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llama.cpp/ggml-backend.c
Georgi Gerganov 38566680cd
ggml : add IQ2 to test-backend-ops + refactoring (#4990) 
* ggml : add IQ2 to test-backend-ops + refactoring

ggml-ci

* cuda : update supports_op for IQ2

ggml-ci

* ci : enable LLAMA_CUBLAS=1 for CUDA nodes

ggml-ci

* cuda : fix out-of-bounds-access in `mul_mat_vec_q`

ggml-ci

* tests : avoid creating RNGs for each Q tensor

ggml-ci

* tests : avoid creating RNGs for each tensor

ggml-ci
2024-01-17 18:54:56 +02:00

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#include "ggml-backend-impl.h"
#include "ggml-alloc.h"
#include "ggml-impl.h"
#include <assert.h>
#include <limits.h>
#include <stdarg.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#define MAX(a, b) ((a) > (b) ? (a) : (b))
// backend buffer type
const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
return buft->iface.get_name(buft);
}
GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
return buft->iface.alloc_buffer(buft, size);
}
size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) {
return buft->iface.get_alignment(buft);
}
GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) {
// get_alloc_size is optional, defaults to ggml_nbytes
if (buft->iface.get_alloc_size) {
return buft->iface.get_alloc_size(buft, tensor);
}
return ggml_nbytes(tensor);
}
bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
return buft->iface.supports_backend(buft, backend);
}
bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
if (buft->iface.is_host) {
return buft->iface.is_host(buft);
}
return false;
}
// backend buffer
GGML_CALL 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) {
ggml_backend_buffer_t buffer = malloc(sizeof(struct ggml_backend_buffer));
GGML_ASSERT(iface.get_base != NULL);
(*buffer) = (struct ggml_backend_buffer) {
/* .interface = */ iface,
/* .buft = */ buft,
/* .context = */ context,
/* .size = */ size,
/* .usage = */ GGML_BACKEND_BUFFER_USAGE_ANY
};
return buffer;
}
const char * ggml_backend_buffer_name(ggml_backend_buffer_t buffer) {
return buffer->iface.get_name(buffer);
}
void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
if (buffer == NULL) {
return;
}
if (buffer->iface.free_buffer != NULL) {
buffer->iface.free_buffer(buffer);
}
free(buffer);
}
size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
return buffer->size;
}
void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
void * base = buffer->iface.get_base(buffer);
GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL");
return base;
}
GGML_CALL void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
// init_tensor is optional
if (buffer->iface.init_tensor) {
buffer->iface.init_tensor(buffer, tensor);
}
}
size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer) {
return ggml_backend_buft_get_alignment(ggml_backend_buffer_get_type(buffer));
}
size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor);
}
void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
buffer->iface.clear(buffer, value);
}
bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) {
return ggml_backend_buft_is_host(ggml_backend_buffer_get_type(buffer));
}
void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
buffer->usage = usage;
}
ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) {
return buffer->buft;
}
void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) {
if (buffer->iface.reset) {
buffer->iface.reset(buffer);
}
}
bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst) {
ggml_backend_buffer_t dst_buf = dst->view_src ? dst->view_src->buffer : dst->buffer;
if (dst_buf->iface.cpy_tensor) {
return src->buffer->iface.cpy_tensor(dst_buf, src, dst);
}
return false;
}
// backend
const char * ggml_backend_name(ggml_backend_t backend) {
if (backend == NULL) {
return "NULL";
}
return backend->iface.get_name(backend);
}
void ggml_backend_free(ggml_backend_t backend) {
if (backend == NULL) {
return;
}
backend->iface.free(backend);
}
ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend) {
return backend->iface.get_default_buffer_type(backend);
}
ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size) {
return ggml_backend_buft_alloc_buffer(ggml_backend_get_default_buffer_type(backend), size);
}
size_t ggml_backend_get_alignment(ggml_backend_t backend) {
return ggml_backend_buft_get_alignment(ggml_backend_get_default_buffer_type(backend));
}
void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
if (backend->iface.set_tensor_async == NULL) {
ggml_backend_tensor_set(tensor, data, offset, size);
} else {
backend->iface.set_tensor_async(backend, tensor, data, offset, size);
}
}
void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
if (backend->iface.get_tensor_async == NULL) {
ggml_backend_tensor_get(tensor, data, offset, size);
} else {
backend->iface.get_tensor_async(backend, tensor, data, offset, size);
}
}
GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(buf != NULL && "tensor buffer not set");
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size);
}
GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set");
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
tensor->buffer->iface.get_tensor(buf, tensor, data, offset, size);
}
void ggml_backend_synchronize(ggml_backend_t backend) {
if (backend->iface.synchronize == NULL) {
return;
}
backend->iface.synchronize(backend);
}
ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
return backend->iface.graph_plan_create(backend, cgraph);
}
void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
backend->iface.graph_plan_free(backend, plan);
}
void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
backend->iface.graph_plan_compute(backend, plan);
}
bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
return backend->iface.graph_compute(backend, cgraph);
}
bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
return backend->iface.supports_op(backend, op);
}
// backend copy
static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
if (a->type != b->type) {
return false;
}
for (int i = 0; i < GGML_MAX_DIMS; i++) {
if (a->ne[i] != b->ne[i]) {
return false;
}
if (a->nb[i] != b->nb[i]) {
return false;
}
}
return true;
}
void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) {
GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
if (src == dst) {
return;
}
if (ggml_backend_buffer_is_host(src->buffer)) {
ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src));
