mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 13:30:35 +00:00
ggml : introduce GGML_CALL function annotation (#4850)
This change makes it possible to build ggml-cuda.cu and ggml-metal.m as independent dynamic shared objects, that may be conditionally linked at runtime in a multiplatform binary. It introduces a GGML_CALL annotation that documents which functions have a cyclic call relationship, between the application code and GPU modules. This change does nothing, unless the build defines -DGGML_MULTIPLATFORM which causes back-references and function pointers to conform to MS ABI which is supported by NVCC, ROCm, XCode, GCC and Clang across platforms
This commit is contained in:
Justine Tunney
GitHub
parent
d75c232e1d
commit
a0b3ac8c48
@ -16,14 +16,14 @@ extern "C" {
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typedef void * ggml_backend_buffer_type_context_t;
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struct ggml_backend_buffer_type_i {
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const char * (*get_name) (ggml_backend_buffer_type_t buft);
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ggml_backend_buffer_t (*alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size);
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size_t (*get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment
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size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
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bool (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
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const char * (*GGML_CALL get_name) (ggml_backend_buffer_type_t buft);
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ggml_backend_buffer_t (*GGML_CALL alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size);
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size_t (*GGML_CALL get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment
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size_t (*GGML_CALL get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
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bool (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
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// check if tensor data is in host memory
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// should be equivalent to supports_backend(buft, ggml_backend_cpu_init())
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bool (*is_host) (ggml_backend_buffer_type_t buft);
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bool (*GGML_CALL is_host) (ggml_backend_buffer_type_t buft);
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};
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struct ggml_backend_buffer_type {
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@ -35,15 +35,15 @@ extern "C" {
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typedef void * ggml_backend_buffer_context_t;
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struct ggml_backend_buffer_i {
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const char * (*get_name) (ggml_backend_buffer_t buffer);
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void (*free_buffer)(ggml_backend_buffer_t buffer);
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void * (*get_base) (ggml_backend_buffer_t buffer);
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void (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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bool (*cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
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void (*clear) (ggml_backend_buffer_t buffer, uint8_t value);
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void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
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const char * (*GGML_CALL get_name) (ggml_backend_buffer_t buffer);
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void (*GGML_CALL free_buffer)(ggml_backend_buffer_t buffer);
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void * (*GGML_CALL get_base) (ggml_backend_buffer_t buffer);
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void (*GGML_CALL init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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void (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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void (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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bool (*GGML_CALL cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
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void (*GGML_CALL clear) (ggml_backend_buffer_t buffer, uint8_t value);
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void (*GGML_CALL reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
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};
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struct ggml_backend_buffer {
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@ -54,7 +54,7 @@ extern "C" {
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enum ggml_backend_buffer_usage usage;
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};
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ggml_backend_buffer_t ggml_backend_buffer_init(
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GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
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ggml_backend_buffer_type_t buft,
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struct ggml_backend_buffer_i iface,
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ggml_backend_buffer_context_t context,
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@ -70,31 +70,31 @@ extern "C" {
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typedef void * ggml_backend_context_t;
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struct ggml_backend_i {
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const char * (*get_name)(ggml_backend_t backend);
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const char * (*GGML_CALL get_name)(ggml_backend_t backend);
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void (*free)(ggml_backend_t backend);
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void (*GGML_CALL free)(ggml_backend_t backend);
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// buffer allocation
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ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend);
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ggml_backend_buffer_type_t (*GGML_CALL get_default_buffer_type)(ggml_backend_t backend);
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// (optional) asynchronous tensor data access
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void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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bool (*cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst);
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void (*GGML_CALL set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst);
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// (optional) complete all pending operations
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void (*synchronize)(ggml_backend_t backend);
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void (*GGML_CALL synchronize)(ggml_backend_t backend);
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// compute graph with a plan
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ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
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void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
