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Unverified Commit b754f5a6 authored by Daniel Hiltgen's avatar Daniel Hiltgen Committed by GitHub
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Remove submodule and shift to Go server - 0.4.0 (#7157) 

* Remove llama.cpp submodule and shift new build to top

* CI: install msys and clang gcc on win

Needed for deepseek to work properly on windows
parent a805e594
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llm/generate/gen_windows.ps1 deleted 100644 → 0
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#!powershell
$ErrorActionPreference = "Stop"
function amdGPUs {
if ($env:AMDGPU_TARGETS) {
return $env:AMDGPU_TARGETS
}
# Current supported rocblas list from ROCm v6.1.2 on windows
# https://rocm.docs.amd.com/projects/install-on-windows/en/latest/reference/system-requirements.html#windows-supported-gpus
$GPU_LIST = @(
"gfx1030"
"gfx1100"
"gfx1101"
"gfx1102"
)
$GPU_LIST -join ';'
}
function init_vars {
write-host "Checking for cmake..."
get-command cmake
write-host "Checking for ninja..."
$d=(get-command -ea 'silentlycontinue' ninja).path
if ($null -eq $d) {
$MSVC_INSTALL=(Get-CimInstance MSFT_VSInstance -Namespace root/cimv2/vs)[0].InstallLocation
$matches=(gci -path $MSVC_INSTALL -r -fi ninja.exe)
if ($matches.count -eq 0) {
throw "Unable to locate ninja"
}
$ninjaDir=($matches[0].FullName | split-path -parent)
$env:PATH="$env:PATH;$ninjaDir"
}
if (!$script:SRC_DIR) {
$script:SRC_DIR = $(resolve-path "..\..\")
}
if (!$script:llamacppDir) {
$script:llamacppDir = "../llama.cpp"
}
if (!$script:cmakeTargets) {
$script:cmakeTargets = @("ollama_llama_server")
}
$script:cmakeDefs = @(
"-DBUILD_SHARED_LIBS=on",
"-DGGML_NATIVE=off",
"-DGGML_OPENMP=off"
)
$script:commonCpuDefs = @("-DCMAKE_POSITION_INDEPENDENT_CODE=on")
$script:ARCH = $Env:PROCESSOR_ARCHITECTURE.ToLower()
$script:DIST_BASE = "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\runners"
md "$script:DIST_BASE" -ea 0 > $null
if ($env:CGO_CFLAGS -contains "-g") {
$script:cmakeDefs += @("-DCMAKE_VERBOSE_MAKEFILE=on", "-DLLAMA_SERVER_VERBOSE=on", "-DCMAKE_BUILD_TYPE=RelWithDebInfo")
$script:config = "RelWithDebInfo"
} else {
$script:cmakeDefs += @("-DLLAMA_SERVER_VERBOSE=off", "-DCMAKE_BUILD_TYPE=Release")
$script:config = "Release"
}
if ($null -ne $env:CMAKE_SYSTEM_VERSION) {
$script:cmakeDefs += @("-DCMAKE_SYSTEM_VERSION=${env:CMAKE_SYSTEM_VERSION}")
}
# Try to find the CUDA dir
if ($env:CUDA_LIB_DIR -eq $null) {
$d=(get-command -ea 'silentlycontinue' nvcc).path
if ($d -ne $null) {
$script:CUDA_LIB_DIR=($d| split-path -parent)
$script:CUDA_INCLUDE_DIR=($script:CUDA_LIB_DIR|split-path -parent)+"\include"
}
} else {
$script:CUDA_LIB_DIR=$env:CUDA_LIB_DIR
}
$script:DUMPBIN=(get-command -ea 'silentlycontinue' dumpbin).path
if ($null -eq $env:CMAKE_CUDA_ARCHITECTURES) {
$script:CMAKE_CUDA_ARCHITECTURES="50;52;61;70;75;80"
} else {
$script:CMAKE_CUDA_ARCHITECTURES=$env:CMAKE_CUDA_ARCHITECTURES
}
# Note: Windows Kits 10 signtool crashes with GCP's plugin
if ($null -eq $env:SIGN_TOOL) {
${script:SignTool}="C:\Program Files (x86)\Windows Kits\8.1\bin\x64\signtool.exe"
} else {
${script:SignTool}=${env:SIGN_TOOL}
}
if ("${env:KEY_CONTAINER}") {
${script:OLLAMA_CERT}=$(resolve-path "${script:SRC_DIR}\ollama_inc.crt")
}
}
function git_module_setup {
# TODO add flags to skip the init/patch logic to make it easier to mod llama.cpp code in-repo
& git submodule init
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
& git submodule update --force "${script:llamacppDir}"
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
}
function apply_patches {
# Apply temporary patches until fix is upstream
foreach ($patch in $(Get-ChildItem "../patches/*.patch")) {
git -c 'user.name=nobody' -c 'user.email=<>' -C "${script:llamacppDir}" am $patch.FullName
}
}
function build {
write-host "generating config with: cmake -S ${script:llamacppDir} -B $script:buildDir $script:cmakeDefs"
& cmake --version
& cmake -S "${script:llamacppDir}" -B $script:buildDir $script:cmakeDefs
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
if ($cmakeDefs -contains "-G") {
$extra=@("-j8")
} else {
$extra= @("--", "/maxCpuCount:8")
}
write-host "building with: cmake --build $script:buildDir --config $script:config $($script:cmakeTargets | ForEach-Object { `"--target`", $_ }) $extra"
& cmake --build $script:buildDir --config $script:config ($script:cmakeTargets | ForEach-Object { "--target", $_ }) $extra
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
# Rearrange output to be consistent between different generators
if ($null -ne ${script:config} -And (test-path -path "${script:buildDir}/bin/${script:config}" ) ) {
mv -force "${script:buildDir}/bin/${script:config}/*" "${script:buildDir}/bin/"
remove-item "${script:buildDir}/bin/${script:config}"
}
}
function sign {
if ("${env:KEY_CONTAINER}") {
write-host "Signing ${script:buildDir}/bin/*.exe ${script:buildDir}/bin/*.dll"
foreach ($file in @(get-childitem "${script:buildDir}/bin/*.exe") + @(get-childitem "${script:buildDir}/bin/*.dll")){
& "${script:SignTool}" sign /v /fd sha256 /t http://timestamp.digicert.com /f "${script:OLLAMA_CERT}" `
/csp "Google Cloud KMS Provider" /kc "${env:KEY_CONTAINER}" $file
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
}
}
}
function install {
write-host "Installing binaries to dist dir ${script:distDir}"
mkdir ${script:distDir} -ErrorAction SilentlyContinue
$binaries = dir "${script:buildDir}/bin/*.exe"
foreach ($file in $binaries) {
copy-item -Path $file -Destination ${script:distDir} -Force
