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build : on Mac OS enable Metal by default (#2901)

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* build : on Mac OS enable Metal by default

* make : try to fix build on Linux

* make : move targets back to the top

* make : fix target clean

* llama : enable GPU inference by default with Metal

* llama : fix vocab_only logic when GPU is enabled

* common : better `n_gpu_layers` assignment

* readme : update Metal instructions

* make : fix merge conflict remnants

* gitignore : metal
This commit is contained in:
Georgi Gerganov 2023-09-04 22:26:24 +03:00 committed by GitHub
parent bd33e5ab92
commit e36ecdccc8
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GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 143 additions and 133 deletions
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29
.gitignore vendored
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@ -31,28 +31,29 @@ tmp/
models/* models/*
models-mnt models-mnt
/main
/quantize
/quantize-stats
/result
/perplexity
/embedding
/train-text-from-scratch
/convert-llama2c-to-ggml
/simple
/benchmark-matmult
/vdot
/server
/Pipfile /Pipfile
/baby-llama
/beam-search
/benchmark-matmult
/convert-llama2c-to-ggml
/embd-input-test /embd-input-test
/embedding
/gguf /gguf
/gguf-llama-simple /gguf-llama-simple
/libllama.so /libllama.so
/llama-bench /llama-bench
/baby-llama /main
/beam-search /metal
/perplexity
/quantize
/quantize-stats
/result
/save-load-state /save-load-state
/server
/simple
/speculative /speculative
/train-text-from-scratch
/vdot
build-info.h build-info.h
arm_neon.h arm_neon.h
compile_commands.json compile_commands.json

56
CMakeLists.txt
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@ -36,6 +36,12 @@ endif()
# Option list # Option list
# #
if (APPLE)
set(LLAMA_METAL_DEFAULT ON)
else()
set(LLAMA_METAL_DEFAULT OFF)
endif()
# general # general
option(LLAMA_STATIC "llama: static link libraries" OFF) option(LLAMA_STATIC "llama: static link libraries" OFF)
option(LLAMA_NATIVE "llama: enable -march=native flag" OFF) option(LLAMA_NATIVE "llama: enable -march=native flag" OFF)
@ -76,7 +82,7 @@ option(LLAMA_CUDA_F16 "llama: use 16 bit floats for some
set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K") set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF) option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF)
option(LLAMA_CLBLAST "llama: use CLBlast" OFF) option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
option(LLAMA_METAL "llama: use Metal" OFF) option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT})
option(LLAMA_MPI "llama: use MPI" OFF) option(LLAMA_MPI "llama: use MPI" OFF)
option(LLAMA_K_QUANTS "llama: use k-quants" ON) option(LLAMA_K_QUANTS "llama: use k-quants" ON)
option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF)
@ -158,6 +164,31 @@ if (APPLE AND LLAMA_ACCELERATE)
endif() endif()
endif() endif()
if (LLAMA_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
message(STATUS "Metal framework found")
set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
add_compile_definitions(GGML_USE_METAL)
#add_compile_definitions(GGML_METAL_NDEBUG)
# get full path to the file
#add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
# copy ggml-metal.metal to bin directory
configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
${FOUNDATION_LIBRARY}
${METAL_FRAMEWORK}
${METALKIT_FRAMEWORK}
)
endif()
if (LLAMA_BLAS) if (LLAMA_BLAS)
if (LLAMA_STATIC) if (LLAMA_STATIC)
set(BLA_STATIC ON) set(BLA_STATIC ON)
@ -293,29 +324,6 @@ if (LLAMA_CUBLAS)
endif() endif()
endif() endif()
if (LLAMA_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
add_compile_definitions(GGML_USE_METAL)
#add_compile_definitions(GGML_METAL_NDEBUG)
# get full path to the file
#add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
# copy ggml-metal.metal to bin directory
configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
${FOUNDATION_LIBRARY}
${METAL_FRAMEWORK}
${METALKIT_FRAMEWORK}
)
endif()
if (LLAMA_MPI) if (LLAMA_MPI)
cmake_minimum_required(VERSION 3.10) cmake_minimum_required(VERSION 3.10)
find_package(MPI) find_package(MPI)

