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Merge branch 'master' into concedo_experimental

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# Conflicts:
#	Makefile
#	README.md
#	flake.lock
#	ggml-cuda.cu
#	llama.cpp
#	tests/test-backend-ops.cpp
#	tests/test-quantize-fns.cpp
This commit is contained in:
Concedo 2024-02-28 13:41:35 +08:00
commit ad638285de
26 changed files with 3393 additions and 589 deletions
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78
.github/workflows/server.yml vendored
View file

@ -6,11 +6,10 @@ on:
push:
branches:
- master
- test/server-add-ci-test # FIXME remove
paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
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: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*']
jobs:
server:
@ -18,45 +17,21 @@ jobs:
strategy:
matrix:
build: [noavx, avx2, avx, avx512, cublas, clblast, openblas, kompute, vulkan]
sanitizer: [ADDRESS, THREAD, UNDEFINED]
build_type: [Debug, Release]
include:
- build: 'noavx'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF'
image: ubuntu:latest
- build: 'avx2'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON'
image: ubuntu:latest
- build: 'avx'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF'
image: ubuntu:latest
- build: 'avx512'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON'
image: ubuntu:latest
experimental: true
- build: 'cublas'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON'
image: nvidia/cuda:12.3.1-devel-ubuntu22.04
arch_not_available: true # require nvidia docker engine
- build: 'clblast'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON'
image: ubuntu:latest
arch_not_available: true
- build: 'openblas'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS'
image: ubuntu:latest
- build: 'kompute'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON'
image: ubuntu:latest
arch_not_available: true
- build: 'vulkan'
defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON'
image: ubuntu:latest
arch_not_available: true
- build_type: Release
sanitizer: ""
exclude:
- build_type: Release
sanitizer: ADDRESS
- build_type: Release
sanitizer: THREAD
- build_type: Release
sanitizer: UNDEFINED
container:
image: ${{ matrix.image }}
image: ubuntu:latest
ports:
- 8888
options: --cpus 4
@ -72,40 +47,22 @@ jobs:
apt-get update
apt-get -y install \
build-essential \
pkg-config \
git \
cmake \
python3-pip \
wget \
psmisc
- name: Download CLBlast
id: get_clblast
if: ${{ matrix.build == 'clblast' }}
run: |
apt install -y libclblast-dev
- name: Download OpenBLAS
id: get_openblas
if: ${{ matrix.build == 'openblas' }}
run: |
apt-get -y install libopenblas-dev
- name: Install Vulkan SDK
id: get_vulkan
if: ${{ matrix.build == 'kompute' || matrix.build == 'vulkan' }}
run: |
wget -qO- https://packages.lunarg.com/lunarg-signing-key-pub.asc | tee /etc/apt/trusted.gpg.d/lunarg.asc
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list http://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
apt-get update
apt-get -y install vulkan-sdk
- name: Build
id: cmake_build
run: |
mkdir build
cd build
cmake .. -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ${{ matrix.defines }}
cmake .. \
-DLLAMA_NATIVE=OFF \
-DLLAMA_BUILD_SERVER=ON \
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server
- name: Tests dependencies
@ -121,7 +78,6 @@ jobs:
- name: Tests
id: server_integration_test
continue-on-error: ${{ matrix.experimental || matrix.arch_not_available }}
run: |
cd examples/server/tests
PORT=8888 ./tests.sh

9
common/common.cpp
View file

@ -336,6 +336,12 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
break;
}
params.yarn_beta_slow = std::stof(argv[i]);
} else if (arg == "--defrag-thold" || arg == "-dt") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.defrag_thold = std::stof(argv[i]);
} else if (arg == "--samplers") {
if (++i >= argc) {
invalid_param = true;
@ -1005,6 +1011,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
printf(" -dt N, --defrag-thold N\n");
printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
printf(" --no-penalize-nl do not penalize newline token\n");
printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp);
@ -1286,6 +1294,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.yarn_beta_fast = params.yarn_beta_fast;
cparams.yarn_beta_slow = params.yarn_beta_slow;
cparams.yarn_orig_ctx = params.yarn_orig_ctx;
cparams.defrag_thold = params.defrag_thold;
cparams.offload_kqv = !params.no_kv_offload;
cparams.type_k = kv_cache_type_from_str(params.cache_type_k);

1
common/common.h
View file

@ -69,6 +69,7 @@ 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
float defrag_thold = -1.0f; // KV cache defragmentation threshold
int32_t rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;

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());
}
}

4
examples/infill/infill.cpp
View file

@ -448,8 +448,8 @@ 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;

10
examples/main/main.cpp
View file

@ -549,8 +549,8 @@ 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 , params.n_keep + n_discard);
llama_kv_cache_seq_shift(ctx, 0, params.n_keep + 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;
@ -577,9 +577,9 @@ 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;
}
}

10
examples/quantize/quantize.cpp
View file

@ -24,18 +24,21 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", },
{ "IQ2_XXS",LLAMA_FTYPE_MOSTLY_IQ2_XXS," 2.06 bpw quantization", },
{ "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", },
{ "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", },
{ "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", },
{ "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", },
{ "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", },
{ "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" , },
{ "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS, " 3.3 bpw quantization" , },
{ "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },
{ "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", },
{ "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", },
{ "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.25 bpw non-linear quantization", },
{ "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
{ "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
{ "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
{ "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.59G, +0.0992 ppl @ LLaMA-v1-7B", },
{ "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0532 ppl @ LLaMA-v1-7B", },
@ -293,6 +296,7 @@ int main(int argc, char ** argv) {
}
if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) && imatrix_data.empty()) {
fprintf(stderr, "\n===============================================================================================\n");
fprintf(stderr, "Please do not use IQ1_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");

18
examples/server/README.md
View file

@ -1,8 +1,20 @@
# llama.cpp/example/server
# LLaMA.cpp HTTP Server
This example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp.
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**.
Command line options:
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.

12
examples/server/server.cpp
View file

@ -1337,6 +1337,10 @@ struct llama_server_context
split_multiprompt_task(task_id, task);
}
} else {
// an empty prompt can make slot become buggy
if (task.data.contains("prompt") && task.data["prompt"].is_string() && task.data["prompt"].get<std::string>().empty()) {
task.data["prompt"] = " "; // add a space so that we have one token
}
queue_tasks.post(task);
}
}
@ -1637,8 +1641,8 @@ struct llama_server_context
{"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_shift(ctx, slot.id, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard);
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 = n_keep + n_discard; i < slot.cache_tokens.size(); i++)
{
@ -1942,9 +1946,9 @@ struct llama_server_context
LOG_TEE("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_TEE("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;

4
examples/server/tests/features/steps/steps.py
View file

@ -699,6 +699,8 @@ async def wait_for_health_status(context,
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:
@ -736,7 +738,7 @@ async def wait_for_health_status(context,
if n_completions > 0:
return
assert False, 'timeout exceeded'
assert False, f'{expected_health_status} timeout exceeded {counter}s>={timeout}'
def assert_embeddings(embeddings):

