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#import "ggml-metal.h" |
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#import "ggml.h" |
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#import <Foundation/Foundation.h> |
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#import <Metal/Metal.h> |
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#import <MetalPerformanceShaders/MetalPerformanceShaders.h> |
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#ifdef GGML_METAL_NDEBUG |
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#define metal_printf(...) |
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#else |
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#define metal_printf(...) fprintf(stderr, __VA_ARGS__) |
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#endif |
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#define UNUSED(x) (void)(x) |
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struct ggml_metal_buffer { |
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const char * name; |
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void * data; |
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size_t size; |
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id<MTLBuffer> metal; |
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}; |
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struct ggml_metal_context { |
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float * logits; |
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id<MTLDevice> device; |
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id<MTLCommandQueue> queue; |
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id<MTLLibrary> library; |
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int n_buffers; |
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struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS]; |
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// custom kernels |
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#define GGML_METAL_DECL_KERNEL(name) \ |
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id<MTLFunction> function_##name; \ |
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id<MTLComputePipelineState> pipeline_##name |
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GGML_METAL_DECL_KERNEL(add); |
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GGML_METAL_DECL_KERNEL(mul); |
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GGML_METAL_DECL_KERNEL(mul_row); // TODO: avoid this extra kernel, instead extend the "mul" kernel to support broadcast |
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GGML_METAL_DECL_KERNEL(scale); |
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GGML_METAL_DECL_KERNEL(silu); |
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GGML_METAL_DECL_KERNEL(relu); |
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GGML_METAL_DECL_KERNEL(gelu); |
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GGML_METAL_DECL_KERNEL(soft_max); |
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GGML_METAL_DECL_KERNEL(diag_mask_inf); |
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GGML_METAL_DECL_KERNEL(get_rows_f16); |
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GGML_METAL_DECL_KERNEL(get_rows_q4_0); |
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GGML_METAL_DECL_KERNEL(get_rows_q4_1); |
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GGML_METAL_DECL_KERNEL(get_rows_q2_k); |
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GGML_METAL_DECL_KERNEL(get_rows_q3_k); |
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GGML_METAL_DECL_KERNEL(get_rows_q4_k); |
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GGML_METAL_DECL_KERNEL(get_rows_q5_k); |
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GGML_METAL_DECL_KERNEL(get_rows_q6_k); |
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GGML_METAL_DECL_KERNEL(rms_norm); |
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GGML_METAL_DECL_KERNEL(norm); |
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GGML_METAL_DECL_KERNEL(mul_mat_f16_f32); |
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GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32); |
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GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32); |
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GGML_METAL_DECL_KERNEL(mul_mat_q2_k_f32); |
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GGML_METAL_DECL_KERNEL(mul_mat_q3_k_f32); |
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GGML_METAL_DECL_KERNEL(mul_mat_q4_k_f32); |
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GGML_METAL_DECL_KERNEL(mul_mat_q5_k_f32); |
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GGML_METAL_DECL_KERNEL(mul_mat_q6_k_f32); |
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GGML_METAL_DECL_KERNEL(rope); |
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GGML_METAL_DECL_KERNEL(alibi_f32); |
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GGML_METAL_DECL_KERNEL(cpy_f32_f16); |
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GGML_METAL_DECL_KERNEL(cpy_f32_f32); |
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GGML_METAL_DECL_KERNEL(cpy_f16_f16); |
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#undef GGML_METAL_DECL_KERNEL |
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}; |
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// MSL code |
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// TODO: move the contents here when ready |
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// for now it is easier to work in a separate file |
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static NSString * const msl_library_source = @"see metal.metal"; |
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// Here to assist with NSBundle Path Hack |
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@interface GGMLMetalClass : NSObject |
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@end |
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@implementation GGMLMetalClass |
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@end |
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struct ggml_metal_context * ggml_metal_init(void) { |
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fprintf(stderr, "%s: allocating\n", __func__); |
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struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context)); |
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ctx->device = MTLCreateSystemDefaultDevice(); |
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ctx->queue = [ctx->device newCommandQueue]; |
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ctx->n_buffers = 0; |
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// determine if we can use MPS |
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if (MPSSupportsMTLDevice(ctx->device)) { |
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fprintf(stderr, "%s: using MPS\n", __func__); |
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} else { |
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fprintf(stderr, "%s: not using MPS\n", __func__); |
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GGML_ASSERT(false && "MPS not supported"); |
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} |
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#if 0 |
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// compile from source string and show compile log |
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{ |
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NSError * error = nil; |
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ctx->library = [ctx->device newLibraryWithSource:msl_library_source options:nil error:&error]; |
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if (error) { |
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fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]); |
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exit(1); |
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} |
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} |
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#else |
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UNUSED(msl_library_source); |
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// read the source from "ggml-metal.metal" into a string and use newLibraryWithSource |
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{ |
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NSError * error = nil; |
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//NSString * path = [[NSBundle mainBundle] pathForResource:@"../../examples/metal/metal" ofType:@"metal"]; |
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NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]]; |
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NSString * path = [bundle pathForResource:@"ggml-metal" ofType:@"metal"]; |
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fprintf(stderr, "%s: loading '%s'\n", __func__, [path UTF8String]); |
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NSString * src = [NSString stringWithContentsOfFile:path encoding:NSUTF8StringEncoding error:&error]; |
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if (error) { |
