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| #import "ggml-metal.h" | |
| #import "ggml-impl.h" | |
| #import "ggml-backend-impl.h" | |
| #import <Foundation/Foundation.h> | |
| #import <Metal/Metal.h> | |
| #undef MIN | |
| #undef MAX | |
| #define MIN(a, b) ((a) < (b) ? (a) : (b)) | |
| #define MAX(a, b) ((a) > (b) ? (a) : (b)) | |
| // max memory buffers that can be mapped to the device | |
| #define GGML_METAL_MAX_BUFFERS 64 | |
| // max number of MTLCommandBuffer used to submit a graph for processing | |
| #define GGML_METAL_MAX_COMMAND_BUFFERS 8 | |
| #define UNUSED(x) (void)(x) | |
| // globals | |
| // overload of MTLGPUFamilyMetal3 (not available in some environments) | |
| static const NSInteger MTLGPUFamilyMetal3_GGML = 5001; | |
| // initialized in ggml_backend_metal_reg | |
| static struct ggml_backend_reg g_ggml_backend_metal_reg; | |
| static struct ggml_backend_device g_ggml_backend_metal_device; | |
| // information about a Metal device | |
| // note: assumes single GPU device - the default one | |
| // TODO: support multiple GPU devices | |
| static struct ggml_backend_metal_device_context { | |
| id<MTLDevice> mtl_device; | |
| int mtl_device_ref_count; | |
| bool support_simdgroup_reduction; | |
| bool support_simdgroup_mm; | |
| char name[128]; | |
| } g_ggml_ctx_dev_main = { | |
| /*.mtl_device =*/ nil, | |
| /*.mtl_device_ref_count =*/ 0, | |
| /*.support_simdgroup_reduction =*/ false, | |
| /*.support_simdgroup_mm =*/ false, | |
| /*.name =*/ "", | |
| }; | |
| // acquire | |
| static id<MTLDevice> ggml_backend_metal_device_acq(struct ggml_backend_metal_device_context * ctx) { | |
| assert(ctx != NULL); | |
| if (ctx->mtl_device == nil) { | |
| ctx->mtl_device = MTLCreateSystemDefaultDevice(); | |
| ctx->support_simdgroup_reduction = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7]; | |
| ctx->support_simdgroup_reduction |= [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML]; | |
| ctx->support_simdgroup_mm = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7]; | |
| strncpy(ctx->name, [[ctx->mtl_device name] UTF8String], sizeof(ctx->name) - 1); | |
| } | |
| ctx->mtl_device_ref_count++; | |
| return ctx->mtl_device; | |
| } | |
| // release | |
| static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_context * ctx) { | |
| assert(ctx != NULL); | |
| assert(ctx->mtl_device_ref_count > 0); | |
| ctx->mtl_device_ref_count--; | |
| if (ctx->mtl_device_ref_count == 0) { | |
| [ctx->mtl_device release]; | |
| ctx->mtl_device = nil; | |
| } | |
| } | |
| // kernels | |
| struct ggml_metal_kernel { | |
| id<MTLComputePipelineState> pipeline; | |
| }; | |
| enum ggml_metal_kernel_type { | |
| GGML_METAL_KERNEL_TYPE_ADD, | |
| GGML_METAL_KERNEL_TYPE_ADD_ROW, | |
| GGML_METAL_KERNEL_TYPE_SUB, | |
| GGML_METAL_KERNEL_TYPE_SUB_ROW, | |
| GGML_METAL_KERNEL_TYPE_MUL, | |
| GGML_METAL_KERNEL_TYPE_MUL_ROW, | |
| GGML_METAL_KERNEL_TYPE_DIV, | |
| GGML_METAL_KERNEL_TYPE_DIV_ROW, | |
| GGML_METAL_KERNEL_TYPE_REPEAT_F32, | |
| GGML_METAL_KERNEL_TYPE_REPEAT_F16, | |
| GGML_METAL_KERNEL_TYPE_REPEAT_I32, | |
| GGML_METAL_KERNEL_TYPE_REPEAT_I16, | |
| GGML_METAL_KERNEL_TYPE_SCALE, | |
| GGML_METAL_KERNEL_TYPE_SCALE_4, | |
| GGML_METAL_KERNEL_TYPE_CLAMP, | |
| GGML_METAL_KERNEL_TYPE_TANH, | |
| GGML_METAL_KERNEL_TYPE_RELU, | |
| GGML_METAL_KERNEL_TYPE_SIGMOID, | |
| GGML_METAL_KERNEL_TYPE_GELU, | |
| GGML_METAL_KERNEL_TYPE_GELU_4, | |
| GGML_METAL_KERNEL_TYPE_GELU_QUICK, | |
| GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, | |
| GGML_METAL_KERNEL_TYPE_SILU, | |
| GGML_METAL_KERNEL_TYPE_SILU_4, | |
| GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, | |
| GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, | |
| GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, | |
| GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4, | |
| GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, | |
| GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, | |
| GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, | |
| GGML_METAL_KERNEL_TYPE_RMS_NORM, | |
| GGML_METAL_KERNEL_TYPE_GROUP_NORM, | |
| GGML_METAL_KERNEL_TYPE_NORM, | |
| GGML_METAL_KERNEL_TYPE_SSM_CONV_F32, | |
| GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, | |
| //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, | |
| //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, | |
| //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, | |
| GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, | |
| GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32, | |
| GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16, | |
| GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32, | |
| GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, | |
| GGML_METAL_KERNEL_TYPE_IM2COL_F16, | |
| GGML_METAL_KERNEL_TYPE_IM2COL_F32, | |
| GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16, | |
| GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32, | |
| GGML_METAL_KERNEL_TYPE_UPSCALE_F32, | |
| GGML_METAL_KERNEL_TYPE_PAD_F32, | |
| GGML_METAL_KERNEL_TYPE_ARANGE_F32, | |
| GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, | |
| 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_FLASH_ATTN_EXT_F16_H64, | |
| GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, | |
| GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, | |
| GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, | |
| GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, | |
| //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261 | |
| GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, | |
| //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261 | |
| GGML_METAL_KERNEL_TYPE_CPY_F32_F32, | |
| GGML_METAL_KERNEL_TYPE_CPY_F32_F16, | |
| GGML_METAL_KERNEL_TYPE_CPY_F16_F16, | |
| GGML_METAL_KERNEL_TYPE_CPY_F16_F32, | |
| GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, | |
| GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, | |
| GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, | |
| GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, | |
| GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, | |
| GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, | |
| GGML_METAL_KERNEL_TYPE_CONCAT, | |
| GGML_METAL_KERNEL_TYPE_SQR, | |
| GGML_METAL_KERNEL_TYPE_SQRT, | |
| GGML_METAL_KERNEL_TYPE_SIN, | |
| GGML_METAL_KERNEL_TYPE_COS, | |
| GGML_METAL_KERNEL_TYPE_SUM_ROWS, | |
| GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32, | |
| GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32, | |
| GGML_METAL_KERNEL_TYPE_COUNT | |
| }; | |
| struct ggml_backend_metal_context { | |
| id<MTLCommandQueue> queue; | |
| dispatch_queue_t d_queue; | |
| struct ggml_metal_kernel kernels[GGML_METAL_KERNEL_TYPE_COUNT]; | |
| // capture state | |
| bool capture_next_compute; | |
| bool capture_started; | |
| id<MTLCaptureScope> capture_scope; | |
| // command buffer state | |
| int n_cb; // number of extra threads used to submit the command buffers | |
| int n_nodes_0; // number of nodes submitted by the main thread | |
| int n_nodes_1; // remaining number of nodes submitted by the n_cb threads | |
| int n_nodes_per_cb; | |
| struct ggml_cgraph * gf; | |
| // the callback given to the thread pool | |
| void (^encode_async)(size_t ith); | |
| // n_cb command buffers + 1 used by the main thread | |
| id<MTLCommandBuffer> command_buffers[GGML_METAL_MAX_COMMAND_BUFFERS + 1]; | |
| // abort ggml_metal_graph_compute if callback returns true | |
| ggml_abort_callback abort_callback; | |
| void * abort_callback_data; | |
| }; | |
| // MSL code | |
| // TODO: move the contents here when ready | |
| // for now it is easier to work in a separate file | |
| // static NSString * const msl_library_source = @"see metal.metal"; | |
| // Here to assist with NSBundle Path Hack | |
| @interface GGMLMetalClass : NSObject | |
| @end | |
| @implementation GGMLMetalClass | |
| @end | |
| static void * ggml_metal_host_malloc(size_t n) { | |
| void * data = NULL; | |
| #if TARGET_OS_OSX | |
| kern_return_t err = vm_allocate((vm_map_t) mach_task_self(), (void *) &data, n, VM_FLAGS_ANYWHERE); | |
| if (err != KERN_SUCCESS) { | |
| GGML_LOG_ERROR("%s: error: vm_allocate failed\n", __func__); | |
| return NULL; | |
| } | |
| #else | |
| const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n); | |
| if (result != 0) { | |
| GGML_LOG_ERROR("%s: error: posix_memalign failed\n", __func__); | |
| return NULL; | |
| } | |
| #endif | |
| return data; | |
| } | |
| static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t dev) { | |
| GGML_LOG_INFO("%s: allocating\n", __func__); | |
| #if TARGET_OS_OSX && !GGML_METAL_NDEBUG | |
| // Show all the Metal device instances in the system | |
| NSArray * devices = MTLCopyAllDevices(); | |
| for (id<MTLDevice> device in devices) { | |
| GGML_LOG_INFO("%s: found device: %s\n", __func__, [[device name] UTF8String]); | |
| } | |
| [devices release]; // since it was created by a *Copy* C method | |
| #endif | |
| // init context | |
| struct ggml_backend_metal_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_context)); | |
| struct ggml_backend_metal_device_context * ctx_dev = dev->context; | |
| id<MTLDevice> device = ggml_backend_metal_device_acq(ctx_dev); | |
| GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]); | |
| ctx->queue = [device newCommandQueue]; | |
| ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT); | |
| id<MTLLibrary> metal_library; | |
| // load library | |
| // | |
| // - first check if the library is embedded | |
| // - then check if the library is in the bundle | |
| // - if not found, load the source and compile it | |
| // - if that fails, return NULL | |
| { | |
| NSBundle * bundle = nil; | |
| #ifdef SWIFT_PACKAGE | |
| bundle = SWIFTPM_MODULE_BUNDLE; | |
| #else | |
| bundle = [NSBundle bundleForClass:[GGMLMetalClass class]]; | |
| #endif | |
| NSError * error = nil; | |
| #if GGML_METAL_EMBED_LIBRARY | |
| const bool try_metallib = false; | |
| #else | |
| const bool try_metallib = true; | |
| #endif | |
| NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"]; | |
| if (try_metallib && path_lib != nil) { | |
| // pre-compiled library found | |
| NSURL * libURL = [NSURL fileURLWithPath:path_lib]; | |
| GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]); | |
| metal_library = [device newLibraryWithURL:libURL error:&error]; | |
| if (error) { | |
| GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); | |
| return NULL; | |
| } | |
| } else { | |
| #if GGML_METAL_EMBED_LIBRARY | |
| GGML_LOG_INFO("%s: using embedded metal library\n", __func__); | |
| extern const char ggml_metallib_start[]; | |
| extern const char ggml_metallib_end[]; | |
| NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding]; | |
| #else | |
| GGML_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__); | |
| NSString * path_source; | |
| NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"]; | |
| GGML_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil"); | |
| if (path_resource) { | |
| path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"]; | |
| } else { | |
| path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"]; | |
| } | |
| if (path_source == nil) { | |
| GGML_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__); | |
| path_source = @"ggml-metal.metal"; | |
| } | |
| GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]); | |
| NSString * src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error]; | |
| if (error) { | |
| GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); | |
| return NULL; | |
| } | |
| #endif // GGML_METAL_EMBED_LIBRARY | |
| @autoreleasepool { | |
| // dictionary of preprocessor macros | |
| NSMutableDictionary * prep = [NSMutableDictionary dictionary]; | |
| MTLCompileOptions* options = [MTLCompileOptions new]; | |
| options.preprocessorMacros = prep; | |
| //[options setFastMathEnabled:false]; | |
| metal_library = [device newLibraryWithSource:src options:options error:&error]; | |
| if (error) { | |
| GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); | |
| return NULL; | |
| } | |
| #if !__has_feature(objc_arc) | |
| [options release]; | |
| #endif | |
| } | |
| #if GGML_METAL_EMBED_LIBRARY | |
| [src release]; | |
| #endif // GGML_METAL_EMBED_LIBRARY | |
| } | |
| } | |
| // print MTL GPU family: | |
| GGML_LOG_INFO("%s: GPU name: %s\n", __func__, [[device name] UTF8String]); | |
| // determine max supported GPU family | |
| // https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf | |
| // https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf | |
| { | |
| for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) { | |
| if ([device supportsFamily:i]) { | |
| GGML_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i); | |
| break; | |
| } | |
| } | |
| for (int i = MTLGPUFamilyCommon1 + 5; i >= MTLGPUFamilyCommon1; --i) { | |
| if ([device supportsFamily:i]) { | |
| GGML_LOG_INFO("%s: GPU family: MTLGPUFamilyCommon%d (%d)\n", __func__, i - (int) MTLGPUFamilyCommon1 + 1, i); | |
| break; | |
| } | |
| } | |
| for (int i = MTLGPUFamilyMetal3_GGML + 5; i >= MTLGPUFamilyMetal3_GGML; --i) { | |
| if ([device supportsFamily:i]) { | |
| GGML_LOG_INFO("%s: GPU family: MTLGPUFamilyMetal%d (%d)\n", __func__, i - (int) MTLGPUFamilyMetal3_GGML + 3, i); | |
| break; | |
| } | |
| } | |
| } | |
| GGML_LOG_INFO("%s: simdgroup reduction support = %s\n", __func__, ctx_dev->support_simdgroup_reduction ? "true" : "false"); | |
| GGML_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx_dev->support_simdgroup_mm ? "true" : "false"); | |
| GGML_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx_dev->mtl_device.hasUnifiedMemory ? "true" : "false"); | |
| ctx->capture_next_compute = false; | |
| ctx->capture_started = false; | |
| ctx->capture_scope = nil; | |
| ctx->gf = nil; | |
| ctx->encode_async = nil; | |
| for (int i = 0; i < GGML_METAL_MAX_COMMAND_BUFFERS; ++i) { | |
| ctx->command_buffers[i] = 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); | |
| } | |
| #endif | |
| // load kernels | |
| { | |
| NSError * error = nil; | |
| for (int i = 0; i < GGML_METAL_KERNEL_TYPE_COUNT; ++i) { | |
| ctx->kernels[i].pipeline = nil; | |
| } | |
| /* | |
| GGML_LOG_INFO("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ | |
| (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ | |
| (int) kernel->pipeline.threadExecutionWidth); \ | |
| */ | |
| #define GGML_METAL_ADD_KERNEL(e, name, supported) \ | |
| if (supported) { \ | |
| struct ggml_metal_kernel * kernel = &ctx->kernels[e]; \ | |
| id<MTLFunction> metal_function = [metal_library newFunctionWithName:@"kernel_"#name]; \ | |
| kernel->pipeline = [device newComputePipelineStateWithFunction:metal_function error:&error]; \ | |