552
ggml-cuda.cu
View file

@ -523,6 +523,17 @@ typedef struct {
} block_iq2_xs;
static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding");
// 2.5625 bpw quants
#define QR2_S 8
#define QI2_S (QK_K / (4*QR2_S))
typedef struct {
half d;
uint8_t qs[QK_K/4];
uint8_t qh[QK_K/32];
uint8_t scales[QK_K/32];
} block_iq2_s;
static_assert(sizeof(block_iq2_s) == sizeof(ggml_fp16_t) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding");
#define QR3_XXS 8
#define QI3_XXS (QK_K / (4*QR3_XXS))
typedef struct {
@ -560,6 +571,18 @@ typedef struct {
} block_iq4_nl;
static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding");
// QR4_XS = 8 is very slightly faster than QR4_XS = 4
#define QR4_XS 8
#define QI4_XS (QK_K / (4*QR4_XS))
typedef struct {
half d;
uint16_t scales_h;
uint8_t scales_l[QK_K/64];
uint8_t qs[QK_K/2];
} block_iq4_xs;
static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
#define WARP_SIZE 32
#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
@ -686,18 +709,20 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
return a;
}
//static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
//#pragma unroll
// for (int mask = 16; mask > 0; mask >>= 1) {
// a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32));
// }
// return a;
//#else
// (void) a;
// NO_DEVICE_CODE;
//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
//}
#ifdef GGML_CUDA_F16
static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32));
}
return a;
#else
(void) a;
NO_DEVICE_CODE;
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
}
#endif // GGML_CUDA_F16
static __device__ __forceinline__ float warp_reduce_max(float x) {
#pragma unroll
@ -1690,6 +1715,265 @@ static const __device__ uint64_t iq2xs_grid[512] = {
0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b,
};
static const __device__ uint64_t iq2s_grid[1024] = {
0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b,
0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919,
0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b,
0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919,
0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b,
0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919,
0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808,
0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908,
0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b,
0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908,
0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08,
0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19,
0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819,
0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919,
0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b,
0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908,
0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908,
0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919,
0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908,
0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b,
0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919,
0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b,
0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919,
0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908,
0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b,
0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b,
0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08,
0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b,
0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819,
0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808,
0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908,
0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b,
0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908,
0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08,
0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819,
0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808,
0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08,
0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819,
0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b,
0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908,
0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919,
0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b,
0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919,
0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808,
0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819,
0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919,
0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919,
0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808,
0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819,
0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b,
0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908,
0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b,
0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908,
0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919,
0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b,
0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919,
0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b,
0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819,
0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919,
0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908,
0x081908190819192b, 0x0819081908192b19, 0x08190819082b0808, 0x08190819082b082b,
0x08190819082b1919, 0x08190819082b2b08, 0x0819081919080819, 0x0819081919081908,
0x081908191908192b, 0x0819081919082b19, 0x0819081919190808, 0x081908191919082b,
0x0819081919191919, 0x0819081919192b08, 0x08190819192b0819, 0x08190819192b1908,
0x081908192b080808, 0x081908192b08082b, 0x081908192b081919, 0x081908192b082b08,
0x081908192b190819, 0x081908192b191908, 0x0819082b08080819, 0x0819082b08081908,
0x0819082b08082b19, 0x0819082b08190808, 0x0819082b08191919, 0x0819082b082b0819,
0x0819082b082b1908, 0x0819082b19080808, 0x0819082b19081919, 0x0819082b19190819,
0x0819082b19191908, 0x0819082b2b080819, 0x0819082b2b081908, 0x0819082b2b190808,
0x0819190808080808, 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08,
0x0819190808190819, 0x0819190808191908, 0x081919080819192b, 0x0819190808192b19,
0x08191908082b0808, 0x08191908082b1919, 0x08191908082b2b08, 0x0819190819080819,
0x0819190819081908, 0x081919081908192b, 0x0819190819082b19, 0x0819190819190808,
0x081919081919082b, 0x0819190819191919, 0x0819190819192b08, 0x08191908192b0819,
0x08191908192b1908, 0x081919082b080808, 0x081919082b08082b, 0x081919082b081919,
0x081919082b082b08, 0x081919082b190819, 0x081919082b191908, 0x081919082b2b0808,
0x0819191908080819, 0x0819191908081908, 0x081919190808192b, 0x0819191908082b19,
0x0819191908190808, 0x081919190819082b, 0x0819191908191919, 0x0819191908192b08,
0x08191919082b0819, 0x08191919082b1908, 0x0819191919080808, 0x081919191908082b,
0x0819191919081919, 0x0819191919082b08, 0x0819191919190819, 0x0819191919191908,
0x08191919192b0808, 0x081919192b080819, 0x081919192b081908, 0x081919192b190808,
0x0819192b08080808, 0x0819192b08081919, 0x0819192b08082b08, 0x0819192b08190819,
0x0819192b08191908, 0x0819192b082b0808, 0x0819192b19080819, 0x0819192b19081908,
0x0819192b19190808, 0x0819192b2b080808, 0x0819192b2b2b2b2b, 0x08192b0808080819,
0x08192b0808081908, 0x08192b080808192b, 0x08192b0808082b19, 0x08192b0808190808,
0x08192b0808191919, 0x08192b0808192b08, 0x08192b08082b0819, 0x08192b0819080808,
0x08192b081908082b, 0x08192b0819081919, 0x08192b0819082b08, 0x08192b0819190819,
0x08192b0819191908, 0x08192b08192b0808, 0x08192b082b080819, 0x08192b082b081908,
0x08192b1908080808, 0x08192b190808082b, 0x08192b1908081919, 0x08192b1908082b08,
0x08192b1908190819, 0x08192b1908191908, 0x08192b19082b0808, 0x08192b1919080819,
0x08192b1919081908, 0x08192b1919190808, 0x08192b19192b2b19, 0x08192b192b2b082b,
0x08192b2b08081908, 0x08192b2b08190808, 0x08192b2b19080808, 0x08192b2b1919192b,
0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, 0x082b080808082b08,
0x082b080808190819, 0x082b080808191908, 0x082b08080819192b, 0x082b080808192b19,
0x082b0808082b0808, 0x082b0808082b1919, 0x082b0808082b2b2b, 0x082b080819080819,
0x082b080819081908, 0x082b080819190808, 0x082b08081919082b, 0x082b080819191919,
0x082b0808192b1908, 0x082b08082b080808, 0x082b08082b082b2b, 0x082b08082b191908,
0x082b08082b2b2b2b, 0x082b081908080819, 0x082b081908081908, 0x082b081908190808,
0x082b08190819082b, 0x082b081908191919, 0x082b0819082b0819, 0x082b081919080808,
0x082b08191908082b, 0x082b081919081919, 0x082b081919190819, 0x082b081919191908,
0x082b0819192b0808, 0x082b08192b080819, 0x082b08192b081908, 0x082b08192b190808,
0x082b082b08080808, 0x082b082b08082b2b, 0x082b082b082b082b, 0x082b082b082b2b08,
0x082b082b082b2b2b, 0x082b082b19081908, 0x082b082b19190808, 0x082b082b2b082b08,
0x082b082b2b082b2b, 0x082b082b2b2b2b08, 0x082b190808080819, 0x082b190808081908,
0x082b19080808192b, 0x082b190808082b19, 0x082b190808190808, 0x082b190808191919,
0x082b190808192b08, 0x082b1908082b0819, 0x082b1908082b1908, 0x082b190819080808,
0x082b19081908082b, 0x082b190819081919, 0x082b190819082b08, 0x082b190819190819,
0x082b190819191908, 0x082b1908192b0808, 0x082b19082b080819, 0x082b19082b081908,
0x082b19082b190808, 0x082b191908080808, 0x082b191908081919, 0x082b191908082b08,
0x082b191908190819, 0x082b191908191908, 0x082b1919082b0808, 0x082b191919080819,
0x082b191919081908, 0x082b191919190808, 0x082b1919192b192b, 0x082b19192b080808,
0x082b192b08080819, 0x082b192b08081908, 0x082b192b08190808, 0x082b192b19080808,
0x082b192b19192b19, 0x082b2b0808080808, 0x082b2b0808081919, 0x082b2b0808190819,
0x082b2b0808191908, 0x082b2b0819080819, 0x082b2b0819081908, 0x082b2b0819190808,
0x082b2b082b082b2b, 0x082b2b082b2b2b2b, 0x082b2b1908080819, 0x082b2b1908081908,
0x082b2b1908190808, 0x082b2b192b191919, 0x082b2b2b08082b2b, 0x082b2b2b082b082b,
0x082b2b2b192b1908, 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819,
0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808,
0x190808080819082b, 0x1908080808191919, 0x1908080808192b08, 0x1908080808192b2b,
0x19080808082b0819, 0x19080808082b1908, 0x19080808082b192b, 0x1908080819080808,
0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, 0x1908080819082b2b,
0x1908080819190819, 0x1908080819191908, 0x190808081919192b, 0x1908080819192b19,
0x19080808192b0808, 0x19080808192b082b, 0x19080808192b1919, 0x190808082b080819,
0x190808082b081908, 0x190808082b190808, 0x190808082b191919, 0x190808082b192b08,
0x190808082b2b0819, 0x190808082b2b1908, 0x1908081908080808, 0x190808190808082b,
0x1908081908081919, 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908,
0x190808190819192b, 0x1908081908192b19, 0x19080819082b0808, 0x19080819082b082b,
0x19080819082b1919, 0x1908081919080819, 0x1908081919081908, 0x190808191908192b,
0x1908081919082b19, 0x1908081919190808, 0x190808191919082b, 0x1908081919191919,
0x1908081919192b08, 0x19080819192b0819, 0x19080819192b1908, 0x190808192b080808,
0x190808192b08082b, 0x190808192b081919, 0x190808192b082b08, 0x190808192b190819,
0x190808192b191908, 0x190808192b2b0808, 0x1908082b08080819, 0x1908082b08081908,
0x1908082b08190808, 0x1908082b0819082b, 0x1908082b08191919, 0x1908082b08192b08,
0x1908082b082b1908, 0x1908082b19080808, 0x1908082b19081919, 0x1908082b19082b08,
0x1908082b19190819, 0x1908082b19191908, 0x1908082b192b0808, 0x1908082b2b080819,
0x1908082b2b081908, 0x1908190808080808, 0x190819080808082b, 0x1908190808081919,
0x1908190808082b08, 0x1908190808082b2b, 0x1908190808190819, 0x1908190808191908,
0x190819080819192b, 0x1908190808192b19, 0x19081908082b0808, 0x19081908082b082b,
0x19081908082b1919, 0x19081908082b2b08, 0x1908190819080819, 0x1908190819081908,
0x190819081908192b, 0x1908190819082b19, 0x1908190819190808, 0x190819081919082b,
0x1908190819191919, 0x1908190819192b08, 0x19081908192b0819, 0x19081908192b1908,
0x190819082b080808, 0x190819082b08082b, 0x190819082b081919, 0x190819082b082b08,
0x190819082b190819, 0x190819082b191908, 0x190819082b2b0808, 0x1908191908080819,
0x1908191908081908, 0x190819190808192b, 0x1908191908082b19, 0x1908191908190808,
0x190819190819082b, 0x1908191908191919, 0x1908191908192b08, 0x19081919082b0819,
0x19081919082b1908, 0x1908191919080808, 0x190819191908082b, 0x1908191919081919,
0x1908191919082b08, 0x1908191919190819, 0x1908191919191908, 0x19081919192b0808,
0x19081919192b2b2b, 0x190819192b080819, 0x190819192b081908, 0x190819192b190808,
0x1908192b08080808, 0x1908192b0808082b, 0x1908192b08081919, 0x1908192b08082b08,
0x1908192b08190819, 0x1908192b08191908, 0x1908192b082b0808, 0x1908192b19080819,
0x1908192b19081908, 0x1908192b19190808, 0x1908192b2b080808, 0x1908192b2b2b1919,
0x19082b0808080819, 0x19082b0808081908, 0x19082b0808082b19, 0x19082b0808190808,
0x19082b080819082b, 0x19082b0808191919, 0x19082b0808192b08, 0x19082b08082b0819,
0x19082b08082b1908, 0x19082b0819080808, 0x19082b081908082b, 0x19082b0819081919,
0x19082b0819082b08, 0x19082b0819190819, 0x19082b0819191908, 0x19082b08192b0808,
0x19082b082b081908, 0x19082b082b190808, 0x19082b1908080808, 0x19082b190808082b,
0x19082b1908081919, 0x19082b1908082b08, 0x19082b1908190819, 0x19082b1908191908,
0x19082b19082b0808, 0x19082b1919080819, 0x19082b1919081908, 0x19082b1919190808,
0x19082b192b080808, 0x19082b192b19192b, 0x19082b2b08080819, 0x19082b2b08081908,
0x19082b2b08190808, 0x19082b2b19080808, 0x1919080808080808, 0x191908080808082b,
0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, 0x1919080808191908,
0x191908080819192b, 0x1919080808192b19, 0x19190808082b0808, 0x19190808082b082b,
0x19190808082b1919, 0x19190808082b2b08, 0x1919080819080819, 0x1919080819081908,
0x191908081908192b, 0x1919080819082b19, 0x1919080819190808, 0x191908081919082b,
0x1919080819191919, 0x1919080819192b08, 0x19190808192b0819, 0x19190808192b1908,
0x191908082b080808, 0x191908082b08082b, 0x191908082b081919, 0x191908082b082b08,
0x191908082b190819, 0x191908082b191908, 0x1919081908080819, 0x1919081908081908,
0x191908190808192b, 0x1919081908082b19, 0x1919081908190808, 0x191908190819082b,
0x1919081908191919, 0x1919081908192b08, 0x19190819082b0819, 0x19190819082b1908,
0x1919081919080808, 0x191908191908082b, 0x1919081919081919, 0x1919081919082b08,
0x1919081919190819, 0x1919081919191908, 0x19190819192b0808, 0x191908192b080819,
0x191908192b081908, 0x191908192b190808, 0x1919082b08080808, 0x1919082b08081919,
0x1919082b08082b08, 0x1919082b08190819, 0x1919082b08191908, 0x1919082b082b0808,
0x1919082b19080819, 0x1919082b19081908, 0x1919082b19190808, 0x1919082b192b2b19,
0x1919082b2b080808, 0x1919190808080819, 0x1919190808081908, 0x191919080808192b,
0x1919190808082b19, 0x1919190808190808, 0x191919080819082b, 0x1919190808191919,
0x1919190808192b08, 0x19191908082b0819, 0x19191908082b1908, 0x1919190819080808,
0x191919081908082b, 0x1919190819081919, 0x1919190819082b08, 0x1919190819190819,
0x1919190819191908, 0x19191908192b0808, 0x191919082b080819, 0x191919082b081908,
0x191919082b190808, 0x1919191908080808, 0x191919190808082b, 0x1919191908081919,
0x1919191908082b08, 0x1919191908190819, 0x1919191908191908, 0x19191919082b0808,
0x1919191919080819, 0x1919191919081908, 0x1919191919190808, 0x191919192b080808,
0x1919192b08080819, 0x1919192b08081908, 0x1919192b08190808, 0x1919192b082b192b,
0x1919192b19080808, 0x19192b0808080808, 0x19192b080808082b, 0x19192b0808081919,
0x19192b0808082b08, 0x19192b0808190819, 0x19192b0808191908, 0x19192b08082b0808,
0x19192b0819080819, 0x19192b0819081908, 0x19192b0819190808, 0x19192b0819192b2b,
0x19192b082b080808, 0x19192b1908080819, 0x19192b1908081908, 0x19192b1908190808,
0x19192b1919080808, 0x19192b2b08080808, 0x19192b2b08192b19, 0x19192b2b2b081919,