}
write-host "Installing dlls to dist dir ${script:distDir}"
$dlls = dir "${script:buildDir}/bin/*.dll"
foreach ($file in $dlls) {
copy-item -Path $file -Destination ${script:distDir} -Force
}
}
function cleanup {
$patches = Get-ChildItem "../patches/*.diff"
foreach ($patch in $patches) {
# Extract file paths from the patch file
$filePaths = Get-Content $patch.FullName | Where-Object { $_ -match '^\+\+\+ ' } | ForEach-Object {
$parts = $_ -split ' '
($parts[1] -split '/', 2)[1]
}
# Checkout each file
foreach ($file in $filePaths) {
git -C "${script:llamacppDir}" checkout $file
}
git -C "${script:llamacppDir}" checkout CMakeLists.txt
}
}
# -DGGML_AVX -- 2011 Intel Sandy Bridge & AMD Bulldozer
# -DGGML_AVX2 -- 2013 Intel Haswell & 2015 AMD Excavator / 2017 AMD Zen
# -DGGML_FMA (FMA3) -- 2013 Intel Haswell & 2012 AMD Piledriver
function build_cpu_x64 {
if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu"))) {
init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DGGML_AVX=off", "-DGGML_AVX2=off", "-DGGML_AVX512=off", "-DGGML_FMA=off", "-DGGML_F16C=off") + $script:cmakeDefs
$script:buildDir="../build/windows/${script:ARCH}/cpu"
$script:distDir="$script:DIST_BASE\cpu"
write-host "Building LCD CPU"
build
sign
install
} else {
write-host "Skipping CPU generation step as requested"
}
}
function build_cpu_arm64 {
if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu"))) {
init_vars
write-host "Checking for clang..."
get-command clang
$env:CFLAGS="-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only"
$env:CXXFLAGS="$env:CFLAGS"
$env:LDFLAGS="-static-libstdc++"
$script:cmakeDefs = $script:commonCpuDefs + @(
"-DCMAKE_VERBOSE_MAKEFILE=on",
"-DCMAKE_C_COMPILER=clang.exe",
"-DCMAKE_CXX_COMPILER=clang++.exe",
"-DMSVC_RUNTIME_LIBRARY=MultiThreaded"
) + $script:cmakeDefs
$script:buildDir="../build/windows/${script:ARCH}/cpu"
$script:distDir="$script:DIST_BASE\cpu"
write-host "Building LCD CPU"
build
sign
install
} else {
write-host "Skipping CPU generation step as requested"
}
}
function build_cpu_avx() {
if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu_avx"))) {
init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DGGML_AVX=on", "-DGGML_AVX2=off", "-DGGML_AVX512=off", "-DGGML_FMA=off", "-DGGML_F16C=off") + $script:cmakeDefs
$script:buildDir="../build/windows/${script:ARCH}/cpu_avx"
$script:distDir="$script:DIST_BASE\cpu_avx"
write-host "Building AVX CPU"
build
sign
install
} else {
write-host "Skipping CPU AVX generation step as requested"
}
}
function build_cpu_avx2() {
if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu_avx2"))) {
init_vars
$script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DGGML_AVX=on", "-DGGML_AVX2=on", "-DGGML_AVX512=off", "-DGGML_FMA=on", "-DGGML_F16C=on") + $script:cmakeDefs
$script:buildDir="../build/windows/${script:ARCH}/cpu_avx2"
$script:distDir="$script:DIST_BASE\cpu_avx2"
write-host "Building AVX2 CPU"
build
sign
install
} else {
write-host "Skipping CPU AVX2 generation step as requested"
}
}
function build_cuda() {
if ((-not "${env:OLLAMA_SKIP_CUDA_GENERATE}") -and ("${script:CUDA_LIB_DIR}")) {
# Then build cuda as a dynamically loaded library
$nvcc = "$script:CUDA_LIB_DIR\nvcc.exe"
$script:CUDA_VERSION=((get-item ($nvcc | split-path | split-path)).Basename -Split "\.")[0]
if ($null -ne $script:CUDA_VERSION) {
$script:CUDA_VARIANT="_"+$script:CUDA_VERSION
}
init_vars
$script:buildDir="../build/windows/${script:ARCH}/cuda$script:CUDA_VARIANT"
$script:distDir="$script:DIST_BASE\cuda$script:CUDA_VARIANT"
$script:cmakeDefs += @(
"-A", "x64",
"-DGGML_CUDA=ON",
"-DGGML_AVX=on",
"-DGGML_AVX2=off",
"-DCMAKE_CUDA_FLAGS=-t6",
"-DCMAKE_CUDA_ARCHITECTURES=${script:CMAKE_CUDA_ARCHITECTURES}",
"-DCMAKE_CUDA_COMPILER_TOOLKIT_ROOT=$env:CUDA_PATH"
)
if ($null -ne $env:OLLAMA_CUSTOM_CUDA_DEFS) {
write-host "OLLAMA_CUSTOM_CUDA_DEFS=`"${env:OLLAMA_CUSTOM_CUDA_DEFS}`""
$script:cmakeDefs +=@("${env:OLLAMA_CUSTOM_CUDA_DEFS}")
write-host "building custom CUDA GPU"
}
build
sign
install
md "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\" -ea 0 > $null
write-host "copying CUDA dependencies to ${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${script:CUDA_LIB_DIR}\cudart64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${script:CUDA_LIB_DIR}\cublas64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${script:CUDA_LIB_DIR}\cublasLt64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
} else {
write-host "Skipping CUDA generation step"
}
}
function build_oneapi() {
if ((-not "${env:OLLAMA_SKIP_ONEAPI_GENERATE}") -and ("${env:ONEAPI_ROOT}")) {
# Get oneAPI version
$script:ONEAPI_VERSION = icpx --version
$script:ONEAPI_VERSION = [regex]::Match($script:ONEAPI_VERSION, '(?<=oneAPI DPC\+\+/C\+\+ Compiler )(?<version>\d+\.\d+\.\d+)').Value
if ($null -ne $script:ONEAPI_VERSION) {
$script:ONEAPI_VARIANT = "_v" + $script:ONEAPI_VERSION
}
init_vars
$script:buildDir = "../build/windows/${script:ARCH}/oneapi$script:ONEAPI_VARIANT"
$script:distDir ="$script:DIST_BASE\oneapi$script:ONEAPI_VARIANT"
$script:cmakeDefs += @(
"-G", "MinGW Makefiles",
"-DGGML_SYCL=ON",
"-DCMAKE_C_COMPILER=icx",
"-DCMAKE_CXX_COMPILER=icx",
"-DCMAKE_BUILD_TYPE=Release"
)
Write-Host "Building oneAPI"
build
# Ninja doesn't prefix with config name
if ($null -ne $script:DUMPBIN) {
& "$script:DUMPBIN" /dependents "${script:buildDir}/bin/ollama_llama_server.exe" | Select-String ".dll"
}
sign
install
md "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\" -ea 0 > $null
cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libirngmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libmmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_level_zero.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_unified_runtime.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_win_proxy_loader.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${env:ONEAPI_ROOT}\compiler\latest\bin\svml_dispmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${env:ONEAPI_ROOT}\compiler\latest\bin\sycl7.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_core.2.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_sycl_blas.4.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_tbb_thread.2.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
} else {
Write-Host "Skipping oneAPI generation step"
}
}
function build_rocm() {
if ((-not "${env:OLLAMA_SKIP_ROCM_GENERATE}") -and ("${env:HIP_PATH}")) {
$script:ROCM_VERSION=(get-item $env:HIP_PATH).Basename
if ($null -ne $script:ROCM_VERSION) {
$script:ROCM_VARIANT="_v"+$script:ROCM_VERSION
}
init_vars
$script:buildDir="../build/windows/${script:ARCH}/rocm$script:ROCM_VARIANT"
$script:distDir="$script:DIST_BASE\rocm$script:ROCM_VARIANT"
$script:cmakeDefs += @(
"-G", "Ninja",
"-DCMAKE_C_COMPILER=clang.exe",
"-DCMAKE_CXX_COMPILER=clang++.exe",
"-DGGML_HIPBLAS=on",
"-DHIP_PLATFORM=amd",
"-DGGML_AVX=on",
"-DGGML_AVX2=off",
"-DCMAKE_POSITION_INDEPENDENT_CODE=on",
"-DAMDGPU_TARGETS=$(amdGPUs)",
"-DGPU_TARGETS=$(amdGPUs)"
)
# Make sure the ROCm binary dir is first in the path
$env:PATH="$env:HIP_PATH\bin;$env:PATH"
# We have to clobber the LIB var from the developer shell for clang to work properly
$env:LIB=""
if ($null -ne $env:OLLAMA_CUSTOM_ROCM_DEFS) {
write-host "OLLAMA_CUSTOM_ROCM_DEFS=`"${env:OLLAMA_CUSTOM_ROCM_DEFS}`""
$script:cmakeDefs += @("${env:OLLAMA_CUSTOM_ROCM_DEFS}")
write-host "building custom ROCM GPU"
}
write-host "Building ROCm"
build
# Ninja doesn't prefix with config name
${script:config}=""
if ($null -ne $script:DUMPBIN) {
& "$script:DUMPBIN" /dependents "${script:buildDir}/bin/ollama_llama_server.exe" | select-string ".dll"
}
sign
install
md "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\rocblas\library\" -ea 0 > $null
cp "${env:HIP_PATH}\bin\hipblas.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
cp "${env:HIP_PATH}\bin\rocblas.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\"
# amdhip64.dll dependency comes from the driver and must be installed on the host to use AMD GPUs
cp "${env:HIP_PATH}\bin\rocblas\library\*" "${script:SRC_DIR}\dist\windows-${script:ARCH}\lib\ollama\rocblas\library\"
} else {
write-host "Skipping ROCm generation step"
}
}
init_vars
if ($($args.count) -eq 0) {
git_module_setup
apply_patches
if ($script:ARCH -eq "arm64") {
build_cpu_arm64
} else { # amd64
build_cpu_x64
build_cpu_avx
build_cpu_avx2
build_cuda
build_oneapi
build_rocm
}
cleanup
write-host "`ngo generate completed. LLM runners: $(get-childitem -path $script:DIST_BASE)"
} else {
for ( $i = 0; $i -lt $args.count; $i++ ) {
write-host "performing $($args[$i])"
& $($args[$i])
}
}
\ No newline at end of file
llm/generate/generate_darwin.go deleted 100644 → 0
View file @ a805e594
package generate
//go:generate bash ./gen_darwin.sh
llm/generate/generate_linux.go deleted 100644 → 0
View file @ a805e594
package generate
//go:generate bash ./gen_linux.sh
llm/generate/generate_windows.go deleted 100644 → 0
View file @ a805e594
package generate
//go:generate powershell -ExecutionPolicy Bypass -File ./gen_windows.ps1
llama.cpp @ 3f1ae2e3
Compare 3f1ae2e3...3f1ae2e3
Subproject commit 3f1ae2e32cde00c39b96be6d01c2997c29bae555
llm/patches/0000-cmakelist.patch deleted 100644 → 0
View file @ a805e594
From 7a3555098d4591c9b329c677654497ed8cee07ec Mon Sep 17 00:00:00 2001
From: Michael Yang <mxyng@pm.me>
Date: Fri, 23 Aug 2024 11:27:48 -0700
Subject: [PATCH] patch cmakelist
---
CMakeLists.txt | 2 ++
1 file changed, 2 insertions(+)
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 415743c2..aaadd13e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -210,3 +210,5 @@ if (LLAMA_BUILD_EXAMPLES)
add_subdirectory(examples)
add_subdirectory(pocs)
endif()
+
+add_subdirectory(../ext_server ext_server) # ollama
--
2.39.3 (Apple Git-146)
llm/patches/0001-load-progress.patch deleted 100644 → 0
View file @ a805e594
From c97ed60c3369294d5551ba099a88ddc509687df1 Mon Sep 17 00:00:00 2001
From: Gabe Goodhart <ghart@us.ibm.com>
Date: Thu, 19 Sep 2024 16:55:15 -0600
Subject: [PATCH] patch load progress
---
common/common.cpp | 2 ++
common/common.h | 7 +++++++
2 files changed, 9 insertions(+)
diff --git a/common/common.cpp b/common/common.cpp
index 8d0ed4f9..a09e8a53 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -955,6 +955,8 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
mparams.use_mmap = params.use_mmap;
mparams.use_mlock = params.use_mlock;
mparams.check_tensors = params.check_tensors;
+ mparams.progress_callback = params.progress_callback;
+ mparams.progress_callback_user_data = params.progress_callback_user_data;
if (params.kv_overrides.empty()) {
mparams.kv_overrides = NULL;
} else {
diff --git a/common/common.h b/common/common.h
index cb87c447..818a4a4a 100644
--- a/common/common.h
+++ b/common/common.h
@@ -266,6 +266,13 @@ struct gpt_params {
std::string mmproj = ""; // path to multimodal projector // NOLINT
std::vector<std::string> image; // path to image file(s)
+ // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
+ // If the provided progress_callback returns true, model loading continues.
