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9 Commits

Author SHA1 Message Date
Georgi Gerganov
3f62ee8bee metal : back to a single queue per device
ggml-ci
2025-09-09 17:06:46 +03:00
Georgi Gerganov
0926cb492d metal : clean-up loose ends, ready for tests
ggml-ci
2025-09-09 15:01:21 +03:00
Georgi Gerganov
f288225d42 metal : remove broken implementation of GGML_OP_SET
ggml-ci
2025-09-09 14:29:54 +03:00
Georgi Gerganov
7fc2b3d503 metal : restore .alloc_buffer for buffer_from_ptr_type
ggml-ci
2025-09-09 14:28:34 +03:00
Georgi Gerganov
85aaf52b7e metal : create only metal buffers, no wrapping of host memory
ggml-ci
2025-09-09 14:01:15 +03:00
Georgi Gerganov
d91ba85d04 metal : remove deprecated ggml_backend_metal_buffer_from_ptr 2025-09-09 09:29:11 +03:00
Georgi Gerganov
bdff7729b1 metal : fix batch size for MUL_MAT_ID 2025-09-09 09:29:11 +03:00
Georgi Gerganov
c5637cf39c cont : add comments, extend op offload, clean up
ggml-ci
2025-09-09 09:29:11 +03:00
Georgi Gerganov
97b96c1ad3 metal : make the backend async
ggml-ci
2025-09-09 09:29:10 +03:00
3 changed files with 431 additions and 280 deletions

View File

@@ -43,10 +43,6 @@ GGML_BACKEND_API ggml_backend_t ggml_backend_metal_init(void);
GGML_BACKEND_API bool ggml_backend_is_metal(ggml_backend_t backend);
GGML_DEPRECATED(
GGML_BACKEND_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size),
"obsoleted by the new device interface - https://github.com/ggml-org/llama.cpp/pull/9713");
GGML_BACKEND_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data);
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);

View File

@@ -48,6 +48,8 @@ static struct ggml_backend_metal_device_context {
int mtl_device_ref_count;
id<MTLLibrary> mtl_library;
id<MTLCommandQueue> mtl_queue;
NSLock * mtl_lock;
bool has_simdgroup_reduction;
@@ -69,6 +71,7 @@ static struct ggml_backend_metal_device_context {
/*.mtl_device =*/ nil,
/*.mtl_device_ref_count =*/ 0,
/*.mtl_library =*/ nil,
/*.mtl_queue =*/ nil,
/*.mtl_lock =*/ nil,
/*.has_simdgroup_reduction =*/ false,
/*.has_simdgroup_mm =*/ false,
@@ -94,6 +97,8 @@ static id<MTLDevice> ggml_backend_metal_device_acq(struct ggml_backend_metal_dev
ctx->mtl_device = MTLCreateSystemDefaultDevice();
if (ctx->mtl_device) {
ctx->mtl_queue = [ctx->mtl_device newCommandQueue];
ctx->has_simdgroup_reduction = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7];
ctx->has_simdgroup_reduction |= [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML];
@@ -161,6 +166,11 @@ static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_conte
ctx->mtl_library = nil;
}
if (ctx->mtl_queue) {
[ctx->mtl_queue release];
ctx->mtl_queue = nil;
}
if (ctx->mtl_device) {
[ctx->mtl_device release];
ctx->mtl_device = nil;
@@ -467,8 +477,6 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,
GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,
GGML_METAL_KERNEL_TYPE_SET_I32,
GGML_METAL_KERNEL_TYPE_SET_F32,
GGML_METAL_KERNEL_TYPE_CPY_F32_F32,
GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
GGML_METAL_KERNEL_TYPE_CPY_F32_BF16,
@@ -803,6 +811,12 @@ struct ggml_backend_metal_context {
// n_cb command buffers + 1 used by the main thread
struct ggml_metal_command_buffer cmd_bufs[GGML_METAL_MAX_COMMAND_BUFFERS + 1];
// extra command buffers for things like getting, setting and copying tensors
NSMutableArray * cmd_bufs_ext;
id<MTLCommandBuffer> cmd_buf_last;
id<MTLCommandBuffer> cmd_buf_ext_last;
// abort ggml_metal_graph_compute if callback returns true
ggml_abort_callback abort_callback;
void * abort_callback_data;
@@ -999,7 +1013,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]);
ctx->device = device;
ctx->queue = [device newCommandQueue];
ctx->queue = ctx_dev->mtl_queue;
if (ctx->queue == nil) {