0x19192b2b2b2b2b08, 0x192b080808080819, 0x192b080808081908, 0x192b08080808192b,
0x192b080808190808, 0x192b08080819082b, 0x192b080808191919, 0x192b080808192b08,
0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919,
0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808,
0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b,
0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908,
0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808,
0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808,
0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808,
0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908,
0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808,
0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808,
0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b,
0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908,
0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808,
0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808,
0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819,
0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919,
0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b,
0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808,
0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819,
0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b,
0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908,
0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08,
0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908,
0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919,
0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819,
0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908,
0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b,
0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808,
0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819,
0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908,
0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919,
0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808,
0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808,
0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808,
0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919,
0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908,
0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908,
0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08,
0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819,
0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b,
0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808,
0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819,
0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908,
0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819,
0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808,
0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808,
0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b,
0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908,
0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808,
0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908,
0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819,
0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819,
0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808,
0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b,
0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b,
0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819,
0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b,
0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b,
0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b,
0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819,
0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19,
0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819,
0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908,
0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808,
0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b,
};
static const __device__ uint32_t iq3xxs_grid[256] = {
0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
@ -2038,6 +2322,27 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst
}
template<typename dst_t>
static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
const int i = blockIdx.x;
const block_iq2_s * x = (const block_iq2_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 * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300)));
const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
#else
assert(false);
#endif
}
template<typename dst_t>
static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
@ -2135,6 +2440,25 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst
}
template<typename dst_t>
static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
const int i = blockIdx.x;
const block_iq4_xs * x = (const block_iq4_xs *)vx;
const int tid = threadIdx.x;
const int il = tid/8; // 0...3
const int ib = tid%8; // 0...7
dst_t * y = yy + i*QK_K + 32*ib + 4*il;
const uint8_t * q4 = x[i].qs + 16*ib + 4*il;
const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32);
for (int j = 0; j < 4; ++j) {
y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
y[j+16] = d * kvalues_iq4nl[q4[j] >> 4];
}
}
static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
@ -2231,10 +2555,7 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx,
#endif
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
tmp = warp_reduce_sum(tmp);
if (threadIdx.x == 0) {
dst[row] = tmp;
@ -2335,10 +2656,7 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx,
#endif
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
tmp = warp_reduce_sum(tmp);
if (threadIdx.x == 0) {
dst[row] = tmp;
@ -2471,10 +2789,7 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx,
#endif
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
tmp = warp_reduce_sum(tmp);
if (tid == 0) {
dst[row] = tmp;
@ -2587,10 +2902,7 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx,
#endif
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
tmp = warp_reduce_sum(tmp);
if (threadIdx.x == 0) {
dst[row] = tmp;
@ -2697,10 +3009,7 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx,
#endif
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
tmp = warp_reduce_sum(tmp);
if (tid == 0) {
dst[row] = tmp;
@ -2735,11 +3044,8 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest
float amax = fabsf(xi);
float sum = xi;
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32));
sum += __shfl_xor_sync(0xffffffff, sum, mask, 32);
}
amax = warp_reduce_max(amax);
sum = warp_reduce_sum(sum);
const float d = amax / 127;
const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
@ -4801,6 +5107,54 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
#endif
}
// TODO
static __device__ __forceinline__ float vec_dot_iq2_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_iq2_s * bq2 = (const block_iq2_s *) vbq;
const int ib32 = iqs;
const int8_t * q8 = bq8_1[ib32].qs;
const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32;
const uint8_t ls1 = bq2->scales[ib32] & 0xf;
const uint8_t ls2 = bq2->scales[ib32] >> 4;
int sumi1 = 0;
for (int l = 0; l < 2; ++l) {
const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
const uint32_t signs1 = __vcmpeq4(((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201);
const int grid_l = __vsub4(grid[0] ^ signs0, signs0);
const int grid_h = __vsub4(grid[1] ^ signs1, signs1);
sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1);
sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1);
q8 += 8;
}
int sumi2 = 0;
for (int l = 2; l < 4; ++l) {
const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
const uint32_t signs1 = __vcmpeq4(((signs[l] >> 4) * 0x01010101) & 0x08040201, 0x08040201);
const int grid_l = __vsub4(grid[0] ^ signs0, signs0);
const int grid_h = __vsub4(grid[1] ^ signs1, signs1);
sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2);
sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2);
q8 += 8;
}
const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f;
return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
#else
(void) ksigns64;
assert(false);
return 0.f;
#endif
#else
(void) ksigns64;
assert(false);
return 0.f;
#endif
}
static __device__ __forceinline__ float vec_dot_iq3_xxs_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
@ -4964,6 +5318,76 @@ static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1(
return d * (sumi1 + sumi2);
}
static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
#if QK_K == 256
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq;
const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
//// iqs is 0...7
//const int ib64 = iqs/2;
//const int il = iqs%2;
//const int32_t * q8_1 = (const int *)bq8_1[2*ib64+0].qs + 2*il;
//const int32_t * q8_2 = (const int *)bq8_1[2*ib64+1].qs + 2*il;
//const uint32_t * q4_1 = (const uint32_t *)bq4->qs + 8*ib64 + 2*il;
//const uint32_t * q4_2 = q4_1 + 4;
//const int8_t ls1 = (bq4->scales_l[ib64] & 0xf) | (((bq4->scales_h >> (4*ib64+0)) & 3) << 4);
//const int8_t ls2 = (bq4->scales_l[ib64] >> 4) | (((bq4->scales_h >> (4*ib64+2)) & 3) << 4);
//const float d1 = (float)bq4->d * (ls1 - 32) * __low2float(bq8_1[2*ib64+0].ds);
//const float d2 = (float)bq4->d * (ls2 - 32) * __low2float(bq8_1[2*ib64+1].ds);
//int v1, v2;
//int sumi1 = 0, sumi2 = 0;
//for (int j = 0; j < 2; ++j) {
// get_int_from_table_16(q4_1[j], values, v1, v2);
// sumi1 = __dp4a(v2, q8_1[j+4], __dp4a(v1, q8_1[j+0], sumi1));
// get_int_from_table_16(q4_2[j], values, v1, v2);
// sumi2 = __dp4a(v2, q8_2[j+4], __dp4a(v1, q8_2[j+0], sumi2));
//}
//return d1 * sumi1 + d2 * sumi2;
// iqs is 0...7
const int ib32 = iqs;
const int32_t * q8 = (const int *)bq8_1[ib32].qs;
const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32;
const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds);
int v1, v2;
int sumi1 = 0, sumi2 = 0;
for (int j = 0; j < 4; ++j) {
get_int_from_table_16(q4[j], values, v1, v2);
sumi1 = __dp4a(v1, q8[j+0], sumi1);
sumi2 = __dp4a(v2, q8[j+4], sumi2);
}
return d * (sumi1 + sumi2);
//// iqs is 0...15
//const int ib32 = iqs/2;
//const int il = iqs%2;
//const int32_t * q8 = (const int *)bq8_1[ib32].qs + 2*il;
//const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32 + 2*il;
//const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
//const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds);
//int v1, v2;
//int sumi1 = 0, sumi2 = 0;
//for (int j = 0; j < 2; ++j) {
// get_int_from_table_16(q4[j], values, v1, v2);
// sumi1 = __dp4a(v1, q8[j+0], sumi1);
// sumi2 = __dp4a(v2, q8[j+4], sumi2);
//}
//return d * (sumi1 + sumi2);
#else
assert(false);
return 0.f;
#endif
#else
assert(false);
return 0.f;
#endif
}
template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
static __device__ __forceinline__ void mul_mat_q(
@ -5884,10 +6308,7 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons
}
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
tmp = warp_reduce_sum(tmp);
if (tid == 0) {
#ifdef GGML_CUDA_F16
@ -5937,10 +6358,7 @@ static __global__ void mul_mat_p021_f16_f32(
const int idst = channel*nrows_dst + row_dst;
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
tmp = warp_reduce_sum(tmp);
if (threadIdx.x == 0) {
dst[idst] = tmp;
@ -5983,10 +6401,7 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
}
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
tmp = warp_reduce_sum(tmp);
if (threadIdx.x == 0) {
dst[idst] = tmp;
@ -6997,6 +7412,12 @@ static void dequantize_row_iq2_xs_cuda(const void * vx, dst_t * y, const int k,
dequantize_block_iq2_xs<<<nb, 32, 0, stream>>>(vx, y);
}
template<typename dst_t>
static void dequantize_row_iq2_s_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
const int nb = k / QK_K;
dequantize_block_iq2_s<<<nb, 32, 0, stream>>>(vx, y);
}
template<typename dst_t>
static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
const int nb = k / QK_K;
@ -7021,6 +7442,12 @@ static void dequantize_row_iq4_nl_cuda(const void * vx, dst_t * y, const int k,
dequantize_block_iq4_nl<<<nb, 32, 0, stream>>>(vx, y);
}
template<typename dst_t>
static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
const int nb = (k + QK_K - 1) / QK_K;
dequantize_block_iq4_xs<<<nb, 32, 0, stream>>>(vx, y);
}
template <typename src_t, typename dst_t>
static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
@ -7058,12 +7485,16 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
return dequantize_row_iq2_xxs_cuda;
case GGML_TYPE_IQ2_XS:
return dequantize_row_iq2_xs_cuda;
case GGML_TYPE_IQ2_S:
return dequantize_row_iq2_s_cuda;
case GGML_TYPE_IQ3_XXS:
return dequantize_row_iq3_xxs_cuda;
case GGML_TYPE_IQ1_S:
return dequantize_row_iq1_s_cuda;
case GGML_TYPE_IQ4_NL:
return dequantize_row_iq4_nl_cuda;
case GGML_TYPE_IQ4_XS:
return dequantize_row_iq4_xs_cuda;
case GGML_TYPE_IQ3_S:
return dequantize_row_iq3_s_cuda;
case GGML_TYPE_F32:
@ -7099,12 +7530,16 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
return dequantize_row_iq2_xxs_cuda;
case GGML_TYPE_IQ2_XS:
return dequantize_row_iq2_xs_cuda;
case GGML_TYPE_IQ2_S:
return dequantize_row_iq2_s_cuda;
case GGML_TYPE_IQ3_XXS:
return dequantize_row_iq3_xxs_cuda;
case GGML_TYPE_IQ1_S:
return dequantize_row_iq1_s_cuda;
case GGML_TYPE_IQ4_NL:
return dequantize_row_iq4_nl_cuda;
case GGML_TYPE_IQ4_XS:
return dequantize_row_iq4_xs_cuda;
case GGML_TYPE_IQ3_S:
return dequantize_row_iq3_s_cuda;
case GGML_TYPE_F16:
@ -8166,7 +8601,7 @@ static void * ggml_cuda_pool_malloc_vmm(int device, size_t size, size_t * actual
g_cuda_pool_used[device] += size;
#ifdef DEBUG_CUDA_MALLOC
printf("cuda pool[%d]: allocated %llu bytes at %llx [%s]\n", id, (unsigned long long) size, ptr);
printf("cuda pool[%d]: allocated %llu bytes at %llx\n", device, (unsigned long long) size, ptr);
#endif
return ptr;
@ -8176,7 +8611,7 @@ static void ggml_cuda_pool_free_vmm(int device, void * ptr, size_t size) {
scoped_spin_lock lock(g_cuda_pool_lock);
#ifdef DEBUG_CUDA_MALLOC
printf("cuda pool[%d]: freed %llu bytes at %llx\n", id, (unsigned long long) size, ptr);
printf("cuda pool[%d]: freed %llu bytes at %llx\n", device, (unsigned long long) size, ptr);
#endif
g_cuda_pool_used[device] -= size;
@ -8840,9 +9275,11 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUD
case GGML_TYPE_Q6_K:
case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
return max_compute_capability >= CC_RDNA2 ? 128 : 64;
default:
@ -8866,9 +9303,11 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_CUD
case GGML_TYPE_Q5_K:
case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
return max_compute_capability >= CC_VOLTA ? 128 : 64;
case GGML_TYPE_Q6_K:
@ -8963,6 +9402,10 @@ static void ggml_cuda_op_mul_mat_vec_q(
mul_mat_vec_q_cuda<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_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_IQ2_S:
mul_mat_vec_q_cuda<QK_K, QI2_S, block_iq2_s, 1, vec_dot_iq2_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;
case GGML_TYPE_IQ3_XXS:
mul_mat_vec_q_cuda<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream);
@ -8975,6 +9418,10 @@ 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_IQ4_XS:
mul_mat_vec_q_cuda<QK_K, QI4_XS, block_iq4_xs, 1, vec_dot_iq4_xs_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);
@ -11708,7 +12155,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
}
ggml_type a_type = a->type;
if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS ||
a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S) {
a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S ||
a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) {
if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
return false;
}

