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llama.cpp
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sync : ggml (new ops, tests, backend, etc.) (#4359) 

* sync : ggml (part 1)

* sync : ggml (part 2, CUDA)

* sync : ggml (part 3, Metal)

* ggml : build fixes

ggml-ci

* cuda : restore lost changes

* cuda : restore lost changes (StableLM rope)

* cmake : enable separable compilation for CUDA

ggml-ci

* ggml-cuda : remove device side dequantize

* Revert "cmake : enable separable compilation for CUDA"

This reverts commit 09e35d04b1.

* cuda : remove assert for rope

* tests : add test-backend-ops

* ggml : fix bug in ggml_concat

* ggml : restore `ggml_get_n_tasks()` logic in `ggml_graph_plan()`

* ci : try to fix macOS

* ggml-backend : remove backend self-registration

* ci : disable Metal for macOS cmake build

ggml-ci

* metal : fix "supports family" call

* metal : fix assert

* metal : print resource path

ggml-ci

---------

Co-authored-by: slaren <slarengh@gmail.com>
pull/4368/head^2 b1620
Georgi Gerganov 2023-12-07 22:26:54 +02:00 committed by GitHub
parent bcc0eb4591
commit fe680e3d10
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
20 changed files with 4637 additions and 1031 deletions
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15
.github/workflows/build.yml vendored
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@ -143,6 +143,9 @@ jobs:
cd build
ctest --verbose
# TODO: build with LLAMA_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know
# how to debug it.
# ref: https://github.com/ggerganov/llama.cpp/actions/runs/7131777249/job/19420981052#step:5:1124
macOS-latest-make:
runs-on: macos-latest
@ -160,14 +163,18 @@ jobs:
- name: Build
id: make_build
run: |
make -j $(sysctl -n hw.logicalcpu)
LLAMA_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu)
- name: Test
id: make_test
run: |
make tests -j $(sysctl -n hw.logicalcpu)
make test -j $(sysctl -n hw.logicalcpu)
LLAMA_NO_METAL=1 make tests -j $(sysctl -n hw.logicalcpu)
LLAMA_NO_METAL=1 make test -j $(sysctl -n hw.logicalcpu)
# TODO: build with LLAMA_METAL=OFF because test-backend-ops fail on "Apple Paravirtual device" and I don't know
# how to debug it.
# ref: https://github.com/ggerganov/llama.cpp/actions/runs/7132125951/job/19422043567?pr=4359#step:5:6584
# would be great if we fix these
macOS-latest-cmake:
runs-on: macos-latest
@ -188,7 +195,7 @@ jobs:
sysctl -a
mkdir build
cd build
cmake ..
cmake -DLLAMA_METAL=OFF ..
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test

1
.gitignore vendored
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@ -101,3 +101,4 @@ poetry.toml
/tests/test-tokenizer-1-llama
/tests/test-tokenizer-1-bpe
/tests/test-rope
/tests/test-backend-ops

14
CMakeLists.txt
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@ -97,9 +97,9 @@ option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging"
option(LLAMA_MPI "llama: use MPI" OFF)
option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF)
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_SERVER "llama: build server example" ON)
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_SERVER "llama: build server example" ON)
# Required for relocatable CMake package
include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
@ -662,11 +662,11 @@ add_library(ggml OBJECT
ggml-backend.h
ggml-quants.c
ggml-quants.h
${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA}
${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA}
${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL}
${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL}
${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI}
${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA}
${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL}
${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI}
${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA}
)
target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES})

6
Makefile
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@ -8,7 +8,8 @@ BUILD_TARGETS = \
TEST_TARGETS = \
tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama \
tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope
tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope \
tests/test-backend-ops
# Code coverage output files
COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
@ -746,3 +747,6 @@ tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS)
tests/test-c.o: tests/test-c.c llama.h
$(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@
tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

49
ggml-alloc.c
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@ -168,10 +168,6 @@ static void ggml_tallocr_free_tensor(ggml_tallocr_t alloc, struct ggml_tensor *
size = aligned_offset(NULL, size, alloc->alignment);
AT_PRINTF("%s: freeing %s at %p (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, ptr, size, alloc->n_free_blocks);
if (!alloc->measure) {
ggml_backend_buffer_free_tensor(alloc->buffer, tensor);
}
#ifdef GGML_ALLOCATOR_DEBUG
remove_allocated_tensor(alloc, tensor);
#endif
@ -237,7 +233,7 @@ void ggml_tallocr_reset(ggml_tallocr_t alloc) {
}
ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment) {
struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(NULL, data, size);
struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(data, size);
ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr));
@ -449,7 +445,6 @@ static ggml_tallocr_t node_tallocr(ggml_gallocr_t galloc, struct ggml_tensor * n
static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool update_backend) {
ggml_tallocr_t alloc = node_tallocr(galloc, view);
//printf("init_view: %s from src %s\n", view->name, view->view_src->name);
GGML_ASSERT(view->view_src != NULL && view->view_src->data != NULL);
if (update_backend) {
view->backend = view->view_src->backend;
@ -459,7 +454,7 @@ static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool upd
// FIXME: the view should be initialized by the owning buffer, but currently this breaks the CUDA backend
// due to the ggml_tensor_extra_gpu ring buffer overwriting the KV cache extras
assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->backend == alloc->buffer->backend);
assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->buft == alloc->buffer->buft);
if (!alloc->measure) {
ggml_backend_buffer_init_tensor(alloc->buffer, view);
@ -765,3 +760,43 @@ size_t ggml_allocr_max_size(ggml_allocr_t alloc) {
size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph) {
return ggml_gallocr_alloc_graph(alloc->galloc, alloc->talloc, graph);
}
// utils
ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
GGML_ASSERT(ggml_get_no_alloc(ctx) == true);
size_t alignment = ggml_backend_buft_get_alignment(buft);
size_t nbytes = 0;
for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->data == NULL && t->view_src == NULL) {
nbytes += GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment);
}
}
if (nbytes == 0) {
fprintf(stderr, "%s: no tensors to allocate\n", __func__);
return NULL;
}
ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, nbytes);
ggml_tallocr_t tallocr = ggml_tallocr_new_from_buffer(buffer);
for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->data == NULL) {
if (t->view_src == NULL) {
ggml_tallocr_alloc(tallocr, t);
} else {
ggml_backend_view_init(buffer, t);
}
}
}
ggml_tallocr_free(tallocr);
return buffer;
}
ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend) {
return ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_get_default_buffer_type(backend));
}

7
ggml-alloc.h
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@ -8,6 +8,7 @@ extern "C" {
struct ggml_backend;
struct ggml_backend_buffer;
struct ggml_backend_buffer_type;
//
// Legacy API
@ -80,6 +81,12 @@ GGML_API void ggml_gallocr_alloc_graph_n(
struct ggml_hash_set hash_set,
ggml_tallocr_t * hash_node_talloc);
// Utils
// Create a buffer and allocate all the tensors in a ggml_context
GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, struct ggml_backend_buffer_type * buft);
GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, struct ggml_backend * backend);
#ifdef __cplusplus
}
#endif

67
ggml-backend-impl.h
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@ -12,31 +12,50 @@ extern "C" {
// Backend buffer
//
// buffer type
typedef void * ggml_backend_buffer_type_context_t;
struct ggml_backend_buffer_type_i {
ggml_backend_buffer_t (*alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size);
size_t (*get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment
size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
bool (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
};
struct ggml_backend_buffer_type {
struct ggml_backend_buffer_type_i iface;
ggml_backend_buffer_type_context_t context;
};
// buffer
typedef void * ggml_backend_buffer_context_t;
struct ggml_backend_buffer_i {
void (*free_buffer) (ggml_backend_buffer_t buffer);
void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer
size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback
void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback
void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback
void (*free_buffer)(ggml_backend_buffer_t buffer);
//void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
void * (*get_base) (ggml_backend_buffer_t buffer);
void (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
// (optional) copy tensor between different buffer-type, allow for single-copy tranfers
void (*cpy_tensor_from)(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*cpy_tensor_to) (ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
};
struct ggml_backend_buffer {
struct ggml_backend_buffer_i iface;
ggml_backend_t backend;
struct ggml_backend_buffer_i iface;
ggml_backend_buffer_type_t buft;
ggml_backend_buffer_context_t context;
size_t size;
};
GGML_API ggml_backend_buffer_t ggml_backend_buffer_init(
struct ggml_backend * backend,
ggml_backend_buffer_t ggml_backend_buffer_init(
ggml_backend_buffer_type_t buft,
struct ggml_backend_buffer_i iface,
ggml_backend_buffer_context_t context,
size_t size);
//
// Backend
//
@ -49,20 +68,17 @@ extern "C" {
void (*free)(ggml_backend_t backend);
// buffer allocation
ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size);
ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend);
// get buffer alignment
size_t (*get_alignment)(ggml_backend_t backend);
// tensor data access
// these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize
// (optional) asynchroneous tensor data access
void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
void (*synchronize) (ggml_backend_t backend);
// (optional) copy tensor between different backends, allow for single-copy tranfers
void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
// (optional) asynchroneous tensor copy
void (*cpy_tensor_from_async)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*cpy_tensor_to_async) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*synchronize) (ggml_backend_t backend);
// compute graph with a plan
ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
@ -82,6 +98,15 @@ extern "C" {
ggml_backend_context_t context;
};
//
// Backend registry
//
typedef ggml_backend_t (*ggml_backend_init_fn)(const char * params, void * user_data);
void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
#ifdef __cplusplus
}
#endif

