#import "ggml-metal.h" #import "ggml-backend-impl.h" #import "ggml.h" #import #import #undef MIN #undef MAX #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) #ifdef GGML_METAL_NDEBUG #define GGML_METAL_LOG_INFO(...) #define GGML_METAL_LOG_WARN(...) #define GGML_METAL_LOG_ERROR(...) #else #define GGML_METAL_LOG_INFO(...) ggml_metal_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__) #define GGML_METAL_LOG_WARN(...) ggml_metal_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__) #define GGML_METAL_LOG_ERROR(...) ggml_metal_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__) #endif #define UNUSED(x) (void)(x) #define GGML_MAX_CONCUR (2*GGML_DEFAULT_GRAPH_SIZE) #define GGML_METAL_MAX_KERNELS 256 struct ggml_metal_buffer { const char * name; void * data; size_t size; id metal; }; struct ggml_metal_kernel { id function; id pipeline; }; enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_ADD, GGML_METAL_KERNEL_TYPE_ADD_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_SCALE, GGML_METAL_KERNEL_TYPE_SCALE_4, GGML_METAL_KERNEL_TYPE_TANH, GGML_METAL_KERNEL_TYPE_RELU, GGML_METAL_KERNEL_TYPE_GELU, GGML_METAL_KERNEL_TYPE_GELU_QUICK, GGML_METAL_KERNEL_TYPE_SILU, GGML_METAL_KERNEL_TYPE_SOFT_MAX, GGML_METAL_KERNEL_TYPE_SOFT_MAX_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_I32, GGML_METAL_KERNEL_TYPE_RMS_NORM, GGML_METAL_KERNEL_TYPE_GROUP_NORM, GGML_METAL_KERNEL_TYPE_NORM, 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_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_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_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_ROPE_F32, GGML_METAL_KERNEL_TYPE_ROPE_F16, GGML_METAL_KERNEL_TYPE_ALIBI_F32, GGML_METAL_KERNEL_TYPE_IM2COL_F16, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, GGML_METAL_KERNEL_TYPE_PAD_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_CPY_F32_F16, GGML_METAL_KERNEL_TYPE_CPY_F32_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_F16_F16, GGML_METAL_KERNEL_TYPE_CPY_F16_F32, GGML_METAL_KERNEL_TYPE_CONCAT, GGML_METAL_KERNEL_TYPE_SQR, GGML_METAL_KERNEL_TYPE_SUM_ROWS, GGML_METAL_KERNEL_TYPE_COUNT }; struct ggml_metal_context { int n_cb; id device; id queue; id library; id command_buffers [GGML_METAL_MAX_COMMAND_BUFFERS]; id command_encoders[GGML_METAL_MAX_COMMAND_BUFFERS]; dispatch_queue_t d_queue; int n_buffers; struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS]; struct ggml_metal_kernel kernels[GGML_METAL_MAX_KERNELS]; int concur_list[GGML_MAX_CONCUR]; int concur_list_len; bool support_simdgroup_reduction; bool support_simdgroup_mm; }; // 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_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) { fprintf(stderr, "%s", msg); UNUSED(level); UNUSED(user_data); } ggml_log_callback ggml_metal_log_callback = ggml_metal_default_log_callback; void * ggml_metal_log_user_data = NULL; void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) { ggml_metal_log_callback = log_callback; ggml_metal_log_user_data = user_data; } GGML_ATTRIBUTE_FORMAT(2, 3) static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ if (ggml_metal_log_callback != NULL) { va_list args; va_start(args, format); char buffer[128]; int len = vsnprintf(buffer, 128, format, args); if (len < 128) { ggml_metal_log_callback(level, buffer, ggml_metal_log_user_data); } else { char* buffer2 = malloc(len+1); va_end(args); va_start(args, format); vsnprintf(buffer2, len+1, format, args); buffer2[len] = 0; ggml_metal_log_callback(level, buffer2, ggml_metal_log_user_data); free(buffer2); } va_end(args); } } struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_LOG_INFO("%s: allocating\n", __func__); id device; NSString * s; #if TARGET_OS_OSX // Show all the Metal device instances in the system NSArray * devices = MTLCopyAllDevices(); for (device in devices) { s = [device name]; GGML_METAL_LOG_INFO("%s: found device: %s\n", __func__, [s UTF8String]); } #endif // Pick and show default Metal device device = MTLCreateSystemDefaultDevice(); s = [device name]; GGML_METAL_LOG_INFO("%s: picking default device: %s\n", __func__, [s UTF8String]); // Configure context struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context)); ctx->device = device; ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); ctx->queue = [ctx->device newCommandQueue]; ctx->n_buffers = 0; ctx->concur_list_len = 0; ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT); // load library { NSBundle * bundle = nil; #ifdef SWIFT_PACKAGE bundle = SWIFTPM_MODULE_BUNDLE; #else bundle = [NSBundle bundleForClass:[GGMLMetalClass class]]; #endif NSError * error = nil; NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"]; if (libPath != nil) { // pre-compiled library found NSURL * libURL = [NSURL fileURLWithPath:libPath]; GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]); ctx->library = [ctx->device newLibraryWithURL:libURL error:&error]; } else { GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__); NSString * sourcePath; NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"]; GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, ggmlMetalPathResources ? [ggmlMetalPathResources UTF8String] : "nil"); if (ggmlMetalPathResources) { sourcePath = [ggmlMetalPathResources stringByAppendingPathComponent:@"ggml-metal.metal"]; } else { sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"]; } if (sourcePath == nil) { GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__); sourcePath = @"ggml-metal.metal"; } GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [sourcePath UTF8String]); NSString * src = [NSString stringWithContentsOfFile:sourcePath encoding:NSUTF8StringEncoding error:&error]; if (error) { GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); return NULL; } // dictionary of preprocessor macros NSMutableDictionary * prep = [NSMutableDictionary dictionary]; #ifdef GGML_QKK_64 prep[@"QK_K"] = @(64); #endif MTLCompileOptions* options = [MTLCompileOptions new]; options.preprocessorMacros = prep; //[options setFastMathEnabled:false]; ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error]; [options release]; [prep release]; } if (error) { GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); return NULL; } } #if TARGET_OS_OSX // print MTL GPU family: GGML_METAL_LOG_INFO("%s: GPU name: %s\n", __func__, [[ctx->device name] UTF8String]); const NSInteger MTLGPUFamilyMetal3 = 5001; // 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 ([ctx->device supportsFamily:i]) { GGML_METAL_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 ([ctx->device supportsFamily:i]) { GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyCommon%d (%d)\n", __func__, i - (int) MTLGPUFamilyCommon1 + 1, i); break; } } for (int i = MTLGPUFamilyMetal3 + 5; i >= MTLGPUFamilyMetal3; --i) { if ([ctx->device supportsFamily:i]) { GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyMetal%d (%d)\n", __func__, i - (int) MTLGPUFamilyMetal3 + 3, i); break; } } } ctx->support_simdgroup_reduction = [ctx->device supportsFamily:MTLGPUFamilyApple7]; ctx->support_simdgroup_reduction |= [ctx->device supportsFamily:MTLGPUFamilyMetal3]; ctx->support_simdgroup_mm = [ctx->device supportsFamily:MTLGPUFamilyApple7]; GGML_METAL_LOG_INFO("%s: simdgroup reduction support = %s\n", __func__, ctx->support_simdgroup_reduction ? "true" : "false"); GGML_METAL_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx->support_simdgroup_mm ? "true" : "false"); GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6); if (ctx->device.maxTransferRate != 0) { GGML_METAL_LOG_INFO("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1e6); } else { GGML_METAL_LOG_INFO("%s: maxTransferRate = built-in GPU\n", __func__); } #endif // load kernels { NSError * error = nil; for (int i = 0; i < GGML_METAL_MAX_KERNELS; ++i) { ctx->kernels[i].function = nil; ctx->kernels[i].pipeline = nil; } /* GGML_METAL_LOG_INFO("%s: loaded %-32s %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]; \ kernel->function = [ctx->library newFunctionWithName:@"kernel_"#name]; \ kernel->pipeline = [ctx->device newComputePipelineStateWithFunction:kernel->function error:&error]; \ if (error) { \ GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ return NULL; \ } \ } else { \ GGML_METAL_LOG_WARN("%s: skipping %-32s (not supported)\n", __func__, "kernel_"#name); \ } // 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_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_SCALE, scale, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, 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_GELU, gelu, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX, soft_max, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, soft_max_4, ctx->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_I32, get_rows_i32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction); //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction); //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, ctx->support_simdgroup_reduction); //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, 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_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_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_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_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_CONCAT, concat, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true); } return ctx; } void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_LOG_INFO("%s: deallocating\n", __func__); for (int i = 0; i < ctx->n_buffers; ++i) { [ctx->buffers[i].metal release]; } for (int i = 0; i < GGML_METAL_MAX_KERNELS; ++i) { if (ctx->kernels[i].pipeline) { [ctx->kernels[i].pipeline release]; } if (ctx->kernels[i].function) { [ctx->kernels[i].function release]; } } [ctx->library release]; [ctx->queue release]; [ctx->device release]; dispatch_release(ctx->d_queue); free(ctx); } void * ggml_metal_host_malloc(size_t n) { void * data = NULL; const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n); if (result != 0) { GGML_METAL_LOG_ERROR("%s: error: posix_memalign failed\n", __func__); return NULL; } return data; } void ggml_metal_host_free(void * data) { free(data); } void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) { ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); } int ggml_metal_if_optimized(struct ggml_metal_context * ctx) { return ctx->concur_list_len; } int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) { return ctx->concur_list; } // temporarily defined here for compatibility between ggml-backend and the old API struct ggml_backend_metal_buffer { void * data; size_t size; id 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 ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) { //GGML_METAL_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; // compatibility with ggml-backend if (buffer && buffer->buft == ggml_backend_metal_buffer_type()) { 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_METAL_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_METAL_LOG_INFO("%s: tensor '%16s', offs = %8ld\n", __func__, t->name, *offs); return buf_ctx->buffers[i].metal; } } GGML_METAL_LOG_ERROR("%s: error: tensor '%s' buffer is nil\n", __func__, t->name); return nil; } // find the view that contains the tensor fully for (int i = 0; i < ctx->n_buffers; ++i) { const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data; //GGML_METAL_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, ctx->buffers[%d].size = %10ld, name = %s\n", ioffs, tsize, ioffs + tsize, i, ctx->buffers[i].size, ctx->buffers[i].name); if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) { *offs = (size_t) ioffs; //GGML_METAL_LOG_INFO("%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs); return ctx->buffers[i].metal; } } GGML_METAL_LOG_ERROR("%s: error: buffer is nil\n", __func__); return nil; } bool ggml_metal_add_buffer( struct ggml_metal_context * ctx, const char * name, void * data, size_t size, size_t max_size) { if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) { GGML_METAL_LOG_ERROR("%s: error: too many buffers\n", __func__); return false; } if (data) { // verify that the buffer does not overlap with any of the existing buffers for (int i = 0; i < ctx->n_buffers; ++i) { const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data; if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) { GGML_METAL_LOG_ERROR("%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name); return false; } } 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)); } // the buffer fits into the max buffer size allowed by the device if (size_aligned <= ctx->device.maxBufferLength) { ctx->buffers[ctx->n_buffers].name = name; ctx->buffers[ctx->n_buffers].data = data; ctx->buffers[ctx->n_buffers].size = size; ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (ctx->buffers[ctx->n_buffers].metal == nil) { GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_aligned / 1024.0 / 1024.0); return false; } GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB", __func__, name, size_aligned / 1024.0 / 1024.0); ++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 = ctx->device.maxBufferLength - size_ovlp; const size_t size_view = ctx->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].name = name; 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 = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (ctx->buffers[ctx->n_buffers].metal == nil) { GGML_METAL_LOG_ERROR("%s: error: failed to allocate '%-16s' buffer, size = %8.2f MiB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0); return false; } GGML_METAL_LOG_INFO("%s: allocated '%-16s' buffer, size = %8.2f MiB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i); if (i + size_step < size) { GGML_METAL_LOG_INFO("\n"); } ++ctx->n_buffers; } } #if TARGET_OS_OSX GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", ctx->device.currentAllocatedSize / 1024.0 / 1024.0, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) { GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); } else { GGML_METAL_LOG_INFO("\n"); } #else GGML_METAL_LOG_INFO(", (%8.2f)\n", ctx->device.currentAllocatedSize / 1024.0 / 1024.0); #endif } return true; } void ggml_metal_set_tensor( struct ggml_metal_context * ctx, struct ggml_tensor * t) { size_t offs; id id_dst = ggml_metal_get_buffer(ctx, t, &offs); memcpy((void *) ((uint8_t *) id_dst.contents + offs), t->data, ggml_nbytes(t)); } void ggml_metal_get_tensor( struct ggml_metal_context * ctx, struct ggml_tensor * t) { size_t offs; id id_src = ggml_metal_get_buffer(ctx, t, &offs); memcpy(t->data, (void *) ((uint8_t *) id_src.contents + offs), ggml_nbytes(t)); } void ggml_metal_graph_find_concurrency( struct ggml_metal_context * ctx, struct ggml_cgraph * gf, bool check_mem) { int search_depth = gf->n_nodes; //we only find concurrency in this range to avoid wasting too much time int nodes_unused[GGML_MAX_CONCUR]; for (int i = 0; i < GGML_MAX_CONCUR; i++) { ctx->concur_list[i] = 0; } for (int i = 0; i < gf->n_nodes; i++) { nodes_unused[i] = 1; } ctx->concur_list_len = 0; int n_left = gf->n_nodes; int n_start = 0; // all nodes before n_start at nodes_unused array have been sorted and store back to ctx->concur_list int level_pos = 0; // at ctx->concur_list, the last layer (level) ends at level_pos while (n_left > 0) { // number of nodes at a layer (that can be issued concurrently) int concurrency = 0; for (int i = n_start; i < ((n_start + search_depth > gf->n_nodes) ? gf->n_nodes : n_start + search_depth); i++) { if (nodes_unused[i]) { // if the requirements for gf->nodes[i] are satisfied int exe_flag = 1; // scan all srcs for (int src_ind = 0; src_ind < GGML_MAX_SRC; src_ind++) { struct ggml_tensor * src_cur = gf->nodes[i]->src[src_ind]; if (src_cur) { // if is leaf nodes it's satisfied. // TODO: ggml_is_leaf() if (src_cur->op == GGML_OP_NONE && src_cur->grad == NULL) { continue; } // otherwise this src should be the output from previous nodes. int is_found = 0; // scan 2*search_depth back because we inserted barrier. //for (int j = ((level_pos - 2*search_depth) < 0 ? 