66
ggml-metal.m
View file

@ -62,8 +62,10 @@ enum ggml_metal_kernel_type {
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_IQ2_S,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,
GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,
GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,
GGML_METAL_KERNEL_TYPE_RMS_NORM,
GGML_METAL_KERNEL_TYPE_GROUP_NORM,
@ -87,8 +89,10 @@ enum ggml_metal_kernel_type {
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_IQ2_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_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,
//GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16,
GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32,
@ -108,8 +112,10 @@ enum ggml_metal_kernel_type {
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_IQ2_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_MV_ID_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32,
@ -126,8 +132,10 @@ enum ggml_metal_kernel_type {
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_IQ2_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_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32,
GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32,
@ -144,8 +152,10 @@ enum ggml_metal_kernel_type {
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_IQ2_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_MUL_MM_ID_IQ4_XS_F32,
GGML_METAL_KERNEL_TYPE_ROPE_F32,
GGML_METAL_KERNEL_TYPE_ROPE_F16,
GGML_METAL_KERNEL_TYPE_ALIBI_F32,
@ -458,8 +468,10 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
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_IQ2_S, get_rows_iq2_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction);
@ -483,8 +495,10 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
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_IQ2_S_F32, mul_mv_iq2_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction);
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction);
@ -504,8 +518,10 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
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_IQ2_S_F32, mul_mv_id_iq2_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, ctx->support_simdgroup_reduction);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm);
@ -522,8 +538,10 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
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_IQ2_S_F32, mul_mm_iq2_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm);
@ -540,8 +558,10 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
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_IQ2_S_F32, mul_mm_id_iq2_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true);
@ -1358,8 +1378,10 @@ static bool ggml_metal_graph_compute(
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_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32 ].pipeline; break;
case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break;
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break;
case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break;
default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
}
@ -1500,6 +1522,12 @@ static bool ggml_metal_graph_compute(
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline;
} break;
case GGML_TYPE_IQ2_S:
{
nth0 = 4;
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32].pipeline;
} break;
case GGML_TYPE_IQ1_S:
{
nth0 = 4;
@ -1512,6 +1540,12 @@ static bool ggml_metal_graph_compute(
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline;
} break;
case GGML_TYPE_IQ4_XS:
{
nth0 = 4;
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline;
} break;
default:
{
GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t);
@ -1544,9 +1578,9 @@ static bool ggml_metal_graph_compute(
[encoder setBytes:&r2 length:sizeof(r2) atIndex:17];
[encoder setBytes:&r3 length:sizeof(r3) atIndex:18];
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 ||
src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_IQ1_S) { // || src0t == GGML_TYPE_Q4_K) {
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 ||
src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ2_S) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) {
@ -1559,7 +1593,7 @@ 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_IQ4_NL) {
else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) {
const int mem_size = 32*sizeof(float);
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@ -1658,8 +1692,10 @@ static bool ggml_metal_graph_compute(
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_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32 ].pipeline; break;
case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break;
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break;
case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break;
default: GGML_ASSERT(false && "MUL_MAT_ID not implemented");
}
@ -1803,6 +1839,12 @@ static bool ggml_metal_graph_compute(
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline;
} break;
case GGML_TYPE_IQ2_S:
{
nth0 = 4;
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32].pipeline;
} break;
case GGML_TYPE_IQ1_S:
{
nth0 = 4;
@ -1815,6 +1857,12 @@ static bool ggml_metal_graph_compute(
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline;
} break;
case GGML_TYPE_IQ4_XS:
{
nth0 = 4;
nth1 = 16;
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline;
} break;
default:
{
GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t);
@ -1863,9 +1911,9 @@ static bool ggml_metal_graph_compute(
[encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j];
}
if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 ||
src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 ||
src2t == GGML_TYPE_Q2_K || src2t == GGML_TYPE_IQ1_S) { // || src2t == GGML_TYPE_Q4_K) {
if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 ||
src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 ||
src2t == GGML_TYPE_Q2_K || src2t == GGML_TYPE_IQ1_S || src2t == GGML_TYPE_IQ2_S) {
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) {
@ -1878,7 +1926,7 @@ 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_IQ4_NL) {
else if (src2t == GGML_TYPE_IQ4_NL || src2t == GGML_TYPE_IQ4_XS) {
const int mem_size = 32*sizeof(float);
[encoder setThreadgroupMemoryLength:mem_size atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@ -1925,8 +1973,10 @@ static bool ggml_metal_graph_compute(
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_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S ].pipeline; break;
case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break;
case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break;
case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break;
case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break;
default: GGML_ASSERT(false && "not implemented");
}