} else if (ggml_backend_buffer_is_host(dst->buffer)) {
ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
} else if (!ggml_backend_buffer_copy_tensor(src, dst)) {
#ifndef NDEBUG
fprintf(stderr, "%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer));
#endif
size_t nbytes = ggml_nbytes(src);
void * data = malloc(nbytes);
ggml_backend_tensor_get(src, data, 0, nbytes);
ggml_backend_tensor_set(dst, data, 0, nbytes);
free(data);
}
}
void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
if (src == dst) {
return;
}
if (ggml_backend_buft_supports_backend(src->buffer->buft, backend) && ggml_backend_buft_supports_backend(dst->buffer->buft, backend)) {
if (backend->iface.cpy_tensor_async != NULL) {
if (backend->iface.cpy_tensor_async(backend, src, dst)) {
return;
}
}
}
size_t nbytes = ggml_nbytes(src);
if (ggml_backend_buffer_is_host(src->buffer)) {
ggml_backend_tensor_set_async(backend, dst, src->data, 0, nbytes);
}
else {
ggml_backend_tensor_copy(src, dst);
}
}
// backend registry
#define GGML_MAX_BACKENDS_REG 16
struct ggml_backend_reg {
char name[128];
ggml_backend_init_fn init_fn;
ggml_backend_buffer_type_t default_buffer_type;
void * user_data;
};
static struct ggml_backend_reg ggml_backend_registry[GGML_MAX_BACKENDS_REG];
static size_t ggml_backend_registry_count = 0;
GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
GGML_CALL static void ggml_backend_registry_init(void) {
static bool initialized = false;
if (initialized) {
return;
}
initialized = true;
ggml_backend_register("CPU", ggml_backend_reg_cpu_init, ggml_backend_cpu_buffer_type(), NULL);
// add forward decls here to avoid including the backend headers
#ifdef GGML_USE_CUBLAS
extern GGML_CALL void ggml_backend_cuda_reg_devices(void);
ggml_backend_cuda_reg_devices();
#endif
#ifdef GGML_USE_METAL
extern GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data);
extern GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
ggml_backend_register("Metal", ggml_backend_reg_metal_init, ggml_backend_metal_buffer_type(), NULL);
#endif
}
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) {
GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG);
size_t id = ggml_backend_registry_count;
ggml_backend_registry[id] = (struct ggml_backend_reg) {
/* .name = */ {0},
/* .fn = */ init_fn,
/* .default_buffer_type = */ default_buffer_type,
/* .user_data = */ user_data,
};
snprintf(ggml_backend_registry[id].name, sizeof(ggml_backend_registry[id].name), "%s", name);
#ifndef NDEBUG
fprintf(stderr, "%s: registered backend %s\n", __func__, name);
#endif
ggml_backend_registry_count++;
}
size_t ggml_backend_reg_get_count(void) {
ggml_backend_registry_init();
return ggml_backend_registry_count;
}
size_t ggml_backend_reg_find_by_name(const char * name) {
ggml_backend_registry_init();
for (size_t i = 0; i < ggml_backend_registry_count; i++) {
// TODO: case insensitive in a portable way
if (strcmp(ggml_backend_registry[i].name, name) == 0) {
return i;
}
}
// not found
return SIZE_MAX;
}
// init from backend:params string
ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str) {
ggml_backend_registry_init();
const char * params = strchr(backend_str, ':');
char backend_name[128];
if (params == NULL) {
snprintf(backend_name, sizeof(backend_name), "%s", backend_str);
params = "";
} else {
snprintf(backend_name, sizeof(backend_name), "%.*s", (int)(params - backend_str), backend_str);
params++;
}
size_t backend_i = ggml_backend_reg_find_by_name(backend_name);
if (backend_i == SIZE_MAX) {
fprintf(stderr, "%s: backend %s not found\n", __func__, backend_name);
return NULL;
}
return ggml_backend_reg_init_backend(backend_i, params);
}
const char * ggml_backend_reg_get_name(size_t i) {
ggml_backend_registry_init();
GGML_ASSERT(i < ggml_backend_registry_count);
return ggml_backend_registry[i].name;
}
ggml_backend_t ggml_backend_reg_init_backend(size_t i, const char * params) {
ggml_backend_registry_init();
GGML_ASSERT(i < ggml_backend_registry_count);
return ggml_backend_registry[i].init_fn(params, ggml_backend_registry[i].user_data);
}
ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i) {
ggml_backend_registry_init();
GGML_ASSERT(i < ggml_backend_registry_count);
return ggml_backend_registry[i].default_buffer_type;
}
ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) {
ggml_backend_registry_init();
GGML_ASSERT(i < ggml_backend_registry_count);
return ggml_backend_buft_alloc_buffer(ggml_backend_registry[i].default_buffer_type, size);
}
// backend CPU
GGML_CALL static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) {
return "CPU";
GGML_UNUSED(buffer);
}
GGML_CALL static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
return (void *)buffer->context;
}
GGML_CALL static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
free(buffer->context);
}
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) {
memcpy((char *)tensor->data + offset, data, size);
GGML_UNUSED(buffer);
}
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) {
memcpy(data, (const char *)tensor->data + offset, size);
GGML_UNUSED(buffer);
}
GGML_CALL static bool ggml_backend_cpu_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;
}
return false;
GGML_UNUSED(buffer);
}
GGML_CALL static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
memset(buffer->context, value, buffer->size);
}
static struct ggml_backend_buffer_i cpu_backend_buffer_i = {
/* .get_name = */ ggml_backend_cpu_buffer_name,
/* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer,
/* .get_base = */ ggml_backend_cpu_buffer_get_base,
/* .init_tensor = */ NULL, // no initialization required
/* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor,
/* .clear = */ ggml_backend_cpu_buffer_clear,
/* .reset = */ NULL,
};
// for buffers from ptr, free is not called
static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = {
/* .get_name = */ ggml_backend_cpu_buffer_name,
/* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed
/* .get_base = */ ggml_backend_cpu_buffer_get_base,
/* .init_tensor = */ NULL, // no initialization required
/* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor,
/* .clear = */ ggml_backend_cpu_buffer_clear,
/* .reset = */ NULL,
};
static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512
GGML_CALL static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
return "CPU";
GGML_UNUSED(buft);
}
GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned
void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC?