76
Makefile
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@ -7,6 +7,39 @@ TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-dou
# Code coverage output files # Code coverage output files
COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
ifndef UNAME_S
UNAME_S := $(shell uname -s)
endif
ifndef UNAME_P
UNAME_P := $(shell uname -p)
endif
ifndef UNAME_M
UNAME_M := $(shell uname -m)
endif
# Mac OS + Arm can report x86_64
# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
ifeq ($(UNAME_S),Darwin)
ifndef LLAMA_NO_METAL
LLAMA_METAL := 1
endif
ifneq ($(UNAME_P),arm)
SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null)
ifeq ($(SYSCTL_M),1)
# UNAME_P := arm
# UNAME_M := arm64
warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789)
endif
endif
endif
ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))'
BUILD_TARGETS += metal
endif
default: $(BUILD_TARGETS) default: $(BUILD_TARGETS)
test: test:
@ -38,18 +71,6 @@ gcovr-report: coverage ## Generate gcovr report
mkdir -p gcovr-report mkdir -p gcovr-report
gcovr --root . --html --html-details --output gcovr-report/coverage.html gcovr --root . --html --html-details --output gcovr-report/coverage.html
ifndef UNAME_S
UNAME_S := $(shell uname -s)
endif
ifndef UNAME_P
UNAME_P := $(shell uname -p)
endif
ifndef UNAME_M
UNAME_M := $(shell uname -m)
endif
ifdef RISCV_CROSS_COMPILE ifdef RISCV_CROSS_COMPILE
CC := riscv64-unknown-linux-gnu-gcc CC := riscv64-unknown-linux-gnu-gcc
CXX := riscv64-unknown-linux-gnu-g++ CXX := riscv64-unknown-linux-gnu-g++
@ -58,19 +79,6 @@ endif
CCV := $(shell $(CC) --version | head -n 1) CCV := $(shell $(CC) --version | head -n 1)
CXXV := $(shell $(CXX) --version | head -n 1) CXXV := $(shell $(CXX) --version | head -n 1)
# Mac OS + Arm can report x86_64
# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
ifeq ($(UNAME_S),Darwin)
ifneq ($(UNAME_P),arm)
SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null)
ifeq ($(SYSCTL_M),1)
# UNAME_P := arm
# UNAME_M := arm64
warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789)
endif
endif
endif
# #
# Compile flags # Compile flags
# #
@ -231,14 +239,24 @@ endif
endif endif
ifndef LLAMA_NO_ACCELERATE ifndef LLAMA_NO_ACCELERATE
# Mac M1 - include Accelerate framework. # Mac OS - include Accelerate framework.
# `-framework Accelerate` works on Mac Intel as well, with negliable performance boost (as of the predict time). # `-framework Accelerate` works both with Apple Silicon and Mac Intel
ifeq ($(UNAME_S),Darwin) ifeq ($(UNAME_S),Darwin)
MK_CPPFLAGS += -DGGML_USE_ACCELERATE MK_CPPFLAGS += -DGGML_USE_ACCELERATE
MK_LDFLAGS += -framework Accelerate MK_LDFLAGS += -framework Accelerate
endif endif
endif # LLAMA_NO_ACCELERATE endif # LLAMA_NO_ACCELERATE
ifdef LLAMA_METAL
# By default - use GPU acceleration on Mac OS
ifeq ($(UNAME_S),Darwin)
CFLAGS += -DGGML_USE_METAL #-DGGML_METAL_NDEBUG
CXXFLAGS += -DGGML_USE_METAL
LDFLAGS += -framework Foundation -framework Metal -framework MetalKit
OBJS += ggml-metal.o
endif
endif # LLAMA_METAL
ifdef LLAMA_MPI ifdef LLAMA_MPI
MK_CPPFLAGS += -DGGML_USE_MPI MK_CPPFLAGS += -DGGML_USE_MPI
MK_CFLAGS += -Wno-cast-qual MK_CFLAGS += -Wno-cast-qual
@ -480,10 +498,6 @@ beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o co
speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o $(OBJS) speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))'
BUILD_TARGETS += metal
endif
ifdef LLAMA_METAL ifdef LLAMA_METAL
metal: examples/metal/metal.cpp ggml.o $(OBJS) metal: examples/metal/metal.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)