711
ggml-metal.metal
View file

@ -2519,6 +2519,14 @@ typedef struct {
} block_iq2_xs;
// 74 bytes / block for QK_K = 256, so 2.3125 bpw
// 2.5625 bpw quants
typedef struct {
half d;
uint8_t qs[QK_K/4];
uint8_t qh[QK_K/32];
uint8_t scales[QK_K/32];
} block_iq2_s;
typedef struct {
half d;
uint8_t qs[3*QK_K/8];
@ -2552,6 +2560,13 @@ typedef struct {
uint8_t qs[QK4_NL/2];
} block_iq4_nl;
typedef struct {
half d;
uint16_t scales_h;
uint8_t scales_l[QK_K/64];
uint8_t qs[QK_K/2];
} block_iq4_xs;
//====================================== dot products =========================
void kernel_mul_mv_q2_K_f32_impl(
@ -3774,6 +3789,265 @@ constexpr constant static uint64_t iq2xs_grid[512] = {
0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b,
};
constexpr constant static uint64_t iq2s_grid[1024] = {
0x0808080808080808, 0x080808080808082b, 0x0808080808081919, 0x0808080808082b08,
0x0808080808082b2b, 0x0808080808190819, 0x0808080808191908, 0x080808080819192b,
0x0808080808192b19, 0x08080808082b0808, 0x08080808082b082b, 0x08080808082b1919,
0x08080808082b2b08, 0x0808080819080819, 0x0808080819081908, 0x080808081908192b,
0x0808080819082b19, 0x0808080819190808, 0x080808081919082b, 0x0808080819191919,
0x0808080819192b08, 0x08080808192b0819, 0x08080808192b1908, 0x08080808192b192b,
0x08080808192b2b19, 0x080808082b080808, 0x080808082b08082b, 0x080808082b081919,
0x080808082b082b08, 0x080808082b190819, 0x080808082b191908, 0x080808082b2b0808,
0x080808082b2b1919, 0x080808082b2b2b2b, 0x0808081908080819, 0x0808081908081908,
0x080808190808192b, 0x0808081908082b19, 0x0808081908190808, 0x080808190819082b,
0x0808081908191919, 0x0808081908192b08, 0x08080819082b0819, 0x08080819082b1908,
0x0808081919080808, 0x080808191908082b, 0x0808081919081919, 0x0808081919082b08,
0x0808081919190819, 0x0808081919191908, 0x080808191919192b, 0x0808081919192b19,
0x08080819192b0808, 0x08080819192b1919, 0x08080819192b2b08, 0x080808192b080819,
0x080808192b081908, 0x080808192b190808, 0x080808192b19082b, 0x080808192b191919,
0x080808192b2b0819, 0x080808192b2b1908, 0x0808082b08080808, 0x0808082b0808082b,
0x0808082b08081919, 0x0808082b08082b08, 0x0808082b08190819, 0x0808082b08191908,
0x0808082b082b0808, 0x0808082b082b2b2b, 0x0808082b19080819, 0x0808082b19081908,
0x0808082b1908192b, 0x0808082b19082b19, 0x0808082b19190808, 0x0808082b19191919,
0x0808082b2b080808, 0x0808082b2b081919, 0x0808082b2b082b2b, 0x0808082b2b191908,
0x0808082b2b2b082b, 0x0808190808080819, 0x0808190808081908, 0x080819080808192b,
0x0808190808082b19, 0x0808190808190808, 0x080819080819082b, 0x0808190808191919,
0x0808190808192b08, 0x08081908082b0819, 0x08081908082b1908, 0x08081908082b192b,
0x08081908082b2b19, 0x0808190819080808, 0x080819081908082b, 0x0808190819081919,
0x0808190819082b08, 0x0808190819082b2b, 0x0808190819190819, 0x0808190819191908,
0x080819081919192b, 0x0808190819192b19, 0x08081908192b0808, 0x08081908192b082b,
0x08081908192b1919, 0x080819082b080819, 0x080819082b081908, 0x080819082b08192b,
0x080819082b082b19, 0x080819082b190808, 0x080819082b191919, 0x080819082b192b08,
0x080819082b2b0819, 0x080819082b2b1908, 0x0808191908080808, 0x080819190808082b,
0x0808191908081919, 0x0808191908082b08, 0x0808191908082b2b, 0x0808191908190819,
0x0808191908191908, 0x080819190819192b, 0x0808191908192b19, 0x08081919082b0808,
0x08081919082b1919, 0x08081919082b2b08, 0x0808191919080819, 0x0808191919081908,
0x080819191908192b, 0x0808191919082b19, 0x0808191919190808, 0x080819191919082b,
0x0808191919191919, 0x0808191919192b08, 0x08081919192b0819, 0x08081919192b1908,
0x080819192b080808, 0x080819192b08082b, 0x080819192b081919, 0x080819192b082b08,
0x080819192b190819, 0x080819192b191908, 0x080819192b2b0808, 0x0808192b08080819,
0x0808192b08081908, 0x0808192b0808192b, 0x0808192b08082b19, 0x0808192b08190808,
0x0808192b08191919, 0x0808192b19080808, 0x0808192b19081919, 0x0808192b19082b08,
0x0808192b19190819, 0x0808192b19191908, 0x0808192b192b0808, 0x0808192b2b080819,
0x0808192b2b081908, 0x0808192b2b190808, 0x08082b0808080808, 0x08082b080808082b,
0x08082b0808081919, 0x08082b0808082b08, 0x08082b0808190819, 0x08082b0808191908,
0x08082b080819192b, 0x08082b0808192b19, 0x08082b08082b0808, 0x08082b08082b1919,
0x08082b08082b2b2b, 0x08082b0819080819, 0x08082b0819081908, 0x08082b081908192b,
0x08082b0819082b19, 0x08082b0819190808, 0x08082b081919082b, 0x08082b0819191919,
0x08082b0819192b08, 0x08082b08192b0819, 0x08082b08192b1908, 0x08082b082b080808,
0x08082b082b081919, 0x08082b082b191908, 0x08082b082b2b2b2b, 0x08082b1908080819,
0x08082b1908081908, 0x08082b1908190808, 0x08082b190819082b, 0x08082b1908191919,
0x08082b1908192b08, 0x08082b19082b0819, 0x08082b1919080808, 0x08082b1919081919,
0x08082b1919082b08, 0x08082b1919190819, 0x08082b1919191908, 0x08082b19192b0808,
0x08082b192b080819, 0x08082b192b190808, 0x08082b2b08080808, 0x08082b2b08190819,
0x08082b2b08191908, 0x08082b2b082b082b, 0x08082b2b082b2b08, 0x08082b2b082b2b2b,
0x08082b2b19190808, 0x08082b2b2b192b19, 0x0819080808080819, 0x0819080808081908,
0x081908080808192b, 0x0819080808082b19, 0x0819080808190808, 0x081908080819082b,
0x0819080808191919, 0x0819080808192b08, 0x08190808082b0819, 0x08190808082b1908,
0x08190808082b192b, 0x0819080819080808, 0x081908081908082b, 0x0819080819081919,
0x0819080819082b08, 0x0819080819190819, 0x0819080819191908, 0x081908081919192b,
0x0819080819192b19, 0x08190808192b0808, 0x08190808192b082b, 0x08190808192b1919,
0x08190808192b2b08, 0x081908082b080819, 0x081908082b081908, 0x081908082b08192b,
0x081908082b190808, 0x081908082b191919, 0x081908082b192b08, 0x081908082b2b0819,
0x081908082b2b1908, 0x0819081908080808, 0x081908190808082b, 0x0819081908081919,
0x0819081908082b08, 0x0819081908082b2b, 0x0819081908190819, 0x0819081908191908,
0x081908190819192b, 0x0819081908192b19, 0x08190819082b0808, 0x08190819082b082b,
0x08190819082b1919, 0x08190819082b2b08, 0x0819081919080819, 0x0819081919081908,
0x081908191908192b, 0x0819081919082b19, 0x0819081919190808, 0x081908191919082b,
0x0819081919191919, 0x0819081919192b08, 0x08190819192b0819, 0x08190819192b1908,
0x081908192b080808, 0x081908192b08082b, 0x081908192b081919, 0x081908192b082b08,
0x081908192b190819, 0x081908192b191908, 0x0819082b08080819, 0x0819082b08081908,
0x0819082b08082b19, 0x0819082b08190808, 0x0819082b08191919, 0x0819082b082b0819,
0x0819082b082b1908, 0x0819082b19080808, 0x0819082b19081919, 0x0819082b19190819,
0x0819082b19191908, 0x0819082b2b080819, 0x0819082b2b081908, 0x0819082b2b190808,
0x0819190808080808, 0x081919080808082b, 0x0819190808081919, 0x0819190808082b08,
0x0819190808190819, 0x0819190808191908, 0x081919080819192b, 0x0819190808192b19,
0x08191908082b0808, 0x08191908082b1919, 0x08191908082b2b08, 0x0819190819080819,
0x0819190819081908, 0x081919081908192b, 0x0819190819082b19, 0x0819190819190808,
0x081919081919082b, 0x0819190819191919, 0x0819190819192b08, 0x08191908192b0819,
0x08191908192b1908, 0x081919082b080808, 0x081919082b08082b, 0x081919082b081919,
0x081919082b082b08, 0x081919082b190819, 0x081919082b191908, 0x081919082b2b0808,
0x0819191908080819, 0x0819191908081908, 0x081919190808192b, 0x0819191908082b19,
0x0819191908190808, 0x081919190819082b, 0x0819191908191919, 0x0819191908192b08,
0x08191919082b0819, 0x08191919082b1908, 0x0819191919080808, 0x081919191908082b,
0x0819191919081919, 0x0819191919082b08, 0x0819191919190819, 0x0819191919191908,
0x08191919192b0808, 0x081919192b080819, 0x081919192b081908, 0x081919192b190808,
0x0819192b08080808, 0x0819192b08081919, 0x0819192b08082b08, 0x0819192b08190819,
0x0819192b08191908, 0x0819192b082b0808, 0x0819192b19080819, 0x0819192b19081908,
0x0819192b19190808, 0x0819192b2b080808, 0x0819192b2b2b2b2b, 0x08192b0808080819,
0x08192b0808081908, 0x08192b080808192b, 0x08192b0808082b19, 0x08192b0808190808,
0x08192b0808191919, 0x08192b0808192b08, 0x08192b08082b0819, 0x08192b0819080808,
0x08192b081908082b, 0x08192b0819081919, 0x08192b0819082b08, 0x08192b0819190819,
0x08192b0819191908, 0x08192b08192b0808, 0x08192b082b080819, 0x08192b082b081908,
0x08192b1908080808, 0x08192b190808082b, 0x08192b1908081919, 0x08192b1908082b08,
0x08192b1908190819, 0x08192b1908191908, 0x08192b19082b0808, 0x08192b1919080819,
0x08192b1919081908, 0x08192b1919190808, 0x08192b19192b2b19, 0x08192b192b2b082b,
0x08192b2b08081908, 0x08192b2b08190808, 0x08192b2b19080808, 0x08192b2b1919192b,
0x082b080808080808, 0x082b08080808082b, 0x082b080808081919, 0x082b080808082b08,
0x082b080808190819, 0x082b080808191908, 0x082b08080819192b, 0x082b080808192b19,
0x082b0808082b0808, 0x082b0808082b1919, 0x082b0808082b2b2b, 0x082b080819080819,
0x082b080819081908, 0x082b080819190808, 0x082b08081919082b, 0x082b080819191919,
0x082b0808192b1908, 0x082b08082b080808, 0x082b08082b082b2b, 0x082b08082b191908,
0x082b08082b2b2b2b, 0x082b081908080819, 0x082b081908081908, 0x082b081908190808,
0x082b08190819082b, 0x082b081908191919, 0x082b0819082b0819, 0x082b081919080808,
0x082b08191908082b, 0x082b081919081919, 0x082b081919190819, 0x082b081919191908,
0x082b0819192b0808, 0x082b08192b080819, 0x082b08192b081908, 0x082b08192b190808,
0x082b082b08080808, 0x082b082b08082b2b, 0x082b082b082b082b, 0x082b082b082b2b08,
0x082b082b082b2b2b, 0x082b082b19081908, 0x082b082b19190808, 0x082b082b2b082b08,
0x082b082b2b082b2b, 0x082b082b2b2b2b08, 0x082b190808080819, 0x082b190808081908,
0x082b19080808192b, 0x082b190808082b19, 0x082b190808190808, 0x082b190808191919,
0x082b190808192b08, 0x082b1908082b0819, 0x082b1908082b1908, 0x082b190819080808,
0x082b19081908082b, 0x082b190819081919, 0x082b190819082b08, 0x082b190819190819,
0x082b190819191908, 0x082b1908192b0808, 0x082b19082b080819, 0x082b19082b081908,
0x082b19082b190808, 0x082b191908080808, 0x082b191908081919, 0x082b191908082b08,
0x082b191908190819, 0x082b191908191908, 0x082b1919082b0808, 0x082b191919080819,
0x082b191919081908, 0x082b191919190808, 0x082b1919192b192b, 0x082b19192b080808,
0x082b192b08080819, 0x082b192b08081908, 0x082b192b08190808, 0x082b192b19080808,
0x082b192b19192b19, 0x082b2b0808080808, 0x082b2b0808081919, 0x082b2b0808190819,
0x082b2b0808191908, 0x082b2b0819080819, 0x082b2b0819081908, 0x082b2b0819190808,
0x082b2b082b082b2b, 0x082b2b082b2b2b2b, 0x082b2b1908080819, 0x082b2b1908081908,
0x082b2b1908190808, 0x082b2b192b191919, 0x082b2b2b08082b2b, 0x082b2b2b082b082b,
0x082b2b2b192b1908, 0x082b2b2b2b082b08, 0x082b2b2b2b082b2b, 0x1908080808080819,
0x1908080808081908, 0x190808080808192b, 0x1908080808082b19, 0x1908080808190808,
0x190808080819082b, 0x1908080808191919, 0x1908080808192b08, 0x1908080808192b2b,
0x19080808082b0819, 0x19080808082b1908, 0x19080808082b192b, 0x1908080819080808,
0x190808081908082b, 0x1908080819081919, 0x1908080819082b08, 0x1908080819082b2b,
0x1908080819190819, 0x1908080819191908, 0x190808081919192b, 0x1908080819192b19,
0x19080808192b0808, 0x19080808192b082b, 0x19080808192b1919, 0x190808082b080819,
0x190808082b081908, 0x190808082b190808, 0x190808082b191919, 0x190808082b192b08,
0x190808082b2b0819, 0x190808082b2b1908, 0x1908081908080808, 0x190808190808082b,
0x1908081908081919, 0x1908081908082b08, 0x1908081908190819, 0x1908081908191908,
0x190808190819192b, 0x1908081908192b19, 0x19080819082b0808, 0x19080819082b082b,
0x19080819082b1919, 0x1908081919080819, 0x1908081919081908, 0x190808191908192b,
0x1908081919082b19, 0x1908081919190808, 0x190808191919082b, 0x1908081919191919,
0x1908081919192b08, 0x19080819192b0819, 0x19080819192b1908, 0x190808192b080808,
0x190808192b08082b, 0x190808192b081919, 0x190808192b082b08, 0x190808192b190819,
0x190808192b191908, 0x190808192b2b0808, 0x1908082b08080819, 0x1908082b08081908,
0x1908082b08190808, 0x1908082b0819082b, 0x1908082b08191919, 0x1908082b08192b08,
0x1908082b082b1908, 0x1908082b19080808, 0x1908082b19081919, 0x1908082b19082b08,
0x1908082b19190819, 0x1908082b19191908, 0x1908082b192b0808, 0x1908082b2b080819,
0x1908082b2b081908, 0x1908190808080808, 0x190819080808082b, 0x1908190808081919,
0x1908190808082b08, 0x1908190808082b2b, 0x1908190808190819, 0x1908190808191908,
0x190819080819192b, 0x1908190808192b19, 0x19081908082b0808, 0x19081908082b082b,
0x19081908082b1919, 0x19081908082b2b08, 0x1908190819080819, 0x1908190819081908,
0x190819081908192b, 0x1908190819082b19, 0x1908190819190808, 0x190819081919082b,
0x1908190819191919, 0x1908190819192b08, 0x19081908192b0819, 0x19081908192b1908,
0x190819082b080808, 0x190819082b08082b, 0x190819082b081919, 0x190819082b082b08,
0x190819082b190819, 0x190819082b191908, 0x190819082b2b0808, 0x1908191908080819,
0x1908191908081908, 0x190819190808192b, 0x1908191908082b19, 0x1908191908190808,
0x190819190819082b, 0x1908191908191919, 0x1908191908192b08, 0x19081919082b0819,
0x19081919082b1908, 0x1908191919080808, 0x190819191908082b, 0x1908191919081919,
0x1908191919082b08, 0x1908191919190819, 0x1908191919191908, 0x19081919192b0808,
0x19081919192b2b2b, 0x190819192b080819, 0x190819192b081908, 0x190819192b190808,
0x1908192b08080808, 0x1908192b0808082b, 0x1908192b08081919, 0x1908192b08082b08,
0x1908192b08190819, 0x1908192b08191908, 0x1908192b082b0808, 0x1908192b19080819,
0x1908192b19081908, 0x1908192b19190808, 0x1908192b2b080808, 0x1908192b2b2b1919,
0x19082b0808080819, 0x19082b0808081908, 0x19082b0808082b19, 0x19082b0808190808,
0x19082b080819082b, 0x19082b0808191919, 0x19082b0808192b08, 0x19082b08082b0819,
0x19082b08082b1908, 0x19082b0819080808, 0x19082b081908082b, 0x19082b0819081919,
0x19082b0819082b08, 0x19082b0819190819, 0x19082b0819191908, 0x19082b08192b0808,
0x19082b082b081908, 0x19082b082b190808, 0x19082b1908080808, 0x19082b190808082b,
0x19082b1908081919, 0x19082b1908082b08, 0x19082b1908190819, 0x19082b1908191908,
0x19082b19082b0808, 0x19082b1919080819, 0x19082b1919081908, 0x19082b1919190808,
0x19082b192b080808, 0x19082b192b19192b, 0x19082b2b08080819, 0x19082b2b08081908,
0x19082b2b08190808, 0x19082b2b19080808, 0x1919080808080808, 0x191908080808082b,
0x1919080808081919, 0x1919080808082b08, 0x1919080808190819, 0x1919080808191908,
0x191908080819192b, 0x1919080808192b19, 0x19190808082b0808, 0x19190808082b082b,
0x19190808082b1919, 0x19190808082b2b08, 0x1919080819080819, 0x1919080819081908,
0x191908081908192b, 0x1919080819082b19, 0x1919080819190808, 0x191908081919082b,
0x1919080819191919, 0x1919080819192b08, 0x19190808192b0819, 0x19190808192b1908,
0x191908082b080808, 0x191908082b08082b, 0x191908082b081919, 0x191908082b082b08,
0x191908082b190819, 0x191908082b191908, 0x1919081908080819, 0x1919081908081908,
0x191908190808192b, 0x1919081908082b19, 0x1919081908190808, 0x191908190819082b,
0x1919081908191919, 0x1919081908192b08, 0x19190819082b0819, 0x19190819082b1908,
0x1919081919080808, 0x191908191908082b, 0x1919081919081919, 0x1919081919082b08,
0x1919081919190819, 0x1919081919191908, 0x19190819192b0808, 0x191908192b080819,
0x191908192b081908, 0x191908192b190808, 0x1919082b08080808, 0x1919082b08081919,
0x1919082b08082b08, 0x1919082b08190819, 0x1919082b08191908, 0x1919082b082b0808,
0x1919082b19080819, 0x1919082b19081908, 0x1919082b19190808, 0x1919082b192b2b19,
0x1919082b2b080808, 0x1919190808080819, 0x1919190808081908, 0x191919080808192b,