GGML_ASSERT(data != NULL && "failed to allocate buffer");
return ggml_backend_buffer_init(buft, cpu_backend_buffer_i, data, size);
}
GGML_CALL static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
return TENSOR_ALIGNMENT;
GGML_UNUSED(buft);
}
GGML_CALL static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
return ggml_backend_is_cpu(backend);
GGML_UNUSED(buft);
}
GGML_CALL static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
return true;
GGML_UNUSED(buft);
}
GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) {
static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = {
/* .iface = */ {
/* .get_name = */ ggml_backend_cpu_buffer_type_get_name,
/* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment,
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend,
/* .is_host = */ ggml_backend_cpu_buffer_type_is_host,
},
/* .context = */ NULL,
};
return &ggml_backend_cpu_buffer_type;
}
#ifdef GGML_USE_CPU_HBM
// buffer type HBM
#include <hbwmalloc.h>
GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
return "CPU_HBM";
GGML_UNUSED(buft);
}
GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) {
return "CPU_HBM";
GGML_UNUSED(buf);
}
GGML_CALL static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) {
hbw_free(buffer->context);
}
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);
if (result != 0) {
fprintf(stderr, "failed to allocate HBM buffer of size %zu\n", size);
return NULL;
}
ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
buffer->buft = buft;
buffer->iface.get_name = ggml_backend_cpu_hbm_buffer_get_name;
buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer;
return buffer;
}
ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) {
static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = {
/* .iface = */ {
/* .get_name = */ ggml_backend_cpu_hbm_buffer_type_get_name,
/* .alloc_buffer = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment,
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend,
/* .is_host = */ ggml_backend_cpu_buffer_type_is_host,
},
/* .context = */ NULL,
};
return &ggml_backend_cpu_buffer_type_hbm;
}
#endif
struct ggml_backend_cpu_context {
int n_threads;
void * work_data;
size_t work_size;
};
GGML_CALL static const char * ggml_backend_cpu_name(ggml_backend_t backend) {
return "CPU";
GGML_UNUSED(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);
}
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);
}
struct ggml_backend_plan_cpu {
struct ggml_cplan cplan;
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));
cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
cpu_plan->cgraph = *cgraph; // FIXME: deep copy
if (cpu_plan->cplan.work_size > 0) {
cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size);
}
return cpu_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);
free(cpu_plan);
GGML_UNUSED(backend);
}
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);
GGML_UNUSED(backend);
}
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);
if (cpu_ctx->work_size < cplan.work_size) {
// TODO: may be faster to free and use malloc to avoid the copy
cpu_ctx->work_data = realloc(cpu_ctx->work_data, cplan.work_size);
cpu_ctx->work_size = cplan.work_size;
}
cplan.work_data = cpu_ctx->work_data;
ggml_graph_compute(cgraph, &cplan);
return true;
}
GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
switch (op->op) {
case GGML_OP_CPY:
return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS; // missing type_traits.from_float
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;
default:
return true;
}
GGML_UNUSED(backend);
}
static struct ggml_backend_i cpu_backend_i = {
/* .get_name = */ ggml_backend_cpu_name,
/* .free = */ ggml_backend_cpu_free,
/* .get_default_buffer_type = */ ggml_backend_cpu_get_default_buffer_type,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
/* .synchronize = */ NULL,
/* .graph_plan_create = */ ggml_backend_cpu_graph_plan_create,
/* .graph_plan_free = */ ggml_backend_cpu_graph_plan_free,
/* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute,
/* .graph_compute = */ ggml_backend_cpu_graph_compute,
/* .supports_op = */ ggml_backend_cpu_supports_op,
};
ggml_backend_t ggml_backend_cpu_init(void) {
struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context));
ctx->n_threads = GGML_DEFAULT_N_THREADS;
ctx->work_data = NULL;
ctx->work_size = 0;
ggml_backend_t cpu_backend = malloc(sizeof(struct ggml_backend));
*cpu_backend = (struct ggml_backend) {
/* .interface = */ cpu_backend_i,
/* .context = */ ctx
};
return cpu_backend;
}
GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) {
return backend && backend->iface.get_name == ggml_backend_cpu_name;
}
void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
GGML_ASSERT(ggml_backend_is_cpu(backend_cpu));
struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context;
ctx->n_threads = n_threads;
}
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);
}
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);
GGML_UNUSED(user_data);
}
// scheduler
#define GGML_MAX_BACKENDS 16
#define GGML_MAX_SPLITS 256
#define GGML_MAX_SPLIT_INPUTS 16
struct ggml_backend_sched_split {
ggml_tallocr_t tallocr;
int i_start;
int i_end;
struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS];
int n_inputs;
// graph view of this split
struct ggml_cgraph graph;
};
struct ggml_backend_sched {
bool is_reset; // true if the scheduler has been reset since the last graph split
int n_backends;
ggml_backend_t backends[GGML_MAX_BACKENDS];