771
ggml-backend.c
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79
ggml-backend.h
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@ -7,41 +7,44 @@
extern "C" {
#endif
typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
typedef struct ggml_backend * ggml_backend_t;
typedef void * ggml_backend_graph_plan_t;
//
// Backend buffer
//
struct ggml_backend_buffer;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
// buffer type
GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size);
GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft);
GGML_API size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
// backend buffer functions
// buffer
GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API void ggml_backend_buffer_free_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer);
//
// Backend
//
struct ggml_backend;
typedef struct ggml_backend * ggml_backend_t;
typedef void * ggml_backend_graph_plan_t;
GGML_API ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor);
GGML_API const char * ggml_backend_name(ggml_backend_t backend);
GGML_API void ggml_backend_free(ggml_backend_t backend);
GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
GGML_API ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend);
GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
GGML_API void ggml_backend_tensor_set_async( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
@ -57,6 +60,7 @@ extern "C" {
// tensor copy between different backends
GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); // automatic fallback to sync copy
//
// CPU backend
@ -68,8 +72,23 @@ extern "C" {
GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
// Create a backend buffer from an existing pointer
GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size);
GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
//
// Backend registry
//
// The backend registry is a registry of all the available backends, and allows initializing backends in a generic way
GGML_API size_t ggml_backend_reg_get_count(void);
GGML_API size_t ggml_backend_reg_find_by_name(const char * name);
GGML_API ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str); // str is name[:params]
GGML_API const char * ggml_backend_reg_get_name(size_t i);
GGML_API ggml_backend_t ggml_backend_reg_init_backend(size_t i, const char * params); // params is backend-specific
GGML_API ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i);
GGML_API ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size);
//
// Backend scheduler
@ -131,6 +150,32 @@ extern "C" {
ggml_backend_sched_t sched,
struct ggml_cgraph * graph);
//
// Utils
//
struct ggml_backend_graph_copy {
ggml_backend_buffer_t buffer;
struct ggml_context * ctx_allocated;
struct ggml_context * ctx_unallocated;
struct ggml_cgraph * graph;
};
// Copy a graph to a different backend
GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
GGML_API void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
// Compare the output of two backends
GGML_API void ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
// Tensor initialization
GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
GGML_API void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
#ifdef __cplusplus
}
#endif

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ggml-cuda.cu
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10
ggml-cuda.h
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@ -49,7 +49,15 @@ GGML_API int ggml_cuda_get_device_count(void);
GGML_API void ggml_cuda_get_device_description(int device, char * description, size_t description_size);
// backend API
GGML_API ggml_backend_t ggml_backend_cuda_init(void); // TODO: take a list of devices to use
GGML_API ggml_backend_t ggml_backend_cuda_init(int device);
GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend);
GGML_API int ggml_backend_cuda_get_device(ggml_backend_t backend);
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
// pinned host buffer for use with CPU backend for faster copies between CPU and GPU
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
#ifdef __cplusplus
}

2
ggml-impl.h
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@ -232,7 +232,7 @@ bool ggml_hash_contains (const struct ggml_hash_set hash_set, struct ggml
// returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted
size_t ggml_hash_find (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
// returns GGML_HAHSHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
// returns GGML_HASHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
size_t ggml_hash_insert ( struct ggml_hash_set hash_set, struct ggml_tensor * key);
// return index, asserts if table is full

6
ggml-metal.h
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@ -99,6 +99,12 @@ GGML_API ggml_backend_t ggml_backend_metal_init(void);
GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
// helper to check if the device supports a specific family
// ideally, the user code should be doing these checks
// ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
GGML_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family);
#ifdef __cplusplus
}

