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16
.devops/nix/package.nix
View file

@ -24,7 +24,7 @@
useOpenCL
useRocm
useVulkan
],
] && blas.meta.available,
useCuda ? config.cudaSupport,
useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL,
useMpi ? false, # Increases the runtime closure size by ~700M
@ -67,10 +67,15 @@ let
strings.optionalString (suffices != [ ])
", accelerated with ${strings.concatStringsSep ", " suffices}";
executableSuffix = effectiveStdenv.hostPlatform.extensions.executable;
# TODO: package the Python in this repository in a Nix-like way.
# It'd be nice to migrate to buildPythonPackage, as well as ensure this repo
# is PEP 517-compatible, and ensure the correct .dist-info is generated.
# https://peps.python.org/pep-0517/
#
# TODO: Package up each Python script or service appropriately, by making
# them into "entrypoints"
llama-python = python3.withPackages (
ps: [
ps.numpy
@ -159,11 +164,6 @@ effectiveStdenv.mkDerivation (
--replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
substituteInPlace ./ggml-metal.m \
--replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";"
# TODO: Package up each Python script or service appropriately.
# If we were to migrate to buildPythonPackage and prepare the `pyproject.toml`,
# we could make those *.py into setuptools' entrypoints
substituteInPlace ./*.py --replace "/usr/bin/env python" "${llama-python}/bin/python"
'';
# With PR#6015 https://github.com/ggerganov/llama.cpp/pull/6015,
@ -244,8 +244,8 @@ effectiveStdenv.mkDerivation (
# TODO(SomeoneSerge): It's better to add proper install targets at the CMake level,
# if they haven't been added yet.
postInstall = ''
mv $out/bin/main $out/bin/llama
mv $out/bin/server $out/bin/llama-server
mv $out/bin/main${executableSuffix} $out/bin/llama${executableSuffix}
mv $out/bin/server${executableSuffix} $out/bin/llama-server${executableSuffix}
mkdir -p $out/include
cp $src/llama.h $out/include/
'';

280
.github/workflows/bench.yml vendored Normal file
View file

@ -0,0 +1,280 @@
# Benchmark
name: Benchmark
on:
workflow_dispatch:
inputs:
gpu-series:
description: 'Azure GPU series to run with'
required: true
type: choice
options:
- Standard_NC4as_T4_v3
- Standard_NC24ads_A100_v4
- Standard_NC80adis_H100_v5
sha:
description: 'Commit SHA1 to build'
required: false
type: string
duration:
description: 'Duration of the bench'
type: string
default: 10m
push:
branches:
- master
paths: ['.github/workflows/bench.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/bench/**.*']
pull_request:
types: [opened, synchronize, reopened]
paths: ['.github/workflows/bench.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/bench/**.*']
schedule:
- cron: '04 2 * * *'
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
jobs:
bench-server-baseline:
runs-on: Standard_NC4as_T4_v3
env:
RUNNER_LABEL: Standard_NC4as_T4_v3 # FIXME Do not find a way to not duplicate it
N_USERS: 8
DURATION: 10m
if: ${{ github.event.inputs.gpu-series == 'Standard_NC4as_T4_v3' || github.event.schedule || github.event.pull_request || github.head_ref == 'master' || github.ref_name == 'master' || github.event.push.ref == 'refs/heads/master' }}
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
with:
fetch-depth: 0
ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
- name: Install python env
id: pipenv
run: |
cd examples/server/bench
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
- name: Prometheus
id: install_prometheus
run: |
wget --quiet https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz
tar xzf prometheus*.tar.gz --strip-components=1
./prometheus --config.file=examples/server/bench/prometheus.yml &
while ! nc -z localhost 9090; do
sleep 0.1
done
- name: Install k6
id: k6_installation
run: |
cd examples/server/bench
wget --quiet https://github.com/grafana/k6/releases/download/v0.49.0/k6-v0.49.0-linux-amd64.tar.gz
tar xzf k6*.tar.gz --strip-components=1
- name: Build
id: cmake_build
run: |
set -eux
mkdir build
cd build
cmake .. \
-DLLAMA_NATIVE=OFF \
-DLLAMA_BUILD_SERVER=ON \
-DLLAMA_CURL=ON \
-DLLAMA_CUBLAS=ON \
-DCUDAToolkit_ROOT=/usr/local/cuda \
-DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \
-DCMAKE_CUDA_ARCHITECTURES=75 \
-DLLAMA_FATAL_WARNINGS=OFF \
-DLLAMA_ALL_WARNINGS=OFF \
-DCMAKE_BUILD_TYPE=Release;
cmake --build . --config Release -j $(nproc) --target server
- name: Download the dataset
id: download_dataset
run: |
cd examples/server/bench
wget --quiet https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
- name: Server bench
id: server_bench
run: |
set -eux
cd examples/server/bench
source venv/bin/activate
BENCH_K6_BIN_PATH=./k6 python bench.py \
--runner-label ${{ env.RUNNER_LABEL }} \
--name ${{ github.job }} \
--branch ${{ github.head_ref || github.ref_name }} \
--commit ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha }} \
--scenario script.js \
--duration ${{ github.event.inputs.duration || env.DURATION }} \
--hf-repo ggml-org/models \
--hf-file phi-2/ggml-model-q4_0.gguf \
--model-path-prefix /models \
--parallel ${{ env.N_USERS }} \
-ngl 33 \
--batch-size 2048 \
--ubatch-size 256 \
--ctx-size 16384 \
--n-prompts 1000 \
--max-prompt-tokens 1024 \
--max-tokens 2048
cat results.github.env >> $GITHUB_ENV
# Remove dataset as we do not want it in the artefact
rm ShareGPT_V3_unfiltered_cleaned_split.json
- uses: actions/upload-artifact@v4
with:
name: benchmark-results
compression-level: 9
path: |
examples/server/bench/*.jpg
examples/server/bench/*.json
examples/server/bench/*.log
- name: Commit status
uses: Sibz/github-status-action@v1
continue-on-error: true # If not authorized on external repo
with:
authToken: ${{secrets.GITHUB_TOKEN}}
sha: ${{ inputs.sha || github.event.pull_request.head.sha || github.sha }}
context: bench-server-baseline
description: |
${{ env.BENCH_RESULTS }}
state: 'success'
- name: Upload benchmark images
uses: devicons/public-upload-to-imgur@v2.2.2
continue-on-error: true # Important as it looks unstable: 503
id: imgur_step
with:
client_id: ${{secrets.IMGUR_CLIENT_ID}}
path: |
examples/server/bench/prompt_tokens_seconds.jpg
examples/server/bench/predicted_tokens_seconds.jpg
examples/server/bench/kv_cache_usage_ratio.jpg
examples/server/bench/requests_processing.jpg
- name: Extract mermaid
id: set_mermaid
run: |
set -eux
cd examples/server/bench
PROMPT_TOKENS_SECONDS=$(cat prompt_tokens_seconds.mermaid)
echo "PROMPT_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
echo "$PROMPT_TOKENS_SECONDS" >> $GITHUB_ENV
echo "EOF" >> $GITHUB_ENV
PREDICTED_TOKENS_SECONDS=$(cat predicted_tokens_seconds.mermaid)
echo "PREDICTED_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
echo "$PREDICTED_TOKENS_SECONDS" >> $GITHUB_ENV
echo "EOF" >> $GITHUB_ENV
KV_CACHE_USAGE_RATIO=$(cat kv_cache_usage_ratio.mermaid)
echo "KV_CACHE_USAGE_RATIO<<EOF" >> $GITHUB_ENV
echo "$KV_CACHE_USAGE_RATIO" >> $GITHUB_ENV
echo "EOF" >> $GITHUB_ENV
REQUESTS_PROCESSING=$(cat requests_processing.mermaid)
echo "REQUESTS_PROCESSING<<EOF" >> $GITHUB_ENV
echo "$REQUESTS_PROCESSING" >> $GITHUB_ENV
echo "EOF" >> $GITHUB_ENV
- name: Extract image url
id: extract_image_url
continue-on-error: true
run: |
set -eux
echo "IMAGE_O=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[0] }}" >> $GITHUB_ENV
echo "IMAGE_1=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[1] }}" >> $GITHUB_ENV
echo "IMAGE_2=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[2] }}" >> $GITHUB_ENV
echo "IMAGE_3=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[3] }}" >> $GITHUB_ENV
- name: Comment PR
uses: mshick/add-pr-comment@v2
id: comment_pr
if: ${{ github.event.pull_request != '' }}
with:
message-id: bench-${{ github.job }}-${{ env.RUNNER_LABEL }}
message: |
📈 **llama.cpp server** for _${{ github.job }}_ on _${{ env.RUNNER_LABEL }}_: **${{ env.BENCH_ITERATIONS}} iterations** 🚀
- Concurrent users: ${{ env.N_USERS }}, duration: ${{ github.event.inputs.duration || env.DURATION }}
- HTTP request : avg=${{ env.HTTP_REQ_DURATION_AVG }}ms p(90)=${{ env.HTTP_REQ_DURATION_P_90_ }}ms fails=${{ env.HTTP_REQ_FAILED_PASSES }}, finish reason: stop=${{ env.LLAMACPP_COMPLETIONS_STOP_RATE_PASSES }} truncated=${{ env.LLAMACPP_COMPLETIONS_TRUNCATED_RATE_PASSES }}
- Prompt processing (pp): avg=${{ env.LLAMACPP_PROMPT_TOKENS_AVG }}tk/s p(90)=${{ env.LLAMACPP_PROMPT_TOKENS_P_90_ }}tk/s **total=${{ env.LLAMACPP_PROMPT_TOKENS_TOTAL_COUNTER_RATE }}tk/s**
- Token generation (tg): avg=${{ env.LLAMACPP_TOKENS_SECOND_AVG }}tk/s p(90)=${{ env.LLAMACPP_TOKENS_SECOND_P_90_ }}tk/s **total=${{ env.LLAMACPP_COMPLETION_TOKENS_TOTAL_COUNTER_RATE }}tk/s**
- ${{ env.BENCH_GRAPH_XLABEL }}
<details>
<summary>Time series</summary>
<p align="center">
<img width="100%" height="100%" src="${{ env.IMAGE_O }}" alt="prompt_tokens_seconds" />
<details>
<summary>More</summary>
```mermaid
${{ env.PROMPT_TOKENS_SECONDS }}
```
</details>
<img width="100%" height="100%" src="${{ env.IMAGE_1 }}" alt="predicted_tokens_seconds"/>
<details>
<summary>More</summary>
```mermaid
${{ env.PREDICTED_TOKENS_SECONDS }}
```
</details>
</p>
<details>
<summary>Details</summary>
<p align="center">
<img width="100%" height="100%" src="${{ env.IMAGE_2 }}" alt="kv_cache_usage_ratio" />
<details>
<summary>More</summary>
```mermaid
${{ env.KV_CACHE_USAGE_RATIO }}
```
</details>
<img width="100%" height="100%" src="${{ env.IMAGE_3 }}" alt="requests_processing"/>
<details>
<summary>More</summary>
```mermaid
${{ env.REQUESTS_PROCESSING }}
```
</details>
</p>
</details>
</details>

2
.github/workflows/build.yml vendored
View file

@ -840,7 +840,7 @@ jobs:
id: pack_artifacts
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
run: |
7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip .\build\bin\*
7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip ./build/bin/*
- name: Upload artifacts
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}

1
CMakeLists.txt
View file

@ -1170,6 +1170,7 @@ add_library(llama
llama.h
unicode.h
unicode.cpp
unicode-data.cpp
)
target_include_directories(llama PUBLIC .)

6
Makefile
View file

@ -556,6 +556,7 @@ ifdef LLAMA_CUDA_NO_PEER_COPY
endif # LLAMA_CUDA_NO_PEER_COPY
OBJS += ggml-cuda.o
OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
@ -678,7 +679,10 @@ ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h ggml-common.h
unicode.o: unicode.cpp unicode.h
$(CXX) $(CXXFLAGS) -c $< -o $@
OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o
unicode-data.o: unicode-data.cpp unicode-data.h
$(CXX) $(CXXFLAGS) -c $< -o $@
OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o
llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
$(CXX) $(CXXFLAGS) -c $< -o $@

1
Package.swift
View file

@ -32,6 +32,7 @@ let package = Package(
"ggml.c",
"llama.cpp",
"unicode.cpp",
"unicode-data.cpp",
"ggml-alloc.c",
"ggml-backend.c",
"ggml-quants.c",

11
README.md
View file

@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
### Recent API changes
- [2024 Mar 26] Logits and embeddings API updated for compactness https://github.com/ggerganov/llama.cpp/pull/6122
- [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017
- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328
- [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796
@ -147,6 +148,7 @@ Typically finetunes of the base models below are supported as well.
- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
- Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326)
**UI:**
@ -630,6 +632,15 @@ Building the program with BLAS support may lead to some performance improvements
- #### Vulkan
> [!WARNING]
>
> Vulkan support has been broken in https://github.com/ggerganov/llama.cpp/pull/6122
> due to relying on `GGML_OP_GET_ROWS` which is not yet properly supported by the Vulkan backend,
> but should be fixed relatively soon (possibly in https://github.com/ggerganov/llama.cpp/pull/6155
> (ref: https://github.com/ggerganov/llama.cpp/pull/6122#issuecomment-2015327635)).
>
> Meanwhile, if you want to use the Vulkan backend, you should use the commit right before the breaking change, https://github.com/ggerganov/llama.cpp/commit/55c1b2a3bbd470e9e2a3a0618b92cf64a885f806
**With docker**:
You don't need to install Vulkan SDK. It will be installed inside the container.

