mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-04 09:32:00 +00:00
2009 lines
76 KiB
C++
2009 lines
76 KiB
C++
// Note: porting this file to C++ is a work in progress
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#ifdef _WIN32
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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# define NOMINMAX
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#endif
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#include <windows.h>
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#endif
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#include "ggml-backend.h"
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#include "ggml-backend-impl.h"
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#include "ggml-alloc.h"
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#include "ggml-impl.h"
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#include <assert.h>
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#include <limits.h>
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#include <stdarg.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <string>
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#include <vector>
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#include <algorithm>
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#ifdef __APPLE__
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#include <sys/types.h>
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#include <sys/sysctl.h>
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#endif
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// backend buffer type
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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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if (size == 0) {
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// return a dummy buffer for zero-sized allocations
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return ggml_backend_buffer_init(buft, {}, NULL, 0);
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}
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return buft->iface.alloc_buffer(buft, size);
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}
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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_max_size(ggml_backend_buffer_type_t buft) {
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// get_max_size is optional, defaults to SIZE_MAX
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if (buft->iface.get_max_size) {
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return buft->iface.get_max_size(buft);
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}
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return SIZE_MAX;
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}
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size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, const 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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size_t size = buft->iface.get_alloc_size(buft, tensor);
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assert(size >= ggml_nbytes(tensor));
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return size;
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}
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return ggml_nbytes(tensor);
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}
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bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
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if (buft->iface.is_host) {
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return buft->iface.is_host(buft);
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}
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return false;
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}
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ggml_backend_dev_t ggml_backend_buft_get_device(ggml_backend_buffer_type_t buft) {
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return buft->device;
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}
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// backend buffer
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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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void * context,
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size_t size) {
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ggml_backend_buffer_t buffer = new ggml_backend_buffer {
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/* .interface = */ iface,
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/* .buft = */ buft,
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/* .context = */ context,
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/* .size = */ size,
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/* .usage = */ GGML_BACKEND_BUFFER_USAGE_ANY
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};
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return buffer;
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}
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const char * ggml_backend_buffer_name(ggml_backend_buffer_t buffer) {
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return ggml_backend_buft_name(ggml_backend_buffer_get_type(buffer));
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}
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void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
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if (buffer == NULL) {
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return;
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}
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if (buffer->iface.free_buffer != NULL) {
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buffer->iface.free_buffer(buffer);
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}
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delete buffer;
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}
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size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
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return buffer->size;
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}
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void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
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// get_base is optional if the buffer is zero-sized
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if (buffer->size == 0) {
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return NULL;
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}
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void * base = buffer->iface.get_base(buffer);
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GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL");
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return base;
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}
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enum ggml_status 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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return buffer->iface.init_tensor(buffer, tensor);
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}
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return GGML_STATUS_SUCCESS;
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}
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void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
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// clear is optional if the buffer is zero-sized
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if (buffer->size == 0) {
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return;
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}
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buffer->iface.clear(buffer, value);
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}
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size_t ggml_backend_buffer_get_alignment(ggml_backend_buffer_t buffer) {
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return ggml_backend_buft_get_alignment(ggml_backend_buffer_get_type(buffer));
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}
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size_t ggml_backend_buffer_get_max_size(ggml_backend_buffer_t buffer) {
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return ggml_backend_buft_get_max_size(ggml_backend_buffer_get_type(buffer));
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}
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size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor) {
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return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor);
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}
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bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) {
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return ggml_backend_buft_is_host(ggml_backend_buffer_get_type(buffer));
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}