676
ggml-metal.m
View File

@ -62,6 +62,8 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(add_row); // TODO: avoid this extra kernel, instead extend the "add" kernel to support broadcast
GGML_METAL_DECL_KERNEL(mul);
GGML_METAL_DECL_KERNEL(mul_row); // TODO: avoid this extra kernel, instead extend the "mul" kernel to support broadcast
GGML_METAL_DECL_KERNEL(div);
GGML_METAL_DECL_KERNEL(div_row);
GGML_METAL_DECL_KERNEL(scale);
GGML_METAL_DECL_KERNEL(scale_4);
GGML_METAL_DECL_KERNEL(silu);
@ -112,10 +114,24 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(mul_mm_q4_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q5_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_q6_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_id_f32_f32);
GGML_METAL_DECL_KERNEL(mul_mm_id_f16_f32);
GGML_METAL_DECL_KERNEL(mul_mm_id_q4_0_f32);
GGML_METAL_DECL_KERNEL(mul_mm_id_q4_1_f32);
GGML_METAL_DECL_KERNEL(mul_mm_id_q5_0_f32);
GGML_METAL_DECL_KERNEL(mul_mm_id_q5_1_f32);
GGML_METAL_DECL_KERNEL(mul_mm_id_q8_0_f32);
GGML_METAL_DECL_KERNEL(mul_mm_id_q2_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_id_q3_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_id_q4_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_id_q5_K_f32);
GGML_METAL_DECL_KERNEL(mul_mm_id_q6_K_f32);
GGML_METAL_DECL_KERNEL(rope_f32);
GGML_METAL_DECL_KERNEL(rope_f16);
GGML_METAL_DECL_KERNEL(alibi_f32);
GGML_METAL_DECL_KERNEL(im2col_f16);
GGML_METAL_DECL_KERNEL(argsort_f32_i32_asc);
GGML_METAL_DECL_KERNEL(argsort_f32_i32_desc);
GGML_METAL_DECL_KERNEL(cpy_f32_f16);
GGML_METAL_DECL_KERNEL(cpy_f32_f32);
GGML_METAL_DECL_KERNEL(cpy_f32_q8_0);
@ -126,6 +142,7 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(cpy_f16_f16);
GGML_METAL_DECL_KERNEL(concat);
GGML_METAL_DECL_KERNEL(sqr);
GGML_METAL_DECL_KERNEL(sum_rows);
#undef GGML_METAL_DECL_KERNEL
};
@ -169,12 +186,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
}
}
struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_LOG_INFO("%s: allocating\n", __func__);
id <MTLDevice> device;
id<MTLDevice> device;
NSString * s;
#if TARGET_OS_OSX
@ -220,6 +235,9 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
NSString * sourcePath;
NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, ggmlMetalPathResources ? [ggmlMetalPathResources UTF8String] : "nil");
if (ggmlMetalPathResources) {
sourcePath = [ggmlMetalPathResources stringByAppendingPathComponent:@"ggml-metal.metal"];
} else {
@ -250,6 +268,29 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
}
}
#if TARGET_OS_OSX
// print MTL GPU family:
GGML_METAL_LOG_INFO("%s: GPU name: %s\n", __func__, [[ctx->device name] UTF8String]);
// determine max supported GPU family
// https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf
// https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) {
if ([ctx->device supportsFamily:i]) {
GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i);
break;
}
}
GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6);
if (ctx->device.maxTransferRate != 0) {
GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1e6);
} else {
GGML_METAL_LOG_INFO("%s: maxTransferRate = built-in GPU\n", __func__);
}
#endif
// load kernels
{
NSError * error = nil;
@ -271,6 +312,8 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(add_row);
GGML_METAL_ADD_KERNEL(mul);
GGML_METAL_ADD_KERNEL(mul_row);
GGML_METAL_ADD_KERNEL(div);
GGML_METAL_ADD_KERNEL(div_row);
GGML_METAL_ADD_KERNEL(scale);
GGML_METAL_ADD_KERNEL(scale_4);
GGML_METAL_ADD_KERNEL(silu);
@ -322,11 +365,25 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(mul_mm_q4_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q5_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_q6_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_id_f32_f32);
GGML_METAL_ADD_KERNEL(mul_mm_id_f16_f32);
GGML_METAL_ADD_KERNEL(mul_mm_id_q4_0_f32);
GGML_METAL_ADD_KERNEL(mul_mm_id_q4_1_f32);
GGML_METAL_ADD_KERNEL(mul_mm_id_q5_0_f32);
GGML_METAL_ADD_KERNEL(mul_mm_id_q5_1_f32);
GGML_METAL_ADD_KERNEL(mul_mm_id_q8_0_f32);
GGML_METAL_ADD_KERNEL(mul_mm_id_q2_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_id_q3_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_id_q4_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_id_q5_K_f32);
GGML_METAL_ADD_KERNEL(mul_mm_id_q6_K_f32);
}
GGML_METAL_ADD_KERNEL(rope_f32);
GGML_METAL_ADD_KERNEL(rope_f16);
GGML_METAL_ADD_KERNEL(alibi_f32);
GGML_METAL_ADD_KERNEL(im2col_f16);
GGML_METAL_ADD_KERNEL(argsort_f32_i32_asc);
GGML_METAL_ADD_KERNEL(argsort_f32_i32_desc);
GGML_METAL_ADD_KERNEL(cpy_f32_f16);
GGML_METAL_ADD_KERNEL(cpy_f32_f32);
GGML_METAL_ADD_KERNEL(cpy_f32_q8_0);
@ -337,33 +394,11 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(cpy_f16_f16);
GGML_METAL_ADD_KERNEL(concat);
GGML_METAL_ADD_KERNEL(sqr);
GGML_METAL_ADD_KERNEL(sum_rows);
#undef GGML_METAL_ADD_KERNEL
}
#if TARGET_OS_OSX
// print MTL GPU family:
GGML_METAL_LOG_INFO("%s: GPU name: %s\n", __func__, [[ctx->device name] UTF8String]);
// determine max supported GPU family
// https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf
// https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) {
if ([ctx->device supportsFamily:i]) {
GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i);
break;
}
}
GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MiB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
if (ctx->device.maxTransferRate != 0) {
GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MiB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
} else {
GGML_METAL_LOG_INFO("%s: maxTransferRate = built-in GPU\n", __func__);
}
#endif
return ctx;
}
@ -377,6 +412,8 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
GGML_METAL_DEL_KERNEL(add_row);
GGML_METAL_DEL_KERNEL(mul);
GGML_METAL_DEL_KERNEL(mul_row);
GGML_METAL_DEL_KERNEL(div);
GGML_METAL_DEL_KERNEL(div_row);
GGML_METAL_DEL_KERNEL(scale);
GGML_METAL_DEL_KERNEL(scale_4);
GGML_METAL_DEL_KERNEL(silu);
@ -428,11 +465,25 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_id_f32_f32);
GGML_METAL_DEL_KERNEL(mul_mm_id_f16_f32);
GGML_METAL_DEL_KERNEL(mul_mm_id_q4_0_f32);
GGML_METAL_DEL_KERNEL(mul_mm_id_q4_1_f32);
GGML_METAL_DEL_KERNEL(mul_mm_id_q5_0_f32);
GGML_METAL_DEL_KERNEL(mul_mm_id_q5_1_f32);
GGML_METAL_DEL_KERNEL(mul_mm_id_q8_0_f32);
GGML_METAL_DEL_KERNEL(mul_mm_id_q2_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_id_q3_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_id_q4_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_id_q5_K_f32);
GGML_METAL_DEL_KERNEL(mul_mm_id_q6_K_f32);
}
GGML_METAL_DEL_KERNEL(rope_f32);
GGML_METAL_DEL_KERNEL(rope_f16);
GGML_METAL_DEL_KERNEL(alibi_f32);
GGML_METAL_DEL_KERNEL(im2col_f16);
GGML_METAL_DEL_KERNEL(argsort_f32_i32_asc);
GGML_METAL_DEL_KERNEL(argsort_f32_i32_desc);
GGML_METAL_DEL_KERNEL(cpy_f32_f16);
GGML_METAL_DEL_KERNEL(cpy_f32_f32);
GGML_METAL_DEL_KERNEL(cpy_f32_q8_0);
@ -443,6 +494,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
GGML_METAL_DEL_KERNEL(cpy_f16_f16);
GGML_METAL_DEL_KERNEL(concat);
GGML_METAL_DEL_KERNEL(sqr);
GGML_METAL_DEL_KERNEL(sum_rows);
#undef GGML_METAL_DEL_KERNEL
@ -486,6 +538,13 @@ int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) {
return ctx->concur_list;
}
// temporarily defined here for compatibility between ggml-backend and the old API
struct ggml_backend_metal_buffer_context {
void * data;
id<MTLBuffer> metal;
};
// finds the Metal buffer that contains the tensor data on the GPU device
// the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
// Metal buffer based on the host memory pointer
@ -495,8 +554,17 @@ static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru
const int64_t tsize = ggml_nbytes(t);
if (t->buffer && t->buffer->backend && t->buffer->backend->context) {
ctx = t->buffer->backend->context;
// compatibility with ggml-backend
if (t->buffer && t->buffer->buft == ggml_backend_metal_buffer_type()) {
struct ggml_backend_metal_buffer_context * buf_ctx = (struct ggml_backend_metal_buffer_context *) t->buffer->context;
const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->data;
GGML_ASSERT(ioffs >= 0 && ioffs + tsize <= (int64_t) t->buffer->size);
*offs = (size_t) ioffs;
return buf_ctx->metal;
}
// find the view that contains the tensor fully
@ -721,6 +789,51 @@ void ggml_metal_graph_find_concurrency(
}
}
static bool ggml_metal_supports_op(const struct ggml_tensor * op) {
switch (op->op) {
case GGML_OP_UNARY:
switch (ggml_get_unary_op(op)) {
case GGML_UNARY_OP_SILU:
case GGML_UNARY_OP_RELU:
case GGML_UNARY_OP_GELU:
return true;
default:
return false;
}
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_TRANSPOSE:
case GGML_OP_PERMUTE:
case GGML_OP_CONCAT:
case GGML_OP_ADD:
case GGML_OP_MUL:
case GGML_OP_DIV:
case GGML_OP_SCALE:
case GGML_OP_SQR:
case GGML_OP_SUM_ROWS:
case GGML_OP_SOFT_MAX:
case GGML_OP_RMS_NORM:
case GGML_OP_NORM:
case GGML_OP_ALIBI:
case GGML_OP_ROPE:
case GGML_OP_IM2COL:
case GGML_OP_ARGSORT:
case GGML_OP_DUP:
case GGML_OP_CPY:
case GGML_OP_CONT:
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
return true;
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_GET_ROWS:
{
return op->ne[0] % 4 == 0;
}
default:
return false;
}
}
void ggml_metal_graph_compute(
struct ggml_metal_context * ctx,
struct ggml_cgraph * gf) {
@ -791,6 +904,8 @@ void ggml_metal_graph_compute(
} break;
}
GGML_ASSERT(ggml_metal_supports_op(dst));
const int64_t ne00 = src0 ? src0->ne[0] : 0;
const int64_t ne01 = src0 ? src0->ne[1] : 0;
const int64_t ne02 = src0 ? src0->ne[2] : 0;