15
build.zig
View file

@ -116,6 +116,7 @@ pub fn build(b: *std.build.Builder) !void {
const ggml_backend = make.obj("ggml-backend", "ggml-backend.c");
const ggml_quants = make.obj("ggml-quants", "ggml-quants.c");
const unicode = make.obj("unicode", "unicode.cpp");
const unicode_data = make.obj("unicode-data", "unicode-data.cpp");
const llama = make.obj("llama", "llama.cpp");
const buildinfo = make.obj("common", "common/build-info.cpp");
const common = make.obj("common", "common/common.cpp");
@ -127,14 +128,14 @@ pub fn build(b: *std.build.Builder) !void {
const clip = make.obj("clip", "examples/llava/clip.cpp");
const llava = make.obj("llava", "examples/llava/llava.cpp");
_ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, sampling, console, grammar_parser });
_ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo });
_ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo });
_ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo });
_ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, train });
_ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, train });
_ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, sampling, console, grammar_parser });
_ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo });
_ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo });
_ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo });
_ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, train });
_ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, train });
const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo, sampling, grammar_parser, json_schema_to_grammar, clip, llava });
const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, buildinfo, sampling, grammar_parser, json_schema_to_grammar, clip, llava });
if (server.target.isWindows()) {
server.linkSystemLibrary("ws2_32");
}

8
common/common.cpp
View file

@ -1062,8 +1062,8 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
params.ignore_eos = true;
return true;
}
if (arg == "--no-penalize-nl") {
sparams.penalize_nl = false;
if (arg == "--penalize-nl") {
sparams.penalize_nl = true;
return true;
}
if (arg == "-l" || arg == "--logit-bias") {
@ -1373,7 +1373,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" -dt N, --defrag-thold N\n");
printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
printf(" --no-penalize-nl do not penalize newline token\n");
printf(" --penalize-nl penalize newline tokens\n");
printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp);
printf(" --all-logits return logits for all tokens in the batch (default: disabled)\n");
printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
@ -2489,7 +2489,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false");
fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);

12
convert-hf-to-gguf.py
View file

@ -331,7 +331,7 @@ class Model(ABC):
tokenizer = SentencePieceProcessor(str(tokenizer_path))
vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
for token_id in range(vocab_size):
for token_id in range(tokenizer.vocab_size()):
piece = tokenizer.id_to_piece(token_id)
text = piece.encode("utf-8")
score = tokenizer.get_score(token_id)
@ -356,9 +356,13 @@ class Model(ABC):
added_tokens_json = json.load(f)
for key in added_tokens_json:
tokens.append(key.encode("utf-8"))
scores.append(-1000.0)
toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
key = key.encode("utf-8")
if key not in tokens:
tokens.append(key)
scores.append(-1000.0)
toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
assert len(tokens) == vocab_size
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_token_list(tokens)

2
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View file

@ -880,7 +880,7 @@ int main(int argc, char ** argv) {
TransformerWeights weights = {};
{
LOG("%s: Loading llama2c model from %s\n", __func__, params.fn_llama2c_model);
FILE *file = fopen(params.fn_llama2c_model, "r");
FILE * file = fopen(params.fn_llama2c_model, "rb");
if (!file) {
LOG("%s: Unable to open the checkpoint file %s!\n", __func__, params.fn_llama2c_model);
return 1;

26
examples/embedding/embedding.cpp
View file

@ -61,6 +61,8 @@ int main(int argc, char ** argv) {
}
params.embedding = true;
// For non-causal models, batch size must be equal to ubatch size
params.n_ubatch = params.n_batch;
print_build_info();
@ -114,7 +116,9 @@ int main(int argc, char ** argv) {
for (const auto & prompt : prompts) {
auto inp = ::llama_tokenize(ctx, prompt, true, false);
if (inp.size() > n_batch) {
inp.resize(n_batch);
fprintf(stderr, "%s: error: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
__func__, (long long int) inp.size(), (long long int) n_batch);
return 1;
}
inputs.push_back(inp);
}
@ -174,25 +178,27 @@ int main(int argc, char ** argv) {
float * out = emb + p * n_embd;
batch_decode(ctx, batch, out, s, n_embd);
// print the first part of the embeddings
// print the first part of the embeddings or for a single prompt, the full embedding
fprintf(stdout, "\n");
for (int j = 0; j < n_prompts; j++) {
fprintf(stdout, "embedding %d: ", j);
for (int i = 0; i < std::min(16, n_embd); i++) {
for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) {
fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
}
fprintf(stdout, "\n");
}
// print cosine similarity matrix
fprintf(stdout, "\n");
printf("cosine similarity matrix:\n\n");
for (int i = 0; i < n_prompts; i++) {
for (int j = 0; j < n_prompts; j++) {
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
fprintf(stdout, "%6.2f ", sim);
}
if (n_prompts > 1) {
fprintf(stdout, "\n");
printf("cosine similarity matrix:\n\n");
for (int i = 0; i < n_prompts; i++) {
for (int j = 0; j < n_prompts; j++) {
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
fprintf(stdout, "%6.2f ", sim);
}
fprintf(stdout, "\n");
}
}
// clean up

1
examples/imatrix/imatrix.cpp
View file

@ -424,6 +424,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
}
// TODO: use batch.logits to save computations instead of relying on logits_all == true
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;

2
examples/llava/MobileVLM-README.md
View file

@ -6,7 +6,7 @@ for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com
The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava.
Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using MobiVLM as an example, the different conversion step will be shown.
Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using MobileVLM as an example, the different conversion step will be shown.
## Usage
Build with cmake or run `make llava-cli` to build it.

4
examples/main/README.md
View file

@ -296,7 +296,9 @@ These options help improve the performance and memory usage of the LLaMA models.
### Batch Size
- `-b N, --batch-size N`: Set the batch size for prompt processing (default: 512). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.
- `-b N, --batch-size N`: Set the batch size for prompt processing (default: `2048`). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.
- `-ub N`, `--ubatch-size N`: physical maximum batch size. This is for pipeline parallelization. Default: `512`.
### Prompt Caching

1
examples/parallel/parallel.cpp
View file

@ -132,7 +132,6 @@ int main(int argc, char ** argv) {
llama_context * ctx = NULL;
// load the target model
params.logits_all = true;
std::tie(model, ctx) = llama_init_from_gpt_params(params);
// load the prompts from an external file if there are any

129
examples/perplexity/perplexity.cpp
View file

@ -380,6 +380,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
const int batch_size = std::min(end - batch_start, n_batch);
//fprintf(stderr, " Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
// TODO: use llama_batch.logits instead of relying on logits_all == true
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
//fprintf(stderr, "%s : failed to eval\n", __func__);
return {tokens, -1, logit_history, prob_history};
@ -552,6 +553,8 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
const int batch_start = start + j * n_batch;
const int batch_size = std::min(end - batch_start, n_batch);
int n_outputs = 0;
batch.n_tokens = 0;
for (int seq = 0; seq < n_seq_batch; seq++) {
int seq_start = batch_start + seq*n_ctx;
@ -566,11 +569,13 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
for (int k = 0; k < batch_size; ++k) {
const int idx = seq*n_ctx + k;
batch.token[idx] = tokens[seq_start + k];
batch.pos[idx] = j*n_batch + k;
batch.n_seq_id[idx] = 1;
batch.seq_id[idx][0] = seq;
batch.logits[idx] = batch.pos[idx] >= first ? 1 : 0;
batch.token [idx] = tokens[seq_start + k];
batch.pos [idx] = j*n_batch + k;
batch.n_seq_id[idx] = 1;
batch.seq_id [idx][0] = seq;
batch.logits [idx] = batch.pos[idx] >= first ? 1 : 0;
n_outputs += batch.logits[idx] != 0;
}
batch.n_tokens += batch_size;
@ -583,9 +588,9 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
return {tokens, -1, logit_history, prob_history};
}
if (num_batches > 1) {
if (num_batches > 1 && n_outputs > 0) {
const auto * batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
logits.insert(logits.end(), batch_logits, batch_logits + n_outputs * n_vocab);
}
}
@ -604,14 +609,15 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
}
for (int seq = 0; seq < n_seq_batch; seq++) {
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx);
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx + first);
llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first;
if (!params.logits_file.empty()) {
process_logits(logits_stream, n_vocab, all_logits + first*n_vocab,
process_logits(logits_stream, n_vocab, all_logits,
tokens_data, n_ctx - 1 - first,
workers, log_probs, nll, nll2);
} else {
process_logits(n_vocab, all_logits + first*n_vocab,
process_logits(n_vocab, all_logits,
tokens_data, n_ctx - 1 - first,
workers, nll, nll2,
logit_history.data() + start + seq*n_ctx + first,
@ -652,6 +658,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
}
static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<float> & batch_logits, int32_t n_batch, int32_t n_vocab) {
int prev_outputs = 0;
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
@ -672,7 +679,14 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<
return false;
}
memcpy(batch_logits.data() + i*n_vocab, llama_get_logits(ctx), n_tokens*n_vocab*sizeof(float));
int n_outputs = 0;
for (int i = 0; i < n_tokens; ++i) {
n_outputs += batch_view.logits[i] != 0;
}
memcpy(batch_logits.data() + prev_outputs*n_vocab, llama_get_logits(ctx), n_outputs*n_vocab*sizeof(float));
prev_outputs += n_outputs;
}
return true;
@ -779,7 +793,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
size_t ending_logprob_count[4];
double ending_logprob[4];
size_t i_batch; // starting index in the llama_batch
size_t i_logits; // starting index of logits in the llama_batch
size_t common_prefix; // max number of initial tokens that are the same in all sentences
size_t required_tokens; // needed number of tokens to evaluate all 4 endings
std::vector<llama_token> seq_tokens[4];
@ -844,9 +858,10 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
const int max_tasks_per_batch = 32;
const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
llama_batch batch = llama_batch_init(n_ctx, 0, 4);
std::vector<float> tok_logits(n_vocab);
// TODO: this could be made smaller; it's currently the worst-case size
std::vector<float> batch_logits(n_vocab*n_ctx);
std::vector<std::pair<size_t, llama_token>> eval_pairs;
@ -857,16 +872,17 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
int n_cur = 0;
size_t i1 = i0;
size_t i_batch = 0; // this tells us where in `llama_batch` we are currently
size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
llama_batch_clear(batch);
// batch as much tasks as possible into the available context
// each task has 4 unique seuqnce ids - one for each ending
// each task has 4 unique sequence ids - one for each ending
// the common prefix is shared among the 4 sequences to save tokens
// we extract logits only from the last common token and from all ending tokens of each sequence
while (n_cur + (int) hs_data[i1].required_tokens <= n_ctx) {
auto & hs_cur = hs_data[i1];
int n_logits = 0;
const int s0 = 4*(i1 - i0);
if (s0 + 4 > max_seq) {
@ -874,18 +890,23 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
}
for (size_t i = 0; i < hs_cur.common_prefix; ++i) {
llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false);
}
batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
n_logits += 1;
for (int s = 0; s < 4; ++s) {
for (size_t i = hs_cur.common_prefix; i < hs_cur.seq_tokens[s].size(); ++i) {
llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, true);
const size_t seq_tokens_size = hs_cur.seq_tokens[s].size();
// TODO: don't evaluate the last token of each sequence
for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) {
const bool needs_logits = i < seq_tokens_size - 1;
llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits);
n_logits += needs_logits;
}
}
hs_cur.i_batch = i_batch;
i_batch += hs_cur.required_tokens;
hs_cur.i_logits = i_logits;
i_logits += n_logits;
n_cur += hs_data[i1].required_tokens;
if (++i1 == hs_task_count) {
@ -911,12 +932,11 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
eval_pairs.clear();
for (size_t i = i0; i < i1; ++i) {
auto & hs_cur = hs_data[i];
size_t li = hs_cur.common_prefix;
size_t li = 1; // skip the last logit of the common prefix (computed separately below)
for (int s = 0; s < 4; ++s) {
for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) {
eval_pairs.emplace_back(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1]);
eval_pairs.emplace_back(hs_cur.i_logits + li++, hs_cur.seq_tokens[s][j + 1]);
}
++li;
}
}
// Then we do the actual calculation
@ -928,7 +948,8 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
for (size_t i = i0; i < i1; ++i) {
auto & hs_cur = hs_data[i];
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(hs_cur.i_batch + hs_cur.common_prefix - 1), n_vocab*sizeof(float));
// get the logits of the last token of the common prefix
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*hs_cur.i_logits, n_vocab*sizeof(float));
const auto first_probs = softmax(tok_logits);
@ -978,7 +999,7 @@ struct winogrande_entry {
std::array<std::string, 2> choices;
int answer;
size_t i_batch;
size_t i_logits;
size_t common_prefix;
size_t required_tokens;
size_t n_base1; // number of tokens for context + choice 1
@ -1104,6 +1125,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
task.common_prefix++;
}
// TODO: the last token of each of the sequences don't need to be evaluated
task.required_tokens = task.common_prefix +
task.seq_tokens[0].size() - task.common_prefix +
task.seq_tokens[1].size() - task.common_prefix;
@ -1121,9 +1143,10 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
const int max_tasks_per_batch = 128;
const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
llama_batch batch = llama_batch_init(n_ctx, 0, 2);
std::vector<float> tok_logits(n_vocab);
// TODO: this could be made smaller; it's currently the worst-case size
std::vector<float> batch_logits(n_vocab*n_ctx);
std::vector<std::pair<size_t, llama_token>> eval_pairs;
@ -1137,29 +1160,33 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
int n_cur = 0;
size_t i1 = i0;
size_t i_batch = 0;
size_t i_logits = 0;
llama_batch_clear(batch);
while (n_cur + (int) data[i1].required_tokens <= n_ctx) {
int n_logits = 0;
const int s0 = 2*(i1 - i0);
if (s0 + 2 > max_seq) {
break;
}
for (size_t i = 0; i < data[i1].common_prefix; ++i) {
llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1}, false);
llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false);
}
batch.logits[batch.n_tokens - 1] = true;
n_logits += 1;
for (int s = 0; s < 2; ++s) {
// TODO: end before the last token, no need to predict past the end of the sequences
for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) {
llama_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true);
n_logits += 1;
}
}
data[i1].i_batch = i_batch;
i_batch += data[i1].required_tokens;
data[i1].i_logits = i_logits;
i_logits += n_logits;
n_cur += data[i1].required_tokens;
if (++i1 == data.size()) {
@ -1190,15 +1217,16 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
size_t li = n_base1 - 1;
size_t li = n_base1 - task.common_prefix;
for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[0][j+1]);
eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[0][j+1]);
}
const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1;
// FIXME: this uses the wrong first logits when not skipping the choice word
li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - task.common_prefix;
for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[1][j+1]);
eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[1][j+1]);
}
}
compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
@ -1287,7 +1315,7 @@ struct multiple_choice_task {
}
// For evaluation
size_t i_batch; // starting index in the llama_batch
size_t i_logits; // starting index of logits in the llama_batch
size_t common_prefix; // max number of initial tokens that are the same in all sentences
size_t required_tokens; // needed number of tokens to evaluate all answers
std::vector<std::vector<llama_token>> seq_tokens;
@ -1366,7 +1394,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
std::vector<uint32_t> task_pos(n_task);
strstream.read((char *)task_pos.data(), task_pos.size()*sizeof(uint32_t));
if (strstream.fail()) {
printf("%s: failed to raad task positions from prompt\n", __func__);
printf("%s: failed to read task positions from prompt\n", __func__);
return;
}
@ -1447,7 +1475,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
return;
}
} else {
int n_dot = n_task/100;
int n_dot = std::max((int) n_task/100, 1);
int i_task = 0;
for (auto& task : tasks) {
++i_task;
@ -1491,17 +1519,18 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
int n_cur = 0;
size_t i1 = i0;
size_t i_batch = 0; // this tells us where in `llama_batch` we are currently
size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
llama_batch_clear(batch);
// batch as much tasks as possible into the available context
// each task has 4 unique seuqnce ids - one for each ending
// each task has 4 unique sequence ids - one for each ending
// the common prefix is shared among the 4 sequences to save tokens
// we extract logits only from the last common token and from all ending tokens of each sequence
int s0 = 0;
while (n_cur + (int) tasks[i1].required_tokens <= n_ctx) {
auto& cur_task = tasks[i1];
int n_logits = 0;
int num_answers = cur_task.seq_tokens.size();
if (s0 + num_answers > max_seq) {
@ -1518,17 +1547,22 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
llama_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
}
batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
n_logits += 1;
for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
for (size_t i = cur_task.common_prefix; i < cur_task.seq_tokens[s].size(); ++i) {
llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, true);
const size_t seq_tokens_size = cur_task.seq_tokens[s].size();
// TODO: don't evaluate the last token of each sequence
for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) {
const bool needs_logits = i < seq_tokens_size - 1;
llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits);
n_logits += needs_logits;
}
}
s0 += num_answers;
cur_task.i_batch = i_batch;
i_batch += cur_task.required_tokens;
cur_task.i_logits = i_logits;
i_logits += n_logits;
n_cur += cur_task.required_tokens;
if (++i1 == tasks.size()) {
@ -1554,12 +1588,11 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
eval_pairs.clear();
for (size_t i = i0; i < i1; ++i) {
auto& cur_task = tasks[i];
size_t li = cur_task.common_prefix;
size_t li = 1; // skip the last logit of the common prefix (computed separately below)
for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
eval_pairs.emplace_back(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1]);
eval_pairs.emplace_back(cur_task.i_logits + li++, cur_task.seq_tokens[s][j + 1]);
}
++li;
}
}
// Then we do the actual calculation
@ -1578,7 +1611,8 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
//}
//printf("\n common_prefix: %zu\n", cur_task.common_prefix);
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(cur_task.i_batch + cur_task.common_prefix - 1), n_vocab*sizeof(float));
// get the logits of the last token of the common prefix
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*cur_task.i_logits, n_vocab*sizeof(float));
const auto first_probs = softmax(tok_logits);
@ -1730,6 +1764,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
}
// TODO: use llama_batch.logits instead of relying on logits_all == true
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;