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void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
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ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
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void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
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void (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
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// compute graph without a plan (async)
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bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
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bool (*GGML_CALL graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
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// check if the backend supports an operation
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bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
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bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
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};
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struct ggml_backend {
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@ -107,9 +107,9 @@ extern "C" {
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// Backend registry
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//
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typedef ggml_backend_t (*ggml_backend_init_fn)(const char * params, void * user_data);
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typedef ggml_backend_t (*GGML_CALL ggml_backend_init_fn)(const char * params, void * user_data);
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void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
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GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
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#ifdef __cplusplus
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}
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@ -19,7 +19,7 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
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return buft->iface.get_name(buft);
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}
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ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
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GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
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return buft->iface.alloc_buffer(buft, size);
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}
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@ -27,7 +27,7 @@ size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) {
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return buft->iface.get_alignment(buft);
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}
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size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) {
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GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) {
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// get_alloc_size is optional, defaults to ggml_nbytes
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if (buft->iface.get_alloc_size) {
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return buft->iface.get_alloc_size(buft, tensor);
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@ -48,7 +48,7 @@ bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
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// backend buffer
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ggml_backend_buffer_t ggml_backend_buffer_init(
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GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
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ggml_backend_buffer_type_t buft,
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struct ggml_backend_buffer_i iface,
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ggml_backend_buffer_context_t context,
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@ -95,7 +95,7 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
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return base;
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}
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void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
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GGML_CALL void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
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// init_tensor is optional
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if (buffer->iface.init_tensor) {
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buffer->iface.init_tensor(buffer, tensor);
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@ -191,7 +191,7 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten
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}
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}
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void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
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GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
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ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
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GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
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@ -201,7 +201,7 @@ void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, siz
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tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size);
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}
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void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
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GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
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ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
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GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
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@ -318,9 +318,9 @@ struct ggml_backend_reg {
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static struct ggml_backend_reg ggml_backend_registry[GGML_MAX_BACKENDS_REG];
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static size_t ggml_backend_registry_count = 0;
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static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
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GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
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static void ggml_backend_registry_init(void) {
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GGML_CALL static void ggml_backend_registry_init(void) {
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static bool initialized = false;
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if (initialized) {
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@ -333,18 +333,18 @@ static void ggml_backend_registry_init(void) {
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// add forward decls here to avoid including the backend headers
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#ifdef GGML_USE_CUBLAS
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extern void ggml_backend_cuda_reg_devices(void);
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extern GGML_CALL void ggml_backend_cuda_reg_devices(void);