ggml_backend_buffer_type_t bufts[GGML_MAX_BACKENDS];
ggml_tallocr_t tallocs[GGML_MAX_BACKENDS];
ggml_gallocr_t galloc;
// hash keys of the nodes in the graph
struct ggml_hash_set hash_set;
// hash values (arrays of [hash_set.size])
ggml_tallocr_t * node_talloc; // tallocr assigned to each node (indirectly this is the backend)
struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // copies of each node for each destination backend
// copy of the graph with modified inputs
struct ggml_cgraph * graph;
struct ggml_backend_sched_split splits[GGML_MAX_SPLITS];
int n_splits;
struct ggml_context * ctx;
// align context_buffer to GGML_MEM_ALIGN
#ifdef _MSC_VER
__declspec(align(GGML_MEM_ALIGN))
#else
__attribute__((aligned(GGML_MEM_ALIGN)))
#endif
char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];
ggml_backend_sched_eval_callback callback_eval;
void * callback_eval_user_data;
};
#define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node)
#define node_allocr(node) sched->node_talloc[hash_id(node)]
static bool ggml_is_view_op(enum ggml_op op) {
return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE;
}
// returns the priority of the backend, lower is better
static int sched_backend_prio(ggml_backend_sched_t sched, ggml_backend_t backend) {
for (int i = 0; i < sched->n_backends; i++) {
if (sched->backends[i] == backend) {
return i;
}
}
return INT_MAX;
}
static int sched_allocr_prio(ggml_backend_sched_t sched, ggml_tallocr_t allocr) {
for (int i = 0; i < sched->n_backends; i++) {
if (sched->tallocs[i] == allocr) {
return i;
}
}
return INT_MAX;
}
static ggml_tallocr_t sched_allocr_from_buffer(ggml_backend_sched_t sched, ggml_backend_buffer_t buffer) {
if (buffer == NULL) {
return NULL;
}
// check if this is already allocate in a allocr buffer (from user manual allocations)
for (int i = 0; i < sched->n_backends; i++) {
if (ggml_tallocr_get_buffer(sched->tallocs[i]) == buffer) {
return sched->tallocs[i];
}
}
// find highest prio backend that supports the buffer type
for (int i = 0; i < sched->n_backends; i++) {
if (ggml_backend_buft_supports_backend(buffer->buft, sched->backends[i])) {
return sched->tallocs[i];
}
}
GGML_ASSERT(false && "tensor buffer type not supported by any backend");
}
static ggml_backend_t get_allocr_backend(ggml_backend_sched_t sched, ggml_tallocr_t allocr) {
if (allocr == NULL) {
return NULL;
}
for (int i = 0; i < sched->n_backends; i++) {
if (sched->tallocs[i] == allocr) {
return sched->backends[i];
}
}
GGML_UNREACHABLE();
}
#if 0
static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug only
#define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__)
#define GET_CAUSE(node) causes[hash_id(node)]
#else
#define SET_CAUSE(node, ...)
#define GET_CAUSE(node) ""
#endif
// returns the backend that should be used for the node based on the current locations
static ggml_tallocr_t sched_allocr_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * node) {
// assign pre-allocated nodes to their backend
// dst
ggml_tallocr_t cur_allocr = sched_allocr_from_buffer(sched, node->buffer);
if (cur_allocr != NULL) {
SET_CAUSE(node, "1.dst");
return cur_allocr;
}
// view_src
if (node->view_src != NULL) {
cur_allocr = sched_allocr_from_buffer(sched, node->view_src->buffer);
if (cur_allocr != NULL) {
SET_CAUSE(node, "1.vsrc");
return cur_allocr;
}
}
// assign nodes that use weights to the backend of the weights
for (int i = 0; i < GGML_MAX_SRC; i++) {
const struct ggml_tensor * src = node->src[i];
if (src == NULL) {
break;
}
if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
ggml_tallocr_t src_allocr = sched_allocr_from_buffer(sched, src->buffer);
// operations with weights are always run on the same backend as the weights
SET_CAUSE(node, "1.wgt%d", i);
return src_allocr;
}
}
return NULL;
}
static char * fmt_size(size_t size) {
static char buffer[128];
if (size >= 1024*1024) {
sprintf(buffer, "%zuM", size/1024/1024);
} else {
sprintf(buffer, "%zuK", size/1024);
}
return buffer;
}
static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
int cur_split = 0;
for (int i = 0; i < graph->n_nodes; i++) {
if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) {
ggml_backend_t split_backend = get_allocr_backend(sched, sched->splits[cur_split].tallocr);
fprintf(stderr, "\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend),
sched->splits[cur_split].n_inputs);
for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) {
fprintf(stderr, "[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name,
fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j])));
}
fprintf(stderr, "\n");
cur_split++;
}
struct ggml_tensor * node = graph->nodes[i];
if (ggml_is_view_op(node->op)) {
continue;
}
ggml_tallocr_t node_allocr = node_allocr(node);
ggml_backend_t node_backend = node_allocr ? get_allocr_backend(sched, node_allocr) : NULL; // FIXME:
fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
fmt_size(ggml_nbytes(node)), node_allocr ? ggml_backend_name(node_backend) : "NULL", GET_CAUSE(node));
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
break;
}
ggml_tallocr_t src_allocr = node_allocr(src);
ggml_backend_t src_backend = src_allocr ? get_allocr_backend(sched, src_allocr) : NULL;
fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src));
}
fprintf(stderr, "\n");
}
}
// creates a copy of the tensor with the same memory layout
static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, const struct ggml_tensor * tensor) {
struct ggml_tensor * dup = ggml_dup_tensor(ctx, tensor);
