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llama.cpp
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fix: Metal backend performance on AMD discrete GPUs 

Fixes two critical issues with Metal backend on AMD/Intel discrete GPUs:

1. Auto-disable concurrent dispatch on non-Apple GPUs
   - MTLDispatchTypeConcurrent has broken memory barriers on AMD
   - Causes L2 cache coherency issues leading to corrupted output
   - Now auto-detects via supports_gpu_family_apple7 and uses serial dispatch

2. Use Managed buffer mode on discrete GPUs for cached PCIe reads
   - Shared mode uses uncached PCIe reads (~3 MB/s) on discrete GPUs
   - Managed mode enables cached reads (~3 GB/s) with explicit sync
   - Adds didModifyRange after CPU writes, synchronizeResource for reads
   - Improves generation speed from ~11 t/s to ~60 t/s on AMD Radeon Pro 5300M

Performance on AMD Radeon Pro 5300M:
- Before: 11.5 t/s generation (corrupted output with concurrent dispatch)
- After: 60+ t/s generation (correct output, 88% of Vulkan performance)

Environment variable overrides for testing:
- GGML_METAL_MANAGED_BUFFERS_DISABLE/ENABLE
- GGML_METAL_CONCURRENCY_DISABLE (still works)

Also adds bench_metal.sh with benchmark and conversation modes.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
hung 2026-02-11 00:52:32 -05:00
parent 57487a64c8
commit e459796110
3 changed files with 78 additions and 9 deletions
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12
ggml/src/ggml-metal/ggml-metal-context.m
View File

@ -129,8 +129,16 @@ ggml_metal_t ggml_metal_init(ggml_metal_device_t dev) {
res->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);
res->use_fusion = getenv("GGML_METAL_FUSION_DISABLE") == nil;
res->use_concurrency = getenv("GGML_METAL_CONCURRENCY_DISABLE") == nil;
res->use_fusion = getenv("GGML_METAL_FUSION_DISABLE") == nil;
// Auto-disable concurrent dispatch on non-Apple GPUs (AMD/Intel)
// MTLDispatchTypeConcurrent has broken memory barriers on these GPUs
bool is_apple_gpu = props_dev->supports_gpu_family_apple7;
res->use_concurrency = is_apple_gpu && (getenv("GGML_METAL_CONCURRENCY_DISABLE") == nil);
if (!is_apple_gpu && getenv("GGML_METAL_CONCURRENCY_DISABLE") == nil) {
GGML_LOG_INFO("%s: disabling concurrent dispatch (non-Apple GPU detected)\n", __func__);
}
{
const char * val = getenv("GGML_METAL_GRAPH_DEBUG");

1
ggml/src/ggml-metal/ggml-metal-device.h
View File

@ -226,6 +226,7 @@ struct ggml_metal_device_props {
bool has_tensor;
bool use_residency_sets;
bool use_shared_buffers;
bool use_managed_buffers; // Use Managed mode for discrete GPUs
bool supports_gpu_family_apple7;