GGML_LOG_ERROR("%s: error: failed to create command queue\n", __func__);
return NULL;
@@ -1073,6 +1087,11 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
ctx->cmd_bufs[i].mem_pool->device = device;
}
ctx->cmd_bufs_ext = [[NSMutableArray alloc] init];
ctx->cmd_buf_last = nil;
ctx->cmd_buf_ext_last = nil;
#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
if (@available(macOS 10.12, iOS 16.0, *)) {
GGML_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, device.recommendedMaxWorkingSetSize / 1e6);
@@ -1390,8 +1409,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_F32, set_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_I32, set_i32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_BF16, cpy_f32_bf16, use_bfloat);
@@ -1663,14 +1680,17 @@ static void ggml_metal_free(struct ggml_backend_metal_context * ctx) {
Block_release(ctx->encode_async);
[ctx->queue release];
for (int i = 0; i < GGML_METAL_MAX_COMMAND_BUFFERS; ++i) {
// ctx->cmd_bufs[i].obj is auto released
if (ctx->cmd_bufs[i].obj) {
[ctx->cmd_bufs[i].obj release];
}
ggml_metal_mem_pool_free(ctx->cmd_bufs[i].mem_pool);
}
[ctx->cmd_bufs_ext removeAllObjects];
[ctx->cmd_bufs_ext release];
dispatch_release(ctx->d_queue);
free(ctx);
@@ -1688,7 +1708,6 @@ struct ggml_backend_metal_buffer {
struct ggml_backend_metal_buffer_context {
void * all_data;
size_t all_size;
bool owned;
// multiple buffers are used only to avoid the maximum buffer size limitation when using mmap
int n_buffers;
@@ -1696,6 +1715,9 @@ struct ggml_backend_metal_buffer_context {
// optional MTLResidencySet
id rset;
id device;
id queue;
};
// rset init
@@ -1761,7 +1783,7 @@ static void ggml_backend_metal_buffer_rset_free(struct ggml_backend_metal_buffer
// the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
// Metal buffer based on the host memory pointer
//
static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_tensor * t, size_t * offs) {
static id<MTLBuffer> ggml_metal_get_buffer(const struct ggml_tensor * t, size_t * offs) {
//GGML_LOG_INFO("%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
const int64_t tsize = ggml_nbytes(t);
@@ -1984,16 +2006,6 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
return false;
};
}
case GGML_OP_SET:
{
switch (op->src[0]->type) {
case GGML_TYPE_F32:
case GGML_TYPE_I32:
return true;
default:
return false;
};
}
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_GET_ROWS:
{
@@ -5569,68 +5581,6 @@ static int ggml_metal_encode_node(
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + nrptg - 1)/nrptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, nrptg, 1)];
} break;
case GGML_OP_SET:
{
GGML_ASSERT(ggml_are_same_shape(src0, dst));
GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0));
// src0 and dst as viewed during set
const size_t dst_nb0 = ggml_element_size(src0);
const size_t dst_nb1 = ((int32_t *) dst->op_params)[0];
const size_t dst_nb2 = ((int32_t *) dst->op_params)[1];
const size_t dst_nb3 = ((int32_t *) dst->op_params)[2];
const size_t offset = ((int32_t *) dst->op_params)[3];
const bool inplace = (bool) ((int32_t *) dst->op_params)[4];
if (!inplace) {
memcpy(((char *) dst->data), ((char *) src0->data), ggml_nbytes(dst));
}
const int im0 = (ne10 == 0 ? 0 : ne10-1);
const int im1 = (ne11 == 0 ? 0 : ne11-1);
const int im2 = (ne12 == 0 ? 0 : ne12-1);
const int im3 = (ne13 == 0 ? 0 : ne13-1);
GGML_ASSERT(offset + im0*dst_nb0 + im1*dst_nb1 + im2*dst_nb2 + im3*dst_nb3 <= ggml_nbytes(dst));
id<MTLComputePipelineState> pipeline = nil;
switch (src0t) {
case GGML_TYPE_F32:
GGML_ASSERT(nb10 == sizeof(float));
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_F32].pipeline; break;
case GGML_TYPE_I32:
GGML_ASSERT(nb10 == sizeof(int32_t));
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_I32].pipeline; break;
