});
std::partial_sum(rbegin(out_lengths),
std::prev(rend(out_lengths)),
std::next(rbegin(out_strides)),
std::multiplies<>{});
// transpose GNHWC/GKYXC/GNHWK to GNCHW/GKCYX/GNCHW
std::rotate(
rbegin(in_lengths), std::next(rbegin(in_lengths)), std::next(rbegin(in_lengths), 3));
std::rotate(
rbegin(in_strides), std::next(rbegin(in_strides)), std::next(rbegin(in_strides), 3));
std::rotate(
rbegin(wei_lengths), std::next(rbegin(wei_lengths)), std::next(rbegin(wei_lengths), 3));
std::rotate(
rbegin(wei_strides), std::next(rbegin(wei_strides)), std::next(rbegin(wei_strides), 3));
std::rotate(
rbegin(out_lengths), std::next(rbegin(out_lengths)), std::next(rbegin(out_lengths), 3));
std::rotate(
rbegin(out_strides), std::next(rbegin(out_strides)), std::next(rbegin(out_strides), 3));
std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1}; std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1};
std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1}; std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1};
std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1}; std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1};
std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1}; std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1};
SimpleDeviceMem in(sizeof(InDataType) * G * N * Hi * Wi * C); SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Y * X * C); SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Y * X * C);
SimpleDeviceMem out(sizeof(OutDataType) * G * N * Ho * Wo * K); SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * G * K);
using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial, using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial,
InLayout, InLayout,
...@@ -155,9 +129,9 @@ int main() ...@@ -155,9 +129,9 @@ int main()
float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true}); float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t flop = std::size_t(2) * G * N * K * C * Ho * Wo * Y * X; std::size_t flop = std::size_t(2) * G * N * K * C * Ho * Wo * Y * X;
std::size_t num_bytes = sizeof(InDataType) * G * N * Hi * Wi * C + std::size_t num_bytes = sizeof(InDataType) * N * Hi * Wi * G * C +
sizeof(WeiDataType) * G * K * Y * X * C + sizeof(WeiDataType) * G * K * Y * X * C +
sizeof(OutDataType) * G * N * Ho * Wo * K; sizeof(OutDataType) * N * Ho * Wo * G * K;
float tflops = static_cast<float>(flop) / 1.E9 / avg_time; float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
float gb_per_sec = num_bytes / 1.E6 / avg_time; float gb_per_sec = num_bytes / 1.E6 / avg_time;
......
client_example/09_quantization/conv2d_fwd_bias_relu_perchannel_quantization.cpp
View file @ 185af92b
...@@ -17,26 +17,26 @@ using BiasDataType = int32_t; ...@@ -17,26 +17,26 @@ using BiasDataType = int32_t;
using RequantScaleDataType = float; using RequantScaleDataType = float;
using OutDataType = int8_t; using OutDataType = int8_t;
using InLayout = ck::tensor_layout::convolution::GNHWC; using InLayout = ck::tensor_layout::convolution::NHWGC;
using WeiLayout = ck::tensor_layout::convolution::GKYXC; using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using BiasLayout = ck::tensor_layout::convolution::G_K; using BiasLayout = ck::tensor_layout::convolution::G_K;
using RequantScaleLayout = ck::tensor_layout::convolution::G_K; using RequantScaleLayout = ck::tensor_layout::convolution::G_K;
using OutLayout = ck::tensor_layout::convolution::GNHWK; using OutLayout = ck::tensor_layout::convolution::NHWGK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ActivationOp = ck::tensor_operation::element_wise::Relu; using ActivationOp = ck::tensor_operation::element_wise::Relu;
using OutElementOp = ck::tensor_operation::element_wise::Add_Activation_Mul2_Clamp<ActivationOp>; using OutElementOp = ck::tensor_operation::element_wise::Add_Activation_Mul2_Clamp<ActivationOp>;
static constexpr ck::index_t NumDimSpatial = 2; static constexpr ck::index_t NumDimSpatial = 2;
static constexpr ck::index_t G = 1; static constexpr ck::index_t G = 4;
static constexpr ck::index_t N = 4; // batch size static constexpr ck::index_t N = 4; // batch size
static constexpr ck::index_t K = 64; // output channel static constexpr ck::index_t K = 32; // output channel
static constexpr ck::index_t C = 192; // input channel static constexpr ck::index_t C = 64; // input channel (per group)
static constexpr ck::index_t Y = 3; // filter H static constexpr ck::index_t Y = 3; // filter H
static constexpr ck::index_t X = 3; // filter W static constexpr ck::index_t X = 3; // filter W
static constexpr ck::index_t Hi = 71; // input H static constexpr ck::index_t Hi = 71; // input H
static constexpr ck::index_t Wi = 71; // input W static constexpr ck::index_t Wi = 71; // input W
static constexpr ck::index_t Ho = 36; // output H static constexpr ck::index_t Ho = 36; // output H
static constexpr ck::index_t Wo = 36; // output W static constexpr ck::index_t Wo = 36; // output W
struct SimpleDeviceMem struct SimpleDeviceMem
{ {
SimpleDeviceMem() = delete; SimpleDeviceMem() = delete;
...@@ -55,8 +55,11 @@ struct SimpleDeviceMem ...@@ -55,8 +55,11 @@ struct SimpleDeviceMem
int main(int argc, char* argv[]) int main(int argc, char* argv[])
{ {
// We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space
// However, CK's API only accept length and stride with order of GNCHW/GKCYX/GNCHW
// Hence, we need to adjust the order of stride
std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi}; std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
std::array<ck::index_t, 5> in_strides{N * Hi * Wi * C, Hi * Wi * C, 1, Wi * C, C}; std::array<ck::index_t, 5> in_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X}; std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X};
std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C}; std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
std::array<ck::index_t, 5> bias_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> bias_lengths{G, N, K, Ho, Wo};