+ // If it returns false, model loading is immediately aborted.
+ llama_progress_callback progress_callback = NULL;
+ // context pointer passed to the progress callback
+ void * progress_callback_user_data;
+
// embedding
bool embedding = false; // get only sentence embedding
int32_t embd_normalize = 2; // normalisation for embendings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
--
2.39.3 (Apple Git-146)
llm/patches/0002-clip-log.patch deleted 100644 → 0
View file @ a805e594
From 6fdf4268e13e56f0050fa6a29b029cbd54be49d2 Mon Sep 17 00:00:00 2001
From: Gabe Goodhart <ghart@us.ibm.com>
Date: Thu, 19 Sep 2024 16:58:03 -0600
Subject: [PATCH] clip log
---
examples/llava/clip.cpp | 1 +
1 file changed, 1 insertion(+)
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 8aa7b075..b8941c74 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -3,6 +3,7 @@
// I'll gradually clean and extend it
// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
#include "clip.h"
+#include "common.h"
#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
--
2.39.3 (Apple Git-146)
llm/patches/0003-load_exception.patch deleted 100644 → 0
View file @ a805e594
From 4f2b9cd0f012c49f40d0784454864ad41ca418b2 Mon Sep 17 00:00:00 2001
From: Gabe Goodhart <ghart@us.ibm.com>
Date: Thu, 19 Sep 2024 17:00:28 -0600
Subject: [PATCH] load exception
---
src/llama.cpp | 25 ++++++++++++++++---------
1 file changed, 16 insertions(+), 9 deletions(-)
diff --git a/src/llama.cpp b/src/llama.cpp
index af8afd84..4d1db3d5 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -8871,7 +8871,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
}
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
- return -1;
+ throw;
}
// loading time will be recalculate after the first eval, so
@@ -18675,16 +18675,23 @@ struct llama_model * llama_load_model_from_file(
}
model->rpc_servers.push_back(servers);
}
- int status = llama_model_load(path_model, *model, params);
- GGML_ASSERT(status <= 0);
- if (status < 0) {
- if (status == -1) {
- LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
- } else if (status == -2) {
- LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
+
+ try {
+ int status = llama_model_load(path_model, *model, params);
+ GGML_ASSERT(status <= 0);
+ if (status < 0) {
+ if (status == -1) {
+ LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
+ } else if (status == -2) {
+ LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
+ }
+ delete model;
+ return nullptr;
}
+ } catch (...) {
+ LLAMA_LOG_ERROR("%s: exception loading model\n", __func__);
delete model;
- return nullptr;
+ throw;
}
return model;
--
2.39.3 (Apple Git-146)
llm/patches/0004-metal.patch deleted 100644 → 0
View file @ a805e594
From 91d3f886f1645b38d9658c0e125603e8d5338146 Mon Sep 17 00:00:00 2001
From: nobody <>
Date: Tue, 1 Oct 2024 13:55:01 -0600
Subject: [PATCH] metal
---
ggml/src/ggml-metal.m | 30 +++++++++++++-----------------
1 file changed, 13 insertions(+), 17 deletions(-)
diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m
index 9da08fe2..3a433703 100644
--- a/ggml/src/ggml-metal.m
+++ b/ggml/src/ggml-metal.m
@@ -1720,27 +1720,23 @@ static void ggml_metal_encode_node(
// to the matrix-vector kernel
int ne11_mm_min = 1;
-#if 0
// the numbers below are measured on M2 Ultra for 7B and 13B models
// these numbers do not translate to other devices or model sizes
// TODO: need to find a better approach
- if ([ctx->device.name isEqualToString:@"Apple M2 Ultra"]) {
- switch (src0t) {
- case GGML_TYPE_F16: ne11_mm_min = 2; break;
- case GGML_TYPE_Q8_0: ne11_mm_min = 7; break;
- case GGML_TYPE_Q2_K: ne11_mm_min = 15; break;
- case GGML_TYPE_Q3_K: ne11_mm_min = 7; break;
- case GGML_TYPE_Q4_0:
- case GGML_TYPE_Q4_1: ne11_mm_min = 15; break;
- case GGML_TYPE_Q4_K: ne11_mm_min = 11; break;
- case GGML_TYPE_Q5_0: // not tested yet
- case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet
- case GGML_TYPE_Q5_K: ne11_mm_min = 7; break;
- case GGML_TYPE_Q6_K: ne11_mm_min = 7; break;
- default: ne11_mm_min = 1; break;
- }
+ switch (src0t) {
+ case GGML_TYPE_F16: ne11_mm_min = 2; break;
+ case GGML_TYPE_Q8_0: ne11_mm_min = 7; break;
+ case GGML_TYPE_Q2_K: ne11_mm_min = 15; break;
+ case GGML_TYPE_Q3_K: ne11_mm_min = 7; break;
+ case GGML_TYPE_Q4_0:
+ case GGML_TYPE_Q4_1: ne11_mm_min = 15; break;
+ case GGML_TYPE_Q4_K: ne11_mm_min = 11; break;
+ case GGML_TYPE_Q5_0: // not tested yet
+ case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet
+ case GGML_TYPE_Q5_K: ne11_mm_min = 7; break;
+ case GGML_TYPE_Q6_K: ne11_mm_min = 7; break;
+ default: ne11_mm_min = 1; break;
}
-#endif
// for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
// AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
--
2.39.3 (Apple Git-146)
llm/patches/0005-default-pretokenizer.patch deleted 100644 → 0
View file @ a805e594
From 0e531d69786c4a96a3a2bcf7b2d576bd6f7edf25 Mon Sep 17 00:00:00 2001
From: Michael Yang <mxyng@pm.me>
Date: Mon, 16 Sep 2024 15:53:13 -0700
Subject: [PATCH] 05-default-pretokenizer.diff
---
src/llama.cpp | 14 +++-----------
1 file changed, 3 insertions(+), 11 deletions(-)
diff --git a/src/llama.cpp b/src/llama.cpp