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void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
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buffer->usage = usage;
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// FIXME: add a generic callback to the buffer interface
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if (ggml_backend_buffer_is_multi_buffer(buffer)) {
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ggml_backend_multi_buffer_set_usage(buffer, usage);
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}
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}
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enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage(ggml_backend_buffer_t buffer) {
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return buffer->usage;
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}
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ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) {
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return buffer->buft;
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}
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void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) {
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if (buffer->iface.reset) {
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buffer->iface.reset(buffer);
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}
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}
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bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst) {
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ggml_backend_buffer_t dst_buf = dst->view_src ? dst->view_src->buffer : dst->buffer;
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if (dst_buf->iface.cpy_tensor) {
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return dst_buf->iface.cpy_tensor(dst_buf, src, dst);
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}
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return false;
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}
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// backend
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ggml_guid_t ggml_backend_guid(ggml_backend_t backend) {
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if (backend == NULL) {
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return NULL;
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}
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return backend->guid;
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}
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const char * ggml_backend_name(ggml_backend_t backend) {
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if (backend == NULL) {
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return "NULL";
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}
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return backend->iface.get_name(backend);
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}
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void ggml_backend_free(ggml_backend_t backend) {
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if (backend == NULL) {
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return;
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}
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backend->iface.free(backend);
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}
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ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend) {
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return ggml_backend_dev_buffer_type(backend->device);
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}
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ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size) {
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return ggml_backend_buft_alloc_buffer(ggml_backend_get_default_buffer_type(backend), size);
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}
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size_t ggml_backend_get_alignment(ggml_backend_t backend) {
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return ggml_backend_buft_get_alignment(ggml_backend_get_default_buffer_type(backend));
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}
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size_t ggml_backend_get_max_size(ggml_backend_t backend) {
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return ggml_backend_buft_get_max_size(ggml_backend_get_default_buffer_type(backend));
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}
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void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
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GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
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GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
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if (backend->iface.set_tensor_async == NULL) {
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ggml_backend_tensor_set(tensor, data, offset, size);
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} else {
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backend->iface.set_tensor_async(backend, tensor, data, offset, size);
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}
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}
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void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
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GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
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GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
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if (backend->iface.get_tensor_async == NULL) {
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ggml_backend_tensor_get(tensor, data, offset, size);
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} else {
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backend->iface.get_tensor_async(backend, tensor, data, offset, size);
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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_ASSERT(tensor);
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ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
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if (size == 0) {
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return;
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}
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GGML_ASSERT(buf != NULL && "tensor buffer not set");
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GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
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GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
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buf->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_ASSERT(tensor);
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ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
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if (size == 0) {
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return;
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}
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GGML_ASSERT(buf != NULL && "tensor buffer not set");
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GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
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GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
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buf->iface.get_tensor(buf, tensor, data, offset, size);
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}
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void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, 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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if (size == 0) {
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return;
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}
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GGML_ASSERT(buf != NULL && "tensor buffer not set");
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GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
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GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
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GGML_ASSERT(buf->iface.memset_tensor != NULL && "memset not implemented by backend buffer");
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buf->iface.memset_tensor(buf, tensor, value, offset, size);
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}
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void ggml_backend_synchronize(ggml_backend_t backend) {
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if (backend->iface.synchronize == NULL) {
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return;
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}
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backend->iface.synchronize(backend);
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}
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ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