0 : (level_pos - 2*search_depth)); j < level_pos; j++) { for (int j = MAX(0, level_pos - 2*search_depth); j < level_pos; j++) { if (ctx->concur_list[j] >= 0 && gf->nodes[ctx->concur_list[j]] == src_cur) { is_found = 1; break; } } if (is_found == 0) { exe_flag = 0; break; } } } if (exe_flag && check_mem) { // check if nodes[i]'s data will be overwritten by a node before nodes[i]. // if node[5] and node[3] write to the same memory region, then we can't issue node[5] before node[3] int64_t data_start = (int64_t) gf->nodes[i]->data; int64_t length = (int64_t) ggml_nbytes(gf->nodes[i]); for (int j = n_start; j < i; j++) { if (nodes_unused[j] && gf->nodes[j]->op != GGML_OP_RESHAPE \ && gf->nodes[j]->op != GGML_OP_VIEW \ && gf->nodes[j]->op != GGML_OP_TRANSPOSE \ && gf->nodes[j]->op != GGML_OP_PERMUTE) { if (((int64_t)gf->nodes[j]->data) >= data_start + length || \ ((int64_t)gf->nodes[j]->data) + (int64_t) ggml_nbytes(gf->nodes[j]) <= data_start) { continue; } exe_flag = 0; } } } if (exe_flag) { ctx->concur_list[level_pos + concurrency] = i; nodes_unused[i] = 0; concurrency++; ctx->concur_list_len++; } } } n_left -= concurrency; // adding a barrier different layer ctx->concur_list[level_pos + concurrency] = -1; ctx->concur_list_len++; // jump all sorted nodes at nodes_bak while (!nodes_unused[n_start]) { n_start++; } level_pos += concurrency + 1; } if (ctx->concur_list_len > GGML_MAX_CONCUR) { GGML_METAL_LOG_WARN("%s: too many elements for metal ctx->concur_list!\n", __func__); } } static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const struct ggml_tensor * op) { 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_GELU: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_SILU: return true; 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_ACC: case GGML_OP_MUL: case GGML_OP_DIV: case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_SUM_ROWS: return true; case GGML_OP_SOFT_MAX: case GGML_OP_RMS_NORM: case GGML_OP_GROUP_NORM: return ctx->support_simdgroup_reduction; case GGML_OP_NORM: case GGML_OP_ALIBI: case GGML_OP_ROPE: case GGML_OP_IM2COL: case GGML_OP_UPSCALE: case GGML_OP_PAD: case GGML_OP_ARGSORT: case GGML_OP_LEAKY_RELU: return true; case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: return ctx->support_simdgroup_reduction; 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_F16: case GGML_TYPE_F32: case GGML_TYPE_Q8_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: return true; default: return false; } case GGML_TYPE_F16: switch (op->type) { case GGML_TYPE_F16: case GGML_TYPE_F32: 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; } } bool ggml_metal_graph_compute( struct ggml_metal_context * ctx, struct ggml_cgraph * gf) { @autoreleasepool { // if there is ctx->concur_list, dispatch concurrently // else fallback to serial dispatch MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor; const bool has_concur = ctx->concur_list_len && ctx->concur_list_len <= GGML_MAX_CONCUR; const int n_nodes = has_concur ? ctx->concur_list_len : gf->n_nodes; edesc.dispatchType = has_concur ? MTLDispatchTypeConcurrent : MTLDispatchTypeSerial; // create multiple command buffers and enqueue them // then, we encode the graph into the command buffers in parallel const int n_cb = ctx->n_cb; for (int i = 0; i < n_cb; ++i) { ctx->command_buffers[i] = [ctx->queue commandBuffer]; // enqueue the command buffers in order to specify their execution order [ctx->command_buffers[i] enqueue]; ctx->command_encoders[i] = [ctx->command_buffers[i] computeCommandEncoderWithDescriptor: edesc]; } for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb; dispatch_async(ctx->d_queue, ^{ size_t offs_src0 = 0; size_t offs_src1 = 0; size_t offs_dst = 0; id command_buffer = ctx->command_buffers[cb_idx]; id encoder = ctx->command_encoders[cb_idx]; const int node_start = (cb_idx + 0) * n_nodes_per_cb; const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes); for (int ind = node_start; ind < node_end; ++ind) { const int i = has_concur ? ctx->concur_list[ind] : ind; if (i == -1) { [encoder memoryBarrierWithScope:MTLBarrierScopeBuffers]; continue; } //GGML_METAL_LOG_INFO("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op)); struct ggml_tensor * src0 = gf->nodes[i]->src[0]; struct ggml_tensor * src1 = gf->nodes[i]->src[1]; struct ggml_tensor * dst = gf->nodes[i]; 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 } continue; default: { } break; } if (!ggml_metal_supports_op(ctx, dst)) { GGML_METAL_LOG_ERROR("%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); GGML_ASSERT(!"unsupported op"); } #ifndef GGML_METAL_NDEBUG [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst) encoding:NSUTF8StringEncoding]]; #endif 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; UNUSED(ne13); 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; UNUSED(nb13); 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; id id_src0 = src0 ? ggml_metal_get_buffer(ctx, src0, &offs_src0) : nil; id id_src1 = src1 ? ggml_metal_get_buffer(ctx, src1, &offs_src1) : nil; id id_dst = dst ? ggml_metal_get_buffer(ctx, dst, &offs_dst) : nil; //GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); //if (src0) { // GGML_METAL_LOG_INFO("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, // ggml_is_contiguous(src0), src0->name); //} //if (src1) { // GGML_METAL_LOG_INFO("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, // ggml_is_contiguous(src1), src1->name); //} //if (dst) { // GGML_METAL_LOG_INFO("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, // dst->name); //} switch (dst->op) { case GGML_OP_CONCAT: { const int64_t nb = ne00; id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].