index 4c0a1bb6..800dfb95 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -6287,16 +6287,7 @@ static void llm_load_vocab(
if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
vocab.tokenizer_add_space_prefix = false;
vocab.tokenizer_clean_spaces = true;
- if (tokenizer_pre.empty()) {
- LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
- LLAMA_LOG_WARN("%s: \n", __func__);
- LLAMA_LOG_WARN("%s: ************************************ \n", __func__);
- LLAMA_LOG_WARN("%s: GENERATION QUALITY WILL BE DEGRADED! \n", __func__);
- LLAMA_LOG_WARN("%s: CONSIDER REGENERATING THE MODEL \n", __func__);
- LLAMA_LOG_WARN("%s: ************************************ \n", __func__);
- LLAMA_LOG_WARN("%s: \n", __func__);
- vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
- } else if (tokenizer_pre == "default") {
+ if (tokenizer_pre == "default") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
} else if (
tokenizer_pre == "llama3" ||
@@ -6398,7 +6389,8 @@ static void llm_load_vocab(
vocab.tokenizer_add_bos = true;
vocab.tokenizer_clean_spaces = false;
} else {
- throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
+ LLAMA_LOG_WARN("%s: missing or unrecognized pre-tokenizer type, using: 'default'\n", __func__);
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
}
} else if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
--
2.39.3 (Apple Git-146)
llm/patches/0006-embeddings.patch deleted 100644 → 0
View file @ a805e594
From 235b6d876a74cb09abe26985fa89ebe5bfc9f562 Mon Sep 17 00:00:00 2001
From: Gabe Goodhart <ghart@us.ibm.com>
Date: Thu, 19 Sep 2024 17:06:17 -0600
Subject: [PATCH] embeddings
---
src/llama.cpp | 15 +++++++++------
1 file changed, 9 insertions(+), 6 deletions(-)
diff --git a/src/llama.cpp b/src/llama.cpp
index 1a8e0c51..e55ec3f8 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -16516,7 +16516,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
const auto n_embd = hparams.n_embd;
// TODO: use a per-batch flag for logits presence instead
- const bool has_logits = !cparams.embeddings;
+ const bool has_logits = cparams.causal_attn;
const bool has_embd = cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
@@ -16794,20 +16794,23 @@ static int llama_decode_internal(
// no output
res = nullptr;
embd = nullptr;
- } else if (cparams.embeddings) {
- res = nullptr; // do not extract logits for embedding case
- embd = nullptr;
+ }
+
+ if (cparams.embeddings) {
for (int i = ggml_graph_n_nodes(gf) - 1; i >= 0; --i) {
+ embd = ggml_graph_node(gf, i);
if (strcmp(ggml_graph_node(gf, i)->name, "result_embd_pooled") == 0) {
- embd = ggml_graph_node(gf, i);
break;
}
}
- GGML_ASSERT(embd != nullptr && "missing embeddings tensor");
} else {
embd = nullptr; // do not extract embeddings when not needed
GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
}
+
+ if (!cparams.causal_attn) {
+ res = nullptr; // do not extract logits when not needed
+ }
// LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
ggml_backend_sched_alloc_graph(lctx.sched, gf);
--
2.39.3 (Apple Git-146)
llm/patches/0007-clip-unicode.patch deleted 100644 → 0
View file @ a805e594
From 01c42149cbdc194644a2f138598029938e0dd447 Mon Sep 17 00:00:00 2001
From: Gabe Goodhart <ghart@us.ibm.com>
Date: Thu, 19 Sep 2024 17:09:57 -0600
Subject: [PATCH] clip unicode
---
examples/llava/clip.cpp | 23 +++++++++++++++++++++++
1 file changed, 23 insertions(+)
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index b8941c74..3a735f17 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -40,6 +40,14 @@
#include <cinttypes>
#include <limits>
+#if defined(_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+ #define NOMINMAX
+#endif
+#include <windows.h>
+#endif
+
#define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0)
#define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0)
#define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0)
@@ -1227,7 +1235,22 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
return nullptr;
}
+#ifdef _WIN32
+ int wlen = MultiByteToWideChar(CP_UTF8, 0, fname, -1, NULL, 0);
+ if (!wlen) {
+ return NULL;
+ }
+ wchar_t * wbuf = (wchar_t *) malloc(wlen * sizeof(wchar_t));
+ wlen = MultiByteToWideChar(CP_UTF8, 0, fname, -1, wbuf, wlen);
+ if (!wlen) {
+ free(wbuf);
+ return NULL;
+ }
+ auto fin = std::ifstream(wbuf, std::ios::binary);
+ free(wbuf);
+#else
auto fin = std::ifstream(fname, std::ios::binary);
+#endif
if (!fin) {
LOG_ERR("cannot open model file for loading tensors\n");
clip_free(new_clip);
--
2.39.3 (Apple Git-146)
llm/patches/0008-solar-pro.patch deleted 100644 → 0
View file @ a805e594
From a8fe40fa7b026d2db9bb6aeecd24fcd2027110ec Mon Sep 17 00:00:00 2001
From: Michael Yang <mxyng@pm.me>
Date: Mon, 16 Sep 2024 15:53:16 -0700
Subject: [PATCH] add solar-pro support
solar-pro introduces block skip connections where blocks are connected
to other, non-sequential blocks with a scale multiple
this change adds 4 new keys to store the skip connections and one new
tensor to store the scalar. the scalar is implemented a 1-dimensional
tensor with 2 elements dervied from the model's bskcn_tv configuration.
