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pudepiedj 2024-02-26 13:31:50 +00:00
commit 2768634743
60 changed files with 5299 additions and 1621 deletions
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37
.devops/nix/docker.nix Normal file
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@ -0,0 +1,37 @@
{
lib,
dockerTools,
buildEnv,
llama-cpp,
interactive ? true,
coreutils,
}:
# A tar that can be fed into `docker load`:
#
# $ nix build .#llamaPackages.docker
# $ docker load < result
# For details and variations cf.
# - https://nixos.org/manual/nixpkgs/unstable/#ssec-pkgs-dockerTools-buildLayeredImage
# - https://discourse.nixos.org/t/a-faster-dockertools-buildimage-prototype/16922
# - https://nixery.dev/
# Approximate (compressed) sizes, at the time of writing, are:
#
# .#llamaPackages.docker: 125M;
# .#llamaPackagesCuda.docker: 537M;
# .#legacyPackages.aarch64-linux.llamaPackagesXavier.docker: 415M.
dockerTools.buildLayeredImage {
name = llama-cpp.pname;
tag = "latest";
contents =
[ llama-cpp ]
++ lib.optionals interactive [
coreutils
dockerTools.binSh
dockerTools.caCertificates
];
}

3
.devops/nix/scope.nix
View file

@ -12,5 +12,8 @@ lib.makeScope newScope (
self: {
inherit llamaVersion;
llama-cpp = self.callPackage ./package.nix { };
docker = self.callPackage ./docker.nix { };
docker-min = self.callPackage ./docker.nix { interactive = false; };
sif = self.callPackage ./sif.nix { };
}
)

27
.devops/nix/sif.nix Normal file
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@ -0,0 +1,27 @@
{
lib,
singularity-tools,
llama-cpp,
bashInteractive,
interactive ? false,
}:
let
optionalInt = cond: x: if cond then x else 0;
in
singularity-tools.buildImage rec {
inherit (llama-cpp) name;
contents = [ llama-cpp ] ++ lib.optionals interactive [ bashInteractive ];
# These are excessive (but safe) for most variants. Building singularity
# images requires superuser privileges, so we build them inside a VM in a
# writable image of pre-determined size.
#
# ROCm is currently affected by https://github.com/NixOS/nixpkgs/issues/276846
#
# Expected image sizes:
# - cpu/blas: 150M,
# - cuda, all gencodes: 560M,
diskSize = 4096 + optionalInt llama-cpp.useRocm 16384;
memSize = diskSize;
}

2
.github/ISSUE_TEMPLATE/bug.md vendored
View file

@ -7,3 +7,5 @@ assignees: ''
---
Please include information about your system, the steps to reproduce the bug, and the version of llama.cpp that you are using. If possible, please provide a minimal code example that reproduces the bug.
If the bug concerns the server, please try to reproduce it first using the [server test scenario framework](https://github.com/ggerganov/llama.cpp/tree/master/examples/server/tests).

3
.github/workflows/build.yml vendored
View file

@ -669,8 +669,7 @@ jobs:
run: |
cd examples/llama.android
# Skip armeabi-v7a for now (https://github.com/llvm/llvm-project/issues/65820).
./gradlew build --no-daemon -Pskip-armeabi-v7a
./gradlew build --no-daemon
# freeBSD-latest:
# runs-on: macos-12

7
.github/workflows/nix-ci-aarch64.yml vendored
View file

@ -19,7 +19,6 @@ on:
jobs:
nix-build-aarch64:
if: ${{ vars.CACHIX_NAME != '' }}
runs-on: ubuntu-latest
steps:
- name: Checkout repository
@ -37,8 +36,8 @@ jobs:
extra-conf: |
extra-platforms = aarch64-linux
extra-system-features = nixos-test kvm
extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org
extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org
extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
- uses: DeterminateSystems/magic-nix-cache-action@v2
with:
upstream-cache: https://${{ matrix.cachixName }}.cachix.org
@ -46,7 +45,7 @@ jobs:
uses: cachix/cachix-action@v13
with:
authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}'
name: ${{ vars.CACHIX_NAME }}
name: llama-cpp
- name: Show all output paths
run: >
nix run github:nix-community/nix-eval-jobs

11
.github/workflows/nix-ci.yml vendored
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@ -23,8 +23,8 @@ jobs:
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
extra-conf: |
extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org
extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org
extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
- uses: DeterminateSystems/magic-nix-cache-action@v2
with:
upstream-cache: https://${{ matrix.cachixName }}.cachix.org
@ -37,7 +37,6 @@ jobs:
--flake
".#packages.$(nix eval --raw --impure --expr builtins.currentSystem)"
nix-build:
if: ${{ vars.CACHIX_NAME != '' }}
strategy:
fail-fast: false
matrix:
@ -51,8 +50,8 @@ jobs:
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
extra-conf: |
extra-substituters = https://${{ vars.CACHIX_NAME }}.cachix.org https://cuda-maintainers.cachix.org
extra-trusted-public-keys = ${{ vars.CACHIX_PUBLIC_KEY }} cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org
extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E=
- uses: DeterminateSystems/magic-nix-cache-action@v2
with:
upstream-cache: https://${{ matrix.cachixName }}.cachix.org
@ -60,7 +59,7 @@ jobs:
uses: cachix/cachix-action@v13
with:
authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}'
name: ${{ vars.CACHIX_NAME }}
name: llama-cpp
- name: Build
run: >
nix run github:Mic92/nix-fast-build

83
.github/workflows/server.yml vendored Normal file
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@ -0,0 +1,83 @@
# Server build and tests
name: Server
on:
workflow_dispatch: # allows manual triggering
push:
branches:
- master
paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*']
pull_request:
types: [opened, synchronize, reopened]
paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*']
jobs:
server:
runs-on: ubuntu-latest
strategy:
matrix:
sanitizer: [ADDRESS, THREAD, UNDEFINED]
build_type: [Debug, Release]
include:
- build_type: Release
sanitizer: ""
exclude:
- build_type: Release
sanitizer: ADDRESS
- build_type: Release
sanitizer: THREAD
- build_type: Release
sanitizer: UNDEFINED
container:
image: ubuntu:latest
ports:
- 8888
options: --cpus 4
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
- name: Dependencies
id: depends
run: |
apt-get update
apt-get -y install \
build-essential \
git \
cmake \
python3-pip \
wget \
psmisc
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake .. \
-DLLAMA_NATIVE=OFF \
-DLLAMA_BUILD_SERVER=ON \
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server
- name: Tests dependencies
id: test_dependencies
run: |
pip install -r examples/server/tests/requirements.txt
- name: Download models
id: download_models
run: |
cd examples/server/tests
../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf
- name: Tests
id: server_integration_test
run: |
cd examples/server/tests
PORT=8888 ./tests.sh

10
CMakeLists.txt
View file

@ -936,10 +936,16 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STR
list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access)
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7")
# Raspberry Pi 2
list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)
if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android")
# Android armeabi-v7a
list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations)
else()
# Raspberry Pi 2
list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations)
endif()
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8")
# Android arm64-v8a
# Raspberry Pi 3, 4, Zero 2 (32-bit)
list(APPEND ARCH_FLAGS -mno-unaligned-access)
endif()

2
Makefile
View file

@ -597,7 +597,7 @@ $(info I CC: $(shell $(CC) --version | head -n 1))
$(info I CXX: $(shell $(CXX) --version | head -n 1))
ifdef LLAMA_CUBLAS
$(info I NVCC: $(shell $(NVCC) --version | tail -n 1))
CUDA_VERSION := $(shell nvcc --version | grep -oP 'release (\K[0-9]+\.[0-9])')
CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])')
ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1)
ifndef CUDA_DOCKER_ARCH
ifndef CUDA_POWER_ARCH

15
README.md
View file

@ -10,13 +10,9 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
### Hot topics
- Remove LLAMA_MAX_DEVICES and LLAMA_SUPPORTS_GPU_OFFLOAD: https://github.com/ggerganov/llama.cpp/pull/5240
- Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138
- [SYCL backend](README-sycl.md) is ready (1/28/2024), support Linux/Windows in Intel GPUs (iGPU, Arc/Flex/Max series)
- New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow
- Collecting Apple Silicon performance stats:
- M-series: https://github.com/ggerganov/llama.cpp/discussions/4167
- A-series: https://github.com/ggerganov/llama.cpp/discussions/4508
- Support for chat templates: [Wiki (contributions welcome)](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
- Support for Gemma models: https://github.com/ggerganov/llama.cpp/pull/5631
- Non-linear quantization IQ4_NL: https://github.com/ggerganov/llama.cpp/pull/5590
- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
----
@ -118,6 +114,9 @@ Typically finetunes of the base models below are supported as well.
- [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
**HTTP server**
[llama.cpp web server](./examples/server) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients.
**Bindings:**
@ -146,6 +145,7 @@ Unless otherwise noted these projects are open-source with permissive licensing:
- [nat/openplayground](https://github.com/nat/openplayground)
- [Faraday](https://faraday.dev/) (proprietary)
- [LMStudio](https://lmstudio.ai/) (proprietary)
- [LocalAI](https://github.com/mudler/LocalAI) (MIT)
- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile)
- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all)
@ -158,6 +158,7 @@ Unless otherwise noted these projects are open-source with permissive licensing:
- [semperai/amica](https://github.com/semperai/amica)
- [withcatai/catai](https://github.com/withcatai/catai)
- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT)
- [Msty](https://msty.app) (proprietary)
---

18
common/common.cpp
View file

@ -295,9 +295,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
break;
}
std::string value(argv[i]);
/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
else { invalid_param = true; break; }
} else if (arg == "--rope-scale") {
if (++i >= argc) {
@ -630,11 +630,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
}
std::string arg_next = argv[i];
if (arg_next == "none") {
params.split_mode = LLAMA_SPLIT_NONE;
params.split_mode = LLAMA_SPLIT_MODE_NONE;
} else if (arg_next == "layer") {
params.split_mode = LLAMA_SPLIT_LAYER;
params.split_mode = LLAMA_SPLIT_MODE_LAYER;
} else if (arg_next == "row") {
params.split_mode = LLAMA_SPLIT_ROW;
params.split_mode = LLAMA_SPLIT_MODE_ROW;
} else {
invalid_param = true;
break;
@ -837,15 +837,15 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_INT;
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_FLOAT;
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_BOOL;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.bool_value = true;
} else if (std::strcmp(sep, "false") == 0) {

4
common/common.h
View file

@ -61,7 +61,7 @@ struct gpt_params {
float p_split = 0.1f; // speculative decoding split probability
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs
llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
int32_t n_beams = 0; // if non-zero then use beam search of given width.
@ -75,7 +75,7 @@ struct gpt_params {
float yarn_beta_fast = 32.0f; // YaRN low correction dim
float yarn_beta_slow = 1.0f; // YaRN high correction dim
int32_t yarn_orig_ctx = 0; // YaRN original context length
int32_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED;
int32_t rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
// // sampling parameters

2
common/sampling.cpp
View file

@ -266,7 +266,7 @@ static llama_token llama_sampling_sample_impl(
// }
//}
LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
//LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
}
}

10
common/train.cpp
View file

@ -31,7 +31,7 @@ struct train_state * init_train_state() {
state->opt = new struct ggml_opt_context;
state->opt->ctx = NULL;
state->opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
state->opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
state->opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;
state->opt->loss_after = 0.0f;
@ -556,7 +556,7 @@ void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_g
std::string opt_type;
GGUF_GET_KEY(fctx, opt_type, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_OPTIMIZER_TYPE);
if (opt_type == LLM_KV_OPTIMIZER_TYPE_ADAM) {
opt->params.type = GGML_OPT_ADAM;
opt->params.type = GGML_OPT_TYPE_ADAM;
GGUF_GET_KEY(fctx, opt->adam.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS);
GGUF_GET_KEY(fctx, opt->adam.fx_prev, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS);
@ -568,7 +568,7 @@ void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_g
copy_tensor_by_name(opt->adam.v, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS);
copy_tensor_by_name(opt->adam.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES);
} else if (opt_type == LLM_KV_OPTIMIZER_TYPE_LBFGS) {
opt->params.type = GGML_OPT_LBFGS;
opt->params.type = GGML_OPT_TYPE_LBFGS;
GGUF_GET_KEY(fctx, opt->params.lbfgs.m, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT);
GGUF_GET_KEY(fctx, opt->lbfgs.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS);
@ -603,7 +603,7 @@ void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context *
gguf_set_val_bool(fctx, LLM_KV_OPTIMIZER_JUST_INITIALIZED, opt->just_initialized);
switch (opt->params.type) {
case GGML_OPT_ADAM:
case GGML_OPT_TYPE_ADAM:
{
gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM);
gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, opt->adam.fx_best);
@ -622,7 +622,7 @@ void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context *
gguf_add_tensor(fctx, opt->adam.pf);
}
} break;
case GGML_OPT_LBFGS:
case GGML_OPT_TYPE_LBFGS:
{
gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS);
gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, opt->params.lbfgs.m);

78
convert-hf-to-gguf.py
View file

@ -192,7 +192,7 @@ class Model:
return RefactModel
if model_architecture == "PersimmonForCausalLM":
return PersimmonModel
if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
if model_architecture in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
return StableLMModel
if model_architecture == "QWenLMHeadModel":
return QwenModel
@ -218,6 +218,8 @@ class Model:
return BertModel
if model_architecture == "NomicBertModel":
return NomicBertModel
if model_architecture == "GemmaForCausalLM":
return GemmaModel
return Model
def _is_model_safetensors(self) -> bool:
@ -251,7 +253,7 @@ class Model:
return gguf.MODEL_ARCH.REFACT
if arch == "PersimmonForCausalLM":
return gguf.MODEL_ARCH.PERSIMMON
if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
if arch in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
return gguf.MODEL_ARCH.STABLELM
if arch == "QWenLMHeadModel":
return gguf.MODEL_ARCH.QWEN
@ -277,6 +279,8 @@ class Model:
return gguf.MODEL_ARCH.BERT
if arch == "NomicBertModel":
return gguf.MODEL_ARCH.NOMIC_BERT
if arch == "GemmaForCausalLM":
return gguf.MODEL_ARCH.GEMMA
raise NotImplementedError(f'Architecture "{arch}" not supported!')
@ -618,11 +622,6 @@ class MPTModel(Model):
self.gguf_writer.add_tensor(new_name, data)
# note: MPT output is tied to (same as) wte in original model;
# for easier implementation in llama.cpp it's duplicated in GGUF, though :/
if new_name == "token_embd.weight":
self.gguf_writer.add_tensor("output.weight", data)
class OrionModel(Model):
def set_vocab(self):
@ -655,6 +654,8 @@ class OrionModel(Model):
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_head_count(head_count)
self.gguf_writer.add_head_count_kv(head_count_kv)
# note: config provides rms norm but it is actually layer norm
# ref: https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571
self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"])
def write_tensors(self):
@ -1031,7 +1032,6 @@ class PersimmonModel(Model):
self.gguf_writer.add_head_count_kv(head_count_kv)
self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
def set_vocab(self):
self._set_vocab_sentencepiece()
@ -1074,10 +1074,11 @@ class StableLMModel(Model):
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(int(hparams["rope_pct"] * (hparams["hidden_size"] // hparams["num_attention_heads"])))
rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
self.gguf_writer.add_layer_norm_eps(1e-5)
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
class MixtralModel(Model):
@ -1785,6 +1786,63 @@ class NomicBertModel(BertModel):
yield name, data
class GemmaModel(Model):
def set_vocab(self):
self._set_vocab_sentencepiece()
def set_gguf_parameters(self):
hparams = self.hparams
block_count = hparams["num_hidden_layers"]
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"] if "num_key_value_heads" in hparams else hparams["num_attention_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_key_length(hparams["head_dim"])
self.gguf_writer.add_value_length(hparams["head_dim"])
self.gguf_writer.add_file_type(self.ftype)
def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
for name, data_torch in self.get_tensors():
# ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
if name.endswith("norm.weight"):
data_torch = data_torch + 1
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
data = data_torch.squeeze().numpy()
# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
###### CONVERSION LOGIC ######

2
examples/baby-llama/baby-llama.cpp
View file

@ -1547,7 +1547,7 @@ int main(int argc, char ** argv) {
float error_before_opt = ggml_get_f32_1d(e, 0);
struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS);
struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_TYPE_LBFGS);
opt_params_lbfgs.print_forward_graph = false;
opt_params_lbfgs.print_backward_graph = false;
opt_params_lbfgs.lbfgs.n_iter = 16;

2
examples/finetune/finetune.cpp
View file

@ -1531,7 +1531,7 @@ int main(int argc, char ** argv) {
lora.hparams.n_rank_output = n_rank_output;
// set opt params from command line
opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
opt->params.print_forward_graph = false;
opt->params.print_backward_graph = false;
opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;

4
examples/infill/infill.cpp
View file

@ -447,8 +447,8 @@ int main(int argc, char ** argv) {
LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
n_past, n_left, n_ctx, params.n_keep, n_discard);
llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);
llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);
llama_kv_cache_seq_add(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
n_past -= n_discard;

14
examples/llama-bench/llama-bench.cpp
View file

@ -157,9 +157,9 @@ static const char * output_format_str(output_formats format) {
static const char * split_mode_str(llama_split_mode mode) {
switch (mode) {
case LLAMA_SPLIT_NONE: return "none";
case LLAMA_SPLIT_LAYER: return "layer";
case LLAMA_SPLIT_ROW: return "row";
case LLAMA_SPLIT_MODE_NONE: return "none";
case LLAMA_SPLIT_MODE_LAYER: return "layer";
case LLAMA_SPLIT_MODE_ROW: return "row";
default: GGML_ASSERT(!"invalid split mode");
}
}
@ -193,7 +193,7 @@ static const cmd_params cmd_params_defaults = {
/* type_v */ {GGML_TYPE_F16},
/* n_threads */ {get_num_physical_cores()},
/* n_gpu_layers */ {99},
/* split_mode */ {LLAMA_SPLIT_LAYER},
/* split_mode */ {LLAMA_SPLIT_MODE_LAYER},
/* main_gpu */ {0},
/* no_kv_offload */ {false},
/* mul_mat_q */ {true},
@ -358,11 +358,11 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
for (const auto & m : p) {
llama_split_mode mode;
if (m == "none") {
mode = LLAMA_SPLIT_NONE;
mode = LLAMA_SPLIT_MODE_NONE;
} else if (m == "layer") {
mode = LLAMA_SPLIT_LAYER;
mode = LLAMA_SPLIT_MODE_LAYER;
} else if (m == "row") {
mode = LLAMA_SPLIT_ROW;
mode = LLAMA_SPLIT_MODE_ROW;
} else {
invalid_param = true;
break;

13
examples/llava/README.md
View file

@ -63,13 +63,12 @@ Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` director
```console
git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
```
2) Backup your pth/safetensor model files as llava-surgery modifies them
3) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
2) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
```console
python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/
```
- you will find a llava.projector and a llava.clip file in your model directory
4) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory:
3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory:
```console
mkdir vit
cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin
@ -77,18 +76,18 @@ cp ../llava-v1.6-vicuna-7b/llava.projector vit/
curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json
```
5) Create the visual gguf model:
4) Create the visual gguf model:
```console
python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
```
- This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP
6) Then convert the model to gguf format:
5) Then convert the model to gguf format:
```console
python ./convert.py ../llava-v1.6-vicuna-7b/
python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown
```
7) And finally we can run the llava-cli using the 1.6 model version:
6) And finally we can run the llava-cli using the 1.6 model version:
```console
./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
```

12
examples/llava/llava-surgery-v2.py
View file

@ -65,9 +65,7 @@ def clean_vision_tower_from_checkpoint(checkpoint_path):
for name in clip_tensors:
del checkpoint[name]
# Save the updated checkpoint
checkpoint_path = checkpoint_path
save_model(checkpoint, checkpoint_path, file_type)
return True
return False
@ -152,16 +150,6 @@ for name in first_mm_tensors:
if len(projector) > 0:
save_model(projector, f"{args.model}/llava.projector", 'pytorch')
for name in mm_tensors:
del last_checkpoint[name]
for name in first_mm_tensors:
del first_checkpoint[name]
if len(mm_tensors) > 0:
save_model(last_checkpoint, projector_checkpoint_path, file_type)
if len(first_mm_tensors) > 0:
save_model(first_checkpoint, newline_checkpoint_path, file_type)
print("Done!")
print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.")
print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")

2
examples/llava/llava.cpp
View file

@ -152,7 +152,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
ggml_tensor * newline_tmp = clip_get_newline_tensor(ctx_clip);
model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]);
if (newline_tmp->backend != GGML_BACKEND_CPU) {
if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) {
if (newline_tmp->buffer == NULL) {
printf("newline_tmp tensor buffer is NULL\n");
}

18
examples/main/main.cpp
View file

@ -334,6 +334,8 @@ int main(int argc, char ** argv) {
// number of tokens to keep when resetting context
if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) {
params.n_keep = (int)embd_inp.size();
} else {
params.n_keep += add_bos; // always keep the BOS token
}
// prefix & suffix for instruct mode
@ -383,8 +385,8 @@ int main(int argc, char ** argv) {
}
}
if (params.n_keep > 0) {
LOG_TEE("%s: static prompt based on n_keep: '", __func__);
if (params.n_keep > add_bos) {
LOG_TEE("%s: static prompt based on n_keep: '", __func__);
for (int i = 0; i < params.n_keep; i++) {
LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
}
@ -540,14 +542,14 @@ int main(int argc, char ** argv) {
break;
}
const int n_left = n_past - params.n_keep - 1;
const int n_left = n_past - params.n_keep;
const int n_discard = n_left/2;
LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
n_past, n_left, n_ctx, params.n_keep, n_discard);
llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);
llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard);
llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);
n_past -= n_discard;
@ -574,9 +576,9 @@ int main(int argc, char ** argv) {
LOG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n);
LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd);
llama_kv_cache_seq_shift(ctx, 0, ga_i, n_past, ib*bd);
llama_kv_cache_seq_div (ctx, 0, ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n);
llama_kv_cache_seq_shift(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd, dd);
llama_kv_cache_seq_add(ctx, 0, ga_i, n_past, ib*bd);
llama_kv_cache_seq_div(ctx, 0, ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n);
llama_kv_cache_seq_add(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd, dd);
n_past -= bd;

25
examples/passkey/passkey.cpp
View file

@ -126,7 +126,7 @@ int main(int argc, char ** argv) {
const int n_batch = ctx_params.n_batch;
const int n_batch_grp = ctx_params.n_batch/n_grp;
LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch);
LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d, n_junk = %d, i_pos = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch, n_junk, i_pos);
// print the prompt token-by-token
@ -146,10 +146,11 @@ int main(int argc, char ** argv) {
const int ib = i/n_batch - 1;
const int bd = n_batch_grp*(n_grp - 1);
llama_kv_cache_seq_shift(ctx, 0, n_past - n_batch, n_past, ib*bd);
llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
llama_kv_cache_seq_add (ctx, 0, n_past - n_batch, n_past, ib*bd);
llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
llama_kv_cache_update (ctx);
n_past -= bd;
n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
}
llama_batch_clear(batch);
@ -179,10 +180,12 @@ int main(int argc, char ** argv) {
LOG_TEE("%s: shifting KV cache with %d\n", __func__, n_discard);
llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard);
llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx, -n_discard);
llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard);
llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard);
llama_kv_cache_defrag (ctx);
llama_kv_cache_update (ctx);
n_past -= n_discard;
n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
llama_batch_clear(batch);
@ -208,10 +211,12 @@ int main(int argc, char ** argv) {
if (n_discard > 0) {
LOG_TEE("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard);
llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard);
llama_kv_cache_seq_shift(ctx, 0, n_keep + n_discard, n_ctx, -n_discard);
llama_kv_cache_seq_rm (ctx, 0, n_keep , n_keep + n_discard);
llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx, -n_discard);
llama_kv_cache_defrag (ctx);
llama_kv_cache_update (ctx);
n_past -= n_discard;
n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
}
}

2
examples/quantize/quantize.cpp
View file

@ -27,6 +27,8 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "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", },
{ "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S, " 3.44 bpw quantization", },
{ "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", },
{ "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
{ "Q3_K_XS",LLAMA_FTYPE_MOSTLY_Q3_K_XS,"3-bit extra small quantization" , },
{ "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },

49
examples/server/README.md
View file

@ -7,6 +7,23 @@ It will then run on http://0.0.0.0:8080 and to access the server on the host mac
So for example http://192.168.1.42:8080 will have the same effect as localhost:8080 when the servername is 127.0.0.1
Command line options:
# LLaMA.cpp HTTP Server
Fast, lightweight, pure C/C++ HTTP server based on [httplib](https://github.com/yhirose/cpp-httplib), [nlohmann::json](https://github.com/nlohmann/json) and **llama.cpp**.
Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
**Features:**
* LLM inference of F16 and quantum models on GPU and CPU
* [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes
* Parallel decoding with multi-user support
* Continuous batching
* Multimodal (wip)
* Monitoring endpoints
The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216).
**Command line options:**
- `--threads N`, `-t N`: Set the number of threads to use during generation.
- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation.
@ -45,8 +62,12 @@ see https://github.com/ggerganov/llama.cpp/issues/1437
- `-skvi` or `--show-interactive-graphics`: display a dynamic graphic of kvcache that requires user intervention to move on after each request
- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`
- `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`
- `-n, --n-predict`: Set the maximum tokens to predict (default: -1)
- `-n N, --n-predict N`: Set the maximum tokens to predict (default: -1)
- `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included.
- `--metrics`: enable prometheus `/metrics` compatible endpoint (default: disabled)
- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name (default: template taken from model's metadata). We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
- `--log-disable`: Output logs to stdout only, default: enabled.
- `--log-format FORMAT`: Define the log output to FORMAT: json or text (default: json)
## Build
@ -105,6 +126,12 @@ curl --request POST \
--data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}'
```
## Advanced testing
We implemented a [server test framework](./tests/README.md) using human-readable scenario.
*Before submitting an issue, please try to reproduce it with this format.*
## Node JS Test
You need to have [Node.js](https://nodejs.org/en) installed.
@ -148,6 +175,8 @@ node index.js
- 200 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slot are currently available.
- 503 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if the query parameter `fail_on_no_slot` is provided and no slot are currently available.
If the query parameter `include_slots` is passed, `slots` field will contain internal slots data except if `--slots-endpoint-disable` is set.
- **POST** `/completion`: Given a `prompt`, it returns the predicted completion.
*Options:*
@ -156,7 +185,7 @@ node index.js
`temperature`: Adjust the randomness of the generated text (default: 0.8).
`dynatemp_range`: Dynamic temperature range (default: 0.0, 0.0 = disabled).
`dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` (default: 0.0, 0.0 = disabled).
`dynatemp_exponent`: Dynamic temperature exponent (default: 1.0).
@ -214,7 +243,7 @@ node index.js
`slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1)
`cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false)
`cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch. (default: false)
`system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
@ -247,7 +276,7 @@ Notice that each `probs` is an array of length `n_probs`.
- `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string.
- `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options)
- `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`
- `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.).
- `model`: The path to the model loaded with `-m`
- `prompt`: The provided `prompt`
- `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token
@ -456,6 +485,18 @@ Notice that each `probs` is an array of length `n_probs`.
]
```
- **GET** `/metrics`: [Prometheus](https://prometheus.io/) compatible metrics exporter endpoint if `--metrics` is enabled:
Available metrics:
- `llamacpp:prompt_tokens_total`: Number of prompt tokens processed.
- `llamacpp:tokens_predicted_total`: Number of generation tokens processed.
- `llamacpp:prompt_tokens_seconds`: Average prompt throughput in tokens/s.
- `llamacpp:predicted_tokens_seconds`: Average generation throughput in tokens/s.
- `llamacpp:kv_cache_usage_ratio`: KV-cache usage. 1 means 100 percent usage.
- `llamacpp:kv_cache_tokens`: KV-cache tokens.
- `llamacpp:requests_processing`: Number of request processing.
- `llamacpp:requests_deferred`: Number of request deferred.
## More examples
### Change system prompt on runtime