for (int i = 0; i < GGML_MAX_DIMS; i++) {
dup->nb[i] = tensor->nb[i];
}
return dup;
}
//#define DEBUG_PASS1
//#define DEBUG_PASS2
//#define DEBUG_PASS3
//#define DEBUG_PASS4
// assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend
static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
// reset splits
sched->n_splits = 0;
sched->is_reset = false;
struct ggml_init_params params = {
/* .mem_size = */ sizeof(sched->context_buffer),
/* .mem_buffer = */ sched->context_buffer,
/* .no_alloc = */ true
};
ggml_free(sched->ctx);
sched->ctx = ggml_init(params);
if (sched->ctx == NULL) {
fprintf(stderr, "%s: failed to initialize context\n", __func__);
GGML_ASSERT(false);
}
// pass 1: assign backends to ops with pre-allocated inputs
for (int i = 0; i < graph->n_leafs; i++) {
struct ggml_tensor * leaf = graph->leafs[i];
if (node_allocr(leaf) != NULL) {
// do not overwrite user assignments
continue;
}
node_allocr(leaf) = sched_allocr_from_cur(sched, leaf);
}
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
if (node_allocr(node) != NULL) {
// do not overwrite user assignments
continue;
}
node_allocr(node) = sched_allocr_from_cur(sched, node);
// src
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
break;
}
if (node_allocr(src) == NULL) {
node_allocr(src) = sched_allocr_from_cur(sched, src);
}
}
}
#ifdef DEBUG_PASS1
fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
#endif
// pass 2: expand current backend assignments
// assign the same backend to adjacent nodes
// expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend)
// thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops
// pass 2.1 expand gpu up
{
ggml_tallocr_t cur_allocr = NULL;
for (int i = graph->n_nodes - 1; i >= 0; i--) {
struct ggml_tensor * node = graph->nodes[i];
if (ggml_is_view_op(node->op)) {
continue;
}
ggml_tallocr_t node_allocr = node_allocr(node);
if (node_allocr != NULL) {
if (sched_allocr_prio(sched, node_allocr) == sched->n_backends - 1) {
// skip cpu (lowest prio backend)
cur_allocr = NULL;
} else {
cur_allocr = node_allocr;
}
} else {
node_allocr(node) = cur_allocr;
SET_CAUSE(node, "2.1");
}
}
}
// pass 2.2 expand gpu down
{
ggml_tallocr_t cur_allocr = NULL;
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
if (ggml_is_view_op(node->op)) {
continue;
}
ggml_tallocr_t node_allocr = node_allocr(node);
if (node_allocr != NULL) {
if (sched_allocr_prio(sched, node_allocr) == sched->n_backends - 1) {
// skip cpu (lowest prio backend)
cur_allocr = NULL;
} else {
cur_allocr = node_allocr;
}
} else {
node_allocr(node) = cur_allocr;
SET_CAUSE(node, "2.2");
}
}
}
// pass 2.3 expand rest up
{
ggml_tallocr_t cur_allocr = NULL;
for (int i = graph->n_nodes - 1; i >= 0; i--) {
struct ggml_tensor * node = graph->nodes[i];
if (ggml_is_view_op(node->op)) {
continue;
}
ggml_tallocr_t node_allocr = node_allocr(node);
if (node_allocr != NULL) {
cur_allocr = node_allocr;
} else {
node_allocr(node) = cur_allocr;
SET_CAUSE(node, "2.3");
}
}
}
// pass 2.4 expand rest down
{
ggml_tallocr_t cur_allocr = NULL;
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
if (ggml_is_view_op(node->op)) {
continue;
}
ggml_tallocr_t node_allocr = node_allocr(node);
if (node_allocr != NULL) {
cur_allocr = node_allocr;
} else {
node_allocr(node) = cur_allocr;
SET_CAUSE(node, "2.4");
}
}
}
#ifdef DEBUG_PASS2
fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
#endif
// pass 3: assign backends to remaining src from dst and view_src
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
ggml_tallocr_t cur_allocr = node_allocr(node);
if (node->view_src != NULL && cur_allocr == NULL) {
cur_allocr = node_allocr(node) = node_allocr(node->view_src);
SET_CAUSE(node, "3.vsrc");
}
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
break;
}
ggml_tallocr_t src_allocr = node_allocr(src);
if (src_allocr == NULL) {
if (src->view_src != NULL) {
// views are always on the same backend as the source
node_allocr(src) = node_allocr(src->view_src);
SET_CAUSE(src, "3.vsrc");
} else {
node_allocr(src) = cur_allocr;
SET_CAUSE(src, "3.cur");
}
}
}
}
#ifdef DEBUG_PASS3
fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
#endif
// pass 4: split graph, find tensors that need to be copied
{
int cur_split = 0;
// find the backend of the first split, skipping view ops
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
if (!ggml_is_view_op(node->op)) {
sched->splits[0].tallocr = node_allocr(node);
break;
}
}
sched->splits[0].i_start = 0;
sched->splits[0].n_inputs = 0;
memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK
ggml_tallocr_t cur_allocr = sched->splits[0].tallocr;
size_t cur_backend_id = sched_allocr_prio(sched, cur_allocr);
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
if (ggml_is_view_op(node->op)) {
continue;
}
ggml_tallocr_t node_allocr = node_allocr(node);
GGML_ASSERT(node_allocr != NULL); // all nodes should be assigned by now
if (node_allocr != cur_allocr) {
sched->splits[cur_split].i_end = i;
cur_split++;
GGML_ASSERT(cur_split < GGML_MAX_SPLITS);
sched->splits[cur_split].tallocr = node_allocr;
sched->splits[cur_split].i_start = i;
sched->splits[cur_split].n_inputs = 0;
cur_allocr = node_allocr;
cur_backend_id = sched_allocr_prio(sched, cur_allocr);
}
// find inputs that are not on the same backend
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
break;
}
ggml_tallocr_t src_allocr = node_allocr(src);
GGML_ASSERT(src_allocr != NULL); // all inputs should be assigned by now
if (src_allocr != node_allocr) {
// check if the input is already in the split