default: GGML_ABORT("fatal error");
}
ggml_metal_kargs_set args = {
/*.ne10 =*/ ne10,
/*.ne11 =*/ ne11,
/*.ne12 =*/ ne12,
/*.nb10 =*/ nb10,
/*.nb11 =*/ nb11,
/*.nb12 =*/ nb12,
/*.nb13 =*/ nb13,
/*.nb1 =*/ dst_nb1,
/*.nb2 =*/ dst_nb2,
/*.nb3 =*/ dst_nb3,
/*.offs =*/ offset,
/*.inplace =*/ inplace,
};
const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne10);
[encoder setComputePipelineState:pipeline];
[encoder setBytes:&args length:sizeof(args) atIndex:0];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:2];
[encoder setBuffer:id_dst offset:offs_dst atIndex:3];
[encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_POOL_2D:
{
GGML_ASSERT(ggml_is_contiguous(src0));
@@ -5781,78 +5731,122 @@ static enum ggml_status ggml_metal_graph_compute(
// the main thread commits the first few commands immediately
// cmd_buf[n_cb]
{
id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBufferWithUnretainedReferences];
// first wait for any previous command buffer to be completed
// note: this checks only yhat the first part of the previous graph has been computed
// the rest of the graph might still be computing, but it is Ok to start queuing the beginning of the
/// new graph
if (ctx->cmd_bufs[n_cb].obj) {
[ctx->cmd_bufs[n_cb].obj waitUntilCompleted];
[ctx->cmd_bufs[n_cb].obj release];
}
id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBuffer];
[cmd_buf retain];
ctx->cmd_bufs[n_cb].obj = cmd_buf;
[cmd_buf enqueue];
ctx->encode_async(n_cb);
}
// prepare the rest of the command buffers asynchronously
// here we guarantee the full previous graph has finished computing
// but note that we have already enqueued the first part of the new graph so it can start processing, while
// continue to encode the rest of the graph
if (ctx->cmd_buf_last) {
[ctx->cmd_buf_last waitUntilCompleted];
ctx->cmd_buf_last = nil;
}
// remember the command buffer for the next iteration
ctx->cmd_buf_last = ctx->cmd_bufs[n_cb].obj;
// prepare the rest of the command buffers asynchronously (optional)
// cmd_buf[0.. n_cb)
for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBufferWithUnretainedReferences];
id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBuffer];
[cmd_buf retain];
if (ctx->cmd_bufs[cb_idx].obj) {
[ctx->cmd_bufs[cb_idx].obj release];
}
ctx->cmd_bufs[cb_idx].obj = cmd_buf;
// always enqueue the first two command buffers
// enqueue all of the command buffers if we don't need to abort
if (cb_idx < 2 || ctx->abort_callback == NULL) {
[cmd_buf enqueue];
// update the pointer to the last queued command buffer
// this is needed to implement synchronize()
ctx->cmd_buf_last = cmd_buf;
}
}
dispatch_apply(n_cb, ctx->d_queue, ctx->encode_async);
// wait for completion and check status of each command buffer
// needed to detect if the device ran out-of-memory for example (#1881)
{
id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs[n_cb].obj;
[cmd_buf waitUntilCompleted];
[ctx->cmd_buf_last waitUntilScheduled];
MTLCommandBufferStatus status = [cmd_buf status];
if (status != MTLCommandBufferStatusCompleted) {
GGML_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, n_cb, status);
if (status == MTLCommandBufferStatusError) {
GGML_LOG_INFO("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]);
}
return GGML_STATUS_FAILED;
}
}
for (int i = 0; i < n_cb; ++i) {
id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs[i].obj;
[cmd_buf waitUntilCompleted];
MTLCommandBufferStatus status = [cmd_buf status];
if (status != MTLCommandBufferStatusCompleted) {
GGML_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
if (status == MTLCommandBufferStatusError) {
GGML_LOG_INFO("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]);
}
return GGML_STATUS_FAILED;
}