607
examples/server/server.cpp
View file

@ -45,9 +45,11 @@ struct server_params
int32_t read_timeout = 600;
int32_t write_timeout = 600;
bool slots_endpoint = true;
bool metrics_endpoint = false;
};
bool server_verbose = false;
bool server_log_json = true;
static size_t common_part(const std::vector<llama_token> &a, const std::vector<llama_token> &b)
{
@ -303,21 +305,77 @@ struct llama_client_slot
};
}
void print_timings(llama_client_slot &slot, bool flag = false) const {
if (flag) {
printf("\033[5;0H"); // needs to be sensitive to the number of slots
};
LOG("Finished processing slot %d.\n", slot.id);
LOG("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed);
LOG("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded);
LOG("%s: total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation);
void print_timings() const {
char buffer[512];
double t_token = t_prompt_processing / num_prompt_tokens_processed;
double n_tokens_second = 1e3 / t_prompt_processing * num_prompt_tokens_processed;
sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
t_prompt_processing, num_prompt_tokens_processed,
t_token, n_tokens_second);
LOG_INFO(buffer, {
{"slot_id", id},
{"task_id", task_id},
{"t_prompt_processing", t_prompt_processing},
{"num_prompt_tokens_processed", num_prompt_tokens_processed},
{"t_token", t_token},
{"n_tokens_second", n_tokens_second},
});
if (flag) {
printf("\033[KPress any key ... \n");
getchar();
}
t_token = t_token_generation / n_decoded;
n_tokens_second = 1e3 / t_token_generation * n_decoded;
sprintf(buffer, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)",
t_token_generation, n_decoded,
t_token, n_tokens_second);
LOG_INFO(buffer, {
{"slot_id", id},
{"task_id", task_id},
{"t_token_generation", t_token_generation},
{"n_decoded", n_decoded},
{"t_token", t_token},
{"n_tokens_second", n_tokens_second},
});
sprintf(buffer, " total time = %10.2f ms", t_prompt_processing + t_token_generation);
LOG_INFO(buffer, {
{"slot_id", id},
{"task_id", task_id},
{"t_prompt_processing", t_prompt_processing},
{"t_token_generation", t_token_generation},
{"t_total", t_prompt_processing + t_token_generation},
});
}
};
struct llama_metrics {
uint64_t n_prompt_tokens_processed_total = 0;
uint64_t n_tokens_predicted_total = 0;
uint64_t n_prompt_tokens_processed = 0;
uint64_t t_prompt_processing = 0;
uint64_t n_tokens_predicted = 0;
uint64_t t_tokens_generation = 0;
void on_prompt_eval(const llama_client_slot &slot) {
n_prompt_tokens_processed_total += slot.num_prompt_tokens_processed;
n_prompt_tokens_processed += slot.num_prompt_tokens_processed;
t_prompt_processing += slot.t_prompt_processing;
}
void on_prediction(const llama_client_slot &slot) {
n_tokens_predicted_total += slot.n_decoded;
n_tokens_predicted += slot.n_decoded;
t_tokens_generation += slot.t_token_generation;
}
void reset_bucket() {
n_prompt_tokens_processed = 0;
t_prompt_processing = 0;
n_tokens_predicted = 0;
t_tokens_generation = 0;
}
};
@ -427,6 +485,8 @@ struct llama_server_context
llama_server_queue queue_tasks;
llama_server_response queue_results;
llama_metrics metrics;
~llama_server_context()
{
if (ctx)
@ -446,7 +506,7 @@ struct llama_server_context
params = params_;
if (!params.mmproj.empty()) {
multimodal = true;
LOG("Multi Modal Mode Enabled");
LOG_INFO("Multi Modal Mode Enabled", {});
clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1);
if(clp_ctx == nullptr) {
LOG_ERROR("unable to load clip model", {{"model", params.mmproj}});
@ -483,13 +543,23 @@ struct llama_server_context
return true;
}
void validate_model_chat_template(server_params & sparams) {
llama_chat_message chat[] = {{"user", "test"}};
std::vector<char> buf(1);
int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size());
if (res < 0) {
LOG_ERROR("The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
sparams.chat_template = "<|im_start|>"; // llama_chat_apply_template only checks if <|im_start|> exist in the template
}
}
void initialize() {
// create slots
all_slots_are_idle = true;
const int32_t n_ctx_slot = n_ctx / params.n_parallel;
LOG_TEE("Available slots:\n");
LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}});
for (int i = 0; i < params.n_parallel; i++)
{
llama_client_slot slot;
@ -498,7 +568,10 @@ struct llama_server_context
slot.n_ctx = n_ctx_slot;
slot.n_predict = params.n_predict;
LOG_TEE(" -> Slot %2i - max context: %i\n", slot.id, n_ctx_slot);
LOG_INFO("new slot", {
{"slot_id", slot.id},
{"n_ctx_slot", slot.n_ctx}
});
const int ga_n = params.grp_attn_n;
const int ga_w = params.grp_attn_w;
@ -508,7 +581,12 @@ struct llama_server_context
GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT
//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT
//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
LOG(" -> Slot %2i - self-extend: ga_n = %d, ga_w = %d\n", slot.id, ga_n, ga_w);
LOG_INFO("slot self-extend", {
{"slot_id", slot.id},
{"ga_n", ga_n},
{"ga_w", ga_w}
});
}
slot.ga_i = 0;
@ -816,10 +894,16 @@ struct llama_server_context
img_sl.img_data = clip_image_u8_init();
if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
{
LOG("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id);
LOG_ERROR("failed to load image", {
{"slot_id", slot->id},
{"img_sl_id", img_sl.id}
});
return false;
}
LOG("slot %i - loaded image\n", slot->id);
LOG_VERBOSE("image loaded", {
{"slot_id", slot->id},
{"img_sl_id", img_sl.id}
});
img_sl.request_encode_image = true;
slot->images.push_back(img_sl);
}
@ -879,7 +963,10 @@ struct llama_server_context
all_slots_are_idle = false;
LOG("slot %i is processing [task id: %i]\n", slot->id, slot->task_id);
LOG_INFO("slot is processing task", {
{"slot_id", slot->id},
{"task_id", slot->task_id},
});
return true;
}
@ -904,10 +991,24 @@ struct llama_server_context
llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
}
if (llama_decode(ctx, batch) != 0)
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch)
{
LOG("%s: llama_decode() failed\n", __func__);
return;
const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i));
llama_batch batch_view = {
n_tokens,
batch.token + i,
nullptr,
batch.pos + i,
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};
if (llama_decode(ctx, batch_view) != 0)
{
LOG_TEE("%s: llama_decode() failed\n", __func__);
return;
}
}
// assign the system KV cache to all parallel sequences
@ -1451,7 +1552,7 @@ struct llama_server_context
if (slot == nullptr)
{
// if no slot is available, we defer this task for processing later
LOG("no slot is available for task %d\n", task.id);
LOG_VERBOSE("no slot is available", {{"task_id", task.id}});
queue_tasks.defer(task);
break;
} else {
@ -1503,6 +1604,70 @@ struct llama_server_context
case TASK_TYPE_NEXT_RESPONSE: {
// do nothing
} break;
case TASK_TYPE_METRICS: {
json slots_data = json::array();
int n_idle_slots = 0;
int n_processing_slots = 0;
for (llama_client_slot &slot: slots) {
json slot_data = get_formated_generation(slot);
slot_data["id"] = slot.id;
slot_data["task_id"] = slot.task_id;
slot_data["state"] = slot.state;
slot_data["prompt"] = slot.prompt;
slot_data["next_token"] = {
{"has_next_token", slot.has_next_token},
{"n_remain", slot.n_remaining},
{"num_tokens_predicted", slot.n_decoded},
{"stopped_eos", slot.stopped_eos},
{"stopped_word", slot.stopped_word},
{"stopped_limit", slot.stopped_limit},
{"stopping_word", slot.stopping_word},
};
if (slot_data["state"] == IDLE) {
n_idle_slots++;
} else {
n_processing_slots++;
}
slots_data.push_back(slot_data);
}
LOG_INFO("slot data", {
{"task_id", task.id},
{"n_idle_slots", n_idle_slots},
{"n_processing_slots", n_processing_slots}
});
LOG_VERBOSE("slot data", {
{"task_id", task.id},
{"n_idle_slots", n_idle_slots},
{"n_processing_slots", n_processing_slots},
{"slots", slots_data}
});
task_result res;
res.id = task.id;
res.multitask_id = task.multitask_id;
res.stop = true;
res.error = false;
res.result_json = {
{ "idle", n_idle_slots },
{ "processing", n_processing_slots },
{ "deferred", queue_tasks.queue_tasks_deferred.size() },
{ "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total},
{ "n_tokens_predicted_total", metrics.n_tokens_predicted_total},
{ "n_prompt_tokens_processed", metrics.n_prompt_tokens_processed},
{ "t_prompt_processing", metrics.t_prompt_processing},
{ "n_tokens_predicted", metrics.n_tokens_predicted},
{ "t_tokens_generation", metrics.t_tokens_generation},
{ "kv_cache_tokens_count", llama_get_kv_cache_token_count(ctx)},
{ "kv_cache_used_cells", llama_get_kv_cache_used_cells(ctx)},
{ "slots", slots_data },
};
metrics.reset_bucket();
queue_results.send(res);
} break;
}
}
@ -1528,7 +1693,7 @@ struct llama_server_context
bool update_slots() {
if (system_need_update)
{
LOG("updating system prompt\n");
LOG_INFO("updating system prompt", {});
update_system_prompt();
}
@ -1538,15 +1703,16 @@ struct llama_server_context
{
if (system_prompt.empty() && clean_kv_cache)
{
LOG("all slots are idle and system prompt is empty; clearing the KV cache\n");
LOG_INFO("all slots are idle and system prompt is empty, clear the KV cache", {});
kv_cache_clear();
}
return true;
}
task_server task; // where is space made (malloc) for all these tasks of indeterminate number?
task.type = TASK_TYPE_NEXT_RESPONSE; // where is this set?
task.target_id = -1; // -1 means here as elsewhere 'available to be set'
LOG_VERBOSE("posting NEXT_RESPONSE", {});
task_server task;
task.type = TASK_TYPE_NEXT_RESPONSE;
task.target_id = -1;
queue_tasks.post(task);
for (llama_client_slot &slot : slots)
@ -1558,15 +1724,25 @@ struct llama_server_context
if (slot.is_processing() && system_tokens.size() + slot.cache_tokens.size() >= (size_t) slot.n_ctx)
{
// Shift context
const int n_left = system_tokens.size() + slot.n_past - slot.params.n_keep - 1;
const int n_discard = n_left / 2; // is this arbitrary?
const int n_keep = slot.params.n_keep + add_bos_token;
const int n_left = (int) system_tokens.size() + slot.n_past - n_keep;
const int n_discard = n_left / 2;
printf("\033[5;0H\033[K");
LOG("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard);
llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1);
llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, system_tokens.size() + slot.n_past, -n_discard);
LOG_INFO("slot context shift", {
{"slot_id", slot.id},
{"task_id", slot.task_id},
{"n_keep", n_keep},
{"n_left", n_left},
{"n_discard", n_discard},
{"n_ctx", n_ctx},
{"n_past", slot.n_past},
{"n_system_tokens", system_tokens.size()},
{"n_cache_tokens", slot.cache_tokens.size()}
});
llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard);
llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard);
for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++)
for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++)
{
slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
}
@ -1576,17 +1752,12 @@ struct llama_server_context
slot.n_past -= n_discard;
slot.truncated = true;
LOG_VERBOSE("context shift", {
{ "n_ctx", n_ctx },
{ "n_keep", params.n_keep },
{ "n_left", n_left },
});
}
}
}
// decode any currently ongoing sequences
LOG_VERBOSE("decoding ongoing sequences", {});
for (auto & slot : slots)
{
// release the slot
@ -1596,9 +1767,16 @@ struct llama_server_context
slot.command = NONE;
slot.t_last_used = ggml_time_us();
printf("\033[6;0H\033[K");
LOG("slot %d released (%d tokens remain in cache)\n", slot.id, (int) slot.cache_tokens.size());
queue_tasks.notify_slot_changed(); // why don't we immediately reallocate the released slot without waiting? Is this what -cb does?
LOG_INFO("slot released", {
{"slot_id", slot.id},
{"task_id", slot.task_id},
{"n_ctx", n_ctx},
{"n_past", slot.n_past},
{"n_system_tokens", system_tokens.size()},
{"n_cache_tokens", slot.cache_tokens.size()},
{"truncated", slot.truncated}
});
queue_tasks.notify_slot_changed();
continue;
}
@ -1724,6 +1902,14 @@ struct llama_server_context
}
slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
// the last token of the cache is not in the KV cache until the next call to llama_decode
// (it was sampled, pushed into the "cache_tokens", but not yet put in the context)
if (slot.n_past > 0 && slot.n_past == (int32_t) slot.cache_tokens.size())
{
slot.n_past -= 1;
}
slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past;
if (slot.ga_n != 1)
@ -1745,8 +1931,12 @@ struct llama_server_context
slot.ga_i = ga_i;
}
printf("\033[7;0H\033[K");
LOG("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed);
LOG_INFO("slot progression", {
{ "slot_id", slot.id },
{ "task_id", slot.task_id },
{ "n_past", slot.n_past },
{ "num_prompt_tokens_processed", slot.num_prompt_tokens_processed }
});
}
slot.cache_tokens = prompt_tokens;
@ -1754,7 +1944,10 @@ struct llama_server_context
if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0)
{
// we have to evaluate at least 1 token to generate logits.
LOG("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id);
LOG_INFO("we have to evaluate at least 1 token to generate logits", {
{ "slot_id", slot.id },
{ "task_id", slot.task_id }
});
slot.n_past--;
if (slot.ga_i > 0)
{
@ -1762,10 +1955,13 @@ struct llama_server_context
}
}
printf("\033[5;0H\033[K");
LOG("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past);
llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1);
int p0 = (int) system_tokens.size() + slot.n_past;
LOG_INFO("kv cache rm [p0, end)", {
{ "slot_id", slot.id },
{ "task_id", slot.task_id },
{ "p0", p0 }
});
llama_kv_cache_seq_rm(ctx, slot.id, p0, -1);
LOG_VERBOSE("prompt ingested", {
{"n_past", slot.n_past},
@ -1800,7 +1996,13 @@ struct llama_server_context
if (has_images && !ingest_images(slot, n_batch))
{
LOG("failed processing images\n");
LOG_ERROR("failed processing images", {
"slot_id", slot.id,
"task_id", slot.task_id,
});
// FIXME @phymbert: to be properly tested
// early returning without changing the slot state will block the slot for ever
// no one at the moment is checking the return value
return false;
}
@ -1842,9 +2044,9 @@ struct llama_server_context
LOG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd);
llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd);
llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n);
llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd);
llama_kv_cache_seq_add(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd);
slot.n_past_se -= bd;
@ -1904,7 +2106,7 @@ struct llama_server_context
send_embedding(slot);
slot.release();
slot.i_batch = -1;
return true;
continue;
}
completion_token_output result;
@ -1917,6 +2119,7 @@ struct llama_server_context
{
slot.t_start_genereration = ggml_time_us();
slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3;
metrics.on_prompt_eval(slot);
}
llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
@ -1939,12 +2142,15 @@ struct llama_server_context
slot.release();
slot.print_timings(slot, skvinteract);
send_final_response(slot);
metrics.on_prediction(slot);
}
slot.i_batch = -1;
}
}
LOG_VERBOSE("slots updated", {});
// we are still inside llama_server_context so we can use an unqualified parameter
if (skvgraphics) {
kvgraphics(slots);
@ -2022,9 +2228,15 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
printf(" -spf FNAME, --system-prompt-file FNAME\n");
printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
printf(" -ctk TYPE, --cache-type-k TYPE\n");
printf(" KV cache data type for K (default: f16)\n");
printf(" -ctv TYPE, --cache-type-v TYPE\n");
printf(" KV cache data type for V (default: f16)\n");
printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n");
printf(" --log-format log output format: json or text (default: json)\n");
printf(" --log-disable disables logging to a file.\n");
printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n");
printf(" --metrics enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled");
printf(" -skvg, --show-graphics enable graphics displaying kvcache occupancy (default: false)\n");
printf(" -skvi, --show-interactive-graphics\n");
printf(" enable graphics displaying kvcache occupancy with user pause (default: false)\n");
@ -2161,9 +2373,9 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
break;
}
std::string value(argv[i]);
/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
/**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
else { invalid_param = true; break; }
}
else if (arg == "--rope-freq-base")
@ -2297,15 +2509,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
std::string arg_next = argv[i];
if (arg_next == "none")
{
params.split_mode = LLAMA_SPLIT_NONE;
params.split_mode = LLAMA_SPLIT_MODE_NONE;
}
else if (arg_next == "layer")
{
params.split_mode = LLAMA_SPLIT_LAYER;
params.split_mode = LLAMA_SPLIT_MODE_LAYER;
}
else if (arg_next == "row")
{
params.split_mode = LLAMA_SPLIT_ROW;
params.split_mode = LLAMA_SPLIT_MODE_ROW;
}
else {
invalid_param = true;
@ -2475,6 +2687,12 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
);
llama.process_system_prompt_data(json::parse(systm_content));
}
else if (arg == "-ctk" || arg == "--cache-type-k") {
params.cache_type_k = argv[++i];
}
else if (arg == "-ctv" || arg == "--cache-type-v") {
params.cache_type_v = argv[++i];
}
else if(arg == "--mmproj")
{
if (++i >= argc)
@ -2484,6 +2702,27 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
params.mmproj = argv[i];
}
else if (arg == "--log-format")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
if (std::strcmp(argv[i], "json") == 0)
{
server_log_json = true;
}
else if (std::strcmp(argv[i], "text") == 0)
{
server_log_json = false;
}
else
{
invalid_param = true;
break;
}
}
else if (arg == "--log-disable")
{
log_set_target(stdout);
@ -2493,6 +2732,10 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
{
sparams.slots_endpoint = false;
}
else if (arg == "--metrics")
{
sparams.metrics_endpoint = true;
}
else if (arg == "--chat-template")
{
if (++i >= argc)
@ -2526,15 +2769,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_INT;
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_FLOAT;
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_BOOL;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.bool_value = true;
} else if (std::strcmp(sep, "false") == 0) {
@ -2593,31 +2836,39 @@ static json format_partial_response(
static json format_tokenizer_response(const std::vector<llama_token> &tokens)
{
return json{
{"tokens", tokens}};
return json {
{"tokens", tokens}
};
}
static json format_detokenized_response(std::string content)
{
return json{
{"content", content}};
return json {
{"content", content}
};
}
static void log_server_request(const httplib::Request &req, const httplib::Response &res)
{
// skip GH copilot requests when using default port
if (req.path == "/v1/health" || req.path == "/v1/completions")
{
return;
}
LOG_INFO("request", {
{"remote_addr", req.remote_addr},
{"remote_port", req.remote_port},
{"status", res.status},
{"method", req.method},
{"path", req.path},
{"params", req.params},
});
{"remote_addr", req.remote_addr},
{"remote_port", req.remote_port},
{"status", res.status},
{"method", req.method},
{"path", req.path},
{"params", req.params},
});
LOG_VERBOSE("request", {
{"request", req.body},
{"response", res.body},
});
{"request", req.body},
{"response", res.body},
});
}
struct token_translator
@ -2697,34 +2948,38 @@ int main(int argc, char **argv)
server_state current_state = state.load();
switch(current_state) {
case SERVER_STATE_READY: {
int available_slots = 0;
int processing_slots = 0;
for (llama_client_slot &slot: llama.slots) {
if (slot.available()) {
available_slots++;
} else {
processing_slots++;
}
// request slots data using task queue
task_server task;
task.id = llama.queue_tasks.get_new_id();
task.type = TASK_TYPE_METRICS;
task.target_id = -1;
llama.queue_results.add_waiting_task_id(task.id);
llama.queue_tasks.post(task);
// get the result
task_result result = llama.queue_results.recv(task.id);
llama.queue_results.remove_waiting_task_id(task.id);
int n_idle_slots = result.result_json["idle"];
int n_processing_slots = result.result_json["processing"];
json health = {
{"status", "ok"},
{"slots_idle", n_idle_slots},
{"slots_processing", n_processing_slots}};
res.status = 200; // HTTP OK
if (sparams.slots_endpoint && req.has_param("include_slots")) {
health["slots"] = result.result_json["slots"];
}
if (available_slots > 0) {
json health = {
{"status", "ok"},
{"slots_idle", available_slots},
{"slots_processing", processing_slots}};
res.set_content(health.dump(), "application/json");
res.status = 200; // HTTP OK
} else {
json health = {
{"status", "no slot available"},
{"slots_idle", available_slots},
{"slots_processing", processing_slots}};
res.set_content(health.dump(), "application/json");
if (n_idle_slots == 0) {
health["status"] = "no slot available";
if (req.has_param("fail_on_no_slot")) {
res.status = 503; // HTTP Service Unavailable
} else {
res.status = 200; // HTTP OK
}
}
res.set_content(health.dump(), "application/json");
break;
}
case SERVER_STATE_LOADING_MODEL:
@ -2740,26 +2995,101 @@ int main(int argc, char **argv)
if (sparams.slots_endpoint) {
svr.Get("/slots", [&](const httplib::Request&, httplib::Response& res) {
json slots;
for (llama_client_slot & slot : llama.slots) {
json slot_data = llama.get_formatted_generation(slot);
slot_data["id"] = slot.id;
slot_data["task_id"] = slot.task_id;
slot_data["state"] = slot.state;
slot_data["prompt"] = slot.prompt;
slot_data["next_token"] = {
{"has_next_token", slot.has_next_token},
{"n_remain", slot.n_remaining},
{"num_tokens_predicted", slot.n_decoded},
{"stopped_eos", slot.stopped_eos},
{"stopped_word", slot.stopped_word},
{"stopped_limit", slot.stopped_limit},
{"stopping_word", slot.stopping_word},
};
// request slots data using task queue
task_server task;
task.id = llama.queue_tasks.get_new_id();
task.type = TASK_TYPE_METRICS;
task.target_id = -1;
slots.push_back(slot_data);
llama.queue_results.add_waiting_task_id(task.id);
llama.queue_tasks.post(task);
// get the result
task_result result = llama.queue_results.recv(task.id);
llama.queue_results.remove_waiting_task_id(task.id);
res.set_content(result.result_json["slots"].dump(), "application/json");
res.status = 200; // HTTP OK
});
}
if (sparams.metrics_endpoint) {
svr.Get("/metrics", [&](const httplib::Request&, httplib::Response& res) {
// request slots data using task queue
task_server task;
task.id = llama.queue_tasks.get_new_id();
task.type = TASK_TYPE_METRICS;
task.target_id = -1;
llama.queue_results.add_waiting_task_id(task.id);
llama.queue_tasks.post(task);
// get the result
task_result result = llama.queue_results.recv(task.id);
llama.queue_results.remove_waiting_task_id(task.id);
json data = result.result_json;
uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"];
uint64_t t_prompt_processing = data["t_prompt_processing"];
uint64_t n_tokens_predicted = data["n_tokens_predicted"];
uint64_t t_tokens_generation = data["t_tokens_generation"];
int32_t kv_cache_used_cells = data["kv_cache_used_cells"];
// metrics definition: https://prometheus.io/docs/practices/naming/#metric-names
json all_metrics_def = json {
{"counter", {{
{"name", "prompt_tokens_total"},
{"help", "Number of prompt tokens processed."},
{"value", data["n_prompt_tokens_processed_total"]}
}, {
{"name", "tokens_predicted_total"},
{"help", "Number of generation tokens processed."},
{"value", data["n_tokens_predicted_total"]}
}}},
{"gauge", {{
{"name", "prompt_tokens_seconds"},
{"help", "Average prompt throughput in tokens/s."},
{"value", n_prompt_tokens_processed ? 1e3 / t_prompt_processing * n_prompt_tokens_processed : 0}
},{
{"name", "predicted_tokens_seconds"},
{"help", "Average generation throughput in tokens/s."},
{"value", n_tokens_predicted ? 1e3 / t_tokens_generation * n_tokens_predicted : 0}
},{
{"name", "kv_cache_usage_ratio"},
{"help", "KV-cache usage. 1 means 100 percent usage."},
{"value", 1. * kv_cache_used_cells / params.n_ctx}
},{
{"name", "kv_cache_tokens"},
{"help", "KV-cache tokens."},
{"value", data["kv_cache_tokens_count"]}
},{
{"name", "requests_processing"},
{"help", "Number of request processing."},
{"value", data["processing"]}
},{
{"name", "requests_deferred"},
{"help", "Number of request deferred."},
{"value", data["deferred"]}
}}}
};
std::stringstream prometheus;
for (const auto& el : all_metrics_def.items()) {
const auto& type = el.key();
const auto& metrics_def = el.value();
for (const auto& metric_def : metrics_def) {
std::string name = metric_def["name"];
std::string help = metric_def["help"];
prometheus << "# HELP llamacpp:" << name << " " << help << "\n"
<< "# TYPE llamacpp:" << name << " " << type << "\n"
<< "llamacpp:" << name << " " << metric_def["value"] << "\n";
}
}
res.set_content(slots.dump(), "application/json");
res.set_content(prometheus.str(), "text/plain; version=0.0.4");
res.status = 200; // HTTP OK
});
}
@ -2816,9 +3146,6 @@ int main(int argc, char **argv)
// Set the base directory for serving static files
svr.set_base_dir(sparams.public_path);
// to make it ctrl+clickable:
LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
std::unordered_map<std::string, std::string> log_data;
log_data["hostname"] = sparams.hostname;
log_data["port"] = std::to_string(sparams.port);
@ -2832,20 +3159,6 @@ int main(int argc, char **argv)
LOG_TEE("Loaded api key #%d: %s\n", i, sparams.api_keys[i].c_str());
}
LOG_INFO("HTTP server listening", log_data);
// run the HTTP server in a thread - see comment below
// is there a limit on the number of threads the HTTP can handle?
std::thread t([&]()
{
if (!svr.listen_after_bind())
{
state.store(SERVER_STATE_ERROR);
return 1;
}
return 0;
}); // end of thread t
// load the model
if (!llama.load_model(params))
{
@ -2857,6 +3170,11 @@ int main(int argc, char **argv)
LOG_INFO("model loaded", {});
}
if (sparams.chat_template.empty()) { // custom chat template is not supplied
// check if the template comes with the model is supported by us
llama.validate_model_chat_template(sparams);
}
// Middleware for API key validation
auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool {
// If API key is not set, because the file is empty, skip validation
@ -3327,6 +3645,19 @@ int main(int argc, char **argv)
}*/
//);
LOG_INFO("HTTP server listening", log_data);
// run the HTTP server in a thread - see comment below
std::thread t([&]()
{
if (!svr.listen_after_bind())
{
state.store(SERVER_STATE_ERROR);
return 1;
}
return 0;
});
llama.queue_tasks.on_new_task(std::bind(
&llama_server_context::process_single_task, &llama, std::placeholders::_1));
llama.queue_tasks.on_finish_multitask(std::bind(

47
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@ -0,0 +1,47 @@
# Server tests
Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development) and [behave](https://behave.readthedocs.io/en/latest/):
* [issues.feature](./features/issues.feature) Pending issues scenario
* [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests
* [security.feature](./features/security.feature) Security, CORS and API Key
* [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc...
Tests target GitHub workflows job runners with 4 vCPU.
Requests are using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html) based http client.
Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. To mitigate it, you can increase values in `n_predict`, `kv_size`.
### Install dependencies
`pip install -r requirements.txt`
### Run tests
1. Build the server
```shell
cd ../../..
mkdir build
cd build
cmake ../
cmake --build . --target server
```
2. download required models:
1. `../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf`
3. Start the test: `./tests.sh`
It's possible to override some scenario steps values with environment variables:
- `PORT` -> `context.server_port` to set the listening port of the server during scenario, default: `8080`
- `LLAMA_SERVER_BIN_PATH` -> to change the server binary path, default: `../../../build/bin/server`
- `DEBUG` -> "ON" to enable steps and server verbose mode `--verbose`
- `SERVER_LOG_FORMAT_JSON` -> if set switch server logs to json format
### Run @bug, @wip or @wrong_usage annotated scenario
Feature or Scenario must be annotated with `@llama.cpp` to be included in the default scope.
- `@bug` annotation aims to link a scenario with a GitHub issue.
- `@wrong_usage` are meant to show user issue that are actually an expected behavior
- `@wip` to focus on a scenario working in progress
To run a scenario annotated with `@bug`, start:
`DEBUG=ON ./tests.sh --no-skipped --tags bug`
After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated.