95
examples/quantize/quantize.cpp
View file

@ -26,6 +26,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", },
{ "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", },
{ "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", },
{ "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M, " 1.75 bpw quantization", },
{ "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
{ "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
{ "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization", },
@ -87,13 +88,17 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
//
[[noreturn]]
static void usage(const char * executable) {
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
printf(" --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n");
printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
printf("Note: --include-weights and --exclude-weights cannot be used together\n");
printf("\nAllowed quantization types:\n");
for (auto & it : QUANT_OPTIONS) {
@ -107,14 +112,14 @@ static void usage(const char * executable) {
exit(1);
}
static void load_imatrix(const std::string& imatrix_file, std::unordered_map<std::string, std::vector<float>>& imatrix_data) {
static void load_imatrix(const std::string & imatrix_file, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
std::ifstream in(imatrix_file.c_str(), std::ios::binary);
if (!in) {
printf("%s: failed to open %s\n",__func__,imatrix_file.c_str());
printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
return;
}
int n_entries;
in.read((char*)&n_entries, sizeof(n_entries));
in.read((char *)&n_entries, sizeof(n_entries));
if (in.fail() || n_entries < 1) {
printf("%s: no data in file %s\n", __func__, imatrix_file.c_str());
return;
@ -124,25 +129,25 @@ static void load_imatrix(const std::string& imatrix_file, std::unordered_map<std
std::vector<char> name_as_vec(len+1);
in.read((char *)name_as_vec.data(), len);
if (in.fail()) {
printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file.c_str());
printf("%s: failed reading name for entry %d from %s\n", __func__, i+1, imatrix_file.c_str());
return;
}
name_as_vec[len] = 0;
std::string name{name_as_vec.data()};
auto& e = imatrix_data[std::move(name)];
auto & e = imatrix_data[std::move(name)];
int ncall;
in.read((char*)&ncall, sizeof(ncall));
in.read((char *)&ncall, sizeof(ncall));
int nval;
in.read((char *)&nval, sizeof(nval));
if (in.fail() || nval < 1) {
printf("%s: failed reading number of values for entry %d\n",__func__,i);
printf("%s: failed reading number of values for entry %d\n", __func__, i);
imatrix_data = {};
return;
}
e.resize(nval);
in.read((char*)e.data(), nval*sizeof(float));
in.read((char *)e.data(), nval*sizeof(float));
if (in.fail()) {
printf("%s: failed reading data for entry %d\n",__func__,i);
printf("%s: failed reading data for entry %d\n", __func__, i);
imatrix_data = {};
return;
}
@ -150,13 +155,13 @@ static void load_imatrix(const std::string& imatrix_file, std::unordered_map<std
for (auto& v : e) v /= ncall;
}
}
printf("%s: loaded %d importance matrix entries from %s\n",__func__,int(imatrix_data.size()),imatrix_file.c_str());
printf("%s: loaded %d importance matrix entries from %s\n", __func__, int(imatrix_data.size()), imatrix_file.c_str());
}
static void prepare_imatrix(const std::string& imatrix_file,
const std::vector<std::string>& included_weights,
const std::vector<std::string>& excluded_weights,
std::unordered_map<std::string, std::vector<float>>& imatrix_data) {
static void prepare_imatrix(const std::string & imatrix_file,
const std::vector<std::string> & included_weights,
const std::vector<std::string> & excluded_weights,
std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
if (!imatrix_file.empty()) {
load_imatrix(imatrix_file, imatrix_data);
}
@ -201,6 +206,43 @@ static ggml_type parse_ggml_type(const char * arg) {
return result;
}
static bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
const char* sep = strchr(data, '=');
if (sep == nullptr || sep - data >= 128) {
fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
return false;
}
llama_model_kv_override kvo;
std::strncpy(kvo.key, data, sep - data);
kvo.key[sep - data] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.int_value = std::atol(sep);
} else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
kvo.float_value = std::atof(sep);
} else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.bool_value = true;
} else if (std::strcmp(sep, "false") == 0) {
kvo.bool_value = false;
} else {
fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
return false;
}
} else {
fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
return false;
}
overrides.emplace_back(std::move(kvo));
return true;
}
int main(int argc, char ** argv) {
if (argc < 3) {
usage(argv[0]);
@ -211,6 +253,7 @@ int main(int argc, char ** argv) {
int arg_idx = 1;
std::string imatrix_file;
std::vector<std::string> included_weights, excluded_weights;
std::vector<llama_model_kv_override> kv_overrides;
for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) {
@ -227,6 +270,10 @@ int main(int argc, char ** argv) {
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--override-kv") == 0) {
if (arg_idx == argc-1 || !parse_kv_override(argv[++arg_idx], kv_overrides)) {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) {
params.allow_requantize = true;
} else if (strcmp(argv[arg_idx], "--pure") == 0) {
@ -267,6 +314,11 @@ int main(int argc, char ** argv) {
if (!imatrix_data.empty()) {
params.imatrix = &imatrix_data;
}
if (!kv_overrides.empty()) {
kv_overrides.emplace_back();
kv_overrides.back().key[0] = 0;
params.kv_overrides = &kv_overrides;
}
llama_backend_init();
@ -288,8 +340,7 @@ int main(int argc, char ** argv) {
if (ftype_str == "COPY") {
params.only_copy = true;
}
}
else {
} else {
fname_out = argv[arg_idx];
arg_idx++;
@ -320,10 +371,12 @@ int main(int argc, char ** argv) {
if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) && imatrix_data.empty()) {
fprintf(stderr, "\n===============================================================================================\n");
fprintf(stderr, "Please do not use IQ1_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
fprintf(stderr, "===============================================================================================\n\n\n");
params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) && imatrix_data.empty()) {
fprintf(stderr, "\n==========================================================================================================\n");
fprintf(stderr, "Please do not use IQ1_S, IQ1_M, IQ2_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
fprintf(stderr, "==========================================================================================================\n\n\n");
return 1;
}

303
examples/server/bench/bench.py Normal file
View file

@ -0,0 +1,303 @@
import argparse
import json
import os
import re
import signal
import socket
import subprocess
import sys
import threading
import time
import traceback
from contextlib import closing
from datetime import datetime
import matplotlib
import matplotlib.dates
import matplotlib.pyplot as plt
import requests
def main(args_in: list[str] | None = None) -> None:
parser = argparse.ArgumentParser(description="Start server benchmark scenario")
parser.add_argument("--name", type=str, help="Bench name", required=True)
parser.add_argument("--runner-label", type=str, help="Runner label", required=True)
parser.add_argument("--branch", type=str, help="Branch name", default="detached")
parser.add_argument("--commit", type=str, help="Commit name", default="dirty")
parser.add_argument("--host", type=str, help="Server listen host", default="0.0.0.0")
parser.add_argument("--port", type=int, help="Server listen host", default="8080")
parser.add_argument("--model-path-prefix", type=str, help="Prefix where to store the model files", default="models")
parser.add_argument("--n-prompts", type=int,
help="SERVER_BENCH_N_PROMPTS: total prompts to randomly select in the benchmark", required=True)
parser.add_argument("--max-prompt-tokens", type=int,
help="SERVER_BENCH_MAX_PROMPT_TOKENS: maximum prompt tokens to filter out in the dataset",
required=True)
parser.add_argument("--max-tokens", type=int,
help="SERVER_BENCH_MAX_CONTEXT: maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens",
required=True)
parser.add_argument("--hf-repo", type=str, help="Hugging Face model repository", required=True)
parser.add_argument("--hf-file", type=str, help="Hugging Face model file", required=True)
parser.add_argument("-ngl", "--n-gpu-layers", type=int, help="layers to the GPU for computation", required=True)
parser.add_argument("--ctx-size", type=int, help="Set the size of the prompt context", required=True)
parser.add_argument("--parallel", type=int, help="Set the number of slots for process requests", required=True)
parser.add_argument("--batch-size", type=int, help="Set the batch size for prompt processing", required=True)
parser.add_argument("--ubatch-size", type=int, help="physical maximum batch size", required=True)
parser.add_argument("--scenario", type=str, help="Scenario to run", required=True)
parser.add_argument("--duration", type=str, help="Bench scenario", required=True)
args = parser.parse_args(args_in)
start_time = time.time()
# Start the server and performance scenario
try:
server_process = start_server(args)
except Exception:
print("bench: server start error :")
traceback.print_exc(file=sys.stdout)
sys.exit(1)
# start the benchmark
try:
start_benchmark(args)
iterations = 0
with open("results.github.env", 'w') as github_env:
# parse output
with open('k6-results.json', 'r') as bench_results:
# Load JSON data from file
data = json.load(bench_results)
for metric_name in data['metrics']:
for metric_metric in data['metrics'][metric_name]:
value = data['metrics'][metric_name][metric_metric]
if isinstance(value, float) or isinstance(value, int):
value = round(value, 2)
data['metrics'][metric_name][metric_metric]=value
github_env.write(
f"{escape_metric_name(metric_name)}_{escape_metric_name(metric_metric)}={value}\n")
token_seconds = data['metrics']['llamacpp_tokens_second']['avg']
iterations = data['root_group']['checks']['success completion']['passes']
except Exception:
print("bench: error :")
traceback.print_exc(file=sys.stdout)
# Stop the server
if server_process:
try:
print(f"bench: shutting down server pid={server_process.pid} ...")
if os.name == 'nt':
interrupt = signal.CTRL_C_EVENT
else:
interrupt = signal.SIGINT
server_process.send_signal(interrupt)
server_process.wait(0.5)
except subprocess.TimeoutExpired:
print(f"server still alive after 500ms, force-killing pid={server_process.pid} ...")
server_process.kill() # SIGKILL
server_process.wait()
while is_server_listening(args.host, args.port):
time.sleep(0.1)
title = (f"llama.cpp {args.name} on {args.runner_label}\n "
f"duration={args.duration} {iterations} iterations")
xlabel = (f"{args.hf_repo}/{args.hf_file}\n"
f"parallel={args.parallel} ctx-size={args.ctx_size} ngl={args.n_gpu_layers} batch-size={args.batch_size} ubatch-size={args.ubatch_size} pp={args.max_prompt_tokens} pp+tg={args.max_tokens}\n"
f"branch={args.branch} commit={args.commit}")
# Prometheus
end_time = time.time()
if is_server_listening("0.0.0.0", 9090):
metrics = ['prompt_tokens_seconds', 'predicted_tokens_seconds',
'kv_cache_usage_ratio', 'requests_processing', 'requests_deferred']
for metric in metrics:
resp = requests.get(f"http://localhost:9090/api/v1/query_range",
params={'query': 'llamacpp:' + metric, 'start': start_time, 'end': end_time, 'step': 2})
with open(f"{metric}.json", 'w') as metric_json:
metric_json.write(resp.text)
if resp.status_code != 200:
print(f"bench: unable to extract prometheus metric {metric}: {resp.text}")
else:
metric_data = resp.json()
values = metric_data['data']['result'][0]['values']
timestamps, metric_values = zip(*values)
metric_values = [float(value) for value in metric_values]
timestamps_dt = [datetime.fromtimestamp(int(ts)) for ts in timestamps]
plt.figure(figsize=(16, 10), dpi=80)
plt.plot(timestamps_dt, metric_values, label=metric)
plt.xticks(rotation=0, fontsize=14, horizontalalignment='center', alpha=.7)
plt.yticks(fontsize=12, alpha=.7)
ylabel = f"llamacpp:{metric}"
plt.title(title,
fontsize=14, wrap=True)
plt.grid(axis='both', alpha=.3)
plt.ylabel(ylabel, fontsize=22)
plt.xlabel(xlabel, fontsize=14, wrap=True)
plt.gca().xaxis.set_major_locator(matplotlib.dates.MinuteLocator())
plt.gca().xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y-%m-%d %H:%M:%S"))
plt.gcf().autofmt_xdate()
# Remove borders
plt.gca().spines["top"].set_alpha(0.0)
plt.gca().spines["bottom"].set_alpha(0.3)
plt.gca().spines["right"].set_alpha(0.0)
plt.gca().spines["left"].set_alpha(0.3)
# Save the plot as a jpg image
plt.savefig(f'{metric}.jpg', dpi=60)
plt.close()
# Mermaid format in case images upload failed
with (open(f"{metric}.mermaid", 'w') as mermaid_f):
mermaid = (
f"""---
config:
xyChart:
titleFontSize: 12
width: 900
height: 600
themeVariables:
xyChart:
titleColor: "#000000"
---
xychart-beta
title "{title}"
y-axis "llamacpp:{metric}"
x-axis "llamacpp:{metric}" {int(min(timestamps))} --> {int(max(timestamps))}
line [{', '.join([str(round(float(value), 2)) for value in metric_values])}]
""")
mermaid_f.write(mermaid)
# 140 chars max for commit status description
bench_results = {
"req": {
"p90": data['metrics']["http_req_duration"]["p(90)"],
"avg": data['metrics']["http_req_duration"]["avg"],
},
"pp": {
"p90": data['metrics']["llamacpp_prompt_tokens"]["p(90)"],
"avg": data['metrics']["llamacpp_prompt_tokens"]["avg"],
},
"tg": {
"p90": data['metrics']["llamacpp_tokens_second"]["p(90)"],
"avg": data['metrics']["llamacpp_tokens_second"]["avg"],
},
}
with open("results.github.env", 'a') as github_env:
github_env.write(f"BENCH_RESULTS={json.dumps(bench_results, indent=None, separators=(',', ':') )}\n")
github_env.write(f"BENCH_ITERATIONS={iterations}\n")
title = title.replace('\n', ' ')
xlabel = xlabel.replace('\n', ' ')
github_env.write(f"BENCH_GRAPH_TITLE={title}\n")
github_env.write(f"BENCH_GRAPH_XLABEL={xlabel}\n")
def start_benchmark(args):
k6_path = 'k6'
if 'BENCH_K6_BIN_PATH' in os.environ:
k6_path = os.environ['BENCH_K6_BIN_PATH']
k6_args = [
'run', args.scenario,
'--no-color',
]
k6_args.extend(['--duration', args.duration])
k6_args.extend(['--iterations', args.n_prompts])
k6_args.extend(['--vus', args.parallel])
k6_args.extend(['--summary-export', 'k6-results.json'])
args = f"SERVER_BENCH_N_PROMPTS={args.n_prompts} SERVER_BENCH_MAX_PROMPT_TOKENS={args.max_prompt_tokens} SERVER_BENCH_MAX_CONTEXT={args.max_tokens} "
args = args + ' '.join([str(arg) for arg in [k6_path, *k6_args]])
print(f"bench: starting k6 with: {args}")
k6_completed = subprocess.run(args, shell=True, stdout=sys.stdout, stderr=sys.stderr)
if k6_completed.returncode != 0:
raise Exception("bench: unable to run k6")
def start_server(args):
server_process = start_server_background(args)
attempts = 0
max_attempts = 20
if 'GITHUB_ACTIONS' in os.environ:
max_attempts *= 2
while not is_server_listening(args.host, args.port):
attempts += 1
if attempts > max_attempts:
assert False, "server not started"
print(f"bench: waiting for server to start ...")
time.sleep(0.5)
print("bench: server started.")
return server_process
def start_server_background(args):
# Start the server
server_path = '../../../build/bin/server'
if 'LLAMA_SERVER_BIN_PATH' in os.environ:
server_path = os.environ['LLAMA_SERVER_BIN_PATH']
server_args = [
'--host', args.host,
'--port', args.port,
]
model_file = args.model_path_prefix + os.path.sep + args.hf_file
model_dir = os.path.dirname(model_file)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
server_args.extend(['--model', model_file])
server_args.extend(['--hf-repo', args.hf_repo])
server_args.extend(['--hf-file', args.hf_file])
server_args.extend(['--n-gpu-layers', args.n_gpu_layers])
server_args.extend(['--ctx-size', args.ctx_size])
server_args.extend(['--parallel', args.parallel])
server_args.extend(['--batch-size', args.batch_size])
server_args.extend(['--ubatch-size', args.ubatch_size])
server_args.extend(['--n-predict', args.max_tokens * 2])
server_args.extend(['--defrag-thold', "0.1"])
server_args.append('--cont-batching')
server_args.append('--metrics')
server_args.extend(['--log-format', "text"])
args = [str(arg) for arg in [server_path, *server_args]]
print(f"bench: starting server with: {' '.join(args)}")
pkwargs = {
'stdout': subprocess.PIPE,
'stderr': subprocess.PIPE
}
server_process = subprocess.Popen(
args,
**pkwargs)
def server_log(in_stream, out_stream):
for line in iter(in_stream.readline, b''):
print(line.decode('utf-8'), end='', file=out_stream)
thread_stdout = threading.Thread(target=server_log, args=(server_process.stdout, sys.stdout))
thread_stdout.start()
thread_stderr = threading.Thread(target=server_log, args=(server_process.stderr, sys.stderr))
thread_stderr.start()
return server_process
def is_server_listening(server_fqdn, server_port):
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
result = sock.connect_ex((server_fqdn, server_port))
_is_server_listening = result == 0
if _is_server_listening:
print(f"server is listening on {server_fqdn}:{server_port}...")
return _is_server_listening
def escape_metric_name(metric_name):
return re.sub('[^A-Z0-9]', '_', metric_name.upper())
if __name__ == '__main__':
main()