@ -883,6 +998,8 @@ void ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ADD:
case GGML_OP_MUL:
case GGML_OP_DIV:
{
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
@ -896,11 +1013,21 @@ void ggml_metal_graph_compute(
GGML_ASSERT(ne11 == 1);
nb = ne00 / 4;
[encoder setComputePipelineState:ctx->pipeline_add_row];
switch (dst->op) {
case GGML_OP_ADD: [encoder setComputePipelineState:ctx->pipeline_add_row]; break;
case GGML_OP_MUL: [encoder setComputePipelineState:ctx->pipeline_mul_row]; break;
case GGML_OP_DIV: [encoder setComputePipelineState:ctx->pipeline_div_row]; break;
default: GGML_ASSERT(false);
}
bcast_row = true;
} else {
[encoder setComputePipelineState:ctx->pipeline_add];
switch (dst->op) {
case GGML_OP_ADD: [encoder setComputePipelineState:ctx->pipeline_add]; break;
case GGML_OP_MUL: [encoder setComputePipelineState:ctx->pipeline_mul]; break;
case GGML_OP_DIV: [encoder setComputePipelineState:ctx->pipeline_div]; break;
default: GGML_ASSERT(false);
}
}
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
@ -941,31 +1068,6 @@ void ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
}
} break;
case GGML_OP_MUL:
{
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
// utilize float4
GGML_ASSERT(ne00 % 4 == 0);
const int64_t nb = ne00/4;
if (ggml_nelements(src1) == ne10) {
// src1 is a row
GGML_ASSERT(ne11 == 1);
[encoder setComputePipelineState:ctx->pipeline_mul_row];
} else {
[encoder setComputePipelineState:ctx->pipeline_mul];
}
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&nb length:sizeof(nb) atIndex:3];
const int64_t n = ggml_nelements(dst)/4;
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SCALE:
{
GGML_ASSERT(ggml_is_contiguous(src0));
@ -1038,6 +1140,40 @@ void ggml_metal_graph_compute(
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SUM_ROWS:
{
GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
[encoder setComputePipelineState:ctx->pipeline_sum_rows];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
[encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
[encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9];
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10];
[encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12];
[encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16];
[encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:18];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:19];
[encoder setBytes:&ne2 length:sizeof(ne2) atIndex:20];
[encoder setBytes:&ne3 length:sizeof(ne3) atIndex:21];
[encoder setBytes:&nb0 length:sizeof(nb0) atIndex:22];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:23];
[encoder setBytes:&nb2 length:sizeof(nb2) atIndex:24];
[encoder setBytes:&nb3 length:sizeof(nb3) atIndex:25];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SOFT_MAX:
{
int nth = 32; // SIMD width
@ -1092,13 +1228,17 @@ void ggml_metal_graph_compute(
case GGML_OP_MUL_MAT:
{
GGML_ASSERT(ne00 == ne10);
GGML_ASSERT(ne03 == ne13);
const uint gqa = ne12/ne02;
// TODO: assert that dim2 and dim3 are contiguous
GGML_ASSERT(ne12 % ne02 == 0);
GGML_ASSERT(ne13 % ne03 == 0);
const uint r2 = ne12/ne02;
const uint r3 = ne13/ne03;
// find the break-even point where the matrix-matrix kernel becomes more efficient compared
// to the matrix-vector kernel
int ne11_mm_min = src0t == GGML_TYPE_F16 ? 1 : 16;
int ne11_mm_min = 1;
#if 0
// the numbers below are measured on M2 Ultra for 7B and 13B models
@ -1159,9 +1299,10 @@ void ggml_metal_graph_compute(
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:10];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:11];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12];
[encoder setBytes:&gqa length:sizeof(gqa) atIndex:13];
[encoder setBytes:&r2 length:sizeof(r2) atIndex:13];
[encoder setBytes:&r3 length:sizeof(r3) atIndex:14];
[encoder setThreadgroupMemoryLength:8192 atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
} else {
int nth0 = 32;
int nth1 = 1;
@ -1197,90 +1338,60 @@ void ggml_metal_graph_compute(
} break;
case GGML_TYPE_Q4_0:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 8;
nth1 = 8;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_0_f32];
} break;
case GGML_TYPE_Q4_1:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 8;
nth1 = 8;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_1_f32];
} break;
case GGML_TYPE_Q5_0:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 8;
nth1 = 8;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_0_f32];
} break;
case GGML_TYPE_Q5_1:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 8;
nth1 = 8;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_1_f32];
} break;
case GGML_TYPE_Q8_0:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 8;
nth1 = 8;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_q8_0_f32];
} break;
case GGML_TYPE_Q2_K:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 2;
nth1 = 32;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_q2_K_f32];
} break;
case GGML_TYPE_Q3_K:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 2;
nth1 = 32;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_q3_K_f32];
} break;
case GGML_TYPE_Q4_K:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 4; //1;
nth1 = 8; //32;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_K_f32];
} break;
case GGML_TYPE_Q5_K:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 2;
nth1 = 32;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_K_f32];
} break;
case GGML_TYPE_Q6_K:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 2;
nth1 = 32;
[encoder setComputePipelineState:ctx->pipeline_mul_mv_q6_K_f32];
@ -1309,34 +1420,127 @@ void ggml_metal_graph_compute(
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16];
[encoder setBytes:&gqa length:sizeof(gqa) atIndex:17];
[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_Q4_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_Q4_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_Q3_K) {
#ifdef GGML_QKK_64
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
#else
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
#endif
}
else if (src0t == GGML_TYPE_Q5_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
else if (src0t == GGML_TYPE_Q6_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else {
int64_t ny = (ne11 + nrows - 1)/nrows;
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
}
}
} break;
case GGML_OP_MUL_MAT_ID:
{
//GGML_ASSERT(ne00 == ne10);
//GGML_ASSERT(ne03 == ne13);
GGML_ASSERT(src0t == GGML_TYPE_I32);
const int n_as = ne00;
// TODO: make this more general
GGML_ASSERT(n_as <= 8);
struct ggml_tensor * src2 = gf->nodes[i]->src[2];
const int64_t ne20 = src2 ? src2->ne[0] : 0;
const int64_t ne21 = src2 ? src2->ne[1] : 0;
const int64_t ne22 = src2 ? src2->ne[2] : 0;
const int64_t ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23);
const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20);
const uint64_t nb21 = src2 ? src2->nb[1] : 0;
const uint64_t nb22 = src2 ? src2->nb[2] : 0;
const uint64_t nb23 = src2 ? src2->nb[3] : 0; GGML_UNUSED(nb23);
const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t);
GGML_ASSERT(!ggml_is_transposed(src2));
GGML_ASSERT(!ggml_is_transposed(src1));
GGML_ASSERT(ne20 % 32 == 0);
// !!!!!!!!! TODO: this assert is probably required but not sure!
//GGML_ASSERT(ne20 >= 64);
GGML_ASSERT(src1t == GGML_TYPE_F32);
const uint r2 = ne12/ne22;
const uint r3 = ne13/ne23;
// find the break-even point where the matrix-matrix kernel becomes more efficient compared
// to the matrix-vector kernel
int ne11_mm_min = 0;
const int idx = ((int32_t *) dst->op_params)[0];
// for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
// AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
ne11 > ne11_mm_min) {
switch (src2->type) {
case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f32_f32]; break;
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_f16_f32]; break;
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_0_f32]; break;
case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_1_f32]; break;
case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_0_f32]; break;
case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_1_f32]; break;
case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q8_0_f32]; break;
case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q2_K_f32]; break;
case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q3_K_f32]; break;
case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q4_K_f32]; break;
case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q5_K_f32]; break;
case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_id_q6_K_f32]; break;
default: GGML_ASSERT(false && "MUL_MAT_ID not implemented");
}
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
[encoder setBytes:&ne20 length:sizeof(ne20) atIndex:3];
[encoder setBytes:&ne22 length:sizeof(ne22) atIndex:4];
[encoder setBytes:&nb21 length:sizeof(nb21) atIndex:5];
[encoder setBytes:&nb22 length:sizeof(nb22) atIndex:6];
[encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7];
[encoder setBytes:&nb10 length:sizeof(nb10) atIndex:8];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:9];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:10];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:11];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12];
[encoder setBytes:&r2 length:sizeof(r2) atIndex:13];
[encoder setBytes:&r3 length:sizeof(r3) atIndex:14];