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GGML_ASSERT(backend->iface.graph_plan_create != NULL);
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return backend->iface.graph_plan_create(backend, cgraph);
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}
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void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
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GGML_ASSERT(backend->iface.graph_plan_free != NULL);
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backend->iface.graph_plan_free(backend, plan);
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}
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enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
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GGML_ASSERT(backend->iface.graph_plan_compute != NULL);
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return backend->iface.graph_plan_compute(backend, plan);
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}
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enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
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enum ggml_status err = ggml_backend_graph_compute_async(backend, cgraph);
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ggml_backend_synchronize(backend);
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return err;
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}
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enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
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return backend->iface.graph_compute(backend, cgraph);
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}
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bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
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return ggml_backend_dev_supports_op(backend->device, op);
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}
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bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
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return ggml_backend_dev_supports_buft(backend->device, buft);
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}
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bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) {
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return ggml_backend_dev_offload_op(backend->device, op);
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}
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ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend) {
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return backend->device;
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}
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// backend copy
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static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
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if (a->type != b->type) {
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return false;
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}
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for (int i = 0; i < GGML_MAX_DIMS; i++) {
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if (a->ne[i] != b->ne[i]) {
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return false;
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}
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if (a->nb[i] != b->nb[i]) {
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return false;
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}
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}
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return true;
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}
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void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) {
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GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
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if (src == dst) {
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return;
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}
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if (ggml_backend_buffer_is_host(src->buffer)) {
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ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src));
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} else if (ggml_backend_buffer_is_host(dst->buffer)) {
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ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
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} else if (!ggml_backend_buffer_copy_tensor(src, dst)) {
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#ifndef NDEBUG
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GGML_LOG_DEBUG("%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer));
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#endif
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size_t nbytes = ggml_nbytes(src);
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void * data = malloc(nbytes);
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ggml_backend_tensor_get(src, data, 0, nbytes);
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ggml_backend_tensor_set(dst, data, 0, nbytes);
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free(data);
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}
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}
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void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst) {
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GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
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if (src == dst) {
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return;
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}
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if (backend_dst->iface.cpy_tensor_async != NULL) {
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if (backend_dst->iface.cpy_tensor_async(backend_src, backend_dst, src, dst)) {
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return;
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}
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}
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// an async copy would normally happen after all the queued operations on both backends are completed
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// to simulate the same behavior, we need to synchronize both backends first, and do a blocking copy
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ggml_backend_synchronize(backend_src);
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ggml_backend_synchronize(backend_dst);
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ggml_backend_tensor_copy(src, dst);
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}
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// events
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ggml_backend_event_t ggml_backend_event_new(ggml_backend_dev_t device) {
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// null device is allowed for the transition period to the device interface
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if (device == NULL || device->iface.event_new == NULL) {
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return NULL;
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}
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return device->iface.event_new(device);
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}
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void ggml_backend_event_free(ggml_backend_event_t event) {
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if (event == NULL) {
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return;
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}
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event->device->iface.event_free(event->device, event);
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}
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void ggml_backend_event_record(ggml_backend_event_t event, ggml_backend_t backend) {
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GGML_ASSERT(backend->iface.event_record != NULL);
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backend->iface.event_record(backend, event);
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}
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void ggml_backend_event_synchronize(ggml_backend_event_t event) {
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GGML_ASSERT(event->device->iface.event_synchronize);
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event->device->iface.event_synchronize(event->device, event);
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}
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void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
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GGML_ASSERT(backend->iface.event_wait != NULL);
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backend->iface.event_wait(backend, event);