...@@ -64,17 +67,18 @@ int main(int argc, char* argv[]) ...@@ -64,17 +67,18 @@ int main(int argc, char* argv[])
std::array<ck::index_t, 5> requant_scale_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> requant_scale_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> requant_scale_strides{K, 0, 1, 0, 0}; std::array<ck::index_t, 5> requant_scale_strides{K, 0, 1, 0, 0};
std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> out_strides{N * Ho * Wo * K, Ho * Wo * K, 1, Wo * K, K}; std::array<ck::index_t, 5> out_strides{C, Ho * Wo * G * C, 1, Wo * G * C, G * C};
std::array<ck::index_t, 2> in_left_pad{1, 1}; std::array<ck::index_t, 2> in_left_pad{1, 1};
std::array<ck::index_t, 2> in_right_pad{1, 1}; std::array<ck::index_t, 2> in_right_pad{1, 1};
std::array<ck::index_t, 2> conv_strides{2, 2}; std::array<ck::index_t, 2> conv_strides{2, 2};
std::array<ck::index_t, 2> conv_dilations{1, 1}; std::array<ck::index_t, 2> conv_dilations{1, 1};
SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C); SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C); SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Y * X * C);
SimpleDeviceMem bias(sizeof(BiasDataType) * K * Y * X * C); SimpleDeviceMem bias(sizeof(BiasDataType) * G * K);
SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * K * Y * X * C); SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * G * K);
SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K); SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * G * K);
using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD< using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<
NumDimSpatial, NumDimSpatial,
...@@ -203,4 +207,4 @@ int main(int argc, char* argv[]) ...@@ -203,4 +207,4 @@ int main(int argc, char* argv[])
} }
return 0; return 0;
} }
\ No newline at end of file
client_example/09_quantization/conv2d_fwd_bias_relu_perlayer_quantization.cpp
View file @ 185af92b
...@@ -16,19 +16,19 @@ using WeiDataType = int8_t; ...@@ -16,19 +16,19 @@ using WeiDataType = int8_t;
using BiasDataType = int32_t; using BiasDataType = int32_t;
using OutDataType = int8_t; using OutDataType = int8_t;
using InLayout = ck::tensor_layout::convolution::GNHWC; using InLayout = ck::tensor_layout::convolution::NHWGC;
using WeiLayout = ck::tensor_layout::convolution::GKYXC; using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using BiasLayout = ck::tensor_layout::convolution::G_K; using BiasLayout = ck::tensor_layout::convolution::G_K;
using OutLayout = ck::tensor_layout::convolution::GNHWK; using OutLayout = ck::tensor_layout::convolution::NHWGK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ActivationOp = ck::tensor_operation::element_wise::Relu; using ActivationOp = ck::tensor_operation::element_wise::Relu;
using OutElementOp = ck::tensor_operation::element_wise::Add_Activation_Mul_Clamp<ActivationOp>; using OutElementOp = ck::tensor_operation::element_wise::Add_Activation_Mul_Clamp<ActivationOp>;
static constexpr ck::index_t NumDimSpatial = 2; static constexpr ck::index_t NumDimSpatial = 2;
static constexpr ck::index_t G = 1; static constexpr ck::index_t G = 4;
static constexpr ck::index_t N = 4; // batch size static constexpr ck::index_t N = 4; // batch size
static constexpr ck::index_t K = 64; // output channel static constexpr ck::index_t K = 32; // output channel
static constexpr ck::index_t C = 192; // input channel static constexpr ck::index_t C = 64; // input channel (per group)
static constexpr ck::index_t Y = 3; // filter H static constexpr ck::index_t Y = 3; // filter H
static constexpr ck::index_t X = 3; // filter W static constexpr ck::index_t X = 3; // filter W
static constexpr ck::index_t Hi = 71; // input H static constexpr ck::index_t Hi = 71; // input H
...@@ -55,23 +55,27 @@ struct SimpleDeviceMem ...@@ -55,23 +55,27 @@ struct SimpleDeviceMem
int main(int argc, char* argv[]) int main(int argc, char* argv[])
{ {
// We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space
// However, CK's API only accept length and stride with order of GNCHW/GKCYX/GNCHW
// Hence, we need to adjust the order of stride
std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi}; std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
std::array<ck::index_t, 5> in_strides{N * Hi * Wi * C, Hi * Wi * C, 1, Wi * C, C}; std::array<ck::index_t, 5> in_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X}; std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X};
std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C}; std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
std::array<ck::index_t, 5> bias_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> bias_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> bias_strides{K, 0, 1, 0, 0}; std::array<ck::index_t, 5> bias_strides{K, 0, 1, 0, 0};
std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> out_strides{N * Ho * Wo * K, Ho * Wo * K, 1, Wo * K, K}; std::array<ck::index_t, 5> out_strides{C, Ho * Wo * G * C, 1, Wo * G * C, G * C};
std::array<ck::index_t, 2> in_left_pad{1, 1}; std::array<ck::index_t, 2> in_left_pad{1, 1};
std::array<ck::index_t, 2> in_right_pad{1, 1}; std::array<ck::index_t, 2> in_right_pad{1, 1};
std::array<ck::index_t, 2> conv_strides{2, 2}; std::array<ck::index_t, 2> conv_strides{2, 2};
std::array<ck::index_t, 2> conv_dilations{1, 1}; std::array<ck::index_t, 2> conv_dilations{1, 1};
SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C); SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C); SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Y * X * C);
SimpleDeviceMem bias(sizeof(BiasDataType) * K * Y * X * C); SimpleDeviceMem bias(sizeof(BiasDataType) * G * K);
SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K); SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * G * K);
using DeviceOp = using DeviceOp =
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial, ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial,
......