0x1919190808082b19, 0x1919190808190808, 0x191919080819082b, 0x1919190808191919,
0x1919190808192b08, 0x19191908082b0819, 0x19191908082b1908, 0x1919190819080808,
0x191919081908082b, 0x1919190819081919, 0x1919190819082b08, 0x1919190819190819,
0x1919190819191908, 0x19191908192b0808, 0x191919082b080819, 0x191919082b081908,
0x191919082b190808, 0x1919191908080808, 0x191919190808082b, 0x1919191908081919,
0x1919191908082b08, 0x1919191908190819, 0x1919191908191908, 0x19191919082b0808,
0x1919191919080819, 0x1919191919081908, 0x1919191919190808, 0x191919192b080808,
0x1919192b08080819, 0x1919192b08081908, 0x1919192b08190808, 0x1919192b082b192b,
0x1919192b19080808, 0x19192b0808080808, 0x19192b080808082b, 0x19192b0808081919,
0x19192b0808082b08, 0x19192b0808190819, 0x19192b0808191908, 0x19192b08082b0808,
0x19192b0819080819, 0x19192b0819081908, 0x19192b0819190808, 0x19192b0819192b2b,
0x19192b082b080808, 0x19192b1908080819, 0x19192b1908081908, 0x19192b1908190808,
0x19192b1919080808, 0x19192b2b08080808, 0x19192b2b08192b19, 0x19192b2b2b081919,
0x19192b2b2b2b2b08, 0x192b080808080819, 0x192b080808081908, 0x192b08080808192b,
0x192b080808190808, 0x192b08080819082b, 0x192b080808191919, 0x192b080808192b08,
0x192b0808082b0819, 0x192b0808082b1908, 0x192b080819080808, 0x192b080819081919,
0x192b080819082b08, 0x192b080819190819, 0x192b080819191908, 0x192b0808192b0808,
0x192b08082b081908, 0x192b08082b190808, 0x192b081908080808, 0x192b08190808082b,
0x192b081908081919, 0x192b081908082b08, 0x192b081908190819, 0x192b081908191908,
0x192b0819082b0808, 0x192b081919080819, 0x192b081919081908, 0x192b081919190808,
0x192b08192b080808, 0x192b08192b192b19, 0x192b082b08081908, 0x192b082b08190808,
0x192b082b19080808, 0x192b082b1919192b, 0x192b082b2b2b0819, 0x192b190808080808,
0x192b190808081919, 0x192b190808082b08, 0x192b190808190819, 0x192b190808191908,
0x192b1908082b0808, 0x192b190819080819, 0x192b190819081908, 0x192b190819190808,
0x192b19082b080808, 0x192b191908080819, 0x192b191908081908, 0x192b191908190808,
0x192b191919080808, 0x192b191919082b2b, 0x192b1919192b2b08, 0x192b19192b19082b,
0x192b192b08080808, 0x192b192b2b191908, 0x192b2b0808080819, 0x192b2b0808081908,
0x192b2b0808190808, 0x192b2b08192b1919, 0x192b2b082b192b08, 0x192b2b1908080808,
0x192b2b19082b2b2b, 0x192b2b2b1908082b, 0x192b2b2b2b2b0819, 0x2b08080808080808,
0x2b0808080808082b, 0x2b08080808081919, 0x2b08080808082b08, 0x2b08080808190819,
0x2b08080808191908, 0x2b08080808192b19, 0x2b080808082b0808, 0x2b080808082b1919,
0x2b08080819080819, 0x2b08080819081908, 0x2b08080819190808, 0x2b0808081919082b,
0x2b08080819191919, 0x2b08080819192b08, 0x2b080808192b0819, 0x2b0808082b080808,
0x2b0808082b081919, 0x2b0808082b190819, 0x2b0808082b191908, 0x2b08081908080819,
0x2b08081908081908, 0x2b08081908082b19, 0x2b08081908190808, 0x2b0808190819082b,
0x2b08081908191919, 0x2b08081908192b08, 0x2b080819082b0819, 0x2b080819082b1908,
0x2b08081919080808, 0x2b0808191908082b, 0x2b08081919081919, 0x2b08081919082b08,
0x2b08081919190819, 0x2b08081919191908, 0x2b0808192b080819, 0x2b0808192b081908,
0x2b0808192b190808, 0x2b0808192b2b2b19, 0x2b08082b08080808, 0x2b08082b08081919,
0x2b08082b08082b2b, 0x2b08082b08190819, 0x2b08082b08191908, 0x2b08082b19080819,
0x2b08082b19081908, 0x2b08082b19190808, 0x2b08190808080819, 0x2b08190808081908,
0x2b0819080808192b, 0x2b08190808082b19, 0x2b08190808190808, 0x2b0819080819082b,
0x2b08190808191919, 0x2b08190808192b08, 0x2b081908082b0819, 0x2b08190819080808,
0x2b0819081908082b, 0x2b08190819081919, 0x2b08190819082b08, 0x2b08190819190819,
0x2b08190819191908, 0x2b081908192b0808, 0x2b0819082b080819, 0x2b0819082b081908,
0x2b0819082b190808, 0x2b08191908080808, 0x2b0819190808082b, 0x2b08191908081919,
0x2b08191908082b08, 0x2b08191908190819, 0x2b08191908191908, 0x2b081919082b0808,
0x2b08191919080819, 0x2b08191919081908, 0x2b08191919190808, 0x2b0819192b080808,
0x2b0819192b082b2b, 0x2b08192b08080819, 0x2b08192b08081908, 0x2b08192b08190808,
0x2b08192b082b2b19, 0x2b08192b19080808, 0x2b082b0808080808, 0x2b082b0808081919,
0x2b082b0808190819, 0x2b082b0808191908, 0x2b082b0819080819, 0x2b082b0819081908,
0x2b082b0819190808, 0x2b082b082b2b082b, 0x2b082b1908080819, 0x2b082b1908081908,
0x2b082b1919080808, 0x2b082b19192b1919, 0x2b082b2b082b082b, 0x2b082b2b19192b08,
0x2b082b2b19192b2b, 0x2b082b2b2b08082b, 0x2b082b2b2b2b082b, 0x2b19080808080819,
0x2b19080808081908, 0x2b19080808082b19, 0x2b19080808190808, 0x2b1908080819082b,
0x2b19080808191919, 0x2b19080808192b08, 0x2b190808082b1908, 0x2b19080819080808,
0x2b1908081908082b, 0x2b19080819081919, 0x2b19080819082b08, 0x2b19080819190819,
0x2b19080819191908, 0x2b190808192b0808, 0x2b1908082b080819, 0x2b1908082b081908,
0x2b1908082b190808, 0x2b19081908080808, 0x2b19081908081919, 0x2b19081908190819,
0x2b19081908191908, 0x2b19081919080819, 0x2b19081919081908, 0x2b19081919190808,
0x2b19081919192b2b, 0x2b19082b08080819, 0x2b19082b08081908, 0x2b19082b08190808,
0x2b19082b19080808, 0x2b19082b2b2b192b, 0x2b19190808080808, 0x2b1919080808082b,
0x2b19190808081919, 0x2b19190808082b08, 0x2b19190808190819, 0x2b19190808191908,
0x2b191908082b0808, 0x2b19190819080819, 0x2b19190819081908, 0x2b19190819190808,
0x2b1919082b080808, 0x2b1919082b19192b, 0x2b19191908080819, 0x2b19191908081908,
0x2b19191908190808, 0x2b19191919080808, 0x2b1919192b192b08, 0x2b1919192b2b0819,
0x2b19192b08080808, 0x2b19192b1908192b, 0x2b19192b192b1908, 0x2b192b0808080819,
0x2b192b0808081908, 0x2b192b0808190808, 0x2b192b08082b192b, 0x2b192b0819080808,
0x2b192b082b2b2b19, 0x2b192b1908080808, 0x2b192b1919082b19, 0x2b192b191919082b,
0x2b192b2b2b190808, 0x2b2b080808080808, 0x2b2b080808081919, 0x2b2b080808082b2b,
0x2b2b080808191908, 0x2b2b0808082b082b, 0x2b2b0808082b2b2b, 0x2b2b080819080819,
0x2b2b080819081908, 0x2b2b080819190808, 0x2b2b08082b2b082b, 0x2b2b08082b2b2b2b,
0x2b2b081919080808, 0x2b2b0819192b1919, 0x2b2b082b0808082b, 0x2b2b082b08082b2b,
0x2b2b082b082b082b, 0x2b2b082b082b2b08, 0x2b2b082b082b2b2b, 0x2b2b082b2b08082b,
0x2b2b082b2b082b08, 0x2b2b082b2b082b2b, 0x2b2b082b2b2b2b08, 0x2b2b190808080819,
0x2b2b190808081908, 0x2b2b190808190808, 0x2b2b190819080808, 0x2b2b19082b082b19,
0x2b2b19082b2b1908, 0x2b2b191908080808, 0x2b2b191908192b19, 0x2b2b192b19190819,
0x2b2b2b0808082b2b, 0x2b2b2b08082b2b08, 0x2b2b2b082b2b082b, 0x2b2b2b1919191908,
0x2b2b2b192b08192b, 0x2b2b2b2b08082b08, 0x2b2b2b2b08082b2b, 0x2b2b2b2b082b0808,
0x2b2b2b2b082b082b, 0x2b2b2b2b082b2b08, 0x2b2b2b2b2b082b08, 0x2b2b2b2b2b2b2b2b,
};
constexpr constant static uint32_t iq3xxs_grid[256] = {
0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414,
0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14,
@ -4572,6 +4846,139 @@ kernel void kernel_mul_mv_iq3_s_f32(
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_iq2_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_iq2_s * x = (device const block_iq2_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 uint64_t * values = (threadgroup uint64_t *)shared_values;
//{
// int nval = 32;
// int pos = (32*sgitg + tiisg)*nval;
// for (int i = 0; i < nval; ++i) values[pos + i] = iq2s_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_iq2_s * xr = x + ibl;
device const uint8_t * qs = xr->qs + 4 * ib;
device const uint8_t * qh = xr->qh + ib;
device const uint8_t * sc = xr->scales + ib;
device const uint8_t * signs = qs + QK_K/8;
device const half * dh = &xr->d;
for (int row = 0; row < N_DST; row++) {
const float db = dh[0];
const float d1 = db * (0.5f + (sc[0] & 0xf));
const float d2 = db * (0.5f + (sc[0] >> 4));
float2 sum = {0};
for (int l = 0; l < 2; ++l) {
//const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300)));
//const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300)));
constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300)));
constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300)));
for (int j = 0; j < 8; ++j) {
sum[0] += yl[8*l + j + 0] * grid1[j] * select(1, -1, signs[l+0] & kmask_iq2xs[j]);
sum[1] += yl[8*l + j + 16] * grid2[j] * select(1, -1, signs[l+2] & kmask_iq2xs[j]);
}
}
sumf[row] += d1 * sum[0] + d2 * sum[1];
dh += nb*sizeof(block_iq2_s)/2;
qs += nb*sizeof(block_iq2_s);
qh += nb*sizeof(block_iq2_s);
sc += nb*sizeof(block_iq2_s);
signs += nb*sizeof(block_iq2_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 * 0.25f;
}
}
}
[[host_name("kernel_mul_mv_iq2_s_f32")]]
kernel void kernel_mul_mv_iq2_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_iq2_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,
@ -4760,6 +5167,100 @@ void kernel_mul_mv_iq4_nl_f32_impl(
}
}
void kernel_mul_mv_iq4_xs_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 float * 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 * 2 + sgitg) * 2;
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_iq4_xs * x = (device const block_iq4_xs *) src0 + ib_row + offset0;
device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1;
const int ix = tiisg/16; // 0 or 1
const int it = tiisg%16; // 0...15
const int ib = it/2;
const int il = it%2;
shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16];
threadgroup_barrier(mem_flags::mem_threadgroup);
float4 yl[4];
float sumf[2]={0.f}, all_sum;
device const float * yb = y + ix * QK_K + ib * 32 + il * 8;
uint32_t aux32[2];
thread const uint8_t * q8 = (thread const uint8_t *)aux32;
float4 qf1, qf2;
for (int ibl = ix; ibl < nb; ibl += 2) {
device const float4 * y4 = (device const float4 *)yb;
yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5];
for (int row = 0; row < 2; ++row) {
device const block_iq4_xs & xb = x[row*nb + ibl];
device const uint32_t * q4 = (device const uint32_t *)(xb.qs + 16*ib + 8*il);
float4 acc1 = {0.f}, acc2 = {0.f};
aux32[0] = q4[0] & 0x0f0f0f0f;
aux32[1] = (q4[0] >> 4) & 0x0f0f0f0f;
qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]};
qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]};
acc1 += yl[0] * qf1;
acc2 += yl[1] * qf2;
aux32[0] = q4[1] & 0x0f0f0f0f;
aux32[1] = (q4[1] >> 4) & 0x0f0f0f0f;
qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]};
qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]};
acc1 += yl[2] * qf1;
acc2 += yl[3] * qf2;
acc1 += acc2;
const int ls = (((xb.scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((xb.scales_h >> 2*ib) & 3) << 4)) - 32;
sumf[row] += (float)xb.d * ls * (acc1[0] + acc1[1] + acc1[2] + acc1[3]);
}
yb += 2 * QK_K;
}
for (int row = 0; row < 2; ++row) {
all_sum = simd_sum(sumf[row]);
if (tiisg == 0) {
dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
}
}
}
[[host_name("kernel_mul_mv_iq1_s_f32")]]
kernel void kernel_mul_mv_iq1_s_f32(
device const void * src0,
@ -4817,6 +5318,35 @@ kernel void kernel_mul_mv_iq4_nl_f32(
kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
}
[[host_name("kernel_mul_mv_iq4_xs_f32")]]
kernel void kernel_mul_mv_iq4_xs_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 float * 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_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg);
}
//============================= templates and their specializations =============================
// NOTE: this is not dequantizing - we are simply fitting the template
@ -5188,6 +5718,25 @@ void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 &
}
}
template <typename type4x4>
void dequantize_iq2_s(device const block_iq2_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 + 4*ib32 + 2*il;
device const uint8_t * signs = qs + QK_K/8;
const uint8_t qh = xb->qh[ib32] >> 4*il;
const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f;
constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300)));
constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300)));
for (int i = 0; i < 8; ++i) {
reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]);
reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]);
}
}
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
@ -5219,6 +5768,26 @@ void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4
}
}
template <typename type4x4>
void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) {
// il is 0...15 for QK_K = 256 => index of block of 32 is il/2
const int ib32 = il/2;
il = il%2;
// il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16
device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32;
const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4);
const float d = (float)xb->d * (ls - 32);
uint32_t aux32;
thread const uint8_t * q8 = (thread const uint8_t *)&aux32;
for (int i = 0; i < 4; ++i) {
aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f;
reg[i][0] = d * kvalues_iq4nl_f[q8[0]];
reg[i][1] = d * kvalues_iq4nl_f[q8[1]];
reg[i][2] = d * kvalues_iq4nl_f[q8[2]];
reg[i][3] = d * kvalues_iq4nl_f[q8[3]];
}
}
template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
kernel void kernel_get_rows(
device const void * src0,
@ -5762,8 +6331,10 @@ template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_r
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_iq2_s")]] kernel get_rows_t kernel_get_rows<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows<block_iq4_nl, 2, dequantize_iq4_nl>;
template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows<block_iq4_nl, 2, dequantize_iq4_nl>;
template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows<block_iq4_xs, QK_NL, dequantize_iq4_xs>;
//
// matrix-matrix multiplication
@ -5804,8 +6375,10 @@ template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_m
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_iq2_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_nl, 2, dequantize_iq4_nl>;
template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_nl, 2, dequantize_iq4_nl>;
template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq4_xs, QK_NL, dequantize_iq4_xs>;
//
// indirect matrix-matrix multiplication
@ -5858,8 +6431,10 @@ template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel
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_iq2_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq2_s, QK_NL, dequantize_iq2_s>;
template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq1_s, QK_NL, dequantize_iq1_s>;
template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_nl, 2, dequantize_iq4_nl>;
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>;
template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id<block_iq4_xs, QK_NL, dequantize_iq4_xs>;
//
// matrix-vector multiplication
@ -6893,6 +7468,71 @@ kernel void kernel_mul_mv_id_iq3_s_f32(
sgitg);
}
[[host_name("kernel_mul_mv_id_iq2_s_f32")]]
kernel void kernel_mul_mv_id_iq2_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_iq2_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,
@ -7020,3 +7660,68 @@ kernel void kernel_mul_mv_id_iq4_nl_f32(
tiisg,
sgitg);
}
[[host_name("kernel_mul_mv_id_iq4_xs_f32")]]
kernel void kernel_mul_mv_id_iq4_xs_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 float * 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_iq4_xs_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);
}

