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https://github.com/ggerganov/llama.cpp.git
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gg/avoid-m
| Author | SHA1 | Date | |
|---|---|---|---|
|
b5af7ad84f |
@@ -9,7 +9,7 @@
|
||||
git,
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python3,
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mpi,
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openblas, # TODO: Use the generic `blas` so users could switch between alternative implementations
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openblas, # TODO: Use the generic `blas` so users could switch betwen alternative implementations
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cudaPackages,
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darwin,
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rocmPackages,
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||||
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@@ -177,29 +177,27 @@ if (LLAMA_METAL)
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if (LLAMA_METAL_SHADER_DEBUG)
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# custom command to do the following:
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# xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
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# xcrun -sdk macosx metallib ggml-metal.air -o default.metallib
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# xcrun -sdk macosx metallib ggml-metal.air -o ggml.metallib
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#
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# note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
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# disabling fast math is needed in order to pass tests/test-backend-ops
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# note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
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# note: unfortunately, we have to call it default.metallib instead of ggml.metallib
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# ref: https://github.com/ggerganov/whisper.cpp/issues/1720
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set(XC_FLAGS -fno-fast-math -fno-inline -g)
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if (LLAMA_QKK_64)
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set(XC_FLAGS ${XC_FLAGS} -DQK_K=64)
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endif()
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add_custom_command(
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OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
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OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib
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COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
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COMMAND xcrun -sdk macosx metallib ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
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COMMAND xcrun -sdk macosx metallib ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib
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DEPENDS ggml-metal.metal
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COMMENT "Compiling Metal kernels"
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)
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add_custom_target(
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ggml-metal ALL
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DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
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DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml.metallib
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)
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endif()
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@@ -230,11 +228,7 @@ if (LLAMA_BLAS)
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if (${LLAMA_BLAS_VENDOR} MATCHES "Generic")
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pkg_check_modules(DepBLAS REQUIRED blas)
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elseif (${LLAMA_BLAS_VENDOR} MATCHES "OpenBLAS")
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# As of openblas v0.3.22, the 64-bit is named openblas64.pc
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pkg_check_modules(DepBLAS openblas64)
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if (NOT DepBLAS_FOUND)
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pkg_check_modules(DepBLAS REQUIRED openblas)
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endif()
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pkg_check_modules(DepBLAS REQUIRED openblas)
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elseif (${LLAMA_BLAS_VENDOR} MATCHES "FLAME")
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pkg_check_modules(DepBLAS REQUIRED blis)
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elseif (${LLAMA_BLAS_VENDOR} MATCHES "ATLAS")
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@@ -13,17 +13,21 @@ let package = Package(
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products: [
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.library(name: "llama", targets: ["llama"]),
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],
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dependencies: [
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.package(url: "https://github.com/ggerganov/ggml.git", .branch("master"))
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],
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targets: [
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.target(
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name: "llama",
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dependencies: ["ggml"],
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path: ".",
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exclude: [],
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sources: [
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"ggml.c",
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"llama.cpp",
|
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"ggml-alloc.c",
|
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"ggml-backend.c",
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"ggml-quants.c",
|
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"ggml-metal.m",
|
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],
|
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resources: [
|
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.process("ggml-metal.metal")
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],
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publicHeadersPath: "spm-headers",
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cSettings: [
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|
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@@ -1107,7 +1107,7 @@ void print_common_train_usage(int /*argc*/, char ** /*argv*/, const struct train
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fprintf(stderr, " --sample-start STR Sets the starting point for samples after the specified pattern. If empty use every token position as sample start. (default '%s')\n", params->sample_start.c_str());
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fprintf(stderr, " --include-sample-start Include the sample start in the samples. (default off)\n");
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fprintf(stderr, " --escape process sample start escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
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fprintf(stderr, " --overlapping-samples Samples may overlap, will include sample-start of second and following samples. When off, samples will end at begin of next sample. (default off)\n");
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fprintf(stderr, " --overlapping-samples Samples my overlap, will include sample-start of second and following samples. When off, samples will end at begin of next sample. (default off)\n");
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fprintf(stderr, " --fill-with-next-samples Samples shorter than context length will be followed by the next (shuffled) samples. (default off)\n");
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fprintf(stderr, " --separate-with-eos When fill-with-next-samples, insert end-of-sequence token between samples.%s\n", params->separate_with_eos ? " (default)" : "");
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fprintf(stderr, " --separate-with-bos When fill-with-next-samples, insert begin-of-sequence token between samples.%s\n", params->separate_with_bos ? " (default)" : "");
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|
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@@ -1,61 +0,0 @@
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#!/bin/bash
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#
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# Few-shot translation example.
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# Requires a base model (i.e. no fine-tuned or instruct models).
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#
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# Usage:
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#
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# cd llama.cpp
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# make -j
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#
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# ./examples/base-translate.sh <model-base> "<text>" [extra-main-args]
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#
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|
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if [ $# -lt 2 ]; then
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echo "Usage: ./base-translate.sh <model-base> \"<text>\" [extra-main-args]"
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exit 1
|
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fi
|
||||
|
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eargs=""
|
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if [ $# -gt 2 ]; then
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eargs="${@:3}"
|
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fi
|
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|
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ftmp="__llama.cpp_example_tmp__.txt"
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trap "rm -f $ftmp" EXIT
|
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|
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echo "Translate from English to French:
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|
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===
|
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sea otter, peppermint, plush girafe:
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sea otter => loutre de mer
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peppermint => menthe poivrée
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plush girafe => girafe peluche
|
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|
||||
===
|
||||
|
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violin
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||||
|
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violin => violon
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||||
|
||||
===
|
||||
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phone, computer, mouse, keyboard:
|
||||
|
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phone => téléphone
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computer => ordinateur
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mouse => souris
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keyboard => clavier
|
||||
|
||||
===
|
||||
" > $ftmp
|
||||
|
||||
echo "$2
|
||||
" >> $ftmp
|
||||
|
||||
model=$1
|
||||
|
||||
# generate the most likely continuation until the string "===" is found
|
||||
./main -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs
|
||||
@@ -3,9 +3,15 @@
|
||||
#include "llama.h"
|
||||
#include "common.h"
|
||||
#include "train.h"
|
||||
#include <unordered_map>
|
||||
#include <vector>
|
||||
#include <cassert>
|
||||
#include <climits>
|
||||
#include <cstring>
|
||||
#include <cstdarg>
|
||||
#include <ctime>
|
||||
#include <random>
|
||||
#include <stdexcept>
|
||||
#include <algorithm>
|
||||
#include <string>
|
||||
|
||||
|
||||
@@ -1,5 +1,8 @@
|
||||
import Foundation
|
||||
import llama
|
||||
|
||||
// To use this in your own project, add llama.cpp as a swift package dependency
|
||||
// and uncomment this import line.
|
||||
// import llama
|
||||
|
||||
enum LlamaError: Error {
|
||||
case couldNotInitializeContext
|
||||
@@ -158,7 +161,7 @@ actor LlamaContext {
|
||||
new_token_id = llama_sample_token_greedy(context, &candidates_p)
|
||||
}
|
||||
|
||||
if new_token_id == llama_token_eos(model) || n_cur == n_len {
|
||||
if new_token_id == llama_token_eos(context) || n_cur == n_len {
|
||||
print("\n")
|
||||
let new_token_str = String(cString: temporary_invalid_cchars + [0])
|
||||
temporary_invalid_cchars.removeAll()
|
||||
|
||||
5
examples/llama.swiftui/llama.cpp.swift/bridging-header.h
Normal file
5
examples/llama.swiftui/llama.cpp.swift/bridging-header.h
Normal file
@@ -0,0 +1,5 @@
|
||||
//
|
||||
// Use this file to import your target's public headers that you would like to expose to Swift.
|
||||
//
|
||||
|
||||
#import "llama.h"
|
||||
@@ -7,32 +7,51 @@
|
||||
objects = {
|
||||
|
||||
/* Begin PBXBuildFile section */
|
||||
542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */ = {isa = PBXBuildFile; fileRef = 542376072B0D9BFB008E6A1C /* ggml-quants.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
|
||||
5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */ = {isa = PBXBuildFile; fileRef = 5423760A2B0D9C4B008E6A1C /* ggml-backend.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
|
||||
542378792ACE3F3500834A7B /* ggml-metal.metal in Resources */ = {isa = PBXBuildFile; fileRef = 549479C82AC9E10B00E0F78B /* ggml-metal.metal */; };
|
||||
542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09B2AC8723900A8AEE9 /* ggml.c */; settings = {COMPILER_FLAGS = "-DGGML_USE_ACCELERATE -DGGML_USE_METAL -DGGML_USE_K_QUANTS -O3"; }; };
|
||||
542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */ = {isa = PBXBuildFile; fileRef = 542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */; settings = {COMPILER_FLAGS = "-O3"; }; };
|
||||
542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */ = {isa = PBXBuildFile; fileRef = 542EA0A12AC8729100A8AEE9 /* llama.cpp */; settings = {COMPILER_FLAGS = "-DGGML_USE_K_QUANTS -DGGML_USE_METAL -O3"; }; };
|
||||
549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; };
|
||||