9
examples/server/bench/prometheus.yml Normal file
View file

@ -0,0 +1,9 @@
global:
scrape_interval: 10s
external_labels:
llamacpp: 'server'
scrape_configs:
- job_name: 'llama.cpp server'
static_configs:
- targets: ['localhost:8080']

2
examples/server/bench/requirements.txt Normal file
View file

@ -0,0 +1,2 @@
matplotlib
requests

866
examples/server/completion.js.hpp
View file

@ -43,444 +43,454 @@ unsigned char completion_js[] = {
0x7d, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x63,
0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x3d, 0x20,
0x63, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x72,
0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x69, 0x66,
0x20, 0x28, 0x21, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65,
0x72, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e,
0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x6e, 0x65,
0x77, 0x20, 0x41, 0x62, 0x6f, 0x72, 0x74, 0x43, 0x6f, 0x6e, 0x74, 0x72,
0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x7d,
0x0a, 0x0a, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f,
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};
size_t completion_js_len = 5796;
unsigned int completion_js_len = 5909;

4828
examples/server/index.html.hpp
View file

File diff suppressed because it is too large Load diff

2
examples/server/index.js.hpp
View file

@ -1928,4 +1928,4 @@ unsigned char index_js[] = {
0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x50, 0x74, 0x20, 0x61, 0x73,
0x20, 0x75, 0x73, 0x65, 0x53, 0x74, 0x61, 0x74, 0x65, 0x7d, 0x3b, 0x0a
};
size_t index_js_len = 23136;
unsigned int index_js_len = 23136;

8
examples/server/public/completion.js
View file

@ -21,6 +21,7 @@ let generation_settings = null;
//
export async function* llama(prompt, params = {}, config = {}) {
let controller = config.controller;
const api_url = config.api_url || "";
if (!controller) {
controller = new AbortController();
@ -28,7 +29,7 @@ export async function* llama(prompt, params = {}, config = {}) {
const completionParams = { ...paramDefaults, ...params, prompt };
const response = await fetch("/completion", {
const response = await fetch(`${api_url}/completion`, {
method: 'POST',
body: JSON.stringify(completionParams),
headers: {
@ -193,9 +194,10 @@ export const llamaComplete = async (params, controller, callback) => {
}
// Get the model info from the server. This is useful for getting the context window and so on.
export const llamaModelInfo = async () => {
export const llamaModelInfo = async (config = {}) => {
if (!generation_settings) {
const props = await fetch("/props").then(r => r.json());
const api_url = config.api_url || "";
const props = await fetch(`${api_url}/props`).then(r => r.json());
generation_settings = props.default_generation_settings;
}
return generation_settings;

8
examples/server/public/index.html
View file

@ -199,10 +199,10 @@
<script type="module">
import {
html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
} from '/index.js';
} from './index.js';
import { llama } from '/completion.js';
import { SchemaConverter } from '/json-schema-to-grammar.mjs';
import { llama } from './completion.js';
import { SchemaConverter } from './json-schema-to-grammar.mjs';
let selected_image = false;
var slot_id = -1;
@ -405,7 +405,7 @@
throw new Error("already running");
}
controller.value = new AbortController();
for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: document.baseURI.replace(/\/+$/, '') })) {
const data = chunk.data;
if (data.stop) {

9
examples/server/server.cpp
View file

@ -99,6 +99,7 @@ struct slot_params {
uint32_t seed = -1; // RNG seed
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
int32_t n_predict = -1; // new tokens to predict
std::vector<std::string> antiprompt;
@ -746,7 +747,8 @@ struct server_context {
{
const int32_t n_batch = llama_n_batch(ctx);
batch = llama_batch_init(n_batch, 0, params.n_parallel);
// only a single seq_id per token is needed
batch = llama_batch_init(n_batch, 0, 1);
}
metrics.init();
@ -846,6 +848,7 @@ struct server_context {
slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep);
slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard);
slot.params.seed = json_value(data, "seed", default_params.seed);
slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
@ -1253,6 +1256,7 @@ struct server_context {
{"stop", slot.params.antiprompt},
{"n_predict", slot.params.n_predict}, // TODO: fix duplicate key n_predict
{"n_keep", slot.params.n_keep},
{"n_discard", slot.params.n_discard},
{"ignore_eos", ignore_eos},
{"stream", slot.params.stream},
{"logit_bias", slot.sparams.logit_bias},
@ -1696,7 +1700,7 @@ struct server_context {
// Shift context
const int n_keep = slot.params.n_keep + add_bos_token;
const int n_left = slot.n_past - n_keep;
const int n_discard = n_left / 2;
const int n_discard = slot.params.n_discard ? slot.params.n_discard : (n_left / 2);
LOG_INFO("slot context shift", {
{"id_slot", slot.id},
@ -3562,6 +3566,7 @@ int main(int argc, char ** argv) {
sigemptyset (&sigint_action.sa_mask);
sigint_action.sa_flags = 0;
sigaction(SIGINT, &sigint_action, NULL);
sigaction(SIGTERM, &sigint_action, NULL);
#elif defined (_WIN32)
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;

19
examples/server/tests/features/steps/steps.py
View file

@ -1114,7 +1114,10 @@ def start_server_background(context):
server_args.append('--verbose')
if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
server_args.extend(['--log-format', "text"])
print(f"starting server with: {context.server_path} {server_args}")
args = [str(arg) for arg in [context.server_path, *server_args]]
print(f"bench: starting server with: {' '.join(args)}")
flags = 0
if 'nt' == os.name:
flags |= subprocess.DETACHED_PROCESS
@ -1130,16 +1133,14 @@ def start_server_background(context):
[str(arg) for arg in [context.server_path, *server_args]],
**pkwargs)
def log_stdout(process):
for line in iter(process.stdout.readline, b''):
print(line.decode('utf-8'), end='')
thread_stdout = threading.Thread(target=log_stdout, args=(context.server_process,))
def server_log(in_stream, out_stream):
for line in iter(in_stream.readline, b''):
print(line.decode('utf-8'), end='', file=out_stream)
thread_stdout = threading.Thread(target=server_log, args=(context.server_process.stdout, sys.stdout))
thread_stdout.start()
def log_stderr(process):
for line in iter(process.stderr.readline, b''):
print(line.decode('utf-8'), end='', file=sys.stderr)
thread_stderr = threading.Thread(target=log_stderr, args=(context.server_process,))
thread_stderr = threading.Thread(target=server_log, args=(context.server_process.stderr, sys.stderr))
thread_stderr.start()
print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}")

1
examples/speculative/speculative.cpp
View file

@ -65,7 +65,6 @@ int main(int argc, char ** argv) {
llama_context * ctx_dft = NULL;
// load the target model
params.logits_all = true;
std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params);
// load the draft model

13
flake.nix
View file

@ -145,6 +145,7 @@
# the same path you would with an overlay.
legacyPackages = {
llamaPackages = pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
llamaPackagesWindows = pkgs.pkgsCross.mingwW64.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
llamaPackagesCuda = pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
llamaPackagesRocm = pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
};
@ -155,6 +156,7 @@
{
default = config.legacyPackages.llamaPackages.llama-cpp;
vulkan = config.packages.default.override { useVulkan = true; };
windows = config.legacyPackages.llamaPackagesWindows.llama-cpp;
}
// lib.optionalAttrs pkgs.stdenv.isLinux {
opencl = config.packages.default.override { useOpenCL = true; };
@ -168,9 +170,14 @@
};
# Packages exposed in `.#checks` will be built by the CI and by
# `nix flake check`. Currently we expose all packages, but we could
# make more granular choices
checks = config.packages;
# `nix flake check`.
#
# We could test all outputs e.g. as `checks = confg.packages`.
#
# TODO: Build more once https://github.com/ggerganov/llama.cpp/issues/6346 has been addressed
checks = {
inherit (config.packages) default vulkan;
};
};
};
}

22
ggml-common.h
View file

@ -377,6 +377,27 @@ typedef struct {
} block_iq1_s;
static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding");
// 1.75 bpw
typedef struct {
uint8_t qs[QK_K/8]; // grid index, low 8 bits
uint8_t qh[QK_K/16]; // grid index, high 3 bits + grid shift bit (for two groups of 8)
#if QK_K == 64
ggml_half d;
#endif
uint8_t scales[QK_K/32]; // 3-bit block scales (4-bit if QK_K == 64)
} block_iq1_m;
#if QK_K == 64
static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32 + sizeof(ggml_half), "wrong iq1_m block size/padding");
#else
static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32, "wrong iq1_m block size/padding");
#endif
// Used by IQ1_M quants
typedef union {
ggml_half f16;
uint16_t u16;
} iq1m_scale_t;
// Non-linear quants
#define QK4_NL 32
typedef struct {
@ -1050,6 +1071,7 @@ GGML_TABLE_END()
#define NGRID_IQ1S 2048
#define IQ1S_DELTA 0.125f
#define IQ1M_DELTA 0.125f
#if defined(GGML_COMMON_IMPL_C)
GGML_TABLE_BEGIN(uint64_t, iq1s_grid, NGRID_IQ1S)
0xffffffffffffffff, 0xffffffffffffff01, 0xffffffffffff0000, 0xffffffffffff01ff,

6
ggml-cuda.cu
View file

@ -615,6 +615,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUD
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -643,6 +644,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUD
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -2503,7 +2505,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
continue;
}
@ -2560,7 +2562,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
ggml_type a_type = a->type;
if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS ||
a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S ||
a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) {
a_type == GGML_TYPE_IQ1_M || a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) {
if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
return false;
}

53
ggml-cuda/convert.cu
View file

@ -373,7 +373,7 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds
const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
#else
assert(false);
NO_DEVICE_CODE;
#endif
}
@ -395,7 +395,7 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst
const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
#else
assert(false);
NO_DEVICE_CODE;
#endif
}
@ -416,7 +416,7 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_
const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
#else
assert(false);
NO_DEVICE_CODE;
#endif
}
@ -444,7 +444,7 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds
y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
}
#else
assert(false);
NO_DEVICE_CODE;
#endif
}
@ -470,7 +470,7 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_
y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
}
#else
assert(false);
NO_DEVICE_CODE;
#endif
}
@ -496,11 +496,42 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_
y[j] = d * (q[j] + delta);
}
#else
assert(false);
NO_DEVICE_CODE;
#endif
}
template<typename dst_t>
static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_t * __restrict__ yy) {
const int i = blockIdx.x;
const block_iq1_m * x = (const block_iq1_m *) vx;
const int tid = threadIdx.x;
#if QK_K == 256
const int il = tid/8; // 0...3
const int ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 8*il;
const uint16_t * sc = (const uint16_t *)x[i].scales;
iq1m_scale_t scale;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
const int ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1);
const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA;
uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)];
grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
grid32[0] &= 0x0f0f0f0f;
for (int j = 0; j < 8; ++j) {
y[j] = d * (q[j] + delta);
}
#else
NO_DEVICE_CODE;
#endif
}
template<typename dst_t>
static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) {
@ -658,6 +689,12 @@ static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k,
dequantize_block_iq4_nl<<<nb, 32, 0, stream>>>(vx, y);
}
template<typename dst_t>
static void dequantize_row_iq1_m_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
const int nb = k / QK_K;
dequantize_block_iq1_m<<<nb, 32, 0, stream>>>(vx, y);
}
template<typename dst_t>
static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
const int nb = (k + QK_K - 1) / QK_K;
@ -724,6 +761,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
return dequantize_row_iq3_xxs_cuda;
case GGML_TYPE_IQ1_S:
return dequantize_row_iq1_s_cuda;
case GGML_TYPE_IQ1_M:
return dequantize_row_iq1_m_cuda;
case GGML_TYPE_IQ4_NL:
return dequantize_row_iq4_nl_cuda;
case GGML_TYPE_IQ4_XS:
@ -769,6 +808,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
return dequantize_row_iq3_xxs_cuda;
case GGML_TYPE_IQ1_S:
return dequantize_row_iq1_s_cuda;
case GGML_TYPE_IQ1_M:
return dequantize_row_iq1_m_cuda;
case GGML_TYPE_IQ4_NL:
return dequantize_row_iq4_nl_cuda;
case GGML_TYPE_IQ4_XS:

11
ggml-cuda/mmvq.cu
View file

@ -282,6 +282,14 @@ static void mul_mat_vec_iq1_s_q8_1_cuda(
(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
}
static void mul_mat_vec_iq1_m_q8_1_cuda(
const void * vx, const void * vy, float * dst,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
mul_mat_vec_q_cuda<QK_K, QI1_S, block_iq1_m, 1, vec_dot_iq1_m_q8_1>
(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream);
}
static void mul_mat_vec_iq4_nl_q8_1_cuda(
const void * vx, const void * vy, float * dst,
const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) {
@ -373,6 +381,9 @@ void ggml_cuda_op_mul_mat_vec_q(
case GGML_TYPE_IQ1_S:
mul_mat_vec_iq1_s_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_IQ1_M:
mul_mat_vec_iq1_m_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
case GGML_TYPE_IQ4_NL:
mul_mat_vec_iq4_nl_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;