1042
ggml-quants.c
View file

File diff suppressed because it is too large Load diff

27
ggml-quants.h
View file

@ -182,6 +182,15 @@ typedef struct {
} block_iq2_xs;
static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding");
// 2.5625 bpw quants
typedef struct {
ggml_fp16_t d;
uint8_t qs[QK_K/4];
uint8_t qh[QK_K/32];
uint8_t scales[QK_K/32];
} block_iq2_s;
static_assert(sizeof(block_iq2_s) == sizeof(ggml_fp16_t) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding");
// (Almost) "true" 3-bit quantization.
// Due to the need to use blocks as per ggml design, it ends up using
// 3.0625 bpw because of the 16-bit scale for each block of 256.
@ -221,6 +230,14 @@ typedef struct {
} block_iq4_nl;
static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding");
typedef struct {
ggml_fp16_t d;
uint16_t scales_h;
uint8_t scales_l[QK_K/64];
uint8_t qs[QK_K/2];
} block_iq4_xs;
static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
#ifdef __cplusplus
extern "C" {
#endif
@ -241,7 +258,9 @@ 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_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs * 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_iq2_s_reference (const float * GGML_RESTRICT x, block_iq2_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);
@ -258,7 +277,9 @@ 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_iq4_xs (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);
void quantize_row_iq2_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);
@ -276,9 +297,11 @@ void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRI
void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
// Dot product
@ -295,9 +318,11 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
void ggml_vec_dot_q6_K_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_iq2_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_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
//
@ -305,9 +330,11 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const
//
size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
size_t quantize_iq2_s (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
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_iq4_xs (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);

248
ggml-sycl.cpp
View file

@ -8126,23 +8126,51 @@ static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, con
dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX;
}
static void soft_max_f32(const float * x, const float * y, float * dst, const int ncols, const int nrows_y, const float scale,
const sycl::nd_item<3> &item_ct1, float *buf) {
template <bool vals_smem, int ncols_template, int block_size_template>
static void soft_max_f32(const float * x, const float * mask, const float *pos, float * dst, const int ncols_par,
const int nrows_y, const float scale, const float max_bias, const float m0,
const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) {
const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
const int tid = item_ct1.get_local_id(2);
const int rowx = item_ct1.get_group(2);
const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension
const int block_size = item_ct1.get_local_range(2);
const int block_size = block_size_template == 0 ? item_ct1.get_local_range(2) : block_size_template;
const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
float slope = 0.0f;
// ALiBi
if (max_bias > 0.0f) {
const uint32_t h = rowx/nrows_y; // head index
const float base = h < n_head_log2 ? m0 : m1;
const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
slope = sycl::pow(base, float(exp));
}
float * vals = vals_smem ? buf + WARP_SIZE : dst + rowx*ncols;
float max_val = -INFINITY;
for (int col = tid; col < ncols; col += block_size) {
for (int col0 = 0; col0 < ncols; col0 += block_size) {
const int col = col0 + tid;
if (ncols_template == 0 && col >= ncols) {
break;
}
const int ix = rowx*ncols + col;
const int iy = rowy*ncols + col;
max_val = sycl::max(max_val, x[ix] * scale + (y ? y[iy] : 0.0f));
const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f);
vals[col] = val;
max_val = sycl::max(max_val, val);
}
// find the max value in the block
@ -8151,30 +8179,12 @@ static void soft_max_f32(const float * x, const float * y, float * dst, const in
if (warp_id == 0) {
buf[lane_id] = -INFINITY;
}
/*
DPCT1118:12: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
/*
DPCT1065:60: Consider replacing sycl::nd_item::barrier() with
sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
better performance if there is no access to global memory.
*/
item_ct1.barrier();
item_ct1.barrier(sycl::access::fence_space::local_space);
if (lane_id == 0) {
buf[warp_id] = max_val;
}
/*
DPCT1118:13: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
/*
DPCT1065:61: Consider replacing sycl::nd_item::barrier() with
sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
better performance if there is no access to global memory.
*/
item_ct1.barrier();
item_ct1.barrier(sycl::access::fence_space::local_space);
max_val = buf[lane_id];
max_val = warp_reduce_max(max_val, item_ct1);
@ -8182,13 +8192,16 @@ static void soft_max_f32(const float * x, const float * y, float * dst, const in
float tmp = 0.f;
for (int col = tid; col < ncols; col += block_size) {
const int ix = rowx*ncols + col;
const int iy = rowy*ncols + col;
const float val =
sycl::native::exp((x[ix] * scale + (y ? y[iy] : 0.0f)) - max_val);
#pragma unroll
for (int col0 = 0; col0 < ncols; col0 += block_size) {
const int col = col0 + tid;
if (ncols_template == 0 && col >= ncols) {
break;
}
const float val = sycl::native::exp(vals[col] - max_val);
tmp += val;
dst[ix] = val;
vals[col] = val;
}
// find the sum of exps in the block
@ -8197,40 +8210,29 @@ static void soft_max_f32(const float * x, const float * y, float * dst, const in
if (warp_id == 0) {
buf[lane_id] = 0.f;
}
/*
DPCT1118:14: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
/*
DPCT1065:62: Consider replacing sycl::nd_item::barrier() with
sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
better performance if there is no access to global memory.
*/
item_ct1.barrier();
item_ct1.barrier(sycl::access::fence_space::local_space);
if (lane_id == 0) {
buf[warp_id] = tmp;
}
/*
DPCT1118:15: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
/*
DPCT1065:63: Consider replacing sycl::nd_item::barrier() with
sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
better performance if there is no access to global memory.
*/
item_ct1.barrier();
item_ct1.barrier(sycl::access::fence_space::local_space);
tmp = buf[lane_id];
tmp = warp_reduce_sum(tmp, item_ct1);
}
const float inv_tmp = 1.f / tmp;
const float inv_sum = 1.f / tmp;
for (int col = tid; col < ncols; col += block_size) {
const int i = rowx*ncols + col;
dst[i] *= inv_tmp;
#pragma unroll
for (int col0 = 0; col0 < ncols; col0 += block_size) {
const int col = col0 + tid;
if (ncols_template == 0 && col >= ncols) {
return;
}
const int idst = rowx*ncols + col;
dst[idst] = vals[col] * inv_sum;
}
}
@ -10867,35 +10869,96 @@ static void diag_mask_inf_f32_sycl(const float *x, float *dst,
});
}
static void soft_max_f32_sycl(const float *x, const float *y, float *dst,
const int ncols_x, const int nrows_x,
const int nrows_y, const float scale,
template <bool vals_smem, int ncols_template, int block_size_template>
static void soft_max_f32_submitter(const float * x, const float * mask, const float *pos, float * dst, const int ncols_par,
const int nrows_y, const float scale, const float max_bias, const float m0,
const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims,
const size_t n_local_scratch, dpct::queue_ptr stream) {
stream->submit([&](sycl::handler &cgh) {
sycl::local_accessor<float, 1> local_buf_acc(n_local_scratch, cgh);
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, pos, dst, ncols_par,
nrows_y, scale, max_bias, m0,
m1, n_head_log2, item_ct1,
local_buf_acc.get_pointer());
});
});
}
static void soft_max_f32_sycl(const float * x, const float * mask, const float * pos,
float * dst, const int ncols_x, const int nrows_x,
const int nrows_y, const float scale, const float max_bias,
dpct::queue_ptr stream) {
int nth = WARP_SIZE;
while (nth < ncols_x && nth < SYCL_SOFT_MAX_BLOCK_SIZE) nth *= 2;
const sycl::range<3> block_dims(1, 1, nth);
const sycl::range<3> block_nums(1, 1, nrows_x);
/*
DPCT1049:46: The work-group size passed to the SYCL kernel may exceed the
limit. To get the device limit, query info::device::max_work_group_size.
Adjust the work-group size if needed.
*/
stream->submit([&](sycl::handler &cgh) {
/*
DPCT1101:96: 'SYCL_SOFT_MAX_BLOCK_SIZE/WARP_SIZE' expression was
replaced with a value. Modify the code to use the original expression,
provided in comments, if it is correct.
*/
sycl::local_accessor<float, 1> buf_acc_ct1(
sycl::range<1>(32 /*SYCL_SOFT_MAX_BLOCK_SIZE/WARP_SIZE*/), cgh);
const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE);
static_assert(SYCL_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted.");
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
soft_max_f32(x, y, dst, ncols_x, nrows_y, scale, item_ct1,
buf_acc_ct1.get_pointer());
});
});
const uint32_t n_head_kv = nrows_x/nrows_y;
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
const size_t local_mem_size = stream->get_device().get_info<sycl::info::device::local_mem_size>();
if (n_local_scratch*sizeof(float) < local_mem_size) {
switch (ncols_x) {
case 32:
soft_max_f32_submitter<true, 32, 32>(x, mask, pos, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 64:
soft_max_f32_submitter<true, 64, 64>(x, mask, pos, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 128:
soft_max_f32_submitter<true, 128, 128>(x, mask, pos, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 256:
soft_max_f32_submitter<true, 256, 256>(x, mask, pos, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 512:
soft_max_f32_submitter<true, 512, 512>(x, mask, pos, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 1024:
soft_max_f32_submitter<true, 1024, 1024>(x, mask, pos, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 2048:
soft_max_f32_submitter<true, 2048, 1024>(x, mask, pos, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
case 4096:
soft_max_f32_submitter<true, 4096, 1024>(x, mask, pos, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
default:
soft_max_f32_submitter<true, 0, 0>(x, mask, pos, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, n_local_scratch, stream);
break;
}
} else {
soft_max_f32_submitter<false, 0, 0>(x, mask, pos, dst, ncols_x, nrows_y, scale,
max_bias, m0, m1, n_head_log2, block_nums,
block_dims, WARP_SIZE, stream);
}
}
template <typename T>
@ -12435,14 +12498,35 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0,
const int64_t ne00 = src0->ne[0];
const int64_t nrows_x = ggml_nrows(src0);
const int64_t nrows_y = src1 ? ggml_nrows(src1) : 1;
const int64_t nrows_y = src0->ne[1];
float scale = 1.0f;
memcpy(&scale, dst->op_params, sizeof(float));
float max_bias = 0.0f;
soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream);
memcpy(&scale, dst->op_params + 0, sizeof(float));
memcpy(&max_bias, dst->op_params + 1, sizeof(float));
(void) dst;
// positions tensor
float * src2_dd = nullptr;
sycl_pool_alloc<float> src2_f;
ggml_tensor * src2 = dst->src[2];
const bool use_src2 = src2 != nullptr;
if (use_src2) {
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;
src2_dd = (float *) src2_extra->data_device[g_main_device];
} else {
src2_dd = src2_f.alloc(ggml_nelements(src2));
SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src2_dd, src2, 0, 0, 0, 1, main_stream));
}
}
soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, src2_dd, dst_dd, ne00,
nrows_x, nrows_y, scale, max_bias, main_stream);
}
inline void ggml_sycl_op_scale(const ggml_tensor *src0, const ggml_tensor *src1,