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}
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// Backend device
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const char * ggml_backend_dev_name(ggml_backend_dev_t device) {
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return device->iface.get_name(device);
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}
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const char * ggml_backend_dev_description(ggml_backend_dev_t device) {
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return device->iface.get_description(device);
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}
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void ggml_backend_dev_memory(ggml_backend_dev_t device, size_t * free, size_t * total) {
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device->iface.get_memory(device, free, total);
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}
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enum ggml_backend_dev_type ggml_backend_dev_type(ggml_backend_dev_t device) {
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return device->iface.get_type(device);
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}
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void ggml_backend_dev_get_props(ggml_backend_dev_t device, struct ggml_backend_dev_props * props) {
|
|
memset(props, 0, sizeof(*props));
|
|
device->iface.get_props(device, props);
|
|
}
|
|
|
|
ggml_backend_reg_t ggml_backend_dev_backend_reg(ggml_backend_dev_t device) {
|
|
return device->reg;
|
|
}
|
|
|
|
ggml_backend_t ggml_backend_dev_init(ggml_backend_dev_t device, const char * params) {
|
|
return device->iface.init_backend(device, params);
|
|
}
|
|
|
|
ggml_backend_buffer_type_t ggml_backend_dev_buffer_type(ggml_backend_dev_t device) {
|
|
return device->iface.get_buffer_type(device);
|
|
}
|
|
|
|
ggml_backend_buffer_type_t ggml_backend_dev_host_buffer_type(ggml_backend_dev_t device) {
|
|
if (device->iface.get_host_buffer_type == NULL) {
|
|
return NULL;
|
|
}
|
|
|
|
return device->iface.get_host_buffer_type(device);
|
|
}
|
|
|
|
ggml_backend_buffer_t ggml_backend_dev_buffer_from_host_ptr(ggml_backend_dev_t device, void * ptr, size_t size, size_t max_tensor_size) {
|
|
return device->iface.buffer_from_host_ptr(device, ptr, size, max_tensor_size);
|
|
}
|
|
|
|
bool ggml_backend_dev_supports_op(ggml_backend_dev_t device, const struct ggml_tensor * op) {
|
|
return device->iface.supports_op(device, op);
|
|
}
|
|
|
|
bool ggml_backend_dev_supports_buft(ggml_backend_dev_t device, ggml_backend_buffer_type_t buft) {
|
|
return device->iface.supports_buft(device, buft);
|
|
}
|
|
|
|
bool ggml_backend_dev_offload_op(ggml_backend_dev_t device, const struct ggml_tensor * op) {
|
|
if (device->iface.offload_op != NULL) {
|
|
return device->iface.offload_op(device, op);
|
|
}
|
|
|
|
return false;
|
|
}
|
|
|
|
// Backend (reg)
|
|
|
|
const char * ggml_backend_reg_name(ggml_backend_reg_t reg) {
|
|
return reg->iface.get_name(reg);
|
|
}
|
|
|
|
size_t ggml_backend_reg_dev_count(ggml_backend_reg_t reg) {
|
|
return reg->iface.get_device_count(reg);
|
|
}
|
|
|
|
ggml_backend_dev_t ggml_backend_reg_dev_get(ggml_backend_reg_t reg, size_t index) {
|
|
return reg->iface.get_device(reg, index);
|
|
}
|
|
|
|
void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * name) {
|
|
if (!reg->iface.get_proc_address) {
|
|
return NULL;
|
|
}
|
|
return reg->iface.get_proc_address(reg, name);
|
|
}
|
|
|
|
// multi-buffer buffer
|
|
|
|
struct ggml_backend_multi_buffer_context {
|
|
ggml_backend_buffer_t * buffers;
|
|
size_t n_buffers;
|
|
};
|
|
|
|
static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
|
ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
|
|
for (size_t i = 0; i < ctx->n_buffers; i++) {
|
|
ggml_backend_buffer_free(ctx->buffers[i]);
|
|
}
|
|
|
|
free(ctx->buffers);
|
|
free(ctx);
|
|
}
|
|
|
|
static void ggml_backend_multi_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
|
ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
|
|
for (size_t i = 0; i < ctx->n_buffers; i++) {
|
|
ggml_backend_buffer_clear(ctx->buffers[i], value);
|
|
}
|
|
}
|
|
|
|
static const struct ggml_backend_buffer_i ggml_backend_multi_buffer_i = {
|
|
/* .free_buffer = */ ggml_backend_multi_buffer_free_buffer,
|
|
/* .get_base = */ NULL,
|
|
/* .init_tensor = */ NULL,
|
|
/* .memset_tensor = */ NULL,
|
|
/* .set_tensor = */ NULL,
|
|
/* .get_tensor = */ NULL,
|
|
/* .cpy_tensor = */ NULL,
|
|
/* .clear = */ ggml_backend_multi_buffer_clear,
|
|
/* .reset = */ NULL,
|
|
};
|
|
|
|
ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers) {
|
|
ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) malloc(sizeof(struct ggml_backend_multi_buffer_context));
|
|
ctx->n_buffers = n_buffers;
|
|
ctx->buffers = (ggml_backend_buffer_t *) malloc(n_buffers * sizeof(ggml_backend_buffer_t));
|
|
|
|
GGML_ASSERT(ctx->buffers != NULL);
|
|
|
|
size_t total_size = 0;
|
|
for (size_t i = 0; i < n_buffers; i++) {
|
|
ctx->buffers[i] = buffers[i];
|
|
total_size += ggml_backend_buffer_get_size(buffers[i]);
|
|
}
|
|
|
|
return ggml_backend_buffer_init(buffers[0]->buft, ggml_backend_multi_buffer_i, ctx, total_size);
|
|
}
|
|
|
|
bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer) {
|
|
return buffer->iface.free_buffer == ggml_backend_multi_buffer_free_buffer;
|
|
}
|
|
|
|
void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
|
|
GGML_ASSERT(ggml_backend_buffer_is_multi_buffer(buffer));
|
|
ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
|
|
for (size_t i = 0; i < ctx->n_buffers; i++) {
|
|
ggml_backend_buffer_set_usage(ctx->buffers[i], usage);
|
|
}
|
|
}
|
|
|
|
// 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;
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
// scheduler
|
|
|
|
#ifndef GGML_SCHED_MAX_BACKENDS
|
|
#define GGML_SCHED_MAX_BACKENDS 16
|
|
#endif
|
|
|
|
#ifndef GGML_SCHED_MAX_SPLIT_INPUTS
|
|
#define GGML_SCHED_MAX_SPLIT_INPUTS GGML_MAX_SRC
|
|
#endif
|
|
|
|
#ifndef GGML_SCHED_MAX_COPIES
|
|
#define GGML_SCHED_MAX_COPIES 4
|
|
#endif
|
|
|
|
struct ggml_backend_sched_split {
|
|
int backend_id;
|
|
int i_start;
|
|
int i_end;
|
|
struct ggml_tensor * inputs[GGML_SCHED_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
|
|
bool is_alloc;
|
|
|
|
int n_backends;
|
|
|
|
ggml_backend_t backends[GGML_SCHED_MAX_BACKENDS];
|
|
ggml_backend_buffer_type_t bufts[GGML_SCHED_MAX_BACKENDS];
|
|
ggml_gallocr_t galloc;
|
|
|
|
// hash map of the nodes in the graph
|
|
struct ggml_hash_set hash_set;
|
|
int * hv_tensor_backend_ids; // [hash_set.size]
|
|
struct ggml_tensor ** hv_tensor_copies; // [hash_set.size][n_backends][n_copies]
|
|
|
|
int * node_backend_ids; // [graph_size]
|
|
int * leaf_backend_ids; // [graph_size]
|
|
|
|
int * prev_node_backend_ids; // [graph_size]
|
|
int * prev_leaf_backend_ids; // [graph_size]
|
|
|
|
// copy of the graph with modified inputs
|
|
struct ggml_cgraph graph;
|
|
|
|
// graph splits
|
|
struct ggml_backend_sched_split * splits;
|
|
int n_splits;
|
|
int splits_capacity;
|
|
|
|
// pipeline parallelism support
|
|
int n_copies;
|
|
int cur_copy;
|
|
ggml_backend_event_t events[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
|
|
struct ggml_tensor * graph_inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
|
|
int n_graph_inputs;
|
|
|
|
struct ggml_context * ctx;
|
|
|
|
ggml_backend_sched_eval_callback callback_eval;
|
|
void * callback_eval_user_data;
|
|
|
|
char * context_buffer;
|
|
size_t context_buffer_size;
|
|
|
|
bool op_offload;
|
|
|
|
int debug;
|
|
};
|
|
|
|
#define hash_id(tensor) ggml_hash_find_or_insert(&sched->hash_set, tensor)
|
|
#define tensor_backend_id(tensor) sched->hv_tensor_backend_ids[hash_id(tensor)]
|
|
#define tensor_id_copy(id, backend_id, copy_id) sched->hv_tensor_copies[(id) * sched->n_backends * sched->n_copies + (backend_id) * sched->n_copies + (copy_id)]
|
|
#define tensor_copy(tensor, backend_id, copy_id) tensor_id_copy(hash_id(tensor), backend_id, copy_id)
|
|
|
|
// returns the priority of the backend, lower id is higher priority
|
|
static int ggml_backend_sched_backend_id(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 -1;
|
|
}
|
|
|
|
static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor, const struct ggml_tensor * op) {
|
|
ggml_backend_buffer_t buffer = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
|
|
if (buffer == NULL) {
|
|
return -1;
|
|
}
|
|
|
|
// find highest prio backend that supports the buffer type and the op
|
|
for (int i = 0; i < sched->n_backends; i++) {
|
|
if (ggml_backend_supports_buft(sched->backends[i], buffer->buft) &&
|
|
ggml_backend_supports_op(sched->backends[i], op)) {
|
|
return i;
|
|
}
|
|
}
|
|
|
|
#ifndef NDEBUG
|
|
GGML_LOG_DEBUG("%s: warning: no backend supports op %s with a weight with buffer type %s used in tensor %s, the weight will need to be copied\n",
|
|
__func__, ggml_op_desc(tensor), ggml_backend_buffer_name(buffer), tensor->name);
|
|
#endif
|
|
|
|
return -1;
|
|
}
|
|
|
|
#if 0
|
|
#define GGML_SCHED_MAX_SPLITS_DEBUG 4096
|
|
static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS_DEBUG*GGML_SCHED_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 int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * tensor) {
|
|