112
ggml-cuda/vecdotq.cuh
View file

@ -961,8 +961,7 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
return d * (sumi1 + sumi2);
#endif
#else
assert(false);
return 0.f;
NO_DEVICE_CODE;
#endif
}
@ -1001,13 +1000,11 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
#else
GGML_UNUSED(ksigns64);
assert(false);
return 0.f;
NO_DEVICE_CODE;
#endif
#else
GGML_UNUSED(ksigns64);
assert(false);
return 0.f;
NO_DEVICE_CODE;
#endif
}
@ -1049,13 +1046,11 @@ static __device__ __forceinline__ float vec_dot_iq2_s_q8_1(
return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
#else
GGML_UNUSED(ksigns64);
assert(false);
return 0.f;
NO_DEVICE_CODE;
#endif
#else
GGML_UNUSED(ksigns64);
assert(false);
return 0.f;
NO_DEVICE_CODE;
#endif
}
@ -1085,12 +1080,10 @@ static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1(
const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f;
return d * sumi;
#else
assert(false);
return 0.f;
NO_DEVICE_CODE;
#endif
#else
assert(false);
return 0.f;
NO_DEVICE_CODE;
#endif
}
@ -1119,12 +1112,10 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1(
const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds);
return d * sumi;
#else
assert(false);
return 0.f;
NO_DEVICE_CODE;
#endif
#else
assert(false);
return 0.f;
NO_DEVICE_CODE;
#endif
}
@ -1159,8 +1150,50 @@ static __device__ __forceinline__ float vec_dot_iq1_s_q8_1(
const float m = d1q * __high2float(bq8_1[ib32].ds);
return d * sumi + m * delta;
#else
assert(false);
return 0.f;
NO_DEVICE_CODE;
#endif
}
static __device__ __forceinline__ float vec_dot_iq1_m_q8_1(
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
#if QK_K == 256
const block_iq1_m * bq1 = (const block_iq1_m *) vbq;
const int ib32 = iqs;
int sumi[2] = {0, 0};
float sumf[2] = {0.f, 0.f};
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
const int * q8 = (const int *)bq8_1[ib32].qs;
for (int l = 0; l < 4; ++l) {
const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 7) << 8)));
int grid0 = grid[0] & 0x0f0f0f0f;
int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
sumi[l/2] = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi[l/2]));
const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
const int sumy = __dp4a(q8[2*l+1], 0x01010101, __dp4a(q8[2*l+0], 0x01010101, 0));
sumf[l/2] += delta*sumy;
}
#else
const int8_t * q8 = bq8_1[ib32].qs;
for (int l = 0; l < 4; ++l) {
const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
int sumy = 0;
for (int j = 0; j < 4; ++j) {
sumi[l/2] += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4);
sumy += q8[j] + q8[j+4];
}
const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
sumf[l/2] += delta*sumy;
q8 += 8;
}
#endif
iq1m_scale_t scale;
const uint16_t * sc = (const uint16_t *)bq1->scales;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
const float d = (float)scale.f16 * __low2float (bq8_1[ib32].ds);
return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1));
#else
NO_DEVICE_CODE;
#endif
}
@ -1223,27 +1256,6 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq;
const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
//// iqs is 0...7
//const int ib64 = iqs/2;
//const int il = iqs%2;
//const int32_t * q8_1 = (const int *)bq8_1[2*ib64+0].qs + 2*il;
//const int32_t * q8_2 = (const int *)bq8_1[2*ib64+1].qs + 2*il;
//const uint32_t * q4_1 = (const uint32_t *)bq4->qs + 8*ib64 + 2*il;
//const uint32_t * q4_2 = q4_1 + 4;
//const int8_t ls1 = (bq4->scales_l[ib64] & 0xf) | (((bq4->scales_h >> (4*ib64+0)) & 3) << 4);
//const int8_t ls2 = (bq4->scales_l[ib64] >> 4) | (((bq4->scales_h >> (4*ib64+2)) & 3) << 4);
//const float d1 = (float)bq4->d * (ls1 - 32) * __low2float(bq8_1[2*ib64+0].ds);
//const float d2 = (float)bq4->d * (ls2 - 32) * __low2float(bq8_1[2*ib64+1].ds);
//int v1, v2;
//int sumi1 = 0, sumi2 = 0;
//for (int j = 0; j < 2; ++j) {
// get_int_from_table_16(q4_1[j], values, v1, v2);
// sumi1 = __dp4a(v2, q8_1[j+4], __dp4a(v1, q8_1[j+0], sumi1));
// get_int_from_table_16(q4_2[j], values, v1, v2);
// sumi2 = __dp4a(v2, q8_2[j+4], __dp4a(v1, q8_2[j+0], sumi2));
//}
//return d1 * sumi1 + d2 * sumi2;
// iqs is 0...7
const int ib32 = iqs;
const int32_t * q8 = (const int *)bq8_1[ib32].qs;
@ -1259,24 +1271,8 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
}
return d * (sumi1 + sumi2);
//// iqs is 0...15
//const int ib32 = iqs/2;
//const int il = iqs%2;
//const int32_t * q8 = (const int *)bq8_1[ib32].qs + 2*il;
//const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32 + 2*il;
//const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
//const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds);
//int v1, v2;
//int sumi1 = 0, sumi2 = 0;
//for (int j = 0; j < 2; ++j) {
// get_int_from_table_16(q4[j], values, v1, v2);
// sumi1 = __dp4a(v1, q8[j+0], sumi1);
// sumi2 = __dp4a(v2, q8[j+4], sumi2);
//}
//return d * (sumi1 + sumi2);
#else
assert(false);
return 0.f;
NO_DEVICE_CODE;
#endif
#else
return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs);

4
ggml-kompute.cpp
View file

@ -1430,6 +1430,10 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
struct ggml_tensor * dst = gf->nodes[i];
GGML_ASSERT(dst->data != nullptr);
if (ggml_is_empty(dst)) {
continue;
}
switch (dst->op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:

41
ggml-metal.m
View file

@ -64,6 +64,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,
GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,
@ -91,6 +92,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,
@ -114,6 +116,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,
@ -134,6 +137,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,
@ -154,6 +158,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_ROPE_F32,
@ -490,6 +495,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M, get_rows_iq1_m, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true);
@ -517,6 +523,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction);
@ -540,6 +547,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm);
@ -560,6 +568,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm);
@ -580,6 +589,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true);
@ -837,6 +847,10 @@ static enum ggml_status ggml_metal_graph_compute(
struct ggml_tensor * src2 = gf->nodes[i]->src[2];
struct ggml_tensor * dst = gf->nodes[i];
if (ggml_is_empty(dst)) {
continue;
}
switch (dst->op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
@ -1421,6 +1435,7 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32 ].pipeline; break;
case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32 ].pipeline; break;
case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break;
case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32 ].pipeline; break;
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break;
case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break;
default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
@ -1575,6 +1590,12 @@ static enum ggml_status ggml_metal_graph_compute(
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline;
} break;
case GGML_TYPE_IQ1_M:
{
nth0 = 4;
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32].pipeline;
} break;
case GGML_TYPE_IQ4_NL:
{
nth0 = 4;
@ -1619,9 +1640,9 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder setBytes:&r2 length:sizeof(r2) atIndex:17];
[encoder setBytes:&r3 length:sizeof(r3) atIndex:18];
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 ||
src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ2_S) {
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 ||
src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K ||
src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) {
@ -1743,6 +1764,7 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32 ].pipeline; break;
case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32 ].pipeline; break;
case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break;
case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32 ].pipeline; break;
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break;
case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break;
default: GGML_ASSERT(false && "MUL_MAT_ID not implemented");
@ -1900,6 +1922,12 @@ static enum ggml_status ggml_metal_graph_compute(
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline;
} break;
case GGML_TYPE_IQ1_M:
{
nth0 = 4;
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32].pipeline;
} break;
case GGML_TYPE_IQ4_NL:
{
nth0 = 4;
@ -1960,9 +1988,9 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j];
}
if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 ||
src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 ||
src2t == GGML_TYPE_Q2_K || src2t == GGML_TYPE_IQ1_S || src2t == GGML_TYPE_IQ2_S) {
if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || src2t == GGML_TYPE_Q5_0 ||
src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || src2t == GGML_TYPE_Q2_K ||
src2t == GGML_TYPE_IQ1_S || src2t == GGML_TYPE_IQ1_M || src2t == GGML_TYPE_IQ2_S) {
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) {
@ -2024,6 +2052,7 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S ].pipeline; break;
case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S ].pipeline; break;
case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break;
case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M ].pipeline; break;
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break;
case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break;
case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break;

234
ggml-metal.metal
View file

@ -4456,6 +4456,114 @@ void kernel_mul_mv_iq1_s_f32_impl(
}
}
void kernel_mul_mv_iq1_m_f32_impl(
device const void * src0,
device const float * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne10,
constant int64_t & ne12,
constant int64_t & ne0,
constant int64_t & ne1,
constant uint & r2,
constant uint & r3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiisg[[thread_index_in_simdgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]]) {
const int nb = ne00/QK_K;
const int r0 = tgpig.x;
const int r1 = tgpig.y;
const int im = tgpig.z;
const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST;
const int ib_row = first_row * nb;
const uint i12 = im%ne12;
const uint i13 = im/ne12;
const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
device const block_iq1_m * x = (device const block_iq1_m *) src0 + ib_row + offset0;
device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1;
float yl[32];
float sumf[N_DST]={0.f}, all_sum;
const int nb32 = nb * (QK_K / 32);
const int ix = tiisg;
device const float * y4 = y + 32 * ix;
#if QK_K != 64
iq1m_scale_t scale;
#endif
for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
float4 sumy = {0.f};
for (int i = 0; i < 8; ++i) {
yl[i+ 0] = y4[i+ 0]; sumy[0] += yl[i+ 0];
yl[i+ 8] = y4[i+ 8]; sumy[1] += yl[i+ 8];
yl[i+16] = y4[i+16]; sumy[2] += yl[i+16];
yl[i+24] = y4[i+24]; sumy[3] += yl[i+24];
}
const int ibl = ib32 / (QK_K / 32);
const int ib = ib32 % (QK_K / 32);
device const block_iq1_m * xr = x + ibl;
device const uint8_t * qs = xr->qs + 4 * ib;
device const uint8_t * qh = xr->qh + 2 * ib;
device const uint16_t * sc = (device const uint16_t *)xr->scales;
for (int row = 0; row < N_DST; row++) {
#if QK_K != 64
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
#endif
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
constant uint8_t * grid3 = (constant uint8_t *)(iq1s_grid_gpu + (qs[2] | ((qh[1] << 8) & 0x700)));
constant uint8_t * grid4 = (constant uint8_t *)(iq1s_grid_gpu + (qs[3] | ((qh[1] << 4) & 0x700)));
float2 sum = {0.f};
for (int j = 0; j < 4; ++j) {
sum[0] += yl[j+ 0] * (grid1[j] & 0xf) + yl[j+ 4] * (grid1[j] >> 4)
+ yl[j+ 8] * (grid2[j] & 0xf) + yl[j+12] * (grid2[j] >> 4);
sum[1] += yl[j+16] * (grid3[j] & 0xf) + yl[j+20] * (grid3[j] >> 4)
+ yl[j+24] * (grid4[j] & 0xf) + yl[j+28] * (grid4[j] >> 4);
}
const float delta1 = sumy[0] * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[1] * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
const float delta2 = sumy[2] * (qh[1] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[3] * (qh[1] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
#if QK_K == 64
const float d = (float) *((device const half *)(sc - 1));
sumf[row] += d * ((sum[0] + delta1) * (2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1) +
(sum[1] + delta2) * (2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1));
#else
sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) +
(sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1));
#endif
sc += nb*sizeof(block_iq1_m)/2;
qs += nb*sizeof(block_iq1_m);
qh += nb*sizeof(block_iq1_m);
}
y4 += 32 * 32;
}
for (int row = 0; row < N_DST; ++row) {
all_sum = simd_sum(sumf[row]);
if (tiisg == 0) {
dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
}
}
}
void kernel_mul_mv_iq4_nl_f32_impl(
device const void * src0,
device const float * src1,
@ -4673,6 +4781,34 @@ kernel void kernel_mul_mv_iq1_s_f32(
kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg);
}
[[host_name("kernel_mul_mv_iq1_m_f32")]]
kernel void kernel_mul_mv_iq1_m_f32(
device const void * src0,
device const float * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
constant uint & r2,
constant uint & r3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiisg[[thread_index_in_simdgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]]) {
kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, tgpig, tiisg, sgitg);
}
[[host_name("kernel_mul_mv_iq4_nl_f32")]]
kernel void kernel_mul_mv_iq4_nl_f32(
device const void * src0,
@ -5146,6 +5282,38 @@ void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 &
}
}
template <typename type4x4>
void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
const int ib32 = il/2;
il = il%2;
device const uint16_t * sc = (device const uint16_t *)xb->scales;
#if QK_K == 64
const float d = xb->d;
#else
iq1m_scale_t scale;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
const float d = scale.f16;
#endif
device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
device const uint8_t * qh = xb->qh + 2*ib32 + il;
#if QK_K == 64
const float dl = d * (2*((sc[ib32/2] >> (8*(ib32%2)+4*il)) & 0xf) + 1);
#else
const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1);
#endif
const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
for (int i = 0; i < 4; ++i) {
reg[0][i] = dl * (grid1[i] & 0xf) + ml1;
reg[1][i] = dl * (grid1[i] >> 4) + ml1;
reg[2][i] = dl * (grid2[i] & 0xf) + ml2;
reg[3][i] = dl * (grid2[i] >> 4) + ml2;
}
}
template <typename type4x4>
void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) {
device const uint16_t * q4 = (device const uint16_t *)xb->qs;
@ -5730,6 +5898,7 @@ template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_r
template [[host_name("kernel_get_rows_iq3_s")]] kernel get_rows_t kernel_get_rows<block_iq3_s, QK_NL, dequantize_iq3_s>;
template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_t kernel_get_rows<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_get_rows_iq1_m")]] kernel get_rows_t kernel_get_rows<block_iq1_m, QK_NL, dequantize_iq1_m>;
template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows<block_iq4_nl, 2, dequantize_iq4_nl>;
#if QK_K == 64
template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows<block_iq4_xs, 2, dequantize_iq4_xs>;
@ -5778,6 +5947,7 @@ template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_m
template [[host_name("kernel_mul_mm_iq3_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq3_s, QK_NL, dequantize_iq3_s>;
template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_mul_mm_iq1_m_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq1_m, QK_NL, dequantize_iq1_m>;
template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_nl, 2, dequantize_iq4_nl>;
#if QK_K == 64
template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_nl, 2, dequantize_iq4_xs>;
@ -5838,6 +6008,7 @@ template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel
template [[host_name("kernel_mul_mm_id_iq3_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq3_s, QK_NL, dequantize_iq3_s>;
template [[host_name("kernel_mul_mm_id_iq2_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_mul_mm_id_iq1_m_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq1_m, QK_NL, dequantize_iq1_m>;
template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_nl, 2, dequantize_iq4_nl>;
#if QK_K == 64
template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_xs, 2, dequantize_iq4_xs>;
@ -7005,6 +7176,69 @@ kernel void kernel_mul_mv_id_iq1_s_f32(
sgitg);
}
[[host_name("kernel_mul_mv_id_iq1_m_f32")]]
kernel void kernel_mul_mv_id_iq1_m_f32(
device const char * ids,
device const char * src1,
device float * dst,
constant uint64_t & nbi1,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant int64_t & ne13,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
constant uint64_t & nb1,
constant uint & r2,
constant uint & r3,
constant int & idx,
device const char * src00,
device const char * src01,
device const char * src02,
device const char * src03,
device const char * src04,
device const char * src05,
device const char * src06,
device const char * src07,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiitg[[thread_index_in_threadgroup]],
uint tiisg[[thread_index_in_simdgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]]) {
device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07};
const int64_t bid = tgpig.z/(ne12*ne13);
tgpig.z = tgpig.z%(ne12*ne13);
const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx];
kernel_mul_mv_iq1_m_f32_impl(
src0[id],
(device const float *) (src1 + bid*nb11),
dst + bid*ne0,
ne00,
ne01,
ne02,
ne10,
ne12,
ne0,
ne1,
r2,
r3,
tgpig,
tiisg,
sgitg);
}
[[host_name("kernel_mul_mv_id_iq4_nl_f32")]]
kernel void kernel_mul_mv_id_iq4_nl_f32(
device const char * ids,

5
ggml-opencl.cpp
View file

@ -2234,6 +2234,11 @@ static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(gg
static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) {
for (int i = 0; i < graph->n_nodes; ++i) {
ggml_tensor * node = graph->nodes[i];
if (ggml_is_empty(node)) {
continue;
}
switch (node->op) {
case GGML_OP_MUL_MAT:
ggml_cl_mul_mat(node->src[0], node->src[1], node, nullptr, 0);