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ggml_backend_cuda_reg_devices();
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#endif
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#ifdef GGML_USE_METAL
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extern ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data);
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extern ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
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extern GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data);
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extern GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
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ggml_backend_register("Metal", ggml_backend_reg_metal_init, ggml_backend_metal_buffer_type(), NULL);
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#endif
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}
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void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
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GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
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GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG);
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size_t id = ggml_backend_registry_count;
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@ -439,33 +439,33 @@ ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) {
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// backend CPU
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static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) {
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GGML_CALL static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) {
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return "CPU";
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GGML_UNUSED(buffer);
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}
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static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
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GGML_CALL static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
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return (void *)buffer->context;
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}
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static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
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GGML_CALL static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
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free(buffer->context);
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}
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static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
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GGML_CALL static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
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memcpy((char *)tensor->data + offset, data, size);
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GGML_UNUSED(buffer);
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}
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static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
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GGML_CALL static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
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memcpy(data, (const char *)tensor->data + offset, size);
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GGML_UNUSED(buffer);
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}
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static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
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GGML_CALL static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
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if (ggml_backend_buffer_is_host(src->buffer)) {
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memcpy(dst->data, src->data, ggml_nbytes(src));
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return true;
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@ -475,7 +475,7 @@ static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, con
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GGML_UNUSED(buffer);
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}
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static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
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GGML_CALL static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
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memset(buffer->context, value, buffer->size);
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}
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@ -506,13 +506,13 @@ static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = {
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static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512
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static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
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GGML_CALL static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
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return "CPU";
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GGML_UNUSED(buft);
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}
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static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
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GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
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size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned
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void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC?
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@ -521,25 +521,25 @@ static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_back
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return ggml_backend_buffer_init(buft, cpu_backend_buffer_i, data, size);
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}
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static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
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GGML_CALL static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
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return TENSOR_ALIGNMENT;
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GGML_UNUSED(buft);
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}
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static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
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GGML_CALL static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
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return ggml_backend_is_cpu(backend);
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GGML_UNUSED(buft);
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}
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static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
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GGML_CALL static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
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return true;
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GGML_UNUSED(buft);
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}
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ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
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GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