bool found = false;
for (int k = 0; k < sched->splits[cur_split].n_inputs; k++) {
if (sched->splits[cur_split].inputs[k] == src) {
found = true;
break;
}
}
if (!found) {
int n_inputs = sched->splits[cur_split].n_inputs++;
//printf("split %d input %d: %s (%s)\n", cur_split, n_inputs, src->name, ggml_backend_name(get_allocr_backend(sched, src_allocr)));
GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS);
sched->splits[cur_split].inputs[n_inputs] = src;
}
// create a copy of the input in the split's backend
size_t id = hash_id(src);
if (sched->node_copies[id][cur_backend_id] == NULL) {
ggml_backend_t backend = get_allocr_backend(sched, cur_allocr);
struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name);
sched->node_copies[id][cur_backend_id] = tensor_copy;
node_allocr(tensor_copy) = cur_allocr;
SET_CAUSE(tensor_copy, "4.cpy");
}
node->src[j] = sched->node_copies[id][cur_backend_id];
}
}
}
sched->splits[cur_split].i_end = graph->n_nodes;
sched->n_splits = cur_split + 1;
}
#ifdef DEBUG_PASS4
fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
#endif
#ifndef NDEBUG
// sanity check: all sources should have the same backend as the node
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
ggml_tallocr_t node_allocr = node_allocr(node);
if (node_allocr == NULL) {
fprintf(stderr, "!!!!!!! %s has no backend\n", node->name);
}
if (node->view_src != NULL && node_allocr != node_allocr(node->view_src)) {
fprintf(stderr, "!!!!!!! %s has backend %s, view_src %s has backend %s\n",
node->name, node_allocr ? ggml_backend_name(get_allocr_backend(sched, node_allocr)) : "NULL",
node->view_src->name, node_allocr(node->view_src) ? ggml_backend_name(get_allocr_backend(sched, node_allocr(node->view_src))) : "NULL");
}
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
break;
}
ggml_tallocr_t src_allocr = node_allocr(src);
if (src_allocr != node_allocr /* && src_backend != NULL */) { // ignore nulls for now
fprintf(stderr, "!!!! %s has backend %s, src %d (%s) has backend %s\n",
node->name, node_allocr ? ggml_backend_name(get_allocr_backend(sched, node_allocr)) : "NULL",
j, src->name, src_allocr ? ggml_backend_name(get_allocr_backend(sched, src_allocr)) : "NULL");
}
if (src->view_src != NULL && src_allocr != node_allocr(src->view_src)) {
fprintf(stderr, "!!!!!!! [src] %s has backend %s, view_src %s has backend %s\n",
src->name, src_allocr ? ggml_backend_name(get_allocr_backend(sched, src_allocr)) : "NULL",
src->view_src->name, node_allocr(src->view_src) ? ggml_backend_name(get_allocr_backend(sched, node_allocr(src->view_src))) : "NULL");
}
}
}
fflush(stderr);
#endif
// create copies of the graph for each split
// FIXME: avoid this copy, pass split inputs to ggml_gallocr_alloc_graph_n in some other way
struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_MAX_SPLIT_INPUTS, false);
for (int i = 0; i < sched->n_splits; i++) {
struct ggml_backend_sched_split * split = &sched->splits[i];
split->graph = ggml_graph_view(graph, split->i_start, split->i_end);
// add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split
for (int j = 0; j < split->n_inputs; j++) {
struct ggml_tensor * input = split->inputs[j];
struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_allocr_prio(sched, split->tallocr)];
// add a dependency to the input source so that it is not freed before the copy is done
GGML_ASSERT(input_cpy->src[0] == NULL || input_cpy->src[0] == input);
input_cpy->src[0] = input;
graph_copy->nodes[graph_copy->n_nodes++] = input_cpy;
}
for (int j = split->i_start; j < split->i_end; j++) {
graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j];
}
}
sched->graph = graph_copy;
}
static void sched_alloc_splits(ggml_backend_sched_t sched) {
ggml_gallocr_alloc_graph_n(
sched->galloc,
sched->graph,
sched->hash_set,
sched->node_talloc);
}
static void sched_compute_splits(ggml_backend_sched_t sched) {
uint64_t copy_us[GGML_MAX_BACKENDS] = {0};
uint64_t compute_us[GGML_MAX_BACKENDS] = {0};
struct ggml_backend_sched_split * splits = sched->splits;
for (int i = 0; i < sched->n_splits; i++) {
struct ggml_backend_sched_split * split = &splits[i];
ggml_backend_t split_backend = get_allocr_backend(sched, split->tallocr);
int split_backend_id = sched_backend_prio(sched, split_backend);
// copy the input tensors to the split backend
uint64_t copy_start_us = ggml_time_us();
for (int j = 0; j < split->n_inputs; j++) {
struct ggml_tensor * input = split->inputs[j];
struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][split_backend_id];
GGML_ASSERT(input->buffer != NULL);
GGML_ASSERT(input_cpy->buffer != NULL);
// TODO: avoid this copy if it was already copied in a previous split, and the input didn't change
// this is important to avoid copying constants such as KQ_mask and inp_pos multiple times
ggml_backend_tensor_copy_async(split_backend, input, input_cpy);
}
//ggml_backend_synchronize(split_backend); // necessary to measure copy time
int64_t copy_end_us = ggml_time_us();
copy_us[split_backend_id] += copy_end_us - copy_start_us;
#if 0
char split_filename[GGML_MAX_NAME];
snprintf(split_filename, GGML_MAX_NAME, "split_%i_%s.dot", i, ggml_backend_name(split_backend));
ggml_graph_dump_dot(split->graph, NULL, split_filename);
#endif
uint64_t compute_start_us = ggml_time_us();
if (!sched->callback_eval) {
ggml_backend_graph_compute(split_backend, &split->graph);
//ggml_backend_synchronize(split_backend); // necessary to measure compute time
} else {
// similar to ggml_backend_compare_graph_backend