[encoder setBytes:&idx length:sizeof(idx) atIndex:15];
// TODO: how to make this an array? read Metal docs
for (int j = 0; j < n_as; ++j) {
struct ggml_tensor * src_cur = dst->src[2 + j];
size_t offs_src_cur = 0;
id<MTLBuffer> id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur);
[encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:16 + j];
}
[encoder setThreadgroupMemoryLength:8192 atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne21 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
}
} break;
case GGML_OP_GET_ROWS:
{
switch (src0->type) {
@ -1560,6 +1764,27 @@ void ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)];
} break;
case GGML_OP_ARGSORT:
{
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_I32);
const int nrows = ggml_nrows(src0);
enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
switch (order) {
case GGML_SORT_ASC: [encoder setComputePipelineState:ctx->pipeline_argsort_f32_i32_asc]; break;
case GGML_SORT_DESC: [encoder setComputePipelineState:ctx->pipeline_argsort_f32_i32_desc]; break;
default: GGML_ASSERT(false);
};
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00, 1, 1)];
} break;
case GGML_OP_DUP:
case GGML_OP_CPY:
case GGML_OP_CONT:
@ -1655,6 +1880,132 @@ void ggml_metal_graph_compute(
// backend interface
static id<MTLDevice> g_backend_device = nil;
static int g_backend_device_ref_count = 0;
static id<MTLDevice> ggml_backend_metal_get_device(void) {
if (g_backend_device == nil) {
g_backend_device = MTLCreateSystemDefaultDevice();
}
g_backend_device_ref_count++;
return g_backend_device;
}
static void ggml_backend_metal_free_device(void) {
assert(g_backend_device_ref_count > 0);
g_backend_device_ref_count--;
if (g_backend_device_ref_count == 0) {
[g_backend_device release];
g_backend_device = nil;
}
}
static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) {
struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
return ctx->data;
}
static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) {
struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
[ctx->metal release];
ggml_backend_metal_free_device();
free(ctx->data);
free(ctx);
UNUSED(buffer);
}
static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
memcpy((char *)tensor->data + offset, data, size);
UNUSED(buffer);
}
static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
memcpy(data, (const char *)tensor->data + offset, size);
UNUSED(buffer);
}
static void ggml_backend_metal_buffer_cpy_tensor_from(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) {
ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
UNUSED(buffer);
}
static void ggml_backend_metal_buffer_cpy_tensor_to(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst) {
ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src));
UNUSED(buffer);
}
static struct ggml_backend_buffer_i metal_backend_buffer_i = {
/* .free_buffer = */ ggml_backend_metal_buffer_free_buffer,
/* .get_base = */ ggml_backend_metal_buffer_get_base,
/* .init_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_metal_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_metal_buffer_get_tensor,
/* .cpy_tensor_from = */ ggml_backend_metal_buffer_cpy_tensor_from,
/* .cpy_tensor_to = */ ggml_backend_metal_buffer_cpy_tensor_to,
};
static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context));
const size_t size_page = sysconf(_SC_PAGESIZE);
size_t size_aligned = size;
if ((size_aligned % size_page) != 0) {
size_aligned += (size_page - (size_aligned % size_page));
}
ctx->data = ggml_metal_host_malloc(size);
ctx->metal = [ggml_backend_metal_get_device() newBufferWithBytesNoCopy:ctx->data
length:size_aligned
options:MTLResourceStorageModeShared
deallocator:nil];
return ggml_backend_buffer_init(buft, metal_backend_buffer_i, ctx, size);
}
static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
return 32;
UNUSED(buft);
}
static bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
return ggml_backend_is_metal(backend) || ggml_backend_is_cpu(backend);
GGML_UNUSED(buft);
}
ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
static struct ggml_backend_buffer_type ggml_backend_buffer_type_metal = {
/* .iface = */ {
/* .alloc_buffer = */ ggml_backend_metal_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment,
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .supports_backend = */ ggml_backend_metal_buffer_type_supports_backend,
},
/* .context = */ NULL,
};
return &ggml_backend_buffer_type_metal;
}
static const char * ggml_backend_metal_name(ggml_backend_t backend) {
return "Metal";
@ -1667,69 +2018,12 @@ static void ggml_backend_metal_free(ggml_backend_t backend) {
free(backend);
}
static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) {
return (void *)buffer->context;
}
static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) {
free(buffer->context);
UNUSED(buffer);
}
static struct ggml_backend_buffer_i metal_backend_buffer_i = {
/* .free_buffer = */ ggml_backend_metal_buffer_free_buffer,
/* .get_base = */ ggml_backend_metal_buffer_get_base,
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .init_tensor = */ NULL, // no initialization required
/* .free_tensor = */ NULL, // no cleanup required
};
static ggml_backend_buffer_t ggml_backend_metal_alloc_buffer(ggml_backend_t backend, size_t size) {
struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
void * data = ggml_metal_host_malloc(size);
// TODO: set proper name of the buffers
ggml_metal_add_buffer(ctx, "backend", data, size, 0);
return ggml_backend_buffer_init(backend, metal_backend_buffer_i, data, size);
}
static size_t ggml_backend_metal_get_alignment(ggml_backend_t backend) {
return 32;
UNUSED(backend);
}
static void ggml_backend_metal_set_tensor_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
memcpy((char *)tensor->data + offset, data, size);
UNUSED(backend);
}
static void ggml_backend_metal_get_tensor_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
memcpy(data, (const char *)tensor->data + offset, size);
UNUSED(backend);
}
static void ggml_backend_metal_synchronize(ggml_backend_t backend) {
UNUSED(backend);
}
static void ggml_backend_metal_cpy_tensor_from(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
UNUSED(backend);
}
static void ggml_backend_metal_cpy_tensor_to(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
ggml_backend_tensor_set_async(dst, src->data, 0, ggml_nbytes(src));
static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) {
return ggml_backend_metal_buffer_type();
UNUSED(backend);
}
@ -1741,32 +2035,43 @@ static void ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml
}
static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
return true;
return ggml_metal_supports_op(op);
UNUSED(backend);
UNUSED(op);
}
static struct ggml_backend_i metal_backend_i = {
/* .get_name = */ ggml_backend_metal_name,
/* .free = */ ggml_backend_metal_free,
/* .alloc_buffer = */ ggml_backend_metal_alloc_buffer,
/* .get_alignment = */ ggml_backend_metal_get_alignment,
/* .set_tensor_async = */ ggml_backend_metal_set_tensor_async,
/* .get_tensor_async = */ ggml_backend_metal_get_tensor_async,
/* .synchronize = */ ggml_backend_metal_synchronize,
/* .cpy_tensor_from = */ ggml_backend_metal_cpy_tensor_from,
/* .cpy_tensor_to = */ ggml_backend_metal_cpy_tensor_to,
/* .graph_plan_create = */ NULL, // the metal implementation does not require creating graph plans atm
/* .graph_plan_free = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_metal_graph_compute,
/* .supports_op = */ ggml_backend_metal_supports_op,
/* .get_name = */ ggml_backend_metal_name,
/* .free = */ ggml_backend_metal_free,
/* .get_default_buffer_type = */ ggml_backend_metal_get_default_buffer_type,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_from_async = */ NULL,
/* .cpy_tensor_to_async = */ NULL,
/* .synchronize = */ ggml_backend_metal_synchronize,
/* .graph_plan_create = */ NULL, // the metal implementation does not require creating graph plans atm
/* .graph_plan_free = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_metal_graph_compute,
/* .supports_op = */ ggml_backend_metal_supports_op,
};
ggml_backend_t ggml_backend_metal_init(void) {
struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
// TODO: make a common log callback for all backends in ggml-backend
static void ggml_backend_log_callback(enum ggml_log_level level, const char * msg, void * user_data) {
fprintf(stderr, "%s", msg);
ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS);
UNUSED(level);
UNUSED(user_data);
}
ggml_backend_t ggml_backend_metal_init(void) {
ggml_metal_log_set_callback(ggml_backend_log_callback, NULL);
struct ggml_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS);
if (ctx == NULL) {
return NULL;
}
ggml_backend_t metal_backend = malloc(sizeof(struct ggml_backend));
@ -1783,7 +2088,26 @@ bool ggml_backend_is_metal(ggml_backend_t backend) {
}
void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
GGML_ASSERT(ggml_backend_is_metal(backend));
struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
ggml_metal_set_n_cb(ctx, n_cb);
}
bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) {
GGML_ASSERT(ggml_backend_is_metal(backend));
struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
return [ctx->device supportsFamily:(MTLGPUFamilyApple1 + family - 1)];
}
ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); // silence warning
ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data) {
return ggml_backend_metal_init();
GGML_UNUSED(params);
GGML_UNUSED(user_data);
}