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static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = {
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/* .iface = */ {
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/* .get_name = */ ggml_backend_cpu_buffer_type_get_name,
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@ -561,23 +561,23 @@ ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
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#include <hbwmalloc.h>
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static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
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GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
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return "CPU_HBM";
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||||
|
||||
GGML_UNUSED(buft);
|
||||
}
|
||||
|
||||
static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) {
|
||||
GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) {
|
||||
return "CPU_HBM";
|
||||
|
||||
GGML_UNUSED(buf);
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
hbw_free(buffer->context);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
//void * ptr = hbw_malloc(size);
|
||||
void * ptr;
|
||||
int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size);
|
||||
@ -617,20 +617,20 @@ struct ggml_backend_cpu_context {
|
||||
size_t work_size;
|
||||
};
|
||||
|
||||
static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
|
||||
GGML_CALL static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
|
||||
return "CPU";
|
||||
|
||||
GGML_UNUSED(backend);
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_free(ggml_backend_t backend) {
|
||||
GGML_CALL static void ggml_backend_cpu_free(ggml_backend_t backend) {
|
||||
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
|
||||
free(cpu_ctx->work_data);
|
||||
free(cpu_ctx);
|
||||
free(backend);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) {
|
||||
GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) {
|
||||
return ggml_backend_cpu_buffer_type();
|
||||
|
||||
GGML_UNUSED(backend);
|
||||
@ -641,7 +641,7 @@ struct ggml_backend_plan_cpu {
|
||||
struct ggml_cgraph cgraph;
|
||||
};
|
||||
|
||||
static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) {
|
||||
GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) {
|
||||
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
|
||||
|
||||
struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu));
|
||||
@ -656,7 +656,7 @@ static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend
|
||||
return cpu_plan;
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
|
||||
GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
|
||||
struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
|
||||
|
||||
free(cpu_plan->cplan.work_data);
|
||||
@ -665,7 +665,7 @@ static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backen
|
||||
GGML_UNUSED(backend);
|
||||
}
|
||||
|
||||
static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
|
||||
GGML_CALL static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
|
||||
struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
|
||||
|
||||
ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
|
||||
@ -673,7 +673,7 @@ static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_bac
|
||||
GGML_UNUSED(backend);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
|
||||
|
||||
struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
|
||||
@ -690,7 +690,7 @@ static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_c
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
|
||||
GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
|
||||
switch (op->op) {
|
||||
case GGML_OP_MUL_MAT:
|
||||
return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
|
||||
@ -732,7 +732,7 @@ ggml_backend_t ggml_backend_cpu_init(void) {
|
||||
return cpu_backend;
|
||||
}
|
||||
|
||||
bool ggml_backend_is_cpu(ggml_backend_t backend) {
|
||||
GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) {
|
||||
return backend && backend->iface.get_name == ggml_backend_cpu_name;
|
||||
}
|
||||
|
||||
@ -743,11 +743,11 @@ void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
|
||||
ctx->n_threads = n_threads;
|
||||
}
|
||||
|
||||
ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) {
|
||||
GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) {
|
||||
return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), cpu_backend_buffer_i_from_ptr, ptr, size);
|
||||
}
|
||||
|
||||
static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) {
|
||||
GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) {
|
||||
return ggml_backend_cpu_init();
|
||||
|
||||
GGML_UNUSED(params);
|
||||
|
||||
@ -18,9 +18,9 @@ extern "C" {
|
||||
|
||||
// buffer type
|
||||
GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft);
|
||||
GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size);
|
||||
GGML_API GGML_CALL 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 GGML_CALL 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);
|
||||
GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft);
|
||||
|
||||
@ -34,7 +34,7 @@ extern "C" {
|
||||
GGML_API void ggml_backend_buffer_free (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 void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL void ggml_backend_buffer_init_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 void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value);
|
||||
@ -58,8 +58,8 @@ extern "C" {
|
||||
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);
|
||||
GGML_API GGML_CALL void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
|
||||
GGML_API GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
|
||||
|
||||
GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
|
||||
|
||||
@ -80,13 +80,13 @@ extern "C" {
|
||||
|
||||
GGML_API ggml_backend_t ggml_backend_cpu_init(void);
|
||||
|
||||
GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend);
|
||||
GGML_API GGML_CALL bool ggml_backend_is_cpu (ggml_backend_t backend);
|
||||
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(void * ptr, size_t size);
|
||||
GGML_API GGML_CALL 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);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
|
||||
|
||||
#ifdef GGML_USE_CPU_HBM
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
|
||||
@ -183,7 +183,7 @@ extern "C" {
|
||||
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);
|
||||
typedef bool (*GGML_CALL 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 bool 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);
|
||||
|
||||
121
ggml-cuda.cu
121
ggml-cuda.cu
@ -7615,11 +7615,11 @@ struct cuda_pool_alloc {
|
||||
|
||||
static bool g_cublas_loaded = false;
|
||||
|
||||
bool ggml_cublas_loaded(void) {
|
||||
GGML_CALL bool ggml_cublas_loaded(void) {
|
||||
return g_cublas_loaded;
|
||||
}
|
||||
|
||||
void ggml_init_cublas() {
|
||||
GGML_CALL void ggml_init_cublas() {
|
||||
static bool initialized = false;
|
||||
|
||||
if (!initialized) {
|
||||
@ -7707,7 +7707,7 @@ void ggml_init_cublas() {
|
||||
}
|
||||
}
|
||||
|
||||
void * ggml_cuda_host_malloc(size_t size) {
|
||||
GGML_CALL void * ggml_cuda_host_malloc(size_t size) {
|
||||
if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
|
||||
return nullptr;
|
||||
}
|
||||
@ -7725,7 +7725,7 @@ void * ggml_cuda_host_malloc(size_t size) {
|
||||
return ptr;
|
||||
}
|
||||
|
||||
void ggml_cuda_host_free(void * ptr) {
|
||||
GGML_CALL void ggml_cuda_host_free(void * ptr) {
|
||||
CUDA_CHECK(cudaFreeHost(ptr));
|
||||
}
|
||||
|
||||
@ -9242,7 +9242,7 @@ static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src