// assign pre-allocated nodes to their backend
|
|
int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor, tensor);
|
|
if (cur_backend_id != -1) {
|
|
SET_CAUSE(tensor, "1.dst");
|
|
return cur_backend_id;
|
|
}
|
|
|
|
// view_src
|
|
if (tensor->view_src != NULL) {
|
|
cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src, tensor);
|
|
if (cur_backend_id != -1) {
|
|
SET_CAUSE(tensor, "1.vsrc");
|
|
return cur_backend_id;
|
|
}
|
|
}
|
|
|
|
if (tensor->buffer || (tensor->view_src && tensor->view_src->buffer)) {
|
|
// since the tensor is pre-allocated, it cannot be moved to another backend
|
|
ggml_backend_buffer_t buffer = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
|
|
GGML_ABORT("pre-allocated tensor (%s) in a buffer (%s) that cannot run the operation (%s)", tensor->name, ggml_backend_buffer_name(buffer), ggml_op_name(tensor->op));
|
|
}
|
|
|
|
// graph input
|
|
if (tensor->flags & GGML_TENSOR_FLAG_INPUT) {
|
|
cur_backend_id = sched->n_backends - 1; // last backend (assumed CPU)
|
|
SET_CAUSE(tensor, "1.inp");
|
|
return cur_backend_id;
|
|
}
|
|
|
|
// operations with weights are preferably run on the same backend as the weights
|
|
for (int i = 0; i < GGML_MAX_SRC; i++) {
|
|
const struct ggml_tensor * src = tensor->src[i];
|
|
if (src == NULL) {
|
|
continue;
|
|
}
|
|
// skip ROPE since the rope freqs tensor is too small to choose a backend based on it
|
|
// not an ideal solution
|
|
if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
|
|
int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor);
|
|
// check if a backend with higher prio wants to offload the op
|
|
if (sched->op_offload && src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) {
|
|
for (int b = 0; b < src_backend_id; b++) {
|
|
if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) {
|
|
SET_CAUSE(tensor, "1.off");
|
|
return b;
|
|
}
|
|
}
|
|
}
|
|
SET_CAUSE(tensor, "1.wgt%d", i);
|
|
return src_backend_id;
|
|
}
|
|
}
|
|
|
|
return -1;
|
|
}
|
|
|
|
static char * fmt_size(size_t size) {
|
|
static char buffer[128];
|
|
if (size >= 1024*1024) {
|
|
snprintf(buffer, sizeof(buffer), "%zuM", size/1024/1024);
|
|
} else {
|
|
snprintf(buffer, sizeof(buffer), "%zuK", size/1024);
|
|
}
|
|
return buffer;
|
|
}
|
|
|
|
static void ggml_backend_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 = sched->backends[sched->splits[cur_split].backend_id];
|
|
GGML_LOG_DEBUG("\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++) {
|
|
if (j == 0) {
|
|
GGML_LOG_DEBUG(": ");
|
|
}
|
|
GGML_LOG_DEBUG("[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name,
|
|
fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j])));
|
|
}
|
|
GGML_LOG_DEBUG("\n");
|
|
cur_split++;
|
|
}
|
|
struct ggml_tensor * node = graph->nodes[i];
|
|
if (ggml_is_view_op(node->op)) {
|
|
continue;
|
|
}
|
|
if (sched->debug > 1) {
|
|
ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
|
|
GGML_LOG_DEBUG("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)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node));
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
struct ggml_tensor * src = node->src[j];
|
|
if (src == NULL) {
|
|
continue;
|
|
}
|
|
ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
|
|
GGML_LOG_DEBUG(" %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));
|
|
}
|
|
GGML_LOG_DEBUG("\n");
|
|
}
|
|
}
|
|
}
|
|
|
|
static bool ggml_backend_sched_buffer_supported(ggml_backend_sched_t sched, struct ggml_tensor * t, int backend_id) {
|
|
ggml_backend_buffer_t buf = t->view_src ? t->view_src->buffer : t->buffer;
|
|
ggml_backend_buffer_type_t buft = NULL;
|
|
|
|
if (buf) {
|
|
// the tensor is already allocated
|
|
buft = buf->buft;
|
|
} else {
|
|
// see if the tensor already has a backend assigned, and use the buffer type of that backend
|
|
int tensor_backend_id = tensor_backend_id(t);
|
|
if (tensor_backend_id == -1 && t->view_src) {
|
|
tensor_backend_id = tensor_backend_id(t->view_src);
|
|
}
|
|
if (tensor_backend_id != -1) {
|
|
buft = sched->bufts[tensor_backend_id];
|
|
}
|
|
}
|
|
|
|
return buft != NULL && ggml_backend_supports_buft(sched->backends[backend_id], buft);
|
|
}
|
|
|
|
static void ggml_backend_sched_set_if_supported(ggml_backend_sched_t sched, struct ggml_tensor * node, int cur_backend_id, int * node_backend_id) {
|
|
if (ggml_backend_supports_op(sched->backends[cur_backend_id], node)) {
|
|
*node_backend_id = cur_backend_id;
|
|
SET_CAUSE(node, "2.sup");
|
|
}
|
|
}
|
|
|
|
// assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend
|
|
static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
|
|
// reset splits
|
|
sched->n_splits = 0;
|
|
sched->n_graph_inputs = 0;
|
|
sched->is_reset = false;
|
|
|
|
struct ggml_init_params params = {
|
|
/* .mem_size = */ sched->context_buffer_size,
|
|
/* .mem_buffer = */ sched->context_buffer,
|
|
/* .no_alloc = */ true
|
|
};
|
|
|
|
ggml_free(sched->ctx);
|
|
|
|
sched->ctx = ggml_init(params);
|
|
if (sched->ctx == NULL) {
|
|
GGML_ABORT("%s: failed to initialize context\n", __func__);
|
|
}
|
|
|
|
// 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];
|
|
int * leaf_backend_id = &tensor_backend_id(leaf);
|
|
// do not overwrite user assignments
|
|
if (*leaf_backend_id == -1) {
|
|
*leaf_backend_id = ggml_backend_sched_backend_id_from_cur(sched, leaf);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < graph->n_nodes; i++) {
|
|
struct ggml_tensor * node = graph->nodes[i];
|
|
int * node_backend_id = &tensor_backend_id(node);
|
|
// do not overwrite user assignments
|
|
if (*node_backend_id == -1) {
|
|
*node_backend_id = ggml_backend_sched_backend_id_from_cur(sched, node);
|
|
|
|
#if 0
|
|
// src
|
|
if (node->op == GGML_OP_NONE) {
|
|
continue;
|
|
}
|
|
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
struct ggml_tensor * src = node->src[j];
|
|
if (src == NULL) {
|
|
continue;
|
|
}
|
|
int * src_backend_id = &tensor_backend_id(src);
|
|
if (*src_backend_id == -1) {
|
|
*src_backend_id = ggml_backend_sched_backend_id_from_cur(sched, src);
|
|
}
|
|
}
|
|
#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
|
|
// ops unsupported by the backend being expanded will be left unassigned so that they can be assigned later when the locations of its inputs are known
|
|
// expand gpu down
|
|
{
|
|
int cur_backend_id = -1;
|
|
for (int i = 0; i < graph->n_nodes; i++) {
|
|
struct ggml_tensor * node = graph->nodes[i];
|
|
if (ggml_is_view_op(node->op)) {
|
|
continue;
|
|
}
|
|
int * node_backend_id = &tensor_backend_id(node);
|
|
if (*node_backend_id != -1) {
|
|
if (*node_backend_id == sched->n_backends - 1) {
|
|
// skip cpu (lowest prio backend)
|
|
cur_backend_id = -1;
|
|
} else {
|
|
cur_backend_id = *node_backend_id;
|
|
}
|
|
} else if (cur_backend_id != -1) {
|
|
ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
|
|
}
|
|
}
|
|
}
|
|
// expand gpu up
|
|
{
|
|
int cur_backend_id = -1;
|
|
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;
|
|
}
|
|
int * node_backend_id = &tensor_backend_id(node);
|
|
if (*node_backend_id != -1) {
|
|
if (*node_backend_id == sched->n_backends - 1) {
|
|
// skip cpu (lowest prio backend)
|
|
cur_backend_id = -1;
|
|
} else {
|
|
cur_backend_id = *node_backend_id;
|
|
}
|
|
} else if (cur_backend_id != -1) {
|
|
ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
|
|
}
|
|
}
|
|
}
|
|
// expand rest down
|
|
{
|
|
int cur_backend_id = -1;
|
|
for (int i = 0; i < graph->n_nodes; i++) {
|
|
struct ggml_tensor * node = graph->nodes[i];
|
|
if (ggml_is_view_op(node->op)) {
|
|
continue;
|
|
}
|
|
int * node_backend_id = &tensor_backend_id(node);
|
|
if (*node_backend_id != -1) {
|
|
cur_backend_id = *node_backend_id;
|
|
} else if (cur_backend_id != -1) {
|
|
ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
|
|
}
|
|
}
|
|
}
|
|
// expand rest up
|
|
{
|
|
int cur_backend_id = -1;
|
|
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;
|
|
}
|
|
int * node_backend_id = &tensor_backend_id(node);
|
|
if (*node_backend_id != -1) {
|
|
cur_backend_id = *node_backend_id;
|
|
} else if (cur_backend_id != -1) {
|
|
ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id);
|
|
}
|
|
}
|
|
}
|
|
|
|
// pass 3: upgrade nodes to higher prio backends with compatible buffer types
|
|
// if the tensor is already in the same buffer type (*) as another higher priority backend, we should move it there
|
|
// however, we also need to verify that the sources are in compatible buffer types
|
|
// (*) the actual requirement is more relaxed, the buffer type of the backend should be supported by all the users of this tensor further down the graph
|
|
// however, this is slow to verify, so we have a more strict requirement that the buffer type is the same
|
|
// this is not uncommon since multiple backends can use host memory, with the same buffer type (eg. BLAS and CPU)
|
|
// additionally, set remaining unassigned nodes to the backend with the most supported inputs
|
|
// only nodes that could not be assigned during expansion due to the backend not supporting the op should be unassigned at this point
|
|
for (int i = 0; i < graph->n_nodes; i++) {
|
|
struct ggml_tensor * node = graph->nodes[i];
|
|
if (ggml_is_view_op(node->op)) {
|
|
continue;
|
|
}
|
|
int * node_backend_id = &tensor_backend_id(node);
|
|
if (*node_backend_id == -1) {
|
|
// unassigned node: find the backend with the most supported inputs