| [metal_function release]; \ | |
| if (error) { \ | |
| GGML_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ | |
| [metal_library release]; \ | |
| return NULL; \ | |
| } \ | |
| } else { \ | |
| GGML_LOG_WARN("%s: skipping %-40s (not supported)\n", __func__, "kernel_"#name); \ | |
| } | |
| const bool support_simdgroup_mm = ctx_dev->support_simdgroup_mm; | |
| const bool support_simdgroup_reduction = ctx_dev->support_simdgroup_reduction; | |
| // simd_sum and simd_max requires MTLGPUFamilyApple7 | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD, add, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW, add_row, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUB, sub, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUB_ROW, sub_row, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL, mul, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F32, repeat_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F16, repeat_f16, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I32, repeat_i32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I16, repeat_i16, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIGMOID, sigmoid, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, soft_max_f16, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, soft_max_f16_4, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, soft_max_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4, soft_max_f32_4, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, diag_mask_inf_8, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, get_rows_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, get_rows_f16, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, get_rows_q4_0, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, get_rows_q4_1, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, get_rows_q5_0, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, get_rows_q5_1, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, get_rows_q8_0, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, get_rows_q2_K, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, get_rows_q3_K, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, get_rows_q4_K, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, get_rows_q5_K, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, get_rows_q6_K, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M, get_rows_iq1_m, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_CONV_F32, ssm_conv_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32, ssm_scan_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, support_simdgroup_reduction); | |
| //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, support_simdgroup_reduction); | |
| //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, support_simdgroup_reduction); | |
| //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, support_simdgroup_reduction); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32, rope_norm_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16, rope_norm_f16, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32, rope_neox_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, rope_neox_f16, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16, im2col_ext_f16, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32, im2col_ext_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true); | |
| 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_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, support_simdgroup_mm); | |
| //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, support_simdgroup_mm); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, support_simdgroup_reduction); | |
| //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, support_simdgroup_reduction); | |
| 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_F32, cpy_f32_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, cpy_f32_iq4_nl, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQRT, sqrt, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIN, sin, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS, cos, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32, pool_2d_avg_f32, true); | |
| GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32, pool_2d_max_f32, true); | |
| } | |
| [metal_library release]; | |
| return ctx; | |
| } | |
| static void ggml_metal_free(struct ggml_backend_metal_context * ctx) { | |
| GGML_LOG_INFO("%s: deallocating\n", __func__); | |
| for (int i = 0; i < GGML_METAL_KERNEL_TYPE_COUNT; ++i) { | |
| [ctx->kernels[i].pipeline release]; | |
| } | |
| Block_release(ctx->encode_async); | |
| [ctx->queue release]; | |
| dispatch_release(ctx->d_queue); | |
| free(ctx); | |
| } | |
| // temporarily defined here for compatibility between ggml-backend and the old API | |
| struct ggml_backend_metal_buffer { | |
| void * data; | |
| size_t size; | |
| id<MTLBuffer> metal; | |
| }; | |
| 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; | |
| struct ggml_backend_metal_buffer buffers[GGML_METAL_MAX_BUFFERS]; | |
| }; | |
| // finds the Metal buffer that contains the tensor data on the GPU device | |
| // 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) { | |
| //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); | |
| ggml_backend_buffer_t buffer = t->view_src ? t->view_src->buffer : t->buffer; | |
| struct ggml_backend_metal_buffer_context * buf_ctx = (struct ggml_backend_metal_buffer_context *) buffer->context; | |
| // find the view that contains the tensor fully | |
| for (int i = 0; i < buf_ctx->n_buffers; ++i) { | |
| const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->buffers[i].data; | |
| //GGML_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, buf_ctx->buffers[%d].size = %10ld\n", ioffs, tsize, ioffs + tsize, i, buf_ctx->buffers[i].size); | |
| if (ioffs >= 0 && ioffs + tsize <= (int64_t) buf_ctx->buffers[i].size) { | |
| *offs = (size_t) ioffs; | |
| //GGML_LOG_INFO("%s: tensor '%16s', offs = %8ld\n", __func__, t->name, *offs); | |
| return buf_ctx->buffers[i].metal; | |
| } | |
| } | |
| GGML_LOG_ERROR("%s: error: tensor '%s' buffer is nil\n", __func__, t->name); | |
| return nil; | |
| } | |
| static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_context * ctx_dev, const struct ggml_tensor * op) { | |
| for (size_t i = 0, n = 3; i < n; ++i) { | |
| if (op->src[i] != NULL && op->src[i]->type == GGML_TYPE_BF16) { | |
| return false; | |
| } | |
| } | |
| const bool support_simdgroup_mm = ctx_dev->support_simdgroup_mm; | |
| const bool support_simdgroup_reduction = ctx_dev->support_simdgroup_reduction; | |
| switch (op->op) { | |
| case GGML_OP_UNARY: | |
| switch (ggml_get_unary_op(op)) { | |
| case GGML_UNARY_OP_TANH: | |
| case GGML_UNARY_OP_RELU: | |
| case GGML_UNARY_OP_SIGMOID: | |
| case GGML_UNARY_OP_GELU: | |
| case GGML_UNARY_OP_GELU_QUICK: | |
| case GGML_UNARY_OP_SILU: | |
| return ggml_is_contiguous(op->src[0]); | |
| default: | |
| return false; | |
| } | |
| case GGML_OP_NONE: | |
| case GGML_OP_RESHAPE: | |
| case GGML_OP_VIEW: | |
| case GGML_OP_TRANSPOSE: | |
| case GGML_OP_PERMUTE: | |
| case GGML_OP_CONCAT: | |
| case GGML_OP_ADD: | |
| case GGML_OP_SUB: | |
| case GGML_OP_ACC: | |
| case GGML_OP_MUL: | |
| case GGML_OP_DIV: | |
| case GGML_OP_REPEAT: | |
| case GGML_OP_SCALE: | |
| case GGML_OP_CLAMP: | |
| return true; | |
| case GGML_OP_SQR: | |
| case GGML_OP_SQRT: | |
| case GGML_OP_SIN: | |
| case GGML_OP_COS: | |
| return ggml_is_contiguous(op->src[0]); | |
| case GGML_OP_SUM_ROWS: | |
| case GGML_OP_SOFT_MAX: | |
| case GGML_OP_RMS_NORM: | |
| case GGML_OP_GROUP_NORM: | |
| return support_simdgroup_reduction; | |
| case GGML_OP_NORM: | |
| case GGML_OP_ROPE: | |
| return true; | |
| case GGML_OP_IM2COL: | |
| return op->src[0]->type == GGML_TYPE_F16; | |
| case GGML_OP_POOL_1D: | |
| return false; | |
| case GGML_OP_POOL_2D: | |
| case GGML_OP_UPSCALE: | |
| case GGML_OP_PAD: | |
| case GGML_OP_ARANGE: | |
| case GGML_OP_TIMESTEP_EMBEDDING: | |
| case GGML_OP_ARGSORT: | |
| case GGML_OP_LEAKY_RELU: | |
| return true; | |
| case GGML_OP_FLASH_ATTN_EXT: | |
| if (op->src[1]->type != GGML_TYPE_F16) { | |
| return false; | |
| } | |
| if (op->src[2]->type != GGML_TYPE_F16) { | |
| return false; | |
| } | |
| if (op->src[0]->ne[0] == 256) { | |
| return false; | |
| } | |
| return support_simdgroup_mm; // TODO: over-restricted for vec-kernels | |
| case GGML_OP_SSM_CONV: | |
| case GGML_OP_SSM_SCAN: | |
| return true; | |
| case GGML_OP_MUL_MAT: | |
| case GGML_OP_MUL_MAT_ID: | |
| return support_simdgroup_reduction && | |
| (op->src[0]->type != GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F32); | |
| case GGML_OP_CPY: | |
| case GGML_OP_DUP: | |
| case GGML_OP_CONT: | |
| { | |
| switch (op->src[0]->type) { | |
| case GGML_TYPE_F32: | |
| switch (op->type) { | |
| case GGML_TYPE_F32: | |
| case GGML_TYPE_F16: | |
| case GGML_TYPE_Q8_0: | |
| case GGML_TYPE_Q4_0: | |
| case GGML_TYPE_Q4_1: | |
| case GGML_TYPE_Q5_0: | |
| case GGML_TYPE_Q5_1: | |
| case GGML_TYPE_IQ4_NL: | |
| return true; | |
| default: | |
| return false; | |
| } | |
| case GGML_TYPE_F16: | |
| switch (op->type) { | |
| case GGML_TYPE_F32: | |
| case GGML_TYPE_F16: | |
| return true; | |
| default: | |
| return false; | |
| } | |
| default: | |
| return false; | |
| }; | |
| } | |
| case GGML_OP_DIAG_MASK_INF: | |
| case GGML_OP_GET_ROWS: | |
| { | |
| return op->ne[3] == 1; | |
| } | |
| default: | |
| return false; | |
| } | |
| } | |
| static void ggml_metal_encode_node( | |
| ggml_backend_t backend, | |
| int idx, | |
| id<MTLComputeCommandEncoder> encoder) { | |
| struct ggml_backend_metal_context * ctx = backend->context; | |
| struct ggml_backend_metal_device_context * ctx_dev = backend->device->context; | |
| struct ggml_cgraph * gf = ctx->gf; | |
| struct ggml_tensor * node = ggml_graph_node(gf, idx); | |
| //GGML_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, idx, ggml_op_name(node->op)); | |
| struct ggml_tensor * src0 = node->src[0]; | |
| struct ggml_tensor * src1 = node->src[1]; | |
| struct ggml_tensor * src2 = node->src[2]; | |
| struct ggml_tensor * dst = node; | |
| if (ggml_is_empty(dst)) { | |
| return; | |
| } | |
| switch (dst->op) { | |
| case GGML_OP_NONE: | |
| case GGML_OP_RESHAPE: | |
| case GGML_OP_VIEW: | |
| case GGML_OP_TRANSPOSE: | |
| case GGML_OP_PERMUTE: | |
| { | |
| // noop -> next node | |
| } return; | |
| default: | |
| { | |
| } break; | |
| } | |
| if (!ggml_metal_supports_op(ctx_dev, dst)) { | |
| GGML_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); | |
| GGML_ABORT("unsupported op"); | |
| } | |
| const int64_t ne00 = src0 ? src0->ne[0] : 0; | |
| const int64_t ne01 = src0 ? src0->ne[1] : 0; | |
| const int64_t ne02 = src0 ? src0->ne[2] : 0; | |
| const int64_t ne03 = src0 ? src0->ne[3] : 0; | |
| const uint64_t nb00 = src0 ? src0->nb[0] : 0; | |
| const uint64_t nb01 = src0 ? src0->nb[1] : 0; | |
| const uint64_t nb02 = src0 ? src0->nb[2] : 0; | |
| const uint64_t nb03 = src0 ? src0->nb[3] : 0; | |
| const int64_t ne10 = src1 ? src1->ne[0] : 0; | |
| const int64_t ne11 = src1 ? src1->ne[1] : 0; | |
| const int64_t ne12 = src1 ? src1->ne[2] : 0; | |
| const int64_t ne13 = src1 ? src1->ne[3] : 0; | |
| const uint64_t nb10 = src1 ? src1->nb[0] : 0; | |
| const uint64_t nb11 = src1 ? src1->nb[1] : 0; | |
| const uint64_t nb12 = src1 ? src1->nb[2] : 0; | |
| const uint64_t nb13 = src1 ? src1->nb[3] : 0; | |
| const int64_t ne20 = src2 ? src2->ne[0] : 0; | |
| const int64_t ne21 = src2 ? src2->ne[1] : 0; | |
| const int64_t ne22 = src2 ? src2->ne[2] : 0; GGML_UNUSED(ne22); | |
| const int64_t ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23); | |
| const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); | |
| const uint64_t nb21 = src2 ? src2->nb[1] : 0; | |
| const uint64_t nb22 = src2 ? src2->nb[2] : 0; | |
| const uint64_t nb23 = src2 ? src2->nb[3] : 0; | |
| const int64_t ne0 = dst ? dst->ne[0] : 0; | |
| const int64_t ne1 = dst ? dst->ne[1] : 0; | |
| const int64_t ne2 = dst ? dst->ne[2] : 0; | |
| const int64_t ne3 = dst ? dst->ne[3] : 0; | |
| const uint64_t nb0 = dst ? dst->nb[0] : 0; | |
| const uint64_t nb1 = dst ? dst->nb[1] : 0; | |
| const uint64_t nb2 = dst ? dst->nb[2] : 0; | |
| const uint64_t nb3 = dst ? dst->nb[3] : 0; | |
| const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; | |
| const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; | |
| const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT; | |
| size_t offs_src0 = 0; | |
| size_t offs_src1 = 0; | |
| size_t offs_src2 = 0; | |
| size_t offs_dst = 0; | |
| id<MTLBuffer> id_src0 = src0 ? ggml_metal_get_buffer(src0, &offs_src0) : nil; | |
| id<MTLBuffer> id_src1 = src1 ? ggml_metal_get_buffer(src1, &offs_src1) : nil; | |
| id<MTLBuffer> id_src2 = src2 ? ggml_metal_get_buffer(src2, &offs_src2) : nil; | |
| id<MTLBuffer> id_dst = dst ? ggml_metal_get_buffer(dst, &offs_dst) : nil; | |
| #if 0 | |
| GGML_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); | |
| if (src0) { | |
| GGML_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, | |
| ggml_is_contiguous(src0), src0->name); | |
| } | |
| if (src1) { | |
| GGML_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, ne13, nb10, nb11, nb12, nb13, | |
| ggml_is_contiguous(src1), src1->name); | |
| } | |
| if (dst) { | |
| GGML_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld, %5lld] [%5lld, %5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, ne3, nb0, nb1, nb2, nb3, | |
| dst->name); | |
| } | |
| #endif | |
| id<MTLDevice> device = ctx_dev->mtl_device; | |
| switch (dst->op) { | |
| case GGML_OP_CONCAT: | |
| { | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; | |
| const int32_t dim = ((const int32_t *) dst->op_params)[0]; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; | |
| [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; | |
| [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; | |
| [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; | |
| [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; | |
| [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; | |
| [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; | |
| [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; | |
| [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; | |
| [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; | |
| [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; | |
| [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; | |
| [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; | |
| [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; | |
| [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; | |
| [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; | |