|
||||
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm);
|
||||
}
|
||||
|
||||
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
|
||||
GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
|
||||
if (!g_cublas_loaded) return false;
|
||||
|
||||
const int64_t ne10 = src1->ne[0];
|
||||
@ -10013,7 +10013,7 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl
|
||||
return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]);
|
||||
}
|
||||
|
||||
static void ggml_cuda_set_main_device(const int main_device) {
|
||||
GGML_CALL static void ggml_cuda_set_main_device(const int main_device) {
|
||||
if (main_device >= g_device_count) {
|
||||
fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n",
|
||||
main_device, g_device_count, g_main_device);
|
||||
@ -10028,7 +10028,7 @@ static void ggml_cuda_set_main_device(const int main_device) {
|
||||
}
|
||||
}
|
||||
|
||||
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
|
||||
GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
|
||||
if (!g_cublas_loaded) return false;
|
||||
|
||||
ggml_cuda_func_t func;
|
||||
@ -10186,7 +10186,7 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
|
||||
return true;
|
||||
}
|
||||
|
||||
int ggml_cuda_get_device_count() {
|
||||
GGML_CALL int ggml_cuda_get_device_count() {
|
||||
int device_count;
|
||||
if (cudaGetDeviceCount(&device_count) != cudaSuccess) {
|
||||
return 0;
|
||||
@ -10194,7 +10194,7 @@ int ggml_cuda_get_device_count() {
|
||||
return device_count;
|
||||
}
|
||||
|
||||
void ggml_cuda_get_device_description(int device, char * description, size_t description_size) {
|
||||
GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size) {
|
||||
cudaDeviceProp prop;
|
||||
CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
|
||||
snprintf(description, description_size, "%s", prop.name);
|
||||
@ -10244,27 +10244,27 @@ struct ggml_backend_cuda_buffer_context {
|
||||
}
|
||||
};
|
||||
|
||||
static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_buffer_get_name(ggml_backend_buffer_t buffer) {
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
return ctx->name.c_str();
|
||||
}
|
||||
|
||||
static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static bool ggml_backend_buffer_is_cuda(ggml_backend_buffer_t buffer) {
|
||||
return buffer->iface.get_name == ggml_backend_cuda_buffer_get_name;
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
CUDA_CHECK(cudaFree(ctx->dev_ptr));
|
||||
delete ctx;
|
||||
}
|
||||
|
||||
static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
return ctx->dev_ptr;
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
|
||||
GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
|
||||
if (tensor->view_src != NULL && tensor->view_offs == 0) {
|
||||
@ -10296,7 +10296,7 @@ static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, g
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
|
||||
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
@ -10307,7 +10307,7 @@ static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, gg
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
|
||||
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
@ -10318,7 +10318,7 @@ static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, co
|
||||
CUDA_CHECK(cudaDeviceSynchronize());
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
|
||||
GGML_CALL static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
|
||||
if (ggml_backend_buffer_is_cuda(src->buffer)) {
|
||||
ggml_backend_cuda_buffer_context * src_ctx = (ggml_backend_cuda_buffer_context *)src->buffer->context;
|
||||
ggml_backend_cuda_buffer_context * dst_ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
@ -10335,7 +10335,7 @@ static bool ggml_backend_cuda_buffer_cpy_tensor(ggml_backend_buffer_t buffer, co
|
||||
return false;
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
GGML_CALL static void ggml_backend_cuda_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
|
||||
|
||||
ggml_cuda_set_device(ctx->device);
|
||||
@ -10357,19 +10357,18 @@ static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = {
|
||||
};
|
||||
|
||||
// cuda buffer type
|
||||
|
||||
struct ggml_backend_cuda_buffer_type_context {
|
||||
int device;
|
||||
std::string name;
|
||||
};
|
||||
|
||||
static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
ggml_backend_cuda_buffer_type_context * ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
|
||||
|
||||
return ctx->name.c_str();
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context;
|
||||
|
||||
ggml_cuda_set_device(buft_ctx->device);
|
||||
@ -10388,13 +10387,13 @@ static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_bac
|
||||
return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size);
|
||||
}
|
||||
|
||||
static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
||||
return 128;
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
|
||||
GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
|
||||
int64_t row_low = 0;
|
||||
int64_t row_high = ggml_nrows(tensor);
|
||||
int64_t nrows_split = row_high - row_low;
|
||||
@ -10414,7 +10413,7 @@ static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_t
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
|
||||
GGML_CALL static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
|
||||
if (!ggml_backend_is_cuda(backend)) {
|
||||
return false;
|
||||
}
|
||||
@ -10434,7 +10433,7 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = {
|
||||
/* .is_host = */ NULL,
|
||||
};
|
||||
|
||||
ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
|
||||
GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
|
||||
// FIXME: this is not thread safe
|
||||
if (device >= ggml_backend_cuda_get_device_count()) {
|
||||
return nullptr;
|
||||
@ -10479,7 +10478,7 @@ struct ggml_backend_cuda_split_buffer_context {
|
||||
std::vector<ggml_tensor_extra_gpu *> tensor_extras;
|
||||
};
|
||||
|
||||
static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_t buffer) {
|
||||
return GGML_CUDA_NAME "_Split";
|
||||
|
||||
UNUSED(buffer);
|
||||
@ -10490,19 +10489,19 @@ static const char * ggml_backend_cuda_split_buffer_get_name(ggml_backend_buffer_
|
||||
// return buffer->iface.get_name == ggml_backend_cuda_split_buffer_get_name;
|
||||
//}
|
||||
|
||||
static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void ggml_backend_cuda_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
|
||||
delete ctx;
|
||||
}
|
||||
|
||||
static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void * ggml_backend_cuda_split_buffer_get_base(ggml_backend_buffer_t buffer) {
|
||||
// the pointers are stored in the tensor extras, this is just a dummy address and never dereferenced
|
||||
return (void *)0x1000;
|
||||
|
||||
UNUSED(buffer);
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
|
||||
GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
|
||||
GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported
|
||||
|
||||
ggml_backend_cuda_split_buffer_context * ctx = (ggml_backend_cuda_split_buffer_context *)buffer->context;
|
||||
@ -10552,7 +10551,7 @@ static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_buffer_t buf
|
||||
tensor->extra = extra;
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
// split tensors must always be set in their entirety at once
|
||||
GGML_ASSERT(offset == 0);
|
||||
GGML_ASSERT(size == ggml_nbytes(tensor));
|
||||
@ -10586,7 +10585,7 @@ static void ggml_backend_cuda_split_buffer_set_tensor(ggml_backend_buffer_t buff
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
// split tensors must always be set in their entirety at once
|
||||
GGML_ASSERT(offset == 0);
|
||||
GGML_ASSERT(size == ggml_nbytes(tensor));
|
||||