client_example/09_quantization/conv2d_fwd_bias_tanh_perchannel_quantization.cpp
View file @ 185af92b
...@@ -17,21 +17,21 @@ using BiasDataType = int32_t; ...@@ -17,21 +17,21 @@ using BiasDataType = int32_t;
using RequantScaleDataType = float; using RequantScaleDataType = float;
using OutDataType = int8_t; using OutDataType = int8_t;
using InLayout = ck::tensor_layout::convolution::GNHWC; using InLayout = ck::tensor_layout::convolution::NHWGC;
using WeiLayout = ck::tensor_layout::convolution::GKYXC; using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using BiasLayout = ck::tensor_layout::convolution::G_K; using BiasLayout = ck::tensor_layout::convolution::G_K;
using RequantScaleLayout = ck::tensor_layout::convolution::G_K; using RequantScaleLayout = ck::tensor_layout::convolution::G_K;
using OutLayout = ck::tensor_layout::convolution::GNHWK; using OutLayout = ck::tensor_layout::convolution::NHWGK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ActivationOp = ck::tensor_operation::element_wise::TanH; using ActivationOp = ck::tensor_operation::element_wise::TanH;
using OutElementOp = using OutElementOp =
ck::tensor_operation::element_wise::Add_Mul2_Activation_Mul_Clamp<ActivationOp>; ck::tensor_operation::element_wise::Add_Mul2_Activation_Mul_Clamp<ActivationOp>;
static constexpr ck::index_t NumDimSpatial = 2; static constexpr ck::index_t NumDimSpatial = 2;
static constexpr ck::index_t G = 1; static constexpr ck::index_t G = 4;
static constexpr ck::index_t N = 4; // batch size static constexpr ck::index_t N = 4; // batch size
static constexpr ck::index_t K = 64; // output channel static constexpr ck::index_t K = 32; // output channel
static constexpr ck::index_t C = 192; // input channel static constexpr ck::index_t C = 64; // input channel (per group)
static constexpr ck::index_t Y = 3; // filter H static constexpr ck::index_t Y = 3; // filter H
static constexpr ck::index_t X = 3; // filter W static constexpr ck::index_t X = 3; // filter W
static constexpr ck::index_t Hi = 71; // input H static constexpr ck::index_t Hi = 71; // input H
...@@ -58,8 +58,11 @@ struct SimpleDeviceMem ...@@ -58,8 +58,11 @@ struct SimpleDeviceMem
int main(int argc, char* argv[]) int main(int argc, char* argv[])
{ {
// We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space
// However, CK's API only accept length and stride with order of GNCHW/GKCYX/GNCHW
// Hence, we need to adjust the order of stride
std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi}; std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
std::array<ck::index_t, 5> in_strides{N * Hi * Wi * C, Hi * Wi * C, 1, Wi * C, C}; std::array<ck::index_t, 5> in_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X}; std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X};
std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C}; std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
std::array<ck::index_t, 5> bias_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> bias_lengths{G, N, K, Ho, Wo};
...@@ -67,17 +70,18 @@ int main(int argc, char* argv[]) ...@@ -67,17 +70,18 @@ int main(int argc, char* argv[])
std::array<ck::index_t, 5> requant_scale_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> requant_scale_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> requant_scale_strides{K, 0, 1, 0, 0}; std::array<ck::index_t, 5> requant_scale_strides{K, 0, 1, 0, 0};
std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> out_strides{N * Ho * Wo * K, Ho * Wo * K, 1, Wo * K, K}; std::array<ck::index_t, 5> out_strides{C, Ho * Wo * G * C, 1, Wo * G * C, G * C};
std::array<ck::index_t, 2> in_left_pad{1, 1}; std::array<ck::index_t, 2> in_left_pad{1, 1};
std::array<ck::index_t, 2> in_right_pad{1, 1}; std::array<ck::index_t, 2> in_right_pad{1, 1};
std::array<ck::index_t, 2> conv_strides{2, 2}; std::array<ck::index_t, 2> conv_strides{2, 2};
std::array<ck::index_t, 2> conv_dilations{1, 1}; std::array<ck::index_t, 2> conv_dilations{1, 1};
SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C); SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C); SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Y * X * C);
SimpleDeviceMem bias(sizeof(BiasDataType) * K * Y * X * C); SimpleDeviceMem bias(sizeof(BiasDataType) * G * K);
SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * K * Y * X * C); SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * G * K);
SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K); SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * G * K);