26
README.md
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@ -280,29 +280,11 @@ In order to build llama.cpp you have three different options.
### Metal Build ### Metal Build
Using Metal allows the computation to be executed on the GPU for Apple devices: On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
To disable the Metal build at compile time use the `LLAMA_NO_METAL=1` flag or the `LLAMA_METAL=OFF` cmake option.
- Using `make`: When built with Metal support, you can explicitly disable GPU inference with the `--gpu-layers|-ngl 0` command-line
argument.
```bash
LLAMA_METAL=1 make
```
- Using `CMake`:
```bash
mkdir build-metal
cd build-metal
cmake -DLLAMA_METAL=ON ..
cmake --build . --config Release
```
When built with Metal support, you can enable GPU inference with the `--gpu-layers|-ngl` command-line argument.
Any value larger than 0 will offload the computation to the GPU. For example:
```bash
./main -m ./models/7B/ggml-model-q4_0.gguf -n 128 -ngl 1
```
### MPI Build ### MPI Build

6
common/common.cpp
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@ -717,7 +717,9 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
lparams.n_ctx = params.n_ctx; lparams.n_ctx = params.n_ctx;
lparams.n_batch = params.n_batch; lparams.n_batch = params.n_batch;
lparams.n_gpu_layers = params.n_gpu_layers; if (params.n_gpu_layers != -1) {
lparams.n_gpu_layers = params.n_gpu_layers;
}
lparams.main_gpu = params.main_gpu; lparams.main_gpu = params.main_gpu;
lparams.tensor_split = params.tensor_split; lparams.tensor_split = params.tensor_split;
lparams.low_vram = params.low_vram; lparams.low_vram = params.low_vram;
@ -1212,7 +1214,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false"); fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false");
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers); fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", params.n_probs); fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", params.n_probs);
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");

2
common/common.h
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@ -34,7 +34,7 @@ struct gpt_params {
int32_t n_keep = 0; // number of tokens to keep from initial prompt int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_draft = 16; // number of tokens to draft during speculative decoding int32_t n_draft = 16; // number of tokens to draft during speculative decoding
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_gpu_layers = 0; // number of layers to store in VRAM int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.

15
examples/main/main.cpp
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@ -151,14 +151,6 @@ int main(int argc, char ** argv) {
LOG_TEE("%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale); LOG_TEE("%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale);
} }
if (params.n_ctx > 2048) {
// TODO: determine the actual max context of the model (e.g. 4096 for LLaMA v2) and use that instead of 2048
LOG_TEE("%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx);
} else if (params.n_ctx < 8) {
LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
params.n_ctx = 8;
}
LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed == LLAMA_DEFAULT_SEED) { if (params.seed == LLAMA_DEFAULT_SEED) {
@ -194,6 +186,13 @@ int main(int argc, char ** argv) {
return 1; return 1;
} }
if (params.n_ctx > llama_n_ctx(ctx)) {
LOG_TEE("%s: warning: base model only supports context sizes no greater than %d tokens (%d specified)\n", __func__, llama_n_ctx(ctx), params.n_ctx);
} else if (params.n_ctx < 8) {
LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
params.n_ctx = 8;
}
// print system information // print system information
{ {
LOG_TEE("\n"); LOG_TEE("\n");

12
examples/perplexity/perplexity.cpp
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@ -368,7 +368,7 @@ results_perplexity perplexity(llama_context * ctx, const gpt_params & params) {
// Example, we have a context window of 512, we will compute perplexity for each of the // Example, we have a context window of 512, we will compute perplexity for each of the
// last 256 tokens. Then, we split the input up into context window size chunks to // last 256 tokens. Then, we split the input up into context window size chunks to
// process the entire prompt. // process the entire prompt.
const int first = std::min(512, params.n_ctx/2); const int first = params.n_ctx/2;
process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first, process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first,
workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first); workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
count += params.n_ctx - first - 1; count += params.n_ctx - first - 1;
@ -668,11 +668,6 @@ int main(int argc, char ** argv) {
params.n_ctx += params.ppl_stride/2; params.n_ctx += params.ppl_stride/2;
} }
if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
"expect poor results\n", __func__, params.n_ctx);
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed == LLAMA_DEFAULT_SEED) { if (params.seed == LLAMA_DEFAULT_SEED) {
@ -698,6 +693,11 @@ int main(int argc, char ** argv) {
return 1; return 1;
} }
if (params.n_ctx > llama_n_ctx(ctx)) {
fprintf(stderr, "%s: warning: model might not support context sizes greater than %d tokens (%d specified);"
"expect poor results\n", __func__, llama_n_ctx(ctx), params.n_ctx);
}
// print system information // print system information
{ {
fprintf(stderr, "\n"); fprintf(stderr, "\n");