@ -10620,7 +10619,7 @@ static void ggml_backend_cuda_split_buffer_get_tensor(ggml_backend_buffer_t buff
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
GGML_CALL static void ggml_backend_cuda_split_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
UNUSED(buffer);
|
||||
UNUSED(value);
|
||||
}
|
||||
@ -10639,13 +10638,13 @@ static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = {
|
||||
|
||||
// cuda split buffer type
|
||||
|
||||
static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_split_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
return GGML_CUDA_NAME "_Split";
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
// since we don't know the exact split after rounding, we cannot allocate the device buffers at this point
|
||||
// instead, we allocate them for each tensor separately in init_tensor
|
||||
// however, the size still represents the maximum cumulative size of all the device buffers after the tensors are allocated,
|
||||
@ -10655,13 +10654,13 @@ static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(gg
|
||||
return ggml_backend_buffer_init(buft, ggml_backend_cuda_split_buffer_interface, ctx, size);
|
||||
}
|
||||
|
||||
static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
||||
return 128;
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
|
||||
GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
|
||||
ggml_backend_cuda_split_buffer_type_context * ctx = (ggml_backend_cuda_split_buffer_type_context *)buft->context;
|
||||
|
||||
size_t total_size = 0;
|
||||
@ -10688,13 +10687,13 @@ static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_backend_bu
|
||||
return total_size;
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
|
||||
GGML_CALL static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
|
||||
return ggml_backend_is_cuda(backend);
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
|
||||
return false;
|
||||
|
||||
UNUSED(buft);
|
||||
@ -10709,7 +10708,7 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface
|
||||
/* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host,
|
||||
};
|
||||
|
||||
ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) {
|
||||
GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split) {
|
||||
// FIXME: this is not thread safe
|
||||
static std::map<std::array<float, GGML_CUDA_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
|
||||
|
||||
@ -10745,23 +10744,23 @@ ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * ten
|
||||
|
||||
// host buffer type
|
||||
|
||||
static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_host_buffer_type_name(ggml_backend_buffer_type_t buft) {
|
||||
return GGML_CUDA_NAME "_Host";
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_host_buffer_name(ggml_backend_buffer_t buffer) {
|
||||
return GGML_CUDA_NAME "_Host";
|
||||
|
||||
UNUSED(buffer);
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
ggml_cuda_host_free(buffer->context);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
void * ptr = ggml_cuda_host_malloc(size);
|
||||
|
||||
if (ptr == nullptr) {
|
||||
@ -10777,7 +10776,7 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm
|
||||
return buffer;
|
||||
}
|
||||
|
||||
ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
|
||||
GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
|
||||
static struct ggml_backend_buffer_type ggml_backend_cuda_buffer_type_host = {
|
||||
/* .iface = */ {
|
||||
/* .get_name = */ ggml_backend_cuda_host_buffer_type_name,
|
||||
@ -10795,26 +10794,26 @@ ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
|
||||
|
||||
// backend
|
||||
|
||||
static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
|
||||
GGML_CALL static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
return cuda_ctx->name.c_str();
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_free(ggml_backend_t backend) {
|
||||
GGML_CALL static void ggml_backend_cuda_free(ggml_backend_t backend) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
delete cuda_ctx;
|
||||
delete backend;
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) {
|
||||
GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
return ggml_backend_cuda_buffer_type(cuda_ctx->device);
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
|
||||
@ -10823,7 +10822,7 @@ static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tens
|
||||
CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0]));
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
GGML_CALL static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
|
||||
@ -10832,7 +10831,7 @@ static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggm
|
||||
CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0]));
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
|
||||
GGML_CALL static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
if (dst->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && ggml_backend_buffer_is_cuda(src->buffer)) {
|
||||
@ -10843,7 +10842,7 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend, const ggm
|
||||
return false;
|
||||
}
|
||||
|
||||
static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
|
||||
GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[cuda_ctx->device][0]));
|
||||
@ -10851,7 +10850,7 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
|
||||
UNUSED(backend);
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
|
||||
GGML_CALL static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
|
||||
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
|
||||
|
||||
ggml_cuda_set_main_device(cuda_ctx->device);
|
||||
@ -10890,7 +10889,7 @@ static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
|
||||
GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
|
||||
switch (op->op) {
|
||||
case GGML_OP_UNARY:
|
||||
switch (ggml_get_unary_op(op)) {
|
||||
@ -11016,7 +11015,7 @@ static ggml_backend_i ggml_backend_cuda_interface = {
|
||||
/* .supports_op = */ ggml_backend_cuda_supports_op,
|
||||
};
|
||||
|
||||
ggml_backend_t ggml_backend_cuda_init(int device) {
|
||||
GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) {
|
||||
ggml_init_cublas(); // TODO: remove from ggml.c
|
||||
|
||||
if (device < 0 || device >= ggml_cuda_get_device_count()) {
|
||||
@ -11040,35 +11039,35 @@ ggml_backend_t ggml_backend_cuda_init(int device) {
|
||||
return cuda_backend;
|
||||
}
|
||||
|
||||
bool ggml_backend_is_cuda(ggml_backend_t backend) {
|
||||
GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend) {
|
||||
return backend && backend->iface.get_name == ggml_backend_cuda_name;
|
||||
}
|
||||
|
||||
int ggml_backend_cuda_get_device_count() {
|
||||
GGML_CALL int ggml_backend_cuda_get_device_count() {
|
||||
return ggml_cuda_get_device_count();
|
||||
}
|
||||
|
||||
void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) {
|
||||
GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size) {
|
||||
ggml_cuda_get_device_description(device, description, description_size);
|
||||
}
|
||||
|
||||
void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) {
|
||||
GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total) {
|
||||
ggml_cuda_set_device(device);
|
||||
|
||||
CUDA_CHECK(cudaMemGetInfo(free, total));
|
||||
}
|
||||
|
||||
// backend registry
|
||||
static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) {
|
||||
GGML_CALL static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) {
|
||||
ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data);
|
||||
return cuda_backend;
|
||||
|
||||
UNUSED(params);
|
||||
}
|
||||
|
||||
extern "C" int ggml_backend_cuda_reg_devices();
|
||||
extern "C" GGML_CALL int ggml_backend_cuda_reg_devices();
|
||||
|
||||
int ggml_backend_cuda_reg_devices() {
|
||||
GGML_CALL int ggml_backend_cuda_reg_devices() {