| [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; | |
| [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; | |
| [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; | |
| [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; | |
| [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; | |
| [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; | |
| [encoder setBytes:&dim length:sizeof(dim) atIndex:27]; | |
| const int nth = MIN(1024, ne0); | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_ADD: | |
| case GGML_OP_SUB: | |
| case GGML_OP_MUL: | |
| case GGML_OP_DIV: | |
| { | |
| GGML_ASSERT(src0t == GGML_TYPE_F32); | |
| GGML_ASSERT(src1t == GGML_TYPE_F32); | |
| const size_t offs = 0; | |
| bool bcast_row = false; | |
| int64_t nb = ne00; // used by the "row" kernels | |
| id<MTLComputePipelineState> pipeline = nil; | |
| if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) { | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| // src1 is a row | |
| GGML_ASSERT(ne11 == 1); | |
| nb = ne00 / 4; | |
| switch (dst->op) { | |
| case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break; | |
| case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB_ROW].pipeline; break; | |
| case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_ROW].pipeline; break; | |
| case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV_ROW].pipeline; break; | |
| default: GGML_ABORT("fatal error"); | |
| } | |
| bcast_row = true; | |
| } else { | |
| switch (dst->op) { | |
| case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; break; | |
| case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB].pipeline; break; | |
| case GGML_OP_MUL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL].pipeline; break; | |
| case GGML_OP_DIV: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIV].pipeline; break; | |
| default: GGML_ABORT("fatal error"); | |
| } | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; | |
| [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; | |
| [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; | |
| [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; | |
| [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; | |
| [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; | |
| [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; | |
| [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; | |
| [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; | |
| [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; | |
| [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; | |
| [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; | |
| [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; | |
| [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; | |
| [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; | |
| [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; | |
| [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; | |
| [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; | |
| [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; | |
| [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; | |
| [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; | |
| [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; | |
| [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; | |
| [encoder setBytes:&nb length:sizeof(nb) atIndex:28]; | |
| if (bcast_row) { | |
| const int64_t n = ggml_nelements(dst)/4; | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } else { | |
| const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } | |
| } break; | |
| case GGML_OP_REPEAT: | |
| { | |
| id<MTLComputePipelineState> pipeline; | |
| switch (src0t) { | |
| case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F32].pipeline; break; | |
| case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F16].pipeline; break; | |
| case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I32].pipeline; break; | |
| case GGML_TYPE_I16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I16].pipeline; break; | |
| default: GGML_ABORT("fatal error"); | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; | |
| [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; | |
| [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; | |
| [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; | |
| [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; | |
| [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; | |
| [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; | |
| [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; | |
| [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; | |
| [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; | |
| [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; | |
| [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; | |
| [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; | |
| [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; | |
| const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_ACC: | |
| { | |
| GGML_ASSERT(src0t == GGML_TYPE_F32); | |
| GGML_ASSERT(src1t == GGML_TYPE_F32); | |
| GGML_ASSERT(dstt == GGML_TYPE_F32); | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| GGML_ASSERT(ggml_is_contiguous(src1)); | |
| const size_t pnb1 = ((const int32_t *) dst->op_params)[0]; | |
| const size_t pnb2 = ((const int32_t *) dst->op_params)[1]; | |
| const size_t pnb3 = ((const int32_t *) dst->op_params)[2]; | |
| const size_t offs = ((const int32_t *) dst->op_params)[3]; | |
| const bool inplace = (bool) ((const int32_t *) dst->op_params)[4]; | |
| if (!inplace) { | |
| // run a separete kernel to cpy src->dst | |
| // not sure how to avoid this | |
| // TODO: make a simpler cpy_bytes kernel | |
| const id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; | |
| [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; | |
| [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; | |
| [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; | |
| [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; | |
| [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; | |
| [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; | |
| [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; | |
| [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; | |
| [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; | |
| [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; | |
| [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; | |
| [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; | |
| [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; | |
| [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; | |
| [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; | |
| const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } | |
| const id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; | |
| [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; | |
| [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; | |
| [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; | |
| [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:8]; | |
| [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:9]; | |
| [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:10]; | |
| [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; | |
| [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; | |
| [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; | |
| [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; | |
| [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; | |
| [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; | |
| [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; | |
| [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; | |
| [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; | |
| [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; | |
| [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; | |
| [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; | |
| [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:24]; | |
| [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:25]; | |
| [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; | |
| [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; | |
| const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_SCALE: | |
| { | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| float scale; | |
| memcpy(&scale, dst->op_params, sizeof(scale)); | |
| int64_t n = ggml_nelements(dst); | |
| id<MTLComputePipelineState> pipeline = nil; | |
| if (n % 4 == 0) { | |
| n /= 4; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE_4].pipeline; | |
| } else { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SCALE].pipeline; | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&scale length:sizeof(scale) atIndex:2]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_OP_CLAMP: | |
| { | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline; | |
| float min; | |
| float max; | |
| memcpy(&min, ((const int32_t *) dst->op_params) + 0, sizeof(float)); | |
| memcpy(&max, ((const int32_t *) dst->op_params) + 1, sizeof(float)); | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&min length:sizeof(min) atIndex:2]; | |
| [encoder setBytes:&max length:sizeof(max) atIndex:3]; | |
| const int64_t n = ggml_nelements(dst); | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_OP_UNARY: | |
| switch (ggml_get_unary_op(node)) { | |
| // we are not taking into account the strides, so for now require contiguous tensors | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| case GGML_UNARY_OP_TANH: | |
| { | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| const int64_t n = ggml_nelements(dst); | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_UNARY_OP_RELU: | |
| { | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RELU].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| const int64_t n = ggml_nelements(dst); | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_UNARY_OP_SIGMOID: | |
| { | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIGMOID].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| const int64_t n = ggml_nelements(dst); | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_UNARY_OP_GELU: | |
| { | |
| int64_t n = ggml_nelements(dst); | |
| id<MTLComputePipelineState> pipeline = nil; | |
| if (n % 4 == 0) { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_4].pipeline; | |
| n /= 4; | |
| } else { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline; | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_UNARY_OP_GELU_QUICK: | |
| { | |
| int64_t n = ggml_nelements(dst); | |
| id<MTLComputePipelineState> pipeline = nil; | |
| if (n % 4 == 0) { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK_4].pipeline; | |
| n /= 4; | |
| } else { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline; | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_UNARY_OP_SILU: | |
| { | |
| int64_t n = ggml_nelements(dst); | |
| id<MTLComputePipelineState> pipeline = nil; | |
| if (n % 4 == 0) { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU_4].pipeline; | |
| n /= 4; | |
| } else { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline; | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| default: | |
| { | |
| GGML_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op)); | |
| GGML_ABORT("fatal error"); | |
| } | |
| } break; | |
| case GGML_OP_SQR: | |
| { | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQR].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| const int64_t n = ggml_nelements(dst); | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_OP_SQRT: | |
| { | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SQRT].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| const int64_t n = ggml_nelements(dst); | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_OP_SIN: | |
| { | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIN].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| const int64_t n = ggml_nelements(dst); | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_OP_COS: | |
| { | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_COS].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| const int64_t n = ggml_nelements(dst); | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_OP_SUM_ROWS: | |
| { | |
| GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; | |
| [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; | |
| [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; | |
| [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; | |
| [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; | |
| [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; | |
| [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; | |
| [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; | |
| [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; | |
| [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; | |
| [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; | |
| [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; | |
| [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; | |
| [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; | |
| [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:18]; | |
| [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:19]; | |
| [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:20]; | |
| [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:21]; | |
| [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:22]; | |
| [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:23]; | |
| [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:24]; | |
| [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:25]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_OP_SOFT_MAX: | |
| { | |
| GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); | |
| int nth = 32; // SIMD width | |
| id<MTLComputePipelineState> pipeline = nil; | |
| const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); | |
| if (ne00%4 == 0) { | |
| while (nth < ne00/4 && nth*ne01*ne02*ne03 < 256) { | |
| nth *= 2; | |
| } | |
| if (use_f16) { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4].pipeline; | |
| } else { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4].pipeline; | |
| } | |
| } else { | |
| while (nth < ne00 && nth*ne01*ne02*ne03 < 256) { | |
| nth *= 2; | |
| } | |
| if (use_f16) { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16].pipeline; | |
| } else { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32].pipeline; | |
| } | |
| } | |
| float scale; | |
| float max_bias; | |
| memcpy(&scale, ((const int32_t *) dst->op_params) + 0, sizeof(scale)); | |
| memcpy(&max_bias, ((const int32_t *) dst->op_params) + 1, sizeof(max_bias)); | |