in general, the values are (bskcn_tv, 1 - bskcn_tv)
---
src/llama.cpp | 270 +++++++++++++++++++++++++++++++++++++++++++++++---
1 file changed, 255 insertions(+), 15 deletions(-)
diff --git a/src/llama.cpp b/src/llama.cpp
index 4c0a1bb6..c6fc0c3f 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -217,6 +217,7 @@ enum llm_arch {
LLM_ARCH_GRANITE,
LLM_ARCH_GRANITE_MOE,
LLM_ARCH_CHAMELEON,
+ LLM_ARCH_SOLAR,
LLM_ARCH_UNKNOWN,
};
@@ -270,6 +271,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_GRANITE, "granite" },
{ LLM_ARCH_GRANITE_MOE, "granitemoe" },
{ LLM_ARCH_CHAMELEON, "chameleon" },
+ { LLM_ARCH_SOLAR, "solar" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@@ -327,6 +329,7 @@ enum llm_kv {
LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
LLM_KV_ATTENTION_SLIDING_WINDOW,
LLM_KV_ATTENTION_SCALE,
+ LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_FREQ_BASE,
@@ -421,20 +424,21 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_RESIDUAL_SCALE, "%s.residual_scale" },
{ LLM_KV_EMBEDDING_SCALE, "%s.embedding_scale" },
- { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
- { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
- { LLM_KV_ATTENTION_MAX_ALIBI_BIAS, "%s.attention.max_alibi_bias" },
- { LLM_KV_ATTENTION_CLAMP_KQV, "%s.attention.clamp_kqv" },
- { LLM_KV_ATTENTION_KEY_LENGTH, "%s.attention.key_length" },
- { LLM_KV_ATTENTION_VALUE_LENGTH, "%s.attention.value_length" },
- { LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" },
- { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" },
- { LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" },
- { LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" },
- { LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" },
- { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
- { LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },
- { LLM_KV_ATTENTION_SCALE, "%s.attention.scale" },
+ { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
+ { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
+ { LLM_KV_ATTENTION_MAX_ALIBI_BIAS, "%s.attention.max_alibi_bias" },
+ { LLM_KV_ATTENTION_CLAMP_KQV, "%s.attention.clamp_kqv" },
+ { LLM_KV_ATTENTION_KEY_LENGTH, "%s.attention.key_length" },
+ { LLM_KV_ATTENTION_VALUE_LENGTH, "%s.attention.value_length" },
+ { LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" },
+ { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" },
+ { LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" },
+ { LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" },
+ { LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" },
+ { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
+ { LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },
+ { LLM_KV_ATTENTION_SCALE, "%s.attention.scale" },
+ { LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION, "%s.attention.block_skip_connection.%d" },
{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
@@ -608,6 +612,7 @@ enum llm_tensor {
LLM_TENSOR_ENC_OUTPUT_NORM,
LLM_TENSOR_CLS,
LLM_TENSOR_CLS_OUT,
+ LLM_TENSOR_BSKCN_TV,
};
static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@@ -1527,6 +1532,25 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
},
},
+
+ {
+ LLM_ARCH_SOLAR,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_BSKCN_TV, "bskcn_tv" },
+ },
+ },
{
LLM_ARCH_UNKNOWN,
{
@@ -2360,6 +2384,7 @@ enum e_model {
MODEL_15B,
MODEL_16B,
MODEL_20B,
+ MODEL_22B,
MODEL_30B,
MODEL_34B,
MODEL_35B,
@@ -2409,6 +2434,8 @@ struct llama_hparams {
std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
+ std::array<std::array<uint32_t, LLAMA_MAX_LAYERS>, 4> n_bskcn_arr;
+
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
uint32_t n_lora_kv = 0;
@@ -2479,6 +2506,7 @@ struct llama_hparams {
if (this->n_head_arr != other.n_head_arr) return true;
if (this->n_head_kv_arr != other.n_head_kv_arr) return true;
if (this->n_ff_arr != other.n_ff_arr) return true;
+ if (this->n_bskcn_arr != other.n_bskcn_arr) return true;
if (this->n_rel_attn_bkts != other.n_rel_attn_bkts) return true;
if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true;
@@ -2588,6 +2616,14 @@ struct llama_hparams {
return ssm_d_state * ssm_d_inner;
}
}
+
+ bool n_bskcn(uint32_t n, uint32_t il = 0) const {
+ if (il < n_layer) {
+ return n_bskcn_arr[n][il] > 0;
+ }
+
+ GGML_ABORT("fatal error");
+ }
};
static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
@@ -2769,6 +2805,8 @@ struct llama_layer {
struct ggml_tensor * ffn_gate_scale;
struct ggml_tensor * ffn_up_scale;
struct ggml_tensor * ffn_down_scale;
+
+ struct ggml_tensor * bskcn_tv;
};
// very similar to llama_batch,
@@ -6134,6 +6172,21 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
+ case LLM_ARCH_SOLAR:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ for (int i = 0; i < hparams.n_bskcn_arr.max_size(); ++i) {
+ auto & bskcn = hparams.n_bskcn_arr.at(i);
+ bskcn.fill(0);
+ ml.get_key_or_arr(::format(LLM_KV_NAMES.at(LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION), LLM_ARCH_NAMES.at(ml.llm_kv.arch), i), bskcn, hparams.n_layer, false);
+ }
+
+ switch (hparams.n_layer) {
+ case 64: model.type = e_model::MODEL_22B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ }
default: (void)0;
}
@@ -8839,6 +8892,37 @@ static bool llm_load_tensors(
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ }
+ } break;
+ case LLM_ARCH_SOLAR:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ ggml_context * ctx_layer = ctx_for_layer(i);
+ ggml_context * ctx_split = ctx_for_layer_split(i);
+
+ auto & layer = model.layers[i];
+
+ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+ layer.bskcn_tv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_BSKCN_TV, "weight"), {2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
@@ -16009,7 +16093,6 @@ struct llm_build_context {
return gf;
}
-
// ref: https://github.com/facebookresearch/chameleon
// based on the original build_llama() function, changes:
// * qk-norm
@@ -16187,6 +16270,158 @@ struct llm_build_context {
return gf;
}
+
+ ggml_cgraph * build_solar() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
+
+ // mutable variable, needed during the last layer of the computation to skip unused tokens
+ int32_t n_tokens = this->n_tokens;
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+ GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ struct ggml_tensor * bskcn_1;
+ struct ggml_tensor * bskcn_2;
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ if (hparams.n_bskcn(0, il)) {
+ bskcn_1 = inpSA;
+ }
+
+ if (hparams.n_bskcn(1, il)) {
+ bskcn_2 = inpSA;
+ }
+
+ if (hparams.n_bskcn(2, il)) {
+ inpSA = ggml_add(
+ ctx0,
+ ggml_mul(ctx0, bskcn_1, ggml_view_1d(ctx0, model.layers[il].bskcn_tv, 1, 0)),
+ ggml_mul(ctx0, inpSA, ggml_view_1d(ctx0, model.layers[il].bskcn_tv, 1, ggml_element_size(model.layers[il].bskcn_tv))));
+ }
+
+ if (hparams.n_bskcn(3, il)) {
+ inpSA = ggml_add(
+ ctx0,
+ ggml_mul(ctx0, bskcn_2, ggml_view_1d(ctx0, model.layers[il].bskcn_tv, 1, 0)),
+ ggml_mul(ctx0, inpSA, ggml_view_1d(ctx0, model.layers[il].bskcn_tv, 1, ggml_element_size(model.layers[il].bskcn_tv))));
+ }
+
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // rope freq factors for llama3; may return nullptr for llama2 and other models
+ struct ggml_tensor * rope_factors = build_rope_factors(il);
+
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ if (model.layers[il].bq) {
+ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+ }
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ if (model.layers[il].bk) {
+ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+ }
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ if (model.layers[il].bv) {
+ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ cb(Vcur, "Vcur", il);
+ }
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
};
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
@@ -16451,6 +16686,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_chameleon();
} break;
+ case LLM_ARCH_SOLAR:
+ {
+ result = llm.build_solar();
+ } break;
default:
GGML_ABORT("fatal error");
}
@@ -19594,6 +19833,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_CHAMELEON:
+ case LLM_ARCH_SOLAR:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
--
2.39.3 (Apple Git-146)
scripts/build_darwin.sh
View file @ b754f5a6
......@@ -7,15 +7,9 @@ set -e
mkdir -p dist
for TARGETARCH in arm64 amd64; do
if [ -n "${OLLAMA_NEW_RUNNERS}" ]; then
echo "Building Go runner darwin $TARGETARCH"
rm -rf llama/build
GOOS=darwin ARCH=$TARGETARCH GOARCH=$TARGETARCH make -C llama -j 8
else
echo "Building C++ runner darwin $TARGETARCH"
rm -rf llm/build
GOOS=darwin GOARCH=$TARGETARCH go generate ./...
fi
echo "Building Go runner darwin $TARGETARCH"
rm -rf llama/build
GOOS=darwin ARCH=$TARGETARCH GOARCH=$TARGETARCH make -C llama -j 8
# These require Xcode v13 or older to target MacOS v11
# If installed to an alternate location use the following to enable
# export SDKROOT=/Applications/Xcode_12.5.1.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk
......
scripts/build_docker.sh
View file @ b754f5a6
......@@ -19,7 +19,7 @@ docker buildx build \
${LOAD_OR_PUSH} \
--platform=${PLATFORM} \
${OLLAMA_COMMON_BUILD_ARGS} \
-f ${DOCKERFILE_DIR}Dockerfile \
-f Dockerfile \
-t ${FINAL_IMAGE_REPO}:$VERSION \
.
......@@ -29,7 +29,7 @@ if echo $PLATFORM | grep "amd64" > /dev/null; then
--platform=linux/amd64 \
${OLLAMA_COMMON_BUILD_ARGS} \
--target runtime-rocm \
-f ${DOCKERFILE_DIR}Dockerfile \
-f Dockerfile \
-t ${FINAL_IMAGE_REPO}:$VERSION-rocm \
.
fi
\ No newline at end of file
scripts/build_linux.sh
View file @ b754f5a6
......@@ -19,7 +19,7 @@ docker buildx build \
--platform=${PLATFORM} \
${OLLAMA_COMMON_BUILD_ARGS} \
--target dist \
-f ${DOCKERFILE_DIR}Dockerfile \
-f Dockerfile \
.
# buildx behavior changes for single vs. multiplatform
......
scripts/build_remote.py deleted 100755 → 0
View file @ a805e594
#!/usr/bin/env python3
import subprocess
import sys
from urllib.parse import urlparse
from git import Repo
# Helper script to be able to build on remote repos using git to push local changes
# (e.g. particularly helpful to target a remote windows build system)
#
# Typical windows remote git config looks like this:
#
#[remote "windows-pa"]
# url = jdoe@desktop-foo:C:/Users/Jdoe/code/ollama
# fetch = +refs/heads/*:refs/remotes/windows-pa/*
# uploadpack = powershell git upload-pack
# receivepack = powershell git receive-pack
#
# TODO - add argpare and make this more configurable
# - force flag becomes optional
# - generate, build or test ...
# Note: remote repo will need this run once:
# git config --local receive.denyCurrentBranch updateInstead
repo = Repo(".")