|
||||
int device_count = ggml_cuda_get_device_count();
|
||||
//int device_count = 1; // DEBUG: some tools require delaying CUDA initialization
|
||||
for (int i = 0; i < device_count; i++) {
|
||||
|
||||
32
ggml-cuda.h
32
ggml-cuda.h
@ -18,34 +18,34 @@ extern "C" {
|
||||
#define GGML_CUDA_MAX_DEVICES 16
|
||||
|
||||
// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`.
|
||||
GGML_API void ggml_init_cublas(void);
|
||||
GGML_API GGML_CALL void ggml_init_cublas(void);
|
||||
|
||||
// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`.
|
||||
GGML_API bool ggml_cublas_loaded(void);
|
||||
GGML_API GGML_CALL bool ggml_cublas_loaded(void);
|
||||
|
||||
GGML_API void * ggml_cuda_host_malloc(size_t size);
|
||||
GGML_API void ggml_cuda_host_free(void * ptr);
|
||||
GGML_API GGML_CALL void * ggml_cuda_host_malloc(size_t size);
|
||||
GGML_API GGML_CALL void ggml_cuda_host_free(void * ptr);
|
||||
|
||||
GGML_API bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
GGML_API bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
GGML_API GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
|
||||
|
||||
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);
|
||||
GGML_API GGML_CALL int ggml_cuda_get_device_count(void);
|
||||
GGML_API GGML_CALL 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(int device);
|
||||
GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device);
|
||||
|
||||
GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend);
|
||||
GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend);
|
||||
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
|
||||
// split tensor buffer that splits matrices by rows across multiple devices
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split);
|
||||
// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
|
||||
|
||||
GGML_API int ggml_backend_cuda_get_device_count(void);
|
||||
GGML_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
|
||||
GGML_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
|
||||
GGML_API GGML_CALL int ggml_backend_cuda_get_device_count(void);
|
||||
GGML_API GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
|
||||
GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
||||
@ -47,11 +47,11 @@ GGML_API ggml_backend_t ggml_backend_metal_init(void);
|
||||
|
||||
GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
|
||||
|
||||
GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size);
|
||||
GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size);
|
||||
|
||||
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);
|
||||
GGML_API GGML_CALL 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
|
||||
|
||||
42
ggml-metal.m
42
ggml-metal.m
@ -2294,13 +2294,13 @@ static void ggml_backend_metal_free_device(void) {
|
||||
}
|
||||
}
|
||||
|
||||
static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) {
|
||||
return "Metal";
|
||||
|
||||
UNUSED(buffer);
|
||||
}
|
||||
|
||||
static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL 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;
|
||||
|
||||
for (int i = 0; i < ctx->n_buffers; i++) {
|
||||
@ -2315,25 +2315,25 @@ static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer)
|
||||
free(ctx);
|
||||
}
|
||||
|
||||
static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) {
|
||||
GGML_CALL 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->all_data;
|
||||
}
|
||||
|
||||
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_CALL 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) {
|
||||
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_CALL 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) {
|
||||
memcpy(data, (const char *)tensor->data + offset, size);
|
||||
|
||||
UNUSED(buffer);
|
||||
}
|
||||
|
||||
static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
|
||||
GGML_CALL static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
|
||||
if (ggml_backend_buffer_is_host(src->buffer)) {
|
||||
memcpy(dst->data, src->data, ggml_nbytes(src));
|
||||
return true;
|
||||
@ -2343,7 +2343,7 @@ static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, c
|
||||
UNUSED(buffer);
|
||||
}
|
||||
|
||||
static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
GGML_CALL static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
||||
struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
|
||||
|
||||
memset(ctx->all_data, value, ctx->all_size);
|
||||
@ -2363,13 +2363,13 @@ static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = {
|
||||
|
||||
// default buffer type
|
||||
|
||||
static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
|
||||
return "Metal";
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
GGML_CALL 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);
|
||||
@ -2421,24 +2421,24 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba
|
||||
return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size);
|
||||
}
|
||||
|
||||
static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL 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) {
|
||||
GGML_CALL 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);
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
|
||||
GGML_CALL static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
|
||||
return true;
|
||||
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
|
||||
GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
|
||||
static struct ggml_backend_buffer_type ggml_backend_buffer_type_metal = {
|
||||
/* .iface = */ {
|
||||
/* .get_name = */ ggml_backend_metal_buffer_type_get_name,
|
||||
@ -2456,7 +2456,7 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
|
||||
|
||||
// buffer from ptr
|
||||
|
||||
ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) {
|
||||
GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) {
|
||||
struct ggml_backend_metal_buffer_context * ctx = malloc(sizeof(struct ggml_backend_metal_buffer_context));
|
||||
|
||||
ctx->all_data = data;
|
||||
@ -2543,31 +2543,31 @@ ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t siz
|
||||
|
||||
// backend
|
||||
|
||||
static const char * ggml_backend_metal_name(ggml_backend_t backend) {
|
||||
GGML_CALL static const char * ggml_backend_metal_name(ggml_backend_t backend) {
|
||||
return "Metal";
|
||||
|
||||
UNUSED(backend);
|
||||
}
|
||||
|
||||
static void ggml_backend_metal_free(ggml_backend_t backend) {
|
||||
GGML_CALL static void ggml_backend_metal_free(ggml_backend_t backend) {
|
||||
struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context;
|
||||
ggml_metal_free(ctx);
|
||||
free(backend);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) {
|
||||
GGML_CALL 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);
|
||||
}
|
||||
|
||||
static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
GGML_CALL static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
|
||||
struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
|
||||
|
||||
return ggml_metal_graph_compute(metal_ctx, cgraph);
|
||||
}
|
||||
|
||||
static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
|
||||
GGML_CALL static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
|
||||
struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
|
||||
|
||||
return ggml_metal_supports_op(metal_ctx, op);
|
||||
@ -2630,9 +2630,9 @@ bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) {
|
||||
return [ctx->device supportsFamily:(MTLGPUFamilyApple1 + family - 1)];
|
||||
}
|
||||
|
||||
ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); // silence warning
|
||||
GGML_CALL 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) {
|
||||
GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data) {
|
||||
return ggml_backend_metal_init();
|
||||
|
||||