| const int64_t nrows_x = ggml_nrows(src0); | |
| const int64_t nrows_y = src0->ne[1]; | |
| const uint32_t n_head = nrows_x/nrows_y; | |
| const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); | |
| const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); | |
| const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| if (id_src1) { | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| } else { | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; | |
| } | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; | |
| [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; | |
| [encoder setBytes:&scale length:sizeof(scale) atIndex:6]; | |
| [encoder setBytes:&max_bias length:sizeof(max_bias) atIndex:7]; | |
| [encoder setBytes:&m0 length:sizeof(m0) atIndex:8]; | |
| [encoder setBytes:&m1 length:sizeof(m1) atIndex:9]; | |
| [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:10]; | |
| [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_DIAG_MASK_INF: | |
| { | |
| const int n_past = ((const int32_t *)(dst->op_params))[0]; | |
| id<MTLComputePipelineState> pipeline = nil; | |
| if (ne00%8 == 0) { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8].pipeline; | |
| } else { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline; | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; | |
| [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; | |
| [encoder setBytes:&n_past length:sizeof(int) atIndex:4]; | |
| if (ne00%8 == 0) { | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne00*ne01*ne02/8, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } | |
| else { | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } | |
| } break; | |
| case GGML_OP_SSM_CONV: | |
| { | |
| GGML_ASSERT(src0t == GGML_TYPE_F32); | |
| GGML_ASSERT(src1t == GGML_TYPE_F32); | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| GGML_ASSERT(ggml_is_contiguous(src1)); | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_CONV_F32].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; | |
| [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; | |
| [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; | |
| [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; | |
| [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; | |
| [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:9]; | |
| [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:10]; | |
| [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:11]; | |
| [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:12]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; | |
| [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; | |
| [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:15]; | |
| [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:16]; | |
| [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:17]; | |
| [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:18]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne1, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_OP_SSM_SCAN: | |
| { | |
| struct ggml_tensor * src3 = node->src[3]; | |
| struct ggml_tensor * src4 = node->src[4]; | |
| struct ggml_tensor * src5 = node->src[5]; | |
| GGML_ASSERT(src3); | |
| GGML_ASSERT(src4); | |
| GGML_ASSERT(src5); | |
| size_t offs_src3 = 0; | |
| size_t offs_src4 = 0; | |
| size_t offs_src5 = 0; | |
| id<MTLBuffer> id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil; | |
| id<MTLBuffer> id_src4 = src4 ? ggml_metal_get_buffer(src4, &offs_src4) : nil; | |
| id<MTLBuffer> id_src5 = src5 ? ggml_metal_get_buffer(src5, &offs_src5) : nil; | |
| const int64_t ne30 = src3->ne[0]; GGML_UNUSED(ne30); | |
| const int64_t ne31 = src3->ne[1]; GGML_UNUSED(ne31); | |
| const uint64_t nb30 = src3->nb[0]; | |
| const uint64_t nb31 = src3->nb[1]; | |
| const int64_t ne40 = src4->ne[0]; GGML_UNUSED(ne40); | |
| const int64_t ne41 = src4->ne[1]; GGML_UNUSED(ne41); | |
| const int64_t ne42 = src4->ne[2]; GGML_UNUSED(ne42); | |
| const uint64_t nb40 = src4->nb[0]; | |
| const uint64_t nb41 = src4->nb[1]; | |
| const uint64_t nb42 = src4->nb[2]; | |
| const int64_t ne50 = src5->ne[0]; GGML_UNUSED(ne50); | |
| const int64_t ne51 = src5->ne[1]; GGML_UNUSED(ne51); | |
| const int64_t ne52 = src5->ne[2]; GGML_UNUSED(ne52); | |
| const uint64_t nb50 = src5->nb[0]; | |
| const uint64_t nb51 = src5->nb[1]; | |
| const uint64_t nb52 = src5->nb[2]; | |
| const int64_t d_state = ne00; | |
| const int64_t d_inner = ne01; | |
| const int64_t n_seq_tokens = ne11; | |
| const int64_t n_seqs = ne02; | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; | |
| [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; | |
| [encoder setBuffer:id_src4 offset:offs_src4 atIndex:4]; | |
| [encoder setBuffer:id_src5 offset:offs_src5 atIndex:5]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:6]; | |
| [encoder setBytes:&d_state length:sizeof(d_state) atIndex:7]; | |
| [encoder setBytes:&d_inner length:sizeof(d_inner) atIndex:8]; | |
| [encoder setBytes:&n_seq_tokens length:sizeof(n_seq_tokens) atIndex:9]; | |
| [encoder setBytes:&n_seqs length:sizeof(n_seqs) atIndex:10]; | |
| [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:11]; | |
| [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:12]; | |
| [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:13]; | |
| [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; | |
| [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; | |
| [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; | |
| [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:17]; | |
| [encoder setBytes:&nb20 length:sizeof(nb20) atIndex:18]; | |
| [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:19]; | |
| [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:20]; | |
| [encoder setBytes:&nb30 length:sizeof(nb30) atIndex:21]; | |
| [encoder setBytes:&nb31 length:sizeof(nb31) atIndex:22]; | |
| [encoder setBytes:&nb40 length:sizeof(nb40) atIndex:23]; | |
| [encoder setBytes:&nb41 length:sizeof(nb41) atIndex:24]; | |
| [encoder setBytes:&nb42 length:sizeof(nb42) atIndex:25]; | |
| [encoder setBytes:&nb50 length:sizeof(nb50) atIndex:26]; | |
| [encoder setBytes:&nb51 length:sizeof(nb51) atIndex:27]; | |
| [encoder setBytes:&nb52 length:sizeof(nb52) atIndex:28]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(d_inner, n_seqs, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_OP_MUL_MAT: | |
| { | |
| GGML_ASSERT(ne00 == ne10); | |
| GGML_ASSERT(ne12 % ne02 == 0); | |
| GGML_ASSERT(ne13 % ne03 == 0); | |
| const uint r2 = ne12/ne02; | |
| const uint r3 = ne13/ne03; | |
| // find the break-even point where the matrix-matrix kernel becomes more efficient compared | |
| // to the matrix-vector kernel | |
| int ne11_mm_min = 1; | |
| #if 0 | |
| // the numbers below are measured on M2 Ultra for 7B and 13B models | |
| // these numbers do not translate to other devices or model sizes | |
| // TODO: need to find a better approach | |
| if ([device.name isEqualToString:@"Apple M2 Ultra"]) { | |
| switch (src0t) { | |
| case GGML_TYPE_F16: ne11_mm_min = 2; break; | |
| case GGML_TYPE_Q8_0: ne11_mm_min = 7; break; | |
| case GGML_TYPE_Q2_K: ne11_mm_min = 15; break; | |
| case GGML_TYPE_Q3_K: ne11_mm_min = 7; break; | |
| case GGML_TYPE_Q4_0: | |
| case GGML_TYPE_Q4_1: ne11_mm_min = 15; break; | |
| case GGML_TYPE_Q4_K: ne11_mm_min = 11; break; | |
| case GGML_TYPE_Q5_0: // not tested yet | |
| case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet | |
| case GGML_TYPE_Q5_K: ne11_mm_min = 7; break; | |
| case GGML_TYPE_Q6_K: ne11_mm_min = 7; break; | |
| default: ne11_mm_min = 1; break; | |
| } | |
| } | |
| #endif | |
| // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs | |
| // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel | |
| if ([device supportsFamily:MTLGPUFamilyApple7] && | |
| !ggml_is_transposed(src0) && | |
| !ggml_is_transposed(src1) && | |
| src1t == GGML_TYPE_F32 && | |
| ne00 % 32 == 0 && ne00 >= 64 && | |
| (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { | |
| //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); | |
| // some Metal matrix data types require aligned pointers | |
| // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) | |
| switch (src0->type) { | |
| case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; | |
| case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; | |
| default: break; | |
| } | |
| id<MTLComputePipelineState> pipeline = nil; | |
| switch (src0->type) { | |
| case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32 ].pipeline; break; | |
| case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32 ].pipeline; break; | |
| case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32 ].pipeline; break; | |
| case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32 ].pipeline; break; | |
| case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32 ].pipeline; break; | |
| case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32 ].pipeline; break; | |
| case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32 ].pipeline; break; | |
| case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32 ].pipeline; break; | |
| case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32 ].pipeline; break; | |
| case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32 ].pipeline; break; | |
| case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32 ].pipeline; break; | |
| case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; | |
| case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break; | |
| case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break; | |
| default: GGML_ABORT("MUL MAT-MAT not implemented"); | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; | |
| [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5]; | |
| [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6]; | |
| [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:7]; | |
| [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:8]; | |
| [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:9]; | |
| [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:10]; | |
| [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:11]; | |
| [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:12]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; | |
| [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; | |
| [encoder setBytes:&r2 length:sizeof(r2) atIndex:15]; | |
| [encoder setBytes:&r3 length:sizeof(r3) atIndex:16]; | |
| [encoder setThreadgroupMemoryLength:8192 atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; | |
| } else { | |
| int nth0 = 32; | |
| int nth1 = 1; | |
| int nrows = 1; | |
| //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); | |
| id<MTLComputePipelineState> pipeline = nil; | |
| // use custom matrix x vector kernel | |
| switch (src0t) { | |
| case GGML_TYPE_F32: | |
| { | |
| GGML_ASSERT(src1t == GGML_TYPE_F32); | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline; | |
| nrows = 4; | |
| } break; | |
| case GGML_TYPE_F16: | |
| { | |
| nth0 = 32; | |
| nth1 = 1; | |
| if (src1t == GGML_TYPE_F32) { | |
| if (ne11 * ne12 < 4) { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline; | |
| } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline; | |
| nrows = ne11; | |
| } else { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline; | |
| nrows = 4; | |
| } | |
| } else { | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline; | |
| nrows = 4; | |
| } | |
| } break; | |
| case GGML_TYPE_Q4_0: | |
| { | |
| nth0 = 8; | |
| nth1 = 8; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q4_1: | |
| { | |
| nth0 = 8; | |
| nth1 = 8; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q5_0: | |
| { | |
| nth0 = 8; | |
| nth1 = 8; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q5_1: | |
| { | |
| nth0 = 8; | |
| nth1 = 8; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q8_0: | |
| { | |
| nth0 = 8; | |
| nth1 = 8; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q2_K: | |
| { | |
| nth0 = 2; | |
| nth1 = 32; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q3_K: | |
| { | |
| nth0 = 2; | |
| nth1 = 32; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q4_K: | |
| { | |
| nth0 = 4; //1; | |
| nth1 = 8; //32; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q5_K: | |
| { | |
| nth0 = 2; | |
| nth1 = 32; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q6_K: | |
| { | |
| nth0 = 2; | |
| nth1 = 32; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ2_XXS: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ2_XS: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ3_XXS: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ3_S: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ2_S: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ1_S: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ1_M: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ4_NL: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ4_XS: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline; | |
| } break; | |
| default: | |
| { | |
| GGML_LOG_ERROR("Asserting on type %d\n", (int)src0t); | |
| GGML_ABORT("not implemented"); | |
| } | |
| }; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; | |
| [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; | |
| [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; | |
| [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; | |
| [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; | |
| [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; | |
| [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; | |
| [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; | |
| [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; | |
| [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:13]; | |
| [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:14]; | |
| [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:15]; | |
| [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:16]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; | |
| [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:18]; | |
| [encoder setBytes:&r2 length:sizeof(r2) atIndex:19]; | |
| [encoder setBytes:&r3 length:sizeof(r3) atIndex:20]; | |
| if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || | |
| src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || | |
| src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { | |
| const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; | |
| [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) { | |
| const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4; | |
| [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) { | |
| const int mem_size = 32*sizeof(float); | |