using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD< using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<
NumDimSpatial, NumDimSpatial,
...@@ -206,4 +210,4 @@ int main(int argc, char* argv[]) ...@@ -206,4 +210,4 @@ int main(int argc, char* argv[])
} }
return 0; return 0;
} }
\ No newline at end of file
client_example/09_quantization/conv2d_fwd_bias_tanh_perlayer_quantization.cpp
View file @ 185af92b
...@@ -16,19 +16,19 @@ using WeiDataType = int8_t; ...@@ -16,19 +16,19 @@ using WeiDataType = int8_t;
using BiasDataType = int32_t; using BiasDataType = int32_t;
using OutDataType = int8_t; using OutDataType = int8_t;
using InLayout = ck::tensor_layout::convolution::GNHWC; using InLayout = ck::tensor_layout::convolution::NHWGC;
using WeiLayout = ck::tensor_layout::convolution::GKYXC; using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using BiasLayout = ck::tensor_layout::convolution::G_K; using BiasLayout = ck::tensor_layout::convolution::G_K;
using OutLayout = ck::tensor_layout::convolution::GNHWK; using OutLayout = ck::tensor_layout::convolution::NHWGK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ActivationOp = ck::tensor_operation::element_wise::TanH; using ActivationOp = ck::tensor_operation::element_wise::TanH;
using OutElementOp = ck::tensor_operation::element_wise::Add_Mul_Activation_Mul_Clamp<ActivationOp>; using OutElementOp = ck::tensor_operation::element_wise::Add_Mul_Activation_Mul_Clamp<ActivationOp>;
static constexpr ck::index_t NumDimSpatial = 2; static constexpr ck::index_t NumDimSpatial = 2;
static constexpr ck::index_t G = 1; static constexpr ck::index_t G = 4;
static constexpr ck::index_t N = 4; // batch size static constexpr ck::index_t N = 4; // batch size
static constexpr ck::index_t K = 64; // output channel static constexpr ck::index_t K = 32; // output channel
static constexpr ck::index_t C = 192; // input channel static constexpr ck::index_t C = 64; // input channel (per group)
static constexpr ck::index_t Y = 3; // filter H static constexpr ck::index_t Y = 3; // filter H
static constexpr ck::index_t X = 3; // filter W static constexpr ck::index_t X = 3; // filter W
static constexpr ck::index_t Hi = 71; // input H static constexpr ck::index_t Hi = 71; // input H
...@@ -56,23 +56,27 @@ struct SimpleDeviceMem ...@@ -56,23 +56,27 @@ struct SimpleDeviceMem
int main(int argc, char* argv[]) int main(int argc, char* argv[])
{ {
// We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space
// However, CK's API only accept length and stride with order of GNCHW/GKCYX/GNCHW
// Hence, we need to adjust the order of stride
std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi}; std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
std::array<ck::index_t, 5> in_strides{N * Hi * Wi * C, Hi * Wi * C, 1, Wi * C, C}; std::array<ck::index_t, 5> in_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X}; std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X};
std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C}; std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
std::array<ck::index_t, 5> bias_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> bias_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> bias_strides{K, 0, 1, 0, 0}; std::array<ck::index_t, 5> bias_strides{K, 0, 1, 0, 0};
std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> out_strides{N * Ho * Wo * K, Ho * Wo * K, 1, Wo * K, K}; std::array<ck::index_t, 5> out_strides{C, Ho * Wo * G * C, 1, Wo * G * C, G * C};
std::array<ck::index_t, 2> in_left_pad{1, 1}; std::array<ck::index_t, 2> in_left_pad{1, 1};
std::array<ck::index_t, 2> in_right_pad{1, 1}; std::array<ck::index_t, 2> in_right_pad{1, 1};
std::array<ck::index_t, 2> conv_strides{2, 2}; std::array<ck::index_t, 2> conv_strides{2, 2};
std::array<ck::index_t, 2> conv_dilations{1, 1}; std::array<ck::index_t, 2> conv_dilations{1, 1};
SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C); SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C); SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Y * X * C);
SimpleDeviceMem bias(sizeof(BiasDataType) * K * Y * X * C); SimpleDeviceMem bias(sizeof(BiasDataType) * G * K);
SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K); SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * G * K);
using DeviceOp = using DeviceOp =
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial, ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial,
......