549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */ = {isa = PBXBuildFile; fileRef = 549479C52AC9E0F200E0F78B /* ggml-metal.m */; settings = {COMPILER_FLAGS = "-fno-objc-arc -DGGML_SWIFT -DGGML_USE_METAL -O3"; }; };
|
||||
7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */; };
|
||||
8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */; };
|
||||
8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A1C83782AC328BD0096AF73 /* ContentView.swift */; };
|
||||
8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 8A1C837A2AC328BE0096AF73 /* Assets.xcassets */; };
|
||||
8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = 8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */; };
|
||||
8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 8A39BE092AC7601000BFEB40 /* Accelerate.framework */; };
|
||||
8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; };
|
||||
8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; };
|
||||
8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; };
|
||||
DF810E132B4A5BA200301144 /* llama in Frameworks */ = {isa = PBXBuildFile; productRef = DF810E122B4A5BA200301144 /* llama */; };
|
||||
F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; };
|
||||
/* End PBXBuildFile section */
|
||||
|
||||
/* Begin PBXFileReference section */
|
||||
542376062B0D9BEA008E6A1C /* ggml-quants.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-quants.h"; path = "../../ggml-quants.h"; sourceTree = "<group>"; };
|
||||
542376072B0D9BFB008E6A1C /* ggml-quants.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-quants.c"; path = "../../ggml-quants.c"; sourceTree = "<group>"; };
|
||||
542376092B0D9C40008E6A1C /* ggml-backend.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; name = "ggml-backend.h"; path = "../../ggml-backend.h"; sourceTree = "<group>"; };
|
||||
5423760A2B0D9C4B008E6A1C /* ggml-backend.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-backend.c"; path = "../../ggml-backend.c"; sourceTree = "<group>"; };
|
||||
542EA09B2AC8723900A8AEE9 /* ggml.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = ggml.c; path = ../../ggml.c; sourceTree = "<group>"; };
|
||||
542EA09C2AC8723900A8AEE9 /* ggml.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = ggml.h; path = ../../ggml.h; sourceTree = "<group>"; };
|
||||
542EA09E2AC8725700A8AEE9 /* ggml-alloc.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-alloc.h"; path = "../../ggml-alloc.h"; sourceTree = "<group>"; };
|
||||
542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.c; name = "ggml-alloc.c"; path = "../../ggml-alloc.c"; sourceTree = "<group>"; };
|
||||
542EA0A12AC8729100A8AEE9 /* llama.cpp */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.cpp; name = llama.cpp; path = ../../llama.cpp; sourceTree = "<group>"; };
|
||||
542EA0A22AC8729100A8AEE9 /* llama.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = llama.h; path = ../../llama.h; sourceTree = "<group>"; };
|
||||
549479C52AC9E0F200E0F78B /* ggml-metal.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; name = "ggml-metal.m"; path = "../../ggml-metal.m"; sourceTree = "<group>"; };
|
||||
549479C62AC9E0F200E0F78B /* ggml-metal.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; name = "ggml-metal.h"; path = "../../ggml-metal.h"; sourceTree = "<group>"; };
|
||||
549479C82AC9E10B00E0F78B /* ggml-metal.metal */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.metal; name = "ggml-metal.metal"; path = "../../ggml-metal.metal"; sourceTree = "<group>"; };
|
||||
549479CA2AC9E16000E0F78B /* Metal.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Metal.framework; path = System/Library/Frameworks/Metal.framework; sourceTree = SDKROOT; };
|
||||
7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.swift; path = DownloadButton.swift; sourceTree = "<group>"; };
|
||||
8A08D20A2AC73B1500FE6CD4 /* bridging-header.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = "bridging-header.h"; sourceTree = "<group>"; };
|
||||
8A1C83732AC328BD0096AF73 /* llama.swiftui.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = llama.swiftui.app; sourceTree = BUILT_PRODUCTS_DIR; };
|
||||
8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = llama_swiftuiApp.swift; sourceTree = "<group>"; };
|
||||
8A1C83782AC328BD0096AF73 /* ContentView.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = ContentView.swift; sourceTree = "<group>"; };
|
||||
8A1C837A2AC328BE0096AF73 /* Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = Assets.xcassets; sourceTree = "<group>"; };
|
||||
8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */ = {isa = PBXFileReference; lastKnownFileType = folder.assetcatalog; path = "Preview Assets.xcassets"; sourceTree = "<group>"; };
|
||||
8A39BE092AC7601000BFEB40 /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = System/Library/Frameworks/Accelerate.framework; sourceTree = SDKROOT; };
|
||||
8A3F84232AC4C891005E2EE8 /* models */ = {isa = PBXFileReference; lastKnownFileType = folder; name = models; path = llama.swiftui/Resources/models; sourceTree = "<group>"; };
|
||||
8A907F322AC7134E006146EA /* LibLlama.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LibLlama.swift; sourceTree = "<group>"; };
|
||||
8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LlamaState.swift; sourceTree = "<group>"; };
|
||||
DF2D2FE72B4A59BE00FCB72D /* llama.cpp */ = {isa = PBXFileReference; lastKnownFileType = wrapper; name = llama.cpp; path = ../..; sourceTree = "<group>"; };
|
||||
F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = LoadCustomButton.swift; sourceTree = "<group>"; };
|
||||
/* End PBXFileReference section */
|
||||
|
||||
/* Begin PBXFrameworksBuildPhase section */
|
||||
@@ -40,7 +59,6 @@
|
||||
isa = PBXFrameworksBuildPhase;
|
||||
buildActionMask = 2147483647;
|
||||
files = (
|
||||
DF810E132B4A5BA200301144 /* llama in Frameworks */,
|
||||
549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */,
|
||||
8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */,
|
||||
);
|
||||
@@ -49,10 +67,30 @@
|
||||
/* End PBXFrameworksBuildPhase section */
|
||||
|
||||
/* Begin PBXGroup section */
|
||||
8A08D1F62AC7383900FE6CD4 /* llama.cpp */ = {
|
||||
isa = PBXGroup;
|
||||
children = (
|
||||
5423760A2B0D9C4B008E6A1C /* ggml-backend.c */,
|
||||
542376092B0D9C40008E6A1C /* ggml-backend.h */,
|
||||
542376062B0D9BEA008E6A1C /* ggml-quants.h */,
|
||||
542376072B0D9BFB008E6A1C /* ggml-quants.c */,
|
||||
549479C82AC9E10B00E0F78B /* ggml-metal.metal */,
|
||||
549479C62AC9E0F200E0F78B /* ggml-metal.h */,
|
||||
549479C52AC9E0F200E0F78B /* ggml-metal.m */,
|
||||
542EA09B2AC8723900A8AEE9 /* ggml.c */,
|
||||
542EA09C2AC8723900A8AEE9 /* ggml.h */,
|
||||
542EA09F2AC8725700A8AEE9 /* ggml-alloc.c */,
|
||||
542EA09E2AC8725700A8AEE9 /* ggml-alloc.h */,
|
||||
542EA0A12AC8729100A8AEE9 /* llama.cpp */,
|
||||
542EA0A22AC8729100A8AEE9 /* llama.h */,
|
||||
);
|
||||
name = llama.cpp;
|
||||
sourceTree = "<group>";
|
||||
};
|
||||
8A1C836A2AC328BD0096AF73 = {
|
||||
isa = PBXGroup;
|
||||
children = (
|
||||
DF2D2FE72B4A59BE00FCB72D /* llama.cpp */,
|
||||
8A08D1F62AC7383900FE6CD4 /* llama.cpp */,
|
||||
8A907F312AC7134E006146EA /* llama.cpp.swift */,
|
||||
8A3F84232AC4C891005E2EE8 /* models */,
|
||||
8A1C83752AC328BD0096AF73 /* llama.swiftui */,
|
||||
@@ -77,10 +115,19 @@
|
||||
8A9F7C4A2AC332BF008AE1EA /* UI */,
|
||||
8A1C83762AC328BD0096AF73 /* llama_swiftuiApp.swift */,
|
||||
8A1C837A2AC328BE0096AF73 /* Assets.xcassets */,
|
||||
8A1C837C2AC328BE0096AF73 /* Preview Content */,
|
||||
);
|
||||
path = llama.swiftui;
|
||||
sourceTree = "<group>";
|
||||
};
|
||||
8A1C837C2AC328BE0096AF73 /* Preview Content */ = {
|
||||
isa = PBXGroup;
|
||||
children = (
|
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8A1C837D2AC328BE0096AF73 /* Preview Assets.xcassets */,
|
||||
);
|
||||
path = "Preview Content";
|
||||
sourceTree = "<group>";
|
||||
};
|
||||
8A39BE082AC7601000BFEB40 /* Frameworks */ = {
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||||
isa = PBXGroup;
|
||||
children = (
|
||||
@@ -108,6 +155,7 @@
|
||||
8A907F312AC7134E006146EA /* llama.cpp.swift */ = {
|
||||
isa = PBXGroup;
|
||||
children = (
|
||||
8A08D20A2AC73B1500FE6CD4 /* bridging-header.h */,
|
||||
8A907F322AC7134E006146EA /* LibLlama.swift */,
|
||||
);
|
||||
path = llama.cpp.swift;
|
||||
@@ -118,7 +166,6 @@
|
||||
children = (
|
||||
7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */,
|
||||
8A1C83782AC328BD0096AF73 /* ContentView.swift */,
|
||||
F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */,
|
||||
);
|
||||
path = UI;
|
||||
sourceTree = "<group>";
|
||||
@@ -148,7 +195,6 @@
|
||||
);
|
||||
name = llama.swiftui;
|
||||
packageProductDependencies = (
|
||||
DF810E122B4A5BA200301144 /* llama */,
|
||||
);
|
||||
productName = llama.swiftui;
|
||||
productReference = 8A1C83732AC328BD0096AF73 /* llama.swiftui.app */;
|
||||
@@ -195,7 +241,9 @@
|
||||
isa = PBXResourcesBuildPhase;
|
||||
buildActionMask = 2147483647;
|
||||
files = (
|
||||
542378792ACE3F3500834A7B /* ggml-metal.metal in Resources */,
|
||||
8A3F84242AC4C891005E2EE8 /* models in Resources */,
|
||||
8A1C837E2AC328BE0096AF73 /* Preview Assets.xcassets in Resources */,
|
||||
8A1C837B2AC328BE0096AF73 /* Assets.xcassets in Resources */,
|
||||
);
|
||||
runOnlyForDeploymentPostprocessing = 0;
|
||||
@@ -207,12 +255,17 @@
|
||||
isa = PBXSourcesBuildPhase;
|
||||
buildActionMask = 2147483647;
|
||||
files = (
|
||||
F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */,
|
||||
542376082B0D9BFB008E6A1C /* ggml-quants.c in Sources */,
|
||||
549479CD2AC9E42A00E0F78B /* ggml-metal.m in Sources */,
|
||||
542EA09D2AC8723900A8AEE9 /* ggml.c in Sources */,
|
||||
8A907F332AC7138A006146EA /* LibLlama.swift in Sources */,
|
||||
542EA0A32AC8729100A8AEE9 /* llama.cpp in Sources */,
|
||||
8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */,
|
||||
8A1C83792AC328BD0096AF73 /* ContentView.swift in Sources */,
|
||||
8A1C83772AC328BD0096AF73 /* llama_swiftuiApp.swift in Sources */,
|
||||
7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */,
|
||||
542EA0A02AC8725700A8AEE9 /* ggml-alloc.c in Sources */,
|
||||
5423760B2B0D9C4B008E6A1C /* ggml-backend.c in Sources */,
|
||||
);
|
||||
runOnlyForDeploymentPostprocessing = 0;
|
||||
};
|
||||
@@ -342,9 +395,11 @@
|
||||
isa = XCBuildConfiguration;
|
||||
buildSettings = {
|
||||
ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
|
||||
ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
|
||||
CLANG_ENABLE_MODULES = YES;
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
CURRENT_PROJECT_VERSION = 1;
|
||||
DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
|
||||
DEVELOPMENT_TEAM = STLSG3FG8Q;
|
||||
ENABLE_PREVIEWS = YES;
|
||||
GENERATE_INFOPLIST_FILE = YES;
|
||||
@@ -361,12 +416,11 @@
|
||||
MARKETING_VERSION = 1.0;
|
||||
PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
|
||||
PRODUCT_NAME = "$(TARGET_NAME)";
|
||||
SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator";
|
||||
SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO;
|
||||
SWIFT_EMIT_LOC_STRINGS = YES;
|
||||
SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
|
||||
SWIFT_OPTIMIZATION_LEVEL = "-Onone";
|
||||
SWIFT_VERSION = 5.0;
|
||||
TARGETED_DEVICE_FAMILY = "1,2,7";
|
||||
TARGETED_DEVICE_FAMILY = "1,2";
|
||||
};
|
||||
name = Debug;
|
||||
};
|
||||
@@ -374,9 +428,11 @@
|
||||
isa = XCBuildConfiguration;
|
||||
buildSettings = {
|
||||
ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
|
||||
ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
|
||||
CLANG_ENABLE_MODULES = YES;
|
||||
CODE_SIGN_STYLE = Automatic;
|
||||
CURRENT_PROJECT_VERSION = 1;
|
||||
DEVELOPMENT_ASSET_PATHS = "\"llama.swiftui/Preview Content\"";
|
||||
DEVELOPMENT_TEAM = STLSG3FG8Q;
|
||||
ENABLE_PREVIEWS = YES;
|
||||
GENERATE_INFOPLIST_FILE = YES;
|
||||
@@ -393,11 +449,10 @@
|
||||
MARKETING_VERSION = 1.0;
|
||||
PRODUCT_BUNDLE_IDENTIFIER = "com.bachittle.llama-swift";
|
||||
PRODUCT_NAME = "$(TARGET_NAME)";
|
||||
SUPPORTED_PLATFORMS = "iphoneos iphonesimulator xros xrsimulator";
|
||||
SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO;
|
||||
SWIFT_EMIT_LOC_STRINGS = YES;
|
||||
SWIFT_OBJC_BRIDGING_HEADER = "llama.cpp.swift/bridging-header.h";
|
||||
SWIFT_VERSION = 5.0;
|
||||
TARGETED_DEVICE_FAMILY = "1,2,7";
|
||||
TARGETED_DEVICE_FAMILY = "1,2";
|
||||
};
|
||||
name = Release;
|
||||
};
|
||||
@@ -423,13 +478,6 @@
|
||||
defaultConfigurationName = Release;
|
||||
};
|
||||
/* End XCConfigurationList section */
|
||||
|
||||
/* Begin XCSwiftPackageProductDependency section */
|
||||
DF810E122B4A5BA200301144 /* llama */ = {
|
||||
isa = XCSwiftPackageProductDependency;
|
||||
productName = llama;
|
||||
};
|
||||
/* End XCSwiftPackageProductDependency section */
|
||||
};
|
||||
rootObject = 8A1C836B2AC328BD0096AF73 /* Project object */;
|
||||
}
|
||||
|
||||
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"colors" : [
|
||||
{
|
||||
"idiom" : "universal"
|
||||
}
|
||||
],
|
||||
"info" : {
|
||||
"author" : "xcode",
|
||||
"version" : 1
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"info" : {
|
||||
"author" : "xcode",
|
||||
"version" : 1
|
||||
}
|
||||
}
|
||||
@@ -103,8 +103,6 @@ struct ContentView: View {
|
||||
ContentView.cleanupModelCaches()
|
||||
llamaState.cacheCleared = true
|
||||
}
|
||||
|
||||
LoadCustomButton(llamaState: llamaState)
|
||||
}
|
||||
.padding(.top, 4)
|
||||
.font(.system(size: 12))
|
||||
|
||||
@@ -1,44 +0,0 @@
|
||||
import SwiftUI
|
||||
import UniformTypeIdentifiers
|
||||
|
||||
struct LoadCustomButton: View {
|
||||
@ObservedObject private var llamaState: LlamaState
|
||||
@State private var showFileImporter = false
|
||||
|
||||
init(llamaState: LlamaState) {
|
||||
self.llamaState = llamaState
|
||||
}
|
||||
|
||||
var body: some View {
|
||||
VStack {
|
||||
Button(action: {
|
||||
showFileImporter = true
|
||||
}) {
|
||||
Text("Load Custom Model")
|
||||
}
|
||||
}
|
||||
.fileImporter(
|
||||
isPresented: $showFileImporter,
|
||||
allowedContentTypes: [UTType(filenameExtension: "gguf", conformingTo: .data)!],
|
||||
allowsMultipleSelection: false
|
||||
) { result in
|
||||
switch result {
|
||||
case .success(let files):
|
||||
files.forEach { file in
|
||||
let gotAccess = file.startAccessingSecurityScopedResource()
|
||||
if !gotAccess { return }
|
||||
|
||||
do {
|
||||
try llamaState.loadModel(modelUrl: file.absoluteURL)
|
||||
} catch let err {
|
||||
print("Error: \(err.localizedDescription)")
|
||||
}
|
||||
|
||||
file.stopAccessingSecurityScopedResource()
|
||||
}
|
||||
case .failure(let error):
|
||||
print(error)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -168,12 +168,6 @@ node index.js
|
||||
|
||||
`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
|
||||
|
||||
`slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1)
|
||||
|
||||
`cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false)
|
||||
|
||||
`system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
|
||||
|
||||
*Result JSON:*
|
||||
|
||||
Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion.