755
ggml-metal.metal
View File

File diff suppressed because it is too large Load Diff

414
ggml.c
View File

@ -233,24 +233,6 @@ inline static void * ggml_aligned_malloc(size_t size) {
#define UNUSED GGML_UNUSED
#define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0)
//
// tensor access macros
//
#define GGML_TENSOR_UNARY_OP_LOCALS \
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
#define GGML_TENSOR_BINARY_OP_LOCALS \
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) \
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
#if defined(GGML_USE_ACCELERATE)
#include <Accelerate/Accelerate.h>
#if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions
@ -1613,6 +1595,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"GROUP_NORM",
"MUL_MAT",
"MUL_MAT_ID",
"OUT_PROD",
"SCALE",
@ -1640,6 +1623,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"POOL_1D",
"POOL_2D",
"UPSCALE",
"ARGSORT",
"FLASH_ATTN",
"FLASH_FF",
@ -1666,7 +1650,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"CROSS_ENTROPY_LOSS_BACK",
};
static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68");
static_assert(GGML_OP_COUNT == 70, "GGML_OP_COUNT != 70");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -1695,6 +1679,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"group_norm(x)",
"X*Y",
"X[i]*Y",
"X*Y",
"x*v",
@ -1722,6 +1707,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"pool_1d(x)",
"pool_2d(x)",
"upscale(x)",
"argsort(x)",
"flash_attn(x)",
"flash_ff(x)",
@ -1748,10 +1734,28 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"cross_entropy_loss_back(x,y)",
};
static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68");
static_assert(GGML_OP_COUNT == 70, "GGML_OP_COUNT != 70");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = {
"ABS",
"SGN",
"NEG",
"STEP",
"TANH",
"ELU",
"RELU",
"GELU",
"GELU_QUICK",
"SILU",
"LEAKY",
};
static_assert(GGML_UNARY_OP_COUNT == 11, "GGML_UNARY_OP_COUNT != 11");
static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
@ -1771,6 +1775,7 @@ static void ggml_setup_op_has_task_pass(void) {
p[GGML_OP_ACC ] = true;
p[GGML_OP_MUL_MAT ] = true;
p[GGML_OP_MUL_MAT_ID ] = true;
p[GGML_OP_OUT_PROD ] = true;
p[GGML_OP_SET ] = true;
p[GGML_OP_GET_ROWS_BACK ] = true;
@ -2023,6 +2028,20 @@ const char * ggml_op_symbol(enum ggml_op op) {
return GGML_OP_SYMBOL[op];
}
const char * ggml_unary_op_name(enum ggml_unary_op op) {
return GGML_UNARY_OP_NAME[op];
}
const char * ggml_op_desc(const struct ggml_tensor * t) {
if (t->op == GGML_OP_UNARY) {
enum ggml_unary_op uop = ggml_get_unary_op(t);
return ggml_unary_op_name(uop);
}
else {
return ggml_op_name(t->op);
}
}
size_t ggml_element_size(const struct ggml_tensor * tensor) {
return ggml_type_size(tensor->type);
}
@ -3154,9 +3173,7 @@ static struct ggml_tensor * ggml_add_impl(
struct ggml_tensor * a,
struct ggml_tensor * b,
bool inplace) {
// TODO: support less-strict constraint
// GGML_ASSERT(ggml_can_repeat(b, a));
GGML_ASSERT(ggml_can_repeat_rows(b, a));
GGML_ASSERT(ggml_can_repeat(b, a));
bool is_node = false;
@ -3371,9 +3388,7 @@ static struct ggml_tensor * ggml_mul_impl(
struct ggml_tensor * a,
struct ggml_tensor * b,
bool inplace) {
// TODO: support less-strict constraint
// GGML_ASSERT(ggml_can_repeat(b, a));
GGML_ASSERT(ggml_can_repeat_rows(b, a));
GGML_ASSERT(ggml_can_repeat(b, a));
bool is_node = false;
@ -3418,7 +3433,7 @@ static struct ggml_tensor * ggml_div_impl(
struct ggml_tensor * a,
struct ggml_tensor * b,
bool inplace) {
GGML_ASSERT(ggml_are_same_shape(a, b));
GGML_ASSERT(ggml_can_repeat(b, a));
bool is_node = false;
@ -4056,6 +4071,49 @@ struct ggml_tensor * ggml_mul_mat(
return result;
}
// ggml_mul_mat_id
struct ggml_tensor * ggml_mul_mat_id(
struct ggml_context * ctx,
struct ggml_tensor * as[],
struct ggml_tensor * ids,
int id,
struct ggml_tensor * b) {
int64_t n_as = ids->ne[0];
GGML_ASSERT(ids->type == GGML_TYPE_I32);
GGML_ASSERT(ggml_is_vector(ids));
GGML_ASSERT(n_as > 0 && n_as <= GGML_MAX_SRC - 2);
GGML_ASSERT(id >= 0 && id < n_as);
bool is_node = false;
if (as[0]->grad || b->grad) {
is_node = true;
}
const int64_t ne[4] = { as[0]->ne[1], b->ne[1], b->ne[2], b->ne[3] };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MAX(as[0]->n_dims, b->n_dims), ne);
ggml_set_op_params_i32(result, 0, id);
result->op = GGML_OP_MUL_MAT_ID;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = ids;
result->src[1] = b;
for (int64_t i = 0; i < n_as; i++) {
struct ggml_tensor * a = as[i];
GGML_ASSERT(ggml_are_same_shape(as[0], a));
GGML_ASSERT(ggml_can_mul_mat(a, b));
GGML_ASSERT(!ggml_is_transposed(a));
result->src[i + 2] = a;
}
return result;
}
// ggml_out_prod
struct ggml_tensor * ggml_out_prod(
@ -4209,7 +4267,7 @@ struct ggml_tensor * ggml_set_2d_inplace(
struct ggml_tensor * b,
size_t nb1,
size_t offset) {
return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false);
return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, true);
}
// ggml_cpy
@ -5468,6 +5526,43 @@ struct ggml_tensor * ggml_upscale(
return ggml_upscale_impl(ctx, a, scale_factor);
}
// ggml_argsort
struct ggml_tensor * ggml_argsort(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_sort_order order) {
bool is_node = false;
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, a->n_dims, a->ne);
ggml_set_op_params_i32(result, 0, (int32_t) order);
result->op = GGML_OP_ARGSORT;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
return result;
}
// ggml_top_k
struct ggml_tensor * ggml_top_k(
struct ggml_context * ctx,
struct ggml_tensor * a,
int k) {
GGML_ASSERT(a->ne[0] >= k);
struct ggml_tensor * result = ggml_argsort(ctx, a, GGML_SORT_DESC);
result = ggml_view_4d(ctx, result,
k, result->ne[1], result->ne[2], result->ne[3],
result->nb[1], result->nb[2], result->nb[3],
0);
return result;
}
// ggml_flash_attn
struct ggml_tensor * ggml_flash_attn(
@ -6827,7 +6922,7 @@ static void ggml_compute_forward_add_f32(
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));
GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
@ -6860,16 +6955,19 @@ static void ggml_compute_forward_add_f32(
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
const int64_t nr0 = ne00 / ne10;
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
for (int64_t r = 0; r < nr0; ++r) {
#ifdef GGML_USE_ACCELERATE
vDSP_vadd(src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00);
vDSP_vadd(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10);
#else
ggml_vec_add_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
ggml_vec_add_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr);
#endif
}
}
} else {
// src1 is not contiguous
@ -6886,8 +6984,9 @@ static void ggml_compute_forward_add_f32(
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
for (int i0 = 0; i0 < ne0; i0++) {
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10);
for (int64_t i0 = 0; i0 < ne0; ++i0) {
const int64_t i10 = i0 % ne10;
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10);
dst_ptr[i0] = src0_ptr[i0] + *src1_ptr;
}
@ -7607,7 +7706,7 @@ static void ggml_compute_forward_mul_f32(
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));
GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
@ -7630,7 +7729,6 @@ static void ggml_compute_forward_mul_f32(
GGML_ASSERT( nb0 == sizeof(float));
GGML_ASSERT(nb00 == sizeof(float));
GGML_ASSERT(ne00 == ne10);
if (nb10 == sizeof(float)) {
for (int64_t ir = ith; ir < nr; ir += nth) {
@ -7642,20 +7740,21 @@ static void ggml_compute_forward_mul_f32(
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
const int64_t nr0 = ne00 / ne10;
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
for (int64_t r = 0 ; r < nr0; ++r) {
#ifdef GGML_USE_ACCELERATE
UNUSED(ggml_vec_mul_f32);
UNUSED(ggml_vec_mul_f32);
vDSP_vmul( src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00);
vDSP_vmul(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10);
#else
ggml_vec_mul_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
ggml_vec_mul_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr);
#endif
// }
// }
}
}
} else {
// src1 is not contiguous
@ -7673,8 +7772,9 @@ static void ggml_compute_forward_mul_f32(
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
for (int64_t i0 = 0; i0 < ne00; i0++) {
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10);
for (int64_t i0 = 0; i0 < ne00; ++i0) {
const int64_t i10 = i0 % ne10;
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10);
dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr);
}
@ -7708,14 +7808,16 @@ static void ggml_compute_forward_div_f32(
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
assert(params->ith == 0);
assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int nr = ggml_nrows(src0);
const int ith = params->ith;
const int nth = params->nth;
const int64_t nr = ggml_nrows(src0);
GGML_TENSOR_BINARY_OP_LOCALS
@ -7723,41 +7825,50 @@ static void ggml_compute_forward_div_f32(
GGML_ASSERT(nb00 == sizeof(float));
if (nb10 == sizeof(float)) {
for (int ir = 0; ir < nr; ++ir) {
// src0, src1 and dst are same shape => same indices
const int i3 = ir/(ne2*ne1);
const int i2 = (ir - i3*ne2*ne1)/ne1;
const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
for (int64_t ir = ith; ir < nr; ir += nth) {
// src0 and dst are same shape => same indices
const int64_t i03 = ir/(ne02*ne01);
const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
const int64_t nr0 = ne00 / ne10;
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
for (int64_t r = 0; r < nr0; ++r) {
#ifdef GGML_USE_ACCELERATE
UNUSED(ggml_vec_div_f32);
UNUSED(ggml_vec_div_f32);
vDSP_vdiv(
(float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1,
(float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1,