client_example/09_quantization/conv2d_fwd_perchannel_quantization.cpp
View file @ 185af92b
...@@ -16,25 +16,25 @@ using WeiDataType = int8_t; ...@@ -16,25 +16,25 @@ using WeiDataType = int8_t;
using RequantScaleDataType = float; using RequantScaleDataType = float;
using OutDataType = int8_t; using OutDataType = int8_t;
using InLayout = ck::tensor_layout::convolution::GNHWC; using InLayout = ck::tensor_layout::convolution::NHWGC;
using WeiLayout = ck::tensor_layout::convolution::GKYXC; using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using RequantScaleLayout = ck::tensor_layout::convolution::G_K; using RequantScaleLayout = ck::tensor_layout::convolution::G_K;
using OutLayout = ck::tensor_layout::convolution::GNHWK; using OutLayout = ck::tensor_layout::convolution::NHWGK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ActivationOp = PassThrough; using ActivationOp = PassThrough;
using OutElementOp = ck::tensor_operation::element_wise::Activation_Mul2_Clamp<ActivationOp>; using OutElementOp = ck::tensor_operation::element_wise::Activation_Mul2_Clamp<ActivationOp>;
static constexpr ck::index_t NumDimSpatial = 2; static constexpr ck::index_t NumDimSpatial = 2;
static constexpr ck::index_t G = 1; static constexpr ck::index_t G = 4;
static constexpr ck::index_t N = 4; // batch size static constexpr ck::index_t N = 4; // batch size
static constexpr ck::index_t K = 64; // output channel static constexpr ck::index_t K = 32; // output channel
static constexpr ck::index_t C = 192; // input channel static constexpr ck::index_t C = 64; // input channel (per group)
static constexpr ck::index_t Y = 3; // filter H static constexpr ck::index_t Y = 3; // filter H
static constexpr ck::index_t X = 3; // filter W static constexpr ck::index_t X = 3; // filter W
static constexpr ck::index_t Hi = 71; // input H static constexpr ck::index_t Hi = 71; // input H
static constexpr ck::index_t Wi = 71; // input W static constexpr ck::index_t Wi = 71; // input W
static constexpr ck::index_t Ho = 36; // output H static constexpr ck::index_t Ho = 36; // output H
static constexpr ck::index_t Wo = 36; // output W static constexpr ck::index_t Wo = 36; // output W
struct SimpleDeviceMem struct SimpleDeviceMem
{ {
...@@ -54,23 +54,27 @@ struct SimpleDeviceMem ...@@ -54,23 +54,27 @@ struct SimpleDeviceMem
int main(int argc, char* argv[]) int main(int argc, char* argv[])
{ {
// We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space
// However, CK's API only accept length and stride with order of GNCHW/GKCYX/GNCHW
// Hence, we need to adjust the order of stride
std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi}; std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
std::array<ck::index_t, 5> in_strides{N * Hi * Wi * C, Hi * Wi * C, 1, Wi * C, C}; std::array<ck::index_t, 5> in_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X}; std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X};
std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C}; std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
std::array<ck::index_t, 5> requant_scale_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> requant_scale_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> requant_scale_strides{K, 0, 1, 0, 0}; std::array<ck::index_t, 5> requant_scale_strides{K, 0, 1, 0, 0};
std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> out_strides{N * Ho * Wo * K, Ho * Wo * K, 1, Wo * K, K}; std::array<ck::index_t, 5> out_strides{C, Ho * Wo * G * C, 1, Wo * G * C, G * C};
std::array<ck::index_t, 2> in_left_pad{1, 1}; std::array<ck::index_t, 2> in_left_pad{1, 1};
std::array<ck::index_t, 2> in_right_pad{1, 1}; std::array<ck::index_t, 2> in_right_pad{1, 1};
std::array<ck::index_t, 2> conv_strides{2, 2}; std::array<ck::index_t, 2> conv_strides{2, 2};
std::array<ck::index_t, 2> conv_dilations{1, 1}; std::array<ck::index_t, 2> conv_dilations{1, 1};
SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C); SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C); SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Y * X * C);
SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * K * Y * X * C); SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * G * K);
SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K); SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * G * K);
using DeviceOp = using DeviceOp =
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial, ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial,
...@@ -196,4 +200,4 @@ int main(int argc, char* argv[]) ...@@ -196,4 +200,4 @@ int main(int argc, char* argv[])
} }
return 0; return 0;
} }
\ No newline at end of file
client_example/09_quantization/conv2d_fwd_perlayer_quantization.cpp
View file @ 185af92b
...@@ -15,18 +15,18 @@ using InDataType = int8_t; ...@@ -15,18 +15,18 @@ using InDataType = int8_t;
using WeiDataType = int8_t; using WeiDataType = int8_t;
using OutDataType = int8_t; using OutDataType = int8_t;
using InLayout = ck::tensor_layout::convolution::GNHWC; using InLayout = ck::tensor_layout::convolution::NHWGC;
using WeiLayout = ck::tensor_layout::convolution::GKYXC; using WeiLayout = ck::tensor_layout::convolution::GKYXC;
using OutLayout = ck::tensor_layout::convolution::GNHWK; using OutLayout = ck::tensor_layout::convolution::NHWGK;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ActivationOp = PassThrough; using ActivationOp = PassThrough;
using OutElementOp = ck::tensor_operation::element_wise::Activation_Mul_Clamp<ActivationOp>; using OutElementOp = ck::tensor_operation::element_wise::Activation_Mul_Clamp<ActivationOp>;
static constexpr ck::index_t NumDimSpatial = 2; static constexpr ck::index_t NumDimSpatial = 2;
static constexpr ck::index_t G = 1; static constexpr ck::index_t G = 4;
static constexpr ck::index_t N = 4; // batch size static constexpr ck::index_t N = 4; // batch size
static constexpr ck::index_t K = 64; // output channel static constexpr ck::index_t K = 32; // output channel
static constexpr ck::index_t C = 192; // input channel static constexpr ck::index_t C = 64; // input channel (per group)
static constexpr ck::index_t Y = 3; // filter H static constexpr ck::index_t Y = 3; // filter H
static constexpr ck::index_t X = 3; // filter W static constexpr ck::index_t X = 3; // filter W
static constexpr ck::index_t Hi = 71; // input H static constexpr ck::index_t Hi = 71; // input H
...@@ -53,20 +53,24 @@ struct SimpleDeviceMem ...@@ -53,20 +53,24 @@ struct SimpleDeviceMem
int main(int argc, char* argv[]) int main(int argc, char* argv[])
{ {
// We have NHWGC/GKYXC/NHWGK (x, weight, y) in memory space
// However, CK's API only accept length and stride with order of GNCHW/GKCYX/GNCHW
// Hence, we need to adjust the order of stride
std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi}; std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
std::array<ck::index_t, 5> in_strides{N * Hi * Wi * C, Hi * Wi * C, 1, Wi * C, C}; std::array<ck::index_t, 5> in_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X}; std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X};
std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C}; std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo}; std::array<ck::index_t, 5> out_lengths{G, N, K, Ho, Wo};
std::array<ck::index_t, 5> out_strides{N * Ho * Wo * K, Ho * Wo * K, 1, Wo * K, K}; std::array<ck::index_t, 5> out_strides{C, Ho * Wo * G * C, 1, Wo * G * C, G * C};
std::array<ck::index_t, 2> in_left_pad{1, 1}; std::array<ck::index_t, 2> in_left_pad{1, 1};
std::array<ck::index_t, 2> in_right_pad{1, 1}; std::array<ck::index_t, 2> in_right_pad{1, 1};
std::array<ck::index_t, 2> conv_strides{2, 2}; std::array<ck::index_t, 2> conv_strides{2, 2};
std::array<ck::index_t, 2> conv_dilations{1, 1}; std::array<ck::index_t, 2> conv_dilations{1, 1};
SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C); SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C); SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Y * X * C);
SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K); SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * G * K);
using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial, using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial,
InLayout, InLayout,
......