|
||||
@@ -204,6 +198,12 @@ node index.js
|
||||
|
||||
`truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`)
|
||||
|
||||
`slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1)
|
||||
|
||||
`cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false)
|
||||
|
||||
`system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
|
||||
|
||||
- **POST** `/tokenize`: Tokenize a given text.
|
||||
|
||||
*Options:*
|
||||
|
||||
@@ -74,376 +74,355 @@ unsigned char completion_js[] = {
|
||||
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|
||||
};
|
||||
unsigned int completion_js_len = 5346;
|
||||
unsigned int completion_js_len = 5099;
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -95,15 +95,6 @@ export async function* llama(prompt, params = {}, config = {}) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (result.error) {
|
||||
result.error = JSON.parse(result.error);
|
||||
if (result.error.content.includes('slot unavailable')) {
|
||||
// Throw an error to be caught by upstream callers
|
||||
throw new Error('slot unavailable');
|
||||
} else {
|
||||
console.error(`llama.cpp error: ${result.error.content}`);
|
||||
}
|
||||
}
|
||||
if (result.error) {
|
||||
result.error = JSON.parse(result.error);
|
||||
console.error(`llama.cpp error: ${result.error.content}`);
|
||||
|
||||
@@ -427,7 +427,7 @@
|
||||
}
|
||||
|
||||
if (data.timings) {
|
||||
llamaStats.value = data;
|
||||
llamaStats.value = data.timings;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -880,7 +880,7 @@
|
||||
}
|
||||
return html`
|
||||
<span>
|
||||
${llamaStats.value.tokens_predicted} predicted, ${llamaStats.value.tokens_cached} cached, ${llamaStats.value.timings.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.timings.predicted_per_second.toFixed(2)} tokens per second
|
||||
${llamaStats.value.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.predicted_per_second.toFixed(2)} tokens per second
|
||||
</span>
|
||||
`
|
||||
}
|
||||
|
||||
@@ -447,14 +447,8 @@ struct llama_client_slot
|
||||
}
|
||||
|
||||
bool has_budget(gpt_params &global_params) {
|
||||
if (params.n_predict == -1 && global_params.n_predict == -1)
|
||||
{
|
||||
return true; // limitless
|
||||
}
|
||||
|
||||
n_remaining = -1;
|
||||
|
||||
if (params.n_predict != -1)
|
||||
if(params.n_predict != -1)
|
||||
{
|
||||
n_remaining = params.n_predict - n_decoded;
|
||||
}
|
||||
@@ -462,8 +456,7 @@ struct llama_client_slot
|
||||
{
|
||||
n_remaining = global_params.n_predict - n_decoded;
|
||||
}
|
||||
|
||||
return n_remaining > 0; // no budget
|
||||
return n_remaining > 0 || n_remaining == -1; // no budget || limitless
|
||||
}
|
||||
|
||||
bool available() const {
|
||||
@@ -1109,7 +1102,7 @@ struct llama_server_context
|
||||
}
|
||||
|
||||
// check the limits
|
||||
if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params))
|
||||
if (slot.n_decoded > 2 && slot.has_next_token && !slot.has_budget(params))
|
||||
{
|
||||
slot.stopped_limit = true;
|
||||
slot.has_next_token = false;
|
||||
@@ -1272,7 +1265,7 @@ struct llama_server_context
|
||||
{
|
||||
std::vector<completion_token_output> probs_output = {};
|
||||
const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
|
||||
size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size());
|
||||
size_t probs_pos = std::min(slot.sent_token_probs_index, slot.generated_token_probs.size());
|
||||
size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size());
|
||||
if (probs_pos < probs_stop_pos)
|
||||
{
|
||||
@@ -1332,7 +1325,7 @@ struct llama_server_context
|
||||
{
|
||||
probs = std::vector<completion_token_output>(
|
||||
slot.generated_token_probs.begin(),
|
||||
slot.generated_token_probs.end());
|
||||
slot.generated_token_probs.begin() + slot.sent_token_probs_index);
|
||||
}
|
||||
res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
|
||||
}
|
||||
@@ -1710,6 +1703,7 @@ struct llama_server_context
|
||||
|
||||
llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true);
|
||||
|
||||
slot.n_decoded += 1;
|
||||
slot.n_past += 1;
|
||||
}
|
||||
|
||||
@@ -1927,7 +1921,6 @@ struct llama_server_context
|
||||
|
||||
llama_sampling_accept(slot.ctx_sampling, ctx, id, true);
|
||||
|
||||
slot.n_decoded += 1;
|
||||
if (slot.n_decoded == 1)
|
||||
{
|
||||
slot.t_start_genereration = ggml_time_us();
|
||||
|
||||
@@ -90,7 +90,7 @@ extern "C" {
|
||||
void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
|
||||
|
||||
// compute graph without a plan
|
||||
bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
|
||||
void (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
|
||||
|
||||
// check if the backend supports an operation
|
||||
bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
|
||||
|
||||
@@ -195,14 +195,11 @@ void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_
|
||||
ggml_backend_synchronize(backend);
|
||||
}
|
||||
|
||||
bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
if (!backend->iface.graph_compute(backend, cgraph)) {
|
||||
return false;
|
||||
}
|
||||
void ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
backend->iface.graph_compute(backend, cgraph);
|
||||
|
||||
// TODO: optional sync
|
||||
ggml_backend_synchronize(backend);
|
||||
return true;
|
||||
}
|
||||
|
||||
bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
|
||||
@@ -600,7 +597,7 @@ static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_bac
|
||||
GGML_UNUSED(backend);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
|
||||
|
||||
struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
|
||||
@@ -614,7 +611,6 @@ static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_c
|
||||
cplan.work_data = cpu_ctx->work_data;
|
||||
|
||||
ggml_graph_compute(cgraph, &cplan);
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
|
||||
|
||||
@@ -58,7 +58,7 @@ extern "C" {
|
||||
|
||||
GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
|
||||
GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
|
||||
GGML_API bool ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
|
||||
GGML_API void ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
|
||||
GGML_API bool ggml_backend_supports_op (ggml_backend_t backend, const struct ggml_tensor * op);
|
||||
|
||||
// tensor copy between different backends
|
||||
|
||||
16
ggml-cuda.cu
16
ggml-cuda.cu
@@ -183,7 +183,7 @@ static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
|
||||
static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
|
||||
#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
|
||||
c = __builtin_amdgcn_sdot4(a, b, c, false);
|
||||
#elif defined(RDNA3)
|
||||
#elif defined(__gfx1100__)
|
||||
c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
|
||||
#elif defined(__gfx1010__) || defined(__gfx900__)
|
||||
int tmp1;
|
||||
@@ -9910,7 +9910,7 @@ static void ggml_backend_cuda_graph_plan_compute(ggml_backend_t backend, ggml_ba
|
||||
UNUSED(plan);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
|
||||
static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
|
||||
ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
|
||||
|
||||
ggml_cuda_set_main_device(cuda_ctx->device);
|
||||
@@ -9967,8 +9967,6 @@ static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph
|
||||
}
|
||||
|
||||
UNUSED(backend);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
|
||||
@@ -10041,19 +10039,14 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten
|
||||
}
|
||||
return false;
|
||||
} break;
|
||||
case GGML_OP_DUP:
|
||||
case GGML_OP_REPEAT:
|
||||
case GGML_OP_CONCAT:
|
||||
{
|
||||
ggml_type src0_type = op->src[0]->type;
|
||||
return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
|
||||
} break;
|
||||
case GGML_OP_NONE:
|
||||
case GGML_OP_RESHAPE:
|
||||
case GGML_OP_VIEW:
|
||||
case GGML_OP_PERMUTE:
|
||||
case GGML_OP_TRANSPOSE:
|
||||
case GGML_OP_NORM:
|
||||
case GGML_OP_REPEAT:
|
||||
case GGML_OP_DUP:
|
||||
case GGML_OP_ADD:
|
||||
case GGML_OP_MUL:
|
||||
case GGML_OP_DIV:
|
||||
@@ -10070,6 +10063,7 @@ static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_ten
|
||||
case GGML_OP_SUM_ROWS:
|
||||
case GGML_OP_ARGSORT:
|
||||
case GGML_OP_ACC:
|
||||
case GGML_OP_CONCAT:
|
||||
case GGML_OP_GROUP_NORM:
|
||||
case GGML_OP_UPSCALE:
|
||||
case GGML_OP_PAD:
|
||||
|
||||
@@ -5,7 +5,6 @@
|
||||
// GGML internal header
|
||||
|
||||
#include <assert.h>
|
||||
#include <stdlib.h> // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/
|
||||
#include <stddef.h>
|
||||
#include <stdbool.h>
|
||||
#include <string.h> // memcpy
|
||||
|
||||
@@ -87,7 +87,7 @@ int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
|
||||
|
||||
// same as ggml_graph_compute but uses Metal
|
||||
// creates gf->n_threads command buffers in parallel
|
||||
bool ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
|
||||
void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
|
||||
|
||||
//
|
||||
// backend API
|
||||
|
||||
50
ggml-metal.m
50
ggml-metal.m
@@ -87,7 +87,6 @@ struct ggml_metal_context {
|
||||
GGML_METAL_DECL_KERNEL(get_rows_q4_K);
|
||||
GGML_METAL_DECL_KERNEL(get_rows_q5_K);
|
||||
GGML_METAL_DECL_KERNEL(get_rows_q6_K);
|
||||
GGML_METAL_DECL_KERNEL(get_rows_i32);
|
||||
GGML_METAL_DECL_KERNEL(rms_norm);
|
||||
GGML_METAL_DECL_KERNEL(group_norm);
|
||||
GGML_METAL_DECL_KERNEL(norm);
|
||||
@@ -258,14 +257,14 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
||||
bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
|
||||
#endif
|
||||
NSError * error = nil;
|
||||
NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"];
|
||||
NSString * libPath = [bundle pathForResource:@"ggml" ofType:@"metallib"];
|
||||
if (libPath != nil) {
|
||||
// pre-compiled library found
|
||||
NSURL * libURL = [NSURL fileURLWithPath:libPath];
|
||||
GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]);
|
||||
ctx->library = [ctx->device newLibraryWithURL:libURL error:&error];
|
||||
} else {
|
||||
GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
|
||||
GGML_METAL_LOG_INFO("%s: ggml.metallib not found, loading from source\n", __func__);
|
||||
|
||||
NSString * sourcePath;
|
||||
NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
|
||||
@@ -295,7 +294,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
||||
#endif
|
||||
// try to disable fast-math
|
||||
// NOTE: this seems to have no effect whatsoever
|
||||
// instead, in order to disable fast-math, we have to build default.metallib from the command line
|
||||
// instead, in order to disable fast-math, we have to build ggml.metallib from the command line
|
||||
// using xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
|
||||
// and go through the "pre-compiled library found" path above
|
||||
//[options setFastMathEnabled:false];
|
||||
@@ -378,7 +377,6 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
||||
GGML_METAL_ADD_KERNEL(get_rows_q4_K);
|
||||
GGML_METAL_ADD_KERNEL(get_rows_q5_K);
|
||||
GGML_METAL_ADD_KERNEL(get_rows_q6_K);
|
||||
GGML_METAL_ADD_KERNEL(get_rows_i32);
|
||||
GGML_METAL_ADD_KERNEL(rms_norm);
|
||||
GGML_METAL_ADD_KERNEL(group_norm);
|
||||
GGML_METAL_ADD_KERNEL(norm);
|
||||
@@ -501,7 +499,6 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
|
||||
GGML_METAL_DEL_KERNEL(get_rows_q4_K);
|
||||
GGML_METAL_DEL_KERNEL(get_rows_q5_K);
|
||||
GGML_METAL_DEL_KERNEL(get_rows_q6_K);
|
||||
GGML_METAL_DEL_KERNEL(get_rows_i32);
|
||||
GGML_METAL_DEL_KERNEL(rms_norm);
|
||||
GGML_METAL_DEL_KERNEL(group_norm);
|
||||
GGML_METAL_DEL_KERNEL(norm);
|
||||
@@ -977,7 +974,7 @@ static bool ggml_metal_supports_op(const struct ggml_tensor * op) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
bool ggml_metal_graph_compute(
|
||||
void ggml_metal_graph_compute(
|
||||
struct ggml_metal_context * ctx,
|
||||
struct ggml_cgraph * gf) {
|
||||
@autoreleasepool {
|
||||
@@ -1660,10 +1657,6 @@ bool ggml_metal_graph_compute(
|
||||
}
|
||||
};
|
||||
|
||||
if (ggml_is_quantized(src0t)) {
|
||||
GGML_ASSERT(ne00 >= nth0*nth1);
|
||||
}
|
||||
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
|
||||
@@ -1722,9 +1715,6 @@ bool ggml_metal_graph_compute(
|
||||
// TODO: make this more general
|
||||
GGML_ASSERT(n_as <= 8);
|
||||
|
||||
// max size of the src1ids array in the kernel stack
|
||||
GGML_ASSERT(ne11 <= 512);
|
||||
|
||||
struct ggml_tensor * src2 = gf->nodes[i]->src[2];
|
||||
|
||||
const int64_t ne20 = src2 ? src2->ne[0] : 0;
|
||||
@@ -1742,6 +1732,9 @@ bool ggml_metal_graph_compute(
|
||||
GGML_ASSERT(!ggml_is_transposed(src2));
|
||||
GGML_ASSERT(!ggml_is_transposed(src1));
|
||||
|
||||
GGML_ASSERT(ne20 % 32 == 0);
|
||||
// !!!!!!!!! TODO: this assert is probably required but not sure!