679
ggml-quants.c
View file

@ -3474,6 +3474,65 @@ void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, in
}
}
void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, int k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
float delta[4];
uint16_t idx[4];
#if QK_K != 64
iq1m_scale_t scale;
#endif
for (int i = 0; i < nb; i++) {
const uint16_t * sc = (const uint16_t *)x[i].scales;
#if QK_K == 64
const float d = GGML_FP16_TO_FP32(x[i].d);
#else
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
const float d = GGML_FP16_TO_FP32(scale.f16);
#endif
const uint8_t * qs = x[i].qs;
const uint8_t * qh = x[i].qh;
for (int ib = 0; ib < QK_K/32; ++ib) {
#if QK_K == 64
const float dl1 = d * (2*((sc[ib/2] >> (8*(ib%2)+0)) & 0xf) + 1);
const float dl2 = d * (2*((sc[ib/2] >> (8*(ib%2)+4)) & 0xf) + 1);
#else
const float dl1 = d * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1);
const float dl2 = d * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1);
#endif
idx[0] = qs[0] | ((qh[0] << 8) & 0x700);
idx[1] = qs[1] | ((qh[0] << 4) & 0x700);
idx[2] = qs[2] | ((qh[1] << 8) & 0x700);
idx[3] = qs[3] | ((qh[1] << 4) & 0x700);
delta[0] = qh[0] & 0x08 ? -IQ1S_DELTA : IQ1S_DELTA;
delta[1] = qh[0] & 0x80 ? -IQ1S_DELTA : IQ1S_DELTA;
delta[2] = qh[1] & 0x08 ? -IQ1S_DELTA : IQ1S_DELTA;
delta[3] = qh[1] & 0x80 ? -IQ1S_DELTA : IQ1S_DELTA;
for (int l = 0; l < 2; ++l) {
const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]);
for (int j = 0; j < 8; ++j) {
y[j] = dl1 * (grid[j] + delta[l]);
}
y += 8;
}
for (int l = 2; l < 4; ++l) {
const int8_t * grid = (const int8_t *)(iq1s_grid + idx[l]);
for (int j = 0; j < 8; ++j) {
y[j] = dl2 * (grid[j] + delta[l]);
}
y += 8;
}
qs += 4;
qh += 2;
}
}
}
static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, int k) {
@ -9695,6 +9754,248 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void
#endif
}
void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
assert(n % QK_K == 0);
assert(nrc == 1);
UNUSED(nrc);
UNUSED(bx);
UNUSED(by);
UNUSED(bs);
const block_iq1_m * restrict x = vx;
const block_q8_K * restrict y = vy;
const int nb = n / QK_K;
#if QK_K != 64
iq1m_scale_t scale;
#endif
#if defined __ARM_NEON
#if QK_K == 64
const int32x4_t mask = vdupq_n_s32(0xf);
#else
const int32x4_t mask = vdupq_n_s32(0x7);
#endif
const int32x4_t mone = vdupq_n_s32(1);
const int32x4_t mzero = vdupq_n_s32(0);
ggml_int8x16x4_t deltas;
deltas.val[0] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(+1));
deltas.val[1] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(+1));
deltas.val[2] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(-1));
deltas.val[3] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(-1));
ggml_int8x16x4_t q1b;
ggml_int8x16x4_t q8b;
uint32_t aux32;
const uint8_t * aux8 = (const uint8_t *)&aux32;
float sumf = 0;
for (int i = 0; i < nb; ++i) {
const int8_t * q8 = y[i].qs;
const uint8_t * qs = x[i].qs;
const uint8_t * qh = x[i].qh;
const uint16_t * sc = (const uint16_t *)x[i].scales;
#if QK_K != 64
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
#endif
int32x4_t sumi1 = mzero;
int32x4_t sumi2 = mzero;
for (int ib = 0; ib < QK_K/32; ib += 2) {
q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[0] << 8) & 0x700)))),
vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[0] << 4) & 0x700)))));
q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[1] << 8) & 0x700)))),
vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[1] << 4) & 0x700)))));
q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[2] << 8) & 0x700)))),
vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[2] << 4) & 0x700)))));
q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[3] << 8) & 0x700)))),
vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[3] << 4) & 0x700)))));
q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
const int32x4_t p1 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[0], q8b.val[0]), ggml_vdotq_s32(mzero, q1b.val[1], q8b.val[1]));
const int32x4_t p2 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[2], q8b.val[2]), ggml_vdotq_s32(mzero, q1b.val[3], q8b.val[3]));
const int32x4_t p12 = vpaddq_s32(p1, p2);
const uint32_t * qh32 = (const uint32_t *)qh; // we are 4-byte aligned, so we can do that
aux32 = ((qh32[0] >> 3) & 0x01010101) | ((qh32[0] >> 6) & 0x02020202);
const int32x4_t p3 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[0]], q8b.val[0]), ggml_vdotq_s32(mzero, deltas.val[aux8[1]], q8b.val[1]));
const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3]));
const int32x4_t p34 = vpaddq_s32(p3, p4);
#if QK_K == 64
int32x4_t scales_4 = ggml_vld1q_u32(sc[0] >> 0, sc[0] >> 4, sc[0] >> 8, sc[0] >> 12);
#else
int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9);
#endif
scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone);
sumi1 = vmlaq_s32(sumi1, scales_4, p12);
sumi2 = vmlaq_s32(sumi2, scales_4, p34);
qs += 8; qh += 4;
}
#if QK_K == 64
sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
#else
sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
#endif
}
*s = sumf;
#elif defined __AVX2__
#if QK_K == 64
const __m256i mask = _mm256_set1_epi16(0xf);
#else
const __m256i mask = _mm256_set1_epi16(0x7);
#endif
const __m256i mone = _mm256_set1_epi16(1);
__m256 accum1 = _mm256_setzero_ps();
__m256 accum2 = _mm256_setzero_ps();
for (int i = 0; i < nb; ++i) {
const int8_t * q8 = y[i].qs;
const uint8_t * qs = x[i].qs;
const uint8_t * qh = x[i].qh;
const uint16_t * sc = (const uint16_t *)x[i].scales;
#if QK_K != 64
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
#endif
__m256i sumi1 = _mm256_setzero_si256();
__m256i sumi2 = _mm256_setzero_si256();
for (int ib = 0; ib < QK_K/32; ib += 2) {
const __m256i q1b_1 = _mm256_set_epi64x(
iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)],
iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)]
);
const __m256i q1b_2 = _mm256_set_epi64x(
iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)],
iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)]
);
const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1);
const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2);
const __m256i delta1 = _mm256_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101,
qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
const __m256i delta2 = _mm256_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101,
qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101,
qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101);
const __m256i dot3 = mul_add_epi8(delta1, q8b_1);
const __m256i dot4 = mul_add_epi8(delta2, q8b_2);
#if QK_K == 64
__m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 4), _mm_set1_epi16(sc[0] >> 0));
__m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 12), _mm_set1_epi16(sc[0] >> 8));
#else
__m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0));
__m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6));
#endif
scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone);
scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone);
const __m256i p1 = _mm256_madd_epi16(dot1, scale1);
const __m256i p2 = _mm256_madd_epi16(dot2, scale2);
const __m256i p3 = _mm256_madd_epi16(dot3, scale1);
const __m256i p4 = _mm256_madd_epi16(dot4, scale2);
sumi1 = _mm256_add_epi32(sumi1, _mm256_add_epi32(p1, p2));
sumi2 = _mm256_add_epi32(sumi2, _mm256_add_epi32(p3, p4));
qs += 8; qh += 4;
}
#if QK_K == 64
const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d));
#else
const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16));
#endif
accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1);
accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2);
}
*s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2);
#else
int sum1[2], sum2[2], delta[4];
float sumf = 0;
for (int i = 0; i < nb; i++) {
const int8_t * q8 = y[i].qs;
const uint8_t * qs = x[i].qs;
const uint8_t * qh = x[i].qh;
const uint16_t * sc = (const uint16_t *)x[i].scales;
#if QK_K != 64
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
#endif
int sumi1 = 0, sumi2 = 0;
for (int ib = 0; ib < QK_K/32; ++ib) {
delta[0] = qh[0] & 0x08 ? -1 : 1;
delta[1] = qh[0] & 0x80 ? -1 : 1;
delta[2] = qh[1] & 0x08 ? -1 : 1;
delta[3] = qh[1] & 0x80 ? -1 : 1;
sum1[0] = sum1[1] = sum2[0] = sum2[1] = 0;
for (int l = 0; l < 4; ++l) {
const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((uint16_t)qh[l/2] << (8 - 4*(l%2))) & 0x700)));
int lsum1 = 0, lsum2 = 0;
for (int j = 0; j < 8; ++j) {
lsum1 += q8[j] * grid[j];
lsum2 += q8[j];
}
q8 += 8;
sum1[l/2] += lsum1;
sum2[l/2] += lsum2*delta[l];
}
#if QK_K == 64
const int ls1 = 2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1;
const int ls2 = 2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1;
#else
const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1;
const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1;
#endif
sumi1 += sum1[0] * ls1 + sum1[1] * ls2;
sumi2 += sum2[0] * ls1 + sum2[1] * ls2;
qs += 4;
qh += 2;
}
#if QK_K == 64
sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
#else
sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
#endif
}
*s = sumf;
#endif
}
void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
assert(nrc == 1);
UNUSED(nrc);
@ -9938,17 +10239,17 @@ static iq2_entry_t iq2_data[4] = {
};
static inline int iq2_data_index(enum ggml_type type) {
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S);
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
return type == GGML_TYPE_IQ2_XXS ? 0 :
type == GGML_TYPE_IQ2_XS ? 1 :
type == GGML_TYPE_IQ1_S ? 2 : 3;
type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? 2 : 3;
}
static inline int iq2_grid_size(enum ggml_type type) {
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S);
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
return type == GGML_TYPE_IQ2_XXS ? 256 :
type == GGML_TYPE_IQ2_XS ? 512 :
type == GGML_TYPE_IQ1_S ? NGRID_IQ1S : 1024;
type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? NGRID_IQ1S : 1024;
}
static int iq2_compare_func(const void * left, const void * right) {
@ -10214,10 +10515,10 @@ void iq2xs_init_impl(enum ggml_type type) {
const int kmap_size = 43692;
//const int nwant = type == GGML_TYPE_IQ1_S ? 3 : 2;
const int nwant = type == GGML_TYPE_IQ1_S ? 3 : type == GGML_TYPE_IQ2_S ? 1 : 2;
const int nwant = type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? 3 : type == GGML_TYPE_IQ2_S ? 1 : 2;
const uint16_t * kgrid = type == GGML_TYPE_IQ2_XXS ? kgrid_2bit_256 :
type == GGML_TYPE_IQ2_XS ? kgrid_2bit_512 :
type == GGML_TYPE_IQ1_S ? kgrid_1bit_2048 : kgrid_2bit_1024;
type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M ? kgrid_1bit_2048 : kgrid_2bit_1024;
uint64_t * kgrid_q2xs;
int * kmap_q2xs;
uint16_t * kneighbors_q2xs;
@ -10314,7 +10615,7 @@ void iq2xs_init_impl(enum ggml_type type) {
}
void iq2xs_free_impl(enum ggml_type type) {
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ2_S);
GGML_ASSERT(type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ1_S || type == GGML_TYPE_IQ1_M || type == GGML_TYPE_IQ2_S);
const int gindex = iq2_data_index(type);
if (iq2_data[gindex].grid) {
free(iq2_data[gindex].grid); iq2_data[gindex].grid = NULL;
@ -11520,7 +11821,16 @@ static int iq1_sort_helper(const void * left, const void * right) {
}
#define IQ1S_BLOCK_SIZE 32
static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) {
#define IQ1M_BLOCK_SIZE 16
static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights,
float * scales,
float * weight,
float * sumx,
float * sumw,
float * pairs,
int8_t * L,
uint16_t * index,
int8_t * shifts) {
const int gindex = iq2_data_index(GGML_TYPE_IQ1_S);
@ -11534,22 +11844,17 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(n%QK_K == 0);
block_iq1_s * y = vy;
const int nbl = n/QK_K;
block_iq1_s * y = vy;
const int block_size = IQ1S_BLOCK_SIZE;
const float x_p[3] = {-1 + IQ1S_DELTA, IQ1S_DELTA, 1 + IQ1S_DELTA};
const float x_m[3] = {-1 - IQ1S_DELTA, -IQ1S_DELTA, 1 - IQ1S_DELTA};
float scales[QK_K/IQ1S_BLOCK_SIZE];
float weight[IQ1S_BLOCK_SIZE];
int8_t L[IQ1S_BLOCK_SIZE];
float sumx[IQ1S_BLOCK_SIZE+1];
float sumw[IQ1S_BLOCK_SIZE+1];
float pairs[2*IQ1S_BLOCK_SIZE];
int * idx = (int *)(pairs + 1);
uint16_t index[IQ1S_BLOCK_SIZE/8];
int8_t shifts[QK_K/IQ1S_BLOCK_SIZE];
for (int ibl = 0; ibl < nbl; ++ibl) {
@ -11564,15 +11869,15 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
float sigma2 = 2*sumx2/QK_K;
for (int ib = 0; ib < QK_K/IQ1S_BLOCK_SIZE; ++ib) {
const float * xb = xbl + IQ1S_BLOCK_SIZE*ib;
const float * qw = quant_weights + QK_K*ibl + IQ1S_BLOCK_SIZE*ib;
for (int i = 0; i < IQ1S_BLOCK_SIZE; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
for (int ib = 0; ib < QK_K/block_size; ++ib) {
const float * xb = xbl + block_size*ib;
const float * qw = quant_weights + QK_K*ibl + block_size*ib;
for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
float max = fabsf(xb[0]);
for (int i = 1; i < IQ1S_BLOCK_SIZE; ++i) max = MAX(max, fabsf(xb[i]));
for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
if (!max) {
scales[ib] = 0;
memset(L, 1, IQ1S_BLOCK_SIZE);
memset(L, 1, block_size);
continue;
}
// Here we solve exactly the sum of squared difference (SSD) weighted minimization problem.
@ -11581,14 +11886,14 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
// in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and
// Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale
// for each possible and score for each split.
for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) {
for (int j = 0; j < block_size; ++j) {
pairs[2*j] = xb[j];
idx[2*j] = j;
}