client_example/18_groupnorm/groupnorm_swish.cpp
View file @ 185af92b
...@@ -13,8 +13,8 @@ ...@@ -13,8 +13,8 @@
#include "ck/library/tensor_operation_instance/gpu/normalization_swish.hpp" #include "ck/library/tensor_operation_instance/gpu/normalization_swish.hpp"
using XDataType = ck::half_t; using XDataType = ck::half_t;
using GammaDataType = ck::half_t; using GammaDataType = float;
using BetaDataType = ck::half_t; using BetaDataType = float;
using YDataType = ck::half_t; using YDataType = ck::half_t;
using ComputeDataType = float; using ComputeDataType = float;
using Swish = ck::tensor_operation::element_wise::Swish; using Swish = ck::tensor_operation::element_wise::Swish;
......
cmake/googletest.cmake
View file @ 185af92b
...@@ -21,6 +21,7 @@ list(APPEND GTEST_CMAKE_CXX_FLAGS ...@@ -21,6 +21,7 @@ list(APPEND GTEST_CMAKE_CXX_FLAGS
-Wno-comma -Wno-comma
-Wno-old-style-cast -Wno-old-style-cast
-Wno-deprecated -Wno-deprecated
-Wno-unsafe-buffer-usage
) )
message(STATUS "Suppressing googltest warnings with flags: ${GTEST_CMAKE_CXX_FLAGS}") message(STATUS "Suppressing googltest warnings with flags: ${GTEST_CMAKE_CXX_FLAGS}")
......
docs/.sphinx/requirements.in
View file @ 185af92b
git+https://github.com/RadeonOpenCompute/rocm-docs-core.git rocm-docs-core==0.2.0
sphinxcontrib-bibtex==2.5.0 sphinxcontrib-bibtex==2.5.0
docs/.sphinx/requirements.txt
View file @ 185af92b
...@@ -2,9 +2,9 @@ ...@@ -2,9 +2,9 @@
# This file is autogenerated by pip-compile with Python 3.10 # This file is autogenerated by pip-compile with Python 3.10
# by the following command: # by the following command:
# #
# pip-compile requirements.in # pip-compile .sphinx/requirements.in
# #
accessible-pygments==0.0.4 accessible-pygments==0.0.3
# via pydata-sphinx-theme # via pydata-sphinx-theme
alabaster==0.7.13 alabaster==0.7.13
# via sphinx # via sphinx
...@@ -20,7 +20,7 @@ babel==2.12.1 ...@@ -20,7 +20,7 @@ babel==2.12.1
# sphinx # sphinx
backcall==0.2.0 backcall==0.2.0
# via ipython # via ipython
beautifulsoup4==4.12.0 beautifulsoup4==4.11.2
# via pydata-sphinx-theme # via pydata-sphinx-theme
breathe==4.34.0 breathe==4.34.0
# via rocm-docs-core # via rocm-docs-core
...@@ -34,7 +34,7 @@ click==8.1.3 ...@@ -34,7 +34,7 @@ click==8.1.3
# via # via
# jupyter-cache # jupyter-cache
# sphinx-external-toc # sphinx-external-toc
comm==0.1.3 comm==0.1.2
# via ipykernel # via ipykernel
debugpy==1.6.6 debugpy==1.6.6
# via ipykernel # via ipykernel
...@@ -65,13 +65,11 @@ idna==3.4 ...@@ -65,13 +65,11 @@ idna==3.4
# via requests # via requests
imagesize==1.4.1 imagesize==1.4.1
# via sphinx # via sphinx
importlib-metadata==6.1.0 importlib-metadata==6.0.0
# via # via
# jupyter-cache # jupyter-cache
# myst-nb # myst-nb
importlib-resources==5.10.4 ipykernel==6.21.3
# via rocm-docs-core
ipykernel==6.22.0
# via myst-nb # via myst-nb
ipython==8.11.0 ipython==8.11.0
# via # via
...@@ -87,7 +85,7 @@ jsonschema==4.17.3 ...@@ -87,7 +85,7 @@ jsonschema==4.17.3
# via nbformat # via nbformat
jupyter-cache==0.5.0 jupyter-cache==0.5.0
# via myst-nb # via myst-nb
jupyter-client==8.1.0 jupyter-client==8.0.3
# via # via
# ipykernel # ipykernel
# nbclient # nbclient
...@@ -124,7 +122,7 @@ nbclient==0.5.13 ...@@ -124,7 +122,7 @@ nbclient==0.5.13