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fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]); |
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exit(1); |
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} |
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ctx->library = [ctx->device newLibraryWithSource:src options:nil error:&error]; |
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if (error) { |
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fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]); |
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exit(1); |
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} |
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} |
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#endif |
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// load kernels |
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{ |
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#define GGML_METAL_ADD_KERNEL(name) \ |
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ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \ |
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ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:nil]; \ |
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fprintf(stderr, "%s: loaded %-32s %16p\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name); |
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GGML_METAL_ADD_KERNEL(add); |
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GGML_METAL_ADD_KERNEL(mul); |
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GGML_METAL_ADD_KERNEL(mul_row); |
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GGML_METAL_ADD_KERNEL(scale); |
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GGML_METAL_ADD_KERNEL(silu); |
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GGML_METAL_ADD_KERNEL(relu); |
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GGML_METAL_ADD_KERNEL(gelu); |
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GGML_METAL_ADD_KERNEL(soft_max); |
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GGML_METAL_ADD_KERNEL(diag_mask_inf); |
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GGML_METAL_ADD_KERNEL(get_rows_f16); |
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GGML_METAL_ADD_KERNEL(get_rows_q4_0); |
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GGML_METAL_ADD_KERNEL(get_rows_q4_1); |
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GGML_METAL_ADD_KERNEL(get_rows_q2_k); |
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GGML_METAL_ADD_KERNEL(get_rows_q3_k); |
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GGML_METAL_ADD_KERNEL(get_rows_q4_k); |
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GGML_METAL_ADD_KERNEL(get_rows_q5_k); |
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GGML_METAL_ADD_KERNEL(get_rows_q6_k); |
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GGML_METAL_ADD_KERNEL(rms_norm); |
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GGML_METAL_ADD_KERNEL(norm); |
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GGML_METAL_ADD_KERNEL(mul_mat_f16_f32); |
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GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32); |
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GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32); |
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GGML_METAL_ADD_KERNEL(mul_mat_q2_k_f32); |
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GGML_METAL_ADD_KERNEL(mul_mat_q3_k_f32); |
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GGML_METAL_ADD_KERNEL(mul_mat_q4_k_f32); |
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GGML_METAL_ADD_KERNEL(mul_mat_q5_k_f32); |
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GGML_METAL_ADD_KERNEL(mul_mat_q6_k_f32); |
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GGML_METAL_ADD_KERNEL(rope); |
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GGML_METAL_ADD_KERNEL(alibi_f32); |
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GGML_METAL_ADD_KERNEL(cpy_f32_f16); |
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GGML_METAL_ADD_KERNEL(cpy_f32_f32); |
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GGML_METAL_ADD_KERNEL(cpy_f16_f16); |
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#undef GGML_METAL_ADD_KERNEL |
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} |
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fprintf(stderr, "%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); |
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fprintf(stderr, "%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); |
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if (ctx->device.maxTransferRate != 0) { |
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fprintf(stderr, "%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0); |
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} else { |
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fprintf(stderr, "%s: maxTransferRate = built-in GPU\n", __func__); |
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} |
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return ctx; |
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} |
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void ggml_metal_free(struct ggml_metal_context * ctx) { |
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fprintf(stderr, "%s: deallocating\n", __func__); |
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free(ctx); |
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} |
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// finds the Metal buffer that contains the tensor data on the GPU device |
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// the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the |
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// Metal buffer based on the host memory pointer |
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// |
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static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) { |
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//fprintf(stderr, "%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach); |
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const int64_t tsize = ggml_nbytes(t); |
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// find the view that contains the tensor fully |
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for (int i = 0; i < ctx->n_buffers; ++i) { |
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const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data; |
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if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) { |
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*offs = (size_t) ioffs; |
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//fprintf(stderr, "%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs); |
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return ctx->buffers[i].metal; |
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} |
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} |
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fprintf(stderr, "%s: error: buffer is nil\n", __func__); |
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return nil; |
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} |
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bool ggml_metal_add_buffer( |
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struct ggml_metal_context * ctx, |
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const char * name, |
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void * data, |
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size_t size, |
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size_t max_size) { |
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if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) { |
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fprintf(stderr, "%s: too many buffers\n", __func__); |
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return false; |
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} |
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if (data) { |
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// verify that the buffer does not overlap with any of the existing buffers |
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for (int i = 0; i < ctx->n_buffers; ++i) { |
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const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data; |
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if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) { |
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fprintf(stderr, "%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name); |
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return false; |
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} |
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} |
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const size_t size_page = getpagesize(); |
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size_t size_aligned = size; |
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if ((size_aligned |