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:&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:&nb length:sizeof(nb) 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_MUL: case GGML_OP_DIV: { const size_t offs = 0; bool bcast_row = false; int64_t nb = ne00; id 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_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_ASSERT(false); } bcast_row = true; } else { switch (dst->op) { case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].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_ASSERT(false); } } [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_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 = ((int32_t *) dst->op_params)[0]; const size_t pnb2 = ((int32_t *) dst->op_params)[1]; const size_t pnb3 = ((int32_t *) dst->op_params)[2]; const size_t offs = ((int32_t *) dst->op_params)[3]; const bool inplace = (bool) ((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 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 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)); const float scale = *(const float *) dst->op_params; int64_t n = ggml_nelements(dst); id 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_UNARY: switch (ggml_get_unary_op(gf->nodes[i])) { case GGML_UNARY_OP_TANH: { id 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 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_GELU: { id 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]; const int64_t n = ggml_nelements(dst); GGML_ASSERT(n % 4 == 0); [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_UNARY_OP_GELU_QUICK: { id 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]; const int64_t n = ggml_nelements(dst); GGML_ASSERT(n % 4 == 0); [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_UNARY_OP_SILU: { id 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]; const int64_t n = ggml_nelements(dst); GGML_ASSERT(n % 4 == 0); [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; default: { GGML_METAL_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); GGML_ASSERT(false); } } break; case GGML_OP_SQR: { GGML_ASSERT(ggml_is_contiguous(src0)); id 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_SUM_ROWS: { GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); id 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: { int nth = 32; // SIMD width id pipeline = nil; if (ne00%4 == 0) { while (nth < ne00/4 && nth < 256) { nth *= 2; } pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline; } else { while (nth < ne00 && nth < 1024) { nth *= 2; } pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline; } const float scale = ((float *) dst->op_params)[0]; [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 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 = ((int32_t *)(dst->op_params))[0]; id 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_MUL_MAT: { GGML_ASSERT(ne00 == ne10); // TODO: assert that dim2 and dim3 are contiguous 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 ([ctx->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 ([ctx->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); id 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; default: GGML_ASSERT(false && "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:&ne12 length:sizeof(ne12) atIndex:7]; [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:8]; [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:9]; [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:10]; [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:11]; [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12]; [encoder setBytes:&r2 length:sizeof(r2) atIndex:13]; [encoder setBytes:&r3 length:sizeof(r3) atIndex:14]; [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 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; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); GGML_ASSERT(false && "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 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:&ne12 length:sizeof(ne12) atIndex:11]; [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:12]; [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:13]; [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14]; [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:15]; [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:16]; [encoder setBytes:&r2 length:sizeof(r2) atIndex:17]; [encoder setBytes:&r3 length:sizeof(r3) atIndex:18]; 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_Q4_K) { [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_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_Q3_K) { #ifdef GGML_QKK_64 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; #else [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; #endif } 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: { //GGML_ASSERT(ne00 == ne10); //GGML_ASSERT(ne03 == ne13); GGML_ASSERT(src0t == GGML_TYPE_I32); const int n_as = ((int32_t *) dst->op_params)[1]; // TODO: make this more general GGML_ASSERT(n_as <= 8); // max size of