# via # via
# jupyter-cache # jupyter-cache
# myst-nb # myst-nb
nbformat==5.8.0 nbformat==5.7.3
# via # via
# jupyter-cache # jupyter-cache
# myst-nb # myst-nb
...@@ -187,7 +185,7 @@ pyyaml==6.0 ...@@ -187,7 +185,7 @@ pyyaml==6.0
# myst-parser # myst-parser
# pybtex # pybtex
# sphinx-external-toc # sphinx-external-toc
pyzmq==25.0.2 pyzmq==25.0.1
# via # via
# ipykernel # ipykernel
# jupyter-client # jupyter-client
...@@ -195,8 +193,8 @@ requests==2.28.2 ...@@ -195,8 +193,8 @@ requests==2.28.2
# via # via
# pygithub # pygithub
# sphinx # sphinx
rocm-docs-core @ git+https://github.com/RadeonOpenCompute/rocm-docs-core.git rocm-docs-core==0.2.0
# via -r requirements.in # via -r .sphinx/requirements.in
six==1.16.0 six==1.16.0
# via # via
# asttokens # asttokens
...@@ -235,9 +233,7 @@ sphinx-notfound-page==0.8.3 ...@@ -235,9 +233,7 @@ sphinx-notfound-page==0.8.3
sphinxcontrib-applehelp==1.0.4 sphinxcontrib-applehelp==1.0.4
# via sphinx # via sphinx
sphinxcontrib-bibtex==2.5.0 sphinxcontrib-bibtex==2.5.0
# via # via -r .sphinx/requirements.in
# -r requirements.in
# rocm-docs-core
sphinxcontrib-devhelp==1.0.2 sphinxcontrib-devhelp==1.0.2
# via sphinx # via sphinx
sphinxcontrib-htmlhelp==2.0.1 sphinxcontrib-htmlhelp==2.0.1
...@@ -248,7 +244,7 @@ sphinxcontrib-qthelp==1.0.3 ...@@ -248,7 +244,7 @@ sphinxcontrib-qthelp==1.0.3
# via sphinx # via sphinx
sphinxcontrib-serializinghtml==1.1.5 sphinxcontrib-serializinghtml==1.1.5
# via sphinx # via sphinx
sqlalchemy==1.4.47 sqlalchemy==1.4.46
# via jupyter-cache # via jupyter-cache
stack-data==0.6.2 stack-data==0.6.2
# via ipython # via ipython
......
example/15_grouped_gemm/CMakeLists.txt
View file @ 185af92b
...@@ -5,6 +5,7 @@ add_example_executable(example_grouped_gemm_xdl_fp16 grouped_gemm_xdl_fp16.cpp) ...@@ -5,6 +5,7 @@ add_example_executable(example_grouped_gemm_xdl_fp16 grouped_gemm_xdl_fp16.cpp)
add_example_executable(example_grouped_gemm_xdl_bfp16 grouped_gemm_xdl_bfp16.cpp) add_example_executable(example_grouped_gemm_xdl_bfp16 grouped_gemm_xdl_bfp16.cpp)
add_example_executable(example_grouped_gemm_xdl_int8 grouped_gemm_xdl_int8.cpp) add_example_executable(example_grouped_gemm_xdl_int8 grouped_gemm_xdl_int8.cpp)
add_example_executable(example_grouped_gemm_multiple_d_dl_fp16 grouped_gemm_multiple_d_dl_fp16.cpp) add_example_executable(example_grouped_gemm_multiple_d_dl_fp16 grouped_gemm_multiple_d_dl_fp16.cpp)
add_example_executable(example_grouped_gemm_xdl_splitk_fp16 grouped_gemm_xdl_splitk_fp16.cpp)
add_dependencies(example_grouped_gemm_xdl add_dependencies(example_grouped_gemm_xdl
...@@ -12,7 +13,8 @@ add_dependencies(example_grouped_gemm_xdl ...@@ -12,7 +13,8 @@ add_dependencies(example_grouped_gemm_xdl
example_grouped_gemm_xdl_fp16 example_grouped_gemm_xdl_fp16
example_grouped_gemm_xdl_bfp16 example_grouped_gemm_xdl_bfp16
example_grouped_gemm_xdl_int8 example_grouped_gemm_xdl_int8
example_grouped_gemm_multiple_d_dl_fp16) example_grouped_gemm_multiple_d_dl_fp16
example_grouped_gemm_xdl_splitk_fp16)
if(USE_BITINT_EXTENSION_INT4) if(USE_BITINT_EXTENSION_INT4)
add_example_executable(example_grouped_gemm_xdl_int4 grouped_gemm_xdl_int4.cpp) add_example_executable(example_grouped_gemm_xdl_int4 grouped_gemm_xdl_int4.cpp)
......