|
||||
//GGML_ASSERT(ne20 >= 64);
|
||||
GGML_ASSERT(src1t == GGML_TYPE_F32);
|
||||
|
||||
const uint r2 = ne12/ne22;
|
||||
@@ -1749,22 +1742,22 @@ bool ggml_metal_graph_compute(
|
||||
|
||||
// find the break-even point where the matrix-matrix kernel becomes more efficient compared
|
||||
// to the matrix-vector kernel
|
||||
int ne11_mm_min = n_as;
|
||||
int ne11_mm_min = 1;
|
||||
|
||||
const int idx = ((int32_t *) dst->op_params)[0];
|
||||
|
||||
// batch size
|
||||
GGML_ASSERT(ne01 == ne11);
|
||||
|
||||
const int64_t _ne1 = 1; // kernel_mul_mm_impl needs a reference in constant memory
|
||||
|
||||
// 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
|
||||
// !!!
|
||||
// TODO: for now, always use mat-vec kernels until we figure out how to improve the
|
||||
// indirect matrix multiplication
|
||||
// !!!
|
||||
if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
|
||||
ne20 % 32 == 0 && ne20 >= 64 &&
|
||||
ne11 > ne11_mm_min) {
|
||||
if ([ctx->device supportsFamily:MTLGPUFamilyApple7] && _ne1 > ne11_mm_min) {
|
||||
switch (src2->type) {
|
||||
case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f32_f32]; break;
|
||||
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f16_f32]; break;
|
||||
@@ -1794,7 +1787,7 @@ bool ggml_metal_graph_compute(
|
||||
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11];
|
||||
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12];
|
||||
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13];
|
||||
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14];
|
||||
[encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:14];
|
||||
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15];
|
||||
[encoder setBytes:&r2 length:sizeof(r2) atIndex:16];
|
||||
[encoder setBytes:&r3 length:sizeof(r3) atIndex:17];
|
||||
@@ -1812,7 +1805,8 @@ bool ggml_metal_graph_compute(
|
||||
|
||||
[encoder setThreadgroupMemoryLength:8192 atIndex:0];
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
|
||||
// TODO: processing one row at a time (ne11 -> 1) is not efficient
|
||||
[encoder dispatchThreadgroups:MTLSizeMake( (_ne1 + 31)/32, (ne21 + 63)/64, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
|
||||
} else {
|
||||
int nth0 = 32;
|
||||
int nth1 = 1;
|
||||
@@ -1895,17 +1889,11 @@ bool ggml_metal_graph_compute(
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t);
|
||||
GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
|
||||
GGML_ASSERT(false && "not implemented");
|
||||
}
|
||||
};
|
||||
|
||||
if (ggml_is_quantized(src2t)) {
|
||||
GGML_ASSERT(ne20 >= nth0*nth1);
|
||||
}
|
||||
|
||||
const int64_t _ne1 = 1; // kernels needs a reference in constant memory
|
||||
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
|
||||
@@ -1981,7 +1969,6 @@ bool ggml_metal_graph_compute(
|
||||
case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_K]; break;
|
||||
case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_K]; break;
|
||||
case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_K]; break;
|
||||
case GGML_TYPE_I32: [encoder setComputePipelineState:ctx->pipeline_get_rows_i32]; break;
|
||||
default: GGML_ASSERT(false && "not implemented");
|
||||
}
|
||||
|
||||
@@ -2405,11 +2392,10 @@ bool ggml_metal_graph_compute(
|
||||
MTLCommandBufferStatus status = (MTLCommandBufferStatus) [ctx->command_buffers[i] status];
|
||||
if (status != MTLCommandBufferStatusCompleted) {
|
||||
GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
|
||||
return false;
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -2689,10 +2675,10 @@ static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggm
|
||||
UNUSED(backend);
|
||||
}
|
||||
|
||||
static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
static void ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
|
||||
|
||||
return ggml_metal_graph_compute(metal_ctx, cgraph);
|
||||
ggml_metal_graph_compute(metal_ctx, cgraph);
|
||||
}
|
||||
|
||||
static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
|
||||
|
||||
234
ggml-metal.metal
234
ggml-metal.metal
@@ -846,7 +846,7 @@ inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thre
|
||||
#define N_SIMDGROUP 2 // number of SIMD groups in a thread group
|
||||
//Note: This is a template, but strictly speaking it only applies to
|
||||
// quantizations where the block size is 32. It also does not
|
||||
// guard against the number of rows not being divisible by
|
||||
// giard against the number of rows not being divisible by
|
||||
// N_DST, so this is another explicit assumption of the implementation.
|
||||
template<typename block_q_type, int nr, int nsg, int nw>
|
||||
void mul_vec_q_n_f32_impl(
|
||||
@@ -3829,35 +3829,6 @@ kernel void kernel_get_rows_f16(
|
||||
}
|
||||
}
|
||||
|
||||
kernel void kernel_get_rows_i32(
|
||||
device const void * src0,
|
||||
device const char * src1,
|
||||
device int32_t * dst,
|
||||
constant int64_t & ne00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb02,
|
||||
constant int64_t & ne10,
|
||||
constant uint64_t & nb10,
|
||||
constant uint64_t & nb11,
|
||||
constant uint64_t & nb1,
|
||||
constant uint64_t & nb2,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint tiitg[[thread_index_in_threadgroup]],
|
||||
uint3 tptg [[threads_per_threadgroup]]) {
|
||||
const int64_t i10 = tgpig.x;
|
||||
const int64_t i11 = tgpig.y;
|
||||
|
||||
const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
|
||||
|
||||
const int64_t i02 = i11;
|
||||
|
||||
for (int ind = tiitg; ind < ne00; ind += tptg.x) {
|
||||
((device int32_t *) ((device char *) dst + i11*nb2 + i10*nb1))[ind] =
|
||||
((device int32_t *) ((device char *) src0 + r*nb01 + i02*nb02))[ind];
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
#define BLOCK_SIZE_M 64 // 8 simdgroup matrices from matrix A
|
||||
#define BLOCK_SIZE_N 32 // 4 simdgroup matrices from matrix B
|
||||
#define BLOCK_SIZE_K 32
|
||||
@@ -4002,131 +3973,6 @@ void kernel_mul_mm_impl(device const uchar * src0,
|
||||
}
|
||||
}
|
||||
|
||||
// same as kernel_mul_mm_impl, but src1 and dst are accessed via indices stored in src1ids
|
||||
template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
|
||||
void kernel_mul_mm_id_impl(
|
||||
device const uchar * src0,
|
||||
device const uchar * src1,
|
||||
thread short * src1ids,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne02,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb02,
|
||||
constant int64_t & ne12,
|
||||
constant uint64_t & nb10,
|
||||
constant uint64_t & nb11,
|
||||
constant uint64_t & nb12,
|
||||
constant int64_t & ne0,
|
||||
int64_t ne1,
|
||||
constant uint & r2,
|
||||
constant uint & r3,
|
||||
threadgroup uchar * shared_memory,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint tiitg[[thread_index_in_threadgroup]],
|
||||
uint sgitg[[simdgroup_index_in_threadgroup]]) {
|
||||
|
||||
threadgroup half * sa = (threadgroup half *)(shared_memory);
|
||||
threadgroup float * sb = (threadgroup float *)(shared_memory + 4096);
|
||||
|
||||
const uint r0 = tgpig.y;
|
||||
const uint r1 = tgpig.x;
|
||||
const uint im = tgpig.z;
|
||||
|
||||
if (r1 * BLOCK_SIZE_N >= ne1) return;
|
||||
|
||||
// if this block is of 64x32 shape or smaller
|
||||
short n_rows = (ne0 - r0 * BLOCK_SIZE_M < BLOCK_SIZE_M) ? (ne0 - r0 * BLOCK_SIZE_M) : BLOCK_SIZE_M;
|
||||
short n_cols = (ne1 - r1 * BLOCK_SIZE_N < BLOCK_SIZE_N) ? (ne1 - r1 * BLOCK_SIZE_N) : BLOCK_SIZE_N;
|
||||
|
||||
// a thread shouldn't load data outside of the matrix
|
||||
short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1;
|
||||
short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1;
|
||||
|
||||
simdgroup_half8x8 ma[4];
|
||||
simdgroup_float8x8 mb[2];
|
||||
simdgroup_float8x8 c_res[8];
|
||||
for (int i = 0; i < 8; i++){
|
||||
c_res[i] = make_filled_simdgroup_matrix<float, 8>(0.f);
|
||||
}
|
||||
|
||||
short il = (tiitg % THREAD_PER_ROW);
|
||||
|
||||
const uint i12 = im%ne12;
|
||||
const uint i13 = im/ne12;
|
||||
|
||||
uint offset0 = (i12/r2)*nb02 + (i13/r3)*(nb02*ne02);
|
||||
ushort offset1 = il/nl;
|
||||
|
||||
device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01 + offset0) + offset1;
|
||||
device const float * y = (device const float *)(src1
|
||||
+ nb12 * im
|
||||
+ nb11 * src1ids[r1 * BLOCK_SIZE_N + thread_col]
|
||||
+ nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL)));
|
||||
|
||||
for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) {
|
||||
// load data and store to threadgroup memory
|
||||
half4x4 temp_a;
|
||||
dequantize_func(x, il, temp_a);
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
for (int i = 0; i < 16; i++) {
|
||||
*(sa + SG_MAT_SIZE * ((tiitg / THREAD_PER_ROW / 8) \
|
||||
+ (tiitg % THREAD_PER_ROW) * 16 + (i / 8) * 8) \
|
||||
+ (tiitg / THREAD_PER_ROW) % 8 + (i & 7) * 8) = temp_a[i/4][i%4];
|
||||
}
|
||||
|
||||
*(threadgroup float2x4 *)(sb + (tiitg % THREAD_PER_COL) * 8 * 32 + 8 * (tiitg / THREAD_PER_COL)) = *((device float2x4 *)y);
|
||||
|
||||
il = (il + 2 < nl) ? il + 2 : il % 2;
|
||||
x = (il < 2) ? x + (2+nl-1)/nl : x;
|
||||
y += BLOCK_SIZE_K;
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// load matrices from threadgroup memory and conduct outer products
|
||||
threadgroup half * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2));
|
||||
threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2));
|
||||
|
||||
for (int ik = 0; ik < BLOCK_SIZE_K / 8; ik++) {
|
||||
for (int i = 0; i < 4; i++) {
|
||||
simdgroup_load(ma[i],lsma + SG_MAT_SIZE * i);
|
||||
}
|
||||
simdgroup_barrier(mem_flags::mem_none);
|
||||
for (int i = 0; i < 2; i++) {
|
||||