69
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@ -0,0 +1,69 @@
import os
import socket
import subprocess
import time
from contextlib import closing
from signal import SIGKILL
def before_scenario(context, scenario):
print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m")
port = 8080
if 'PORT' in os.environ:
port = int(os.environ['PORT'])
if is_server_listening("localhost", port):
assert False, "Server already started"
def after_scenario(context, scenario):
if context.server_process is None:
return
if scenario.status == "failed":
if 'GITHUB_ACTIONS' in os.environ:
print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n\n")
if os.path.isfile('llama.log'):
with closing(open('llama.log', 'r')) as f:
for line in f:
print(line)
if not is_server_listening(context.server_fqdn, context.server_port):
print("\x1b[33;101mERROR: Server stopped listening\x1b[0m")
if not pid_exists(context.server_process.pid):
assert False, f"Server not running pid={context.server_process.pid} ..."
print(f"stopping server pid={context.server_process.pid} ...")
context.server_process.kill()
# Wait few for socket to free up
time.sleep(0.05)
attempts = 0
while is_server_listening(context.server_fqdn, context.server_port):
print(f"stopping server pid={context.server_process.pid} ...")
os.kill(context.server_process.pid, SIGKILL)
time.sleep(0.1)
attempts += 1
if attempts > 5:
print(f"Server dangling exits, killing all {context.server_path} ...")
process = subprocess.run(['killall', '-9', context.server_path],
stderr=subprocess.PIPE,
universal_newlines=True)
print(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))
return result == 0
def pid_exists(pid):
"""Check whether pid exists in the current process table."""
import errno
if pid < 0:
return False
try:
os.kill(pid, 0)
except OSError as e:
return e.errno == errno.EPERM
else:
return True

4
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@ -0,0 +1,4 @@
# List of ongoing issues
@bug
Feature: Issues
# No confirmed issue at the moment

123
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@ -0,0 +1,123 @@
@llama.cpp
Feature: Parallel
Background: Server startup
Given a server listening on localhost:8080
And a model file stories260K.gguf
And a model alias tinyllama-2
And 42 as server seed
And 64 KV cache size
And 2 slots
And embeddings extraction
And continuous batching
Then the server is starting
Then the server is healthy
Scenario Outline: Multi users completion
Given a prompt:
"""
Write a very long story about AI.
"""
And a prompt:
"""
Write another very long music lyrics.
"""
And <n_predict> max tokens to predict
Given concurrent completion requests
Then the server is busy
Then the server is idle
And all slots are idle
Then all prompts are predicted with <n_predict> tokens
Examples:
| n_predict |
| 128 |
Scenario Outline: Multi users OAI completions compatibility
Given a system prompt You are a writer.
And a model tinyllama-2
Given a prompt:
"""
Write a very long book.
"""
And a prompt:
"""
Write another a poem.
"""
And <n_predict> max tokens to predict
And streaming is <streaming>
Given concurrent OAI completions requests
Then the server is busy
Then the server is idle
Then all prompts are predicted with <n_predict> tokens
Examples:
| streaming | n_predict |
| disabled | 128 |
| enabled | 64 |
Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969
Given a prompt:
"""
Write a very long story about AI.
"""
And a prompt:
"""
Write another very long music lyrics.
"""
And a prompt:
"""
Write a very long poem.
"""
And a prompt:
"""
Write a very long joke.
"""
And 128 max tokens to predict
Given concurrent completion requests
Then the server is busy
Then the server is idle
Then all prompts are predicted
Scenario: Multi users embeddings
Given a prompt:
"""
Write a very long story about AI.
"""
And a prompt:
"""
Write another very long music lyrics.
"""
And a prompt:
"""
Write a very long poem.
"""
And a prompt:
"""
Write a very long joke.
"""
Given concurrent embedding requests
Then the server is busy
Then the server is idle
Then all embeddings are generated
Scenario: Multi users OAI compatibility embeddings
Given a prompt:
"""
In which country Paris is located ?
"""
And a prompt:
"""
Is Madrid the capital of Spain ?
"""
And a prompt:
"""
What is the biggest US city ?
"""
And a prompt:
"""
What is the capital of Bulgaria ?
"""
And a model tinyllama-2
Given concurrent OAI embedding requests
Then the server is busy
Then the server is idle
Then all embeddings are generated

50
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@ -0,0 +1,50 @@
@llama.cpp
Feature: Security
Background: Server startup with an api key defined
Given a server listening on localhost:8080
And a model file stories260K.gguf
And a server api key llama.cpp
Then the server is starting
Then the server is healthy
Scenario Outline: Completion with some user api key
Given a prompt test
And a user api key <api_key>
And 4 max tokens to predict
And a completion request with <api_error> api error
Examples: Prompts
| api_key | api_error |
| llama.cpp | no |
| llama.cpp | no |
| hackeme | raised |
| | raised |
Scenario Outline: OAI Compatibility
Given a system prompt test
And a user prompt test
And a model test
And 2 max tokens to predict
And streaming is disabled
And a user api key <api_key>
Given an OAI compatible chat completions request with <api_error> api error
Examples: Prompts
| api_key | api_error |
| llama.cpp | no |
| llama.cpp | no |
| hackme | raised |
Scenario Outline: CORS Options
When an OPTIONS request is sent from <origin>
Then CORS header <cors_header> is set to <cors_header_value>
Examples: Headers
| origin | cors_header | cors_header_value |
| localhost | Access-Control-Allow-Origin | localhost |
| web.mydomain.fr | Access-Control-Allow-Origin | web.mydomain.fr |
| origin | Access-Control-Allow-Credentials | true |
| web.mydomain.fr | Access-Control-Allow-Methods | POST |
| web.mydomain.fr | Access-Control-Allow-Headers | * |

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@ -0,0 +1,84 @@
@llama.cpp
Feature: llama.cpp server
Background: Server startup
Given a server listening on localhost:8080
And a model file stories260K.gguf
And a model alias tinyllama-2
And 42 as server seed
# KV Cache corresponds to the total amount of tokens
# that can be stored across all independent sequences: #4130
# see --ctx-size and #5568
And 32 KV cache size
And 1 slots
And embeddings extraction
And 32 server max tokens to predict
And prometheus compatible metrics exposed
Then the server is starting
Then the server is healthy
Scenario: Health
Then the server is ready
And all slots are idle
Scenario Outline: Completion
Given a prompt <prompt>
And <n_predict> max tokens to predict
And a completion request with no api error
Then <n_predicted> tokens are predicted matching <re_content>
And prometheus metrics are exposed
Examples: Prompts
| prompt | n_predict | re_content | n_predicted |
| I believe the meaning of life is | 8 | (read<or>going)+ | 8 |
| Write a joke about AI | 64 | (park<or>friends<or>scared<or>always)+ | 32 |
Scenario Outline: OAI Compatibility
Given a model <model>
And a system prompt <system_prompt>
And a user prompt <user_prompt>
And <max_tokens> max tokens to predict
And streaming is <enable_streaming>
Given an OAI compatible chat completions request with no api error
Then <n_predicted> tokens are predicted matching <re_content>
Examples: Prompts
| model | system_prompt | user_prompt | max_tokens | re_content | n_predicted | enable_streaming |
| llama-2 | Book | What is the best book | 8 | (Mom<or>what)+ | 8 | disabled |
| codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64 | (thanks<or>happy<or>bird)+ | 32 | enabled |
Scenario: Embedding
When embeddings are computed for:
"""
What is the capital of Bulgaria ?
"""
Then embeddings are generated
Scenario: OAI Embeddings compatibility
Given a model tinyllama-2
When an OAI compatible embeddings computation request for:
"""
What is the capital of Spain ?
"""
Then embeddings are generated
Scenario: OAI Embeddings compatibility with multiple inputs
Given a model tinyllama-2
Given a prompt:
"""
In which country Paris is located ?
"""
And a prompt:
"""
Is Madrid the capital of Spain ?
"""
When an OAI compatible embeddings computation request for multiple inputs
Then embeddings are generated
Scenario: Tokenize / Detokenize
When tokenizing:
"""
What is the capital of France ?
"""
Then tokens can be detokenize

803
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@ -0,0 +1,803 @@
import asyncio
import collections
import json
import os
import re
import socket
import subprocess
import time
from contextlib import closing
from re import RegexFlag
import aiohttp
import openai
from behave import step
from behave.api.async_step import async_run_until_complete
from prometheus_client import parser
@step(u"a server listening on {server_fqdn}:{server_port}")
def step_server_config(context, server_fqdn, server_port):
context.server_fqdn = server_fqdn
context.server_port = int(server_port)
if 'PORT' in os.environ:
context.server_port = int(os.environ['PORT'])
print(f"$PORT set, overriding server port with to {context.server_port}")
context.base_url = f'http://{context.server_fqdn}:{context.server_port}'
context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON'
context.model_alias = None
context.n_ctx = None
context.n_predict = None
context.n_server_predict = None
context.n_slots = None
context.server_api_key = None
context.server_continuous_batching = False
context.server_embeddings = False
context.server_metrics = False
context.server_process = None
context.server_seed = None
context.user_api_key = None
context.tasks_result = []
context.concurrent_tasks = []
context.prompts = []
@step(u'a model file {model_file}')
def step_model_file(context, model_file):
context.model_file = model_file
@step(u'a model alias {model_alias}')
def step_model_alias(context, model_alias):
context.model_alias = model_alias
@step(u'{seed} as server seed')
def step_seed(context, seed):
context.server_seed = int(seed)
@step(u'{n_ctx} KV cache size')
def step_n_ctx(context, n_ctx):
context.n_ctx = int(n_ctx)
@step(u'{n_slots} slots')
def step_n_slots(context, n_slots):
context.n_slots = int(n_slots)
@step(u'{n_predict} server max tokens to predict')
def step_server_n_predict(context, n_predict):
context.n_server_predict = int(n_predict)
@step(u'continuous batching')
def step_server_continuous_batching(context):
context.server_continuous_batching = True
@step(u'embeddings extraction')
def step_server_embeddings(context):
context.server_embeddings = True
@step(u'prometheus compatible metrics exposed')
def step_server_metrics(context):
context.server_metrics = True
@step(u"the server is starting")
def step_start_server(context):
start_server_background(context)
attempts = 0
while True:
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
result = sock.connect_ex((context.server_fqdn, context.server_port))
if result == 0:
print("\x1b[33;46mserver started!\x1b[0m")
return
attempts += 1
if attempts > 20:
assert False, "server not started"
print(f"waiting for server to start, connect error code = {result}...")
time.sleep(0.1)
@step(u"the server is {expecting_status}")
@async_run_until_complete
async def step_wait_for_the_server_to_be_started(context, expecting_status):
match expecting_status:
case 'healthy':
await wait_for_health_status(context, context.base_url, 200, 'ok')
case 'ready' | 'idle':
await wait_for_health_status(context, context.base_url, 200, 'ok',
params={'fail_on_no_slot': 0, 'include_slots': 0},
slots_idle=context.n_slots,
slots_processing=0,
expected_slots=[{'id': slot_id, 'state': 0}
for slot_id in range(context.n_slots)])
case 'busy':
await wait_for_health_status(context, context.base_url, 503,
'no slot available',
params={'fail_on_no_slot': 0, 'include_slots': 0},
slots_idle=0,
slots_processing=context.n_slots,
expected_slots=[{'id': slot_id, 'state': 1}
for slot_id in range(context.n_slots)])
case _:
assert False, "unknown status"
@step(u'all slots are {expected_slot_status_string}')
@async_run_until_complete
async def step_all_slots_status(context, expected_slot_status_string):
match expected_slot_status_string:
case 'idle':
expected_slot_status = 0
case 'busy':
expected_slot_status = 1
case _:
assert False, "unknown status"
expected_slots = [{'id': slot_id, 'state': expected_slot_status}
for slot_id in range(context.n_slots)]
await request_slots_status(context, expected_slots)
@step(u'a completion request with {api_error} api error')
@async_run_until_complete
async def step_request_completion(context, api_error):
expect_api_error = api_error == 'raised'
completion = await request_completion(context.prompts.pop(),
context.base_url,
debug=context.debug,
n_predict=context.n_predict,
server_seed=context.server_seed,
expect_api_error=expect_api_error,
user_api_key=context.user_api_key)
context.tasks_result.append(completion)
if context.debug:
print(f"Completion response: {completion}")
if expect_api_error:
assert completion == 401, f"completion must be an 401 status code: {completion}"
@step(u'{predicted_n} tokens are predicted matching {re_content}')
def step_n_tokens_predicted_with_content(context, predicted_n, re_content):
assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n), re_content)
@step(u'{predicted_n} tokens are predicted')
def step_n_tokens_predicted(context, predicted_n):
assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n))
@step(u'a user prompt {user_prompt}')
def step_user_prompt(context, user_prompt):
context.prompts.append(user_prompt)
@step(u'a system prompt {system_prompt}')
def step_system_prompt(context, system_prompt):
context.system_prompt = system_prompt
@step(u'a model {model}')
def step_model(context, model):
context.model = model
@step(u'{max_tokens} max tokens to predict')
def step_max_tokens(context, max_tokens):
context.n_predict = int(max_tokens)
@step(u'streaming is {enable_streaming}')
def step_streaming(context, enable_streaming):
context.enable_streaming = enable_streaming == 'enabled'
@step(u'a user api key {user_api_key}')
def step_user_api_key(context, user_api_key):
context.user_api_key = user_api_key
@step(u'no user api key')
def step_no_user_api_key(context):
context.user_api_key = None
@step(u'a user api key ')
def step_no_user_api_key_space(context):
context.user_api_key = None
@step(u'a server api key {server_api_key}')
def step_server_api_key(context, server_api_key):
context.server_api_key = server_api_key
@step(u'an OAI compatible chat completions request with {api_error} api error')
@async_run_until_complete
async def step_oai_chat_completions(context, api_error):
if context.debug:
print(f"Submitting OAI compatible completions request...")
expect_api_error = api_error == 'raised'
completion = await oai_chat_completions(context.prompts.pop(),
context.system_prompt,
context.base_url,
False,
model=context.model if hasattr(context, 'model') else None,
n_predict=context.n_predict
if hasattr(context, 'n_predict') else None,
enable_streaming=context.enable_streaming
if hasattr(context, 'enable_streaming') else None,
server_seed=context.server_seed
if hasattr(context, 'server_seed') else None,
user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None,
expect_api_error=expect_api_error)
context.tasks_result.append(completion)
if context.debug:
print(f"Completion response: {completion}")
if expect_api_error:
assert completion == 401, f"completion must be an 401 status code: {completion}"
if context.debug:
print(f"Completion response: {completion}")
@step(u'a prompt')
def step_a_prompt(context):
context.prompts.append(context.text)
@step(u'a prompt {prompt}')
def step_a_prompt_prompt(context, prompt):
context.prompts.append(prompt)
@step(u'concurrent completion requests')
@async_run_until_complete()
async def step_concurrent_completion_requests(context):
await concurrent_requests(context,
request_completion,
# prompt is inserted automatically
context.base_url,
debug=context.debug,
n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
server_seed=context.server_seed if hasattr(context, 'server_seed') else None,
user_api_key=context.user_api_key if hasattr(context,
'user_api_key') else None)
@step(u'concurrent OAI completions requests')
@async_run_until_complete
async def step_oai_chat_completions(context):
await concurrent_requests(context, oai_chat_completions,
# user_prompt is inserted automatically
context.system_prompt,
context.base_url,
True, # async_client
model=context.model
if hasattr(context, 'model') else None,
n_predict=context.n_predict
if hasattr(context, 'n_predict') else None,
enable_streaming=context.enable_streaming
if hasattr(context, 'enable_streaming') else None,
server_seed=context.server_seed
if hasattr(context, 'server_seed') else None,
user_api_key=context.user_api_key
if hasattr(context, 'user_api_key') else None)
@step(u'all prompts are predicted')
@async_run_until_complete
async def step_all_prompts_are_predicted(context):
await all_prompts_are_predicted(context)
@step(u'all prompts are predicted with {n_predict} tokens')
@async_run_until_complete
async def step_all_prompts_are_predicted_with_n_tokens(context, n_predict):
expected_predicted_n = int(n_predict)
await all_prompts_are_predicted(context, expected_predicted_n)
async def all_prompts_are_predicted(context, expected_predicted_n=None):
n_completions = await gather_tasks_results(context)
assert n_completions > 0
for i in range(n_completions):
assert_n_tokens_predicted(context.tasks_result.pop(), expected_predicted_n=expected_predicted_n)
assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests"
@step(u'embeddings are computed for')
@async_run_until_complete
async def step_compute_embedding(context):
context.embeddings = await request_embedding(context.text, base_url=context.base_url)
@step(u'embeddings are generated')
def step_assert_embeddings(context):
if len(context.prompts) == 0:
assert_embeddings(context.embeddings)
else:
assert len(context.embeddings) == len(context.prompts), (f"unexpected response:\n"
f"context.prompts={context.prompts}\n"
f"context.embeddings={context.embeddings}")
for embedding in context.embeddings:
context.prompts.pop()
assert_embeddings(embedding)
@step(u'an OAI compatible embeddings computation request for')
@async_run_until_complete
async def step_oai_compute_embeddings(context):
context.embeddings = await request_oai_embeddings(context.text,
base_url=context.base_url,
user_api_key=context.user_api_key,
model=context.model)
@step(u'an OAI compatible embeddings computation request for multiple inputs')
@async_run_until_complete
async def step_oai_compute_embeddings_multiple_inputs(context):
context.embeddings = await request_oai_embeddings(context.prompts,
base_url=context.base_url,
user_api_key=context.user_api_key,
model=context.model)
@step(u'concurrent embedding requests')
@async_run_until_complete()
async def step_concurrent_embedding_requests(context):
await concurrent_requests(context,
request_embedding,
# prompt is inserted automatically
base_url=context.base_url)
@step(u'concurrent OAI embedding requests')
@async_run_until_complete()
async def step_concurrent_oai_embedding_requests(context):
await concurrent_requests(context,
request_oai_embeddings,
# prompt is inserted automatically
base_url=context.base_url,
async_client=True,
model=context.model)
@step(u'all embeddings are generated')
@async_run_until_complete()
async def all_embeddings_are_generated(context):
n_embedding_requests = await gather_tasks_results(context)
assert n_embedding_requests > 0
for i in range(n_embedding_requests):
assert_embeddings(context.tasks_result.pop())
@step(u'tokenizing')
@async_run_until_complete
async def step_tokenize(context):
context.tokenized_text = context.text
async with aiohttp.ClientSession() as session:
async with session.post(f'{context.base_url}/tokenize',
json={
"content": context.tokenized_text,
}) as response:
assert response.status == 200
tokenize_json = await response.json()
context.tokens = tokenize_json['tokens']
@step(u'tokens can be detokenize')
@async_run_until_complete
async def step_detokenize(context):
assert len(context.tokens) > 0
async with aiohttp.ClientSession() as session:
async with session.post(f'{context.base_url}/detokenize',
json={
"tokens": context.tokens,
}) as response:
assert response.status == 200
detokenize_json = await response.json()
# SPM tokenizer adds a whitespace prefix: https://github.com/google/sentencepiece/issues/15
assert context.tokenized_text == detokenize_json['content'].strip()
@step(u'an OPTIONS request is sent from {origin}')
@async_run_until_complete
async def step_options_request(context, origin):
async with aiohttp.ClientSession() as session:
async with session.options(f'{context.base_url}/v1/chat/completions',
headers={"Origin": origin}) as response:
assert response.status == 200
context.options_response = response
@step(u'CORS header {cors_header} is set to {cors_header_value}')
def step_check_options_header_value(context, cors_header, cors_header_value):
assert context.options_response.headers[cors_header] == cors_header_value
@step(u'prometheus metrics are exposed')
@async_run_until_complete
async def step_prometheus_metrics_exported(context):
async with aiohttp.ClientSession() as session:
async with await session.get(f'{context.base_url}/metrics') as metrics_response:
assert metrics_response.status == 200
assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4"
metrics_raw = await metrics_response.text()
metric_exported = False
for metric in parser.text_string_to_metric_families(metrics_raw):
match metric.name:
case "llamacpp:kv_cache_usage_ratio":
assert len(metric.samples) > 0
metric_exported = True
assert metric_exported, "No metrics exported"
async def concurrent_requests(context, f_completion, *args, **kwargs):
n_prompts = len(context.prompts)
if context.debug:
print(f"starting {n_prompts} concurrent completion requests...")
assert n_prompts > 0
for prompt_no in range(n_prompts):
shifted_args = [context.prompts.pop(), *args]
context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
await asyncio.sleep(0.1)
async def request_completion(prompt,
base_url,
debug=False,
n_predict=None,
server_seed=None,
expect_api_error=None,
user_api_key=None):
if debug:
print(f"Sending completion request: {prompt}")
origin = "my.super.domain"
headers = {
'Origin': origin
}
if user_api_key is not None:
if debug:
print(f"Set user_api_key: {user_api_key}")
headers['Authorization'] = f'Bearer {user_api_key}'
async with aiohttp.ClientSession() as session:
async with session.post(f'{base_url}/completion',
json={
"prompt": prompt,
"n_predict": int(n_predict) if n_predict is not None else -1,
"seed": server_seed if server_seed is not None else 42
},
headers=headers) as response:
if expect_api_error is None or not expect_api_error:
assert response.status == 200
assert response.headers['Access-Control-Allow-Origin'] == origin
return await response.json()
else:
return response.status
async def oai_chat_completions(user_prompt,
system_prompt,
base_url,
async_client,
debug=False,
model=None,
n_predict=None,
enable_streaming=None,
server_seed=None,
user_api_key=None,
expect_api_error=None):
if debug:
print(f"Sending OAI Chat completions request: {user_prompt}")
# openai client always expects an api key
user_api_key = user_api_key if user_api_key is not None else 'nope'
seed = server_seed if server_seed is not None else 42
enable_streaming = enable_streaming if enable_streaming is not None else False
payload = {
"messages": [
{
"role": "system",
"content": system_prompt,
},
{
"role": "user",
"content": user_prompt,
}
],
"model": model,
"max_tokens": n_predict,
"stream": enable_streaming,
"seed": seed
}
completion_response = {
'content': '',
'timings': {
'predicted_n': 0
}
}
if async_client:
origin = 'llama.cpp'
headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
async with aiohttp.ClientSession() as session:
async with session.post(f'{base_url}/v1/chat/completions',
json=payload,
headers=headers) as response:
if enable_streaming:
assert response.status == 200
assert response.headers['Access-Control-Allow-Origin'] == origin
assert response.headers['Content-Type'] == "text/event-stream"
event_received = True
while event_received:
event_received = False
async for line_in_bytes in response.content:
line = line_in_bytes.decode('utf8')
line = line.rstrip('\n').rstrip('\r')
if line == '':
continue
event_data = line.split(': ', 1)
assert event_data[0] == 'data', f'Bad event code received: ```{event_data}```'
chunk_raw = event_data[1]
chunk = json.loads(chunk_raw)
assert len(chunk['choices']) == 1, f"no choices provided, line ```{line}```"
delta = chunk['choices'][0]['delta']
if 'content' in delta:
completion_response['content'] += delta['content']
completion_response['timings']['predicted_n'] += 1
else:
if expect_api_error is None or not expect_api_error:
assert response.status == 200
assert response.headers['Access-Control-Allow-Origin'] == origin
assert response.headers['Content-Type'] == "application/json; charset=utf-8"
chat_completion_raw = await response.json()
completion_response = {
'content': chat_completion_raw['choices'][0]['message'],
'timings': {
'predicted_n': chat_completion_raw['usage']['completion_tokens']
}
}
else:
return response.status
else:
try:
openai.api_key = user_api_key
openai.api_base = f'{base_url}/v1/chat'
chat_completion = openai.Completion.create(
messages=payload['messages'],
model=model,
max_tokens=n_predict,
stream=enable_streaming,
seed=seed
)
except openai.error.APIError as e:
if expect_api_error is not None and expect_api_error:
return 401
else:
assert False, f'error raised: {e}'
if enable_streaming:
for chunk in chat_completion:
assert len(chunk.choices) == 1
delta = chunk.choices[0].delta
if 'content' in delta:
completion_response['content'] += delta['content']
completion_response['timings']['predicted_n'] += 1
else:
assert len(chat_completion.choices) == 1
completion_response = {
'content': chat_completion.choices[0].message.content,
'timings': {
'predicted_n': chat_completion.usage.completion_tokens
}
}
if debug:
print("OAI response formatted to llama.cpp:", completion_response)
return completion_response
async def request_embedding(content, base_url=None):
async with aiohttp.ClientSession() as session:
async with session.post(f'{base_url}/embedding',
json={
"content": content,
}) as response:
assert response.status == 200
response_json = await response.json()
return response_json['embedding']
async def request_oai_embeddings(input,
base_url=None, user_api_key=None,
model=None, async_client=False):
# openai client always expects an api_key
user_api_key = user_api_key if user_api_key is not None else 'nope'
if async_client:
origin = 'llama.cpp'
if user_api_key is not None:
headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
async with aiohttp.ClientSession() as session:
async with session.post(f'{base_url}/v1/embeddings',
json={
"input": input,
"model": model,
},
headers=headers) as response:
assert response.status == 200, f"received status code not expected: {response.status}"
assert response.headers['Access-Control-Allow-Origin'] == origin
assert response.headers['Content-Type'] == "application/json; charset=utf-8"
response_json = await response.json()
assert response_json['model'] == model, f"invalid model received: {response_json['model']}"
assert response_json['object'] == 'list'
return response_json['data']
else:
openai.api_key = user_api_key
openai.api_base = f'{base_url}/v1'
oai_embeddings = openai.Embedding.create(
model=model,
input=input,
)
if isinstance(input, collections.abc.Sequence):
embeddings = []
for an_oai_embeddings in oai_embeddings.data:
embeddings.append(an_oai_embeddings.embedding)
else:
embeddings = oai_embeddings.data.embedding
return embeddings
def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re_content=None):
content = completion_response['content']
n_predicted = completion_response['timings']['predicted_n']
assert len(content) > 0, "no token predicted"
if expected_predicted_n is not None:
assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:'
f' {n_predicted} <> {expected_predicted_n}')
if re_content is not None:
re_content = '^.*' + re_content.replace('<or>', '|') + '.*$'
assert re.match(re_content, content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL), (
f'invalid tokens predicted:'
f' ```\n{content}\n``` do not match /{re_content}/')
async def gather_tasks_results(context):
n_tasks = len(context.concurrent_tasks)
if context.debug:
print(f"Waiting for all {n_tasks} tasks results...")
for task_no in range(n_tasks):
context.tasks_result.append(await context.concurrent_tasks.pop())
n_completions = len(context.tasks_result)
return n_completions
async def wait_for_health_status(context,
base_url,
expected_http_status_code,
expected_health_status,
params=None,
slots_idle=None,
slots_processing=None,
expected_slots=None):
if context.debug:
print(f"Starting checking for health for expected_health_status={expected_health_status}")
timeout = 3 # seconds
if expected_health_status == 'ok':
timeout = 10 # CI slow inference
interval = 0.5
counter = 0
async with aiohttp.ClientSession() as session:
while True:
async with await session.get(f'{base_url}/health', params=params) as health_response:
status_code = health_response.status
health = await health_response.json()
if context.debug:
print(f"HEALTH - response for expected health status='{expected_health_status}' on "
f"'{base_url}/health'?{params} is {health}")
if (status_code == expected_http_status_code
and health['status'] == expected_health_status
and (slots_idle is None or health['slots_idle'] == slots_idle)
and (slots_processing is None or health['slots_processing'] == slots_processing)):
if expected_slots is not None:
assert_slots_status(health['slots'], expected_slots)
return
if (status_code == expected_http_status_code
and health['status'] == expected_health_status
and (slots_idle is None or health['slots_idle'] == slots_idle)
and (slots_processing is None or health['slots_processing'] == slots_processing)):
if expected_slots is not None:
assert_slots_status(health['slots'], expected_slots)
return
await asyncio.sleep(interval)
counter += interval
if counter >= timeout:
# Sometimes health requests are triggered after completions are predicted
if expected_http_status_code == 503:
if len(context.tasks_result) == 0:
print("\x1b[5;37;43mWARNING: forcing concurrent tasks,"
" busy health check missed, probably too fast inference\x1b[0m")
n_completions = await gather_tasks_results(context)
if n_completions > 0:
return
assert False, f'{expected_health_status} timeout exceeded {counter}s>={timeout}'
def assert_embeddings(embeddings):
assert len(embeddings) > 0
embeddings_computed = False
for emb in embeddings:
if emb != 0:
embeddings_computed = True
assert embeddings_computed, f"Embeddings: {embeddings}"
async def request_slots_status(context, expected_slots):
async with aiohttp.ClientSession() as session:
async with await session.get(f'{context.base_url}/slots') as slots_response:
assert slots_response.status == 200
slots = await slots_response.json()
assert_slots_status(slots, expected_slots)
def assert_slots_status(slots, expected_slots):
assert len(slots) == len(expected_slots)
for slot_id, (expected, slot) in enumerate(zip(expected_slots, slots)):
for key in expected:
assert expected[key] == slot[key], (f"invalid slot {slot_id}"
f" expected[{key}] != slot[{key}]"
f" = {expected[key]} != {slot[key]}")
def start_server_background(context):
context.server_path = '../../../build/bin/server'
if 'LLAMA_SERVER_BIN_PATH' in os.environ:
context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
server_args = [
'--host', context.server_fqdn,
'--port', context.server_port,
'--model', context.model_file
]
if context.server_continuous_batching:
server_args.append('--cont-batching')
if context.server_embeddings:
server_args.append('--embedding')
if context.server_metrics:
server_args.append('--metrics')
if context.model_alias is not None:
server_args.extend(['--alias', context.model_alias])
if context.n_ctx is not None:
server_args.extend(['--ctx-size', context.n_ctx])
if context.n_slots is not None:
server_args.extend(['--parallel', context.n_slots])
if context.n_server_predict is not None:
server_args.extend(['--n-predict', context.n_server_predict])
if context.server_api_key is not None:
server_args.extend(['--api-key', context.server_api_key])
if context.debug:
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)
context.server_process = subprocess.Popen(
[str(arg) for arg in [context.server_path, *server_args]],
close_fds=True)
print(f"server pid={context.server_process.pid}")