| [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_Q4_K) { | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_Q3_K) { | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_Q5_K) { | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_Q6_K) { | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } else { | |
| const int64_t ny = (ne11 + nrows - 1)/nrows; | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| } | |
| } break; | |
| case GGML_OP_MUL_MAT_ID: | |
| { | |
| const int n_as = src0->ne[2]; | |
| // src2 = ids | |
| const enum ggml_type src2t = src2->type; GGML_UNUSED(src2t); | |
| GGML_ASSERT(src2t == GGML_TYPE_I32); | |
| GGML_ASSERT(!ggml_is_transposed(src0)); | |
| GGML_ASSERT(!ggml_is_transposed(src1)); | |
| GGML_ASSERT(src1t == GGML_TYPE_F32); | |
| GGML_ASSERT(ne03 == 1); | |
| GGML_ASSERT(ne13 == 1); | |
| // find the break-even point where the matrix-matrix kernel becomes more efficient compared | |
| // to the matrix-vector kernel | |
| // ne20 = n_used_experts | |
| // ne21 = n_rows | |
| const int dst_rows = ne20*ne21; | |
| const int dst_rows_min = n_as; | |
| const int dst_rows_max = (device.maxThreadgroupMemoryLength - 32 - 8192)/4; | |
| // max size of the rowids array in the kernel shared buffer | |
| GGML_ASSERT(dst_rows <= dst_rows_max); | |
| // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs | |
| // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel | |
| // !!! | |
| // TODO: for now, always use mat-vec kernels until we figure out how to improve the | |
| // indirect matrix multiplication | |
| // !!! | |
| if ([device supportsFamily:MTLGPUFamilyApple7] && | |
| ne00 % 32 == 0 && ne00 >= 64 && | |
| dst_rows > dst_rows_min) { | |
| // some Metal matrix data types require aligned pointers | |
| // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) | |
| switch (src0->type) { | |
| case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; | |
| case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; | |
| default: break; | |
| } | |
| id<MTLComputePipelineState> pipeline = nil; | |
| switch (src0->type) { | |
| case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32 ].pipeline; break; | |
| case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32 ].pipeline; break; | |
| case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32 ].pipeline; break; | |
| case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32 ].pipeline; break; | |
| case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32 ].pipeline; break; | |
| case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32 ].pipeline; break; | |
| case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32 ].pipeline; break; | |
| case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32 ].pipeline; break; | |
| case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32 ].pipeline; break; | |
| case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32 ].pipeline; break; | |
| case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32 ].pipeline; break; | |
| case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; | |
| case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break; | |
| case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32 ].pipeline; break; | |
| case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32 ].pipeline; break; | |
| default: GGML_ABORT("MUL_MAT_ID not implemented"); | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; | |
| [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; | |
| [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; | |
| [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; | |
| [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:8]; | |
| [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:9]; | |
| [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:10]; | |
| [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; | |
| [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; | |
| [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; | |
| [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; | |
| [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; | |
| [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; | |
| [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:18]; | |
| [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:19]; | |
| [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + dst_rows*4/*sizeof(ushort2)*/, 16) atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 31)/32, (ne01 + 63)/64, n_as) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; | |
| } else { | |
| int nth0 = 32; | |
| int nth1 = 1; | |
| int nrows = 1; | |
| //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); | |
| id<MTLComputePipelineState> pipeline = nil; | |
| // use custom matrix x vector kernel | |
| switch (src0t) { | |
| case GGML_TYPE_F32: | |
| { | |
| GGML_ASSERT(src1t == GGML_TYPE_F32); | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_F16: | |
| { | |
| GGML_ASSERT(src1t == GGML_TYPE_F32); | |
| nth0 = 32; | |
| nth1 = 1; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q4_0: | |
| { | |
| nth0 = 8; | |
| nth1 = 8; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q4_1: | |
| { | |
| nth0 = 8; | |
| nth1 = 8; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q5_0: | |
| { | |
| nth0 = 8; | |
| nth1 = 8; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q5_1: | |
| { | |
| nth0 = 8; | |
| nth1 = 8; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q8_0: | |
| { | |
| nth0 = 8; | |
| nth1 = 8; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q2_K: | |
| { | |
| nth0 = 2; | |
| nth1 = 32; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q3_K: | |
| { | |
| nth0 = 2; | |
| nth1 = 32; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q4_K: | |
| { | |
| nth0 = 4; //1; | |
| nth1 = 8; //32; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q5_K: | |
| { | |
| nth0 = 2; | |
| nth1 = 32; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_Q6_K: | |
| { | |
| nth0 = 2; | |
| nth1 = 32; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ2_XXS: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ2_XS: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ3_XXS: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ3_S: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ2_S: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ1_S: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ1_M: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ4_NL: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline; | |
| } break; | |
| case GGML_TYPE_IQ4_XS: | |
| { | |
| nth0 = 4; | |
| nth1 = 16; | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline; | |
| } break; | |
| default: | |
| { | |
| GGML_LOG_ERROR("Asserting on type %d\n", (int)src2t); | |
| GGML_ABORT("not implemented"); | |
| } | |
| }; | |
| if (ggml_is_quantized(src0t)) { | |
| GGML_ASSERT(ne00 >= nth0*nth1); | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; | |
| [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; | |
| [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; | |
| [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; | |
| [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; | |
| [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:8]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:9]; | |
| [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:10]; | |
| [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:11]; | |
| [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:12]; | |
| [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:13]; | |
| [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:14]; | |
| [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:15]; | |
| [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:16]; | |
| [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:17]; | |
| [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:18]; | |
| [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:19]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:20]; | |
| [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:21]; | |
| [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:22]; | |
| const int64_t _ne1 = 1; | |
| const int tgz = dst_rows; | |
| if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || | |
| src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || | |
| src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { | |
| const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; | |
| [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) { | |
| const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4; | |
| [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) { | |
| const int mem_size = 32*sizeof(float); | |
| [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_Q4_K) { | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_Q3_K) { | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_Q5_K) { | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| else if (src0t == GGML_TYPE_Q6_K) { | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } else { | |
| const int64_t ny = (_ne1 + nrows - 1)/nrows; // = _ne1 | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; | |
| } | |
| } | |
| } break; | |
| case GGML_OP_GET_ROWS: | |
| { | |
| id<MTLComputePipelineState> pipeline = nil; | |
| switch (src0->type) { | |
| case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32 ].pipeline; break; | |
| case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16 ].pipeline; break; | |
| case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0 ].pipeline; break; | |
| case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1 ].pipeline; break; | |
| case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0 ].pipeline; break; | |
| case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1 ].pipeline; break; | |
| case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0 ].pipeline; break; | |
| case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K ].pipeline; break; | |
| case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K ].pipeline; break; | |
| case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K ].pipeline; break; | |
| case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K ].pipeline; break; | |
| case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K ].pipeline; break; | |
| case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; | |
| case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; | |
| case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break; | |
| case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S ].pipeline; break; | |
| case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S ].pipeline; break; | |
| case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S ].pipeline; break; | |
| case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M ].pipeline; break; | |
| case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL ].pipeline; break; | |
| case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS ].pipeline; break; | |
| case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; | |
| default: GGML_ABORT("not implemented"); | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; | |
| [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; | |
| [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:4]; | |
| [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:5]; | |
| [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:6]; | |
| [encoder setBytes:&nb10 length:sizeof( int64_t) atIndex:7]; | |
| [encoder setBytes:&nb11 length:sizeof( int64_t) atIndex:8]; | |
| [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:9]; | |
| [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:10]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne10, ne11, 1) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)]; | |
| } break; | |
| case GGML_OP_RMS_NORM: | |
| { | |
| GGML_ASSERT(ne00 % 4 == 0); | |
| GGML_ASSERT(ggml_is_contiguous_1(src0)); | |
| float eps; | |
| memcpy(&eps, dst->op_params, sizeof(float)); | |
| int nth = 32; // SIMD width | |
| while (nth < ne00/4 && nth < 1024) { | |
| nth *= 2; | |
| } | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; | |
| [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; | |
| [encoder setBytes:&eps length:sizeof( float) atIndex:4]; | |
| [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; | |
| const int64_t nrows = ggml_nrows(src0); | |
| [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_GROUP_NORM: | |
| { | |
| GGML_ASSERT(ne00 % 4 == 0); | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| float eps; | |
| memcpy(&eps, dst->op_params + 1, sizeof(float)); | |
| const int32_t n_groups = ((const int32_t *) dst->op_params)[0]; | |
| int nth = 32; // SIMD width | |
| //while (nth < ne00/4 && nth < 1024) { | |
| // nth *= 2; | |
| //} | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; | |
| [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; | |
| [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; | |
| [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:5]; | |
| [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:6]; | |
| [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:7]; | |
| [encoder setBytes:&n_groups length:sizeof( int32_t) atIndex:8]; | |
| [encoder setBytes:&eps length:sizeof( float) atIndex:9]; | |
| [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(n_groups, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_NORM: | |
| { | |
| GGML_ASSERT(ggml_is_contiguous_1(src0)); | |
| float eps; | |
| memcpy(&eps, dst->op_params, sizeof(float)); | |
| const int nth = MIN(256, ne00); | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; | |
| [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; | |
| [encoder setBytes:&eps length:sizeof( float) atIndex:4]; | |
| [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0]; | |
| const int64_t nrows = ggml_nrows(src0); | |
| [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_ROPE: | |
| { | |
| GGML_ASSERT(ne10 == ne02); | |
| const int nth = MIN(1024, ne00); | |
| const int n_past = ((const int32_t *) dst->op_params)[0]; | |
| const int n_dims = ((const int32_t *) dst->op_params)[1]; | |
| const int mode = ((const int32_t *) dst->op_params)[2]; | |
| // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal | |
| const int n_ctx_orig = ((const int32_t *) dst->op_params)[4]; | |
| float freq_base; | |
| float freq_scale; | |
| float ext_factor; | |
| float attn_factor; | |
| float beta_fast; | |
| float beta_slow; | |
| memcpy(&freq_base, (const int32_t *) dst->op_params + 5, sizeof(float)); | |
| memcpy(&freq_scale, (const int32_t *) dst->op_params + 6, sizeof(float)); | |