qsort(pairs, IQ1S_BLOCK_SIZE, 2*sizeof(float), iq1_sort_helper);
qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper);
{
sumx[0] = sumw[0] = 0;
for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) {
for (int j = 0; j < block_size; ++j) {
int i = idx[2*j];
sumx[j+1] = sumx[j] + weight[i]*xb[i];
sumw[j+1] = sumw[j] + weight[i];
@ -11596,16 +11901,16 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
}
float best_score = 0, scale = max;
int besti1 = -1, besti2 = -1, best_shift = 0;
for (int i1 = 0; i1 <= IQ1S_BLOCK_SIZE; ++i1) {
for (int i2 = i1; i2 <= IQ1S_BLOCK_SIZE; ++i2) {
float sumqx = (sumx[i1] - sumx[0])*x_p[0] + (sumx[i2] - sumx[i1])*x_p[1] + (sumx[IQ1S_BLOCK_SIZE] - sumx[i2])*x_p[2];
float sumq2 = (sumw[i1] - sumw[0])*x_p[0]*x_p[0] + (sumw[i2] - sumw[i1])*x_p[1]*x_p[1] + (sumw[IQ1S_BLOCK_SIZE] - sumw[i2])*x_p[2]*x_p[2];
for (int i1 = 0; i1 <= block_size; ++i1) {
for (int i2 = i1; i2 <= block_size; ++i2) {
float sumqx = (sumx[i1] - sumx[0])*x_p[0] + (sumx[i2] - sumx[i1])*x_p[1] + (sumx[block_size] - sumx[i2])*x_p[2];
float sumq2 = (sumw[i1] - sumw[0])*x_p[0]*x_p[0] + (sumw[i2] - sumw[i1])*x_p[1]*x_p[1] + (sumw[block_size] - sumw[i2])*x_p[2]*x_p[2];
if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) {
scale = sumqx/sumq2; best_score = scale*sumqx;
besti1 = i1; besti2 = i2; best_shift = 1;
}
sumqx = (sumx[i1] - sumx[0])*x_m[0] + (sumx[i2] - sumx[i1])*x_m[1] + (sumx[IQ1S_BLOCK_SIZE] - sumx[i2])*x_m[2];
sumq2 = (sumw[i1] - sumw[0])*x_m[0]*x_m[0] + (sumw[i2] - sumw[i1])*x_m[1]*x_m[1] + (sumw[IQ1S_BLOCK_SIZE] - sumw[i2])*x_m[2]*x_m[2];
sumqx = (sumx[i1] - sumx[0])*x_m[0] + (sumx[i2] - sumx[i1])*x_m[1] + (sumx[block_size] - sumx[i2])*x_m[2];
sumq2 = (sumw[i1] - sumw[0])*x_m[0]*x_m[0] + (sumw[i2] - sumw[i1])*x_m[1]*x_m[1] + (sumw[block_size] - sumw[i2])*x_m[2]*x_m[2];
if (sumq2 > 0 && sumqx*sumqx > best_score*sumq2) {
scale = sumqx/sumq2; best_score = scale*sumqx;
besti1 = i1; besti2 = i2; best_shift = -1;
@ -11615,14 +11920,14 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_shift != 0);
for (int j = 0; j < besti1; ++j) L[idx[2*j]] = 0;
for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1;
for (int j = besti2; j < IQ1S_BLOCK_SIZE; ++j) L[idx[2*j]] = 2;
for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2;
if (scale < 0) {
for (int j = 0; j < IQ1S_BLOCK_SIZE; ++j) L[j] = 2 - L[j];
for (int j = 0; j < block_size; ++j) L[j] = 2 - L[j];
scale = -scale; best_shift = -best_shift;
}
bool all_on_grid = true;
const float * xx = best_shift == 1 ? x_p : x_m;
for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) {
for (int k = 0; k < block_size/8; ++k) {
uint16_t u = 0;
for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j);
int grid_index = kmap_q2xs[u];
@ -11636,7 +11941,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
}
if (!all_on_grid) {
float sumqx = 0, sumq2 = 0;
for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) {
for (int k = 0; k < block_size/8; ++k) {
const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]);
for (int j = 0; j < 8; ++j) {
float w = weight[8*k + j];
@ -11648,8 +11953,8 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
if (sumqx > 0 && sumq2 > 0) scale = sumqx/sumq2;
}
uint16_t h = 0;
for (int k = 0; k < IQ1S_BLOCK_SIZE/8; ++k) {
y[ibl].qs[(IQ1S_BLOCK_SIZE/8)*ib + k] = index[k] & 255;
for (int k = 0; k < block_size/8; ++k) {
y[ibl].qs[(block_size/8)*ib + k] = index[k] & 255;
h |= (index[k] >> 8) << 3*k;
}
y[ibl].qh[ib] = h;
@ -11660,14 +11965,13 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
}
if (!max_scale) {
memset(y[ibl].qs, 0, QK_K/8);
continue;
}
float d = max_scale/15;
y[ibl].d = GGML_FP32_TO_FP16(d*1.125f); // 1.085f is another fudge factor. Don't ask me why it is needed.
y[ibl].d = GGML_FP32_TO_FP16(d*1.125f); // 1.125f is another fudge factor. Don't ask me why it is needed.
float id = 1/d;
for (int ib = 0; ib < QK_K/IQ1S_BLOCK_SIZE; ++ib) {
for (int ib = 0; ib < QK_K/block_size; ++ib) {
int l = nearest_int(0.5f*(id*scales[ib]-1));
l = MAX(0, MIN(7, l));
if (shifts[ib] == -1) l |= 8;
@ -11678,16 +11982,307 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy
size_t quantize_iq1_s(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
GGML_ASSERT(n_per_row%QK_K == 0);
float scales[QK_K/IQ1S_BLOCK_SIZE];
float weight[IQ1S_BLOCK_SIZE];
int8_t L[IQ1S_BLOCK_SIZE];
float sumx[IQ1S_BLOCK_SIZE+1];
float sumw[IQ1S_BLOCK_SIZE+1];
float pairs[2*IQ1S_BLOCK_SIZE];
uint16_t index[IQ1S_BLOCK_SIZE/8];
int8_t shifts[QK_K/IQ1S_BLOCK_SIZE];
int nblock = n_per_row/QK_K;
char * qrow = (char *)dst;
for (int row = 0; row < nrow; ++row) {
quantize_row_iq1_s_impl(src, qrow, n_per_row, quant_weights);
quantize_row_iq1_s_impl(src, qrow, n_per_row, quant_weights, scales, weight, sumx, sumw, pairs, L, index, shifts);
src += n_per_row;
qrow += nblock*sizeof(block_iq1_s);
}
return nrow * nblock * sizeof(block_iq1_s);
}
static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights,
float * scales,
float * weight,
float * pairs,
int8_t * L,
uint16_t * index,
int8_t * shifts) {
const int gindex = iq2_data_index(GGML_TYPE_IQ1_M);
const uint64_t * kgrid_q2xs = iq2_data[gindex].grid;
const int * kmap_q2xs = iq2_data[gindex].map;
const uint16_t * kneighbors_q2xs = iq2_data[gindex].neighbours;
//GGML_ASSERT(quant_weights && "missing quantization weights");
GGML_ASSERT(kgrid_q2xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(kmap_q2xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(kneighbors_q2xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(n%QK_K == 0);
block_iq1_m * y = vy;
const int nbl = n/QK_K;
const int block_size = IQ1M_BLOCK_SIZE;
const float x_p[3] = {-1 + IQ1M_DELTA, IQ1M_DELTA, 1 + IQ1M_DELTA};
const float x_m[3] = {-1 - IQ1M_DELTA, -IQ1M_DELTA, 1 - IQ1M_DELTA};
const uint8_t masks[4] = {0x00, 0x80, 0x08, 0x88};
int * idx = (int *)(pairs + 1);
float sumqx[4], sumq2[4];
iq1m_scale_t s;
const float * xx;
for (int ibl = 0; ibl < nbl; ++ibl) {
#if QK_K == 64
y[ibl].d = GGML_FP32_TO_FP16(0.f);
#endif
memset(y[ibl].qs, 0, QK_K/8);
memset(y[ibl].qh, 0, QK_K/16);
memset(y[ibl].scales, 0, QK_K/32);
float max_scale = 0;
const float * xbl = x + QK_K*ibl;
float sumx2 = 0;
for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i];
float sigma2 = 2*sumx2/QK_K;
for (int ib = 0; ib < QK_K/block_size; ++ib) {
const float * xb = xbl + block_size*ib;
if (quant_weights) {
const float * qw = quant_weights + QK_K*ibl + block_size*ib;
for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
} else {
for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i];
}
float max = fabsf(xb[0]);
for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i]));
if (!max) {
scales[ib] = 0;
memset(L, 1, block_size);
continue;
}
// Here we solve exactly the sum of squared difference (SSD) weighted minimization problem.
// With just 3 allowed quant values (-1, 0, 1), we can search exhaustively for the two
// boundaries that split the weights xb[i] into 3 groups. To do so, we sort the weights
// in ascending order, compute Si = sum[weight[j] xb[j], j = 0...i] and
// Wi = sum[weight[j], j = 0...i], and use these to quckly get get the optimum scale
// for each possible and score for each split.
for (int j = 0; j < block_size; ++j) {
pairs[2*j] = xb[j];
idx[2*j] = j;
}
qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper);
float best_score = 0, scale = max;
int besti1 = -1, besti2 = -1, best_k = -1;
// 0: +, +
// 1: +, -
// 2: -, +
// 3: -, -
for (int i1 = 0; i1 <= block_size; ++i1) {
for (int i2 = i1; i2 <= block_size; ++i2) {
memset(sumqx, 0, 4*sizeof(float));
memset(sumq2, 0, 4*sizeof(float));
for (int j = 0; j < i1; ++j) {
int i = idx[2*j];
if (i < block_size/2) {
sumqx[0] += weight[i]*x_p[0]*xb[i];
sumqx[1] += weight[i]*x_p[0]*xb[i];
sumqx[2] += weight[i]*x_m[0]*xb[i];
sumqx[3] += weight[i]*x_m[0]*xb[i];
sumq2[0] += weight[i]*x_p[0]*x_p[0];
sumq2[1] += weight[i]*x_p[0]*x_p[0];
sumq2[2] += weight[i]*x_m[0]*x_m[0];
sumq2[3] += weight[i]*x_m[0]*x_m[0];
} else {
sumqx[0] += weight[i]*x_p[0]*xb[i];
sumqx[2] += weight[i]*x_p[0]*xb[i];
sumqx[1] += weight[i]*x_m[0]*xb[i];
sumqx[3] += weight[i]*x_m[0]*xb[i];
sumq2[0] += weight[i]*x_p[0]*x_p[0];
sumq2[2] += weight[i]*x_p[0]*x_p[0];
sumq2[1] += weight[i]*x_m[0]*x_m[0];
sumq2[3] += weight[i]*x_m[0]*x_m[0];
}
}
for (int j = i1; j < i2; ++j) {
int i = idx[2*j];
if (i < block_size/2) {
sumqx[0] += weight[i]*x_p[1]*xb[i];
sumqx[1] += weight[i]*x_p[1]*xb[i];
sumqx[2] += weight[i]*x_m[1]*xb[i];
sumqx[3] += weight[i]*x_m[1]*xb[i];
sumq2[0] += weight[i]*x_p[1]*x_p[1];
sumq2[1] += weight[i]*x_p[1]*x_p[1];
sumq2[2] += weight[i]*x_m[1]*x_m[1];
sumq2[3] += weight[i]*x_m[1]*x_m[1];
} else {
sumqx[0] += weight[i]*x_p[1]*xb[i];
sumqx[2] += weight[i]*x_p[1]*xb[i];
sumqx[1] += weight[i]*x_m[1]*xb[i];
sumqx[3] += weight[i]*x_m[1]*xb[i];
sumq2[0] += weight[i]*x_p[1]*x_p[1];
sumq2[2] += weight[i]*x_p[1]*x_p[1];
sumq2[1] += weight[i]*x_m[1]*x_m[1];
sumq2[3] += weight[i]*x_m[1]*x_m[1];
}
}
for (int j = i2; j < block_size; ++j) {
int i = idx[2*j];
if (i < block_size/2) {
sumqx[0] += weight[i]*x_p[2]*xb[i];
sumqx[1] += weight[i]*x_p[2]*xb[i];
sumqx[2] += weight[i]*x_m[2]*xb[i];
sumqx[3] += weight[i]*x_m[2]*xb[i];
sumq2[0] += weight[i]*x_p[2]*x_p[2];
sumq2[1] += weight[i]*x_p[2]*x_p[2];
sumq2[2] += weight[i]*x_m[2]*x_m[2];
sumq2[3] += weight[i]*x_m[2]*x_m[2];
} else {
sumqx[0] += weight[i]*x_p[2]*xb[i];
sumqx[2] += weight[i]*x_p[2]*xb[i];
sumqx[1] += weight[i]*x_m[2]*xb[i];
sumqx[3] += weight[i]*x_m[2]*xb[i];
sumq2[0] += weight[i]*x_p[2]*x_p[2];
sumq2[2] += weight[i]*x_p[2]*x_p[2];
sumq2[1] += weight[i]*x_m[2]*x_m[2];
sumq2[3] += weight[i]*x_m[2]*x_m[2];
}
}
for (int k = 0; k < 4; ++k) {
if (sumq2[k] > 0 && sumqx[k]*sumqx[k] > best_score*sumq2[k]) {
scale = sumqx[k]/sumq2[k]; best_score = scale*sumqx[k];
besti1 = i1; besti2 = i2; best_k = k;
}
}
}
}
GGML_ASSERT(besti1 >= 0 && besti2 >= 0 && best_k >= 0);
for (int j = 0; j < besti1; ++j) L[idx[2*j]] = 0;
for (int j = besti1; j < besti2; ++j) L[idx[2*j]] = 1;
for (int j = besti2; j < block_size; ++j) L[idx[2*j]] = 2;
if (scale < 0) {
for (int j = 0; j < block_size; ++j) L[j] = 2 - L[j];
scale = -scale;
best_k = best_k == 0 ? 3 : best_k == 1 ? 2 : best_k == 2 ? 1 : 0;
}
bool all_on_grid = true;
for (int k = 0; k < block_size/8; ++k) {
if (k == 0) xx = best_k < 2 ? x_p : x_m;
else xx = best_k%2 == 0 ? x_p : x_m;
uint16_t u = 0;
for (int j = 0; j < 8; ++j) u |= (L[8*k+j] << 2*j);
int grid_index = kmap_q2xs[u];
if (grid_index < 0) {
all_on_grid = false;
const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1;
grid_index = iq1_find_best_neighbour2(neighbours, kgrid_q2xs, xb + 8*k, weight + 8*k, scale, xx, L + 8*k, NGRID_IQ1S);
GGML_ASSERT(grid_index >= 0);
}
index[k] = grid_index;
}
if (!all_on_grid) {
float sumqx_f = 0, sumq2_f = 0;
for (int k = 0; k < block_size/8; ++k) {
if (k == 0) xx = best_k < 2 ? x_p : x_m;
else xx = best_k%2 == 0 ? x_p : x_m;
const int8_t * pg = (const int8_t *)(kgrid_q2xs + index[k]);
for (int j = 0; j < 8; ++j) {
float w = weight[8*k + j];
float q = xx[(pg[j] - 1)/2];
sumqx_f += w*q*xb[8*k+j];
sumq2_f += w*q*q;
}
}
if (sumqx_f > 0 && sumq2_f > 0) scale = sumqx_f/sumq2_f;
}
y[ibl].qs[2*ib + 0] = index[0] & 255;
y[ibl].qs[2*ib + 1] = index[1] & 255;
y[ibl].qh[ib] = (index[0] >> 8) | ((index[1] >> 8) << 4);
GGML_ASSERT(scale >= 0);
scales[ib] = scale;
shifts[ib] = best_k;
max_scale = MAX(max_scale, scale);
}
if (!max_scale) {
continue;
}
uint16_t * sc = (uint16_t *)y[ibl].scales;
#if QK_K == 64
float d = max_scale/31;
#else
float d = max_scale/15;
#endif
float id = 1/d;
float sumqx_f = 0, sumq2_f = 0;
for (int ib = 0; ib < QK_K/block_size; ++ib) {
int l = nearest_int(0.5f*(id*scales[ib+0]-1));
#if QK_K == 64
l = MAX(0, MIN(15, l));
sc[ib/4] |= (l << 4*(ib%4));
#else
l = MAX(0, MIN(7, l));
sc[ib/4] |= (l << 3*(ib%4));
#endif
y[ibl].qh[ib] |= masks[shifts[ib]];
const float * xb = xbl + block_size*ib;
if (quant_weights) {
const float * qw = quant_weights + QK_K*ibl + block_size*ib;
for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
} else {
for (int i = 0; i < block_size; ++i) weight[i] = xb[i]*xb[i];
}
for (int k = 0; k < block_size/8; ++k) {
if (k == 0) xx = shifts[ib] < 2 ? x_p : x_m;
else xx = shifts[ib]%2 == 0 ? x_p : x_m;
const int8_t * pg = (const int8_t *)(kgrid_q2xs + y[ibl].qs[2*ib+k] + ((y[ibl].qh[ib] << (8 - 4*k)) & 0x700));
for (int j = 0; j < 8; ++j) {
float w = weight[8*k + j];
float q = xx[(pg[j] - 1)/2]*(2*l+1);
sumqx_f += w*q*xb[8*k+j];
sumq2_f += w*q*q;
}
}
}
if (sumq2_f > 0) d = sumqx_f/sumq2_f;
s.f16 = GGML_FP32_TO_FP16(d*1.1125f); // 1.1125f is another fudge factor. Don't ask me why it is needed.
#if QK_K == 64
y[ibl].d = s.f16;
#else
sc[0] |= ((s.u16 & 0x000f) << 12);
sc[1] |= ((s.u16 & 0x00f0) << 8);
sc[2] |= ((s.u16 & 0x0f00) << 4);
sc[3] |= ((s.u16 & 0xf000) << 0);
#endif
}
}
size_t quantize_iq1_m(const float * restrict src, void * restrict dst, int nrow, int n_per_row, const float * quant_weights) {
GGML_ASSERT(n_per_row%QK_K == 0);
float scales[QK_K/IQ1M_BLOCK_SIZE];
float weight[IQ1M_BLOCK_SIZE];
int8_t L[IQ1M_BLOCK_SIZE];
float pairs[2*IQ1M_BLOCK_SIZE];
uint16_t index[IQ1M_BLOCK_SIZE/8];
int8_t shifts[QK_K/IQ1M_BLOCK_SIZE];
int nblock = n_per_row/QK_K;
char * qrow = (char *)dst;
for (int row = 0; row < nrow; ++row) {
quantize_row_iq1_m_impl(src, qrow, n_per_row, quant_weights, scales, weight, pairs, L, index, shifts);
src += n_per_row;
qrow += nblock*sizeof(block_iq1_m);
}
return nrow * nblock * sizeof(block_iq1_m);
}
// ============================ 4-bit non-linear quants
static inline int best_index_int8(int n, const int8_t * val, float x) {