74
ggml/src/ggml-metal/ggml-metal-device.m
View File

@ -787,6 +787,17 @@ ggml_metal_device_t ggml_metal_device_init(int device) {
dev->props.use_shared_buffers = true;
}
// Use Managed mode on discrete GPUs for cached PCIe reads
dev->props.use_managed_buffers = !dev->props.has_unified_memory;
// Environment variable overrides for testing
if (getenv("GGML_METAL_MANAGED_BUFFERS_DISABLE") != NULL) {
dev->props.use_managed_buffers = false;
}
if (getenv("GGML_METAL_MANAGED_BUFFERS_ENABLE") != NULL) {
dev->props.use_managed_buffers = true;
}
dev->props.supports_gpu_family_apple7 = [dev->mtl_device supportsFamily:MTLGPUFamilyApple7];
dev->props.op_offload_min_batch_size = getenv("GGML_OP_OFFLOAD_MIN_BATCH") ? atoi(getenv("GGML_OP_OFFLOAD_MIN_BATCH")) : 32;
@ -849,6 +860,7 @@ ggml_metal_device_t ggml_metal_device_init(int device) {
GGML_LOG_INFO("%s: has tensor = %s\n", __func__, dev->props.has_tensor ? "true" : "false");
GGML_LOG_INFO("%s: use residency sets = %s\n", __func__, dev->props.use_residency_sets ? "true" : "false");
GGML_LOG_INFO("%s: use shared buffers = %s\n", __func__, dev->props.use_shared_buffers ? "true" : "false");
GGML_LOG_INFO("%s: use managed buffers = %s\n", __func__, dev->props.use_managed_buffers ? "true" : "false");
#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
if (@available(macOS 10.12, iOS 16.0, *)) {
@ -1424,10 +1436,19 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size,
if (size_aligned > 0) {
if (props_dev->use_shared_buffers && shared) {
MTLResourceOptions storage_mode = props_dev->use_managed_buffers
? MTLResourceStorageModeManaged
: MTLResourceStorageModeShared;
res->buffers[0].metal = [res->dev->mtl_device newBufferWithBytesNoCopy:res->all_data
length:size_aligned
options:MTLResourceStorageModeShared
options:storage_mode
deallocator:nil];
// For Managed buffers, sync CPU→GPU after creation
if (props_dev->use_managed_buffers && res->buffers[0].metal) {
[(id<MTLBuffer>)res->buffers[0].metal didModifyRange:NSMakeRange(0, size_aligned)];
}
} else {
res->buffers[0].metal = [res->dev->mtl_device newBufferWithLength:size_aligned options:MTLResourceStorageModePrivate];
}
@ -1493,13 +1514,22 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s
res->buffers[res->n_buffers].metal = nil;
if (size_aligned > 0) {
res->buffers[res->n_buffers].metal = [res->dev->mtl_device newBufferWithBytesNoCopy:ptr length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
MTLResourceOptions storage_mode = props_dev->use_managed_buffers
? MTLResourceStorageModeManaged
: MTLResourceStorageModeShared;
res->buffers[res->n_buffers].metal = [res->dev->mtl_device newBufferWithBytesNoCopy:ptr length:size_aligned options:storage_mode deallocator:nil];
if (res->buffers[res->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);
free(res);
return NULL;
}
// For Managed buffers, sync CPU→GPU after creation
if (props_dev->use_managed_buffers) {
[(id<MTLBuffer>)res->buffers[res->n_buffers].metal didModifyRange:NSMakeRange(0, size_aligned)];
}
}
ggml_metal_log_allocated_size(res->dev->mtl_device, size_aligned);
@ -1520,13 +1550,22 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s
res->buffers[res->n_buffers].metal = nil;
if (size_step_aligned > 0) {
res->buffers[res->n_buffers].metal = [res->dev->mtl_device newBufferWithBytesNoCopy:(void *) ((uint8_t *) ptr + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
MTLResourceOptions storage_mode = props_dev->use_managed_buffers
? MTLResourceStorageModeManaged
: MTLResourceStorageModeShared;
res->buffers[res->n_buffers].metal = [res->dev->mtl_device newBufferWithBytesNoCopy:(void *) ((uint8_t *) ptr + i) length:size_step_aligned options:storage_mode deallocator:nil];
if (res->buffers[res->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);
free(res);
return NULL;
}
// For Managed buffers, sync CPU→GPU after creation
if (props_dev->use_managed_buffers) {
[(id<MTLBuffer>)res->buffers[res->n_buffers].metal didModifyRange:NSMakeRange(0, size_step_aligned)];
}
}
ggml_metal_log_allocated_size(res->dev->mtl_device, size_step_aligned);
@ -1583,6 +1622,13 @@ bool ggml_metal_buffer_is_shared(ggml_metal_buffer_t buf) {
void ggml_metal_buffer_memset_tensor(ggml_metal_buffer_t buf, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
if (buf->is_shared) {
memset((char *) tensor->data + offset, value, size);
// Sync Managed buffer after CPU write
if (buf->dev->props.use_managed_buffers) {
struct ggml_metal_buffer_id bid = ggml_metal_buffer_get_id(buf, tensor);
[(id<MTLBuffer>)bid.metal didModifyRange:NSMakeRange(bid.offs + offset, size)];
}
return;
}
@ -1611,6 +1657,13 @@ void ggml_metal_buffer_memset_tensor(ggml_metal_buffer_t buf, struct ggml_tensor
void ggml_metal_buffer_set_tensor(ggml_metal_buffer_t buf, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
if (buf->is_shared) {
memcpy((char *) tensor->data + offset, data, size);
// Sync Managed buffer after CPU write
if (buf->dev->props.use_managed_buffers) {
struct ggml_metal_buffer_id bid = ggml_metal_buffer_get_id(buf, tensor);
[(id<MTLBuffer>)bid.metal didModifyRange:NSMakeRange(bid.offs + offset, size)];
}
return;
}
@ -1647,9 +1700,6 @@ void ggml_metal_buffer_set_tensor(ggml_metal_buffer_t buf, struct ggml_tensor *
}
[cmd_buf addCompletedHandler:^(id<MTLCommandBuffer> cb) {
// TODO: can check for errors here
GGML_UNUSED(cb);
dispatch_semaphore_signal(completion_semaphore);
}];
@ -1664,6 +1714,14 @@ void ggml_metal_buffer_set_tensor(ggml_metal_buffer_t buf, struct ggml_tensor *
void ggml_metal_buffer_get_tensor(ggml_metal_buffer_t buf, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
if (buf->is_shared) {
// For Managed buffers, sync GPU→CPU then direct memcpy
if (buf->dev->props.use_managed_buffers) {
struct ggml_metal_buffer_id bid = ggml_metal_buffer_get_id(buf, tensor);
@autoreleasepool {
[(id<MTLBuffer>)bid.metal synchronizeResource];
}
}
// Direct memcpy (Shared or Managed after sync)
memcpy(data, (const char *) tensor->data + offset, size);
return;
}
@ -1701,7 +1759,9 @@ void ggml_metal_buffer_get_tensor(ggml_metal_buffer_t buf, const struct ggml_ten
}
void ggml_metal_buffer_clear(ggml_metal_buffer_t buf, uint8_t value) {
if (buf->is_shared) {
// For Managed buffers, use GPU blit to avoid reading unsynced data
if (buf->is_shared && !buf->dev->props.use_managed_buffers) {
// True Shared mode (unified memory): direct memset OK
memset(buf->all_data, value, buf->all_size);
return;
}