| memcpy(&ext_factor, (const int32_t *) dst->op_params + 7, sizeof(float)); | |
| memcpy(&attn_factor, (const int32_t *) dst->op_params + 8, sizeof(float)); | |
| memcpy(&beta_fast, (const int32_t *) dst->op_params + 9, sizeof(float)); | |
| memcpy(&beta_slow, (const int32_t *) dst->op_params + 10, sizeof(float)); | |
| const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; | |
| id<MTLComputePipelineState> pipeline = nil; | |
| if (!is_neox) { | |
| switch (src0->type) { | |
| case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32].pipeline; break; | |
| case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16].pipeline; break; | |
| default: GGML_ABORT("fatal error"); | |
| }; | |
| } else { | |
| switch (src0->type) { | |
| case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32].pipeline; break; | |
| case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16].pipeline; break; | |
| default: GGML_ABORT("fatal error"); | |
| }; | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| if (id_src2 != nil) { | |
| [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; | |
| } else { | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:2]; | |
| } | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; | |
| [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:4]; | |
| [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; | |
| [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; | |
| [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; | |
| [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:8]; | |
| [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:9]; | |
| [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:10]; | |
| [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:11]; | |
| [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:12]; | |
| [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:13]; | |
| [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:14]; | |
| [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:15]; | |
| [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:16]; | |
| [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:17]; | |
| [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:18]; | |
| [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:19]; | |
| [encoder setBytes:&n_past length:sizeof( int) atIndex:20]; | |
| [encoder setBytes:&n_dims length:sizeof( int) atIndex:21]; | |
| [encoder setBytes:&n_ctx_orig length:sizeof( int) atIndex:22]; | |
| [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; | |
| [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; | |
| [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; | |
| [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; | |
| [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; | |
| [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_IM2COL: | |
| { | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| GGML_ASSERT(ggml_is_contiguous(src1)); | |
| GGML_ASSERT(src0->type == GGML_TYPE_F16); | |
| GGML_ASSERT(src1->type == GGML_TYPE_F32); | |
| GGML_ASSERT( dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); | |
| const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; | |
| const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; | |
| const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; | |
| const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; | |
| const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; | |
| const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; | |
| const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; | |
| const int32_t N = src1->ne[is_2D ? 3 : 2]; | |
| const int32_t IC = src1->ne[is_2D ? 2 : 1]; | |
| const int32_t IH = is_2D ? src1->ne[1] : 1; | |
| const int32_t IW = src1->ne[0]; | |
| const int32_t KH = is_2D ? src0->ne[1] : 1; | |
| const int32_t KW = src0->ne[0]; | |
| const int32_t OH = is_2D ? dst->ne[2] : 1; | |
| const int32_t OW = dst->ne[1]; | |
| const int32_t CHW = IC * KH * KW; | |
| const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; | |
| const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; | |
| const bool is_gt_mttpt = ((size_t)(N * KH * KW)) > pipeline.maxTotalThreadsPerThreadgroup; | |
| switch (dst->type) { | |
| case GGML_TYPE_F32: { | |
| pipeline = (is_gt_mttpt ? | |
| ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32].pipeline | |
| : | |
| ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline); | |
| } break; | |
| case GGML_TYPE_F16: { | |
| pipeline = (is_gt_mttpt ? | |
| ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16].pipeline | |
| : | |
| ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline); | |
| } break; | |
| default: GGML_ABORT("fatal error"); | |
| }; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&ofs0 length:sizeof(int32_t) atIndex:2]; | |
| [encoder setBytes:&ofs1 length:sizeof(int32_t) atIndex:3]; | |
| [encoder setBytes:&IW length:sizeof(int32_t) atIndex:4]; | |
| [encoder setBytes:&IH length:sizeof(int32_t) atIndex:5]; | |
| [encoder setBytes:&CHW length:sizeof(int32_t) atIndex:6]; | |
| [encoder setBytes:&s0 length:sizeof(int32_t) atIndex:7]; | |
| [encoder setBytes:&s1 length:sizeof(int32_t) atIndex:8]; | |
| [encoder setBytes:&p0 length:sizeof(int32_t) atIndex:9]; | |
| [encoder setBytes:&p1 length:sizeof(int32_t) atIndex:10]; | |
| [encoder setBytes:&d0 length:sizeof(int32_t) atIndex:11]; | |
| [encoder setBytes:&d1 length:sizeof(int32_t) atIndex:12]; | |
| if (is_gt_mttpt) { | |
| [encoder setBytes:&N length:sizeof(int32_t) atIndex:13]; | |
| [encoder setBytes:&KH length:sizeof(int32_t) atIndex:14]; | |
| [encoder setBytes:&KW length:sizeof(int32_t) atIndex:15]; | |
| const uint64_t n_threads = MIN(pipeline.maxTotalThreadsPerThreadgroup, (uint64_t)N); | |
| const int64_t quotient = N / n_threads + (N % n_threads > 0 ? 1 : 0); | |
| [encoder dispatchThreadgroups:MTLSizeMake(quotient * CHW, OH, OW) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)]; | |
| } else { | |
| [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; | |
| } | |
| } break; | |
| case GGML_OP_UPSCALE: | |
| { | |
| GGML_ASSERT(src0->type == GGML_TYPE_F32); | |
| const float sf0 = (float)ne0/src0->ne[0]; | |
| const float sf1 = (float)ne1/src0->ne[1]; | |
| const float sf2 = (float)ne2/src0->ne[2]; | |
| const float sf3 = (float)ne3/src0->ne[3]; | |
| const id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; | |
| [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; | |
| [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; | |
| [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; | |
| [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; | |
| [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; | |
| [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; | |
| [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; | |
| [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; | |
| [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; | |
| [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; | |
| [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; | |
| [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; | |
| [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; | |
| [encoder setBytes:&sf0 length:sizeof(sf0) atIndex:18]; | |
| [encoder setBytes:&sf1 length:sizeof(sf1) atIndex:19]; | |
| [encoder setBytes:&sf2 length:sizeof(sf2) atIndex:20]; | |
| [encoder setBytes:&sf3 length:sizeof(sf3) atIndex:21]; | |
| const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_PAD: | |
| { | |
| GGML_ASSERT(src0->type == GGML_TYPE_F32); | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; | |
| [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; | |
| [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; | |
| [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; | |
| [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; | |
| [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; | |
| [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; | |
| [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; | |
| [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; | |
| [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; | |
| [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; | |
| [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; | |
| [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; | |
| [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; | |
| [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; | |
| const int nth = MIN(1024, ne0); | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_ARANGE: | |
| { | |
| GGML_ASSERT(dst->type == GGML_TYPE_F32); | |
| float start; | |
| float step; | |
| memcpy(&start, ((const int32_t *) dst->op_params) + 0, sizeof(float)); | |
| memcpy(&step, ((const int32_t *) dst->op_params) + 2, sizeof(float)); | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:0]; | |
| [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:1]; | |
| [encoder setBytes:&start length:sizeof(start) atIndex:2]; | |
| [encoder setBytes:&step length:sizeof(step) atIndex:3]; | |
| const int nth = MIN(1024, ne0); | |
| [encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_TIMESTEP_EMBEDDING: | |
| { | |
| GGML_ASSERT(src0->type == GGML_TYPE_F32); | |
| const int dim = dst->op_params[0]; | |
| const int max_period = dst->op_params[1]; | |
| const int half = dim / 2; | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:2]; | |
| [encoder setBytes:&dim length:sizeof(dim) atIndex:3]; | |
| [encoder setBytes:&max_period length:sizeof(max_period) atIndex:4]; | |
| const int nth = MIN(1024, half); | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne00, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_ARGSORT: | |
| { | |
| GGML_ASSERT(src0->type == GGML_TYPE_F32); | |
| GGML_ASSERT( dst->type == GGML_TYPE_I32); | |
| const int nrows = ggml_nrows(src0); | |
| enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; | |
| // bitonic sort requires the number of elements to be power of 2 | |
| int64_t ne00_padded = 1; | |
| while (ne00_padded < ne00) { | |
| ne00_padded *= 2; | |
| } | |
| // Metal kernels require the buffer size to be multiple of 16 bytes | |
| // https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/1443142-setthreadgroupmemorylength | |
| const int mem_size = GGML_PAD(ne00_padded*sizeof(int32_t), 16); | |
| id<MTLComputePipelineState> pipeline = nil; | |
| switch (order) { | |
| case GGML_SORT_ORDER_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; | |
| case GGML_SORT_ORDER_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; | |
| default: GGML_ABORT("fatal error"); | |
| }; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; | |
| [encoder setBytes:&ne00_padded length:sizeof( int64_t) atIndex:3]; | |
| [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00_padded, 1, 1)]; | |
| } break; | |
| case GGML_OP_LEAKY_RELU: | |
| { | |
| GGML_ASSERT(src0->type == GGML_TYPE_F32); | |
| float slope; | |
| memcpy(&slope, dst->op_params, sizeof(float)); | |
| id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&slope length:sizeof(slope) atIndex:2]; | |
| const int64_t n = ggml_nelements(dst); | |
| [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; | |
| } break; | |
| case GGML_OP_FLASH_ATTN_EXT: | |
| { | |
| GGML_ASSERT(ne00 % 4 == 0); | |
| GGML_ASSERT(ne11 % 32 == 0); | |
| GGML_ASSERT(src0->type == GGML_TYPE_F32); | |
| GGML_ASSERT(ggml_are_same_shape (src1, src2)); | |
| struct ggml_tensor * src3 = node->src[3]; | |
| size_t offs_src3 = 0; | |
| id<MTLBuffer> id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil; | |
| GGML_ASSERT(!src3 || src3->type == GGML_TYPE_F16); | |
| GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) && | |
| "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big"); | |
| const int64_t ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30); | |
| //const int64_t ne31 = src3 ? src3->ne[1] : 0; | |
| const int64_t ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32); | |
| const int64_t ne33 = src3 ? src3->ne[3] : 0; GGML_UNUSED(ne33); | |
| const uint64_t nb30 = src3 ? src3->nb[0] : 0; GGML_UNUSED(nb30); | |
| const uint64_t nb31 = src3 ? src3->nb[1] : 0; | |
| const uint64_t nb32 = src3 ? src3->nb[2] : 0; GGML_UNUSED(nb32); | |
| const uint64_t nb33 = src3 ? src3->nb[3] : 0; GGML_UNUSED(nb33); | |
| const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t); | |
| float scale; | |
| float max_bias; | |
| float logit_softcap; | |
| memcpy(&scale, ((const int32_t *) dst->op_params) + 0, sizeof(scale)); | |
| memcpy(&max_bias, ((const int32_t *) dst->op_params) + 1, sizeof(max_bias)); | |
| memcpy(&logit_softcap, ((const int32_t *) dst->op_params) + 2, sizeof(logit_softcap)); | |
| if (logit_softcap != 0.0f) { | |
| scale /= logit_softcap; | |
| } | |
| const uint32_t n_head = src0->ne[2]; | |
| const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); | |
| const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); | |
| const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); | |
| id<MTLComputePipelineState> pipeline = nil; | |
| bool use_vec_kernel = false; | |
| if (ne01 >= 4 || (ne00%128 != 0)) { | |
| switch (ne00) { | |
| case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break; | |
| case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break; | |
| case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break; | |
| case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break; | |
| case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break; | |
| //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; | |
| default: | |
| { | |
| GGML_LOG_ERROR("unsupported size: %lld\n", ne00); | |
| GGML_LOG_ERROR("add template specialization for this size\n"); | |
| GGML_ABORT("add template specialization for this size"); | |
| } | |
| } | |
| } else { | |
| use_vec_kernel = true; | |
| switch (ne00) { | |
| case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break; | |
| //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; | |
| default: | |
| { | |
| GGML_LOG_ERROR("unsupported size: %lld\n", ne00); | |
| GGML_LOG_ERROR("add template specialization for this size\n"); | |
| GGML_ABORT("add template specialization for this size"); | |
| } | |
| } | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; | |
| [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; | |
| if (id_src3) { | |
| [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; | |
| } else { | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:3]; | |
| } | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; | |
| [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; | |
| [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; | |
| [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; | |