simdgroup_load(mb[i],lsmb + SG_MAT_SIZE * i);
|
||||
}
|
||||
|
||||
lsma += BLOCK_SIZE_M / SG_MAT_ROW * SG_MAT_SIZE;
|
||||
lsmb += BLOCK_SIZE_N / SG_MAT_ROW * SG_MAT_SIZE;
|
||||
|
||||
for (int i = 0; i < 8; i++){
|
||||
simdgroup_multiply_accumulate(c_res[i], mb[i/4], ma[i%4], c_res[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
{
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
threadgroup float * temp_str = ((threadgroup float *)shared_memory) \
|
||||
+ 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M;
|
||||
for (int i = 0; i < 8; i++) {
|
||||
simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M);
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
device float * C = dst + (BLOCK_SIZE_M * r0) + im*ne1*ne0;
|
||||
if (sgitg == 0) {
|
||||
for (int i = 0; i < n_rows; i++) {
|
||||
for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) {
|
||||
*(C + i + src1ids[j + r1*BLOCK_SIZE_N] * ne0) = *(temp_str + i + j * BLOCK_SIZE_M);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
|
||||
kernel void kernel_mul_mm(device const uchar * src0,
|
||||
device const uchar * src1,
|
||||
@@ -4173,7 +4019,7 @@ template<typename block_q, short nl, void (*dequantize_func)(device const block_
|
||||
kernel void kernel_mul_mm_id(
|
||||
device const uchar * ids,
|
||||
device const uchar * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne02,
|
||||
@@ -4202,28 +4048,18 @@ kernel void kernel_mul_mm_id(
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint tiitg[[thread_index_in_threadgroup]],
|
||||
uint sgitg[[simdgroup_index_in_threadgroup]]) {
|
||||
device const uchar * src0s[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
|
||||
device const uchar * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
|
||||
|
||||
// expert id
|
||||
const int32_t id = tgpig.z/(ne12*ne13);
|
||||
const int64_t bid = tgpig.z/(ne12*ne13);
|
||||
|
||||
tgpig.z = tgpig.z%(ne12*ne13);
|
||||
|
||||
// row indices of src1 for expert id
|
||||
int64_t _ne1 = 0;
|
||||
short src1ids[512];
|
||||
const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
|
||||
|
||||
for (int64_t i1 = 0; i1 < ne1; i1++) {
|
||||
if (((device int32_t *) (ids + i1*nbi1))[idx] == id) {
|
||||
src1ids[_ne1++] = i1;
|
||||
}
|
||||
}
|
||||
|
||||
kernel_mul_mm_id_impl<block_q, nl, dequantize_func>(
|
||||
src0s[id],
|
||||
src1,
|
||||
src1ids,
|
||||
dst,
|
||||
kernel_mul_mm_impl<block_q, nl, dequantize_func>(
|
||||
src0[id],
|
||||
src1 + bid*nb11,
|
||||
(device float *) (dst + bid*nb1),
|
||||
ne00,
|
||||
ne02,
|
||||
nb01,
|
||||
@@ -4233,7 +4069,7 @@ kernel void kernel_mul_mm_id(
|
||||
nb11,
|
||||
nb12,
|
||||
ne0,
|
||||
_ne1,
|
||||
ne1,
|
||||
r2,
|
||||
r3,
|
||||
shared_memory,
|
||||
@@ -4322,7 +4158,7 @@ template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm<b
|
||||
typedef void (mat_mm_id_t)(
|
||||
device const uchar * ids,
|
||||
device const uchar * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne02,
|
||||
@@ -4371,7 +4207,7 @@ template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mu
|
||||
kernel void kernel_mul_mv_id_f32_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
@@ -4415,7 +4251,7 @@ kernel void kernel_mul_mv_id_f32_f32(
|
||||
kernel_mul_mv_f32_f32_impl(
|
||||
src0[id],
|
||||
src1 + bid*nb11,
|
||||
dst + bid*ne0,
|
||||
(device float *) (dst + bid*nb1),
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
@@ -4440,7 +4276,7 @@ kernel void kernel_mul_mv_id_f32_f32(
|
||||
kernel void kernel_mul_mv_id_f16_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
@@ -4484,7 +4320,7 @@ kernel void kernel_mul_mv_id_f16_f32(
|
||||
kernel_mul_mv_f16_f32_impl(
|
||||
src0[id],
|
||||
src1 + bid*nb11,
|
||||
dst + bid*ne0,
|
||||
(device float *) (dst + bid*nb1),
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
@@ -4509,7 +4345,7 @@ kernel void kernel_mul_mv_id_f16_f32(
|
||||
kernel void kernel_mul_mv_id_q8_0_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
@@ -4553,7 +4389,7 @@ kernel void kernel_mul_mv_id_q8_0_f32(
|
||||
kernel_mul_mv_q8_0_f32_impl(
|
||||
src0[id],
|
||||
(device const float *) (src1 + bid*nb11),
|
||||
dst + bid*ne0,
|
||||
(device float *) ( dst + bid*nb1),
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
@@ -4572,7 +4408,7 @@ kernel void kernel_mul_mv_id_q8_0_f32(
|
||||
kernel void kernel_mul_mv_id_q4_0_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
@@ -4616,7 +4452,7 @@ kernel void kernel_mul_mv_id_q4_0_f32(
|
||||
mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
|
||||
src0[id],
|
||||
(device const float *) (src1 + bid*nb11),
|
||||
dst + bid*ne0,
|
||||
(device float *) ( dst + bid*nb1),
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
@@ -4635,7 +4471,7 @@ kernel void kernel_mul_mv_id_q4_0_f32(
|
||||
kernel void kernel_mul_mv_id_q4_1_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
@@ -4679,7 +4515,7 @@ kernel void kernel_mul_mv_id_q4_1_f32(
|
||||
mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
|
||||
src0[id],
|
||||
(device const float *) (src1 + bid*nb11),
|
||||
dst + bid*ne0,
|
||||
(device float *) ( dst + bid*nb1),
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
@@ -4698,7 +4534,7 @@ kernel void kernel_mul_mv_id_q4_1_f32(
|
||||
kernel void kernel_mul_mv_id_q5_0_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
@@ -4742,7 +4578,7 @@ kernel void kernel_mul_mv_id_q5_0_f32(
|
||||
mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
|
||||
src0[id],
|
||||
(device const float *) (src1 + bid*nb11),
|
||||
dst + bid*ne0,
|
||||
(device float *) ( dst + bid*nb1),
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
@@ -4761,7 +4597,7 @@ kernel void kernel_mul_mv_id_q5_0_f32(
|
||||
kernel void kernel_mul_mv_id_q5_1_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
@@ -4805,7 +4641,7 @@ kernel void kernel_mul_mv_id_q5_1_f32(
|
||||
mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(
|
||||
src0[id],
|
||||
(device const float *) (src1 + bid*nb11),
|
||||
dst + bid*ne0,
|
||||
(device float *) ( dst + bid*nb1),
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
@@ -4824,7 +4660,7 @@ kernel void kernel_mul_mv_id_q5_1_f32(
|
||||
kernel void kernel_mul_mv_id_q2_K_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
@@ -4868,7 +4704,7 @@ kernel void kernel_mul_mv_id_q2_K_f32(
|
||||
kernel_mul_mv_q2_K_f32_impl(
|
||||
src0[id],
|
||||
(device const float *) (src1 + bid*nb11),
|
||||
dst + bid*ne0,
|
||||
(device float *) ( dst + bid*nb1),
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
@@ -4887,7 +4723,7 @@ kernel void kernel_mul_mv_id_q2_K_f32(
|
||||
kernel void kernel_mul_mv_id_q3_K_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
@@ -4931,7 +4767,7 @@ kernel void kernel_mul_mv_id_q3_K_f32(
|
||||
kernel_mul_mv_q3_K_f32_impl(
|
||||
src0[id],
|
||||
(device const float *) (src1 + bid*nb11),
|
||||
dst + bid*ne0,
|
||||
(device float *) ( dst + bid*nb1),
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
@@ -4950,7 +4786,7 @@ kernel void kernel_mul_mv_id_q3_K_f32(
|
||||
kernel void kernel_mul_mv_id_q4_K_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
@@ -4994,7 +4830,7 @@ kernel void kernel_mul_mv_id_q4_K_f32(
|
||||
kernel_mul_mv_q4_K_f32_impl(
|
||||
src0[id],
|
||||
(device const float *) (src1 + bid*nb11),
|
||||
dst + bid*ne0,
|
||||
(device float *) ( dst + bid*nb1),
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
@@ -5013,7 +4849,7 @@ kernel void kernel_mul_mv_id_q4_K_f32(
|
||||
kernel void kernel_mul_mv_id_q5_K_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
@@ -5057,7 +4893,7 @@ kernel void kernel_mul_mv_id_q5_K_f32(
|
||||
kernel_mul_mv_q5_K_f32_impl(
|
||||
src0[id],
|
||||
(device const float *) (src1 + bid*nb11),
|
||||
dst + bid*ne0,
|
||||
(device float *) ( dst + bid*nb1),
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
@@ -5076,7 +4912,7 @@ kernel void kernel_mul_mv_id_q5_K_f32(
|
||||
kernel void kernel_mul_mv_id_q6_K_f32(
|
||||
device const char * ids,
|
||||
device const char * src1,
|
||||
device float * dst,
|
||||
device uchar * dst,
|
||||
constant uint64_t & nbi1,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
@@ -5120,7 +4956,7 @@ kernel void kernel_mul_mv_id_q6_K_f32(
|
||||
kernel_mul_mv_q6_K_f32_impl(
|
||||
src0[id],
|
||||
(device const float *) (src1 + bid*nb11),
|
||||
dst + bid*ne0,
|
||||
(device float *) ( dst + bid*nb1),
|
||||
ne00,
|
||||
ne01,
|
||||
ne02,
|
||||
|
||||
@@ -70,7 +70,7 @@ static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block s
|
||||
// 2-bit quantization
|
||||
// weight is represented as x = a * q + b
|
||||
// 16 blocks of 16 elements each
|
||||
// Effectively 2.625 bits per weight
|
||||
// Effectively 2.5625 bits per weight
|
||||
typedef struct {
|
||||
uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
|
||||
uint8_t qs[QK_K/4]; // quants
|
||||
|
||||
207
ggml.c
207
ggml.c
@@ -4766,11 +4766,8 @@ struct ggml_tensor * ggml_get_rows(
|
||||
}
|
||||
|
||||
// TODO: implement non F32 return
|
||||
enum ggml_type type = GGML_TYPE_F32;
|
||||
if (a->type == GGML_TYPE_I32) {
|
||||
type = a->type;
|
||||
}
|
||||
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, type, a->ne[0], b->ne[0], b->ne[1], b->ne[2]);
|
||||
//struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]);
|
||||
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0], b->ne[1], b->ne[2]);
|
||||
|
||||
result->op = GGML_OP_GET_ROWS;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
@@ -6941,165 +6938,14 @@ static void ggml_compute_forward_dup_f32(
|
||||
}
|
||||
}
|
||||
|
||||
// A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy.