for (int j0 = 0; j0 < split->graph.n_nodes; j0++) {
struct ggml_tensor * t = split->graph.nodes[j0];
// check if the user needs data from this node
bool need = sched->callback_eval(t, true, sched->callback_eval_user_data);
int j1 = j0;
// determine the range [j0, j1] of nodes that can be computed together
while (!need && j1 < split->graph.n_nodes - 1) {
t = split->graph.nodes[++j1];
need = sched->callback_eval(t, true, sched->callback_eval_user_data);
}
struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1);
ggml_backend_graph_compute(split_backend, &gv);
if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) {
break;
}
j0 = j1;
}
}
uint64_t compute_end_us = ggml_time_us();
compute_us[split_backend_id] += compute_end_us - compute_start_us;
}
#if 0
// per-backend timings
fprintf(stderr, "sched_compute_splits times (%d splits):\n", sched->n_splits);
for (int i = 0; i < sched->n_backends; i++) {
if (copy_us[i] > 0 || compute_us[i] > 0) {
fprintf(stderr, "\t%5.5s: %lu us copy, %lu us compute\n", ggml_backend_name(sched->backends[i]), copy_us[i], compute_us[i]);
}
}
#endif
}
static void sched_reset(ggml_backend_sched_t sched) {
for (int i = 0; i < sched->n_backends; i++) {
ggml_tallocr_reset(sched->tallocs[i]);
}
// reset state for the next run
size_t hash_size = sched->hash_set.size;
memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size);
memset(sched->node_talloc, 0, sizeof(sched->node_talloc[0]) * hash_size);
memset(sched->node_copies, 0, sizeof(sched->node_copies[0]) * hash_size);
sched->is_reset = true;
}
ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size) {
GGML_ASSERT(n_backends > 0);
GGML_ASSERT(n_backends <= GGML_MAX_BACKENDS);
struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1);
// initialize hash table
sched->hash_set = ggml_hash_set_new(graph_size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS);
sched->node_talloc = calloc(sizeof(sched->node_talloc[0]) * sched->hash_set.size, 1);
sched->node_copies = calloc(sizeof(sched->node_copies[0]) * sched->hash_set.size, 1);
sched->n_backends = n_backends;
for (int i = 0; i < n_backends; i++) {
sched->backends[i] = backends[i];
sched->bufts[i] = bufts ? bufts[i] : ggml_backend_get_default_buffer_type(backends[i]);
}
sched->galloc = ggml_gallocr_new();
// init measure allocs for each backend
for (int i = 0; i < n_backends; i++) {
sched->tallocs[i] = ggml_tallocr_new_measure_from_buft(sched->bufts[i]);
}
sched_reset(sched);
return sched;
}
void ggml_backend_sched_free(ggml_backend_sched_t sched) {
if (sched == NULL) {
return;
}
for (int i = 0; i < sched->n_backends; i++) {
ggml_tallocr_free(sched->tallocs[i]);
}
ggml_gallocr_free(sched->galloc);
ggml_free(sched->ctx);
free(sched->hash_set.keys);
free(sched->node_talloc);
free(sched->node_copies);
free(sched);
}
void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
GGML_ASSERT(ggml_tallocr_is_measure(sched->tallocs[0])); // can only be initialized once
sched_split_graph(sched, measure_graph);
sched_alloc_splits(sched);
// allocate buffers and reset allocators
for (int i = 0; i < sched->n_backends; i++) {
size_t size = ggml_tallocr_max_size(sched->tallocs[i]);
ggml_tallocr_free(sched->tallocs[i]);
sched->tallocs[i] = ggml_tallocr_new_from_buft(sched->bufts[i], size);
}
sched_reset(sched);
}
void ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS);
if (!sched->is_reset) {
sched_reset(sched);
}
sched_split_graph(sched, graph);
sched_alloc_splits(sched);
sched_compute_splits(sched);
}
void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
sched_reset(sched);
}
void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
sched->callback_eval = callback;
sched->callback_eval_user_data = user_data;
}
int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) {
return sched->n_splits;
}
ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend) {
int backend_index = sched_backend_prio(sched, backend);
GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
return sched->tallocs[backend_index];
}
ggml_backend_buffer_t ggml_backend_sched_get_buffer(ggml_backend_sched_t sched, ggml_backend_t backend) {
int backend_index = sched_backend_prio(sched, backend);
GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
return ggml_tallocr_get_buffer(sched->tallocs[backend_index]);
}
void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
int backend_index = sched_backend_prio(sched, backend);
GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
node_allocr(node) = sched->tallocs[backend_index];
}
ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
ggml_tallocr_t allocr = node_allocr(node);
if (allocr == NULL) {
return NULL;
}
return get_allocr_backend(sched, allocr);
}
// utils
void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
GGML_ASSERT(tensor->buffer == NULL);
//GGML_ASSERT(tensor->data == NULL); // views of pre-allocated tensors may have the data set in ggml_new_tensor, but still need to be initialized by the backend
GGML_ASSERT(tensor->view_src != NULL);
GGML_ASSERT(tensor->view_src->buffer != NULL);
GGML_ASSERT(tensor->view_src->data != NULL);
tensor->buffer = buffer;
tensor->data = (char *)tensor->view_src->data + tensor->view_offs;
tensor->backend = tensor->view_src->backend;
ggml_backend_buffer_init_tensor(buffer, tensor);
}
void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr) {
GGML_ASSERT(tensor->buffer == NULL);
GGML_ASSERT(tensor->data == NULL);
GGML_ASSERT(tensor->view_src == NULL);
GGML_ASSERT(addr >= ggml_backend_buffer_get_base(buffer));