| [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; | |
| [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; | |
| [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; | |
| [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:11]; | |
| [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:12]; | |
| [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:13]; | |
| [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:14]; | |
| [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:15]; | |
| [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:16]; | |
| [encoder setBytes:&nb21 length:sizeof(uint64_t) atIndex:17]; | |
| [encoder setBytes:&nb22 length:sizeof(uint64_t) atIndex:18]; | |
| [encoder setBytes:&nb23 length:sizeof(uint64_t) atIndex:19]; | |
| [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:20]; | |
| [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:21]; | |
| [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:22]; | |
| [encoder setBytes:&scale length:sizeof( float) atIndex:23]; | |
| [encoder setBytes:&max_bias length:sizeof( float) atIndex:24]; | |
| [encoder setBytes:&m0 length:sizeof(m0) atIndex:25]; | |
| [encoder setBytes:&m1 length:sizeof(m1) atIndex:26]; | |
| [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:27]; | |
| [encoder setBytes:&logit_softcap length:sizeof(logit_softcap) atIndex:28]; | |
| if (!use_vec_kernel) { | |
| // half8x8 kernel | |
| const int64_t nqptg = 8; // queries per threadgroup !! sync with kernel template arguments !! | |
| const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! | |
| GGML_ASSERT(nqptg <= 32); | |
| GGML_ASSERT(nqptg % 8 == 0); | |
| GGML_ASSERT(ncpsg % 32 == 0); | |
| int64_t nsgmax = 2; | |
| while (true) { | |
| const size_t smem = nqptg*(ne00 + 2*nsgmax*(ncpsg + nqptg))*(sizeof(float)/2); | |
| if (smem > device.maxThreadgroupMemoryLength) { | |
| break; | |
| } | |
| nsgmax *= 2; | |
| } | |
| nsgmax /= 2; | |
| // simdgroups per threadgroup (a.k.a. warps) | |
| const int64_t nsg = ne01 <= nqptg ? MAX(4, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))) : 4; | |
| const size_t smem = nqptg*(ne00 + 2*nsg*(ncpsg + nqptg))*(sizeof(float)/2); | |
| //printf("smem: %zu, max: %zu\n", smem, device.maxThreadgroupMemoryLength); | |
| GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength); | |
| [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; | |
| } else { | |
| // half1x4 kernel | |
| const int64_t nqptg = 1; // queries per threadgroup !! sync with kernel template arguments !! | |
| const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! | |
| GGML_ASSERT(nqptg <= 32); | |
| GGML_ASSERT(nqptg % 1 == 0); | |
| GGML_ASSERT(ncpsg % 32 == 0); | |
| // simdgroups per threadgroup (a.k.a. warps) | |
| const int64_t nsgt = MAX(2, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32)); | |
| int64_t nsg = 1; | |
| while (nsg <= nsgt) { | |
| nsg *= 2; | |
| } | |
| nsg /= 2; | |
| const size_t smem = (nqptg*(ne00 + 2*nsg*(ncpsg + nqptg)) + nsg*ne00)*(sizeof(float)/2); | |
| //printf("smem: %zu, max: %zu\n", smem, device.maxThreadgroupMemoryLength); | |
| GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength); | |
| [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0]; | |
| [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; | |
| } | |
| } break; | |
| case GGML_OP_DUP: | |
| case GGML_OP_CPY: | |
| case GGML_OP_CONT: | |
| { | |
| GGML_ASSERT(ne00 % ggml_blck_size(src0->type) == 0); | |
| int nth = MIN(1024, ne00/ggml_blck_size(src0->type)); | |
| id<MTLComputePipelineState> pipeline = nil; | |
| switch (src0t) { | |
| case GGML_TYPE_F32: | |
| { | |
| GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0); | |
| switch (dstt) { | |
| case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; | |
| case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; | |
| case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; | |
| case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; | |
| case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; | |
| case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0].pipeline; break; | |
| case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1].pipeline; break; | |
| case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL].pipeline; break; | |
| default: GGML_ABORT("not implemented"); | |
| }; | |
| } break; | |
| case GGML_TYPE_F16: | |
| { | |
| switch (dstt) { | |
| case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break; | |
| case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break; | |
| default: GGML_ABORT("not implemented"); | |
| }; | |
| } break; | |
| default: GGML_ABORT("not implemented"); | |
| } | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; | |
| [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; | |
| [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; | |
| [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; | |
| [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; | |
| [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; | |
| [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; | |
| [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; | |
| [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; | |
| [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; | |
| [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; | |
| [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; | |
| [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; | |
| [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; | |
| [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; | |
| [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; | |
| } break; | |
| case GGML_OP_POOL_2D: | |
| { | |
| GGML_ASSERT(ggml_is_contiguous(src0)); | |
| GGML_ASSERT(src0t == GGML_TYPE_F32 && src0t == dstt); | |
| const int32_t * opts = dst->op_params; | |
| enum ggml_op_pool op = opts[0]; | |
| id<MTLComputePipelineState> pipeline = nil; | |
| switch (src0t) { | |
| case GGML_TYPE_F32: { | |
| switch(op) { | |
| case GGML_OP_POOL_AVG: | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32].pipeline; break; | |
| case GGML_OP_POOL_MAX: | |
| pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32].pipeline; break; | |
| default: GGML_ASSERT(false && "not implemented"); | |
| } | |
| } break; | |
| default: GGML_ASSERT(false && "not implemented"); | |
| } | |
| const int32_t k0 = opts[1]; | |
| const int32_t k1 = opts[2]; | |
| const int32_t s0 = opts[3]; | |
| const int32_t s1 = opts[4]; | |
| const int32_t p0 = opts[5]; | |
| const int32_t p1 = opts[6]; | |
| const int64_t IH = src0->ne[1]; | |
| const int64_t IW = src0->ne[0]; | |
| const int64_t N = dst->ne[3]; | |
| const int64_t OC = dst->ne[2]; | |
| const int64_t OH = dst->ne[1]; | |
| const int64_t OW = dst->ne[0]; | |
| const int64_t parallel_elements = N * OC * OH * OW; | |
| const int64_t n_threads = MIN((int64_t)[pipeline maxTotalThreadsPerThreadgroup], parallel_elements); | |
| const int64_t n_tg = (parallel_elements + n_threads - 1) / n_threads; | |
| [encoder setComputePipelineState:pipeline]; | |
| [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; | |
| [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; | |
| [encoder setBytes:&k0 length:sizeof(int32_t) atIndex:2]; | |
| [encoder setBytes:&k1 length:sizeof(int32_t) atIndex:3]; | |
| [encoder setBytes:&s0 length:sizeof(int32_t) atIndex:4]; | |
| [encoder setBytes:&s1 length:sizeof(int32_t) atIndex:5]; | |
| [encoder setBytes:&p0 length:sizeof(int32_t) atIndex:6]; | |
| [encoder setBytes:&p1 length:sizeof(int32_t) atIndex:7]; | |
| [encoder setBytes:&IH length:sizeof(int64_t) atIndex:8]; | |
| [encoder setBytes:&IW length:sizeof(int64_t) atIndex:9]; | |
| [encoder setBytes:&OH length:sizeof(int64_t) atIndex:10]; | |
| [encoder setBytes:&OW length:sizeof(int64_t) atIndex:11]; | |
| [encoder setBytes:¶llel_elements length:sizeof(int64_t) atIndex:12]; | |
| [encoder dispatchThreadgroups:MTLSizeMake(n_tg, 1, 1) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)]; | |
| } break; | |
| default: | |
| { | |
| GGML_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op)); | |
| GGML_ABORT("fatal error"); | |
| } | |
| } | |
| } | |
| static enum ggml_status ggml_metal_graph_compute( | |
| ggml_backend_t backend, | |
| struct ggml_cgraph * gf) { | |
| struct ggml_backend_metal_context * ctx = backend->context; | |
| struct ggml_backend_metal_device_context * ctx_dev = backend->device->context; | |
| // number of nodes encoded by the main thread (empirically determined) | |
| const int n_main = 128; | |
| // number of threads in addition to the main thread | |
| const int n_cb = ctx->n_cb; | |
| // submit the ggml compute graph to the GPU by creating command buffers and encoding the ops in them | |
| // the first n_nodes_0 are encoded and submitted for processing directly by the calling thread | |
| // while these nodes are processing, we start n_cb threads to enqueue the rest of the nodes | |
| // each thread creates it's own command buffer and enqueues the ops in parallel | |
| // | |
| // tests on M1 Pro and M2 Ultra using LLaMA models, show that optimal values for n_cb are 1 or 2 | |
| @autoreleasepool { | |
| ctx->gf = gf; | |
| ctx->n_nodes_0 = MIN(n_main, gf->n_nodes); | |
| ctx->n_nodes_1 = gf->n_nodes - ctx->n_nodes_0; | |
| ctx->n_nodes_per_cb = (ctx->n_nodes_1 + ctx->n_cb - 1) / ctx->n_cb; | |
| const bool should_capture = ctx->capture_next_compute; | |
| if (should_capture) { | |
| ctx->capture_next_compute = false; | |
| if (!ctx->capture_started) { | |
| // create capture scope | |
| ctx->capture_scope = [[MTLCaptureManager sharedCaptureManager] newCaptureScopeWithDevice:ctx_dev->mtl_device]; | |
| MTLCaptureDescriptor * descriptor = [MTLCaptureDescriptor new]; | |
| descriptor.captureObject = ctx->capture_scope; | |
| descriptor.destination = MTLCaptureDestinationGPUTraceDocument; | |
| descriptor.outputURL = [NSURL fileURLWithPath:[NSString stringWithFormat:@"/tmp/perf-metal.gputrace"]]; | |
| NSError * error = nil; | |
| if (![[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]) { | |
| GGML_LOG_ERROR("%s: error: unable to start capture '%s'\n", __func__, [[error localizedDescription] UTF8String]); | |
| } else { | |
| [ctx->capture_scope beginScope]; | |
| ctx->capture_started = true; | |
| } | |
| } | |
| } | |
| // the main thread commits the first few commands immediately | |
| // command_buffer[n_cb] | |
| { | |
| id<MTLCommandBuffer> command_buffer = [ctx->queue commandBufferWithUnretainedReferences]; | |
| ctx->command_buffers[n_cb] = command_buffer; | |
| [command_buffer enqueue]; | |
| ctx->encode_async(n_cb); | |
| } | |
| // prepare the rest of the command buffers asynchronously | |
| // command_buffer[0.. n_cb) | |
| for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { | |
| id<MTLCommandBuffer> command_buffer = [ctx->queue commandBufferWithUnretainedReferences]; | |
| ctx->command_buffers[cb_idx] = command_buffer; | |
| // 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) { | |
| [command_buffer enqueue]; | |
| } | |
| } | |
| 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> command_buffer = ctx->command_buffers[n_cb]; | |
| [command_buffer waitUntilCompleted]; | |
| MTLCommandBufferStatus status = [command_buffer 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", [[command_buffer error].localizedDescription UTF8String]); | |
| } | |
| return GGML_STATUS_FAILED; | |
| } | |
| } | |
| for (int i = 0; i < n_cb; ++i) { | |
| id<MTLCommandBuffer> command_buffer = ctx->command_buffers[i]; | |
| [command_buffer waitUntilCompleted]; | |
| MTLCommandBufferStatus status = [command_buffer 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", [[command_buffer error].localizedDescription UTF8String]); | |
| } | |
| return GGML_STATUS_FAILED; | |
| } | |
| id<MTLCommandBuffer> next_buffer = (i + 1 < n_cb ? ctx->command_buffers[i + 1] : 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 (!should_capture && ctx->capture_started) { | |
| [ctx->capture_scope endScope]; | |
| [[MTLCaptureManager sharedCaptureManager] stopCapture]; | |
| } | |
| } | |
| return GGML_STATUS_SUCCESS; | |
| } | |
| //////////////////////////////////////////////////////////////////////////////// | |
| // backend interface | |
| static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer) { | |
| struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; | |
| for (int i = 0; i < ctx->n_buffers; i++) { | |
| [ctx->buffers[i].metal release]; | |
| } | |
| ggml_backend_metal_device_rel(buffer->buft->device->context); | |
| 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); | |
| } | |
| static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { | |
| struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; | |
| return ctx->all_data; | |
| } | |
| static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { | |
| memcpy((char *)tensor->data + offset, data, size); | |
| UNUSED(buffer); | |
| } | |
| static void ggml_backend_metal_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { | |
| memcpy(data, (const char *)tensor->data + offset, size); | |
| UNUSED(buffer); | |
| } | |
| static bool ggml_backend_metal_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { | |
| if (ggml_backend_buffer_is_host(src->buffer)) { | |
| memcpy(dst->data, src->data, ggml_nbytes(src)); | |
| return true; | |
| } | |
| return false; | |
| UNUSED(buffer); | |
| } | |
| static void ggml_backend_metal_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { | |
| struct ggml_backend_metal_buffer_context * ctx = (struct ggml_backend_metal_buffer_context *)buffer->context; | |
| memset(ctx->all_data, value, ctx->all_size); | |
| } | |
| static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = { | |
| /* .free_buffer = */ ggml_backend_metal_buffer_free_buffer, | |
| /* .get_base = */ ggml_backend_metal_buffer_get_base, | |
| /* .init_tensor = */ NULL, | |
| /* .memset_tensor = */ NULL, | |
| /* .set_tensor = */ ggml_backend_metal_buffer_set_tensor, | |
| /* .get_tensor = */ ggml_backend_metal_buffer_get_tensor, | |
| /* .cpy_tensor = */ ggml_backend_metal_buffer_cpy_tensor, | |
| /* .clear = */ ggml_backend_metal_buffer_clear, | |
| /* .reset = */ NULL, | |
| }; | |
| // default buffer type | |
| static const char * ggml_backend_metal_buffer_type_get_name(ggml_backend_buffer_type_t buft) { | |
| return "Metal"; | |
| UNUSED(buft); | |
| } | |
| static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device, size_t size_aligned) { | |
| #ifndef GGML_METAL_NDEBUG | |
| #if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15) | |
| if (@available(macOS 10.12, iOS 16.0, *)) { | |
| GGML_LOG_DEBUG("%s: allocated buffer, size = %8.2f MiB, (%8.2f / %8.2f)\n", | |