21
examples/server/tests/features/wrong_usages.feature Normal file
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@ -0,0 +1,21 @@
# run with ./test.sh --tags wrong_usage
@wrong_usage
Feature: Wrong usage of llama.cpp server
#3969 The user must always set --n-predict option
# to cap the number of tokens any completion request can generate
# or pass n_predict/max_tokens in the request.
Scenario: Infinite loop
Given a server listening on localhost:8080
And a model file stories260K.gguf
# Uncomment below to fix the issue
#And 64 server max tokens to predict
Then the server is starting
Given a prompt:
"""
Go to: infinite loop
"""
# Uncomment below to fix the issue
#And 128 max tokens to predict
Given concurrent completion requests
Then all prompts are predicted

4
examples/server/tests/requirements.txt Normal file
View file

@ -0,0 +1,4 @@
aiohttp~=3.9.3
behave~=1.2.6
openai~=0.25.0
prometheus-client~=0.20.0

12
examples/server/tests/tests.sh Executable file
View file

@ -0,0 +1,12 @@
#!/bin/bash
set -eu
if [ $# -lt 1 ]
then
# Start @llama.cpp scenario
behave --summary --stop --no-capture --exclude 'issues|wrong_usages' --tags llama.cpp
else
behave "$@"
fi

88
examples/server/utils.hpp
View file

@ -14,6 +14,7 @@
using json = nlohmann::json;
extern bool server_verbose;
extern bool server_log_json;
#ifndef SERVER_VERBOSE
#define SERVER_VERBOSE 1
@ -27,15 +28,14 @@ extern bool server_verbose;
{ \
if (server_verbose) \
{ \
server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \
server_log("VERB", __func__, __LINE__, MSG, __VA_ARGS__); \
} \
} while (0)
#endif
#define LOG_ERROR( MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_ERROR( MSG, ...) server_log("ERR", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
//
// parallel
@ -50,7 +50,8 @@ enum server_state {
enum task_type {
TASK_TYPE_COMPLETION,
TASK_TYPE_CANCEL,
TASK_TYPE_NEXT_RESPONSE
TASK_TYPE_NEXT_RESPONSE,
TASK_TYPE_METRICS
};
struct task_server {
@ -133,26 +134,48 @@ struct completion_token_output
std::string text_to_send;
};
static inline void server_log(const char *level, const char *function, int line,
const char *message, const nlohmann::ordered_json &extra)
static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra)
{
nlohmann::ordered_json log
{
std::stringstream ss_tid;
ss_tid << std::this_thread::get_id();
json log = nlohmann::ordered_json{
{"tid", ss_tid.str()},
{"timestamp", time(nullptr)},
{"level", level},
{"function", function},
{"line", line},
{"message", message},
};
if (!extra.empty())
{
log.merge_patch(extra);
}
if (server_log_json) {
log.merge_patch(
{
{"level", level},
{"function", function},
{"line", line},
{"msg", message},
});
if (!extra.empty()) {
log.merge_patch(extra);
}
const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace);
LOG("%.*s\n", (int)str.size(), str.data());
fflush(stdout);
std::cout << log.dump(-1, ' ', false, json::error_handler_t::replace) << "\n" << std::flush;
} else {
char buf[1024];
snprintf(buf, 1024, "%4s [%24s] %s", level, function, message);
if (!extra.empty()) {
log.merge_patch(extra);
}
std::stringstream ss;
ss << buf << " |";
for (const auto& el : log.items())
{
const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace);
snprintf(buf, 1024, " %s=%s", el.key().c_str(), value.c_str());
ss << buf;
}
const std::string str = ss.str();
printf("%.*s\n", (int)str.size(), str.data());
fflush(stdout);
}
}
//
@ -233,7 +256,8 @@ struct llama_server_queue {
int post(task_server task) {
std::unique_lock<std::mutex> lock(mutex_tasks);
if (task.id == -1) {
task.id = id; // originally id++ but this repeats get_new_id below
task.id = id++;
LOG_VERBOSE("new task id", {{"new_id", task.id}});
}
queue_tasks.push_back(std::move(task));
//LOG("Queue now has %2zu members.\n", queue_tasks.size());
@ -251,8 +275,9 @@ struct llama_server_queue {
// Get the next id for creating a new task
int get_new_id() {
std::unique_lock<std::mutex> lock(mutex_tasks);
LOG("New task id returned with value %d.\n", id);
return id++;
int new_id = id++;
LOG_VERBOSE("new task id", {{"new_id", new_id}});
return new_id;
}
// Register function to process a new task
@ -297,8 +322,7 @@ struct llama_server_queue {
running = true;
//LOG("In start_loop have new task number %d.\n", id);
while (true) {
// new task arrived
// LOG("In start_loop have new task number %d.\n", id);
LOG_VERBOSE("new task may arrive", {});
{
while (true)
{
@ -310,6 +334,7 @@ struct llama_server_queue {
task_server task = queue_tasks.front();
queue_tasks.erase(queue_tasks.begin());
lock.unlock();
LOG_VERBOSE("callback_new_task", {{"task_id", task.id}});
callback_new_task(task);
}
// process and update all the multitasks
@ -389,12 +414,14 @@ struct llama_server_response {
std::condition_variable condition_results;
void add_waiting_task_id(int task_id) {
LOG_VERBOSE("waiting for task id", {{"task_id", task_id}});
std::unique_lock<std::mutex> lock(mutex_results);
waiting_task_ids.insert(task_id);
LOG("Waiting task list size after addition: %2zu.\n", waiting_task_ids.size());
}
void remove_waiting_task_id(int task_id) {
LOG_VERBOSE("remove waiting for task id", {{"task_id", task_id}});
std::unique_lock<std::mutex> lock(mutex_results);
waiting_task_ids.erase(task_id);
LOG("Waiting task list size after removal: %zu.\n", waiting_task_ids.size());
@ -408,7 +435,6 @@ struct llama_server_response {
condition_results.wait(lock, [&]{
return !queue_results.empty();
});
LOG_VERBOSE("condition_results unblock", {});
for (int i = 0; i < (int) queue_results.size(); i++)
{
@ -433,22 +459,22 @@ struct llama_server_response {
// Send a new result to a waiting task_id
void send(task_result result) {
std::unique_lock<std::mutex> lock(mutex_results);
LOG_VERBOSE("send new result", {});
LOG_VERBOSE("send new result", {{"task_id", result.id}});
for (auto& task_id : waiting_task_ids) {
// LOG("waiting task id %i \n", task_id);
// for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
if (result.multitask_id == task_id)
{
LOG_VERBOSE("callback_update_multitask", {});
LOG_VERBOSE("callback_update_multitask", {{"task_id", task_id}});
callback_update_multitask(task_id, result.id, result);
continue;
}
if (result.id == task_id)
{
LOG_VERBOSE("queue_results.push_back", {});
LOG_VERBOSE("queue_results.push_back", {{"task_id", task_id}});
queue_results.push_back(result);
condition_results.notify_one();
condition_results.notify_all();
return;
}
}

2
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

@ -960,7 +960,7 @@ int main(int argc, char ** argv) {
struct ggml_opt_context * opt = train->opt;
// set opt params from command line
opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
opt->params.print_forward_graph = false;
opt->params.print_backward_graph = false;
opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;

6
flake.lock generated
View file

@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1708118438,
"narHash": "sha256-kk9/0nuVgA220FcqH/D2xaN6uGyHp/zoxPNUmPCMmEE=",
"lastModified": 1708655239,
"narHash": "sha256-ZrP/yACUvDB+zbqYJsln4iwotbH6CTZiTkANJ0AgDv4=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "5863c27340ba4de8f83e7e3c023b9599c3cb3c80",
"rev": "cbc4211f0afffe6dfd2478a62615dd5175a13f9a",
"type": "github"
},
"original": {

318
ggml-cuda.cu
View file

@ -1,3 +1,7 @@
#include "ggml-cuda.h"
#include "ggml.h"
#include "ggml-backend-impl.h"
#include <algorithm>
#include <assert.h>
#include <atomic>
@ -121,11 +125,6 @@
#endif // defined(GGML_USE_HIPBLAS)
// ggml-cuda need half type so keep ggml headers include at last
#include "ggml-cuda.h"
#include "ggml.h"
#include "ggml-backend-impl.h"
#define CUDART_HMAX 11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed)
#define CC_PASCAL 600
@ -173,6 +172,7 @@
#endif
typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
@ -197,6 +197,18 @@ static __device__ __forceinline__ int __vsub4(const int a, const int b) {
return __vsubss4(a, b);
}
static __device__ __forceinline__ unsigned int __vcmpeq4(unsigned int a, unsigned int b) {
const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
unsigned int c;
uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
#pragma unroll
for (int i = 0; i < 4; ++i) {
vc[i] = va[i] == vb[i] ? 0xff : 0x00;
}
return c;
}
static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
c = __builtin_amdgcn_sdot4(a, b, c, false);
@ -519,6 +531,17 @@ typedef struct {
} block_iq3_xxs;
static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
#define QR3_XS 8
#define QI3_XS (QK_K / (4*QR3_XS))
typedef struct {
half d;
uint8_t qs[QK_K/4];
uint8_t qh[QK_K/32];
uint8_t signs[QK_K/8];
uint8_t scales[QK_K/64];
} block_iq3_s;
static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 27*(QK_K/64), "wrong iq3_s block size/padding");
#define QR1_S 8
#define QI1_S (QK_K / (4*QR1_S))
typedef struct {
@ -1701,6 +1724,74 @@ static const __device__ uint32_t iq3xxs_grid[256] = {
0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
};
static const __device__ uint32_t iq3xs_grid[512] = {
0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14,
0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414,
0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24,
0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c,
0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c,
0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34,
0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c,
0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414,
0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c,
0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404,
0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434,
0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c,
0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404,
0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414,
0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414,
0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404,
0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c,
0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c,
0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404,
0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e,
0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14,
0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c,
0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424,
0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c,
0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c,
0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e,
0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e,
0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e,
0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424,
0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e,
0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424,
0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404,
0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c,
0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e,
0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c,
0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c,
0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c,
0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404,
0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04,
0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c,
0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414,
0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c,
0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c,
0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424,
0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c,
0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c,
0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414,
0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c,
0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e,
0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04,
0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424,
0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14,
0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34,
0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c,
0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434,
0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c,
0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424,
0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24,
0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24,
0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e,
0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c,
0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c,
0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c,
0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404,
};
static const __device__ uint64_t iq1s_grid[512] = {
0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000,
0xffffffff01ff00ff, 0xffffffff01ff0001, 0xffffffff0101ffff, 0xffffffff0101ff01,
@ -1974,6 +2065,32 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds
}
template<typename dst_t>
static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
const int i = blockIdx.x;
const block_iq3_s * x = (const block_iq3_s *) 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 uint8_t * qs = x[i].qs + 8*ib;
const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256)));
const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)) * 0.5f;
const uint8_t signs = x[i].signs[4*ib + il];
for (int j = 0; j < 4; ++j) {
y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
}
#else
assert(false);
#endif
}
template<typename dst_t>
static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
@ -4718,6 +4835,41 @@ static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1(
#endif
}
// TODO: don't use lookup table for signs
static __device__ __forceinline__ float vec_dot_iq3_s_q8_1(
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
#if QK_K == 256
const block_iq3_s * bq2 = (const block_iq3_s *) vbq;
const int ib32 = iqs;
const uint8_t * qs = bq2->qs + 8*ib32;
const int8_t * q8 = bq8_1[ib32].qs;
int sumi = 0;
for (int l = 0; l < 4; ++l) {
const uint32_t * grid1 = iq3xs_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256));
const uint32_t * grid2 = iq3xs_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256));
uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >> 4) * 0x01010101) & 0x08040201, 0x08040201);
const int grid_l = __vsub4(grid1[0] ^ signs0, signs0);
const int grid_h = __vsub4(grid2[0] ^ signs1, signs1);
sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
q8 += 8;
}
const float d = (float)bq2->d * (0.5f + ((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds) * 0.5f;
return d * sumi;
#else
assert(false);
return 0.f;
#endif
#else
assert(false);
return 0.f;
#endif
}
static __device__ __forceinline__ float vec_dot_iq1_s_q8_1(
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
#if QK_K == 256
@ -6217,11 +6369,11 @@ static __global__ void k_argsort_f32_i32(const float * x, int * dst, const int n
int ixj = col ^ j;
if (ixj > col) {
if ((col & k) == 0) {
if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
swap(dst_row[col], dst_row[ixj]);
}
} else {
if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
swap(dst_row[col], dst_row[ixj]);
}
}
@ -6850,6 +7002,12 @@ static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k,
dequantize_block_iq3_xxs<<<nb, 32, 0, stream>>>(vx, y);
}
template<typename dst_t>
static void dequantize_row_iq3_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
const int nb = k / QK_K;
dequantize_block_iq3_s<<<nb, 32, 0, stream>>>(vx, y);
}
template<typename dst_t>
static void dequantize_row_iq1_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
const int nb = k / QK_K;
@ -6905,6 +7063,8 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
return dequantize_row_iq1_s_cuda;
case GGML_TYPE_IQ4_NL:
return dequantize_row_iq4_nl_cuda;
case GGML_TYPE_IQ3_S:
return dequantize_row_iq3_s_cuda;
case GGML_TYPE_F32:
return convert_unary_cuda<float>;
default:
@ -6944,6 +7104,8 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
return dequantize_row_iq1_s_cuda;
case GGML_TYPE_IQ4_NL:
return dequantize_row_iq4_nl_cuda;
case GGML_TYPE_IQ3_S:
return dequantize_row_iq3_s_cuda;
case GGML_TYPE_F16:
return convert_unary_cuda<half>;
default:
@ -7765,10 +7927,10 @@ static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, co
const dim3 block_dims(ncols, 1, 1);
const dim3 block_nums(1, nrows, 1);
if (order == GGML_SORT_ASC) {
k_argsort_f32_i32<GGML_SORT_ASC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
} else if (order == GGML_SORT_DESC) {
k_argsort_f32_i32<GGML_SORT_DESC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
if (order == GGML_SORT_ORDER_ASC) {
k_argsort_f32_i32<GGML_SORT_ORDER_ASC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
} else if (order == GGML_SORT_ORDER_DESC) {
k_argsort_f32_i32<GGML_SORT_ORDER_DESC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
} else {
GGML_ASSERT(false);
}
@ -8200,11 +8362,11 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
cudaMemcpyKind kind;
char * src_ptr;
if (src->backend == GGML_BACKEND_CPU) {
if (src->backend == GGML_BACKEND_TYPE_CPU) {
kind = cudaMemcpyHostToDevice;
src_ptr = (char *) src->data;
} else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) {
GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
} else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
kind = cudaMemcpyDeviceToDevice;
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
int id;
@ -8609,7 +8771,7 @@ static void ggml_cuda_op_mul_mat_q(
// the main device has a larger memory buffer to hold the results from all GPUs
// nrows_dst == nrows of the matrix that the kernel writes into
const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff;
switch (src0->type) {
case GGML_TYPE_Q4_0:
@ -8689,6 +8851,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUD
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ3_S:
return max_compute_capability >= CC_RDNA2 ? 128 : 64;
default:
GGML_ASSERT(false);
@ -8714,6 +8877,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUD
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ3_S:
return max_compute_capability >= CC_VOLTA ? 128 : 64;
case GGML_TYPE_Q6_K:
return 64;
@ -8756,7 +8920,7 @@ static void ggml_cuda_op_mul_mat_vec_q(
// the main device has a larger memory buffer to hold the results from all GPUs
// nrows_dst == nrows of the matrix that the kernel writes into
const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff;
switch (src0->type) {
case GGML_TYPE_Q4_0:
@ -8819,6 +8983,10 @@ static void ggml_cuda_op_mul_mat_vec_q(
mul_mat_vec_q_cuda<QK4_NL, QI4_NL, block_iq4_nl, VDR_Q4_0_Q8_1_MMVQ, vec_dot_iq4_nl_q8_1>
(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_IQ3_S:
mul_mat_vec_q_cuda<QK_K, QI3_XS, block_iq3_s, 1, vec_dot_iq3_s_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
break;
default:
GGML_ASSERT(false);
break;
@ -8928,7 +9096,7 @@ static void ggml_cuda_op_mul_mat_cublas(
// the main device has a larger memory buffer to hold the results from all GPUs
// ldc == nrows of the matrix that cuBLAS writes into
int ldc = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff;
const int compute_capability = g_device_caps[id].cc;
@ -9276,7 +9444,7 @@ static void ggml_cuda_op_soft_max(
const bool use_src2 = src2 != nullptr;
if (use_src2) {
const bool src2_on_device = src2->backend == GGML_BACKEND_GPU;
const bool src2_on_device = src2->backend == GGML_BACKEND_TYPE_GPU;
if (src2_on_device) {
ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra;
@ -9334,16 +9502,16 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s
const bool use_src1 = src1 != nullptr;
const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT( dst->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT( dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU;
const bool dst_on_device = dst->backend == GGML_BACKEND_GPU;
const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU;
const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU;
// dd = data device
float * src0_ddf = nullptr;
@ -9387,7 +9555,7 @@ static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * s
CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream));
}
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
CUDA_CHECK(cudaDeviceSynchronize());
}
}
@ -9468,8 +9636,8 @@ static void ggml_cuda_op_mul_mat(
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1));
GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
@ -9485,20 +9653,20 @@ static void ggml_cuda_op_mul_mat(
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
const bool src0_is_contiguous = ggml_is_contiguous(src0);
const bool src1_is_contiguous = ggml_is_contiguous(src1);
const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
GGML_ASSERT(!(split && ne02 > 1));
GGML_ASSERT(!(split && ne03 > 1));
GGML_ASSERT(!(split && ne02 < ne12));
std::array<float, GGML_CUDA_MAX_DEVICES> tensor_split;
if (split) {
// TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_GPU_SPLIT check
// TODO: check that src0->buffer->buft is a split buffer type, replace GGML_BACKEND_TYPE_GPU_SPLIT check
// GGML_ASSERT(src0->buffer != nullptr && src0->buffer->buft == ...);
ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context;
tensor_split = buft_ctx->tensor_split;
@ -9556,8 +9724,8 @@ static void ggml_cuda_op_mul_mat(
used_devices++;
const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device;
const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device;
const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device;
ggml_cuda_set_device(id);
cudaStream_t stream = g_cudaStreams[id][0];
@ -9608,8 +9776,8 @@ static void ggml_cuda_op_mul_mat(
continue;
}
const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device;
const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device;
const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device;
const int64_t row_diff = dev[id].row_high - dev[id].row_low;
ggml_cuda_set_device(id);
@ -9634,12 +9802,12 @@ static void ggml_cuda_op_mul_mat(
// the main device memory buffer can be on VRAM scratch, with space for all partial results
// in that case an offset on dst_ddf_i is needed
if (dst->backend == GGML_BACKEND_GPU && id == g_main_device) {
if (dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device) {
dst_dd_i += dev[id].row_low; // offset is 0 if no tensor split
}
// copy src0, src1 to device if necessary
if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) {
if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) {
if (id != g_main_device) {
if (convert_src1_to_q8_1) {
char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset;
@ -9652,14 +9820,14 @@ static void ggml_cuda_op_mul_mat(
src1_ncols*ne10*sizeof(float), stream));
}
}
} else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) {
} else if (src1->backend == GGML_BACKEND_TYPE_CPU || (src1_on_device && !src1_is_contiguous)) {
CUDA_CHECK(ggml_cuda_cpy_tensor_2d(
src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
} else {
GGML_ASSERT(false);
}
if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) {
if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_TYPE_CPU || !src1_is_contiguous)) {
quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
CUDA_CHECK(cudaGetLastError());
}
@ -9677,10 +9845,10 @@ static void ggml_cuda_op_mul_mat(
if (!dst_on_device) {
void * dst_off_device;
cudaMemcpyKind kind;
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
dst_off_device = dst->data;
kind = cudaMemcpyDeviceToHost;
} else if (dst->backend == GGML_BACKEND_GPU) {
} else if (dst->backend == GGML_BACKEND_TYPE_GPU) {
dst_off_device = dst_extra->data_device[g_main_device];
kind = cudaMemcpyDeviceToDevice;
} else {
@ -9745,7 +9913,7 @@ static void ggml_cuda_op_mul_mat(
}
}
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
ggml_cuda_set_device(g_main_device);
CUDA_CHECK(cudaDeviceSynchronize());
}
@ -9851,7 +10019,7 @@ GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const stru
static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
GGML_ASSERT(src0->type == GGML_TYPE_F16);
@ -9882,7 +10050,7 @@ static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor
GGML_ASSERT(!ggml_is_transposed(src0));
GGML_ASSERT(!ggml_is_transposed(src1));
GGML_ASSERT(!ggml_is_permuted(src0));
GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src0->type == GGML_TYPE_F16);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
@ -9941,7 +10109,7 @@ static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggm
GGML_ASSERT(!ggml_is_transposed(src0));
GGML_ASSERT(!ggml_is_transposed(src1));
GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src0->type == GGML_TYPE_F16);
GGML_TENSOR_BINARY_OP_LOCALS
@ -10087,11 +10255,11 @@ static void ggml_cuda_mul_mat_batched_cublas(const ggml_tensor * src0, const ggm
static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const bool all_on_device =
(src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
(src1->backend == GGML_BACKEND_GPU) &&
( dst->backend == GGML_BACKEND_GPU);
(src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) &&
(src1->backend == GGML_BACKEND_TYPE_GPU) &&
( dst->backend == GGML_BACKEND_TYPE_GPU);
const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
int64_t min_compute_capability = INT_MAX;
@ -10241,7 +10409,7 @@ static void ggml_cuda_mul_mat_id_cublas(ggml_tensor * dst) {
GGML_ASSERT(!ggml_is_transposed(src00));
GGML_ASSERT(!ggml_is_transposed(src1));
GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
const int64_t ne00 = src00->ne[0]; GGML_UNUSED(ne00);
@ -10385,7 +10553,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
cudaStream_t stream = g_cudaStreams[g_main_device][0];
if (ids->backend == GGML_BACKEND_GPU) {
if (ids->backend == GGML_BACKEND_TYPE_GPU) {
const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device];
CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream));
CUDA_CHECK(cudaStreamSynchronize(stream));
@ -10402,20 +10570,20 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
ggml_tensor src1_row = *src1;
ggml_tensor dst_row = *dst;
src1_row.backend = GGML_BACKEND_GPU;
dst_row.backend = GGML_BACKEND_GPU;
src1_row.backend = GGML_BACKEND_TYPE_GPU;
dst_row.backend = GGML_BACKEND_TYPE_GPU;
src1_row.extra = &src1_row_extra;
dst_row.extra = &dst_row_extra;
char * src1_original = src1->backend == GGML_BACKEND_CPU ?
char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ?
(char *) src1->data : (char *) src1_extra->data_device[g_main_device];
char * dst_original = dst->backend == GGML_BACKEND_CPU ?
char * dst_original = dst->backend == GGML_BACKEND_TYPE_CPU ?
(char *) dst->data : (char *) dst_extra->data_device[g_main_device];
if (src1->ne[1] == 1) {
GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
GGML_ASSERT(dst->backend == GGML_BACKEND_GPU);
GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU);
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
//int32_t row_id;
@ -10443,9 +10611,9 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
src1_row_extra.data_device[g_main_device] = src1_contiguous.get();
dst_row_extra.data_device[g_main_device] = dst_contiguous.get();
const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_CPU ?
const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_TYPE_CPU ?
cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice;
const cudaMemcpyKind dst_kind = dst->backend == GGML_BACKEND_CPU ?
const cudaMemcpyKind dst_kind = dst->backend == GGML_BACKEND_TYPE_CPU ?
cudaMemcpyDeviceToHost : cudaMemcpyDeviceToDevice;
for (int32_t row_id = 0; row_id < n_as; ++row_id) {
@ -10500,7 +10668,7 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s
}
}
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
CUDA_CHECK(cudaStreamSynchronize(stream));
}
}
@ -10517,8 +10685,8 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg
const int64_t ne = ggml_nelements(src0);
GGML_ASSERT(ne == ggml_nelements(src1));
GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
@ -10649,9 +10817,9 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st
if (!g_cublas_loaded) return false;
ggml_cuda_func_t func;
const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
|| (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
|| (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU
|| (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
|| (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU);
if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) {
return false;
@ -10798,14 +10966,14 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st
return false;
}
if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) {
if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
ggml_cuda_set_peer_access(tensor->src[1]->ne[1]);
}
if (params->ith != 0) {
return true;
}
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return true;
}
func(tensor->src[0], tensor->src[1], tensor);
@ -10904,7 +11072,7 @@ GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t
extra->data_device[ctx->device] = tensor->data;
tensor->backend = GGML_BACKEND_GPU;
tensor->backend = GGML_BACKEND_TYPE_GPU;
tensor->extra = extra;
if (ggml_is_quantized(tensor->type)) {
@ -10919,7 +11087,7 @@ GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t
}
GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
@ -10930,7 +11098,7 @@ GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t
}
GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
@ -11165,7 +11333,7 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_bu
CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming));
}
}
tensor->backend = GGML_BACKEND_GPU_SPLIT;
tensor->backend = GGML_BACKEND_TYPE_GPU_SPLIT;
tensor->extra = extra;
}
@ -11437,7 +11605,7 @@ GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend,
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0]));
}
@ -11446,7 +11614,7 @@ GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend,
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0]));
}
@ -11476,7 +11644,7 @@ GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, gg
ggml_cuda_set_main_device(cuda_ctx->device);
ggml_compute_params params = {};
params.type = GGML_TASK_COMPUTE;
params.type = GGML_TASK_TYPE_COMPUTE;
params.ith = 0;
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
@ -11486,13 +11654,13 @@ GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, gg
}
#ifndef NDEBUG
assert(node->backend == GGML_BACKEND_GPU || node->backend == GGML_BACKEND_GPU_SPLIT);
assert(node->backend == GGML_BACKEND_TYPE_GPU || node->backend == GGML_BACKEND_TYPE_GPU_SPLIT);
assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
assert(node->extra != nullptr);
for (int j = 0; j < GGML_MAX_SRC; j++) {
if (node->src[j] != nullptr) {
assert(node->src[j]->backend == GGML_BACKEND_GPU || node->src[j]->backend == GGML_BACKEND_GPU_SPLIT);
assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU || node->src[j]->backend == GGML_BACKEND_TYPE_GPU_SPLIT);
assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer));
assert(node->src[j]->extra != nullptr);
}
@ -11542,7 +11710,7 @@ 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_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S) {
if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
return false;
}