example/15_grouped_gemm/grouped_gemm_xdl_splitk_fp16.cpp 0 → 100644
View file @ 185af92b
// 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/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_splitk_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.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"
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 = F16;
using DsDataType = ck::Tuple<>;
using EDataType = F16;
using ALayout = Row;
using BLayout = Col;
using DsLayout = ck::Tuple<>;
using ELayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemmXdlSplitKCShuffle
// clang-format off
//######| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//######| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//######| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
// clang-format on
#include "run_grouped_gemm_example.inc"
int main(int argc, char* argv[])
{
ProblemSize problem_size;
ExecutionConfig config;
problem_size.group_count = 16;
problem_size.Ms = {
167, 183, 177, 181, 153, 139, 156, 173, 163, 150, 204, 184, 168, 156, 168, 148};
for(int i = 0; i < problem_size.group_count; i++)
{
problem_size.Ns.push_back(768);
problem_size.Ks.push_back(4608);
problem_size.stride_As.push_back(problem_size.Ks[i]);
problem_size.stride_Bs.push_back(problem_size.Ks[i]);
problem_size.stride_Cs.push_back(problem_size.Ns[i]);
}
if(argc == 4)
{
config.do_verification = std::stoi(argv[1]);
config.init_method = std::stoi(argv[2]);
config.time_kernel = std::stoi(argv[3]);
}
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=n0, 1=yes)\n");
exit(0);
}
return !run_grouped_gemm(problem_size, config);
}
example/15_grouped_gemm/run_grouped_gemm_example.inc
View file @ 185af92b
...@@ -147,6 +147,7 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co ...@@ -147,6 +147,7 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
#else #else
a_tensors_device[i]->ToDevice(a_tensors[i].mData.data()); a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data()); b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
c_tensors_device[i]->SetZero();
#endif #endif
p_a.push_back(a_tensors_device[i]->GetDeviceBuffer()); p_a.push_back(a_tensors_device[i]->GetDeviceBuffer());
......
example/40_conv2d_fwd_quantization/run_conv2d_fwd_bias_perchannel_quantization_example.inc
View file @ 185af92b
...@@ -190,11 +190,11 @@ int run_conv2d_fwd_bias_perchannel_quantization_example(const OutElementOp& out_ ...@@ -190,11 +190,11 @@ int run_conv2d_fwd_bias_perchannel_quantization_example(const OutElementOp& out_
const auto in_element_op = InElementOp{}; const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{}; const auto wei_element_op = WeiElementOp{};
using InLayout = ck::tensor_layout::convolution::GNHWC; using InLayout = ck::tensor_layout::convolution::NHWGC;
using WeiLayout = ck::tensor_layout::convolution::GKYXC; using WeiLayout = ck::tensor_layout::convolution::KYXGC;
using BiasLayout = ck::tensor_layout::convolution::G_K; using BiasLayout = ck::tensor_layout::convolution::G_K;
using RequantScaleLayout = ck::tensor_layout::convolution::G_K; using RequantScaleLayout = ck::tensor_layout::convolution::G_K;
using OutLayout = ck::tensor_layout::convolution::GNHWK; using OutLayout = ck::tensor_layout::convolution::NHWGK;
const auto in_g_n_c_wis_desc = const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param); ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param);
......
example/40_conv2d_fwd_quantization/run_conv2d_fwd_bias_perlayer_quantization_example.inc
View file @ 185af92b
...@@ -178,10 +178,10 @@ int run_conv2d_fwd_bias_perlayer_quantization_example(const OutElementOp& out_el ...@@ -178,10 +178,10 @@ int run_conv2d_fwd_bias_perlayer_quantization_example(const OutElementOp& out_el
const auto in_element_op = InElementOp{}; const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{}; const auto wei_element_op = WeiElementOp{};
using InLayout = ck::tensor_layout::convolution::GNHWC; using InLayout = ck::tensor_layout::convolution::NHWGC;
using WeiLayout = ck::tensor_layout::convolution::GKYXC; using WeiLayout = ck::tensor_layout::convolution::KYXGC;
using BiasLayout = ck::tensor_layout::convolution::G_K; using BiasLayout = ck::tensor_layout::convolution::G_K;
using OutLayout = ck::tensor_layout::convolution::GNHWK; using OutLayout = ck::tensor_layout::convolution::NHWGK;
const auto in_g_n_c_wis_desc = const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param); ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param);
......
example/40_conv2d_fwd_quantization/run_conv2d_fwd_perchannel_quantization_example.inc
View file @ 185af92b
...@@ -180,10 +180,10 @@ int run_conv2d_fwd_perchannel_quantization_example(const OutElementOp& out_eleme ...@@ -180,10 +180,10 @@ int run_conv2d_fwd_perchannel_quantization_example(const OutElementOp& out_eleme
const auto in_element_op = InElementOp{}; const auto in_element_op = InElementOp{};
const auto wei_element_op = WeiElementOp{}; const auto wei_element_op = WeiElementOp{};
using InLayout = ck::tensor_layout::convolution::GNHWC; using InLayout = ck::tensor_layout::convolution::NHWGC;
using WeiLayout = ck::tensor_layout::convolution::GKYXC; using WeiLayout = ck::tensor_layout::convolution::KYXGC;
using RequantScaleLayout = ck::tensor_layout::convolution::G_K; using RequantScaleLayout = ck::tensor_layout::convolution::G_K;
using OutLayout = ck::tensor_layout::convolution::GNHWK; using OutLayout = ck::tensor_layout::convolution::NHWGK;
const auto in_g_n_c_wis_desc = const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param); ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param);
......
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