# On linux, add links in /usr/local/bin to the go binaries to avoid needing this
# GoCmd = "/usr/local/go/bin/go"
GoCmd = "go"
if repo.is_dirty():
print("Tree is dirty. Commit your changes before running this script")
sys.exit(1)
if len(sys.argv) != 2:
print("Please specify the remote name: " + ', '.join([r.name for r in repo.remotes]))
sys.exit(1)
remote_name = sys.argv[1]
remote = {r.name: r for r in repo.remotes}[remote_name]
raw_url = list(remote.urls)[0]
url = urlparse(raw_url)
# Windows urls don't quite parse properly
if url.scheme == "" and url.netloc == "":
url = urlparse("ssh://" + raw_url)
print("URL: " + str(url))
netloc = url.netloc.split(":")[0]
path = url.path
branch_name = repo.active_branch.name
print("Force pushing content to remote...")
# Use with care given the force push
remote.push(force=True).raise_if_error()
print("Ensuring correct branch checked out on remote via ssh...")
subprocess.check_call(['ssh', netloc, 'cd', path, ';', 'git', 'checkout', branch_name])
# TODO - add some hardening to try to figure out how to set up the path properly
# subprocess.check_call(['ssh', netloc, 'cd', path, ';', 'env'])
# TODO - or consider paramiko maybe
print("Running Windows Build Script")
subprocess.check_call(['ssh', netloc, 'cd', path, ';', "powershell", "-ExecutionPolicy", "Bypass", "-File", "./scripts/build_windows.ps1"])
# print("Building")
# subprocess.check_call(['ssh', netloc, 'cd', path, ';', GoCmd, 'build', '.'])
print("Copying built result")
subprocess.check_call(['scp', netloc +":"+ path + "/ollama.exe", './dist/'])
print("Copying installer")
subprocess.check_call(['scp', netloc +":"+ path + "/dist/Ollama Setup.exe", './dist/'])
scripts/build_windows.ps1
View file @ b754f5a6
......@@ -83,51 +83,8 @@ function buildOllama() {
if ($null -eq ${env:OLLAMA_SKIP_GENERATE}) {
write-host "Building ollama runners"
Remove-Item -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}"
if ($null -eq ${env:OLLAMA_NEW_RUNNERS}) {
# Start by skipping CUDA to build everything else
write-host "Building ollama runners"
powershell -Command { $env:OLLAMA_SKIP_CUDA_GENERATE="1"; & go generate ./... }
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
# Then skip everyhting else and build all the CUDA variants
foreach ($env:CUDA_LIB_DIR in $script:CUDA_DIRS) {
write-host "Building CUDA ${env:CUDA_LIB_DIR} runner"
if ($env:CUDA_LIB_DIR.Contains("v12")) {
powershell -Command {
$env:OLLAMA_SKIP_CUDA_GENERATE=""
$env:OLLAMA_SKIP_STATIC_GENERATE="1"
$env:OLLAMA_SKIP_CPU_GENERATE="1"
$env:OLLAMA_SKIP_ONEAPI_GENERATE="1"
$env:OLLAMA_SKIP_ROCM_GENERATE="1"
$env:CMAKE_CUDA_ARCHITECTURES="60;61;62;70;72;75;80;86;87;89;90;90a"
$env:OLLAMA_CUSTOM_CUDA_DEFS="-DGGML_CUDA_USE_GRAPHS=on"
$env:CUDA_PATH=split-path -path $env:CUDA_LIB_DIR -parent
$env:PATH="$envs:CUDA_LIB_DIR;$env:PATH"
& go generate ./...
}
} else {
powershell -Command {
$env:OLLAMA_SKIP_CUDA_GENERATE=""
$env:OLLAMA_SKIP_STATIC_GENERATE="1"
$env:OLLAMA_SKIP_CPU_GENERATE="1"
$env:OLLAMA_SKIP_ONEAPI_GENERATE="1"
$env:OLLAMA_SKIP_ROCM_GENERATE="1"
$env:CMAKE_CUDA_ARCHITECTURES="50;52;53;60;61;62;70;72;75;80;86"
$env:OLLAMA_CUSTOM_CUDA_DEFS=""
$env:CUDA_PATH=split-path -path $env:CUDA_LIB_DIR -parent
$env:PATH="$envs:CUDA_LIB_DIR;$env:PATH"
& go generate ./...
}
}
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
}
} else {
& make -C llama -j 12
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
}
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
& make -C llama -j 12
if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
} else {
write-host "Skipping generate step with OLLAMA_SKIP_GENERATE set"
}
......@@ -172,7 +129,7 @@ function gatherDependencies() {
} else {
$depArch=$script:TARGET_ARCH
}
if ($depArch -eq "amd64") {
if ($depArch -eq "x64") {
cp "${env:VCToolsRedistDir}\${depArch}\Microsoft.VC*.CRT\msvcp140*.dll" "${script:DIST_DIR}\lib\ollama\"
cp "${env:VCToolsRedistDir}\${depArch}\Microsoft.VC*.CRT\vcruntime140.dll" "${script:DIST_DIR}\lib\ollama\"
cp "${env:VCToolsRedistDir}\${depArch}\Microsoft.VC*.CRT\vcruntime140_1.dll" "${script:DIST_DIR}\lib\ollama\"
......
scripts/env.sh
View file @ b754f5a6
......@@ -20,12 +20,6 @@ OLLAMA_COMMON_BUILD_ARGS="--build-arg=VERSION \
--build-arg=CUSTOM_CPU_FLAGS \
--build-arg=GPU_RUNNER_CPU_FLAGS \
--build-arg=AMDGPU_TARGETS"
OLLAMA_NEW_RUNNERS=${OLLAMA_NEW_RUNNERS:-""}
if [ -n "${OLLAMA_NEW_RUNNERS}" ]; then
DOCKERFILE_DIR="./llama/"
else
DOCKERFILE_DIR="./"
fi
echo "Building Ollama"
echo "VERSION=$VERSION"
......
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