27
ggml-impl.h
View file

@ -53,11 +53,23 @@ extern "C" {
//
#include <arm_neon.h>
#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x))
#define GGML_COMPUTE_FP32_TO_FP16(x) (x)
#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
#define GGML_FP16_TO_FP32(x) ((float) (x))
#define GGML_FP32_TO_FP16(x) (x)
#define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
__fp16 tmp;
memcpy(&tmp, &h, sizeof(ggml_fp16_t));
return (float)tmp;
}
static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
ggml_fp16_t res;
__fp16 tmp = f;
memcpy(&res, &tmp, sizeof(ggml_fp16_t));
return res;
}
#else
@ -214,8 +226,7 @@ extern float ggml_table_f32_f16[1 << 16];
// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
// This is also true for POWER9.
#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16)
#if !defined(GGML_FP16_TO_FP32)
inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
uint16_t s;
memcpy(&s, &f, sizeof(uint16_t));
@ -223,8 +234,10 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
}
#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x)
#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
#endif
#if !defined(GGML_FP32_TO_FP16)
#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
#endif
#define GGML_HASHTABLE_FULL ((size_t)-1)

37
ggml-metal.m
View file

@ -61,6 +61,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,
GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,
@ -85,6 +86,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,
@ -105,6 +107,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,
@ -122,6 +125,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,
@ -139,6 +143,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32,
GGML_METAL_KERNEL_TYPE_ROPE_F32,
@ -452,6 +457,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true);
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_IQ1_S, get_rows_iq1_s, 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_I32, get_rows_i32, true);
@ -476,6 +482,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction);
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_IQ1_S_F32, mul_mv_iq1_s_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_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction);
@ -496,6 +503,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction);
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_IQ1_S_F32, mul_mv_id_iq1_s_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_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm);
@ -513,6 +521,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm);
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_IQ1_S_F32, mul_mm_iq1_s_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_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm);
@ -530,6 +539,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm);
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_IQ1_S_F32, mul_mm_id_iq1_s_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_ROPE_F32, rope_f32, true);
@ -1347,6 +1357,7 @@ static bool ggml_metal_graph_compute(
case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break;
case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break;
case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break;
case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_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_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break;
default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
@ -1483,6 +1494,12 @@ static bool ggml_metal_graph_compute(
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline;
} break;
case GGML_TYPE_IQ3_S:
{
nth0 = 4;
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline;
} break;
case GGML_TYPE_IQ1_S:
{
nth0 = 4;
@ -1537,8 +1554,8 @@ static bool ggml_metal_graph_compute(
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_IQ3_XXS) {
const int mem_size = 256*4+128;
else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) {
const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4;
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
@ -1640,6 +1657,7 @@ static bool ggml_metal_graph_compute(
case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break;
case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break;
case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break;
case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_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_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break;
default: GGML_ASSERT(false && "MUL_MAT_ID not implemented");
@ -1779,6 +1797,12 @@ static bool ggml_metal_graph_compute(
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline;
} break;
case GGML_TYPE_IQ3_S:
{
nth0 = 4;
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline;
} break;
case GGML_TYPE_IQ1_S:
{
nth0 = 4;
@ -1849,8 +1873,8 @@ static bool ggml_metal_graph_compute(
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src2t == GGML_TYPE_IQ3_XXS) {
const int mem_size = 256*4+128;
else if (src2t == GGML_TYPE_IQ3_XXS || src2t == GGML_TYPE_IQ3_S) {
const int mem_size = src2t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4;
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
@ -1900,6 +1924,7 @@ static bool ggml_metal_graph_compute(
case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break;
case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break;
case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break;
case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S ].pipeline; break;
case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break;
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break;
case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break;
@ -2237,8 +2262,8 @@ static bool ggml_metal_graph_compute(
id<MTLComputePipelineState> pipeline = nil;
switch (order) {
case GGML_SORT_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break;
case GGML_SORT_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break;
case GGML_SORT_ORDER_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break;
case GGML_SORT_ORDER_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break;
default: GGML_ASSERT(false);
};

304
ggml-metal.metal
View file

@ -2525,6 +2525,20 @@ typedef struct {
} block_iq3_xxs;
// 98 bytes / block for QK_K = 256, so 3.0625 bpw
// 3.4375 bpw
#if QK_K == 64
#define IQ3S_N_SCALE 2
#else
#define IQ3S_N_SCALE QK_K/64
#endif
typedef struct {
half d;
uint8_t qs[QK_K/4];
uint8_t qh[QK_K/32];
uint8_t signs[QK_K/8];
uint8_t scales[IQ3S_N_SCALE];
} block_iq3_s;
typedef struct {
half d;
uint8_t qs[QK_K/8];
@ -3795,6 +3809,73 @@ constexpr constant static uint32_t iq3xxs_grid[256] = {
0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
};
constexpr constant static uint32_t iq3xs_grid[512] = {
0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14,
0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414,
0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24,
0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c,
0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c,
0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34,
0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c,
0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414,
0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c,
0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404,
0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434,
0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c,
0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404,
0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414,
0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414,
0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404,
0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c,
0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c,
0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404,
0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e,
0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14,
0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c,
0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424,
0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c,
0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c,
0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e,
0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e,
0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e,
0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424,
0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e,
0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424,
0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404,
0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c,
0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e,
0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c,
0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c,
0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c,
0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404,
0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04,
0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c,
0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414,
0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c,
0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c,
0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424,
0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c,
0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c,
0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414,
0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c,
0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e,
0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04,
0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424,
0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14,
0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34,
0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c,
0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434,
0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c,
0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424,
0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24,
0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24,
0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e,
0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c,
0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c,
0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c,
0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404,
};
#define NGRID_IQ1S 512
constexpr constant static uint64_t iq1s_grid[NGRID_IQ1S] = {
0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000,
@ -4361,6 +4442,136 @@ kernel void kernel_mul_mv_iq3_xxs_f32(
kernel_mul_mv_iq3_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
}
void kernel_mul_mv_iq3_s_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,
threadgroup int8_t * shared_values [[threadgroup(0)]],
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_iq3_s * x = (device const block_iq3_s *) 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);
threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values;
{
int nval = 8;
int pos = (32*sgitg + tiisg)*nval;
for (int i = 0; i < nval; ++i) values[pos + i] = iq3xs_grid[pos + i];
threadgroup_barrier(mem_flags::mem_threadgroup);
}
const int ix = tiisg;
device const float * y4 = y + 32 * ix;
for (int ib32 = ix; ib32 < nb32; ib32 += 32) {
for (int i = 0; i < 32; ++i) {
yl[i] = y4[i];
}
const int ibl = ib32 / (QK_K / 32);
const int ib = ib32 % (QK_K / 32);
device const block_iq3_s * xr = x + ibl;
device const uint8_t * qs = xr->qs + 8 * ib;
device const uint8_t * qh = xr->qh + ib;
device const uint8_t * sc = xr->scales + (ib/2);
device const uint8_t * signs = xr->signs + 4 * ib;
device const half * dh = &xr->d;
for (int row = 0; row < N_DST; row++) {
const float db = dh[0];
const float d = db * (0.5f + ((sc[0] >> 4*(ib%2)) & 0xf));
float2 sum = {0};
for (int l = 0; l < 4; ++l) {
const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256)));
const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256)));
for (int j = 0; j < 4; ++j) {
sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l] & kmask_iq2xs[j+0]);
sum[1] += yl[8*l + j + 4] * grid2[j] * select(1, -1, signs[l] & kmask_iq2xs[j+4]);
}
}
sumf[row] += d * (sum[0] + sum[1]);
dh += nb*sizeof(block_iq3_s)/2;
qs += nb*sizeof(block_iq3_s);
qh += nb*sizeof(block_iq3_s);
sc += nb*sizeof(block_iq3_s);
signs += nb*sizeof(block_iq3_s);
}
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 * 0.5f;
}
}
}
[[host_name("kernel_mul_mv_iq3_s_f32")]]
kernel void kernel_mul_mv_iq3_s_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,
threadgroup int8_t * shared_values [[threadgroup(0)]],
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiisg[[thread_index_in_simdgroup]],
uint sgitg[[simdgroup_index_in_threadgroup]]) {
kernel_mul_mv_iq3_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
}
void kernel_mul_mv_iq1_s_f32_impl(
device const void * src0,
device const float * src1,
@ -4952,6 +5163,31 @@ void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x
}
}
template <typename type4x4>
void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
const float d = xb->d;
const int ib32 = il/2;
il = il%2;
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
device const uint8_t * qs = xb->qs + 8*ib32;
device const uint8_t * signs = xb->signs + 4*ib32 + 2*il;
const uint8_t qh = xb->qh[ib32] >> 4*il;
const float dl = d * (0.5f + ((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * 0.5f;
constant uint8_t * grid1 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+0] | ((qh << 8) & 256)));
constant uint8_t * grid2 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+1] | ((qh << 7) & 256)));
for (int i = 0; i < 4; ++i) {
reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]);
reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]);
}
grid1 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+2] | ((qh << 6) & 256)));
grid2 = (constant uint8_t *)(iq3xs_grid + (qs[4*il+3] | ((qh << 5) & 256)));
for (int i = 0; i < 4; ++i) {
reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]);
reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]);
}
}
template <typename type4x4>
void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
@ -5525,6 +5761,7 @@ template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows
template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_rows<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_t kernel_get_rows<block_iq2_xs, QK_NL, dequantize_iq2_xs>;
template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
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_iq1_s")]] kernel get_rows_t kernel_get_rows<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows<block_iq4_nl, 2, dequantize_iq4_nl>;
@ -5566,6 +5803,7 @@ template [[host_name("kernel_mul_mm_q6_K_f32")]] kernel mat_mm_t kernel_mul_mm<b
template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_xs, QK_NL, dequantize_iq2_xs>;
template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
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_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_nl, 2, dequantize_iq4_nl>;
@ -5619,6 +5857,7 @@ template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mu
template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_xs, QK_NL, dequantize_iq2_xs>;
template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
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_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_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_nl, 2, dequantize_iq4_nl>;
@ -6589,6 +6828,71 @@ kernel void kernel_mul_mv_id_iq3_xxs_f32(
sgitg);
}
[[host_name("kernel_mul_mv_id_iq3_s_f32")]]
kernel void kernel_mul_mv_id_iq3_s_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,
threadgroup int8_t * shared_values [[threadgroup(0)]],
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_iq3_s_f32_impl(
src0[id],
(device const float *) (src1 + bid*nb11),
dst + bid*ne0,
ne00,
ne01,
ne02,
ne10,
ne12,
ne0,
ne1,
r2,
r3,
shared_values,
tgpig,
tiisg,
sgitg);
}
[[host_name("kernel_mul_mv_id_iq1_s_f32")]]
kernel void kernel_mul_mv_id_iq1_s_f32(
device const char * ids,

50
ggml-opencl.cpp
View file

@ -1354,7 +1354,7 @@ static void ggml_cl_pool_free(cl_mem mem, size_t size) {
}
void ggml_cl_free_data(const struct ggml_tensor* tensor) {
if (tensor->backend != GGML_BACKEND_GPU) {
if (tensor->backend != GGML_BACKEND_TYPE_GPU) {
return;
}
@ -1412,7 +1412,7 @@ static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t o
}
static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
@ -1476,7 +1476,7 @@ void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src
}
static void ggml_cl_add_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
@ -1566,13 +1566,13 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
size_t y_size;
size_t d_size;
cl_mem d_X;
if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT
d_X = (cl_mem) src0->extra;
} else {
d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size);
}
cl_mem d_Y = src1->backend == GGML_BACKEND_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
cl_mem d_D = dst->backend == GGML_BACKEND_GPU ? (cl_mem) dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
cl_mem d_Y = src1->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
cl_mem d_D = dst->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
size_t x_offset = 0;
@ -1580,7 +1580,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
// TODO: copy src0 here when r3>1
for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
if (src0->backend == GGML_BACKEND_GPU) {
if (src0->backend == GGML_BACKEND_TYPE_GPU) {
x_offset = (i03 * ne02 + i02) * x_ne;
} else {
// copy src0 to device
@ -1589,7 +1589,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
// copy src1 to device
if (src1->backend == GGML_BACKEND_CPU) {
if (src1->backend == GGML_BACKEND_TYPE_CPU) {
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
}
@ -1612,7 +1612,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
}
// copy dst to host
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
}
@ -1621,13 +1621,13 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
}
}
if (src0->backend != GGML_BACKEND_GPU) {
if (src0->backend != GGML_BACKEND_TYPE_GPU) {
ggml_cl_pool_free(d_X, x_size);
}
if (src1->backend != GGML_BACKEND_GPU) {
if (src1->backend != GGML_BACKEND_TYPE_GPU) {
ggml_cl_pool_free(d_Y, y_size);
}
if (dst->backend != GGML_BACKEND_GPU) {
if (dst->backend != GGML_BACKEND_TYPE_GPU) {
ggml_cl_pool_free(d_D, d_size);
}
}
@ -1670,7 +1670,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
size_t y_size;
size_t d_size;
cl_mem d_X;
if (src0->backend == GGML_BACKEND_GPU) { // NOLINT
if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT
d_X = (cl_mem) src0->extra;
} else {
d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size);
@ -1687,7 +1687,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
// TODO: copy src0 here when r3>1
for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
if (src0->backend == GGML_BACKEND_GPU) {
if (src0->backend == GGML_BACKEND_TYPE_GPU) {
x_offset = (i03 * ne02 + i02) * x_ne;
} else {
// copy src0 to device
@ -1741,7 +1741,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
}
// copy dst to host, then convert to float
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL));
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
ggml_fp16_to_fp32_row(tmp, d, d_ne);
@ -1753,7 +1753,7 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
}
}
if (src0->backend != GGML_BACKEND_GPU) {
if (src0->backend != GGML_BACKEND_TYPE_GPU) {
ggml_cl_pool_free(d_X, x_size);
}
ggml_cl_pool_free(d_Y, y_size);
@ -1798,7 +1798,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size);
cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
cl_mem d_Q;
if (src0->backend == GGML_BACKEND_CPU) {
if (src0->backend == GGML_BACKEND_TYPE_CPU) {
d_Q = ggml_cl_pool_malloc(q_sz, &q_size);
}
@ -1817,10 +1817,10 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
// copy src0 to device if necessary
if (src0->backend == GGML_BACKEND_CPU) {
if (src0->backend == GGML_BACKEND_TYPE_CPU) {
events.emplace_back();
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
} else if (src0->backend == GGML_BACKEND_GPU) {
} else if (src0->backend == GGML_BACKEND_TYPE_GPU) {
d_Q = (cl_mem) src0->extra;
} else {
GGML_ASSERT(false);
@ -1829,7 +1829,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
if (!mul_mat_vec) {
// convert src0 to fp32 on device
const size_t global = x_ne / global_denom;
const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0;
CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
@ -1843,7 +1843,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
// compute
const size_t global = ne01 * local;
const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0;
const cl_int ncols = ne00;
events.emplace_back();
CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q));
@ -1895,7 +1895,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
}
ggml_cl_pool_free(d_Y, y_size);
ggml_cl_pool_free(d_D, d_size);
if (src0->backend == GGML_BACKEND_CPU) {
if (src0->backend == GGML_BACKEND_TYPE_CPU) {
ggml_cl_pool_free(d_Q, q_size);
}
}
@ -1911,7 +1911,7 @@ bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens
if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
src1->type == GGML_TYPE_F32 &&
dst->type == GGML_TYPE_F32 &&
((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU)) {
((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU)) {
return true;
}
@ -1993,7 +1993,7 @@ void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) {
CL_CHECK(clFinish(queue));
tensor->extra = dst;
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
}
// ggml-backend
@ -2045,7 +2045,7 @@ static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer,
ctx->sub_buffers.push_back(sub_buffer);
tensor->extra = sub_buffer;
}
tensor->backend = GGML_BACKEND_GPU;
tensor->backend = GGML_BACKEND_TYPE_GPU;
}
static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {

762
ggml-quants.c
View file

@ -438,6 +438,54 @@ inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
return res;
}
// NOTE: not tested
inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) {
int8x16_t res;
res[ 0] = a[b[ 0]];
res[ 1] = a[b[ 1]];
res[ 2] = a[b[ 2]];
res[ 3] = a[b[ 3]];
res[ 4] = a[b[ 4]];
res[ 5] = a[b[ 5]];
res[ 6] = a[b[ 6]];
res[ 7] = a[b[ 7]];
res[ 8] = a[b[ 8]];
res[ 9] = a[b[ 9]];
res[10] = a[b[10]];
res[11] = a[b[11]];
res[12] = a[b[12]];
res[13] = a[b[13]];
res[14] = a[b[14]];
res[15] = a[b[15]];
return res;
}
// NOTE: not tested
inline static int8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) {
int8x16_t res;
res[ 0] = a[b[ 0]];
res[ 1] = a[b[ 1]];
res[ 2] = a[b[ 2]];
res[ 3] = a[b[ 3]];
res[ 4] = a[b[ 4]];
res[ 5] = a[b[ 5]];
res[ 6] = a[b[ 6]];
res[ 7] = a[b[ 7]];
res[ 8] = a[b[ 8]];
res[ 9] = a[b[ 9]];
res[10] = a[b[10]];
res[11] = a[b[11]];
res[12] = a[b[12]];
res[13] = a[b[13]];
res[14] = a[b[14]];
res[15] = a[b[15]];
return res;
}
#else
#define ggml_int16x8x2_t int16x8x2_t
@ -451,6 +499,8 @@ inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
#define ggml_vld1q_u8_x4 vld1q_u8_x4
#define ggml_vld1q_s8_x2 vld1q_s8_x2
#define ggml_vld1q_s8_x4 vld1q_s8_x4
#define ggml_vqtbl1q_s8 vqtbl1q_s8
#define ggml_vqtbl1q_u8 vqtbl1q_u8
#endif
@ -3480,6 +3530,73 @@ static const uint32_t iq3xxs_grid[256] = {
0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04,
};
static const uint32_t iq3xs_grid[512] = {
0x04040404, 0x0404040c, 0x04040414, 0x0404042c, 0x0404043e, 0x04040c04, 0x04040c0c, 0x04040c14,
0x04040c24, 0x04040c34, 0x04041404, 0x0404140c, 0x0404142c, 0x04041c1c, 0x04042404, 0x04042414,
0x0404242c, 0x0404243e, 0x04042c0c, 0x04042c1c, 0x04043404, 0x04043414, 0x04043e0c, 0x04043e24,
0x04043e3e, 0x040c0404, 0x040c040c, 0x040c0414, 0x040c0424, 0x040c0c04, 0x040c0c0c, 0x040c0c2c,
0x040c1404, 0x040c141c, 0x040c143e, 0x040c1c0c, 0x040c1c2c, 0x040c2424, 0x040c340c, 0x040c342c,
0x040c3e14, 0x04140404, 0x0414040c, 0x0414042c, 0x0414043e, 0x04140c04, 0x04140c1c, 0x04140c34,
0x0414140c, 0x0414142c, 0x04141c04, 0x04141c24, 0x04142414, 0x0414242c, 0x0414243e, 0x04142c0c,
0x04142c1c, 0x04143e04, 0x04143e1c, 0x041c041c, 0x041c0c0c, 0x041c0c2c, 0x041c1404, 0x041c1414,
0x041c1c0c, 0x041c1c1c, 0x041c1c34, 0x041c2424, 0x041c2c04, 0x041c2c14, 0x041c343e, 0x041c3e0c,
0x041c3e2c, 0x04240404, 0x04240c1c, 0x04240c3e, 0x0424140c, 0x04241424, 0x04241c14, 0x04242404,
0x0424241c, 0x04242c0c, 0x04243e04, 0x042c0414, 0x042c0424, 0x042c1404, 0x042c1414, 0x042c1434,
0x042c1c1c, 0x042c240c, 0x042c242c, 0x042c243e, 0x042c3434, 0x042c3e1c, 0x04340434, 0x04340c0c,
0x04340c1c, 0x04341c0c, 0x04342c14, 0x04343e0c, 0x043e0404, 0x043e0414, 0x043e0424, 0x043e1404,
0x043e1414, 0x043e1434, 0x043e1c1c, 0x043e2c04, 0x043e2c24, 0x0c040404, 0x0c04040c, 0x0c040414,
0x0c040424, 0x0c040c04, 0x0c040c0c, 0x0c040c1c, 0x0c040c2c, 0x0c040c3e, 0x0c041404, 0x0c041414,
0x0c041c0c, 0x0c041c24, 0x0c041c34, 0x0c042c24, 0x0c042c34, 0x0c04340c, 0x0c043e14, 0x0c0c0404,
0x0c0c040c, 0x0c0c041c, 0x0c0c0434, 0x0c0c0c04, 0x0c0c0c24, 0x0c0c140c, 0x0c0c1c04, 0x0c0c1c1c,
0x0c0c240c, 0x0c0c2c04, 0x0c0c2c14, 0x0c0c3e04, 0x0c0c3e34, 0x0c140404, 0x0c140c14, 0x0c140c2c,
0x0c140c3e, 0x0c141404, 0x0c141424, 0x0c141c14, 0x0c142404, 0x0c14241c, 0x0c142c2c, 0x0c143404,
0x0c143e14, 0x0c1c040c, 0x0c1c0424, 0x0c1c043e, 0x0c1c0c04, 0x0c1c0c1c, 0x0c1c140c, 0x0c1c143e,
0x0c1c1c04, 0x0c1c1c24, 0x0c1c240c, 0x0c1c3414, 0x0c1c3e04, 0x0c24041c, 0x0c24042c, 0x0c240c14,
0x0c240c24, 0x0c241c0c, 0x0c241c1c, 0x0c242414, 0x0c242434, 0x0c242c04, 0x0c242c24, 0x0c2c040c,
0x0c2c0c04, 0x0c2c0c1c, 0x0c2c140c, 0x0c2c1c04, 0x0c2c1c14, 0x0c2c2c0c, 0x0c341404, 0x0c341424,
0x0c34143e, 0x0c342424, 0x0c342434, 0x0c3e040c, 0x0c3e041c, 0x0c3e0c04, 0x0c3e0c14, 0x0c3e140c,
0x0c3e1c2c, 0x0c3e240c, 0x0c3e3414, 0x0c3e3e04, 0x14040404, 0x1404040c, 0x1404041c, 0x1404042c,
0x1404043e, 0x14040c04, 0x14040c14, 0x14040c24, 0x14040c34, 0x1404140c, 0x1404141c, 0x1404143e,
0x14041c04, 0x14041c14, 0x1404240c, 0x1404241c, 0x1404242c, 0x14042c04, 0x14042c14, 0x1404343e,
0x14043e04, 0x14043e1c, 0x14043e2c, 0x140c0404, 0x140c0414, 0x140c0c04, 0x140c0c1c, 0x140c0c3e,
0x140c1414, 0x140c142c, 0x140c1c0c, 0x140c1c24, 0x140c2414, 0x140c2c0c, 0x1414040c, 0x14140424,
0x1414043e, 0x1414140c, 0x1414141c, 0x14141c04, 0x14141c3e, 0x1414240c, 0x14142c1c, 0x14142c3e,
0x14143e0c, 0x14143e24, 0x141c0404, 0x141c0414, 0x141c042c, 0x141c0c0c, 0x141c1414, 0x141c1424,
0x141c1c0c, 0x141c1c1c, 0x141c2414, 0x141c2c04, 0x141c3434, 0x1424040c, 0x1424043e, 0x14241404,
0x1424141c, 0x14241c14, 0x14241c2c, 0x1424240c, 0x14243e14, 0x14243e2c, 0x142c0424, 0x142c0c0c,
0x142c1414, 0x142c1c3e, 0x142c2404, 0x142c2c1c, 0x142c3e04, 0x14340404, 0x14340414, 0x1434043e,
0x1434140c, 0x14342c2c, 0x1434340c, 0x143e042c, 0x143e0c0c, 0x143e1434, 0x143e1c04, 0x143e241c,
0x143e2c04, 0x1c040414, 0x1c040c0c, 0x1c040c1c, 0x1c040c2c, 0x1c040c3e, 0x1c041414, 0x1c041c0c,
0x1c041c1c, 0x1c041c2c, 0x1c042414, 0x1c042424, 0x1c04243e, 0x1c042c0c, 0x1c04341c, 0x1c043e0c,
0x1c0c040c, 0x1c0c041c, 0x1c0c042c, 0x1c0c0c24, 0x1c0c140c, 0x1c0c141c, 0x1c0c2404, 0x1c0c3404,
0x1c0c3e14, 0x1c0c3e34, 0x1c140404, 0x1c140c14, 0x1c141404, 0x1c141c14, 0x1c141c24, 0x1c142c04,
0x1c1c040c, 0x1c1c0c04, 0x1c1c0c24, 0x1c1c140c, 0x1c1c141c, 0x1c1c143e, 0x1c1c1c04, 0x1c1c240c,
0x1c1c241c, 0x1c1c243e, 0x1c1c2c2c, 0x1c1c3e1c, 0x1c24041c, 0x1c240c0c, 0x1c240c34, 0x1c241414,
0x1c241c0c, 0x1c242c14, 0x1c243404, 0x1c243424, 0x1c2c040c, 0x1c2c0c04, 0x1c2c0c14, 0x1c2c142c,
0x1c2c1c14, 0x1c2c2424, 0x1c2c2c34, 0x1c2c3e1c, 0x1c340c34, 0x1c34240c, 0x1c3e040c, 0x1c3e041c,
0x1c3e1404, 0x1c3e1414, 0x1c3e1c2c, 0x24040404, 0x24040424, 0x24040c14, 0x24041404, 0x24041424,
0x2404143e, 0x24041c14, 0x2404240c, 0x24042c04, 0x24043e04, 0x240c0414, 0x240c043e, 0x240c0c0c,
0x240c0c1c, 0x240c1414, 0x240c1c04, 0x240c1c2c, 0x240c241c, 0x240c2c0c, 0x240c2c2c, 0x2414040c,
0x2414041c, 0x24140c04, 0x24140c2c, 0x2414140c, 0x24141c1c, 0x24142404, 0x24142c3e, 0x24143414,
0x24143e04, 0x241c0424, 0x241c0c0c, 0x241c0c1c, 0x241c1404, 0x241c1414, 0x241c1c0c, 0x241c1c2c,
0x24240404, 0x24240414, 0x24241424, 0x24241c3e, 0x24242404, 0x24243e0c, 0x242c042c, 0x242c043e,
0x242c140c, 0x242c3414, 0x24340c1c, 0x24341c24, 0x24343404, 0x243e0c04, 0x243e0c2c, 0x243e1c04,
0x243e241c, 0x243e2c0c, 0x2c040414, 0x2c040c04, 0x2c040c24, 0x2c041414, 0x2c042404, 0x2c042424,
0x2c04243e, 0x2c042c14, 0x2c043434, 0x2c043e24, 0x2c0c040c, 0x2c0c041c, 0x2c0c042c, 0x2c0c0c14,
0x2c0c140c, 0x2c0c1c14, 0x2c0c3e14, 0x2c140404, 0x2c140c0c, 0x2c14141c, 0x2c141c04, 0x2c141c34,
0x2c142c1c, 0x2c1c0414, 0x2c1c043e, 0x2c1c0c04, 0x2c1c143e, 0x2c1c2424, 0x2c1c2c0c, 0x2c1c342c,
0x2c1c3e1c, 0x2c24040c, 0x2c240424, 0x2c241404, 0x2c241c14, 0x2c242434, 0x2c2c0c14, 0x2c2c1434,
0x2c2c2c0c, 0x2c2c2c1c, 0x2c342414, 0x2c3e0414, 0x2c3e0424, 0x2c3e1414, 0x34040c0c, 0x34040c1c,
0x34040c2c, 0x34041c0c, 0x34041c1c, 0x34043404, 0x340c0404, 0x340c1404, 0x340c143e, 0x340c3424,
0x34140c14, 0x34141c24, 0x34142414, 0x34142c2c, 0x34143414, 0x34143e04, 0x341c0404, 0x341c0c24,
0x341c140c, 0x341c2404, 0x3424142c, 0x3424241c, 0x34243414, 0x342c0404, 0x342c041c, 0x342c1c24,
0x342c3404, 0x3434042c, 0x34342404, 0x343e0c0c, 0x343e0c1c, 0x3e040404, 0x3e040424, 0x3e04043e,
0x3e041404, 0x3e041414, 0x3e041c34, 0x3e042404, 0x3e042c24, 0x3e043414, 0x3e0c0414, 0x3e0c0c0c,
0x3e0c1424, 0x3e0c241c, 0x3e0c242c, 0x3e14040c, 0x3e140424, 0x3e140c04, 0x3e140c34, 0x3e14140c,
0x3e141c04, 0x3e142c0c, 0x3e1c0414, 0x3e1c1c14, 0x3e1c1c2c, 0x3e1c2c1c, 0x3e24040c, 0x3e24042c,
0x3e240c1c, 0x3e241404, 0x3e242c04, 0x3e2c1414, 0x3e2c2414, 0x3e340414, 0x3e341c0c, 0x3e3e0404,
};
#define NGRID_IQ2XXS 512
static const uint64_t iq1s_grid[NGRID_IQ2XXS] = {
0xffffffffffff0101, 0xffffffffff01ff00, 0xffffffffff010100, 0xffffffff00000000,
@ -3711,6 +3828,49 @@ void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y
}
}
// ====================== 3.3125 bpw (de)-quantization
void dequantize_row_iq3_s(const block_iq3_s * restrict x, float * restrict y, int k) {
assert(k % QK_K == 0);
const int nb = k / QK_K;
for (int i = 0; i < nb; i++) {
const float d = GGML_FP16_TO_FP32(x[i].d);
const uint8_t * qs = x[i].qs;
const uint8_t * qh = x[i].qh;
const uint8_t * signs = x[i].signs;
for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
const float db1 = d * (0.5f + (x[i].scales[ib32/2] & 0xf)) * 0.5f;
const float db2 = d * (0.5f + (x[i].scales[ib32/2] >> 4)) * 0.5f;
for (int l = 0; l < 4; ++l) {
const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[0] << (8-2*l)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[0] << (7-2*l)) & 256)));
for (int j = 0; j < 4; ++j) {
y[j+0] = db1 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f);
y[j+4] = db1 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f);
}
y += 8;
}
qs += 8;
signs += 4;
for (int l = 0; l < 4; ++l) {
const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[1] << (8-2*l)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[1] << (7-2*l)) & 256)));
for (int j = 0; j < 4; ++j) {
y[j+0] = db2 * grid1[j] * (signs[l] & kmask_iq2xs[j+0] ? -1.f : 1.f);
y[j+4] = db2 * grid2[j] * (signs[l] & kmask_iq2xs[j+4] ? -1.f : 1.f);
}
y += 8;
}
qh += 2;
qs += 8;
signs += 4;
}
}
}
// ====================== 1.5625 bpw (de)-quantization
void dequantize_row_iq1_s(const block_iq1_s * restrict x, float * restrict y, int k) {
@ -5629,8 +5789,8 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
for (int i = 0; i < nb; ++i) {
const float d = y[i].d * (float)x[i].d;
const float dmin = -y[i].d * (float)x[i].dmin;
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
const uint8_t * restrict q2 = x[i].qs;
const int8_t * restrict q8 = y[i].qs;
@ -5779,8 +5939,8 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
for (int i = 0; i < nb; ++i) {
const float d = y[i].d * (float)x[i].d;
const float dmin = -y[i].d * (float)x[i].dmin;
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
const uint8_t * restrict q2 = x[i].qs;
const int8_t * restrict q8 = y[i].qs;
@ -6433,7 +6593,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]);
const float d = y[i].d * (float)x[i].d;
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const uint8x16_t htmp = vcombine_u8(hbits, vshr_n_u8(hbits, 1));
q3h.val[0] = vandq_u8(mh, vshlq_n_u8(htmp, 2));
@ -6635,7 +6795,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]);
const float d = y[i].d * (float)x[i].d;
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1);
@ -7138,9 +7298,9 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
aux16[1] = (a[0] >> 4) & 0x0f0f;
const int32_t summi = scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]);
sum_mins += y[i].d * (float)x[i].d[1] * summi;
sum_mins += y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * summi;
const float d = y[i].d * (float)x[i].d[0];
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]);
const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4);
@ -7798,7 +7958,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
for (int i = 0; i < nb; ++i) {
const float d = y[i].d * (float)x[i].d;
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const int8_t * sc = x[i].scales;
const uint8_t * restrict q5 = x[i].qs;
@ -7940,7 +8100,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
for (int i = 0; i < nb; ++i) {
const float d = y[i].d * (float)x[i].d;
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const int8_t * sc = x[i].scales;
const uint8_t * restrict q5 = x[i].qs;
@ -8508,7 +8668,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
for (int i = 0; i < nb; ++i) {
const float d_all = (float)x[i].d;
const float d_all = GGML_FP16_TO_FP32(x[i].d);
const uint8_t * restrict q6 = x[i].ql;
const uint8_t * restrict qh = x[i].qh;
@ -8679,7 +8839,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
for (int i = 0; i < nb; ++i) {
const float d_all = (float)x[i].d;
const float d_all = GGML_FP16_TO_FP32(x[i].d);
const uint8_t * restrict q6 = x[i].ql;
const uint8_t * restrict qh = x[i].qh;
@ -8781,6 +8941,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r
#endif
#if defined (__AVX2__) || defined (__ARM_NEON)
static const int8_t keven_signs_q2xs[1024] = {
1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1,
1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1,
@ -8815,6 +8976,7 @@ static const int8_t keven_signs_q2xs[1024] = {
1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1,
1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1,
};
#endif
void ggml_vec_dot_iq2_xxs_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);
@ -9302,6 +9464,202 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void
#endif
}
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) {
assert(n % QK_K == 0);
assert(nrc == 1);
UNUSED(nrc);
UNUSED(bx);
UNUSED(by);
UNUSED(bs);
const block_iq3_s * restrict x = vx;
const block_q8_K * restrict y = vy;
const int nb = n / QK_K;
#if defined(__ARM_NEON)
static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
};
static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,};
const uint8x16x2_t mask1 = vld1q_u8_x2(k_mask1);
const uint8x16_t mask2 = vld1q_u8(k_mask2);
uint8x16x2_t vs;
ggml_int8x16x4_t q3s;
ggml_int8x16x4_t q8b;
float sumf = 0;
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const uint8_t * restrict qs = x[i].qs;
const uint8_t * restrict qh = x[i].qh;
const uint16_t * restrict signs = (const uint16_t *)x[i].signs;
const int8_t * restrict q8 = y[i].qs;
int sumi1 = 0, sumi2 = 0;
for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
q8b = ggml_vld1q_s8_x4(q8); q8 += 64;
const uint32x4_t aux32x4_0 = {iq3xs_grid[qs[ 0] | ((qh[ib32+0] << 8) & 256)], iq3xs_grid[qs[ 1] | ((qh[ib32+0] << 7) & 256)],
iq3xs_grid[qs[ 2] | ((qh[ib32+0] << 6) & 256)], iq3xs_grid[qs[ 3] | ((qh[ib32+0] << 5) & 256)]};
const uint32x4_t aux32x4_1 = {iq3xs_grid[qs[ 4] | ((qh[ib32+0] << 4) & 256)], iq3xs_grid[qs[ 5] | ((qh[ib32+0] << 3) & 256)],
iq3xs_grid[qs[ 6] | ((qh[ib32+0] << 2) & 256)], iq3xs_grid[qs[ 7] | ((qh[ib32+0] << 1) & 256)]};
const uint32x4_t aux32x4_2 = {iq3xs_grid[qs[ 8] | ((qh[ib32+1] << 8) & 256)], iq3xs_grid[qs[ 9] | ((qh[ib32+1] << 7) & 256)],
iq3xs_grid[qs[10] | ((qh[ib32+1] << 6) & 256)], iq3xs_grid[qs[11] | ((qh[ib32+1] << 5) & 256)]};
const uint32x4_t aux32x4_3 = {iq3xs_grid[qs[12] | ((qh[ib32+1] << 4) & 256)], iq3xs_grid[qs[13] | ((qh[ib32+1] << 3) & 256)],
iq3xs_grid[qs[14] | ((qh[ib32+1] << 2) & 256)], iq3xs_grid[qs[15] | ((qh[ib32+1] << 1) & 256)]};
qs += 16;
vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | (signs[1] << 16)));
vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
vs.val[0] = vceqq_u8(vs.val[0], mask2);
vs.val[1] = vceqq_u8(vs.val[1], mask2);
q3s.val[0] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[0], vreinterpretq_u8_u32(aux32x4_0))), vreinterpretq_s8_u8(vs.val[0]));
q3s.val[1] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_1))), vreinterpretq_s8_u8(vs.val[1]));
vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | (signs[3] << 16)));
vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2);
vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2);
vs.val[0] = vceqq_u8(vs.val[0], mask2);
vs.val[1] = vceqq_u8(vs.val[1], mask2);
signs += 4;
q3s.val[2] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[0], vreinterpretq_u8_u32(aux32x4_2))), vreinterpretq_s8_u8(vs.val[0]));
q3s.val[3] = vsubq_s8(vreinterpretq_s8_u8(veorq_u8(vs.val[1], vreinterpretq_u8_u32(aux32x4_3))), vreinterpretq_s8_u8(vs.val[1]));
const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]);
const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]);
sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32/2] & 0xf));
sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32/2] >> 4));
}
sumf += d*(sumi1 + sumi2);
}
*s = 0.25f * sumf;
#elif defined(__AVX2__)
static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03
};
static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,
};
const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1);
const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2);
__m256 accumf = _mm256_setzero_ps();
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const uint8_t * restrict qs = x[i].qs;
const uint8_t * restrict qh = x[i].qh;
const uint16_t * restrict signs = (const uint16_t *)x[i].signs;
const int8_t * restrict q8 = y[i].qs;
__m256i sumi1 = _mm256_setzero_si256();
__m256i sumi2 = _mm256_setzero_si256();
for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32;
const __m256i q2_1 = _mm256_set_epi32(iq3xs_grid[qs[7] | ((qh[ib32+0] << 1) & 256)],
iq3xs_grid[qs[6] | ((qh[ib32+0] << 2) & 256)],
iq3xs_grid[qs[5] | ((qh[ib32+0] << 3) & 256)],
iq3xs_grid[qs[4] | ((qh[ib32+0] << 4) & 256)],
iq3xs_grid[qs[3] | ((qh[ib32+0] << 5) & 256)],
iq3xs_grid[qs[2] | ((qh[ib32+0] << 6) & 256)],
iq3xs_grid[qs[1] | ((qh[ib32+0] << 7) & 256)],
iq3xs_grid[qs[0] | ((qh[ib32+0] << 8) & 256)]);
qs += 8;
const __m256i q2_2 = _mm256_set_epi32(iq3xs_grid[qs[7] | ((qh[ib32+1] << 1) & 256)],
iq3xs_grid[qs[6] | ((qh[ib32+1] << 2) & 256)],
iq3xs_grid[qs[5] | ((qh[ib32+1] << 3) & 256)],
iq3xs_grid[qs[4] | ((qh[ib32+1] << 4) & 256)],
iq3xs_grid[qs[3] | ((qh[ib32+1] << 5) & 256)],
iq3xs_grid[qs[2] | ((qh[ib32+1] << 6) & 256)],
iq3xs_grid[qs[1] | ((qh[ib32+1] << 7) & 256)],
iq3xs_grid[qs[0] | ((qh[ib32+1] << 8) & 256)]);
qs += 8;
__m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16));
aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2);
const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1);
aux256 = _mm256_set1_epi32(signs[2] | (signs[3] << 16));
aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2);
const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2);
const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2);
signs += 4;
const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1);
const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2);
const uint16_t ls1 = x[i].scales[ib32/2] & 0xf;
const uint16_t ls2 = x[i].scales[ib32/2] >> 4;
const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1));
const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1));
sumi1 = _mm256_add_epi32(sumi1, p1);
sumi2 = _mm256_add_epi32(sumi2, p2);
}
accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf);
}
*s = 0.25f * hsum_float_8(accumf);
#else
float sumf = 0.f;
for (int i = 0; i < nb; ++i) {
const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
const uint8_t * restrict qs = x[i].qs;
const uint8_t * restrict qh = x[i].qh;
const uint8_t * restrict signs = x[i].signs;
const int8_t * restrict q8 = y[i].qs;
int32_t bsum = 0;
for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1;
const uint32_t ls2 = 2*(x[i].scales[ib32/2] >> 4) + 1;
int32_t sumi = 0;
for (int l = 0; l < 4; ++l) {
const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256)));
for (int j = 0; j < 4; ++j) {
sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
}
q8 += 8;
}
qs += 8;
signs += 4;
bsum += sumi * ls1;
sumi = 0;
for (int l = 0; l < 4; ++l) {
const uint8_t * grid1 = (const uint8_t *)(iq3xs_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256)));
const uint8_t * grid2 = (const uint8_t *)(iq3xs_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256)));
for (int j = 0; j < 4; ++j) {
sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
}
q8 += 8;
}
qs += 8;
signs += 4;
bsum += sumi * ls2;
}
sumf += d * bsum;
}
*s = 0.25f * sumf;
#endif
}
#ifdef __AVX2__
static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) {
const __m256i ax = _mm256_sign_epi8(x, x);
@ -9333,7 +9691,7 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const
uint16_t gindex[8];
uint16x8x2_t vindex;
int8x16x4_t q1b;
int8x16x4_t q8b;
ggml_int8x16x4_t q8b;
uint16x8x4_t scales;
int32x4x2_t sumi;
int32x4x2_t dotq;
@ -9506,16 +9864,17 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
q8b.val[2] = vld1q_s8(y[ib+1].qs);
q8b.val[3] = vld1q_s8(y[ib+1].qs + 16);
q4b.val[0] = vqtbl1q_s8(values, vandq_u8(q4bits.val[0], m4b));
q4b.val[1] = vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
q4b.val[2] = vqtbl1q_s8(values, vandq_u8(q4bits.val[1], m4b));
q4b.val[3] = vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4));
q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b));
q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b));
q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4));
prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]);
prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]);
sumf += (float)x[ib+0].d * (float)y[ib+0].d * vaddvq_s32(prod_1) + (float)x[ib+1].d * (float)y[ib+1].d * vaddvq_s32(prod_2);
sumf +=
GGML_FP16_TO_FP32(x[ib+0].d) * GGML_FP16_TO_FP32(y[ib+0].d) * vaddvq_s32(prod_1) +
GGML_FP16_TO_FP32(x[ib+1].d) * GGML_FP16_TO_FP32(y[ib+1].d) * vaddvq_s32(prod_2);
}
*s = sumf;
@ -10214,14 +10573,15 @@ typedef struct {
uint16_t * neighbours;
} iq3_entry_t;
static iq3_entry_t iq3_data[1] = {
static iq3_entry_t iq3_data[2] = {
{NULL, NULL, NULL},
{NULL, NULL, NULL},
};
static inline int iq3_data_index(int grid_size) {
(void)grid_size;
GGML_ASSERT(grid_size == 256);
return 0;
GGML_ASSERT(grid_size == 256 || grid_size == 512);
return grid_size == 256 ? 0 : 1;
}
static int iq3_compare_func(const void * left, const void * right) {
@ -10253,9 +10613,44 @@ void iq3xs_init_impl(int grid_size) {
3185, 3215, 3252, 3288, 3294, 3364, 3397, 3434, 3483, 3523, 3537, 3587, 3589, 3591, 3592, 3610,
3626, 3670, 3680, 3722, 3749, 3754, 3776, 3789, 3803, 3824, 3857, 3873, 3904, 3906, 3924, 3992,
};
static const uint16_t kgrid_512[512] = {
0, 1, 2, 5, 7, 8, 9, 10, 12, 14, 16, 17, 21, 27, 32, 34,
37, 39, 41, 43, 48, 50, 57, 60, 63, 64, 65, 66, 68, 72, 73, 77,
80, 83, 87, 89, 93, 100, 113, 117, 122, 128, 129, 133, 135, 136, 139, 142,
145, 149, 152, 156, 162, 165, 167, 169, 171, 184, 187, 195, 201, 205, 208, 210,
217, 219, 222, 228, 232, 234, 247, 249, 253, 256, 267, 271, 273, 276, 282, 288,
291, 297, 312, 322, 324, 336, 338, 342, 347, 353, 357, 359, 374, 379, 390, 393,
395, 409, 426, 441, 448, 450, 452, 464, 466, 470, 475, 488, 492, 512, 513, 514,
516, 520, 521, 523, 525, 527, 528, 530, 537, 540, 542, 556, 558, 561, 570, 576,
577, 579, 582, 584, 588, 593, 600, 603, 609, 616, 618, 632, 638, 640, 650, 653,
655, 656, 660, 666, 672, 675, 685, 688, 698, 705, 708, 711, 712, 715, 721, 727,
728, 732, 737, 754, 760, 771, 773, 778, 780, 793, 795, 802, 806, 808, 812, 833,
840, 843, 849, 856, 858, 873, 912, 916, 919, 932, 934, 961, 963, 968, 970, 977,
989, 993, 1010, 1016, 1024, 1025, 1027, 1029, 1031, 1032, 1034, 1036, 1038, 1041, 1043, 1047,
1048, 1050, 1057, 1059, 1061, 1064, 1066, 1079, 1080, 1083, 1085, 1088, 1090, 1096, 1099, 1103,
1106, 1109, 1113, 1116, 1122, 1129, 1153, 1156, 1159, 1169, 1171, 1176, 1183, 1185, 1195, 1199,
1209, 1212, 1216, 1218, 1221, 1225, 1234, 1236, 1241, 1243, 1250, 1256, 1270, 1281, 1287, 1296,
1299, 1306, 1309, 1313, 1338, 1341, 1348, 1353, 1362, 1375, 1376, 1387, 1400, 1408, 1410, 1415,
1425, 1453, 1457, 1477, 1481, 1494, 1496, 1507, 1512, 1538, 1545, 1547, 1549, 1551, 1554, 1561,
1563, 1565, 1570, 1572, 1575, 1577, 1587, 1593, 1601, 1603, 1605, 1612, 1617, 1619, 1632, 1648,
1658, 1662, 1664, 1674, 1680, 1690, 1692, 1704, 1729, 1736, 1740, 1745, 1747, 1751, 1752, 1761,
1763, 1767, 1773, 1787, 1795, 1801, 1806, 1810, 1817, 1834, 1840, 1844, 1857, 1864, 1866, 1877,
1882, 1892, 1902, 1915, 1934, 1953, 1985, 1987, 2000, 2002, 2013, 2048, 2052, 2058, 2064, 2068,
2071, 2074, 2081, 2088, 2104, 2114, 2119, 2121, 2123, 2130, 2136, 2141, 2147, 2153, 2157, 2177,
2179, 2184, 2189, 2193, 2203, 2208, 2223, 2226, 2232, 2244, 2249, 2251, 2256, 2258, 2265, 2269,
2304, 2306, 2324, 2335, 2336, 2361, 2373, 2375, 2385, 2418, 2443, 2460, 2480, 2504, 2509, 2520,
2531, 2537, 2562, 2568, 2572, 2578, 2592, 2596, 2599, 2602, 2614, 2620, 2625, 2627, 2629, 2634,
2641, 2650, 2682, 2688, 2697, 2707, 2712, 2718, 2731, 2754, 2759, 2760, 2775, 2788, 2793, 2805,
2811, 2817, 2820, 2832, 2842, 2854, 2890, 2902, 2921, 2923, 2978, 3010, 3012, 3026, 3081, 3083,
3085, 3097, 3099, 3120, 3136, 3152, 3159, 3188, 3210, 3228, 3234, 3245, 3250, 3256, 3264, 3276,
3281, 3296, 3349, 3363, 3378, 3392, 3395, 3420, 3440, 3461, 3488, 3529, 3531, 3584, 3588, 3591,
3600, 3602, 3614, 3616, 3628, 3634, 3650, 3657, 3668, 3683, 3685, 3713, 3716, 3720, 3726, 3729,
3736, 3753, 3778, 3802, 3805, 3819, 3841, 3845, 3851, 3856, 3880, 3922, 3938, 3970, 3993, 4032,
};
const int kmap_size = 4096;
const int nwant = 2;
const uint16_t * kgrid = kgrid_256;
const int nwant = grid_size == 256 ? 2 : 3;
const uint16_t * kgrid = grid_size == 256 ? kgrid_256 : kgrid_512;
uint32_t * kgrid_q3xs;
int * kmap_q3xs;
uint16_t * kneighbors_q3xs;
@ -10352,7 +10747,7 @@ void iq3xs_init_impl(int grid_size) {
}
void iq3xs_free_impl(int grid_size) {
GGML_ASSERT(grid_size == 256);
GGML_ASSERT(grid_size == 256 || grid_size == 512);
const int gindex = iq3_data_index(grid_size);
if (iq3_data[gindex].grid) {
free(iq3_data[gindex].grid); iq3_data[gindex].grid = NULL;
@ -10385,9 +10780,10 @@ static int iq3_find_best_neighbour(const uint16_t * restrict neighbours, const u
return grid_index;
}
static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) {
static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, void * restrict vy, int n,
const float * restrict quant_weights) {
const int gindex = iq3_data_index(256);
const int gindex = iq3_data_index(grid_size);
const uint32_t * kgrid_q3xs = iq3_data[gindex].grid;
const int * kmap_q3xs = iq3_data[gindex].map;
@ -10401,9 +10797,23 @@ static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict
const int kMaxQ = 8;
const int nbl = n/256;
const int nbl = n/QK_K;
block_iq3_xxs * y = vy;
ggml_fp16_t * dh;
uint8_t * qs;
int block_size;
if (grid_size == 256) {
block_iq3_xxs * y = vy;
dh = &y->d;
qs = y->qs;
block_size = sizeof(block_iq3_xxs);
} else {
block_iq3_s * y = vy;
dh = &y->d;
qs = y->qs;
block_size = sizeof(block_iq3_s);
}
int quant_size = block_size - sizeof(ggml_fp16_t);
float scales[QK_K/32];
float weight[32];
@ -10414,20 +10824,21 @@ static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict
bool is_on_grid[8];
bool is_on_grid_aux[8];
uint8_t block_signs[8];
uint8_t q3[3*(QK_K/8)];
uint8_t q3[3*(QK_K/8)+QK_K/32];
uint32_t * scales_and_signs = (uint32_t *)(q3 + QK_K/4);
uint8_t * qh = q3 + 3*(QK_K/8);
for (int ibl = 0; ibl < nbl; ++ibl) {
y[ibl].d = GGML_FP32_TO_FP16(0.f);
memset(q3, 0, 3*QK_K/8);
dh[0] = GGML_FP32_TO_FP16(0.f);
memset(q3, 0, 3*QK_K/8+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 = sumx2/QK_K;
float sigma2 = 2*sumx2/QK_K;
for (int ib = 0; ib < QK_K/32; ++ib) {
const float * xb = xbl + 32*ib;
@ -10545,7 +10956,13 @@ static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict
printf("\n");
GGML_ASSERT(false);
}
q3[8*ib+k] = grid_index;
if (grid_size == 256) {
q3[8*ib+k] = grid_index;
} else {
q3[8*ib+k] = grid_index & 255;
qh[ib] |= ((grid_index >> 8) << k);
}
}
scales_and_signs[ib] = block_signs[0] | (block_signs[1] << 7) | (block_signs[2] << 14) | (block_signs[3] << 21);
GGML_ASSERT(scale >= 0);
@ -10554,63 +10971,25 @@ static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict
}
if (!max_scale) {
memset(y[ibl].qs, 0, 3*QK_K/8);
memset(qs, 0, quant_size);
dh += block_size/sizeof(ggml_fp16_t);
qs += block_size;
continue;
}
float d = max_scale/31;
y[ibl].d = GGML_FP32_TO_FP16(d);
dh[0] = GGML_FP32_TO_FP16(d * 1.0125f); // small improvement via this fudge factor
float id = 1/d;
float sumqx = 0, sumq2 = 0;
for (int ib = 0; ib < QK_K/32; ++ib) {
int l = nearest_int(0.5f*(id*scales[ib]-1));
l = MAX(0, MIN(15, l));
scales_and_signs[ib] |= ((uint32_t)l << 28);
if (false) {
const float * xb = xbl + 32*ib;
if (quant_weights) {
const float * qw = quant_weights + QK_K*ibl + 32*ib;
for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]);
} else {
for (int i = 0; i < 32; ++i) weight[i] = xb[i]*xb[i];
}
const float db = 0.25f * d * (1 + 2*l);
for (int k = 0; k < 8; ++k) {
const int8_t * signs = keven_signs_q2xs + 8*((scales_and_signs[ib] >> 7*(k/2)) & 127) + 4*(k%2);
const float * xk = xb + 4*k;
const float * wk = weight + 4*k;
//const uint8_t * grid = (const uint8_t *)(kgrid_q3xs + q3[8*ib+k]);
const uint8_t * grid = (const uint8_t *)(iq3xxs_grid + q3[8*ib+k]);
float best_mse = 0; int best_index = q3[8*ib+k];
for (int j = 0; j < 4; ++j) {
float diff = db * grid[j] * signs[j] - xk[j];
best_mse += wk[j] * diff * diff;
}
for (int idx = 0; idx < 256; ++idx) {
//grid = (const uint8_t *)(kgrid_q3xs + idx);
grid = (const uint8_t *)(iq3xxs_grid + idx);
float mse = 0;
for (int j = 0; j < 4; ++j) {
float diff = db * grid[j] * signs[j] - xk[j];
mse += wk[j] * diff * diff;
}
if (mse < best_mse) {
best_mse = mse; best_index = idx;
}
}
q3[8*ib+k] = best_index;
//grid = (const uint8_t *)(kgrid_q3xs + best_index);
grid = (const uint8_t *)(iq3xxs_grid + best_index);
for (int j = 0; j < 4; ++j) {
float q = db * grid[j] * signs[j];
sumqx += wk[j] * q * xk[j];
sumq2 += wk[j] * q * q;
}
}
if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(d*sumqx/sumq2);
}
}
memcpy(y[ibl].qs, q3, 3*QK_K/8);
memcpy(qs, q3, quant_size);
dh += block_size/sizeof(ggml_fp16_t);
qs += block_size;
}
}
@ -10620,7 +10999,7 @@ size_t quantize_iq3_xxs(const float * src, void * dst, int nrow, int n_per_row,
int nblock = n_per_row/QK_K;
char * qrow = (char *)dst;
for (int row = 0; row < nrow; ++row) {
quantize_row_iq3_xxs_impl(src, qrow, n_per_row, quant_weights);
quantize_row_iq3_xxs_impl(256, src, qrow, n_per_row, quant_weights);
src += n_per_row;
qrow += nblock*sizeof(block_iq3_xxs);
}
@ -10635,9 +11014,226 @@ void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int k) {
void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k) {
assert(k % QK_K == 0);
quantize_row_iq3_xxs_impl(x, y, k, NULL);
quantize_row_iq3_xxs_impl(256, x, y, k, NULL);
}
static void quantize_row_iq3_s_impl(int block_size, const float * restrict x, void * restrict vy, int n,
const float * restrict quant_weights,
float * scales,
float * weight,
float * xval,
int8_t * L,
int8_t * Laux,
float * waux,
bool * is_on_grid,
bool * is_on_grid_aux,
uint8_t * block_signs) {
const int gindex = iq3_data_index(512);
const uint32_t * kgrid_q3xs = iq3_data[gindex].grid;
const int * kmap_q3xs = iq3_data[gindex].map;
const uint16_t * kneighbors_q3xs = iq3_data[gindex].neighbours;
//GGML_ASSERT(quant_weights && "missing quantization weights");
GGML_ASSERT(kgrid_q3xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(kmap_q3xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(kneighbors_q3xs && "forgot to call ggml_quantize_init()?");
GGML_ASSERT(n%QK_K == 0);
const int kMaxQ = 8;
const int nbl = n/QK_K;
block_iq3_s * y = vy;
const int bs4 = block_size/4;
const int bs8 = block_size/8;
for (int ibl = 0; ibl < nbl; ++ibl) {
memset(&y[ibl], 0, sizeof(block_iq3_s));
y[ibl].d = GGML_FP32_TO_FP16(0.f);
uint8_t * qs = y[ibl].qs;
uint8_t * qh = y[ibl].qh;
uint8_t * signs = y[ibl].signs;
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];
}
for (int i = 0; i < block_size; ++i) waux[i] = sqrtf(weight[i]);
for (int k = 0; k < bs8; ++k) {
uint8_t s = 0;
for (int i = 0; i < 8; ++i) {
if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i];
else {
xval[8*k + i] = -xb[8*k + i]; s |= (1 << i);
}
}
block_signs[k] = s;
}
float max = xval[0];
for (int i = 1; i < block_size; ++i) max = MAX(max, xval[i]);
if (!max) {
scales[ib] = 0;
continue;
}
float best = 0;
float scale = max/(2*kMaxQ-1);
for (int is = -15; is <= 15; ++is) {
float id = (2*kMaxQ-1+is*0.2f)/max;
float this_scale = 1/id;
for (int k = 0; k < bs4; ++k) {
for (int i = 0; i < 4; ++i) {
int l = nearest_int(0.5f*(id*xval[4*k+i]-1));
Laux[4*k+i] = MAX(0, MIN(kMaxQ-1, l));
}
uint16_t u = 0;
for (int i = 0; i < 4; ++i) u |= (Laux[4*k+i] << 3*i);
int grid_index = kmap_q3xs[u];
is_on_grid_aux[k] = true;
if (grid_index < 0) {
is_on_grid_aux[k] = false;
const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1;
grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, this_scale, Laux + 4*k);
}
}
float sumqx = 0, sumq2 = 0;
for (int i = 0; i < block_size; ++i) {
float w = weight[i];
float q = 2*Laux[i] + 1;
sumqx += w*xval[i]*q;
sumq2 += w*q*q;
}
if (sumq2 > 0 && sumqx*sumqx > best*sumq2) {
scale = sumqx/sumq2; best = scale*sumqx;
for (int i = 0; i < block_size; ++i) L[i] = Laux[i];
for (int k = 0; k < bs4; ++k) is_on_grid[k] = is_on_grid_aux[k];
}
}
int n_not_ongrid = 0;
for (int k = 0; k < bs4; ++k) if (!is_on_grid[k]) ++n_not_ongrid;
if (n_not_ongrid > 0 && scale > 0) {
float id = 1/scale;
for (int k = 0; k < bs4; ++k) {
if (is_on_grid[k]) continue;
uint16_t u = 0;
for (int i = 0; i < 4; ++i) {
int l = nearest_int(0.5f*(id*xval[4*k+i]-1));
l = MAX(0, MIN(kMaxQ-1, l));
u |= (l << 3*i);
}
int grid_index = kmap_q3xs[u];
if (grid_index < 0) {
const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1;
grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, scale, L + 4*k);
}
const int8_t * pg = (const int8_t *)(kgrid_q3xs + grid_index);
for (int i = 0; i < 4; ++i) L[4*k+i] = (pg[i] - 1)/2;
}
float sumqx = 0, sumq2 = 0;
for (int i = 0; i < block_size; ++i) {
float w = weight[i];
float q = 2*L[i] + 1;
sumqx += w*xval[i]*q;
sumq2 += w*q*q;
}
if (sumq2 > 0) scale = sumqx/sumq2;
}
if (scale < 0) {
// This should never happen, but just in case, flip scale so that it is positive (we use uint's to encode the scale)
// and correspondingly flip quant signs.
scale = -scale;
for (int k = 0; k < bs8; ++k) block_signs[k] = ~block_signs[k];
}
for (int k = 0; k < bs4; ++k) {
uint16_t u = 0;
for (int i = 0; i < 4; ++i) u |= (L[4*k+i] << 3*i);
int grid_index = kmap_q3xs[u];
if (grid_index < 0) {
printf("Oops: found point %u not on grid:", u);
for (int i = 0; i < 4; ++i) printf(" %d", L[4*k+i]);
printf("\n");
GGML_ASSERT(false);
}
qs[k] = grid_index & 255;
qh[(ib*bs4+k)/8] |= ((grid_index >> 8) << ((ib*bs4+k)%8));
}
qs += bs4;
for (int k = 0; k < bs8; ++k) signs[k] = block_signs[k];
signs += bs8;
GGML_ASSERT(scale >= 0);
scales[ib] = scale;
max_scale = MAX(max_scale, scale);
}
if (!max_scale) {
continue;
}
float d = max_scale/31;
y[ibl].d = GGML_FP32_TO_FP16(d);
float id = 1/d;
for (int ib = 0; ib < QK_K/block_size; ib += 2) {
int l1 = nearest_int(0.5f*(id*scales[ib+0]-1));
l1 = MAX(0, MIN(15, l1));
int l2 = nearest_int(0.5f*(id*scales[ib+1]-1));
l2 = MAX(0, MIN(15, l2));
y[ibl].scales[ib/2] = l1 | (l2 << 4);
}
}
}
#define IQ3S_BLOCK_SIZE 32
size_t quantize_iq3_s(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) {
(void)hist;
GGML_ASSERT(n_per_row%QK_K == 0);
int nblock = n_per_row/QK_K;
float scales[QK_K/IQ3S_BLOCK_SIZE];
float weight[IQ3S_BLOCK_SIZE];
float xval[IQ3S_BLOCK_SIZE];
int8_t L[IQ3S_BLOCK_SIZE];
int8_t Laux[IQ3S_BLOCK_SIZE];
float waux[IQ3S_BLOCK_SIZE];
bool is_on_grid[IQ3S_BLOCK_SIZE/4];
bool is_on_grid_aux[IQ3S_BLOCK_SIZE/4];
uint8_t block_signs[IQ3S_BLOCK_SIZE/8];
char * qrow = (char *)dst;
for (int row = 0; row < nrow; ++row) {
quantize_row_iq3_s_impl(IQ3S_BLOCK_SIZE, src, qrow, n_per_row, quant_weights,
scales, weight, xval, L, Laux, waux, is_on_grid, is_on_grid_aux, block_signs);
src += n_per_row;
qrow += nblock*sizeof(block_iq3_s);
}
return nrow * nblock * sizeof(block_iq3_s);
}
void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int k) {
assert(k % QK_K == 0);
block_iq3_s * restrict y = vy;
quantize_row_iq3_s_reference(x, y, k);
}
void quantize_row_iq3_s_reference(const float * restrict x, block_iq3_s * restrict y, int k) {
assert(k % QK_K == 0);
quantize_iq3_s(x, y, 1, k, NULL, NULL);
}
// =================================== 1.5 bpw ===================================================
static int iq1_find_best_neighbour(const uint16_t * restrict neighbours, const uint64_t * restrict grid,