the src1ids array in the kernel stack GGML_ASSERT(ne11 <= 512); struct ggml_tensor * src2 = gf->nodes[i]->src[2]; 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; 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; GGML_UNUSED(nb23); const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t); GGML_ASSERT(!ggml_is_transposed(src2)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(src1t == GGML_TYPE_F32); const uint r2 = ne12/ne22; const uint r3 = ne13/ne23; // find the break-even point where the matrix-matrix kernel becomes more efficient compared // to the matrix-vector kernel int ne11_mm_min = n_as; const int idx = ((int32_t *) dst->op_params)[0]; // batch size GGML_ASSERT(ne01 == ne11); // 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 ([ctx->device supportsFamily:MTLGPUFamilyApple7] && ne20 % 32 == 0 && ne20 >= 64 && ne11 > ne11_mm_min) { id pipeline = nil; switch (src2->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; default: GGML_ASSERT(false && "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 setBytes:&nb01 length:sizeof(nb01) atIndex:3]; [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; [encoder setBytes:&ne22 length:sizeof(ne22) atIndex:5]; [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:7]; [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:8]; [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:9]; [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:10]; [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:11]; [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:12]; [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; [encoder setBytes:&r2 length:sizeof(r2) atIndex:16]; [encoder setBytes:&r3 length:sizeof(r3) atIndex:17]; [encoder setBytes:&idx length:sizeof(idx) atIndex:18]; // TODO: how to make this an array? read Metal docs for (int j = 0; j < 8; ++j) { // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8 struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; size_t offs_src_cur = 0; id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j]; } [encoder setThreadgroupMemoryLength:8192 atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*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 pipeline = nil; // use custom matrix x vector kernel switch (src2t) { 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; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); GGML_ASSERT(false && "not implemented"); } }; if (ggml_is_quantized(src2t)) { GGML_ASSERT(ne20 >= nth0*nth1); } const int64_t _ne1 = 1; // kernels needs a reference in constant memory [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:&nb01 length:sizeof(nb01) atIndex:3]; [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; [encoder setBytes:&ne22 length:sizeof(ne22) atIndex:6]; [encoder setBytes:&nb20 length:sizeof(nb20) atIndex:7]; [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:8]; [encoder setBytes:&nb22 length:sizeof(nb22) atIndex:9]; [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; [encoder setBytes:&_ne1 length:sizeof(_ne1) 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 setBytes:&r2 length:sizeof(r2) atIndex:20]; [encoder setBytes:&r3 length:sizeof(r3) atIndex:21]; [encoder setBytes:&idx length:sizeof(idx) atIndex:22]; // TODO: how to make this an array? read Metal docs for (int j = 0; j < 8; ++j) { // NOTE: this is done like this to avoid uninitialized kernel arguments when n_as < 8 struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; size_t offs_src_cur = 0; id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j]; } if (src2t == GGML_TYPE_Q4_0 || src2t == GGML_TYPE_Q4_1 || src2t == GGML_TYPE_Q5_0 || src2t == GGML_TYPE_Q5_1 || src2t == GGML_TYPE_Q8_0 || src2t == GGML_TYPE_Q2_K) { // || src2t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src2t == GGML_TYPE_IQ2_XXS || src2t == GGML_TYPE_IQ2_XS) { const int mem_size = src2t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src2t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src2t == GGML_TYPE_Q3_K) { #ifdef GGML_QKK_64 [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; #else [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; #endif } else if (src2t == GGML_TYPE_Q5_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src2t == GGML_TYPE_Q6_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 1)/2, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else { const int64_t ny = (_ne1 + nrows - 1)/nrows; [encoder dispatchThreadgroups:MTLSizeMake(ne21, ny, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } } } break; case GGML_OP_GET_ROWS: { id 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_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; default: GGML_ASSERT(false && "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); float eps; memcpy(&eps, dst->op_params, sizeof(float)); int nth = 32; // SIMD width while (nth < ne00/4 && nth < 1024) { nth *= 2; } id 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); //float eps; //memcpy(&eps, dst->op_params, sizeof(float)); const float eps = 1e-6f; // TODO: temporarily hardcoded const int32_t n_groups = ((int32_t *) dst->op_params)[0]; int nth = 32; // SIMD width //while (nth < ne00/4 && nth < 1024) { // nth *= 2; //} id 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: { float eps; memcpy(&eps, dst->op_params, sizeof(float)); const int nth = MIN(256, ne00); id 