GGML_UNUSED(params);
|
||||
|
||||
32
ggml.c
32
ggml.c
@ -1990,19 +1990,19 @@ void ggml_print_objects(const struct ggml_context * ctx) {
|
||||
GGML_PRINT("%s: --- end ---\n", __func__);
|
||||
}
|
||||
|
||||
int64_t ggml_nelements(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL int64_t ggml_nelements(const struct ggml_tensor * tensor) {
|
||||
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
||||
|
||||
return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
|
||||
}
|
||||
|
||||
int64_t ggml_nrows(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL int64_t ggml_nrows(const struct ggml_tensor * tensor) {
|
||||
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
||||
|
||||
return tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
|
||||
}
|
||||
|
||||
size_t ggml_nbytes(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL size_t ggml_nbytes(const struct ggml_tensor * tensor) {
|
||||
size_t nbytes;
|
||||
size_t blck_size = ggml_blck_size(tensor->type);
|
||||
if (blck_size == 1) {
|
||||
@ -2025,15 +2025,15 @@ size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) {
|
||||
return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN);
|
||||
}
|
||||
|
||||
int ggml_blck_size(enum ggml_type type) {
|
||||
GGML_CALL int ggml_blck_size(enum ggml_type type) {
|
||||
return type_traits[type].blck_size;
|
||||
}
|
||||
|
||||
size_t ggml_type_size(enum ggml_type type) {
|
||||
GGML_CALL size_t ggml_type_size(enum ggml_type type) {
|
||||
return type_traits[type].type_size;
|
||||
}
|
||||
|
||||
size_t ggml_row_size(enum ggml_type type, int64_t ne) {
|
||||
GGML_CALL size_t ggml_row_size(enum ggml_type type, int64_t ne) {
|
||||
assert(ne % ggml_blck_size(type) == 0);
|
||||
return ggml_type_size(type)*ne/ggml_blck_size(type);
|
||||
}
|
||||
@ -2042,15 +2042,15 @@ double ggml_type_sizef(enum ggml_type type) {
|
||||
return ((double)(type_traits[type].type_size))/type_traits[type].blck_size;
|
||||
}
|
||||
|
||||
const char * ggml_type_name(enum ggml_type type) {
|
||||
GGML_CALL const char * ggml_type_name(enum ggml_type type) {
|
||||
return type_traits[type].type_name;
|
||||
}
|
||||
|
||||
bool ggml_is_quantized(enum ggml_type type) {
|
||||
GGML_CALL bool ggml_is_quantized(enum ggml_type type) {
|
||||
return type_traits[type].is_quantized;
|
||||
}
|
||||
|
||||
const char * ggml_op_name(enum ggml_op op) {
|
||||
GGML_CALL const char * ggml_op_name(enum ggml_op op) {
|
||||
return GGML_OP_NAME[op];
|
||||
}
|
||||
|
||||
@ -2062,7 +2062,7 @@ 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) {
|
||||
GGML_CALL 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);
|
||||
@ -2072,7 +2072,7 @@ const char * ggml_op_desc(const struct ggml_tensor * t) {
|
||||
}
|
||||
}
|
||||
|
||||
size_t ggml_element_size(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor) {
|
||||
return ggml_type_size(tensor->type);
|
||||
}
|
||||
|
||||
@ -2154,11 +2154,11 @@ size_t ggml_tensor_overhead(void) {
|
||||
return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE;
|
||||
}
|
||||
|
||||
bool ggml_is_transposed(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor) {
|
||||
return tensor->nb[0] > tensor->nb[1];
|
||||
}
|
||||
|
||||
bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
|
||||
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
||||
|
||||
return
|
||||
@ -2177,7 +2177,7 @@ static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * te
|
||||
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
|
||||
}
|
||||
|
||||
bool ggml_is_permuted(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) {
|
||||
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
||||
|
||||
return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3];
|
||||
@ -3079,7 +3079,7 @@ float * ggml_get_data_f32(const struct ggml_tensor * tensor) {
|
||||
return (float *)(tensor->data);
|
||||
}
|
||||
|
||||
enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
|
||||
GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) {
|
||||
GGML_ASSERT(tensor->op == GGML_OP_UNARY);
|
||||
return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0);
|
||||
}
|
||||
@ -11653,7 +11653,7 @@ static void ggml_rope_cache_init(
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_rope_yarn_corr_dims(
|
||||
GGML_CALL void ggml_rope_yarn_corr_dims(
|
||||
int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]
|
||||
) {
|
||||
// start and end correction dims
|
||||
|
||||
42
ggml.h
42
ggml.h
@ -187,6 +187,16 @@
|
||||
# define GGML_API
|
||||
#endif
|
||||
|
||||
#ifdef GGML_MULTIPLATFORM
|
||||
# if defined(_WIN32)
|
||||
# define GGML_CALL
|
||||
# else
|
||||
# define GGML_CALL __attribute__((__ms_abi__))
|
||||
# endif
|
||||
#else
|
||||
# define GGML_CALL
|
||||
#endif
|
||||
|
||||
// TODO: support for clang
|
||||
#ifdef __GNUC__
|
||||
# define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
|
||||
@ -649,36 +659,36 @@ extern "C" {
|
||||
GGML_API void ggml_print_object (const struct ggml_object * obj);
|
||||
GGML_API void ggml_print_objects(const struct ggml_context * ctx);
|
||||
|
||||
GGML_API int64_t ggml_nelements (const struct ggml_tensor * tensor);
|
||||
GGML_API int64_t ggml_nrows (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL int64_t ggml_nelements (const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL int64_t ggml_nrows (const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL size_t ggml_nbytes (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
|
||||
|
||||
GGML_API int ggml_blck_size(enum ggml_type type);
|
||||
GGML_API size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block
|
||||
GGML_API size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
|
||||
GGML_API GGML_CALL int ggml_blck_size(enum ggml_type type);
|
||||
GGML_API GGML_CALL size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block
|
||||
GGML_API GGML_CALL size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
|
||||
|
||||
GGML_DEPRECATED(
|
||||
GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
|
||||
"use ggml_row_size() instead");
|
||||
|
||||
GGML_API const char * ggml_type_name(enum ggml_type type);
|
||||
GGML_API const char * ggml_op_name (enum ggml_op op);
|
||||
GGML_API GGML_CALL const char * ggml_type_name(enum ggml_type type);
|
||||
GGML_API GGML_CALL 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 GGML_CALL 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 GGML_CALL size_t ggml_element_size(const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API bool ggml_is_quantized(enum ggml_type type);
|
||||
GGML_API GGML_CALL bool ggml_is_quantized(enum ggml_type type);
|
||||
|
||||
// TODO: temporary until model loading of ggml examples is refactored
|
||||
GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
|
||||
|
||||
GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL bool ggml_is_permuted (const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_vector (const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_matrix (const struct ggml_tensor * tensor);
|
||||
@ -770,7 +780,7 @@ extern "C" {
|
||||
GGML_API void * ggml_get_data (const struct ggml_tensor * tensor);
|
||||
GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
|
||||
GGML_API GGML_CALL enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor);
|
||||
GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name);
|
||||
@ -1413,7 +1423,7 @@ extern "C" {
|
||||
float beta_slow);
|
||||
|
||||
// compute correction dims for YaRN RoPE scaling
|
||||
void ggml_rope_yarn_corr_dims(
|
||||
GGML_CALL void ggml_rope_yarn_corr_dims(
|
||||
int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]);
|
||||
|
||||
// xPos RoPE, in-place, returns view(a)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user