|
|
int n_supported_best = -1;
|
|
for (int b = 0; b < sched->n_backends; b++) {
|
|
if (ggml_backend_supports_op(sched->backends[b], node)) {
|
|
int n_supported = 0;
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
struct ggml_tensor * src = node->src[j];
|
|
if (src == NULL) {
|
|
continue;
|
|
}
|
|
if ((tensor_backend_id(src) != -1 || tensor_backend_id(src->view_src) != -1) && ggml_backend_sched_buffer_supported(sched, src, b)) {
|
|
n_supported++;
|
|
}
|
|
}
|
|
if (n_supported > n_supported_best) {
|
|
n_supported_best = n_supported;
|
|
*node_backend_id = b;
|
|
SET_CAUSE(node, "3.best");
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
// assigned node: upgrade to higher prio backend if possible
|
|
for (int b = 0; b < *node_backend_id; b++) {
|
|
if (sched->bufts[b] == sched->bufts[*node_backend_id] && ggml_backend_supports_op(sched->backends[b], node)) {
|
|
bool supported = true;
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
struct ggml_tensor * src = node->src[j];
|
|
if (src == NULL) {
|
|
continue;
|
|
}
|
|
if (!ggml_backend_sched_buffer_supported(sched, src, b)) {
|
|
supported = false;
|
|
break;
|
|
}
|
|
}
|
|
if (supported) {
|
|
*node_backend_id = b;
|
|
SET_CAUSE(node, "3.upg");
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// pass 4: 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];
|
|
int * cur_backend_id = &tensor_backend_id(node);
|
|
if (node->view_src != NULL && *cur_backend_id == -1) {
|
|
*cur_backend_id = tensor_backend_id(node->view_src);
|
|
SET_CAUSE(node, "4.vsrc");
|
|
}
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
struct ggml_tensor * src = node->src[j];
|
|
if (src == NULL) {
|
|
continue;
|
|
}
|
|
int * src_backend_id = &tensor_backend_id(src);
|
|
if (*src_backend_id == -1) {
|
|
if (src->view_src != NULL) {
|
|
// views are always on the same backend as the source
|
|
*src_backend_id = tensor_backend_id(src->view_src);
|
|
SET_CAUSE(src, "4.vsrc");
|
|
} else {
|
|
*src_backend_id = *cur_backend_id;
|
|
SET_CAUSE(src, "4.cur");
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// pass 5: split graph, find tensors that need to be copied
|
|
{
|
|
int i_split = 0;
|
|
struct ggml_backend_sched_split * split = &sched->splits[0];
|
|
// find the backend of the first split, skipping view ops
|
|
int i = 0;
|
|
for (; i < graph->n_nodes; i++) {
|
|
struct ggml_tensor * node = graph->nodes[i];
|
|
if (!ggml_is_view_op(node->op)) {
|
|
split->backend_id = tensor_backend_id(node);
|
|
break;
|
|
}
|
|
}
|
|
split->i_start = 0;
|
|
split->n_inputs = 0;
|
|
int cur_backend_id = split->backend_id;
|
|
for (; i < graph->n_nodes; i++) {
|
|
struct ggml_tensor * node = graph->nodes[i];
|
|
|
|
if (ggml_is_view_op(node->op)) {
|
|
continue;
|
|
}
|
|
|
|
const int node_backend_id = tensor_backend_id(node);
|
|
|
|
assert(node_backend_id != -1); // all nodes should be assigned by now
|
|
|
|
// check if we should start a new split based on the sources of the current node
|
|
bool need_new_split = false;
|
|
if (node_backend_id == cur_backend_id && split->n_inputs > 0) {
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
struct ggml_tensor * src = node->src[j];
|
|
if (src == NULL) {
|
|
continue;
|
|
}
|
|
// check if a weight is on a different and incompatible backend
|
|
// by starting a new split, the memory of the previously offloaded weights can be reused
|
|
if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
|
|
int src_backend_id = tensor_backend_id(src);
|
|
if (src_backend_id != cur_backend_id && !ggml_backend_sched_buffer_supported(sched, src, cur_backend_id)) {
|
|
need_new_split = true;
|
|
break;
|
|
}
|
|
}
|
|
// check if the split has too many inputs
|
|
// FIXME: count the number of inputs instead of only checking when full
|
|
if (split->n_inputs == GGML_SCHED_MAX_SPLIT_INPUTS) {
|
|
const size_t id = hash_id(src);
|
|
int src_backend_id = sched->hv_tensor_backend_ids[id];
|
|
bool supported = ggml_backend_sched_buffer_supported(sched, src, cur_backend_id);
|
|
if (src_backend_id != cur_backend_id && tensor_id_copy(id, cur_backend_id, 0) == NULL && !supported) {
|
|
need_new_split = true;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (node_backend_id != cur_backend_id || need_new_split) {
|
|
split->i_end = i;
|
|
i_split++;
|
|
if (i_split >= sched->splits_capacity) {
|
|
sched->splits_capacity *= 2;
|
|
sched->splits = (ggml_backend_sched_split *)
|
|
realloc(sched->splits, sched->splits_capacity * sizeof(struct ggml_backend_sched_split));
|
|
GGML_ASSERT(sched->splits != NULL);
|
|
}
|
|
split = &sched->splits[i_split];
|
|
split->backend_id = node_backend_id;
|
|
split->i_start = i;
|
|
split->n_inputs = 0;
|
|
cur_backend_id = node_backend_id;
|
|
}
|
|
|
|
// 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) {
|
|
continue;
|
|
}
|
|
|
|
size_t src_id = hash_id(src);
|
|
const int src_backend_id = sched->hv_tensor_backend_ids[src_id];
|
|
assert(src_backend_id != -1); // all inputs should be assigned by now
|
|
|
|
if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) {
|
|
if (tensor_id_copy(src_id, src_backend_id, 0) == NULL) {
|
|
ggml_backend_t backend = sched->backends[src_backend_id];
|
|
for (int c = 0; c < sched->n_copies; c++) {
|
|
struct ggml_tensor * tensor_copy;
|
|
if (c == sched->cur_copy) {
|
|
tensor_copy = src; // use the original tensor as the current copy
|
|
} else {
|
|
tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
|
|
ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
|
|
}
|
|
if (sched->n_copies > 1) {
|
|
ggml_set_input(tensor_copy);
|
|
ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
|
|
}
|
|
tensor_id_copy(src_id, src_backend_id, c) = tensor_copy;
|
|
SET_CAUSE(tensor_copy, "4.cpy");
|
|
}
|
|
int n_graph_inputs = sched->n_graph_inputs++;
|
|
GGML_ASSERT(n_graph_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
|
|
sched->graph_inputs[n_graph_inputs] = src;
|
|
}
|
|
}
|
|
|
|
if (src_backend_id != cur_backend_id && !ggml_backend_sched_buffer_supported(sched, src, cur_backend_id)) {
|
|
// create a copy of the input in the split's backend
|
|
if (tensor_id_copy(src_id, cur_backend_id, 0) == NULL) {
|
|
ggml_backend_t backend = sched->backends[cur_backend_id];
|
|
for (int c = 0; c < sched->n_copies; c++) {
|
|
struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
|
|
ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
|
|
if (sched->n_copies > 1) {
|
|
ggml_set_input(tensor_copy);
|
|
ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
|
|
}
|
|
tensor_id_copy(src_id, cur_backend_id, c) = tensor_copy;
|
|
SET_CAUSE(tensor_copy, "4.cpy");
|
|
}
|
|
int n_inputs = split->n_inputs++;
|
|
GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
|
|
split->inputs[n_inputs] = src;
|
|
}
|
|
node->src[j] = tensor_id_copy(src_id, cur_backend_id, sched->cur_copy);
|
|
}
|
|
}
|
|
}
|
|
split->i_end = graph->n_nodes;
|
|
sched->n_splits = i_split + 1;
|
|
}
|
|
|
|
if (sched->debug) {
|
|
ggml_backend_sched_print_assignments(sched, graph);
|
|
}
|
|
|
|
// swap node_backend_ids and leaf _backend_ids with prevs
|
|
{
|
|
int * tmp = sched->node_backend_ids;
|
|
sched->node_backend_ids = sched->prev_node_backend_ids;
|
|
sched->prev_node_backend_ids = tmp;
|
|
|
|
tmp = sched->leaf_backend_ids;
|
|
sched->leaf_backend_ids = sched->prev_leaf_backend_ids;
|
|
sched->prev_leaf_backend_ids = tmp;
|
|
}
|
|
|
|
int graph_size = std::max(graph->n_nodes, graph->n_leafs) + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2*sched->n_copies;
|
|
if (sched->graph.size < graph_size) {
|
|
sched->graph.size = graph_size;
|
|
sched->graph.nodes = (ggml_tensor **) realloc(sched->graph.nodes, graph_size * sizeof(struct ggml_tensor *));
|
|
sched->graph.leafs = (ggml_tensor **) realloc(sched->graph.leafs, graph_size * sizeof(struct ggml_tensor *));
|
|
GGML_ASSERT(sched->graph.nodes != NULL);
|
|
GGML_ASSERT(sched->graph.leafs != NULL);
|
|
}
|
|
sched->graph.n_nodes = 0;
|
|
sched->graph.n_leafs = 0;
|
|
|
|
struct ggml_cgraph * graph_copy = &sched->graph;
|
|
|
|
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++) {
|
|
assert(graph_copy->size > (graph_copy->n_nodes + 1));
|
|
|
|
struct ggml_tensor * input = split->inputs[j];
|
|
const size_t input_id = hash_id(input);
|
|
struct ggml_tensor * input_cpy = tensor_id_copy(input_id, split->backend_id, sched->cur_copy);
|
|
|
|
// add a dependency to the input source so that it is not freed before the copy is done
|
|
struct ggml_tensor * input_dep = ggml_view_tensor(sched->ctx, input);
|
|
input_dep->src[0] = input;
|
|
sched->node_backend_ids[graph_copy->n_nodes] = sched->hv_tensor_backend_ids[input_id];
|
|
graph_copy->nodes[graph_copy->n_nodes++] = input_dep;
|
|
|
|
// add a dependency to the input copy so that it is allocated at the start of the split
|
|
sched->node_backend_ids[graph_copy->n_nodes] = split->backend_id;
|
|
graph_copy->nodes[graph_copy->n_nodes++] = input_cpy;
|
|
}
|
|
|
|
for (int j = split->i_start; j < split->i_end; j++) {
|
|
assert(graph_copy->size > graph_copy->n_nodes);
|
|
sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(graph->nodes[j]);
|
|
graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j];
|
|
}
|
|
}
|
|
|
|
if (sched->n_copies > 1) {
|
|
// add input copies as leafs so that they are allocated first
|
|