|
||||
static void ggml_compute_forward_dup_bytes(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * src0,
|
||||
struct ggml_tensor * dst) {
|
||||
GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
|
||||
GGML_ASSERT(src0->type == dst->type);
|
||||
|
||||
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) {
|
||||
ggml_compute_forward_dup_same_cont(params, src0, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
GGML_TENSOR_UNARY_OP_LOCALS;
|
||||
|
||||
const size_t type_size = ggml_type_size(src0->type);
|
||||
const int ith = params->ith; // thread index
|
||||
const int nth = params->nth; // number of threads
|
||||
|
||||
|
||||
// parallelize by rows
|
||||
const int nr = ne01;
|
||||
// number of rows per thread
|
||||
const int dr = (nr + nth - 1) / nth;
|
||||
// row range for this thread
|
||||
const int ir0 = dr * ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
|
||||
if (src0->type == dst->type &&
|
||||
ne00 == ne0 &&
|
||||
nb00 == type_size && nb0 == type_size) {
|
||||
// copy by rows
|
||||
const size_t rs = ne00 * type_size;
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
for (int64_t i01 = ir0; i01 < ir1; i01++) {
|
||||
memcpy(
|
||||
((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3),
|
||||
((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03),
|
||||
rs);
|
||||
}
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (ggml_is_contiguous(dst)) {
|
||||
size_t id = 0;
|
||||
char * dst_ptr = (char *) dst->data;
|
||||
const size_t rs = ne00 * type_size;
|
||||
|
||||
if (nb00 == type_size) {
|
||||
// src0 is contigous on first dimension, copy by rows
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
id += rs * ir0;
|
||||
for (int64_t i01 = ir0; i01 < ir1; i01++) {
|
||||
const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
|
||||
memcpy(dst_ptr + id, src0_ptr, rs);
|
||||
id += rs;
|
||||
}
|
||||
id += rs * (ne01 - ir1);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
//printf("%s: this is not optimal - fix me\n", __func__);
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
id += rs * ir0;
|
||||
for (int64_t i01 = ir0; i01 < ir1; i01++) {
|
||||
for (int64_t i00 = 0; i00 < ne00; i00++) {
|
||||
const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03;
|
||||
memcpy(dst_ptr + id, src0_ptr, type_size);
|
||||
|
||||
id += type_size;
|
||||
}
|
||||
}
|
||||
id += rs * (ne01 - ir1);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
// dst counters
|
||||
|
||||
int64_t i10 = 0;
|
||||
int64_t i11 = 0;
|
||||
int64_t i12 = 0;
|
||||
int64_t i13 = 0;
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
i10 += ne00 * ir0;
|
||||
while (i10 >= ne0) {
|
||||
i10 -= ne0;
|
||||
if (++i11 == ne1) {
|
||||
i11 = 0;
|
||||
if (++i12 == ne2) {
|
||||
i12 = 0;
|
||||
if (++i13 == ne3) {
|
||||
i13 = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
for (int64_t i01 = ir0; i01 < ir1; i01++) {
|
||||
for (int64_t i00 = 0; i00 < ne00; i00++) {
|
||||
const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
|
||||
char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3);
|
||||
|
||||
memcpy(dst_ptr, src0_ptr, type_size);
|
||||
|
||||
if (++i10 == ne0) {
|
||||
i10 = 0;
|
||||
if (++i11 == ne1) {
|
||||
i11 = 0;
|
||||
if (++i12 == ne2) {
|
||||
i12 = 0;
|
||||
if (++i13 == ne3) {
|
||||
i13 = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
i10 += ne00 * (ne01 - ir1);
|
||||
while (i10 >= ne0) {
|
||||
i10 -= ne0;
|
||||
if (++i11 == ne1) {
|
||||
i11 = 0;
|
||||
if (++i12 == ne2) {
|
||||
i12 = 0;
|
||||
if (++i13 == ne3) {
|
||||
i13 = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_compute_forward_dup(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * src0,
|
||||
struct ggml_tensor * dst) {
|
||||
if (src0->type == dst->type) {
|
||||
ggml_compute_forward_dup_bytes(params, src0, dst);
|
||||
if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
|
||||
ggml_compute_forward_dup_same_cont(params, src0, dst);
|
||||
return;
|
||||
}
|
||||
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_F16:
|
||||
{
|
||||
@@ -8558,12 +8404,10 @@ static void ggml_compute_forward_repeat(
|
||||
struct ggml_tensor * dst) {
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_F16:
|
||||
case GGML_TYPE_I16:
|
||||
{
|
||||
ggml_compute_forward_repeat_f16(params, src0, dst);
|
||||
} break;
|
||||
case GGML_TYPE_F32:
|
||||
case GGML_TYPE_I32:
|
||||
{
|
||||
ggml_compute_forward_repeat_f32(params, src0, dst);
|
||||
} break;
|
||||
@@ -8706,7 +8550,6 @@ static void ggml_compute_forward_concat(
|
||||
struct ggml_tensor* dst) {
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_F32:
|
||||
case GGML_TYPE_I32:
|
||||
{
|
||||
ggml_compute_forward_concat_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
@@ -9704,10 +9547,10 @@ static void ggml_compute_forward_group_norm(
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
// helper function to determine if it is better to use BLAS or not
|
||||
// for large matrices, BLAS is faster
|
||||
static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) {
|
||||
const struct ggml_tensor * src0 = dst->src[0];
|
||||
const struct ggml_tensor * src1 = dst->src[1];
|
||||
|
||||
static bool ggml_compute_forward_mul_mat_use_blas(
|
||||
const struct ggml_tensor * src0,
|
||||
const struct ggml_tensor * src1,
|
||||
struct ggml_tensor * dst) {
|
||||
//const int64_t ne00 = src0->ne[0];
|
||||
//const int64_t ne01 = src0->ne[1];
|
||||
|
||||
@@ -9787,7 +9630,7 @@ static void ggml_compute_forward_mul_mat(
|
||||
#endif
|
||||
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(dst)) {
|
||||
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
|
||||
if (params->ith != 0) {
|
||||
return;
|
||||
}
|
||||
@@ -10831,7 +10674,6 @@ static void ggml_compute_forward_get_rows(
|
||||
ggml_compute_forward_get_rows_f16(params, src0, src1, dst);
|
||||
} break;
|
||||
case GGML_TYPE_F32:
|
||||
case GGML_TYPE_I32:
|
||||
{
|
||||
ggml_compute_forward_get_rows_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
@@ -16301,6 +16143,24 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
|
||||
|
||||
//n_tasks = MIN(n_threads, MAX(1, nr0/128));
|
||||
//printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks%d\n", nr0, nr1, nr0*nr1, n_tasks);
|
||||
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
if (ggml_cuda_can_mul_mat(node->src[0], node->src[1], node)) {
|
||||
n_tasks = 1; // TODO: this actually is doing nothing
|
||||
// the threads are still spinning
|
||||
}
|
||||
#elif defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) {
|
||||
n_tasks = 1; // TODO: this actually is doing nothing
|
||||
// the threads are still spinning
|
||||
}
|
||||
#endif
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) {
|
||||
n_tasks = 1; // TODO: this actually is doing nothing
|
||||
// the threads are still spinning
|
||||
}
|
||||
#endif
|
||||
} break;
|
||||
case GGML_OP_MUL_MAT_ID:
|
||||
{
|
||||
@@ -16473,7 +16333,6 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
|
||||
state->shared->node_n += 1;
|
||||
return (thread_ret_t) GGML_EXIT_ABORTED;
|
||||
}
|
||||
|
||||
if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
|
||||
// all other threads are finished and spinning
|
||||
// do finalize and init here so we don't have synchronize again
|
||||
@@ -16539,18 +16398,14 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
|
||||
} else {
|
||||
// wait for other threads to finish
|
||||
const int last = node_n;
|
||||
|
||||
const bool do_yield = last < 0 || cgraph->nodes[last]->op == GGML_OP_MUL_MAT;
|
||||
|
||||
while (true) {
|
||||
// TODO: this sched_yield can have significant impact on the performance - either positive or negative
|
||||
// depending on the workload and the operating system.
|
||||
// since it is not clear what is the best approach, it should potentially become user-configurable
|
||||
// ref: https://github.com/ggerganov/ggml/issues/291
|
||||
// UPD: adding the do_yield flag seems to resolve the issue universally
|
||||
if (do_yield) {
|
||||
sched_yield();
|
||||
}
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
sched_yield();
|
||||
#endif
|
||||
|
||||
node_n = atomic_load(&state->shared->node_n);
|
||||
if (node_n != last) break;
|
||||
@@ -16629,7 +16484,7 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
|
||||
} else
|
||||
#endif
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(node)) {
|
||||
if (ggml_compute_forward_mul_mat_use_blas(node->src[0], node->src[1], node)) {
|
||||
if (node->src[0]->type != GGML_TYPE_F32) {
|
||||
// here we need memory just for single 2D matrix from src0
|
||||
cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]);
|
||||
|
||||
142
llama.cpp
142
llama.cpp
@@ -68,7 +68,6 @@
|
||||
#include <initializer_list>
|
||||
#include <map>
|
||||
#include <memory>
|
||||
#include <mutex>
|
||||
#include <numeric>
|
||||
#include <queue>
|
||||
#include <random>
|
||||
@@ -2180,11 +2179,7 @@ struct llama_model_loader {
|
||||
type_max = type;
|
||||
}
|
||||
|
||||
// TODO: make runtime configurable
|
||||
#if 0
|
||||
struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
|
||||
LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, ggml_get_name(meta), ggml_type_name(type), llama_format_tensor_shape(meta).c_str());
|
||||
#endif
|
||||
// LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str());
|
||||
}
|
||||
|
||||
switch (type_max) {
|
||||
@@ -4776,6 +4771,7 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
@@ -4899,6 +4895,7 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * pos;
|
||||
@@ -4999,6 +4996,7 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
const int64_t n_rot = n_embd_head_k / 2;
|
||||
|
||||
@@ -5212,6 +5210,7 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
@@ -5304,6 +5303,7 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
@@ -5399,6 +5399,7 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
@@ -5725,6 +5726,7 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * attn_norm_output;
|
||||
@@ -5948,6 +5950,7 @@ struct llm_build_context {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_gqa == n_embd);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * pos;
|
||||
@@ -8026,7 +8029,7 @@ void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * c
|
||||
}
|
||||
}
|
||||
|
||||
void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
|
||||
void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep) {
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
k = std::max(k, (int) min_keep);
|
||||
@@ -8386,7 +8389,7 @@ void llama_sample_classifier_free_guidance(
|
||||
}
|
||||
}
|
||||
|
||||
llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
|
||||
llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu) {
|
||||
GGML_ASSERT(ctx);
|
||||
|
||||
auto N = float(llama_n_vocab(llama_get_model(ctx)));
|
||||
@@ -9081,7 +9084,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
|
||||
std::vector<std::thread> workers;
|
||||
workers.reserve(nthread);
|
||||
std::mutex mutex;
|
||||
|
||||
int idx = 0;
|
||||
|
||||
@@ -9155,7 +9157,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
new_size = ggml_nbytes(tensor);
|
||||
LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0);
|
||||
} else {
|
||||
const size_t nelements = ggml_nelements(tensor);
|
||||
const size_t ne = ggml_nelements(tensor);
|
||||
|
||||
float * f32_data;
|
||||
|
||||
@@ -9164,53 +9166,60 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
} else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
|
||||
throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
|
||||
} else {
|
||||
llama_convert_tensor_internal(tensor, f32_conv_buf, workers, nelements, nthread);
|
||||
llama_convert_tensor_internal(tensor, f32_conv_buf, workers, ne, nthread);
|
||||
f32_data = (float *) f32_conv_buf.data();
|
||||
}
|
||||
|
||||
LLAMA_LOG_INFO("quantizing to %s .. ", ggml_type_name(new_type));
|
||||
fflush(stdout);
|
||||
|
||||
if (work.size() < nelements * 4) {
|
||||
work.resize(nelements * 4); // upper bound on size
|
||||
if (work.size() < ne * 4) {
|
||||
work.resize(ne * 4); // upper bound on size
|
||||
}
|
||||
new_data = work.data();
|
||||
|
||||
std::array<int64_t, 1 << 4> hist_cur = {};
|
||||
|
||||
static const int chunk_size = 32 * 512;
|
||||
const int nchunk = (nelements + chunk_size - 1)/chunk_size;
|
||||
const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
|
||||
if (nthread_use < 2) {
|
||||
new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data());
|
||||
} else {
|
||||
size_t counter = 0;
|
||||
new_size = 0;
|
||||
auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements]() {
|
||||
std::array<int64_t, 1 << 4> local_hist = {};
|
||||
size_t local_size = 0;
|
||||
while (true) {
|
||||
std::unique_lock<std::mutex> lock(mutex);
|
||||
size_t first = counter; counter += chunk_size;
|
||||
if (first >= nelements) {
|
||||
if (local_size > 0) {
|
||||
for (int j=0; j<int(local_hist.size()); ++j) {
|
||||
hist_cur[j] += local_hist[j];
|
||||
}
|
||||
new_size += local_size;
|
||||
}
|
||||
break;
|
||||
}
|
||||
lock.unlock();
|
||||
size_t last = std::min(nelements, first + chunk_size);
|
||||
{
|
||||
static const size_t chunk_size = 32*512;
|
||||
|
||||
const int nchunk = GGML_PAD(ne, chunk_size)/chunk_size;
|
||||
const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
|
||||
|
||||
std::vector<size_t> size_th(nthread_use, 0);
|
||||
std::vector<std::array<int64_t, 1 << 4>> hist_cur_th(nthread_use);
|
||||
|
||||
auto compute = [&size_th, &hist_cur_th, new_type, f32_data, new_data, ne, nchunk, nthread_use](int tid) {
|
||||
auto & local_size = size_th[tid];
|
||||
auto & local_hist = hist_cur_th[tid];
|
||||
|
||||
for (int ch = tid; ch < nchunk; ch += nthread_use) {
|
||||