(float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1,
ne0);
vDSP_vdiv(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10);
#else
ggml_vec_div_f32(ne0,
(float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ),
(float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
(float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11));
ggml_vec_div_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr);
#endif
// }
// }
}
}
} else {
// src1 is not contiguous
for (int ir = 0; ir < nr; ++ir) {
// src0, src1 and dst are same shape => same indices
const int i3 = ir/(ne2*ne1);
const int i2 = (ir - i3*ne2*ne1)/ne1;
const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
for (int64_t ir = ith; ir < nr; ir += nth) {
// src0 and dst are same shape => same indices
// src1 is broadcastable across src0 and dst in i1, i2, i3
const int64_t i03 = ir/(ne02*ne01);
const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
for (int i0 = 0; i0 < ne0; i0++) {
float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11 + i0*nb10);
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
for (int64_t i0 = 0; i0 < ne00; ++i0) {
const int64_t i10 = i0 % ne10;
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10);
dst_ptr[i0] = src0_ptr[i0] / (*src1_ptr);
}
@ -8203,7 +8314,7 @@ static void ggml_compute_forward_repeat_f16(
return;
}
GGML_TENSOR_UNARY_OP_LOCALS;
GGML_TENSOR_UNARY_OP_LOCALS
// guaranteed to be an integer due to the check in ggml_can_repeat
const int nr0 = (int)(ne0/ne00);
@ -8348,6 +8459,7 @@ static void ggml_compute_forward_concat_f32(
GGML_ASSERT(src0->nb[0] == sizeof(float));
const int ith = params->ith;
const int nth = params->nth;
GGML_TENSOR_BINARY_OP_LOCALS
@ -8357,7 +8469,7 @@ static void ggml_compute_forward_concat_f32(
GGML_ASSERT(nb10 == sizeof(float));
for (int i3 = 0; i3 < ne3; i3++) {
for (int i2 = ith; i2 < ne2; i2++) {
for (int i2 = ith; i2 < ne2; i2 += nth) {
if (i2 < ne02) { // src0
for (int i1 = 0; i1 < ne1; i1++) {
for (int i0 = 0; i0 < ne0; i0++) {
@ -9517,6 +9629,8 @@ static void ggml_compute_forward_mul_mat(
char * wdata = params->wdata;
const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
assert(params->wsize >= ne11*ne12*ne13*row_size);
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = 0; i11 < ne11; ++i11) {
@ -9618,6 +9732,26 @@ static void ggml_compute_forward_mul_mat(
}
}
// ggml_compute_forward_mul_mat_id
static void ggml_compute_forward_mul_mat_id(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * ids = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
const int id = ggml_get_op_params_i32(dst, 0);
const int a_id = ((int32_t *)ids->data)[id];
GGML_ASSERT(a_id >= 0 && a_id < ids->ne[0]);
const struct ggml_tensor * src0 = dst->src[a_id + 2];
ggml_compute_forward_mul_mat(params, src0, src1, dst);
}
// ggml_compute_forward_out_prod
static void ggml_compute_forward_out_prod_f32(
@ -12021,6 +12155,67 @@ static void ggml_compute_forward_upscale(
}
}
// ggml_compute_forward_argsort
static void ggml_compute_forward_argsort_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
GGML_TENSOR_UNARY_OP_LOCALS
GGML_ASSERT(nb0 == sizeof(float));
const int ith = params->ith;
const int nth = params->nth;
const int64_t nr = ggml_nrows(src0);
enum ggml_sort_order order = (enum ggml_sort_order) ggml_get_op_params_i32(dst, 0);
for (int64_t i = ith; i < nr; i += nth) {
int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1);
const float * src_data = (float *)((char *) src0->data + i*nb01);
for (int64_t j = 0; j < ne0; j++) {
dst_data[j] = j;
}
// C doesn't have a functional sort, so we do a bubble sort instead
for (int64_t j = 0; j < ne0; j++) {
for (int64_t k = j + 1; k < ne0; k++) {
if ((order == GGML_SORT_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) ||
(order == GGML_SORT_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) {
int32_t tmp = dst_data[j];
dst_data[j] = dst_data[k];
dst_data[k] = tmp;
}
}
}
}
}
static void ggml_compute_forward_argsort(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst) {
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_argsort_f32(params, src0, dst);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_flash_attn
static void ggml_compute_forward_flash_attn_f32(
@ -13844,6 +14039,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor);
} break;
case GGML_OP_MUL_MAT_ID:
{
ggml_compute_forward_mul_mat_id(params, tensor);
} break;
case GGML_OP_OUT_PROD:
{
ggml_compute_forward_out_prod(params, tensor->src[0], tensor->src[1], tensor);
@ -13948,6 +14147,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_upscale(params, tensor->src[0], tensor);
} break;
case GGML_OP_ARGSORT:
{
ggml_compute_forward_argsort(params, tensor->src[0], tensor);
} break;
case GGML_OP_FLASH_ATTN:
{
const int32_t t = ggml_get_op_params_i32(tensor, 0);
@ -14598,6 +14801,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
zero_table);
}
} break;
case GGML_OP_MUL_MAT_ID:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_OUT_PROD:
{
GGML_ASSERT(false); // TODO: not implemented
@ -14936,6 +15143,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_ARGSORT:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_FLASH_ATTN:
{
struct ggml_tensor * flash_grad = NULL;
@ -15296,12 +15507,8 @@ struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) {
return ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, false);
}
struct ggml_cgraph * ggml_graph_view(struct ggml_context * ctx, struct ggml_cgraph * cgraph0, int i0, int i1) {
const size_t obj_size = sizeof(struct ggml_cgraph);
struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, obj_size);
struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs);
*cgraph = (struct ggml_cgraph) {
struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph0, int i0, int i1) {
struct ggml_cgraph cgraph = {
/*.size =*/ 0,
/*.n_nodes =*/ i1 - i0,
/*.n_leafs =*/ 0,
@ -15536,7 +15743,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
n_tasks = n_threads;
} break;
case GGML_OP_SUB:
case GGML_OP_DIV:
case GGML_OP_SQR:
case GGML_OP_SQRT:
case GGML_OP_LOG:
@ -15569,10 +15775,13 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
{
n_tasks = n_threads;
} break;
default:
GGML_ASSERT(false);
}
break;
case GGML_OP_SILU_BACK:
case GGML_OP_MUL:
case GGML_OP_DIV:
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
case GGML_OP_RMS_NORM_BACK:
@ -15610,6 +15819,11 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
}
#endif
} break;
case GGML_OP_MUL_MAT_ID:
{
// FIXME: blas
n_tasks = n_threads;
} break;
case GGML_OP_OUT_PROD:
{
n_tasks = n_threads;
@ -15669,6 +15883,10 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
{
n_tasks = n_threads;
} break;
case GGML_OP_ARGSORT:
{
n_tasks = n_threads;
} break;
case GGML_OP_FLASH_ATTN:
{
n_tasks = n_threads;
@ -15731,6 +15949,10 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
{
n_tasks = 1;
} break;
case GGML_OP_COUNT:
{
GGML_ASSERT(false);
} break;
default:
{
fprintf(stderr, "%s: op not implemented: ", __func__);
@ -15927,6 +16149,23 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
cur = ggml_type_size(vec_dot_type)*ggml_nelements(node->src[1])/ggml_blck_size(vec_dot_type);
}
} break;
case GGML_OP_MUL_MAT_ID:
{
const struct ggml_tensor * a = node->src[2];
const struct ggml_tensor * b = node->src[1];
const enum ggml_type vec_dot_type = type_traits[a->type].vec_dot_type;
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(a, b, node)) {
if (a->type != GGML_TYPE_F32) {
// here we need memory just for single 2D matrix from src0
cur = ggml_type_size(GGML_TYPE_F32)*(a->ne[0]*a->ne[1]);
}
} else
#endif
if (b->type != vec_dot_type) {
cur = ggml_type_size(vec_dot_type)*ggml_nelements(b)/ggml_blck_size(vec_dot_type);
}
} break;
case GGML_OP_OUT_PROD:
{
if (ggml_is_quantized(node->src[0]->type)) {
@ -15962,9 +16201,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
GGML_ASSERT(false);
}
} break;
case GGML_OP_IM2COL:
{
} break;
case GGML_OP_CONV_TRANSPOSE_2D:
{
const int64_t ne00 = node->src[0]->ne[0]; // W
@ -17803,8 +18039,8 @@ size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t *
memcpy(&qh, &y[i].qh, sizeof(qh));
for (int j = 0; j < QK5_0; j += 2) {
const uint8_t vh0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
const uint8_t vh1 = ((qh & (1u << (j + 16))) >> (j + 12));
const uint8_t vh0 = ((qh & (1u << (j/2 + 0 ))) >> (j/2 + 0 )) << 4;
const uint8_t vh1 = ((qh & (1u << (j/2 + 16))) >> (j/2 + 12));
// cast to 16 bins
const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2;
@ -17833,8 +18069,8 @@ size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t *
memcpy(&qh, &y[i].qh, sizeof(qh));
for (int j = 0; j < QK5_1; j += 2) {
const uint8_t vh0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
const uint8_t vh1 = ((qh & (1u << (j + 16))) >> (j + 12));
const uint8_t vh0 = ((qh & (1u << (j/2 + 0 ))) >> (j/2 + 0 )) << 4;
const uint8_t vh1 = ((qh & (1u << (j/2 + 16))) >> (j/2 + 12));
// cast to 16 bins
const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2;
@ -18024,6 +18260,7 @@ struct gguf_kv {
struct gguf_header {
char magic[4];
uint32_t version;
uint64_t n_tensors; // GGUFv2
uint64_t n_kv; // GGUFv2
@ -18113,7 +18350,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
for (uint32_t i = 0; i < sizeof(magic); i++) {
if (magic[i] != GGUF_MAGIC[i]) {
fprintf(stderr, "%s: invalid magic characters %s.\n", __func__, magic);
fprintf(stderr, "%s: invalid magic characters '%c%c%c%c'\n", __func__, magic[0], magic[1], magic[2], magic[3]);
fclose(file);
return NULL;
}
@ -18128,7 +18365,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
{
strncpy(ctx->header.magic, magic, 4);
ctx->kv = NULL;
ctx->infos = NULL;
ctx->data = NULL;