3
ggml-quants.h
View file

@ -72,6 +72,7 @@ void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_
void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq1_m (const block_iq1_m * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
@ -94,6 +95,7 @@ void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const
void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq1_m_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
@ -104,6 +106,7 @@ size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT ds
size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq1_m (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);

56
ggml-sycl.cpp
View file

@ -2968,7 +2968,7 @@ namespace dpct
#include "ggml-common.h"
static int g_ggml_sycl_debug=0;
#define GGML_SYCL_DEBUG(...) do{if(g_ggml_sycl_debug) printf(__VA_ARGS__);}while(0)
#define GGML_SYCL_DEBUG(...) do{if(g_ggml_sycl_debug) fprintf(stderr, __VA_ARGS__);}while(0)
#define CHECK_TRY_ERROR(expr) \
[&]() { \
@ -12868,6 +12868,7 @@ void print_device_detail(int id, sycl::device &device, std::string device_type)
}
void ggml_backend_sycl_print_sycl_devices() {
GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n");
int device_count = dpct::dev_mgr::instance().device_count();
std::map<std::string, size_t> DeviceNums;
fprintf(stderr, "found %d SYCL devices:\n", device_count);
@ -12925,7 +12926,9 @@ static void ggml_init_sycl() try {
static bool initialized = false;
if (!initialized) {
fprintf(stderr, "[SYCL] call ggml_init_sycl\n");
g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0);
fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug);
#if defined(GGML_SYCL_F16)
@ -14986,6 +14989,9 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
SYCL_CHECK(ggml_sycl_set_device(g_main_device));
dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];
bool no_mixed_dtypes = main_stream->get_backend() == sycl::backend::ext_oneapi_cuda ||
main_stream->get_backend() == sycl::backend::ext_oneapi_hip;
SYCL_CHECK(
CHECK_TRY_ERROR(g_sycl_handles[g_main_device] = main_stream));
@ -15016,24 +15022,38 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float;
dpct::library_data_t cu_data_type = dpct::library_data_t::real_float;
if (no_mixed_dtypes) {
cu_compute_type = dpct::library_data_t::real_half;
cu_data_type = dpct::library_data_t::real_half;
}
// dst strides
size_t nbd2 = dst->nb[2];
size_t nbd3 = dst->nb[3];
const float alpha_f32 = 1.0f;
const float beta_f32 = 0.0f;
const sycl::half alpha_f16 = 1.0f;
const sycl::half beta_f16 = 0.0f;
const float alpha_f32 = 1.0f;
const float beta_f32 = 0.0f;
const void * alpha = &alpha_f32;
const void * beta = &beta_f32;
if (no_mixed_dtypes) {
alpha = &alpha_f16;
beta = &beta_f16;
}
// TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway
// oneMKL open source supports half, half, float, float: datatypes
// when oneMKL open source supports half, half, float, float: datatypes
dst_t = (char *) dst_ddf;
if (no_mixed_dtypes) {
dst_t = (char *) dst_f16.alloc(ne_dst);
nbd2 /= sizeof(float) / sizeof(sycl::half);
nbd3 /= sizeof(float) / sizeof(sycl::half);
}
GGML_ASSERT(ne12 % ne02 == 0);
GGML_ASSERT(ne13 % ne03 == 0);
@ -15119,6 +15139,10 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
}
#endif
if (no_mixed_dtypes) {
const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream);
}
}
catch (sycl::exception const &exc) {
std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@ -16018,6 +16042,7 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_
}
GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len) try {
GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_gpu_list\n");
for(int i=0;i<max_len;i++) id_list[i] = -1;
if (!g_sycl_gpu_mgr) {
@ -16052,6 +16077,7 @@ catch (sycl::exception const &exc) {
GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description,
size_t description_size) try {
GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_device_description\n");
dpct::device_info prop;
int device_id = g_sycl_gpu_mgr->gpus[device];
SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
@ -16066,6 +16092,7 @@ catch (sycl::exception const &exc) {
GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free,
size_t *total) try {
GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_memory\n");
ggml_sycl_set_device(device);
/*
@ -16417,7 +16444,8 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = {
};
ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device_index) {
ggml_init_sycl();
GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n");
if (device_index>=g_device_count or device_index<0) {
printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n",
device_index, g_device_count-1);
@ -16787,6 +16815,7 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface
};
GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) {
GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_split_buffer_type\n");
ggml_init_sycl();
// FIXME: this is not thread safe
static std::map<std::array<float, GGML_SYCL_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
@ -16859,6 +16888,7 @@ static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggm
}
ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() {
GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_host_buffer_type\n");
static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_type_host = {
/* .iface = */ {
/* .get_name = */ ggml_backend_sycl_host_buffer_type_name,
@ -16973,7 +17003,7 @@ GGML_CALL static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t back
params.ith = 0;
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
continue;
}
#ifndef NDEBUG
@ -17155,6 +17185,7 @@ static ggml_guid_t ggml_backend_sycl_guid() {
}
GGML_CALL ggml_backend_t ggml_backend_sycl_init(int device) {
GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_init\n");
ggml_init_sycl();
check_allow_gpu_index(device);
@ -17181,6 +17212,7 @@ bool ggml_backend_is_sycl(ggml_backend_t backend) {
}
GGML_CALL int ggml_backend_sycl_get_device_count() {
GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_count\n");
if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr();
return g_sycl_gpu_mgr->get_gpu_count();
}
@ -17193,16 +17225,21 @@ GGML_CALL static ggml_backend_t ggml_backend_reg_sycl_init(const char * params,
}
GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) {
GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_index\n");
return g_sycl_gpu_mgr->get_index(device_id);
}
GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index) {
GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_id\n");
return g_sycl_gpu_mgr->gpus[device_index];
}
GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id) {
GGML_ASSERT(main_gpu_id<g_all_sycl_device_count);
ggml_init_sycl();
GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_set_single_device_mode\n");
fprintf(stderr, "ggml_backend_sycl_set_single_device: use single device: [%d]\n", main_gpu_id);
GGML_ASSERT(main_gpu_id<g_all_sycl_device_count);
if (g_sycl_gpu_mgr) {
delete g_sycl_gpu_mgr;
}
@ -17213,6 +17250,9 @@ GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id
}
GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode() {
ggml_init_sycl();
GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_set_mul_device_mode\n");
if (g_ggml_sycl_backend_gpu_mode == SYCL_MUL_GPU_MODE) {
return;
}

2
ggml-vulkan.cpp
View file

@ -5566,7 +5566,7 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
continue;
}

50
ggml.c
View file

@ -794,6 +794,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ1_M] = {
.type_name = "iq1_m",
.blck_size = QK_K,
.type_size = sizeof(block_iq1_m),
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq1_m,
.from_float = NULL,
.from_float_reference = NULL,
.vec_dot = ggml_vec_dot_iq1_m_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ4_NL] = {
.type_name = "iq4_nl",
.blck_size = QK4_NL,
@ -2539,6 +2551,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break;
case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break;
case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break;
case GGML_FTYPE_MOSTLY_IQ1_M: wtype = GGML_TYPE_IQ1_M; break;
case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break;
case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break;
@ -2594,6 +2607,16 @@ static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
}
GGML_CALL bool ggml_is_empty(const struct ggml_tensor * tensor) {
for (int i = 0; i < GGML_MAX_DIMS; ++i) {
if (tensor->ne[i] == 0) {
// empty if any dimension has no elements
return true;
}
}
return false;
}
bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
@ -2608,7 +2631,7 @@ bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor
static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
return
return ggml_is_empty(t0) ? ggml_is_empty(t1) :
(t1->ne[0]%t0->ne[0] == 0) &&
(t1->ne[1]%t0->ne[1] == 0) &&
(t1->ne[2]%t0->ne[2] == 0) &&
@ -2915,7 +2938,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
data_size *= ne[i];
}
GGML_ASSERT(view_src == NULL || data_size + view_offs <= ggml_nbytes(view_src));
GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src));
void * data = view_src != NULL ? view_src->data : NULL;
if (data != NULL) {
@ -8135,6 +8158,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -8417,6 +8441,7 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -8544,6 +8569,7 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -11447,6 +11473,7 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -11638,6 +11665,7 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -11861,6 +11889,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -12564,6 +12593,7 @@ static void ggml_compute_forward_alibi(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -12652,6 +12682,7 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
@ -16093,7 +16124,7 @@ static void ggml_compute_forward_cross_entropy_loss_back(
static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
GGML_ASSERT(params);
if (tensor->op == GGML_OP_NONE) {
if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) {
return;
}
@ -17962,6 +17993,12 @@ static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const
static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_threads) {
int n_tasks = 0;
if (ggml_is_empty(node)) {
// no need to multi-thread a no-op
n_tasks = 1;
return n_tasks;
}
switch (node->op) {
case GGML_OP_CPY:
case GGML_OP_DUP:
@ -20306,7 +20343,8 @@ void ggml_quantize_init(enum ggml_type type) {
case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ1_S: iq2xs_init_impl(type); break;
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M: iq2xs_init_impl(type); break;
case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break;
case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break;
default: // nothing
@ -20331,7 +20369,8 @@ bool ggml_quantize_requires_imatrix(enum ggml_type type) {
return
type == GGML_TYPE_IQ2_XXS ||
type == GGML_TYPE_IQ2_XS ||
type == GGML_TYPE_IQ1_S;
type == GGML_TYPE_IQ1_S;// ||
//type == GGML_TYPE_IQ1_M;
}
size_t ggml_quantize_chunk(
@ -20375,6 +20414,7 @@ size_t ggml_quantize_chunk(
case GGML_TYPE_IQ3_S: result = quantize_iq3_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ2_S: result = quantize_iq2_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ1_M: result = quantize_iq1_m (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
#if QK_K == 64
case GGML_TYPE_IQ4_XS: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;

3
ggml.h
View file

@ -369,6 +369,7 @@ extern "C" {
GGML_TYPE_I32 = 26,
GGML_TYPE_I64 = 27,
GGML_TYPE_F64 = 28,
GGML_TYPE_IQ1_M = 29,
GGML_TYPE_COUNT,
};
@ -408,6 +409,7 @@ extern "C" {
GGML_FTYPE_MOSTLY_IQ3_S = 20, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ2_S = 21, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ4_XS = 22, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ1_M = 23, // except 1d tensors
};
// available tensor operations:
@ -748,6 +750,7 @@ extern "C" {
GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor);
GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor);
GGML_API GGML_CALL bool ggml_is_permuted (const struct ggml_tensor * tensor);
GGML_API GGML_CALL bool ggml_is_empty (const struct ggml_tensor * tensor);
GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor);
GGML_API bool ggml_is_vector (const struct ggml_tensor * tensor);
GGML_API bool ggml_is_matrix (const struct ggml_tensor * tensor);

1
gguf-py/gguf/constants.py
View file

@ -706,6 +706,7 @@ class GGMLQuantizationType(IntEnum):
I32 = 26
I64 = 27
F64 = 28
IQ1_M = 29
class GGUFEndian(IntEnum):

777
llama.cpp
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File diff suppressed because it is too large Load diff

26
llama.h
View file

@ -39,7 +39,7 @@
#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
#define LLAMA_SESSION_VERSION 4
#define LLAMA_SESSION_VERSION 5
#ifdef __cplusplus
extern "C" {
@ -117,6 +117,7 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ1_M = 31, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};
@ -284,6 +285,7 @@ extern "C" {
bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
bool pure; // quantize all tensors to the default type
void * imatrix; // pointer to importance matrix data
void * kv_overrides; // pointer to vector containing overrides
} llama_model_quantize_params;
// grammar types
@ -676,23 +678,29 @@ extern "C" {
LLAMA_API void llama_synchronize(struct llama_context * ctx);
// Token logits obtained from the last call to llama_decode()
// The logits for the last token are stored in the last row
// Logits for which llama_batch.logits[i] == 0 are undefined
// Rows: n_tokens provided with llama_batch
// The logits for which llama_batch.logits[i] != 0 are stored contiguously
// in the order they have appeared in the batch.
// Rows: number of tokens for which llama_batch.logits[i] != 0
// Cols: n_vocab
LLAMA_API float * llama_get_logits(struct llama_context * ctx);
// Logits for the ith token. Equivalent to:
// llama_get_logits(ctx) + i*n_vocab
// llama_get_logits(ctx) + ctx->output_ids[i]*n_vocab
// returns NULL for invalid ids.
LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
// Get all output token embeddings
// shape: [n_tokens*n_embd] (1-dimensional)
// Get all output token embeddings.
// when pooling_type == LLAMA_POOLING_TYPE_NONE or when using a generative model,
// the embeddings for which llama_batch.logits[i] != 0 are stored contiguously
// in the order they have appeared in the batch.
// shape: [n_outputs*n_embd]
// Otherwise, returns NULL.
LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
// Get the embeddings for the ith token
// llama_get_embeddings(ctx) + i*n_embd
// Get the embeddings for the ith token. Equivalent to:
// llama_get_embeddings(ctx) + ctx->output_ids[i]*n_embd
// shape: [n_embd] (1-dimensional)
// returns NULL for invalid ids.
LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
// Get the embeddings for a sequence id

2
tests/test-backend-ops.cpp
View file

@ -1960,7 +1960,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
GGML_TYPE_Q4_K, GGML_TYPE_Q5_K,
GGML_TYPE_Q6_K,
GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S,
GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
};

1651
unicode-data.cpp Normal file
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File diff suppressed because it is too large Load diff

16
unicode-data.h Normal file
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@ -0,0 +1,16 @@
#pragma once
#include <cstdint>
#include <map>
#include <utility>
#include <vector>
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_digit;
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_letter;
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_whitespace;
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_accent_mark;
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_punctuation;
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_symbol;
extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_control;
extern const std::multimap<uint32_t, uint32_t> unicode_map_nfd;
extern const std::map<char32_t, char32_t> unicode_map_lowercase;

1411
unicode.cpp
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File diff suppressed because it is too large Load diff

2
unicode.h
View file

@ -24,3 +24,5 @@ int unicode_cpt_type(const std::string & utf8);
std::string unicode_byte_to_utf8(uint8_t byte);
uint8_t unicode_utf8_to_byte(const std::string & utf8);
// simple tolower that only implements one-to-one mapping, not one-to-many
char32_t unicode_tolower(char32_t cp);