GGML_ASSERT((char *)addr + ggml_backend_buffer_get_alloc_size(buffer, tensor) <=
(char *)ggml_backend_buffer_get_base(buffer) + ggml_backend_buffer_get_size(buffer));
tensor->buffer = buffer;
tensor->data = addr;
ggml_backend_buffer_init_tensor(buffer, tensor);
}
static struct ggml_tensor * graph_dup_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies,
struct ggml_context * ctx_allocated, struct ggml_context * ctx_unallocated, struct ggml_tensor * src) {
GGML_ASSERT(src != NULL);
GGML_ASSERT(src->data && "graph must be allocated");
size_t id = ggml_hash_insert(hash_set, src);
if (id == GGML_HASHTABLE_ALREADY_EXISTS) {
return node_copies[ggml_hash_find(hash_set, src)];
}
struct ggml_tensor * dst = ggml_dup_tensor_layout(src->data && !src->view_src ? ctx_allocated : ctx_unallocated, src);
if (src->view_src != NULL) {
dst->view_src = graph_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, src->view_src);
dst->view_offs = src->view_offs;
}
dst->op = src->op;
memcpy(dst->op_params, src->op_params, sizeof(dst->op_params));
ggml_set_name(dst, src->name);
// copy src
for (int i = 0; i < GGML_MAX_SRC; i++) {
struct ggml_tensor * s = src->src[i];
if (s == NULL) {
break;
}
dst->src[i] = graph_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, s);
}
node_copies[id] = dst;
return dst;
}
static void graph_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies, bool * node_init, struct ggml_tensor * src) {
size_t id = ggml_hash_find(hash_set, src);
if (node_init[id]) {
return;
}
node_init[id] = true;
struct ggml_tensor * dst = node_copies[id];
if (dst->view_src != NULL) {
graph_init_tensor(hash_set, node_copies, node_init, src->view_src);
ggml_backend_view_init(dst->view_src->buffer, dst);
}
else {
ggml_backend_tensor_copy(src, dst);
}
// init src
for (int i = 0; i < GGML_MAX_SRC; i++) {
struct ggml_tensor * s = src->src[i];
if (s == NULL) {
break;
}
graph_init_tensor(hash_set, node_copies, node_init, s);
}
}
struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) {
struct ggml_hash_set hash_set = {
/* .size = */ graph->visited_hash_table.size,
/* .keys = */ calloc(sizeof(hash_set.keys[0]) * graph->visited_hash_table.size, 1)
};
struct ggml_tensor ** node_copies = calloc(sizeof(node_copies[0]) * hash_set.size, 1);
bool * node_init = calloc(sizeof(node_init[0]) * hash_set.size, 1);
struct ggml_init_params params = {
/* .mem_size = */ ggml_tensor_overhead()*hash_set.size + ggml_graph_overhead_custom(graph->size, false),
/* .mem_buffer = */ NULL,
/* .no_alloc = */ true
};
struct ggml_context * ctx_allocated = ggml_init(params);
struct ggml_context * ctx_unallocated = ggml_init(params);
if (ctx_allocated == NULL || ctx_unallocated == NULL) {
fprintf(stderr, "failed to allocate context for graph copy\n");
free(hash_set.keys);
free(node_copies);
free(node_init);
ggml_free(ctx_allocated);
ggml_free(ctx_unallocated);
return (struct ggml_backend_graph_copy) {
/* .buffer = */ NULL,
/* .ctx_allocated = */ NULL,
/* .ctx_unallocated = */ NULL,
/* .graph = */ NULL,
};
}
// dup nodes
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
graph_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, node);
}
// allocate nodes
ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx_allocated, backend);
if (buffer == NULL) {
fprintf(stderr, "failed to allocate buffer for graph copy\n");
free(hash_set.keys);
free(node_copies);
free(node_init);
ggml_free(ctx_allocated);
ggml_free(ctx_unallocated);
return (struct ggml_backend_graph_copy) {
/* .buffer = */ NULL,
/* .ctx_allocated = */ NULL,
/* .ctx_unallocated = */ NULL,
/* .graph = */ NULL,
};
}
//printf("copy buffer size: %zu MB\n", ggml_backend_buffer_get_size(buffer) / 1024 / 1024);
// copy data and init views
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
graph_init_tensor(hash_set, node_copies, node_init, node);
}
// build graph copy
struct ggml_cgraph * graph_copy = ggml_new_graph_custom(ctx_allocated, graph->size, false);
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
struct ggml_tensor * node_copy = node_copies[ggml_hash_find(hash_set, node)];
graph_copy->nodes[i] = node_copy;
}
graph_copy->n_nodes = graph->n_nodes;
free(hash_set.keys);
free(node_copies);
free(node_init);
return (struct ggml_backend_graph_copy) {
/* .buffer = */ buffer,
/* .ctx_allocated = */ ctx_allocated,
/* .ctx_unallocated = */ ctx_unallocated,
/* .graph = */ graph_copy,
};
}
void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy) {
ggml_backend_buffer_free(copy.buffer);
ggml_free(copy.ctx_allocated);
ggml_free(copy.ctx_unallocated);
}
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) {
struct ggml_backend_graph_copy copy = ggml_backend_graph_copy(backend2, graph);
if (copy.buffer == NULL) {
return false;
}
struct ggml_cgraph * g1 = graph;
struct ggml_cgraph * g2 = copy.graph;
assert(g1->n_nodes == g2->n_nodes);
for (int i = 0; i < g1->n_nodes; i++) {
//printf("eval %d/%d\n", i, g1->n_nodes);
struct ggml_tensor * t1 = g1->nodes[i];
struct ggml_tensor * t2 = g2->nodes[i];
assert(t1->op == t2->op && ggml_are_same_layout(t1, t2));
struct ggml_cgraph g1v = ggml_graph_view(g1, i, i + 1);
struct ggml_cgraph g2v = ggml_graph_view(g2, i, i + 1);
ggml_backend_graph_compute(backend1, &g1v);
ggml_backend_graph_compute(backend2, &g2v);
if (ggml_is_view_op(t1->op)) {
continue;
}
// compare results, calculate rms etc
if (!callback(i, t1, t2, user_data)) {
break;
}
}
ggml_backend_graph_copy_free(copy);
return true;
}
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