53
ggml.h
View File

@ -283,6 +283,20 @@
const type prefix##3 = (pointer)->array[3]; \
GGML_UNUSED(prefix##3);
#define GGML_TENSOR_UNARY_OP_LOCALS \
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
#define GGML_TENSOR_BINARY_OP_LOCALS \
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) \
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
#ifdef __cplusplus
extern "C" {
#endif
@ -381,6 +395,7 @@ extern "C" {
GGML_OP_GROUP_NORM,
GGML_OP_MUL_MAT,
GGML_OP_MUL_MAT_ID,
GGML_OP_OUT_PROD,
GGML_OP_SCALE,
@ -407,8 +422,8 @@ extern "C" {
GGML_OP_CONV_TRANSPOSE_2D,
GGML_OP_POOL_1D,
GGML_OP_POOL_2D,
GGML_OP_UPSCALE, // nearest interpolate
GGML_OP_ARGSORT,
GGML_OP_FLASH_ATTN,
GGML_OP_FLASH_FF,
@ -448,7 +463,9 @@ extern "C" {
GGML_UNARY_OP_GELU,
GGML_UNARY_OP_GELU_QUICK,
GGML_UNARY_OP_SILU,
GGML_UNARY_OP_LEAKY
GGML_UNARY_OP_LEAKY,
GGML_UNARY_OP_COUNT,
};
enum ggml_object_type {
@ -631,6 +648,9 @@ extern "C" {
GGML_API const char * ggml_op_name (enum ggml_op op);
GGML_API const char * ggml_op_symbol(enum ggml_op op);
GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op);
GGML_API const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name
GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);
GGML_API bool ggml_is_quantized(enum ggml_type type);
@ -1027,6 +1047,15 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
// indirect matrix multiplication
// ggml_mul_mat_id(ctx, as, ids, id, b) ~= ggml_mul_mat(as[ids[id]], b)
GGML_API struct ggml_tensor * ggml_mul_mat_id(
struct ggml_context * ctx,
struct ggml_tensor * as[],
struct ggml_tensor * ids,
int id,
struct ggml_tensor * b);
// A: m columns, n rows,
// B: p columns, n rows,
// result is m columns, p rows
@ -1520,6 +1549,23 @@ extern "C" {
struct ggml_tensor * a,
int scale_factor);
// sort rows
enum ggml_sort_order {
GGML_SORT_ASC,
GGML_SORT_DESC,
};
GGML_API struct ggml_tensor * ggml_argsort(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_sort_order order);
// top k elements per row
GGML_API struct ggml_tensor * ggml_top_k(
struct ggml_context * ctx,
struct ggml_tensor * a,
int k);
GGML_API struct ggml_tensor * ggml_flash_attn(
struct ggml_context * ctx,
struct ggml_tensor * q,
@ -1581,7 +1627,6 @@ extern "C" {
int kh);
// used in sam
GGML_API struct ggml_tensor * ggml_add_rel_pos(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -1756,7 +1801,7 @@ extern "C" {
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
GGML_API struct ggml_cgraph * ggml_new_graph_custom (struct ggml_context * ctx, size_t size, bool grads);
GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
GGML_API struct ggml_cgraph * ggml_graph_view (struct ggml_context * ctx, struct ggml_cgraph * cgraph, int i0, int i1);
GGML_API struct ggml_cgraph ggml_graph_view (struct ggml_cgraph * cgraph, int i0, int i1);
GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst);
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // zero grads
GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph);

5
scripts/sync-ggml.sh
View File

@ -20,5 +20,6 @@ cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h
cp -rpv ../ggml/include/ggml/ggml-alloc.h ./ggml-alloc.h
cp -rpv ../ggml/include/ggml/ggml-backend.h ./ggml-backend.h
cp -rpv ../ggml/tests/test-opt.cpp ./tests/test-opt.cpp
cp -rpv ../ggml/tests/test-grad0.cpp ./tests/test-grad0.cpp
cp -rpv ../ggml/tests/test-opt.cpp ./tests/test-opt.cpp
cp -rpv ../ggml/tests/test-grad0.cpp ./tests/test-grad0.cpp
cp -rpv ../ggml/tests/test-backend-ops.cpp ./tests/test-backend-ops.cpp

28
tests/CMakeLists.txt
View File

@ -22,26 +22,32 @@ endfunction()
llama_build_and_test_executable(test-quantize-fns.cpp)
llama_build_and_test_executable(test-quantize-perf.cpp)
llama_build_and_test_executable(test-sampling.cpp)
llama_build_executable(test-tokenizer-0-llama.cpp)
llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_build_executable(test-tokenizer-0-falcon.cpp)
llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
llama_build_executable(test-tokenizer-1-llama.cpp)
llama_test_executable (test-tokenizer-1-llama test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_test_executable(test-tokenizer-1-baichuan test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
llama_test_executable (test-tokenizer-1-llama test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_test_executable (test-tokenizer-1-baichuan test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
llama_build_executable(test-tokenizer-1-bpe.cpp)
llama_test_executable (test-tokenizer-1-falcon test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
llama_test_executable(test-tokenizer-1-aquila test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
llama_test_executable(test-tokenizer-1-mpt test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
llama_test_executable(test-tokenizer-1-stablelm-3b-4e1t test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm-3b-4e1t.gguf)
llama_test_executable(test-tokenizer-1-gpt-neox test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
llama_test_executable(test-tokenizer-1-refact test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
llama_test_executable(test-tokenizer-1-starcoder test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
# llama_test_executable(test-tokenizer-1-bloom test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) # BIG
llama_test_executable (test-tokenizer-1-falcon test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
llama_test_executable (test-tokenizer-1-aquila test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
llama_test_executable (test-tokenizer-1-mpt test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
llama_test_executable (test-tokenizer-1-stablelm-3b-4e1t test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm-3b-4e1t.gguf)
llama_test_executable (test-tokenizer-1-gpt-neox test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
llama_test_executable (test-tokenizer-1-refact test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
llama_test_executable (test-tokenizer-1-starcoder test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
# llama_test_executable (test-tokenizer-1-bloom test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) # BIG
llama_build_and_test_executable(test-grammar-parser.cpp)
llama_build_and_test_executable(test-llama-grammar.cpp)
llama_build_and_test_executable(test-grad0.cpp) # SLOW
llama_build_and_test_executable(test-grad0.cpp)
# llama_build_and_test_executable(test-opt.cpp) # SLOW
llama_build_and_test_executable(test-backend-ops.cpp)
llama_build_and_test_executable(test-rope.cpp)

1357
tests/test-backend-ops.cpp 100644
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