for (int i = 0; i < sched->n_graph_inputs; i++) {
|
|
struct ggml_tensor * input = sched->graph_inputs[i];
|
|
size_t id = hash_id(input);
|
|
int backend_id = tensor_backend_id(input);
|
|
for (int c = 0; c < sched->n_copies; c++) {
|
|
struct ggml_tensor * input_cpy = tensor_id_copy(id, backend_id, c);
|
|
sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
|
|
assert(graph_copy->size > graph_copy->n_leafs);
|
|
graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < sched->n_splits; i++) {
|
|
struct ggml_backend_sched_split * split = &sched->splits[i];
|
|
int backend_id = split->backend_id;
|
|
for (int j = 0; j < split->n_inputs; j++) {
|
|
struct ggml_tensor * input = split->inputs[j];
|
|
size_t id = hash_id(input);
|
|
for (int c = 0; c < sched->n_copies; c++) {
|
|
struct ggml_tensor * input_cpy = tensor_id_copy(id, backend_id, c);
|
|
sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
|
|
assert(graph_copy->size > graph_copy->n_leafs);
|
|
graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// add leafs from the original graph
|
|
for (int i = 0; i < graph->n_leafs; i++) {
|
|
struct ggml_tensor * leaf = graph->leafs[i];
|
|
sched->leaf_backend_ids[graph_copy->n_leafs] = tensor_backend_id(leaf);
|
|
assert(graph_copy->size > graph_copy->n_leafs);
|
|
graph_copy->leafs[graph_copy->n_leafs++] = leaf;
|
|
}
|
|
}
|
|
|
|
static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
|
|
bool backend_ids_changed = false;
|
|
for (int i = 0; i < sched->graph.n_nodes; i++) {
|
|
if (sched->node_backend_ids[i] != sched->prev_node_backend_ids[i] &&
|
|
sched->bufts[sched->node_backend_ids[i]] != sched->bufts[sched->prev_node_backend_ids[i]]) {
|
|
backend_ids_changed = true;
|
|
break;
|
|
}
|
|
}
|
|
if (!backend_ids_changed) {
|
|
for (int i = 0; i < sched->graph.n_leafs; i++) {
|
|
if (sched->leaf_backend_ids[i] != sched->prev_leaf_backend_ids[i] &&
|
|
sched->bufts[sched->leaf_backend_ids[i]] != sched->bufts[sched->prev_leaf_backend_ids[i]]) {
|
|
backend_ids_changed = true;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
// allocate graph
|
|
if (backend_ids_changed || !ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
|
|
// the re-allocation may cause the split inputs to be moved to a different address
|
|
ggml_backend_sched_synchronize(sched);
|
|
#ifndef NDEBUG
|
|
GGML_LOG_DEBUG("%s: failed to allocate graph, reserving (backend_ids_changed = %d)\n", __func__, backend_ids_changed);
|
|
#endif
|
|
ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
|
|
if (!ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
|
|
GGML_LOG_ERROR("%s: failed to allocate graph\n", __func__);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
|
|
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];
|
|
int split_backend_id = split->backend_id;
|
|
ggml_backend_t split_backend = sched->backends[split_backend_id];
|
|
|
|
// copy the input tensors to the split backend
|
|
for (int j = 0; j < split->n_inputs; j++) {
|
|
ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[j]);
|
|
struct ggml_tensor * input = split->inputs[j];
|
|
struct ggml_tensor * input_cpy = tensor_copy(input, split_backend_id, sched->cur_copy);
|
|
|
|
if (input->flags & GGML_TENSOR_FLAG_INPUT) {
|
|
// inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
|
|
if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
|
|
ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
|
|
} else {
|
|
ggml_backend_synchronize(split_backend);
|
|
}
|
|
ggml_backend_tensor_copy(input, input_cpy);
|
|
} else {
|
|
// wait for the split backend to finish using the input before overwriting it
|
|
if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
|
|
ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]);
|
|
} else {
|
|
ggml_backend_synchronize(split_backend);
|
|
}
|
|
// try async copy, but if not possible, we can still use a sync copy without synchronizing the dst backend, since we handle the synchronization here with multiple copies and events
|
|
// TODO: add public function to facilitate this, since applications do not have direct access to the backend interface
|
|
if (!split_backend->iface.cpy_tensor_async || !split_backend->iface.cpy_tensor_async(input_backend, split_backend, input, input_cpy)) {
|
|
ggml_backend_synchronize(input_backend);
|
|
if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
|
|
ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
|
|
} else {
|
|
ggml_backend_synchronize(split_backend);
|
|
}
|
|
ggml_backend_tensor_copy(input, input_cpy);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (!sched->callback_eval) {
|
|
enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
|
|
if (ec != GGML_STATUS_SUCCESS) {
|
|
return ec;
|
|
}
|
|
} 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);
|
|
|
|
enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &gv);
|
|
if (ec != GGML_STATUS_SUCCESS) {
|
|
return ec;
|
|
}
|
|
|
|
// TODO: pass backend to the callback, then the user can decide if they want to synchronize
|
|
ggml_backend_synchronize(split_backend);
|
|
|
|
if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) {
|
|
break;
|
|
}
|
|
|
|
j0 = j1;
|
|
}
|
|
}
|
|
|
|
// record the event of this copy
|
|
if (split->n_inputs > 0) {
|
|
if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
|
|
ggml_backend_event_record(sched->events[split_backend_id][sched->cur_copy], split_backend);
|
|
}
|
|
}
|
|
}
|
|
|
|
sched->cur_copy = (sched->cur_copy + 1) % sched->n_copies;
|
|
|
|
return GGML_STATUS_SUCCESS;
|
|
}
|
|
|
|
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,
|
|
bool parallel,
|
|
bool op_offload) {
|
|
GGML_ASSERT(n_backends > 0);
|
|
GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS);
|
|
GGML_ASSERT(ggml_backend_dev_type(ggml_backend_get_device(backends[n_backends - 1])) == GGML_BACKEND_DEVICE_TYPE_CPU);
|
|
|
|
struct ggml_backend_sched * sched = (ggml_backend_sched *) calloc(1, sizeof(struct ggml_backend_sched));
|
|
|
|
const char * GGML_SCHED_DEBUG = getenv("GGML_SCHED_DEBUG");
|
|
sched->debug = GGML_SCHED_DEBUG ? atoi(GGML_SCHED_DEBUG) : 0;
|
|
sched->n_backends = n_backends;
|
|
sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;
|
|
|
|
// initialize hash table
|
|
// FIXME: needs to be size*2 to account for leafs (do it in graph_split instead)
|
|
sched->hash_set = ggml_hash_set_new(graph_size);
|
|
sched->hv_tensor_backend_ids = (int *) malloc(sched->hash_set.size * sizeof(sched->hv_tensor_backend_ids[0]));
|
|
sched->hv_tensor_copies = (ggml_tensor **) malloc(sched->hash_set.size * sched->n_backends * sched->n_copies * sizeof(struct ggml_tensor *));
|
|
|
|
const size_t ggml_sched_max_splits = graph_size; // at most there is one split for each node in the graph
|
|
const size_t nodes_size = graph_size + ggml_sched_max_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2;
|
|
sched->node_backend_ids = (int *) calloc(nodes_size, sizeof(sched->node_backend_ids[0]));
|
|
sched->leaf_backend_ids = (int *) calloc(nodes_size, sizeof(sched->leaf_backend_ids[0]));
|
|
sched->prev_node_backend_ids = (int *) calloc(nodes_size, sizeof(sched->prev_node_backend_ids[0]));
|
|
sched->prev_leaf_backend_ids = (int *) calloc(nodes_size, sizeof(sched->prev_leaf_backend_ids[0]));
|
|
|
|
sched->context_buffer_size = ggml_sched_max_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + ggml_graph_overhead_custom(graph_size, false);
|
|
sched->context_buffer = (char *) malloc(sched->context_buffer_size);
|
|
|
|
const int initial_splits_capacity = 16;
|
|
sched->splits = (ggml_backend_sched_split *) calloc(initial_splits_capacity, sizeof(sched->splits[0]));
|
|
sched->splits_capacity = initial_splits_capacity;
|
|
|
|
for (int b = 0; b < n_backends; b++) {
|
|
sched->backends[b] = backends[b];
|
|
sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]);
|
|
GGML_ASSERT(ggml_backend_supports_buft(backends[b], sched->bufts[b]));
|
|
|
|
if (sched->n_copies > 1) {
|
|
for (int c = 0; c < sched->n_copies; c++) {
|
|
sched->events[b][c] = ggml_backend_event_new(backends[b]->device);
|
|
}
|
|
}
|
|
}
|
|
|
|
sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends);
|
|
sched->op_offload = op_offload;
|
|
|
|
ggml_backend_sched_reset(sched);
|
|
|
|
return sched;
|
|
}
|
|
|
|
void ggml_backend_sched_free(ggml_backend_sched_t sched) {
|
|
if (sched == NULL) {
|
|
return;
|
|
}
|
|
for (int b = 0; b < sched->n_backends; b++) {
|
|
for (int c = 0; c < sched->n_copies; c++) {
|
|
ggml_backend_event_free(sched->events[b][c]);
|
|
}
|
|
}
|
|
ggml_gallocr_free(sched->galloc);
|
|
ggml_free(sched->ctx);
|
|
ggml_hash_set_free(&sched->hash_set);
|
|
free(sched->splits);
|
|
free(sched->hv_tensor_backend_ids);
|
|
free(sched->hv_tensor_copies);
|
|
free(sched->node_backend_ids);
|
|
free(sched->leaf_backend_ids);
|
|
free(sched->prev_node_backend_ids);
|
|
free(sched->prev_leaf_backend_ids);
|
|
free(sched->context_buffer);
|
|
free(sched->graph.nodes);
|
|
free(sched->graph.leafs);
|
|
free(sched);
|
|
}
|
|
|
|
void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
|
|
// reset state for the next run
|
|
if (!sched->is_reset) {
|
|
ggml_hash_set_reset(&sched->hash_set);
|
|
memset(sched->hv_tensor_backend_ids, -1, sched->hash_set.size * sizeof(sched->hv_tensor_backend_ids[0]));
|
|
memset(sched->hv_tensor_copies, 0, sched->hash_set.size * sched->n_backends * sched->n_copies * sizeof(struct ggml_tensor *));