id<MTLCommandBuffer> next_buffer = (i + 1 < n_cb ? ctx->cmd_bufs[i + 1].obj : nil);
if (!next_buffer) {
continue;
}
const bool next_queued = ([next_buffer status] != MTLCommandBufferStatusNotEnqueued);
if (next_queued) {
continue;
}
if (ctx->abort_callback && ctx->abort_callback(ctx->abort_callback_data)) {
GGML_LOG_INFO("%s: command buffer %d aborted", __func__, i);
return GGML_STATUS_ABORTED;
}
[next_buffer commit];
}
// for debugging: block until graph is computed
//[ctx->cmd_buf_last waitUntilCompleted];
// enter here only when capturing in order to wait for all computation to finish
// otherwise, we leave the graph to compute asynchronously
if (!should_capture && ctx->capture_started) {
// wait for completion and check status of each command buffer
// needed to detect if the device ran out-of-memory for example (#1881)
{
id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs[n_cb].obj;
[cmd_buf waitUntilCompleted];
MTLCommandBufferStatus status = [cmd_buf status];
if (status != MTLCommandBufferStatusCompleted) {
GGML_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, n_cb, status);
if (status == MTLCommandBufferStatusError) {
GGML_LOG_INFO("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]);
}
return GGML_STATUS_FAILED;
}
}
for (int i = 0; i < n_cb; ++i) {
id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs[i].obj;
[cmd_buf waitUntilCompleted];
MTLCommandBufferStatus status = [cmd_buf status];
if (status != MTLCommandBufferStatusCompleted) {
GGML_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
if (status == MTLCommandBufferStatusError) {
GGML_LOG_INFO("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]);
}
return GGML_STATUS_FAILED;
}
id<MTLCommandBuffer> next_buffer = (i + 1 < n_cb ? ctx->cmd_bufs[i + 1].obj : nil);
if (!next_buffer) {
continue;
}
const bool next_queued = ([next_buffer status] != MTLCommandBufferStatusNotEnqueued);
if (next_queued) {
continue;
}
if (ctx->abort_callback && ctx->abort_callback(ctx->abort_callback_data)) {
GGML_LOG_INFO("%s: command buffer %d aborted", __func__, i);
return GGML_STATUS_ABORTED;
}
[next_buffer commit];
}
if (ctx->cmd_buf_last) {
[ctx->cmd_buf_last waitUntilCompleted];
ctx->cmd_buf_last = nil;
}
[ctx->capture_scope endScope];
[[MTLCaptureManager sharedCaptureManager] stopCapture];
}
@@ -5874,14 +5868,6 @@ static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer)
ggml_backend_metal_buffer_rset_free(ctx);
if (ctx->owned) {
#if TARGET_OS_OSX
vm_deallocate((vm_map_t)mach_task_self(), (vm_address_t)ctx->all_data, ctx->all_size);
#else
free(ctx->all_data);
#endif
}
free(ctx);
}
@@ -5892,25 +5878,117 @@ static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) {
}
static void ggml_backend_metal_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
#if 1
struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
@autoreleasepool {
id<MTLCommandQueue> queue = ctx->queue;
id<MTLCommandBuffer> cmd_buf = [queue commandBuffer];
id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
[cmd_buf enqueue];
size_t buf_dst_offset = 0;
id<MTLBuffer> buf_dst = ggml_metal_get_buffer(tensor, &buf_dst_offset);
buf_dst_offset += offset;
[encoder fillBuffer:buf_dst
range:NSMakeRange(buf_dst_offset, buf_dst_offset + size)
value:value];
[encoder endEncoding];
[cmd_buf commit];
[cmd_buf waitUntilScheduled];
}
#else
memset((char *)tensor->data + offset, value, size);
#endif
GGML_UNUSED(buffer);
}
static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
#if 1
struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
@autoreleasepool {
id<MTLCommandQueue> queue = ctx->queue;
id<MTLCommandBuffer> cmd_buf = [queue commandBuffer];
id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
[cmd_buf enqueue];
// TODO: is this an extra copy? can we avoid it?