const size_t first = ch * chunk_size;
|
||||
const size_t last = std::min(ne, first + chunk_size);
|
||||
|
||||
local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first, last - first, local_hist.data());
|
||||
}
|
||||
};
|
||||
|
||||
for (int it = 0; it < nthread_use - 1; ++it) {
|
||||
workers.emplace_back(compute);
|
||||
workers.emplace_back(compute, it);
|
||||
}
|
||||
compute();
|
||||
for (auto & w : workers) { w.join(); }
|
||||
|
||||
compute(nthread_use - 1);
|
||||
|
||||
for (auto & w : workers) {
|
||||
w.join();
|
||||
}
|
||||
|
||||
workers.clear();
|
||||
|
||||
new_size = 0;
|
||||
for (int it = 0; it < nthread_use; ++it) {
|
||||
for (int j = 0; j < int(hist_cur.size()); ++j) {
|
||||
hist_cur[j] += hist_cur_th[it][j];
|
||||
}
|
||||
new_size += size_th[it];
|
||||
}
|
||||
}
|
||||
|
||||
LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB | hist: ", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
|
||||
@@ -9222,7 +9231,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
|
||||
if (tot_count > 0) {
|
||||
for (size_t i = 0; i < hist_cur.size(); i++) {
|
||||
LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements));
|
||||
LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(ne));
|
||||
}
|
||||
}
|
||||
LLAMA_LOG_INFO("\n");
|
||||
@@ -9594,7 +9603,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
|
||||
return result;
|
||||
}
|
||||
|
||||
int32_t llama_max_devices(void) {
|
||||
int llama_max_devices(void) {
|
||||
return LLAMA_MAX_DEVICES;
|
||||
}
|
||||
|
||||
@@ -9905,15 +9914,15 @@ enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
|
||||
return model->vocab.type;
|
||||
}
|
||||
|
||||
int32_t llama_n_vocab(const struct llama_model * model) {
|
||||
int llama_n_vocab(const struct llama_model * model) {
|
||||
return model->vocab.id_to_token.size();
|
||||
}
|
||||
|
||||
int32_t llama_n_ctx_train(const struct llama_model * model) {
|
||||
int llama_n_ctx_train(const struct llama_model * model) {
|
||||
return model->hparams.n_ctx_train;
|
||||
}
|
||||
|
||||
int32_t llama_n_embd(const struct llama_model * model) {
|
||||
int llama_n_embd(const struct llama_model * model) {
|
||||
return model->hparams.n_embd;
|
||||
}
|
||||
|
||||
@@ -9921,7 +9930,7 @@ float llama_rope_freq_scale_train(const struct llama_model * model) {
|
||||
return model->hparams.rope_freq_scale_train;
|
||||
}
|
||||
|
||||
int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
|
||||
int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
|
||||
const auto & it = model->gguf_kv.find(key);
|
||||
if (it == model->gguf_kv.end()) {
|
||||
if (buf_size > 0) {
|
||||
@@ -9932,11 +9941,11 @@ int32_t llama_model_meta_val_str(const struct llama_model * model, const char *
|
||||
return snprintf(buf, buf_size, "%s", it->second.c_str());
|
||||
}
|
||||
|
||||
int32_t llama_model_meta_count(const struct llama_model * model) {
|
||||
int llama_model_meta_count(const struct llama_model * model) {
|
||||
return (int)model->gguf_kv.size();
|
||||
}
|
||||
|
||||
int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
|
||||
int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
|
||||
if (i < 0 || i >= (int)model->gguf_kv.size()) {
|
||||
if (buf_size > 0) {
|
||||
buf[0] = '\0';
|
||||
@@ -9948,7 +9957,7 @@ int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, c
|
||||
return snprintf(buf, buf_size, "%s", it->first.c_str());
|
||||
}
|
||||
|
||||
int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
|
||||
int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
|
||||
if (i < 0 || i >= (int)model->gguf_kv.size()) {
|
||||
if (buf_size > 0) {
|
||||
buf[0] = '\0';
|
||||
@@ -9960,10 +9969,9 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3
|
||||
return snprintf(buf, buf_size, "%s", it->second.c_str());
|
||||
}
|
||||
|
||||
int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
|
||||
return snprintf(buf, buf_size, "%s %s%s %s",
|
||||
int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
|
||||
return snprintf(buf, buf_size, "%s %s %s",
|
||||
llama_model_arch_name(model->arch).c_str(),
|
||||
model->hparams.n_expert > 0 ? (std::to_string(model->hparams.n_expert) + "x").c_str() : "",
|
||||
llama_model_type_name(model->type),
|
||||
llama_model_ftype_name(model->ftype).c_str());
|
||||
}
|
||||
@@ -9988,7 +9996,7 @@ struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const ch
|
||||
return ggml_get_tensor(model->ctx, name);
|
||||
}
|
||||
|
||||
uint32_t llama_model_quantize(
|
||||
int llama_model_quantize(
|
||||
const char * fname_inp,
|
||||
const char * fname_out,
|
||||
const llama_model_quantize_params * params) {
|
||||
@@ -10001,7 +10009,7 @@ uint32_t llama_model_quantize(
|
||||
}
|
||||
}
|
||||
|
||||
int32_t llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
|
||||
int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int n_threads) {
|
||||
try {
|
||||
return llama_apply_lora_from_file_internal(ctx->model, path_lora, scale, path_base_model, n_threads);
|
||||
} catch (const std::exception & err) {
|
||||
@@ -10010,7 +10018,7 @@ int32_t llama_apply_lora_from_file(struct llama_context * ctx, const char * path
|
||||
}
|
||||
}
|
||||
|
||||
int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
|
||||
int llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int n_threads) {
|
||||
try {
|
||||
return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
|
||||
} catch (const std::exception & err) {
|
||||
@@ -10108,7 +10116,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
|
||||
}
|
||||
}
|
||||
|
||||
int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx) {
|
||||
int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
|
||||
int result = 0;
|
||||
|
||||
for (uint32_t i = 0; i < ctx->kv_self.size; i++) {
|
||||
@@ -10118,7 +10126,7 @@ int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx) {
|
||||
return result;
|
||||
}
|
||||
|
||||
int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx) {
|
||||
int llama_get_kv_cache_used_cells(const struct llama_context * ctx) {
|
||||
return ctx->kv_self.used;
|
||||
}
|
||||
|
||||
@@ -10600,7 +10608,7 @@ int llama_eval(
|
||||
struct llama_context * ctx,
|
||||
llama_token * tokens,
|
||||
int32_t n_tokens,
|
||||
int32_t n_past) {
|
||||
int n_past) {
|
||||
llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
|
||||
|
||||
const int ret = llama_decode_internal(*ctx, llama_batch_get_one(tokens, n_tokens, n_past, 0));
|
||||
@@ -10615,7 +10623,7 @@ int llama_eval_embd(
|
||||
struct llama_context * ctx,
|
||||
float * embd,
|
||||
int32_t n_tokens,
|
||||
int32_t n_past) {
|
||||
int n_past) {
|
||||
llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);
|
||||
|
||||
llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, };
|
||||
@@ -10686,7 +10694,7 @@ void llama_batch_free(struct llama_batch batch) {
|
||||
if (batch.logits) free(batch.logits);
|
||||
}
|
||||
|
||||
int32_t llama_decode(
|
||||
int llama_decode(
|
||||
struct llama_context * ctx,
|
||||
struct llama_batch batch) {
|
||||
const int ret = llama_decode_internal(*ctx, batch);
|
||||
@@ -10734,11 +10742,11 @@ llama_token llama_token_nl(const struct llama_model * model) {
|
||||
return model->vocab.linefeed_id;
|
||||
}
|
||||
|
||||
int32_t llama_add_bos_token(const struct llama_model * model) {
|
||||
int llama_add_bos_token(const struct llama_model * model) {
|
||||
return model->vocab.special_add_bos;
|
||||
}
|
||||
|
||||
int32_t llama_add_eos_token(const struct llama_model * model) {
|
||||
int llama_add_eos_token(const struct llama_model * model) {
|
||||
return model->vocab.special_add_eos;
|
||||
}
|
||||
|
||||
@@ -10758,12 +10766,12 @@ llama_token llama_token_eot(const struct llama_model * model) {
|
||||
return model->vocab.special_eot_id;
|
||||
}
|
||||
|
||||
int32_t llama_tokenize(
|
||||
int llama_tokenize(
|
||||
const struct llama_model * model,
|
||||
const char * text,
|
||||
int32_t text_len,
|
||||
int text_len,
|
||||
llama_token * tokens,
|
||||
int32_t n_max_tokens,
|
||||
int n_max_tokens,
|
||||
bool add_bos,
|
||||
bool special) {
|
||||
auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special);
|
||||
@@ -10791,7 +10799,7 @@ static std::string llama_decode_text(const std::string & text) {
|
||||
}
|
||||
|
||||
// does not write null-terminator to buf
|
||||
int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) {
|
||||
int llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int length) {
|
||||
if (0 <= token && token < llama_n_vocab(model)) {
|
||||
switch (llama_vocab_get_type(model->vocab)) {
|
||||
case LLAMA_VOCAB_TYPE_SPM: {
|
||||
|
||||
63
llama.h
63
llama.h
@@ -226,7 +226,7 @@ extern "C" {
|
||||
|
||||
// model quantization parameters
|
||||
typedef struct llama_model_quantize_params {
|
||||
int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
|
||||
int nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
|
||||
enum llama_ftype ftype; // quantize to this llama_ftype
|
||||
bool allow_requantize; // allow quantizing non-f32/f16 tensors
|
||||
bool quantize_output_tensor; // quantize output.weight
|
||||
@@ -310,20 +310,21 @@ extern "C" {
|
||||
|
||||
LLAMA_API int64_t llama_time_us(void);
|
||||
|
||||
LLAMA_API int32_t llama_max_devices(void);
|
||||
LLAMA_API int llama_max_devices (void);
|
||||
LLAMA_API bool llama_mmap_supported (void);
|
||||
LLAMA_API bool llama_mlock_supported(void);
|
||||
|
||||
LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
|
||||
|
||||
// TODO: become more consistent with returned int types across the API
|
||||
LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
|
||||
LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
|
||||
|
||||
LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
|
||||
|
||||
LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
|
||||
LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
|
||||
LLAMA_API int32_t llama_n_embd (const struct llama_model * model);
|
||||
LLAMA_API int llama_n_vocab (const struct llama_model * model);
|
||||
LLAMA_API int llama_n_ctx_train(const struct llama_model * model);
|
||||
LLAMA_API int llama_n_embd (const struct llama_model * model);
|
||||
|
||||
// Get the model's RoPE frequency scaling factor
|
||||
LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
|
||||
@@ -334,19 +335,19 @@ extern "C" {
|
||||
// - GGUF array values are not supported by these functions
|
||||
|
||||
// Get metadata value as a string by key name
|
||||
LLAMA_API int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
|
||||
LLAMA_API int llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size);
|
||||
|
||||
// Get the number of metadata key/value pairs
|
||||
LLAMA_API int32_t llama_model_meta_count(const struct llama_model * model);
|
||||
LLAMA_API int llama_model_meta_count(const struct llama_model * model);
|
||||
|
||||
// Get metadata key name by index
|
||||
LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
|
||||
LLAMA_API int llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);
|
||||
|
||||
// Get metadata value as a string by index
|
||||
LLAMA_API int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
|
||||
LLAMA_API int llama_model_meta_val_str_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size);
|
||||
|
||||
// Get a string describing the model type
|
||||
LLAMA_API int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
|
||||
LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
|
||||
|
||||
// Returns the total size of all the tensors in the model in bytes
|
||||
LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
|
||||
@@ -358,7 +359,7 @@ extern "C" {
|
||||
LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
|
||||
|
||||
// Returns 0 on success
|
||||
LLAMA_API uint32_t llama_model_quantize(
|
||||
LLAMA_API int llama_model_quantize(
|
||||
const char * fname_inp,
|
||||
const char * fname_out,
|
||||
const llama_model_quantize_params * params);
|
||||
@@ -369,20 +370,20 @@ extern "C" {
|
||||
// The model needs to be reloaded before applying a new adapter, otherwise the adapter
|
||||
// will be applied on top of the previous one
|
||||
// Returns 0 on success
|
||||
LLAMA_API DEPRECATED(int32_t llama_apply_lora_from_file(
|
||||
LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
|
||||
struct llama_context * ctx,
|
||||
const char * path_lora,
|
||||
float scale,
|
||||
const char * path_base_model,
|
||||
int32_t n_threads),
|
||||
int n_threads),
|
||||
"use llama_model_apply_lora_from_file instead");
|
||||
|
||||
LLAMA_API int32_t llama_model_apply_lora_from_file(
|
||||
LLAMA_API int llama_model_apply_lora_from_file(
|
||||
const struct llama_model * model,
|
||||
const char * path_lora,
|
||||
float scale,
|
||||
const char * path_base_model,
|
||||
int32_t n_threads);
|
||||
int n_threads);
|
||||
|
||||
//
|
||||
// KV cache
|
||||
@@ -438,10 +439,10 @@ extern "C" {
|
||||
|
||||
// Returns the number of tokens in the KV cache (slow, use only for debug)
|
||||
// If a KV cell has multiple sequences assigned to it, it will be counted multiple times
|
||||
LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx);
|
||||
LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
|
||||
|
||||
// Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
|
||||
LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
|
||||
LLAMA_API int llama_get_kv_cache_used_cells(const struct llama_context * ctx);
|
||||
|
||||
// Clear the KV cache
|
||||
LLAMA_API void llama_kv_cache_clear(
|
||||
@@ -532,7 +533,7 @@ extern "C" {
|
||||
struct llama_context * ctx,
|
||||
llama_token * tokens,
|
||||
int32_t n_tokens,
|
||||
int32_t n_past),
|
||||
int n_past),
|
||||
"use llama_decode() instead");
|
||||
|
||||
// Same as llama_eval, but use float matrix input directly.