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_ALIBI: { GGML_ASSERT((src0t == GGML_TYPE_F32)); const int nth = MIN(1024, ne00); //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_head = ((int32_t *) dst->op_params)[1]; float max_bias; memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ALIBI_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]; [encoder setBytes:&m0 length:sizeof( float) atIndex:18]; [encoder setBytes:&m1 length:sizeof( float) atIndex:19]; [encoder setBytes:&n_heads_log2_floor length:sizeof(int) atIndex:20]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; case GGML_OP_ROPE: { GGML_ASSERT(ne10 == ne02); const int nth = MIN(1024, ne00); const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); id pipeline = nil; switch (src0->type) { case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F32].pipeline; break; case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F16].pipeline; break; default: GGML_ASSERT(false); }; [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:&ne01 length:sizeof( int64_t) atIndex:4]; [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; [encoder setBytes:&nb00 length:sizeof(uint64_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:&ne0 length:sizeof( int64_t) atIndex:11]; [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; [encoder setBytes:&n_past length:sizeof( int) atIndex:19]; [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; [encoder setBytes:&mode length:sizeof( int) atIndex:21]; [encoder setBytes:&n_orig_ctx 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(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F16); 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 pipeline = nil; switch (src0->type) { case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break; case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; default: GGML_ASSERT(false); }; [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]; [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; } break; case GGML_OP_UPSCALE: { GGML_ASSERT(src0->type == GGML_TYPE_F32); const int sf = dst->op_params[0]; const id 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:&sf length:sizeof(sf) atIndex:18]; 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 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_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]; id pipeline = nil; switch (order) { case GGML_SORT_ASC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC].pipeline; break; case GGML_SORT_DESC: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC].pipeline; break; default: GGML_ASSERT(false); }; [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 dispatchThreadgroups:MTLSizeMake(1, nrows, 1) threadsPerThreadgroup:MTLSizeMake(ne00, 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 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_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 pipeline = nil; switch (src0t) { case GGML_TYPE_F32: { GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0); switch (dstt) { case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].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; default: GGML_ASSERT(false && "not implemented"); }; } break; case GGML_TYPE_F16: { switch (dstt) { case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break; case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break; default: GGML_ASSERT(false && "not implemented"); }; } break; default: GGML_ASSERT(false && "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; default: { GGML_METAL_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); GGML_ASSERT(false); } } #ifndef GGML_METAL_NDEBUG [encoder popDebugGroup]; #endif } if (encoder != nil) { [encoder endEncoding]; encoder = nil; } [command_buffer commit]; }); } // wait for all threads to finish dispatch_barrier_sync(ctx->d_queue, ^{}); // check status of command buffers // needed to detect if the device ran out-of-memory for example (#1881) for (int i = 0; i < n_cb; i++) { [ctx->command_buffers[i] waitUntilCompleted]; MTLCommandBufferStatus status = (MTLCommandBufferStatus) [ctx->command_buffers[i] status]; if (status != MTLCommandBufferStatusCompleted) { GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status); return false; } } return true; } } //////////////////////////////////////////////////////////////////////////////// // backend interface // default buffer static id g_backend_device = nil; static int g_backend_device_ref_count = 0; static id ggml_backend_metal_get_device(void) { if (g_backend_device == nil) { g_backend_device = MTLCreateSystemDefaultDevice(); } g_backend_device_ref_count++; return g_backend_device; } static void ggml_backend_metal_free_device(void) { assert(g_backend_device_ref_count > 0); g_backend_device_ref_count--; if (g_backend_device_ref_count == 0) { [g_backend_device release]; g_backend_device = nil; } } static const char * ggml_backend_metal_buffer_get_name(ggml_backend_buffer_t buffer) { return "Metal"; UNUSED(buffer); } 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_free_device(); if (ctx->owned) { free(ctx->all_data); } 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 = { /* .get_name = */ ggml_backend_metal_buffer_get_name, /* .free_buffer = */ ggml_backend_metal_buffer_free_buffer, /* .get_base = */ ggml_backend_metal_buffer_get_base, /* .init_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 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 = malloc(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 device = ggml_backend_metal_get_device(); ctx->all_data = ggml_metal_host_malloc(size_aligned); ctx->all_size = size_aligned; ctx->owned = true; ctx->n_buffers = 1; ctx->buffers[0].data = ctx->all_data; ctx->buffers[0].size = size; ctx->buffers[0].metal = [device newBufferWithBytesNoCopy:ctx->all_data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (ctx->buffers[0].metal == nil) { GGML_METAL_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_free_device(); return NULL; } GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0); #if TARGET_OS_OSX GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", device.currentAllocatedSize / 1024.0 / 1024.0, device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) { GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); } else { GGML_METAL_LOG_INFO("\n"); } #else GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0); #endif 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 bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { return ggml_backend_is_metal(backend) || ggml_backend_is_cpu(backend); 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_alloc_size = */ NULL, // defaults to ggml_nbytes /* .supports_backend = */ ggml_backend_metal_buffer_type_supports_backend, /* .is_host = */ ggml_backend_metal_buffer_type_is_host, }, /* .context = */ NULL, }; return &ggml_backend_buffer_type_metal; } // 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 = malloc(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 device = ggml_backend_metal_get_device(); // the buffer fits into the max buffer size allowed by the device if (size_aligned <= device.maxBufferLength) { ctx->buffers[ctx->n_buffers].data = data; ctx->buffers[ctx->n_buffers].size = size; ctx->buffers[ctx->n_buffers].metal = [device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (ctx->buffers[ctx->n_buffers].metal == nil) { GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0); return false; } GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0); ++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 = [device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (ctx->buffers[ctx->n_buffers].metal == nil) { GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_step_aligned / 1024.0 / 1024.0); return false; } GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, offs = %12ld", __func__, size_step_aligned / 1024.0 / 1024.0, i); if (i + size_step < size) { GGML_METAL_LOG_INFO("\n"); } ++ctx->n_buffers; } } #if TARGET_OS_OSX GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", device.currentAllocatedSize / 1024.0 / 1024.0, device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); if (device.currentAllocatedSize > device.recommendedMaxWorkingSetSize) { GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__); } else { GGML_METAL_LOG_INFO("\n"); } #else GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0); #endif return ggml_backend_buffer_init(ggml_backend_metal_buffer_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_metal_context * ctx = (struct ggml_metal_context *)backend->context; ggml_metal_free(ctx); free(backend); } static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffer_type(ggml_backend_t backend) { return ggml_backend_metal_buffer_type(); UNUSED(backend); } static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context; return ggml_metal_graph_compute(metal_ctx, cgraph); } static bool ggml_backend_metal_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context; return ggml_metal_supports_op(metal_ctx, op); } static struct ggml_backend_i ggml_backend_metal_i = { /* .get_name = */ ggml_backend_metal_name, /* .free = */ ggml_backend_metal_free, /* .get_default_buffer_type = */ ggml_backend_metal_get_default_buffer_type, /* .set_tensor_async = */ NULL, /* .get_tensor_async = */ NULL, /* .cpy_tensor_async = */ NULL, /* .synchronize = */ NULL, /* .graph_plan_create = */ NULL, /* .graph_plan_free = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_metal_graph_compute, /* .supports_op = */ ggml_backend_metal_supports_op, }; ggml_backend_t ggml_backend_metal_init(void) { struct ggml_metal_context * ctx = ggml_metal_init(GGML_DEFAULT_N_THREADS); if (ctx == NULL) { return NULL; } ggml_backend_t metal_backend = malloc(sizeof(struct ggml_backend)); *metal_backend = (struct ggml_backend) { /* .interface = */ ggml_backend_metal_i, /* .context = */ ctx, }; return metal_backend; } bool ggml_backend_is_metal(ggml_backend_t backend) { return backend && backend->iface.get_name == ggml_backend_metal_name; } void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) { GGML_ASSERT(ggml_backend_is_metal(backend)); struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context; ggml_metal_set_n_cb(ctx, n_cb); } bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) { GGML_ASSERT(ggml_backend_is_metal(backend)); struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context; return [ctx->device supportsFamily:(MTLGPUFamilyApple1 + family - 1)]; } ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); // silence warning ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data) { return ggml_backend_metal_init(); GGML_UNUSED(params); GGML_UNUSED(user_data); }