id<MTLBuffer> buf_src = [ctx->device newBufferWithBytesNoCopy:data
length:size
options:MTLResourceStorageModeShared
deallocator:nil];
size_t buf_dst_offset = 0;
id<MTLBuffer> buf_dst = ggml_metal_get_buffer(tensor, &buf_dst_offset);
buf_dst_offset += offset;
[encoder copyFromBuffer:buf_src
sourceOffset:0
toBuffer:buf_dst
destinationOffset:buf_dst_offset
size:size];
[encoder endEncoding];
// note: no need to wait for completion here
[cmd_buf commit];
[cmd_buf waitUntilScheduled];
}
#else
memcpy((char *)tensor->data + offset, data, size);
#endif
GGML_UNUSED(buffer);
}
static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
#if 1
struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
@autoreleasepool {
id<MTLCommandQueue> queue = ctx->queue;
id<MTLCommandBuffer> cmd_buf = [queue commandBuffer];
id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
[cmd_buf enqueue];
size_t buf_src_offset = 0;
id<MTLBuffer> buf_src = ggml_metal_get_buffer(tensor, &buf_src_offset);
buf_src_offset += offset;
id<MTLBuffer> buf_dst = [ctx->device newBufferWithBytesNoCopy:data
length:size
options:MTLResourceStorageModeShared
deallocator:nil];
[encoder copyFromBuffer:buf_src
sourceOffset:buf_src_offset
toBuffer:buf_dst
destinationOffset:0
size:size];
[encoder endEncoding];
[cmd_buf commit];
[cmd_buf waitUntilCompleted];
}
#else
memcpy(data, (const char *)tensor->data + offset, size);
#endif
GGML_UNUSED(buffer);
}
static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
if (ggml_backend_buffer_is_host(src->buffer)) {
GGML_ASSERT(false && "TODO");
memcpy(dst->data, src->data, ggml_nbytes(src));
return true;
}
@@ -5920,9 +5998,27 @@ static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, c
}
static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
#if 1
struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context;
@autoreleasepool {
id<MTLCommandQueue> queue = ctx->queue;
id<MTLCommandBuffer> cmd_buf = [queue commandBuffer];
id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
[cmd_buf enqueue];
[encoder fillBuffer:ctx->buffers[0].metal
range:NSMakeRange(0, ctx->buffers[0].size)
value:value];
[encoder endEncoding];
[cmd_buf commit];
[cmd_buf waitUntilScheduled];
}
#else
memset(ctx->all_data, value, ctx->all_size);
#endif
}
static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = {
@@ -5986,22 +6082,37 @@ static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_ba
id<MTLDevice> device = ctx_dev->mtl_device;
#if 1
// we'll populate this after creating the Metal buffer below
ctx->all_data = (void *) 0x000000400ULL;
#else
ctx->all_data = ggml_metal_host_malloc(size_aligned);
#endif
ctx->all_size = size_aligned;
ctx->owned = true;
ctx->device = device;
ctx->queue = ctx_dev->mtl_queue;
ctx->n_buffers = 1;
if (ctx->all_data != NULL) {
ctx->buffers[0].data = ctx->all_data;
ctx->buffers[0].size = size;
ctx->buffers[0].metal = nil;
if (size_aligned > 0) {
#if 1
ctx->buffers[0].metal = [device newBufferWithLength:size_aligned options:MTLResourceStorageModePrivate];
ctx->all_data = (void *) (ctx->buffers[0].metal.gpuAddress);
#else
ctx->buffers[0].metal = [device newBufferWithBytesNoCopy:ctx->all_data
length:size_aligned
options:MTLResourceStorageModeShared
deallocator:nil];
#endif
}
ctx->buffers[0].data = ctx->all_data;
}
if (size_aligned > 0 && (ctx->all_data == NULL || ctx->buffers[0].metal == nil)) {
@@ -6047,7 +6158,7 @@ ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) {
/* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment,
/* .get_max_size = */ ggml_backend_metal_buffer_type_get_max_size,
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .is_host = */ ggml_backend_metal_buffer_type_is_host,
/* .is_host = */ NULL,
},
/* .device = */ &g_ggml_backend_metal_device,
/* .context = */ NULL,
@@ -6063,6 +6174,8 @@ static const char * ggml_backend_metal_buffer_from_ptr_type_get_name(ggml_backen
}
static ggml_backend_buffer_type_t ggml_backend_metal_buffer_from_ptr_type(void) {
// note: not obvious, but this buffer type still needs to implement .alloc_buffer:
// https://github.com/ggml-org/llama.cpp/pull/15832#discussion_r2333177099
static struct ggml_backend_buffer_type ggml_backend_buffer_from_ptr_type_metal = {
/* .iface = */ {
/* .get_name = */ ggml_backend_metal_buffer_from_ptr_type_get_name,
@@ -6079,95 +6192,6 @@ static ggml_backend_buffer_type_t ggml_backend_metal_buffer_from_ptr_type(void)
return &ggml_backend_buffer_from_ptr_type_metal;
}
// TODO: obsoleted by ggml_backend_metal_device_buffer_from_ptr
ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size) {
struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context));
ctx->all_data = data;
ctx->all_size = size;
ctx->owned = false;
ctx->n_buffers = 0;
const size_t size_page = sysconf(_SC_PAGESIZE);
// page-align the data ptr
{
const uintptr_t offs = (uintptr_t) data % size_page;
data = (void *) ((char *) data - offs);
size += offs;
}
size_t size_aligned = size;
if ((size_aligned % size_page) != 0) {
size_aligned += (size_page - (size_aligned % size_page));
}
struct ggml_backend_metal_device_context * ctx_dev = &g_ggml_ctx_dev_main;
GGML_ASSERT(ctx_dev->mtl_device != nil);
id<MTLDevice> device = ctx_dev->mtl_device;
// the buffer fits into the max buffer size allowed by the device
if (size_aligned <= device.maxBufferLength) {
ctx->buffers[ctx->n_buffers].data = data;
ctx->buffers[ctx->n_buffers].size = size;
ctx->buffers[ctx->n_buffers].metal = nil;
if (size_aligned > 0) {
ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
if (ctx->buffers[ctx->n_buffers].metal == nil) {
GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0);
return false;
}
}
ggml_backend_metal_log_allocated_size(device, size_aligned);
++ctx->n_buffers;
} else {
// this overlap between the views will guarantee that the tensor with the maximum size will fully fit into
// one of the views
const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case
const size_t size_step = device.maxBufferLength - size_ovlp;
const size_t size_view = device.maxBufferLength;
for (size_t i = 0; i < size; i += size_step) {
const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i);
ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) data + i);
ctx->buffers[ctx->n_buffers].size = size_step_aligned;
ctx->buffers[ctx->n_buffers].metal = nil;
if (size_step_aligned > 0) {
ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
if (ctx->buffers[ctx->n_buffers].metal == nil) {
GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_step_aligned / 1024.0 / 1024.0);
return false;
}
}
ggml_backend_metal_log_allocated_size(device, size_step_aligned);
if (i + size_step < size) {
GGML_LOG_INFO("\n");
}
++ctx->n_buffers;
}
}
if (!ggml_backend_metal_buffer_rset_init(ctx, ctx_dev, device)) {
GGML_LOG_ERROR("%s: error: failed to initialize residency set\n", __func__);
free(ctx);
return NULL;
}
return ggml_backend_buffer_init(ggml_backend_metal_buffer_from_ptr_type(), ggml_backend_metal_buffer_i, ctx, size);
}
// backend
static const char * ggml_backend_metal_name(ggml_backend_t backend) {
@@ -6184,6 +6208,147 @@ static void ggml_backend_metal_free(ggml_backend_t backend) {
free(backend);
}
static void ggml_backend_metal_synchronize(ggml_backend_t backend) {
struct ggml_backend_metal_context * ctx = backend->context;
// wait for the computation of the graph to finish
if (ctx->cmd_buf_last) {
[ctx->cmd_buf_last waitUntilCompleted];
ctx->cmd_buf_last = nil;
}
// wait for any pending async get/set operations
if (ctx->cmd_buf_ext_last) {
[ctx->cmd_buf_ext_last waitUntilCompleted];
ctx->cmd_buf_ext_last = nil;
}
// release any completed command buffers
if (ctx->cmd_bufs_ext.count > 0) {
for (size_t i = 0; i < ctx->cmd_bufs_ext.count; ++i) {
id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs_ext[i];
MTLCommandBufferStatus status = [cmd_buf status];
if (status != MTLCommandBufferStatusCompleted) {
GGML_LOG_ERROR("%s: error: command buffer %d failed with status %d\n", __func__, (int) i, (int) status);
if (status == MTLCommandBufferStatusError) {