|
|
sched->is_reset = true;
|
|
}
|
|
sched->is_alloc = false;
|
|
}
|
|
|
|
bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
|
|
GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
|
|
|
|
ggml_backend_sched_split_graph(sched, measure_graph);
|
|
|
|
ggml_backend_sched_synchronize(sched);
|
|
|
|
if (!ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) {
|
|
return false;
|
|
}
|
|
|
|
ggml_backend_sched_reset(sched);
|
|
|
|
return true;
|
|
}
|
|
|
|
bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
|
|
GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + graph->n_leafs);
|
|
|
|
ggml_backend_sched_split_graph(sched, graph);
|
|
|
|
|
|
if (!ggml_backend_sched_alloc_splits(sched)) {
|
|
return false;
|
|
}
|
|
|
|
sched->is_alloc = true;
|
|
|
|
return true;
|
|
}
|
|
|
|
enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
|
|
enum ggml_status err = ggml_backend_sched_graph_compute_async(sched, graph);
|
|
ggml_backend_sched_synchronize(sched);
|
|
return err;
|
|
}
|
|
|
|
enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
|
|
if (!sched->is_reset && !sched->is_alloc) {
|
|
ggml_backend_sched_reset(sched);
|
|
}
|
|
|
|
if (!sched->is_alloc) {
|
|
if (!ggml_backend_sched_alloc_graph(sched, graph)) {
|
|
return GGML_STATUS_ALLOC_FAILED;
|
|
}
|
|
}
|
|
|
|
return ggml_backend_sched_compute_splits(sched);
|
|
}
|
|
|
|
void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
|
|
for (int i = 0; i < sched->n_backends; i++) {
|
|
ggml_backend_synchronize(sched->backends[i]);
|
|
}
|
|
}
|
|
|
|
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;
|
|
}
|
|
|
|
int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) {
|
|
return sched->n_copies;
|
|
}
|
|
|
|
int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched) {
|
|
return sched->n_backends;
|
|
}
|
|
|
|
ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i) {
|
|
GGML_ASSERT(i >= 0 && i < sched->n_backends);
|
|
return sched->backends[i];
|
|
}
|
|
|
|
size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
|
|
int backend_index = ggml_backend_sched_backend_id(sched, backend);
|
|
GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
|
|
|
|
return ggml_gallocr_get_buffer_size(sched->galloc, backend_index);
|
|
}
|
|
|
|
void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
|
|
int backend_index = ggml_backend_sched_backend_id(sched, backend);
|
|
GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
|
|
tensor_backend_id(node) = backend_index;
|
|
SET_CAUSE(node, "usr");
|
|
sched->is_reset = false;
|
|
}
|
|
|
|
ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
|
|
int backend_index = tensor_backend_id(node);
|
|
if (backend_index == -1) {
|
|
return NULL;
|
|
}
|
|
return sched->backends[backend_index];
|
|
}
|
|
|
|
// utils
|
|
|
|
enum ggml_status ggml_backend_view_init(struct ggml_tensor * tensor) {
|
|
GGML_ASSERT(tensor->buffer == NULL);
|
|
GGML_ASSERT(tensor->view_src != NULL);
|
|
GGML_ASSERT(tensor->view_src->buffer != NULL);
|
|
GGML_ASSERT(tensor->view_src->data != NULL);
|
|
|
|
tensor->buffer = tensor->view_src->buffer;
|
|
tensor->data = (char *)tensor->view_src->data + tensor->view_offs;
|
|
return ggml_backend_buffer_init_tensor(tensor->buffer, tensor);
|
|
}
|
|
|
|
enum ggml_status 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;
|
|
return ggml_backend_buffer_init_tensor(buffer, tensor);
|
|
}
|
|
|
|
static struct ggml_tensor * graph_copy_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_HASHSET_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_copy_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) {
|
|
continue;
|
|
}
|
|
dst->src[i] = graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, s);
|
|
}
|
|
|
|
node_copies[id] = dst;
|
|
return dst;
|
|
}
|
|
|
|
static void graph_copy_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_copy_init_tensor(hash_set, node_copies, node_init, src->view_src);
|
|
enum ggml_status status = ggml_backend_view_init(dst);
|
|
GGML_ASSERT(status == GGML_STATUS_SUCCESS);
|
|
}
|
|
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) {
|
|
continue;
|
|
}
|
|
graph_copy_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 = ggml_hash_set_new(graph->visited_hash_set.size);
|
|
struct ggml_tensor ** node_copies = (ggml_tensor **) calloc(hash_set.size, sizeof(node_copies[0])); // NOLINT
|
|
bool * node_init = (bool *) calloc(hash_set.size, sizeof(node_init[0]));
|
|
|
|
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) {
|
|
GGML_LOG_ERROR("%s: failed to allocate context for graph copy\n", __func__);
|
|
ggml_hash_set_free(&hash_set);
|
|
free(node_copies);
|
|
free(node_init);
|
|
ggml_free(ctx_allocated);
|
|
ggml_free(ctx_unallocated);
|
|
return {
|
|
/* .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_copy_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) {
|
|
GGML_LOG_ERROR("%s: failed to allocate buffer for graph copy\n", __func__);
|
|
ggml_hash_set_free(&hash_set);
|
|
free(node_copies);
|
|
free(node_init);
|
|
ggml_free(ctx_allocated);
|
|
ggml_free(ctx_unallocated);
|
|
return {
|
|
/* .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_copy_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;
|
|
|
|
ggml_hash_set_free(&hash_set);
|
|
free(node_copies);
|
|
free(node_init);
|
|
|
|
return {
|
|
/* .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++) {
|
|
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;
|
|
}
|
|
|
|
// CPU backend - buffer
|
|
|
|
static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
|
|
uintptr_t data = (uintptr_t)buffer->context;
|
|
|
|
// align the buffer
|
|
if (data % TENSOR_ALIGNMENT != 0) {
|
|
data = GGML_PAD(data, TENSOR_ALIGNMENT);
|
|
}
|
|
|
|
return (void *)data;
|
|
}
|
|
|
|
static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
|
ggml_aligned_free(buffer->context, buffer->size);
|
|
}
|
|
|
|
static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
|
|
memset((char *)tensor->data + offset, value, size);
|
|
|
|
GGML_UNUSED(buffer);
|
|
}
|
|
|
|
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);
|
|
}
|
|
|
|
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);
|
|
}
|
|
|
|
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);
|
|
}
|
|
|
|
static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
|
|
memset(buffer->context, value, buffer->size);
|
|
}
|
|
|
|
static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_i = {
|
|
/* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer,
|
|
/* .get_base = */ ggml_backend_cpu_buffer_get_base,
|
|
/* .init_tensor = */ NULL, // no initialization required
|
|
/* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor,
|
|
/* .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 struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = {
|
|
/* .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
|
|
/* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor,
|
|
/* .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,
|
|
};
|
|
|
|
// CPU backend buffer type
|
|
|
|
// this buffer type is defined here to make it available to all backends
|
|
|
|
static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
|
|
return "CPU";
|
|
|
|
GGML_UNUSED(buft);
|
|
}
|
|
|
|
static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
|
void * data = ggml_aligned_malloc(size);
|
|
|
|
if (data == NULL) {
|
|
GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, size);
|
|
return NULL;
|
|
}
|
|
|
|
return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, size);
|
|
}
|
|
|
|
static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
|
return TENSOR_ALIGNMENT;
|
|
|
|
GGML_UNUSED(buft);
|
|
}
|
|
|
|
static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
|
|
return true;
|
|
|
|
GGML_UNUSED(buft);
|
|
}
|
|
|
|
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_max_size = */ NULL, // defaults to SIZE_MAX
|
|
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
|
|
/* .is_host = */ ggml_backend_cpu_buffer_type_is_host,
|
|
},
|
|
/* .device = */ NULL, // FIXME ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0),
|
|
/* .context = */ NULL,
|
|
};
|
|
|
|
return &ggml_backend_cpu_buffer_type;
|
|
}
|
|
|
|
static const char * ggml_backend_cpu_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) {
|
|
return "CPU_Mapped";
|
|
|
|
GGML_UNUSED(buft);
|
|
}
|
|
|
|
static ggml_backend_buffer_type_t ggml_backend_cpu_buffer_from_ptr_type(void) {
|
|
static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = {
|
|
/* .iface = */ {
|
|
/* .get_name = */ ggml_backend_cpu_buffer_from_ptr_type_get_name,
|
|
/* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer,
|
|
/* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment,
|
|
/* .get_max_size = */ NULL, // defaults to SIZE_MAX
|
|
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
|
|
/* .is_host = */ ggml_backend_cpu_buffer_type_is_host,
|
|
},
|
|
/* .device = */ NULL, // FIXME ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0),
|
|
/* .context = */ NULL,
|
|
};
|
|
|
|
return &ggml_backend_cpu_buffer_type;
|
|
}
|
|
|
|
ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) {
|
|
GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned");
|
|
return ggml_backend_buffer_init(ggml_backend_cpu_buffer_from_ptr_type(), ggml_backend_cpu_buffer_from_ptr_i, ptr, size);
|
|
}
|