61
ggml.c
View file

@ -690,6 +690,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ2_S] = {
.type_name = "iq2_s",
.blck_size = QK_K,
.type_size = sizeof(block_iq2_s),
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq2_s,
.from_float = quantize_row_iq2_s,
.from_float_reference = (ggml_from_float_t)quantize_row_iq2_s_reference,
.vec_dot = ggml_vec_dot_iq2_s_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_IQ1_S] = {
.type_name = "iq1_s",
.blck_size = QK_K,
@ -714,6 +726,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
.vec_dot_type = GGML_TYPE_Q8_0,
.nrows = 1,
},
[GGML_TYPE_IQ4_XS] = {
.type_name = "iq4_xs",
.blck_size = QK_K,
.type_size = sizeof(block_iq4_xs),
.is_quantized = true,
.to_float = (ggml_to_float_t) dequantize_row_iq4_xs,
.from_float = quantize_row_iq4_xs,
.from_float_reference = (ggml_from_float_t)quantize_row_iq4_xs_reference,
.vec_dot = ggml_vec_dot_iq4_xs_q8_K,
.vec_dot_type = GGML_TYPE_Q8_K,
.nrows = 1,
},
[GGML_TYPE_Q8_K] = {
.type_name = "q8_K",
.blck_size = QK_K,
@ -2317,7 +2341,9 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break;
case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break;
case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break;
case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break;
case GGML_FTYPE_MOSTLY_IQ2_S: wtype = GGML_TYPE_IQ2_S; break;
case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break;
case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
}
@ -7752,7 +7778,9 @@ static void ggml_compute_forward_add(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
{
ggml_compute_forward_add_q_f32(params, dst);
} break;
@ -8032,7 +8060,9 @@ static void ggml_compute_forward_add1(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
{
ggml_compute_forward_add1_q_f32(params, dst);
} break;
@ -8157,7 +8187,9 @@ static void ggml_compute_forward_acc(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
default:
{
GGML_ASSERT(false);
@ -11056,7 +11088,9 @@ static void ggml_compute_forward_out_prod(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
{
ggml_compute_forward_out_prod_q_f32(params, dst);
} break;
@ -11245,7 +11279,9 @@ static void ggml_compute_forward_set(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
default:
{
GGML_ASSERT(false);
@ -11448,7 +11484,9 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
{
ggml_compute_forward_get_rows_q(params, dst);
} break;
@ -12149,7 +12187,9 @@ static void ggml_compute_forward_alibi(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q8_K:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
@ -12233,7 +12273,9 @@ static void ggml_compute_forward_clamp(
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_Q8_K:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
@ -19483,6 +19525,7 @@ void ggml_quantize_init(enum ggml_type type) {
switch (type) {
case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ1_S: iq2xs_init_impl(type); break;
case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break;
case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break;
@ -19769,6 +19812,15 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
result = quantize_iq3_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
GGML_ASSERT(result == row_size * nrows);
} break;
case GGML_TYPE_IQ2_S:
{
GGML_ASSERT(start % QK_K == 0);
GGML_ASSERT(start % n_per_row == 0);
size_t start_row = start / n_per_row;
size_t row_size = ggml_row_size(type, n_per_row);
result = quantize_iq2_s(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
GGML_ASSERT(result == row_size * nrows);
} break;
case GGML_TYPE_IQ1_S:
{
GGML_ASSERT(start % QK_K == 0);
@ -19787,6 +19839,15 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
result = quantize_iq4_nl(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
GGML_ASSERT(result == row_size * nrows);
} break;
case GGML_TYPE_IQ4_XS:
{
GGML_ASSERT(start % QK4_NL == 0);
GGML_ASSERT(start % n_per_row == 0);
size_t start_row = start / n_per_row;
size_t row_size = ggml_row_size(type, n_per_row);
result = quantize_iq4_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix);
GGML_ASSERT(result == row_size * nrows);
} break;
case GGML_TYPE_F16:
{
size_t elemsize = sizeof(ggml_fp16_t);

4
ggml.h
View file

@ -358,6 +358,8 @@ extern "C" {
GGML_TYPE_IQ1_S = 19,
GGML_TYPE_IQ4_NL = 20,
GGML_TYPE_IQ3_S = 21,
GGML_TYPE_IQ2_S = 22,
GGML_TYPE_IQ4_XS = 23,
GGML_TYPE_I8,
GGML_TYPE_I16,
GGML_TYPE_I32,
@ -398,6 +400,8 @@ extern "C" {
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
GGML_FTYPE_MOSTLY_IQ2_S = 21, // except 1d tensors
GGML_FTYPE_MOSTLY_IQ4_XS = 22, // except 1d tensors
};
// available tensor operations:

2
gpttype_adapter.cpp
View file

@ -666,7 +666,7 @@ void PurgeMissingTokens(llama_context * ctx, std::vector<int> &current_context_t
//extract the unwanted tokens out from context and KV
int diff = found - trimstart;
llama_kv_cache_seq_rm(llama_ctx_v4, 0, trimstart, trimstart + diff);
llama_kv_cache_seq_shift(llama_ctx_v4, 0, trimstart + diff, -1, -diff);
llama_kv_cache_seq_add(llama_ctx_v4, 0, trimstart + diff, -1, -diff);
for (size_t i = trimstart + diff; i < current_context_tokens.size() - 1; i++)
{

40
klite.embd
View file

@ -6,7 +6,7 @@ It requires no dependencies, installation or setup.
Just copy this single static HTML file anywhere and open it in a browser, or from a webserver.
Please go to https://github.com/LostRuins/lite.koboldai.net for updates on Kobold Lite.
Kobold Lite is under the AGPL v3.0 License unless otherwise exempted. Please do not remove this line.
Current version: 115
Current version: 116
-Concedo
-->
@ -6747,6 +6747,33 @@ Current version: 115
}
}
var allow_update_kobold_model_display_timestamp = performance.now() + 60000;
function update_custom_kobold_endpoint_model_display()
{
if(custom_kobold_endpoint!="" && selected_workers.length==0 && selected_models.length==1)
{
if(performance.now() >= allow_update_kobold_model_display_timestamp)
{
allow_update_kobold_model_display_timestamp = performance.now() + 60000;
console.log("Updating selected model name...");
let murl = apply_proxy_url(custom_kobold_endpoint + kobold_custom_mdl_endpoint);
fetch(murl)
.then(x => x.json())
.then(values => {
if(custom_kobold_endpoint!="" && values && values.result!="")
{
let mdlname = values.result;
selected_models = [{ "performance": 100.0, "queued": 0.0, "eta": 0, "name": mdlname, "count": 1 }];
selected_workers = [];
console.log("Updating selected model name done.");
}
})
.catch(error => {
console.log("Update Kobold Model Error: " + error);
});
}
}
}
function connect_custom_endpoint()
{
@ -9646,6 +9673,7 @@ Current version: 115
let trackedgenid = pending_response_id; //if it changes, stop streaming
kobold_api_stream(sub_endpt, submit_payload, submit_payload.max_length, trackedgenid, "", streamchunk);
}
update_custom_kobold_endpoint_model_display();
}
else if (custom_oai_key != "")//handle for OAI
{
@ -11362,7 +11390,7 @@ Current version: 115
render_gametext(false);
}
handle_autoscroll();
handle_autoscroll(false);
}
var allow_reenable_submitbtn_timestamp = performance.now();
@ -11416,7 +11444,7 @@ Current version: 115
}
}
function handle_autoscroll()
function handle_autoscroll(alwaysscroll=true)
{
if (localsettings.autoscroll) {
let box1 = document.getElementById("gametext");
@ -11424,11 +11452,11 @@ Current version: 115
function isScrolledToBottom(element) {
return element.scrollHeight - element.scrollTop <= element.clientHeight + 250;
}
if(isScrolledToBottom(box1))
if(alwaysscroll || isScrolledToBottom(box1))
{
box1.scrollTop = box1.scrollHeight - box1.clientHeight + 10;
}
if(isScrolledToBottom(box2))
if(alwaysscroll || isScrolledToBottom(box2))
{
box2.scrollTop = box2.scrollHeight - box2.clientHeight + 10;
}
@ -11813,7 +11841,7 @@ Current version: 115
autosave();
}
handle_autoscroll();
handle_autoscroll(true);
if(localsettings.printer_view)
{

2
koboldcpp.py
View file

@ -475,7 +475,7 @@ maxhordelen = 256
modelbusy = threading.Lock()
requestsinqueue = 0
defaultport = 5001
KcppVersion = "1.59.1"
KcppVersion = "1.60"
showdebug = True
showsamplerwarning = True
showmaxctxwarning = True

1009
llama.cpp
View file

File diff suppressed because it is too large Load diff

40
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,
@ -98,12 +107,15 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q3_K_XS = 22, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ3_XS = 22, // except 1d tensors
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_MOSTLY_IQ2_S = 28, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};
@ -234,6 +246,7 @@ extern "C" {
float yarn_beta_fast; // YaRN low correction dim
float yarn_beta_slow; // YaRN high correction dim
uint32_t yarn_orig_ctx; // YaRN original context size
float defrag_thold; // defragment the KV cache if holes/size > thold, < 0 disabled (default)
ggml_backend_sched_eval_callback cb_eval;
void * cb_eval_user_data;
@ -360,6 +373,7 @@ 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);
@ -516,10 +530,12 @@ 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,
@ -527,7 +543,9 @@ 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(
@ -537,6 +555,20 @@ 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
//

2
otherarch/tools/unused/mainfn.txt
View file

@ -94,7 +94,7 @@ int mainfn() {
const int n_left = n_past - kcpp_params->n_keep - 1;
const int n_discard = n_left/2;
llama_kv_cache_seq_rm (llama_ctx_v4, 0, kcpp_params->n_keep + 1 , kcpp_params->n_keep + n_discard + 1);
llama_kv_cache_seq_shift(llama_ctx_v4, 0, kcpp_params->n_keep + 1 + n_discard, n_past, -n_discard);
llama_kv_cache_seq_add(llama_ctx_v4, 0, kcpp_params->n_keep + 1 + n_discard, n_past, -n_discard);
n_past -= n_discard;
}

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) {