GGML_LOG_ERROR("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]);
}
GGML_ABORT("fatal error");
}
[cmd_buf release];
}
[ctx->cmd_bufs_ext removeAllObjects];
}
}
static void ggml_backend_metal_set_tensor_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
struct ggml_backend_metal_context * ctx = backend->context;
struct ggml_backend_metal_device_context * ctx_dev = backend->device->context;
@autoreleasepool {
id<MTLDevice> device = ctx_dev->mtl_device;
// wrap the source data into a Metal buffer
id<MTLBuffer> buf_src = [device newBufferWithBytes:data
length:size
options:MTLResourceStorageModeShared];
size_t buf_dst_offset = 0;
id<MTLBuffer> buf_dst = ggml_metal_get_buffer(tensor, &buf_dst_offset);
if (buf_dst == nil) {
GGML_ABORT("%s: failed to find buffer for tensor '%s'\n", __func__, tensor->name);
}
buf_dst_offset += offset;
// queue the copy operation into the queue of the Metal context
// this will be queued at the end, after any currently ongoing GPU operations
id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBuffer];
[cmd_buf enqueue];
id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
[encoder copyFromBuffer:buf_src
sourceOffset:0
toBuffer:buf_dst
destinationOffset:buf_dst_offset
size:size];
[encoder endEncoding];
[cmd_buf commit];
// do not wait here for completion
//[cmd_buf waitUntilCompleted];
// instead, remember a reference to the command buffer and wait for it later if needed
[ctx->cmd_bufs_ext addObject:cmd_buf];
ctx->cmd_buf_ext_last = cmd_buf;
[cmd_buf retain];
}
}
static void ggml_backend_metal_get_tensor_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
struct ggml_backend_metal_context * ctx = backend->context;
struct ggml_backend_metal_device_context * ctx_dev = backend->device->context;
@autoreleasepool {
id<MTLDevice> device = ctx_dev->mtl_device;
id<MTLBuffer> buf_dst = [device newBufferWithBytesNoCopy:data
length:size
options:MTLResourceStorageModeShared
deallocator:nil];
size_t buf_src_offset = 0;
id<MTLBuffer> buf_src = ggml_metal_get_buffer(tensor, &buf_src_offset);
if (buf_src == nil) {
GGML_ABORT("%s: failed to find buffer for tensor '%s'\n", __func__, tensor->name);
}
buf_src_offset += offset;
// queue the copy operation into the queue of the Metal context
// this will be queued at the end, after any currently ongoing GPU operations
id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBuffer];
[cmd_buf enqueue];
id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
[encoder copyFromBuffer:buf_src
sourceOffset:buf_src_offset
toBuffer:buf_dst
destinationOffset:0
size:size];
[encoder endEncoding];
[cmd_buf commit];
// do not wait here for completion
//[cmd_buf waitUntilCompleted];
// instead, remember a reference to the command buffer and wait for it later if needed
[ctx->cmd_bufs_ext addObject:cmd_buf];
ctx->cmd_buf_ext_last = cmd_buf;
[cmd_buf retain];
}
}
static bool ggml_backend_metal_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst) {
return false;
GGML_UNUSED(backend_src);
GGML_UNUSED(backend_dst);
GGML_UNUSED(src);
GGML_UNUSED(dst);
}
static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
return ggml_metal_graph_compute(backend, cgraph);
}
@@ -6214,7 +6379,10 @@ static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
const int n_nodes_per_cb = ctx->n_nodes_per_cb;
id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs[cb_idx].obj;
id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs[cb_idx].obj;
struct ggml_metal_mem_pool * mem_pool = ctx->cmd_bufs[cb_idx].mem_pool;
ggml_metal_mem_pool_reset(mem_pool);
id<MTLComputeCommandEncoder> encoder = [cmd_buf computeCommandEncoder];
@@ -6228,9 +6396,6 @@ static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
const bool should_capture = ctx->capture_next_compute;
struct ggml_metal_mem_pool * mem_pool = ctx->cmd_bufs[cb_idx].mem_pool;
ggml_metal_mem_pool_reset(mem_pool);
for (int idx = node_start; idx < node_end;) {
if (should_capture) {
[encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(ggml_graph_node(ctx->gf, idx)) encoding:NSUTF8StringEncoding]];