54
llama.cpp
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@ -5340,7 +5340,7 @@ struct llama_context_params llama_context_default_params() {
/*.seed =*/ LLAMA_DEFAULT_SEED, /*.seed =*/ LLAMA_DEFAULT_SEED,
/*.n_ctx =*/ 512, /*.n_ctx =*/ 512,
/*.n_batch =*/ 512, /*.n_batch =*/ 512,
/*.gpu_layers =*/ 0, /*.n_gpu_layers =*/ 0,
/*.main_gpu =*/ 0, /*.main_gpu =*/ 0,
/*.tensor_split =*/ nullptr, /*.tensor_split =*/ nullptr,
/*.rope_freq_base =*/ 10000.0f, /*.rope_freq_base =*/ 10000.0f,
@ -5357,6 +5357,10 @@ struct llama_context_params llama_context_default_params() {
/*.embedding =*/ false, /*.embedding =*/ false,
}; };
#ifdef GGML_USE_METAL
result.n_gpu_layers = 1;
#endif
return result; return result;
} }
@ -5549,43 +5553,43 @@ struct llama_context * llama_new_context_with_model(
} }
#endif #endif
} }
}
#ifdef GGML_USE_METAL #ifdef GGML_USE_METAL
if (params.n_gpu_layers > 0) { if (params.n_gpu_layers > 0) {
// this allocates all Metal resources and memory buffers // this allocates all Metal resources and memory buffers
void * data_ptr = NULL; void * data_ptr = NULL;
size_t data_size = 0; size_t data_size = 0;
if (params.use_mmap) { if (params.use_mmap) {
data_ptr = ctx->model.mapping->addr; data_ptr = ctx->model.mapping->addr;
data_size = ctx->model.mapping->size; data_size = ctx->model.mapping->size;
} else { } else {
data_ptr = ggml_get_mem_buffer(ctx->model.ctx); data_ptr = ggml_get_mem_buffer(ctx->model.ctx);
data_size = ggml_get_mem_size (ctx->model.ctx); data_size = ggml_get_mem_size (ctx->model.ctx);
} }
const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx); const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx);
LLAMA_LOG_INFO("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0); LLAMA_LOG_INFO("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0);
#define LLAMA_METAL_CHECK_BUF(result) \ #define LLAMA_METAL_CHECK_BUF(result) \
if (!(result)) { \ if (!(result)) { \
LLAMA_LOG_ERROR("%s: failed to add buffer\n", __func__); \ LLAMA_LOG_ERROR("%s: failed to add buffer\n", __func__); \
llama_free(ctx); \ llama_free(ctx); \
return NULL; \ return NULL; \
} }
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size)); LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.data, ctx->buf_compute.size, 0)); LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.data, ctx->buf_compute.size, 0));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->kv_self.buf.data, ctx->kv_self.buf.size, 0)); LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->kv_self.buf.data, ctx->kv_self.buf.size, 0));
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "alloc", ctx->buf_alloc.data, ctx->buf_alloc.size, 0)); LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "alloc", ctx->buf_alloc.data, ctx->buf_alloc.size, 0));
#undef LLAMA_METAL_CHECK_BUF #undef LLAMA_METAL_CHECK_BUF
} }
#endif #endif
}
#ifdef GGML_USE_MPI #ifdef GGML_USE_MPI
ctx->ctx_mpi = ggml_mpi_init(); ctx->ctx_mpi = ggml_mpi_init();