| __func__, | |
| size_aligned / 1024.0 / 1024.0, | |
| device.currentAllocatedSize / 1024.0 / 1024.0, | |
| device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); | |
| if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) { | |
| GGML_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); | |
| } | |
| } else { | |
| GGML_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f)\n", | |
| __func__, | |
| size_aligned / 1024.0 / 1024.0, | |
| device.currentAllocatedSize / 1024.0 / 1024.0); | |
| } | |
| #endif | |
| #endif | |
| UNUSED(device); | |
| UNUSED(size_aligned); | |
| } | |
| static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { | |
| struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context)); | |
| const size_t size_page = sysconf(_SC_PAGESIZE); | |
| size_t size_aligned = size; | |
| if ((size_aligned % size_page) != 0) { | |
| size_aligned += (size_page - (size_aligned % size_page)); | |
| } | |
| id<MTLDevice> device = ggml_backend_metal_device_acq(buft->device->context); | |
| ctx->all_data = ggml_metal_host_malloc(size_aligned); | |
| ctx->all_size = size_aligned; | |
| ctx->owned = true; | |
| 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) { | |
| ctx->buffers[0].metal = [device newBufferWithBytesNoCopy:ctx->all_data | |
| length:size_aligned | |
| options:MTLResourceStorageModeShared | |
| deallocator:nil]; | |
| } | |
| } | |
| if (size_aligned > 0 && (ctx->all_data == NULL || ctx->buffers[0].metal == nil)) { | |
| GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0); | |
| free(ctx); | |
| ggml_backend_metal_device_rel(buft->device->context); | |
| return NULL; | |
| } | |
| //ggml_backend_metal_log_allocated_size(device, size_aligned); | |
| return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size); | |
| } | |
| static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { | |
| return 32; | |
| UNUSED(buft); | |
| } | |
| static size_t ggml_backend_metal_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { | |
| id<MTLDevice> device = ggml_backend_metal_device_acq(buft->device->context); | |
| const size_t max_size = device.maxBufferLength; | |
| ggml_backend_metal_device_rel(buft->device->context); | |
| return max_size; | |
| UNUSED(buft); | |
| } | |
| static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) { | |
| return true; | |
| UNUSED(buft); | |
| } | |
| ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { | |
| static struct ggml_backend_buffer_type ggml_backend_buffer_type_metal = { | |
| /* .iface = */ { | |
| /* .get_name = */ ggml_backend_metal_buffer_type_get_name, | |
| /* .alloc_buffer = */ ggml_backend_metal_buffer_type_alloc_buffer, | |
| /* .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, | |
| }, | |
| /* .device = */ &g_ggml_backend_metal_device, | |
| /* .context = */ NULL, | |
| }; | |
| return &ggml_backend_buffer_type_metal; | |
| } | |
| static const char * ggml_backend_metal_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) { | |
| return "Metal_Mapped"; | |
| UNUSED(buft); | |
| } | |
| static ggml_backend_buffer_type_t ggml_backend_metal_buffer_from_ptr_type(void) { | |
| 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, | |
| /* .alloc_buffer = */ ggml_backend_metal_buffer_type_alloc_buffer, | |
| /* .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, | |
| }, | |
| /* .device = */ &g_ggml_backend_metal_device, | |
| /* .context = */ NULL, | |
| }; | |
| 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)); | |
| } | |
| id<MTLDevice> device = ggml_backend_metal_device_acq(&g_ggml_ctx_dev_main); | |
| // 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; | |
| } | |
| } | |
| 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) { | |
| return "Metal"; | |
| UNUSED(backend); | |
| } | |
| static void ggml_backend_metal_free(ggml_backend_t backend) { | |
| struct ggml_backend_metal_context * ctx = backend->context; | |
| struct ggml_backend_metal_device_context * ctx_dev = backend->device->context; | |
| ggml_backend_metal_device_rel(ctx_dev); | |
| ggml_metal_free(ctx); | |
| free(backend); | |
| } | |
| static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { | |
| return ggml_metal_graph_compute(backend, cgraph); | |
| } | |
| static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { | |
| GGML_ASSERT(ggml_backend_is_metal(backend)); | |
| struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context; | |
| if (ctx->n_cb != n_cb) { | |
| ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_COMMAND_BUFFERS); | |
| if (ctx->n_cb > 2) { | |
| GGML_LOG_WARN("%s: n_cb = %d, using n_cb > 2 is not recommended and can degrade the performance in some cases\n", __func__, n_cb); | |
| } | |
| } | |
| if (ctx->encode_async) { | |
| Block_release(ctx->encode_async); | |
| } | |
| ctx->encode_async = Block_copy(^(size_t iter) { | |
| const int cb_idx = iter; | |
| const int n_cb_l = ctx->n_cb; | |
| const int n_nodes_0 = ctx->n_nodes_0; | |
| const int n_nodes_1 = ctx->n_nodes_1; | |
| const int n_nodes_per_cb = ctx->n_nodes_per_cb; | |
| id<MTLCommandBuffer> command_buffer = ctx->command_buffers[cb_idx]; | |
| id<MTLComputeCommandEncoder> encoder = [command_buffer computeCommandEncoder]; | |
| int node_start = 0; | |
| int node_end = n_nodes_0; | |
| if (cb_idx < n_cb_l) { | |
| node_start = n_nodes_0 + ( (cb_idx + 0) * n_nodes_per_cb); | |
| node_end = n_nodes_0 + (MIN((cb_idx == n_cb_l - 1) ? n_nodes_1 : (cb_idx + 1) * n_nodes_per_cb, n_nodes_1)); | |
| } | |
| const bool should_capture = ctx->capture_next_compute; | |
| for (int idx = node_start; idx < node_end; ++idx) { | |
| if (should_capture) { | |
| [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(ggml_graph_node(ctx->gf, idx)) encoding:NSUTF8StringEncoding]]; | |
| } | |
| ggml_metal_encode_node(backend, idx, encoder); | |
| if (should_capture) { | |
| [encoder popDebugGroup]; | |
| } | |
| } | |
| [encoder endEncoding]; | |
| if (cb_idx < 2 || ctx->abort_callback == NULL) { | |
| [command_buffer commit]; | |
| } | |
| }); | |
| } | |
| 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, | |
| /* .graph_plan_create = */ NULL, | |
| /* .graph_plan_free = */ NULL, | |
| /* .graph_plan_update = */ NULL, | |
| /* .graph_plan_compute = */ NULL, | |
| /* .graph_compute = */ ggml_backend_metal_graph_compute, | |
| /* .event_record = */ NULL, | |
| /* .event_wait = */ NULL, | |
| }; | |
| static ggml_guid_t ggml_backend_metal_guid(void) { | |
| static ggml_guid guid = { 0x81, 0xa1, 0x8b, 0x1e, 0x71, 0xec, 0x79, 0xed, 0x2b, 0x85, 0xdc, 0x8a, 0x61, 0x98, 0x30, 0xe6 }; | |
| return &guid; | |
| } | |
| // TODO: remove in the future | |
| ggml_backend_t ggml_backend_metal_init(void) { | |
| ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_metal_reg(), 0); | |
| struct ggml_backend_metal_context * ctx = ggml_metal_init(dev); | |
| if (ctx == NULL) { | |
| GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__); | |
| return NULL; | |
| } | |
| ggml_backend_t backend = malloc(sizeof(struct ggml_backend)); | |
| *backend = (struct ggml_backend) { | |
| /* .guid = */ ggml_backend_metal_guid(), | |
| /* .interface = */ ggml_backend_metal_i, | |
| /* .device = */ dev, | |
| /* .context = */ ctx, | |
| }; | |
| ggml_backend_metal_set_n_cb(backend, 1); | |
| return backend; | |
| } | |
| bool ggml_backend_is_metal(ggml_backend_t backend) { | |
| return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_metal_guid()); | |
| } | |
| void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data) { | |
| GGML_ASSERT(ggml_backend_is_metal(backend)); | |
| struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context; | |
| ctx->abort_callback = abort_callback; | |
| ctx->abort_callback_data = user_data; | |
| } | |
| bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) { | |
| GGML_ASSERT(ggml_backend_is_metal(backend)); | |
| struct ggml_backend_metal_device_context * ctx_dev = backend->device->context; | |
| return [ctx_dev->mtl_device supportsFamily:(MTLGPUFamilyApple1 + family - 1)]; | |
| } | |
| void ggml_backend_metal_capture_next_compute(ggml_backend_t backend) { | |
| GGML_ASSERT(ggml_backend_is_metal(backend)); | |
| struct ggml_backend_metal_context * ctx = (struct ggml_backend_metal_context *)backend->context; | |
| ctx->capture_next_compute = true; | |
| } | |
| // backend device | |
| static const char * ggml_backend_metal_device_get_name(ggml_backend_dev_t dev) { | |
| return "Metal"; | |
| GGML_UNUSED(dev); | |
| } | |
| static const char * ggml_backend_metal_device_get_description(ggml_backend_dev_t dev) { | |
| // acq/rel just to populate ctx->name in case it hasn't been done yet | |
| struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context; | |
| ggml_backend_metal_device_acq(ctx_dev); | |
| ggml_backend_metal_device_rel(ctx_dev); | |
| return ctx_dev->name; | |
| } | |
| static void ggml_backend_metal_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { | |
| if (@available(macOS 10.12, iOS 16.0, *)) { | |
| struct ggml_backend_metal_device_context * ctx_dev = (struct ggml_backend_metal_device_context *)dev->context; | |
| id<MTLDevice> device = ggml_backend_metal_device_acq(ctx_dev); | |
| *total = device.recommendedMaxWorkingSetSize; | |
| *free = *total - device.currentAllocatedSize; | |
| ggml_backend_metal_device_rel(ctx_dev); | |
| } else { | |
| *free = 1; | |
| *total = 1; | |
| } | |
| } | |
| static enum ggml_backend_dev_type ggml_backend_metal_device_get_type(ggml_backend_dev_t dev) { | |
| return GGML_BACKEND_DEVICE_TYPE_GPU; | |
| GGML_UNUSED(dev); | |
| } | |
| static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { | |
| props->name = ggml_backend_metal_device_get_name(dev); | |
| props->description = ggml_backend_metal_device_get_description(dev); | |
| 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, | |
| /* .buffer_from_host_ptr = */ true, | |
| /* .events = */ false, | |
| }; | |
| } | |
| static ggml_backend_t ggml_backend_metal_device_init(ggml_backend_dev_t dev, const char * params) { | |
| struct ggml_backend_metal_context * ctx = ggml_metal_init(dev); | |
| if (ctx == NULL) { | |
| GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__); | |
| return NULL; | |
| } | |
| ggml_backend_t backend = malloc(sizeof(struct ggml_backend)); | |
| *backend = (struct ggml_backend) { | |
| /* .guid = */ ggml_backend_metal_guid(), | |
| /* .interface = */ ggml_backend_metal_i, | |
| /* .device = */ dev, | |
| /* .context = */ ctx, | |
| }; | |
| ggml_backend_metal_set_n_cb(backend, 1); | |
| return backend; | |
| GGML_UNUSED(params); | |
| } | |
| static ggml_backend_buffer_type_t ggml_backend_metal_device_get_buffer_type(ggml_backend_dev_t dev) { | |
| return ggml_backend_metal_buffer_type(); | |
| GGML_UNUSED(dev); | |
| } | |
| static ggml_backend_buffer_t ggml_backend_metal_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { | |
| struct ggml_backend_metal_buffer_context * ctx = calloc(1, sizeof(struct ggml_backend_metal_buffer_context)); | |
| ctx->all_data = ptr; | |
| 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) ptr % size_page; | |
| ptr = (void *) ((char *) ptr - 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 = (struct ggml_backend_metal_device_context *)dev->context; | |
| id<MTLDevice> device = ggml_backend_metal_device_acq(ctx_dev); | |
| // the buffer fits into the max buffer size allowed by the device | |
| if (size_aligned <= device.maxBufferLength) { | |
| ctx->buffers[ctx->n_buffers].data = ptr; | |
| 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:ptr 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_tensor_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 *) ptr + 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 *) ptr + 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; | |
| } | |
| } | |
| return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size); | |
| } | |
| static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { | |
| struct ggml_backend_metal_device_context * ctx_dev = dev->context; | |
| return ggml_metal_supports_op(ctx_dev, op); | |
| } | |
| static bool ggml_backend_metal_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { | |
| return buft->iface.get_name == ggml_backend_metal_buffer_type_get_name; | |
| UNUSED(dev); | |
| } | |
| static bool ggml_backend_metal_device_offload_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { | |
| return false; | |
| GGML_UNUSED(dev); | |
| GGML_UNUSED(op); | |
| } | |
| static struct ggml_backend_device_i ggml_backend_metal_device_i = { | |
| /* .get_name = */ ggml_backend_metal_device_get_name, | |
| /* .get_description = */ ggml_backend_metal_device_get_description, | |
| /* .get_memory = */ ggml_backend_metal_device_get_memory, | |
| /* .get_type = */ ggml_backend_metal_device_get_type, | |
| /* .get_props = */ ggml_backend_metal_device_get_props, | |
| /* .init_backend = */ ggml_backend_metal_device_init, | |
| /* .get_buffer_type = */ ggml_backend_metal_device_get_buffer_type, | |
| /* .get_host_buffer_type = */ NULL, | |
| /* .buffer_from_host_ptr = */ ggml_backend_metal_device_buffer_from_ptr, | |
| /* .supports_op = */ ggml_backend_metal_device_supports_op, | |
| /* .supports_buft = */ ggml_backend_metal_device_supports_buft, | |
| /* .offload_op = */ ggml_backend_metal_device_offload_op, | |
| /* .event_new = */ NULL, | |
| /* .event_free = */ NULL, | |
| /* .event_synchronize = */ NULL, | |
| }; | |
| // backend registry | |
| static const char * ggml_backend_metal_reg_get_name(ggml_backend_reg_t reg) { | |
| return "Metal"; | |
| GGML_UNUSED(reg); | |
| } | |
| static size_t ggml_backend_metal_reg_device_count(ggml_backend_reg_t reg) { | |
| return 1; | |
| GGML_UNUSED(reg); | |
| } | |
| static ggml_backend_dev_t ggml_backend_metal_reg_device_get(ggml_backend_reg_t reg, size_t index) { | |
| GGML_ASSERT(index == 0); | |
| return &g_ggml_backend_metal_device; | |
| GGML_UNUSED(reg); | |
| GGML_UNUSED(index); | |
| } | |
| static struct ggml_backend_reg_i ggml_backend_metal_reg_i = { | |
| /* .get_name = */ ggml_backend_metal_reg_get_name, | |
| /* .device_count = */ ggml_backend_metal_reg_device_count, | |
| /* .device_get = */ ggml_backend_metal_reg_device_get, | |
| /* .get_proc_address = */ NULL, | |
| }; | |
| ggml_backend_reg_t ggml_backend_metal_reg(void) { | |
| // TODO: make this thread-safe somehow? | |
| { | |
| g_ggml_backend_metal_reg = (struct ggml_backend_reg) { | |
| /* .iface = */ ggml_backend_metal_reg_i, | |
| /* .context = */ NULL, | |
| }; | |
| g_ggml_backend_metal_device = (struct ggml_backend_device) { | |
| /* .iface = */ ggml_backend_metal_device_i, | |
| /* .reg = */ &g_ggml_backend_metal_reg, | |
| /* .context = */ &g_ggml_ctx_dev_main, | |
| }; | |
| } | |
| return &g_ggml_backend_metal_reg; | |
| } | |