20
ggml-quants.h
View file

@ -191,6 +191,21 @@ typedef struct {
} block_iq3_xxs;
static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
// 3.4375 bpw
#if QK_K == 64
#define IQ3S_N_SCALE 2
#else
#define IQ3S_N_SCALE QK_K/64
#endif
typedef struct {
ggml_fp16_t d;
uint8_t qs[QK_K/4];
uint8_t qh[QK_K/32];
uint8_t signs[QK_K/8];
uint8_t scales[IQ3S_N_SCALE];
} block_iq3_s;
static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding");
typedef struct {
ggml_fp16_t d;
uint8_t qs[QK_K/8];
@ -226,6 +241,7 @@ void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGM
void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k);
void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k);
void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int k);
void quantize_row_iq3_s_reference (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int k);
void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
@ -242,6 +258,7 @@ void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
void quantize_row_iq3_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
// Dequantization
void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
@ -262,6 +279,7 @@ void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_
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_iq4_nl (const block_iq4_nl * 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);
// Dot product
void ggml_vec_dot_q4_0_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);
@ -280,6 +298,7 @@ void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const
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_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_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);
//
// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
@ -289,6 +308,7 @@ size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row,
size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq1_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq4_nl (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq3_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_q4_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);

152
ggml-sycl.cpp
View file

@ -3338,7 +3338,7 @@ void print_ggml_tensor(const char*name, struct ggml_tensor *src){
size_t total_elements = ggml_nelements(src);
const bool src_on_device = src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT;
const bool src_on_device = src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
float *src_data =NULL;
if(src_on_device) {
ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra;
@ -8086,11 +8086,11 @@ static void k_argsort_f32_i32(const float * x, int * dst, const int ncols,
int ixj = col ^ j;
if (ixj > col) {
if ((col & k) == 0) {
if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
swap(dst_row[col], dst_row[ixj]);
}
} else {
if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
if (order == GGML_SORT_ORDER_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
swap(dst_row[col], dst_row[ixj]);
}
}
@ -10825,7 +10825,7 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
const sycl::range<3> block_dims(1, 1, ncols);
const sycl::range<3> block_nums(1, nrows, 1);
if (order == GGML_SORT_ASC) {
if (order == GGML_SORT_ORDER_ASC) {
/*
DPCT1049:44: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
@ -10834,9 +10834,9 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
k_argsort_f32_i32<GGML_SORT_ASC>(x, dst, ncols, item_ct1);
k_argsort_f32_i32<GGML_SORT_ORDER_ASC>(x, dst, ncols, item_ct1);
});
} else if (order == GGML_SORT_DESC) {
} else if (order == GGML_SORT_ORDER_DESC) {
/*
DPCT1049:45: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
@ -10845,7 +10845,7 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
k_argsort_f32_i32<GGML_SORT_DESC>(x, dst, ncols, item_ct1);
k_argsort_f32_i32<GGML_SORT_ORDER_DESC>(x, dst, ncols, item_ct1);
});
} else {
GGML_ASSERT(false);
@ -11407,12 +11407,12 @@ static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst,
dpct::memcpy_direction kind;
char * src_ptr;
if (src->backend == GGML_BACKEND_CPU) {
if (src->backend == GGML_BACKEND_TYPE_CPU) {
kind = dpct::host_to_device;
src_ptr = (char *) src->data;
// GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d GGML_BACKEND_CPU src_ptr %p\n", src_ptr);
} else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) {
GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
// GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d GGML_BACKEND_TYPE_CPU src_ptr %p\n", src_ptr);
} else if (src->backend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
GGML_ASSERT(src->backend != GGML_BACKEND_TYPE_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
kind = dpct::device_to_device;
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
int id;
@ -11846,7 +11846,7 @@ inline void ggml_sycl_op_mul_mat_q(
// the main device has a larger memory buffer to hold the results from all GPUs
// nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into
const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff;
const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff;
switch (src0->type) {
case GGML_TYPE_Q4_0:
@ -12119,7 +12119,7 @@ inline void ggml_sycl_op_mul_mat_sycl(
// the main device has a larger memory buffer to hold the results from all GPUs
// ldc == nrows of the matrix that cuBLAS writes into
int ldc = dst->backend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff;
int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff;
#ifdef GGML_SYCL_F16
bool use_fp16 = true; // TODO(Yu) SYCL capability check
@ -12501,16 +12501,16 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0,
const bool use_src1 = src1 != nullptr;
const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT( dst->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT( dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU;
const bool dst_on_device = dst->backend == GGML_BACKEND_GPU;
const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU;
const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU;
// dd = data device
float * src0_ddf = nullptr;
@ -12565,7 +12565,7 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0,
main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst))));
}
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
SYCL_CHECK(CHECK_TRY_ERROR(
dpct::get_current_device().queues_wait_and_throw()));
}
@ -12640,8 +12640,8 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
const int nb2 = dst->nb[2];
const int nb3 = dst->nb[3];
GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
@ -12656,13 +12656,13 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
const bool src0_is_contiguous = ggml_is_contiguous(src0);
const bool src1_is_contiguous = ggml_is_contiguous(src1);
int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
GGML_ASSERT(!(split && ne02 > 1));
GGML_ASSERT(!(split && ne03 > 1));
GGML_ASSERT(!(split && ne02 < ne12));
@ -12717,8 +12717,8 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
used_devices++;
const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device_index;
const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device_index;
const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index;
const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index;
ggml_sycl_set_device(get_device_id_by_index(id));
const dpct::queue_ptr stream = g_syclStreams[id][0];
@ -12782,8 +12782,8 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
continue;
}
const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device_index;
const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device_index;
const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index;
const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index;
const int64_t row_diff = row_high[id] - row_low[id];
ggml_sycl_set_device(get_device_id_by_index(id));
@ -12809,12 +12809,12 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
// the main device memory buffer can be on VRAM scratch, with space for all partial results
// in that case an offset on dst_ddf_i is needed
if (dst->backend == GGML_BACKEND_GPU && id == g_main_device_index) {
if (dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device_index) {
dst_dd_i += row_low[id]; // offset is 0 if no tensor split
}
// copy src0, src1 to device if necessary
if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) {
if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) {
if (id != g_main_device_index) {
if (convert_src1_to_q8_1) {
char * src1_ddq_i_source = src1_ddq[g_main_device_index] + src1_ddq_i_offset;
@ -12830,14 +12830,14 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
src1_ncols * ne10 * sizeof(float))));
}
}
} else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) {
} else if (src1->backend == GGML_BACKEND_TYPE_CPU || (src1_on_device && !src1_is_contiguous)) {
SYCL_CHECK(ggml_sycl_cpy_tensor_2d(
src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
} else {
GGML_ASSERT(false);
}
if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) {
if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_TYPE_CPU || !src1_is_contiguous)) {
quantize_row_q8_1_sycl(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
/*
DPCT1010:92: SYCL uses exceptions to report errors and does
@ -12867,10 +12867,10 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
if (!dst_on_device) {
void * dst_off_device;
dpct::memcpy_direction kind;
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
dst_off_device = dst->data;
kind = dpct::device_to_host;
} else if (dst->backend == GGML_BACKEND_GPU) {
} else if (dst->backend == GGML_BACKEND_TYPE_GPU) {
dst_off_device = dst_extra->data_device[g_main_device_index];
kind = dpct::device_to_device;
} else {
@ -12954,7 +12954,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0,
}
}
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
SYCL_CHECK(ggml_sycl_set_device(g_main_device));
SYCL_CHECK(CHECK_TRY_ERROR(
dpct::get_current_device().queues_wait_and_throw()));
@ -13091,7 +13091,7 @@ static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0,
const ggml_tensor *src1,
ggml_tensor *dst) try {
GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
GGML_ASSERT(src0->type == GGML_TYPE_F16);
@ -13129,7 +13129,7 @@ static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0,
GGML_ASSERT(!ggml_is_transposed(src0));
GGML_ASSERT(!ggml_is_transposed(src1));
GGML_ASSERT(!ggml_is_permuted(src0));
GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src0->type == GGML_TYPE_F16);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
@ -13196,7 +13196,7 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0,
GGML_ASSERT(!ggml_is_transposed(src0));
GGML_ASSERT(!ggml_is_transposed(src1));
GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src0->type == GGML_TYPE_F16);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
@ -13372,11 +13372,11 @@ catch (sycl::exception const &exc) {
static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const bool all_on_device =
(src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
(src1->backend == GGML_BACKEND_GPU) &&
( dst->backend == GGML_BACKEND_GPU);
(src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) &&
(src1->backend == GGML_BACKEND_TYPE_GPU) &&
( dst->backend == GGML_BACKEND_TYPE_GPU);
const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT;
int64_t min_compute_capability = INT_MAX;
for (int64_t id = 0; id < g_device_count; ++id) {
@ -13505,7 +13505,7 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) {
GGML_ASSERT(!ggml_is_transposed(src00));
GGML_ASSERT(!ggml_is_transposed(src1));
GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT);
GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_TENSOR_LOCALS(int64_t, ne0, src00, ne);
@ -13643,7 +13643,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0];
if (ids->backend == GGML_BACKEND_GPU) {
if (ids->backend == GGML_BACKEND_TYPE_GPU) {
const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index];
SYCL_CHECK(CHECK_TRY_ERROR(
stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids))));
@ -13661,20 +13661,20 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
ggml_tensor src1_row = *src1;
ggml_tensor dst_row = *dst;
src1_row.backend = GGML_BACKEND_GPU;
dst_row.backend = GGML_BACKEND_GPU;
src1_row.backend = GGML_BACKEND_TYPE_GPU;
dst_row.backend = GGML_BACKEND_TYPE_GPU;
src1_row.extra = &src1_row_extra;
dst_row.extra = &dst_row_extra;
char * src1_original = src1->backend == GGML_BACKEND_CPU ?
char * src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ?
(char *) src1->data : (char *) src1_extra->data_device[g_main_device_index];
char * dst_original = dst->backend == GGML_BACKEND_CPU ?
char * dst_original = dst->backend == GGML_BACKEND_TYPE_CPU ?
(char *) dst->data : (char *) dst_extra->data_device[g_main_device_index];
if (src1->ne[1] == 1) {
GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
GGML_ASSERT(dst->backend == GGML_BACKEND_GPU);
GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU);
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
//int32_t row_id;
@ -13756,7 +13756,7 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0,
}
}
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
}
}
@ -13779,8 +13779,8 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1,
const int64_t ne = ggml_nelements(src0);
GGML_ASSERT(ne == ggml_nelements(src1));
GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU);
GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
@ -13887,17 +13887,17 @@ void ggml_sycl_transform_tensor(void *data, struct ggml_tensor *tensor) try {
memset(extra, 0, sizeof(*extra));
for (int64_t id = 0; id < g_device_count; ++id) {
if (backend == GGML_BACKEND_GPU && id != g_main_device_index) {
if (backend == GGML_BACKEND_TYPE_GPU && id != g_main_device_index) {
continue;
}
ggml_sycl_set_device(get_device_id_by_index(id));
const dpct::queue_ptr stream = g_syclStreams[id][0];
int64_t row_low, row_high;
if (backend == GGML_BACKEND_GPU) {
if (backend == GGML_BACKEND_TYPE_GPU) {
row_low = 0;
row_high = nrows;
} else if (backend == GGML_BACKEND_GPU_SPLIT) {
} else if (backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
const int64_t rounding = get_row_rounding(tensor->type);
row_low = id == 0 ? 0 : nrows*g_tensor_split[id];
@ -13946,7 +13946,7 @@ void ggml_sycl_transform_tensor(void *data, struct ggml_tensor *tensor) try {
extra->data_device[id] = buf;
if (backend == GGML_BACKEND_GPU_SPLIT) {
if (backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
for (int64_t is = 0; is < MAX_STREAMS; ++is) {
SYCL_CHECK(CHECK_TRY_ERROR(extra->events[id][is] =
new sycl::event()));
@ -13963,7 +13963,7 @@ catch (sycl::exception const &exc) {
}
void ggml_sycl_free_data(struct ggml_tensor *tensor) try {
if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) {
if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_TYPE_GPU && tensor->backend != GGML_BACKEND_TYPE_GPU_SPLIT) ) {
return;
}
@ -14016,15 +14016,15 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor,
return;
}
tensor->backend = GGML_BACKEND_GPU;
tensor->backend = GGML_BACKEND_TYPE_GPU;
if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) {
if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU) {
const ggml_op src0_op = tensor->src[0]->op;
if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) {
ggml_sycl_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc);
}
}
if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) {
if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU) {
ggml_sycl_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc);
}
@ -14042,7 +14042,7 @@ static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor,
SYCL_CHECK(ggml_sycl_set_device(g_main_device));
const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0];
if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
if (inplace && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) {
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index];
size_t offset = 0;
@ -14111,7 +14111,7 @@ void ggml_sycl_assign_scratch_offset(struct ggml_tensor *tensor,
const bool inplace = tensor->view_src != nullptr;
if (inplace && (tensor->view_src->backend == GGML_BACKEND_GPU || tensor->view_src->backend == GGML_BACKEND_GPU_SPLIT)) {
if (inplace && (tensor->view_src->backend == GGML_BACKEND_TYPE_GPU || tensor->view_src->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) {
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->view_src->extra;
char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index];
size_t view_offset = 0;
@ -14132,7 +14132,7 @@ catch (sycl::exception const &exc) {
}
void ggml_sycl_copy_to_device(struct ggml_tensor *tensor) try {
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
GGML_ASSERT(ggml_is_contiguous(tensor));
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
@ -14219,9 +14219,9 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_
if (!g_sycl_loaded) return false;
ggml_sycl_func_t func;
const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
|| (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
|| (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU
|| (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
|| (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU);
if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) {
return false;
@ -14359,14 +14359,14 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_
return false;
}
if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) {
if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT) {
ggml_sycl_set_peer_access(tensor->src[1]->ne[1]);
}
if (params->ith != 0) {
return true;
}
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return true;
}
func(tensor->src[0], tensor->src[1], tensor);
@ -14517,7 +14517,7 @@ static void ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
extra->data_device[ctx->device] = tensor->data;
tensor->backend = GGML_BACKEND_GPU;
tensor->backend = GGML_BACKEND_TYPE_GPU;
tensor->extra = extra;
if (ggml_is_quantized(tensor->type)) {
@ -14548,7 +14548,7 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer,
ggml_tensor *tensor,
const void *data, size_t offset,
size_t size) try {
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
@ -14573,7 +14573,7 @@ static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer,
const ggml_tensor *tensor,
void *data, size_t offset,
size_t size) try {
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
@ -14809,7 +14809,7 @@ static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend,
ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type");
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
(char *)tensor->data + offset, data, size)));
@ -14827,7 +14827,7 @@ static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend,
ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type");
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy(
data, (const char *)tensor->data + offset, size)));
@ -14880,7 +14880,7 @@ static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph
ggml_sycl_set_main_device(sycl_ctx->device);
ggml_compute_params params = {};
params.type = GGML_TASK_COMPUTE;
params.type = GGML_TASK_TYPE_COMPUTE;
params.ith = 0;
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
@ -14888,13 +14888,13 @@ static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph
if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE)
continue;
assert(node->backend == GGML_BACKEND_GPU);
assert(node->backend == GGML_BACKEND_TYPE_GPU);
assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device));
assert(node->extra != nullptr);
for (int j = 0; j < GGML_MAX_SRC; j++) {
if (node->src[j] != nullptr) {
assert(node->src[j]->backend == GGML_BACKEND_GPU);
assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU);
assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device));
assert(node->src[j]->extra != nullptr);
}