@@ -6264,15 +6429,19 @@ static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
static struct ggml_backend_i ggml_backend_metal_i = {
/* .get_name = */ ggml_backend_metal_name,
/* .free = */ ggml_backend_metal_free,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
/* .synchronize = */ NULL,
/* .set_tensor_async = */ ggml_backend_metal_set_tensor_async,
/* .get_tensor_async = */ ggml_backend_metal_get_tensor_async,
/* .cpy_tensor_async = */ ggml_backend_metal_cpy_tensor_async, // only needed for multi-GPU setups
/* .synchronize = */ ggml_backend_metal_synchronize,
/* .graph_plan_create = */ NULL,
/* .graph_plan_free = */ NULL,
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_metal_graph_compute,
// the events API is needed only for multi-GPU setups, so likely no need to implement it for Metal
// in any case, these docs seem relevant if we ever decide to implement it:
// https://developer.apple.com/documentation/metal/mtlcommandbuffer#Synchronizing-Passes-with-Events
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
/* .optimize_graph = */ NULL,
@@ -6376,8 +6545,8 @@ static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, struct g
props->type = ggml_backend_metal_device_get_type(dev);
ggml_backend_metal_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->caps = (struct ggml_backend_dev_caps) {
/* .async = */ false,
/* .host_buffer = */ false,
/* .async = */ true,
/* .host_buffer = */ true,
/* .buffer_from_host_ptr = */ true,
/* .events = */ false,
};
@@ -6417,7 +6586,7 @@ static ggml_backend_buffer_t ggml_backend_metal_device_buffer_from_ptr(ggml_back
ctx->all_data = ptr;
ctx->all_size = size;
ctx->owned = false;
ctx->n_buffers = 0;
const size_t size_page = sysconf(_SC_PAGESIZE);
@@ -6440,6 +6609,9 @@ static ggml_backend_buffer_t ggml_backend_metal_device_buffer_from_ptr(ggml_back
id<MTLDevice> device = ctx_dev->mtl_device;
ctx->device = device;
ctx->queue = ctx_dev->mtl_queue;
// the buffer fits into the max buffer size allowed by the device
if (size_aligned <= device.maxBufferLength) {
ctx->buffers[ctx->n_buffers].data = ptr;
@@ -6514,8 +6686,23 @@ static bool ggml_backend_metal_device_supports_buft(ggml_backend_dev_t dev, ggml
GGML_UNUSED(dev);
}
static int64_t get_op_batch_size(const struct ggml_tensor * op) {
switch (op->op) {
case GGML_OP_MUL_MAT:
return op->ne[1];
case GGML_OP_MUL_MAT_ID:
return op->ne[2];
default:
return ggml_nrows(op);
}
}
static bool ggml_backend_metal_device_offload_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
return false;
const int min_batch_size = 32;
return (op->op == GGML_OP_MUL_MAT ||
op->op == GGML_OP_MUL_MAT_ID) &&
get_op_batch_size(op) >= min_batch_size;
GGML_UNUSED(dev);
GGML_UNUSED(op);

View File

@@ -5571,38 +5571,6 @@ kernel void kernel_flash_attn_ext_vec_reduce(
#undef DV
}
template<typename T>
kernel void kernel_set(
constant ggml_metal_kargs_set & args,
device const char * src0,
device const char * src1,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
const int i13 = tgpig[2];
const int i12 = tgpig[1];
const int i11 = tgpig[0];
const int64_t n = i13*args.ne12*args.ne11*args.ne10 + i12*args.ne11*args.ne10 + i11*args.ne10;
const int64_t i3 = n / (args.ne12*args.ne11*args.ne10);
const int64_t i2 = (n - i3*args.ne12*args.ne11*args.ne10) / (args.ne11*args.ne10);
const int64_t i1 = (n - i3*args.ne12*args.ne11*args.ne10 - i2*args.ne11*args.ne10) / args.ne10;
device T * dst_data = (device T *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + args.offs);
for (int64_t i10 = tpitg.x; i10 < args.ne10; i10 += ntg.x) {
device const T * src = (device T *) (src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11 + i10*args.nb10);
dst_data[i10] = (T) src[0];
}
}
typedef decltype(kernel_set<float>) kernel_set_t;
template [[host_name("kernel_set_f32")]] kernel kernel_set_t kernel_set<float>;
template [[host_name("kernel_set_i32")]] kernel kernel_set_t kernel_set<int32_t>;
template<typename T0, typename T1>
kernel void kernel_cpy(
constant ggml_metal_kargs_cpy & args,