|
||||
@@ -541,7 +542,7 @@ extern "C" {
|
||||
struct llama_context * ctx,
|
||||
float * embd,
|
||||
int32_t n_tokens,
|
||||
int32_t n_past),
|
||||
int n_past),
|
||||
"use llama_decode() instead");
|
||||
|
||||
// Return batch for single sequence of tokens starting at pos_0
|
||||
@@ -573,7 +574,7 @@ extern "C" {
|
||||
// 0 - success
|
||||
// 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
|
||||
// < 0 - error
|
||||
LLAMA_API int32_t llama_decode(
|
||||
LLAMA_API int llama_decode(
|
||||
struct llama_context * ctx,
|
||||
struct llama_batch batch);
|
||||
|
||||
@@ -613,10 +614,10 @@ extern "C" {
|
||||
LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
|
||||
|
||||
// Returns -1 if unknown, 1 for true or 0 for false.
|
||||
LLAMA_API int32_t llama_add_bos_token(const struct llama_model * model);
|
||||
LLAMA_API int llama_add_bos_token(const struct llama_model * model);
|
||||
|
||||
// Returns -1 if unknown, 1 for true or 0 for false.
|
||||
LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model);
|
||||
LLAMA_API int llama_add_eos_token(const struct llama_model * model);
|
||||
|
||||
// codellama infill tokens
|
||||
LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
|
||||
@@ -634,12 +635,12 @@ extern "C" {
|
||||
/// @return Returns a negative number on failure - the number of tokens that would have been returned
|
||||
/// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
|
||||
/// Does not insert a leading space.
|
||||
LLAMA_API int32_t llama_tokenize(
|
||||
LLAMA_API int llama_tokenize(
|
||||
const struct llama_model * model,
|
||||
const char * text,
|
||||
int32_t text_len,
|
||||
int text_len,
|
||||
llama_token * tokens,
|
||||
int32_t n_max_tokens,
|
||||
int n_max_tokens,
|
||||
bool add_bos,
|
||||
bool special);
|
||||
|
||||
@@ -647,11 +648,11 @@ extern "C" {
|
||||
// Uses the vocabulary in the provided context.
|
||||
// Does not write null terminator to the buffer.
|
||||
// User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
|
||||
LLAMA_API int32_t llama_token_to_piece(
|
||||
LLAMA_API int llama_token_to_piece(
|
||||
const struct llama_model * model,
|
||||
llama_token token,
|
||||
char * buf,
|
||||
int32_t length);
|
||||
int length);
|
||||
|
||||
//
|
||||
// Grammar
|
||||
@@ -703,7 +704,7 @@ extern "C" {
|
||||
LLAMA_API void llama_sample_top_k(
|
||||
struct llama_context * ctx,
|
||||
llama_token_data_array * candidates,
|
||||
int32_t k,
|
||||
int k,
|
||||
size_t min_keep);
|
||||
|
||||
/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
|
||||
@@ -762,7 +763,7 @@ extern "C" {
|
||||
llama_token_data_array * candidates,
|
||||
float tau,
|
||||
float eta,
|
||||
int32_t m,
|
||||
int m,
|
||||
float * mu);
|
||||
|
||||
/// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
|
||||
@@ -835,8 +836,8 @@ extern "C" {
|
||||
llama_beam_search_callback_fn_t callback,
|
||||
void * callback_data,
|
||||
size_t n_beams,
|
||||
int32_t n_past,
|
||||
int32_t n_predict);
|
||||
int n_past,
|
||||
int n_predict);
|
||||
|
||||
// Performance information
|
||||
LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
|
||||
|
||||
@@ -27,7 +27,7 @@ echo "Syncing ggml changes since commit $lc"
|
||||
cd $SRC_GGML
|
||||
|
||||
git log --oneline $lc..HEAD
|
||||
git log --oneline $lc..HEAD --reverse | grep -v "(llama/[0-9]*)" | cut -d' ' -f1 > $SRC_LLAMA/ggml-commits
|
||||
git log --oneline $lc..HEAD | grep -v "(llama/[0-9]*)" | cut -d' ' -f1 > $SRC_LLAMA/ggml-commits
|
||||
|
||||
if [ ! -s $SRC_LLAMA/ggml-commits ]; then
|
||||
rm -v $SRC_LLAMA/ggml-commits
|
||||
@@ -87,6 +87,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
|
||||
# src/ggml-impl.h -> ggml-impl.h
|
||||
# src/ggml-metal.h -> ggml-metal.h
|
||||
# src/ggml-metal.m -> ggml-metal.m
|
||||
# src/ggml-metal.metal -> ggml-metal.metal
|
||||
# src/ggml-mpi.h -> ggml-mpi.h
|
||||
# src/ggml-mpi.c -> ggml-mpi.c
|
||||
# src/ggml-opencl.cpp -> ggml-opencl.cpp
|
||||
@@ -113,6 +114,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
|
||||
-e 's/src\/ggml-impl\.h/ggml-impl.h/g' \
|
||||
-e 's/src\/ggml-metal\.h/ggml-metal.h/g' \
|
||||
-e 's/src\/ggml-metal\.m/ggml-metal.m/g' \
|
||||
-e 's/src\/ggml-metal\.metal/ggml-metal.metal/g' \
|
||||
-e 's/src\/ggml-mpi\.h/ggml-mpi.h/g' \
|
||||
-e 's/src\/ggml-mpi\.c/ggml-mpi.c/g' \
|
||||
-e 's/src\/ggml-opencl\.cpp/ggml-opencl.cpp/g' \
|
||||
|
||||
@@ -1 +1 @@
|
||||
f96711108d55bdbbd277e6be07204dce6a94fb93
|
||||
df098ea908764cba4a4889a1cbe7b026b2d31a14
|
||||
|
||||
@@ -58,9 +58,6 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
|
||||
int64_t hist[16];
|
||||
ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size, hist);
|
||||
ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
|
||||
} else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
|
||||
// This is going to create some weird integers though.
|
||||
ggml_backend_tensor_set(tensor, data.data(), 0, ggml_nbytes(tensor));
|
||||
} else {
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
@@ -90,13 +87,8 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) {
|
||||
tv.push_back(*(float *) &buf[i]);
|
||||
} else if (t->type == GGML_TYPE_I32) {
|
||||
tv.push_back((float)*(int32_t *) &buf[i]);
|
||||
} else if (t->type == GGML_TYPE_I16) {
|
||||
tv.push_back((float)*(int16_t *) &buf[i]);
|
||||
} else if (t->type == GGML_TYPE_I8) {
|
||||
tv.push_back((float)*(int8_t *) &buf[i]);
|
||||
} else if (quantized) {
|
||||
std::vector<float> vq(ggml_blck_size(t->type));
|
||||
tt.to_float(&buf[i], vq.data(), ggml_blck_size(t->type));
|
||||
tt.to_float(&buf[i], vq.data(), bs);
|
||||
tv.insert(tv.end(), vq.begin(), vq.end());
|
||||
} else {
|
||||
GGML_ASSERT(false);
|
||||
@@ -392,21 +384,15 @@ struct test_case {
|
||||
struct callback_userdata {
|
||||
bool ok;
|
||||
double max_err;
|
||||
ggml_backend_t backend1;
|
||||
ggml_backend_t backend2;
|
||||
};
|
||||
|
||||
callback_userdata ud {
|
||||
true,
|
||||
max_nmse_err(),
|
||||
backend1,
|
||||
backend2
|
||||
};
|
||||
|
||||
auto callback = [](int index, ggml_tensor * t1, ggml_tensor * t2, void * user_data) -> bool {
|
||||
callback_userdata * ud = (callback_userdata *) user_data;
|
||||
const char * bn1 = ggml_backend_name(ud->backend1);
|
||||
const char * bn2 = ggml_backend_name(ud->backend2);
|
||||
|
||||
if (t1->op == GGML_OP_NONE) {
|
||||
// sentinels must be unchanged
|
||||
@@ -428,7 +414,7 @@ struct test_case {
|
||||
for (size_t i = 0; i < f1.size(); i++) {
|
||||
// check for nans
|
||||
if (std::isnan(f1[i]) || std::isnan(f2[i])) {
|
||||
printf("[%s] NaN at index %zu (%s=%f %s=%f) ", ggml_op_desc(t1), i, bn1, f1[i], bn2, f2[i]);
|
||||
printf("[%s] NaN at index %zu (%f %f) ", ggml_op_desc(t1), i, f1[i], f2[i]);
|
||||
ud->ok = false;
|
||||
return true;
|
||||
}
|
||||
@@ -436,12 +422,12 @@ struct test_case {
|
||||
if (isinf_or_max(f1[i]) || isinf_or_max(f2[i])) {
|
||||
if (isinf_or_max(f1[i]) && isinf_or_max(f2[i])) {
|
||||
if (std::signbit(f1[i]) != std::signbit(f2[i])) {
|
||||
printf("[%s] inf sign mismatch: %s=%f %s=%f ", ggml_op_desc(t1), bn1, f1[i], bn2, f2[i]);
|
||||
printf("[%s] inf sign mismatch: %f %f ", ggml_op_desc(t1), f1[i], f2[i]);
|
||||
ud->ok = false;
|
||||
return true;
|
||||
}
|
||||
} else {
|
||||
printf("[%s] inf mismatch: %s=%f %s=%f ", ggml_op_desc(t1), bn1, f1[i], bn2, f2[i]);
|
||||
printf("[%s] inf mismatch: %f %f ", ggml_op_desc(t1), f1[i], f2[i]);
|
||||
ud->ok = false;
|
||||
return true;
|
||||
}
|
||||
@@ -675,26 +661,17 @@ struct test_repeat : public test_case {
|
||||
struct test_dup : public test_case {
|
||||
const ggml_type type;
|
||||
const std::array<int64_t, 4> ne;
|
||||
const std::array<int64_t, 4> permute;
|
||||
bool _use_permute;
|
||||
|
||||
std::string vars() override {
|
||||
std::string v = VARS_TO_STR2(type, ne);
|
||||
if (_use_permute) v += "," + VAR_TO_STR(permute);
|
||||
return v;
|
||||
return VARS_TO_STR2(type, ne);
|
||||
}
|
||||
|
||||
test_dup(ggml_type type = GGML_TYPE_F32,
|
||||
std::array<int64_t, 4> ne = {10, 10, 10, 1},
|
||||
std::array<int64_t, 4> permute = {0, 0, 0, 0})
|
||||
: type(type), ne(ne), permute(permute),
|
||||
_use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
|
||||
std::array<int64_t, 4> ne = {10, 10, 10, 1})
|
||||
: type(type), ne(ne) {}
|
||||
|
||||
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||||
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
|
||||
if (_use_permute) {
|
||||
src = ggml_permute(ctx, src, permute[0], permute[1], permute[2], permute[3]);
|
||||
}
|
||||
ggml_tensor * out = ggml_dup(ctx, src);
|
||||
return out;
|
||||
}
|
||||
@@ -1473,26 +1450,14 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
|
||||
}
|
||||
}
|
||||
}
|
||||
for (int b : {1, 7}) {
|
||||
for (bool v : {false, true}) {
|
||||
test_cases.emplace_back(new test_get_rows(GGML_TYPE_I32, 256, 5, 4, b, v));
|
||||
}
|
||||
}
|
||||
|
||||
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1}));
|
||||
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1}));
|
||||
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 2, 1, 1}));
|
||||
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 2, 1}));
|
||||
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 2}));
|
||||
test_cases.emplace_back(new test_repeat(GGML_TYPE_I32, {10, 10, 10, 10}, {2, 1, 1, 1}));
|
||||
test_cases.emplace_back(new test_repeat(GGML_TYPE_I16, {10, 10, 10, 10}, {1, 1, 1, 2}));
|
||||
|
||||
test_cases.emplace_back(new test_dup(GGML_TYPE_F32));
|
||||
test_cases.emplace_back(new test_dup(GGML_TYPE_F16));
|
||||
test_cases.emplace_back(new test_dup(GGML_TYPE_I32));
|
||||
test_cases.emplace_back(new test_dup(GGML_TYPE_I16));
|
||||
test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {0, 2, 1, 3}));
|
||||
test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3}));
|
||||
test_cases.emplace_back(new test_dup());
|
||||
|
||||
for (ggml_type type : all_types) {
|
||||
test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, type, {256, 10, 10, 1}));
|
||||
@@ -1600,8 +1565,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
|
||||
|
||||
test_cases.emplace_back(new test_alibi());
|
||||
test_cases.emplace_back(new test_im2col());
|
||||
test_cases.emplace_back(new test_concat(GGML_TYPE_F32));
|
||||
test_cases.emplace_back(new test_concat(GGML_TYPE_I32));
|
||||
test_cases.emplace_back(new test_concat());
|
||||
|
||||
for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) {
|
||||
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
|
||||
|
||||
Reference in New Issue
Block a user