102
ggml-vulkan.cpp
View file

@ -2320,8 +2320,8 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su
src1_uma = d_Qy != nullptr;
}
const bool load_x = src0->backend != GGML_BACKEND_GPU && !src0_uma;
const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma;
const bool load_x = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma;
const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0);
const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1);
@ -2453,7 +2453,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su
// compute
ggml_vk_matmul(ctx, subctx, *pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
// copy dst to host
float * d = (float *) ((char *) dst->data);
ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13);
@ -2506,8 +2506,8 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context
src1_uma = d_Qy != nullptr;
}
const bool load_x = src0->backend != GGML_BACKEND_GPU && !src0_uma;
const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma;
const bool load_x = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma;
const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0);
const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1);
@ -2630,7 +2630,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context
ggml_vk_sync_buffers(subctx);
ggml_vk_dispatch_pipeline(ctx, subctx, *dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1});
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
// copy dst to host
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
ggml_vk_sync_buffers(subctx);
@ -2647,7 +2647,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl;
#endif
GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // NOLINT
GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // NOLINT
GGML_ASSERT(src0->type == GGML_TYPE_F16);
@ -2679,7 +2679,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
src1_uma = d_Qy != nullptr;
}
const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma;
const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
const uint64_t x_ne = ne00 * ne01 * ne02;
const uint64_t y_ne = ne10 * ne11 * ne12;
@ -2721,7 +2721,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
ggml_vk_sync_buffers(subctx);
ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
// copy dst to host
float * d = (float *) dst->data;
ggml_vk_sync_buffers(subctx);
@ -2738,7 +2738,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
GGML_ASSERT(!ggml_is_transposed(src0));
GGML_ASSERT(!ggml_is_transposed(src1));
GGML_ASSERT(!ggml_is_permuted(src0));
GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
GGML_ASSERT(src0->type == GGML_TYPE_F16);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
@ -2771,7 +2771,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
src1_uma = d_Qy != nullptr;
}
const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma;
const bool load_y = src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
const uint64_t d_ne = ne01 * ne11 * ne12;
@ -2814,7 +2814,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
ggml_vk_sync_buffers(subctx);
ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
// copy dst to host
float * d = (float *) dst->data;
ggml_vk_sync_buffers(subctx);
@ -2832,7 +2832,7 @@ static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * sr
return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
(src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 || ggml_is_quantized(src1->type)) &&
dst->type == GGML_TYPE_F32 &&
((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU);
((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU);
}
static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
@ -2880,8 +2880,8 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx
// TODO: support for transposed / permuted tensors
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb00 == sizeof(float));
GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
GGML_ASSERT(dst->backend == GGML_BACKEND_GPU);
GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU);
GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU);
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
@ -3110,8 +3110,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
}
}
const bool transfer_src0 = src0->backend != GGML_BACKEND_GPU && !src0_uma;
const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_GPU && !src1_uma;
const bool transfer_src0 = src0->backend != GGML_BACKEND_TYPE_GPU && !src0_uma;
const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_TYPE_GPU && !src1_uma;
uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment);
uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : 0;
@ -3120,7 +3120,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
vk_buffer d_D = extra->buffer_gpu.lock();
// Workaround for tiny tensor inputs on ROPE
if (use_src1 && src1->backend == GGML_BACKEND_GPU && y_sz > d_D->size) {
if (use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU && y_sz > d_D->size) {
y_sz = VK_WHOLE_SIZE;
}
@ -3209,9 +3209,9 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
ggml_vk_sync_buffers(subctx);
ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements);
}
if (dst->backend == GGML_BACKEND_CPU && op == GGML_OP_CPY) {
if (dst->backend == GGML_BACKEND_TYPE_CPU && op == GGML_OP_CPY) {
ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst);
} else if(dst->backend == GGML_BACKEND_CPU) {
} else if(dst->backend == GGML_BACKEND_TYPE_CPU) {
// copy dst to host
float * d = (float *) dst->data;
ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz);
@ -3253,7 +3253,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
ggml_vk_sync_buffers(subctx);
ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements);
}
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
// copy dst to host
ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz);
}
@ -3359,7 +3359,7 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con
static void ggml_vk_nop(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) {
// If backend is CPU, data from src0 has to be copied off the device
if (dst->backend == GGML_BACKEND_CPU) {
if (dst->backend == GGML_BACKEND_TYPE_CPU) {
ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
vk_buffer d_D = extra_src0->buffer_gpu.lock();
ggml_vk_sync_buffers(subctx);
@ -3994,9 +3994,9 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm
#ifdef GGML_VULKAN_DEBUG
std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl;
#endif
const bool any_on_device = node->backend == GGML_BACKEND_GPU
|| (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
|| (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_GPU));
const bool any_on_device = node->backend == GGML_BACKEND_TYPE_GPU
|| (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_TYPE_GPU || node->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
|| (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_TYPE_GPU));
if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) {
return;
@ -4215,9 +4215,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
}
static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){
const bool any_on_device = node->backend == GGML_BACKEND_GPU
|| (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
|| (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_GPU);
const bool any_on_device = node->backend == GGML_BACKEND_TYPE_GPU
|| (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_TYPE_GPU || node->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
|| (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_TYPE_GPU);
if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) {
return;
@ -4371,7 +4371,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
last_node = true;
#endif
if (node->backend == GGML_BACKEND_CPU || last_node) {
if (node->backend == GGML_BACKEND_TYPE_CPU || last_node) {
ggml_vk_ctx_end(ctx->compute_ctx);
ctx->compute_ctx->exit_tensor = node;
ctx->compute_ctx = nullptr;
@ -4379,9 +4379,9 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
}
static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){
const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
|| (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
|| (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU
|| (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT))
|| (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU);
if (ctx->disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) {
return false;
@ -4442,7 +4442,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_
if (params->ith != 0) {
return true;
}
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return true;
}
@ -4745,7 +4745,7 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b
extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base;
}
tensor->backend = GGML_BACKEND_GPU;
tensor->backend = GGML_BACKEND_TYPE_GPU;
tensor->extra = extra;
}
@ -4753,7 +4753,7 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu
#ifdef GGML_VULKAN_DEBUG
std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl;
#endif
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
@ -4768,7 +4768,7 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu
#ifdef GGML_VULKAN_DEBUG
std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl;
#endif
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
@ -4999,7 +4999,7 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g
#endif
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
@ -5020,7 +5020,7 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c
#endif
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type");
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU);
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
@ -5097,7 +5097,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml
int last_node = cgraph->n_nodes - 1;
// If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly
while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_GPU) {
while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_TYPE_GPU) {
last_node -= 1;
}
@ -5106,7 +5106,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml
}
ggml_compute_params params = {};
params.type = GGML_TASK_COMPUTE;
params.type = GGML_TASK_TYPE_COMPUTE;
params.ith = 0;
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
@ -5410,7 +5410,7 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d
static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tensor * tensor, const char * name) {
void * tensor_data = tensor->data;
if (tensor->backend == GGML_BACKEND_GPU) {
if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
const size_t tensor_size = ggml_nbytes(tensor);
tensor_data = malloc(tensor_size);
@ -5436,14 +5436,14 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso
std::vector<const ggml_tensor *> done;
ggml_vk_print_graph_origin(tensor, done);
if (tensor->backend == GGML_BACKEND_GPU) {
if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
free(tensor_data);
}
}
static void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) {
return;
GGML_ASSERT(tensor->backend == GGML_BACKEND_CPU);
GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_CPU);
if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) {
return;
}
@ -5481,7 +5481,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
if (params->ith != 0) {
return;
}
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
return;
}
@ -5518,10 +5518,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
src0_buffer = malloc(src0_size);
src0_clone->data = src0_buffer;
if (src0->backend == GGML_BACKEND_CPU) {
if (src0->backend == GGML_BACKEND_TYPE_CPU) {
memcpy(src0_clone->data, src0->data, src0_size);
memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
} else if (src0->backend == GGML_BACKEND_GPU) {
} else if (src0->backend == GGML_BACKEND_TYPE_GPU) {
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra;
uint64_t offset = extra->offset;
if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) {
@ -5561,10 +5561,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
src1_buffer = malloc(src1_size);
src1_clone->data = src1_buffer;
if (src1->backend == GGML_BACKEND_CPU) {
if (src1->backend == GGML_BACKEND_TYPE_CPU) {
memcpy(src1_clone->data, src1->data, src1_size);
memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
} else if (src1->backend == GGML_BACKEND_GPU) {
} else if (src1->backend == GGML_BACKEND_TYPE_GPU) {
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra;
uint64_t offset = extra->offset;
if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) {
@ -5723,7 +5723,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_
if (params->ith != 0) {
return;
}
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) {
return;
}
if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) {
@ -5735,7 +5735,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_
void * tensor_data = tensor->data;
if (tensor->backend == GGML_BACKEND_GPU) {
if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
size_t tensor_size = ggml_nbytes(tensor);
tensor_data = malloc(tensor_size);
@ -5868,7 +5868,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_
comp_result = nullptr;
comp_size = 0;
if (tensor->backend == GGML_BACKEND_GPU) {
if (tensor->backend == GGML_BACKEND_TYPE_GPU) {
free(tensor_data);
}
}

1537
ggml.c
View file

File diff suppressed because it is too large Load diff

46
ggml.h
View file

@ -315,13 +315,7 @@
extern "C" {
#endif
#if defined(__ARM_NEON) && defined(__CUDACC__)
typedef half ggml_fp16_t;
#elif defined(__ARM_NEON) && !defined(_MSC_VER)
typedef __fp16 ggml_fp16_t;
#else
typedef uint16_t ggml_fp16_t;
#endif
// convert FP16 <-> FP32
GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x);
@ -356,6 +350,7 @@ extern "C" {
GGML_TYPE_IQ3_XXS = 18,
GGML_TYPE_IQ1_S = 19,
GGML_TYPE_IQ4_NL = 20,
GGML_TYPE_IQ3_S = 21,
GGML_TYPE_I8,
GGML_TYPE_I16,
GGML_TYPE_I32,
@ -369,9 +364,9 @@ extern "C" {
};
enum ggml_backend_type {
GGML_BACKEND_CPU = 0,
GGML_BACKEND_GPU = 10,
GGML_BACKEND_GPU_SPLIT = 20,
GGML_BACKEND_TYPE_CPU = 0,
GGML_BACKEND_TYPE_GPU = 10,
GGML_BACKEND_TYPE_GPU_SPLIT = 20,
};
// model file types
@ -395,6 +390,7 @@ extern "C" {
GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ1_S = 18, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ4_NL = 19, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ3_S = 20, // except 1d tensors
};
// available tensor operations:
@ -502,9 +498,9 @@ extern "C" {
};
enum ggml_object_type {
GGML_OBJECT_TENSOR,
GGML_OBJECT_GRAPH,
GGML_OBJECT_WORK_BUFFER
GGML_OBJECT_TYPE_TENSOR,
GGML_OBJECT_TYPE_GRAPH,
GGML_OBJECT_TYPE_WORK_BUFFER
};
enum ggml_log_level {
@ -646,9 +642,9 @@ extern "C" {
// NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled.
// This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995.
enum ggml_task_type {
GGML_TASK_INIT = 0,
GGML_TASK_COMPUTE,
GGML_TASK_FINALIZE,
GGML_TASK_TYPE_INIT = 0,
GGML_TASK_TYPE_COMPUTE,
GGML_TASK_TYPE_FINALIZE,
};
struct ggml_compute_params {
@ -1653,8 +1649,8 @@ extern "C" {
// sort rows
enum ggml_sort_order {
GGML_SORT_ASC,
GGML_SORT_DESC,
GGML_SORT_ORDER_ASC,
GGML_SORT_ORDER_DESC,
};
GGML_API struct ggml_tensor * ggml_argsort(
@ -1947,8 +1943,8 @@ extern "C" {
// optimization methods
enum ggml_opt_type {
GGML_OPT_ADAM,
GGML_OPT_LBFGS,
GGML_OPT_TYPE_ADAM,
GGML_OPT_TYPE_LBFGS,
};
// linesearch methods
@ -1962,12 +1958,12 @@ extern "C" {
// optimization return values
enum ggml_opt_result {
GGML_OPT_OK = 0,
GGML_OPT_DID_NOT_CONVERGE,
GGML_OPT_NO_CONTEXT,
GGML_OPT_INVALID_WOLFE,
GGML_OPT_FAIL,
GGML_OPT_CANCEL,
GGML_OPT_RESULT_OK = 0,
GGML_OPT_RESULT_DID_NOT_CONVERGE,
GGML_OPT_RESULT_NO_CONTEXT,
GGML_OPT_RESULT_INVALID_WOLFE,
GGML_OPT_RESULT_FAIL,
GGML_OPT_RESULT_CANCEL,
GGML_LINESEARCH_FAIL = -128,
GGML_LINESEARCH_MINIMUM_STEP,

1080
llama.cpp
View file

File diff suppressed because it is too large Load diff

66
llama.h
View file

@ -64,6 +64,15 @@ extern "C" {
LLAMA_VOCAB_TYPE_WPM = 2, // WordPiece
};
// note: these values should be synchronized with ggml_rope
// TODO: maybe move this enum to ggml.h (ggml_rope_type)
enum llama_rope_type {
LLAMA_ROPE_TYPE_NONE = -1,
LLAMA_ROPE_TYPE_NORM = 0,
LLAMA_ROPE_TYPE_NEOX = 2,
LLAMA_ROPE_TYPE_GLM = 4,
};
enum llama_token_type {
LLAMA_TOKEN_TYPE_UNDEFINED = 0,
LLAMA_TOKEN_TYPE_NORMAL = 1,
@ -102,28 +111,30 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ4_NL = 25, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ3_S = 26, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};
enum llama_rope_scaling_type {
LLAMA_ROPE_SCALING_UNSPECIFIED = -1,
LLAMA_ROPE_SCALING_NONE = 0,
LLAMA_ROPE_SCALING_LINEAR = 1,
LLAMA_ROPE_SCALING_YARN = 2,
LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN,
LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1,
LLAMA_ROPE_SCALING_TYPE_NONE = 0,
LLAMA_ROPE_SCALING_TYPE_LINEAR = 1,
LLAMA_ROPE_SCALING_TYPE_YARN = 2,
LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN,
};
enum llama_pooling_type {
LLAMA_POOLING_NONE = 0,
LLAMA_POOLING_MEAN = 1,
LLAMA_POOLING_CLS = 2,
LLAMA_POOLING_TYPE_NONE = 0,
LLAMA_POOLING_TYPE_MEAN = 1,
LLAMA_POOLING_TYPE_CLS = 2,
};
enum llama_split_mode {
LLAMA_SPLIT_NONE = 0, // single GPU
LLAMA_SPLIT_LAYER = 1, // split layers and KV across GPUs
LLAMA_SPLIT_ROW = 2, // split rows across GPUs
LLAMA_SPLIT_MODE_NONE = 0, // single GPU
LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs
};
typedef struct llama_token_data {
@ -171,9 +182,9 @@ extern "C" {
} llama_batch;
enum llama_model_kv_override_type {
LLAMA_KV_OVERRIDE_INT,
LLAMA_KV_OVERRIDE_FLOAT,
LLAMA_KV_OVERRIDE_BOOL,
LLAMA_KV_OVERRIDE_TYPE_INT,
LLAMA_KV_OVERRIDE_TYPE_FLOAT,
LLAMA_KV_OVERRIDE_TYPE_BOOL,
};
struct llama_model_kv_override {
@ -358,6 +369,7 @@ extern "C" {
LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model);
LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
@ -512,10 +524,12 @@ extern "C" {
llama_seq_id seq_id);
// Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
// If the KV cache is RoPEd, the KV data is updated accordingly
// If the KV cache is RoPEd, the KV data is updated accordingly:
// - lazily on next llama_decode()
// - explicitly with llama_kv_cache_update()
// p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_cache_seq_shift(
LLAMA_API void llama_kv_cache_seq_add(
struct llama_context * ctx,
llama_seq_id seq_id,
llama_pos p0,
@ -523,7 +537,9 @@ extern "C" {
llama_pos delta);
// Integer division of the positions by factor of `d > 1`
// If the KV cache is RoPEd, the KV data is updated accordingly
// If the KV cache is RoPEd, the KV data is updated accordingly:
// - lazily on next llama_decode()
// - explicitly with llama_kv_cache_update()
// p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_cache_seq_div(
@ -533,6 +549,20 @@ extern "C" {
llama_pos p1,
int d);
// Returns the largest position present in the KV cache for the specified sequence
LLAMA_API llama_pos llama_kv_cache_seq_pos_max(
struct llama_context * ctx,
llama_seq_id seq_id);
// Defragment the KV cache
// This will be applied:
// - lazily on next llama_decode()
// - explicitly with llama_kv_cache_update()
LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx);
// Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
LLAMA_API void llama_kv_cache_update(struct llama_context * ctx);
//
// State / sessions
//
@ -708,7 +738,7 @@ extern "C" {
/// Apply chat template. Inspired by hf apply_chat_template() on python.
/// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
/// NOTE: This function only support some known jinja templates. It is not a jinja parser.
/// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
/// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead.
/// @param chat Pointer to a list of multiple llama_chat_message
/// @param n_msg Number of llama_chat_message in this chat

2
scripts/sync-ggml.last
View file

@ -1 +1 @@
818eeb8a3be99125746a90ec63af8f51516a2ec6
8cdf783f288a98eddf521b0ab1b4d405be9e18ba

6
tests/test-backend-ops.cpp
View file

@ -1264,7 +1264,7 @@ struct test_argsort : public test_case {
test_argsort(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {16, 10, 10, 10},
ggml_sort_order order = GGML_SORT_ASC)
ggml_sort_order order = GGML_SORT_ORDER_ASC)
: type(type), ne(ne), order(order) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
@ -1918,7 +1918,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
GGML_TYPE_Q6_K,
GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS,
GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S,
GGML_TYPE_IQ4_NL,
GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S,
};
// unary ops
@ -2116,7 +2116,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_concat(GGML_TYPE_F32));
test_cases.emplace_back(new test_concat(GGML_TYPE_I32));
for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) {
for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) {
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {16, 10, 10, 10}, order));
}

27
tests/test-chat-template.cpp
View file

@ -27,12 +27,24 @@ int main(void) {
"{%- for idx in range(0, messages|length) -%}\\n{%- if messages[idx]['role'] == 'user' -%}\\n{%- if idx > 1 -%}\\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\\n{%- else -%}\\n{{- messages[idx]['content'] + ' [/INST]' -}}\\n{%- endif -%}\\n{% elif messages[idx]['role'] == 'system' %}\\n{{- '[INST] <<SYS>>\\\\n' + messages[idx]['content'] + '\\\\n<</SYS>>\\\\n\\\\n' -}}\\n{%- elif messages[idx]['role'] == 'assistant' -%}\\n{{- ' ' + messages[idx]['content'] + ' ' + eos_token -}}\\n{% endif %}\\n{% endfor %}",
// bofenghuang/vigogne-2-70b-chat
"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif true == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'Vous êtes Vigogne, un assistant IA créé par Zaion Lab. Vous suivez extrêmement bien les instructions. Aidez autant que vous le pouvez.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\\\n' + system_message + '\\\\n<</SYS>>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\\\\n' + content.strip() + '\\\\n<</SYS>>\\\\n\\\\n' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
// mlabonne/AlphaMonarch-7B
"{% for message in messages %}{{bos_token + message['role'] + '\\n' + message['content'] + eos_token + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ bos_token + 'assistant\\n' }}{% endif %}",
// google/gemma-7b-it
"{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\\n' + message['content'] | trim + '<end_of_turn>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}",
};
std::vector<std::string> expected_substr = {
"<|im_start|>assistant\n I am an assistant <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant",
"[/INST]Hi there</s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
"</s><s>[INST] Who are you [/INST] I am an assistant </s><s>[INST] Another question [/INST]",
"[/INST] Hi there </s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
std::vector<std::string> expected_output = {
// teknium/OpenHermes-2.5-Mistral-7B
"<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi there<|im_end|>\n<|im_start|>user\nWho are you<|im_end|>\n<|im_start|>assistant\n I am an assistant <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant\n",
// mistralai/Mistral-7B-Instruct-v0.2
"[INST] You are a helpful assistant\nHello [/INST]Hi there</s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
// TheBloke/FusionNet_34Bx2_MoE-AWQ
"[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s><s>[INST] Who are you [/INST] I am an assistant </s><s>[INST] Another question [/INST]",
// bofenghuang/vigogne-2-70b-chat
"[INST] <<SYS>>\nYou are a helpful assistant\n<</SYS>>\n\nHello [/INST] Hi there </s>[INST] Who are you [/INST] I am an assistant </s>[INST] Another question [/INST]",
// mlabonne/AlphaMonarch-7B
"system\nYou are a helpful assistant</s>\n<s>user\nHello</s>\n<s>assistant\nHi there</s>\n<s>user\nWho are you</s>\n<s>assistant\n I am an assistant </s>\n<s>user\nAnother question</s>\n<s>assistant\n",
// google/gemma-7b-it
"<start_of_turn>user\nYou are a helpful assistant\n\nHello<end_of_turn>\n<start_of_turn>model\nHi there<end_of_turn>\n<start_of_turn>user\nWho are you<end_of_turn>\n<start_of_turn>model\nI am an assistant<end_of_turn>\n<start_of_turn>user\nAnother question<end_of_turn>\n<start_of_turn>model\n",
};
std::vector<char> formatted_chat(1024);
int32_t res;
@ -43,7 +55,7 @@ int main(void) {
for (size_t i = 0; i < templates.size(); i++) {
std::string custom_template = templates[i];
std::string substr = expected_substr[i];
std::string expected = expected_output[i];
formatted_chat.resize(1024);
res = llama_chat_apply_template(
nullptr,
@ -57,8 +69,7 @@ int main(void) {
formatted_chat.resize(res);
std::string output(formatted_chat.data(), formatted_chat.size());
std::cout << output << "\n-------------------------\n";
// expect the "formatted_chat" to contain pre-defined strings
assert(output.find(substr) != std::string::npos);
assert(output == expected);
}
return 0;
}

2
tests/test-opt.cpp
View file

@ -118,7 +118,7 @@ int main(void) {
const float fe = ggml_get_f32_1d(e, 0);
printf("%s: e = %.4f\n", __func__, fe);
struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_ADAM);
struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
ggml_opt(ctx, opt_params, e);

4
tests/test-quantize-fns.cpp
View file

@ -151,6 +151,7 @@ int main(int argc, char * argv[]) {
const float max_quantization_error =
type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
type == GGML_TYPE_IQ3_S ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS : MAX_QUANTIZATION_TOTAL_ERROR;
failed = !(total_error < max_quantization_error);
num_failed += failed;
@ -167,7 +168,8 @@ int main(int argc, char * argv[]) {
const float vec_dot_error = dot_product_error(qfns, test_size, test_data.data(), test_data2.data());
const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS ||
type == GGML_TYPE_IQ3_XXS ? MAX_DOT_PRODUCT_ERROR_LOWBIT : MAX_DOT_PRODUCT_ERROR;
type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S ? MAX_DOT_PRODUCT_ERROR_LOWBIT
: MAX_DOT_PRODUCT_ERROR;
failed = !(vec_dot_error < max_allowed_error);
num_failed += failed;
if (failed || verbose) {

3
unicode.h
View file

@ -404,7 +404,8 @@ static std::unordered_map<uint32_t, int> codepoint_type_map() {
static int codepoint_type(uint32_t cp) {
static std::unordered_map<uint32_t, int> codepoint_types = codepoint_type_map();
return codepoint_types.find(cp) == codepoint_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : codepoint_types.at(cp);
const auto it = codepoint_types.find(cp);
return it == codepoint_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : it->second;
}
static int codepoint_type(const std::string & utf8) {