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size_aligned += (size_page - (size_aligned |
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} |
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// the buffer fits into the max buffer size allowed by the device |
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if (size_aligned <= ctx->device.maxBufferLength) { |
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ctx->buffers[ctx->n_buffers].name = name; |
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ctx->buffers[ctx->n_buffers].data = data; |
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ctx->buffers[ctx->n_buffers].size = size; |
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ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; |
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if (ctx->buffers[ctx->n_buffers].metal == nil) { |
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fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0); |
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return false; |
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} |
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fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0); |
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++ctx->n_buffers; |
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} else { |
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// this overlap between the views will guarantee that the tensor with the maximum size will fully fit into |
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// one of the views |
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const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case |
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const size_t size_step = ctx->device.maxBufferLength - size_ovlp; |
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const size_t size_view = ctx->device.maxBufferLength; |
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for (size_t i = 0; i < size; i += size_step) { |
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const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i); |
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ctx->buffers[ctx->n_buffers].name = name; |
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ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) data + i); |
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ctx->buffers[ctx->n_buffers].size = size_step_aligned; |
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ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; |
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if (ctx->buffers[ctx->n_buffers].metal == nil) { |
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fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0); |
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return false; |
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} |
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fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i); |
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if (i + size_step < size) { |
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fprintf(stderr, "\n"); |
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} |
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++ctx->n_buffers; |
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} |
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} |
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fprintf(stderr, ", (%8.2f / %8.2f)", |
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ctx->device.currentAllocatedSize / 1024.0 / 1024.0, |
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ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); |
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if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) { |
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fprintf(stderr, ", warning: current allocated size is greater than the recommended max working set size\n"); |
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} else { |
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fprintf(stderr, "\n"); |
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} |
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} |
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return true; |
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} |
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void ggml_metal_set_tensor( |
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struct ggml_metal_context * ctx, |
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struct ggml_tensor * t) { |
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metal_printf("%s: set input for tensor '%s'\n", __func__, t->name); |
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size_t offs; |
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id<MTLBuffer> id_dst = ggml_metal_get_buffer(ctx, t, &offs); |
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memcpy((void *) ((uint8_t *) id_dst.contents + offs), t->data, ggml_nbytes(t)); |
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} |
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void ggml_metal_get_tensor( |
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struct ggml_metal_context * ctx, |
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struct ggml_tensor * t) { |
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metal_printf("%s: extract results for tensor '%s'\n", __func__, t->name); |
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size_t offs; |
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id<MTLBuffer> id_src = ggml_metal_get_buffer(ctx, t, &offs); |
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memcpy(t->data, (void *) ((uint8_t *) id_src.contents + offs), ggml_nbytes(t)); |
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} |
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void ggml_metal_graph_compute( |
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struct ggml_metal_context * ctx, |
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struct ggml_cgraph * gf) { |
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metal_printf("%s: evaluating graph\n", __func__); |
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// create multiple command buffers and enqueue them |
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// then, we encode the graph into the command buffers in parallel |
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const int n_cb = gf->n_threads; |
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NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb]; |
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for (int i = 0; i < n_cb; ++i) { |
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command_buffers[i] = [ctx->queue commandBuffer]; |
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// enqueue the command buffers in order to specify their execution order |
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[command_buffers[i] enqueue]; |
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} |
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// TODO: is this the best way to start threads? |
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dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT); |
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for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { |
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const int n_nodes_per_cb = (gf->n_nodes + n_cb - 1) / n_cb; |
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dispatch_async(queue, ^{ |
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size_t offs_src0 = 0; |
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size_t offs_src1 = 0; |
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size_t offs_dst = 0; |
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id<MTLCommandBuffer> command_buffer = command_buffers[cb_idx]; |
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id<MTLComputeCommandEncoder> encoder = nil; |
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const int node_start = (cb_idx + 0) * n_nodes_per_cb; |
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const int node_end = (cb_idx == n_cb - 1) ? gf->n_nodes : (cb_idx + 1) * n_nodes_per_cb; |
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for (int i = node_start; i < node_end; ++i) { |
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metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op)); |
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struct ggml_tensor * src0 = gf->nodes[i]->src0; |
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struct ggml_tensor * src1 = gf->nodes[i]->src1; |
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struct ggml_tensor * dst = gf->nodes[i]; |
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const int64_t ne00 = src0 ? src0->ne[0] : 0; |
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const int64_t ne01 = src0 ? src0->ne[1] : 0; |
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const int64_t ne02 = src0 ? src0->ne[2] : 0; |
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const int64_t ne03 = src0 ? src0->ne[3] : 0; |
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const uint64_t nb00 = src0 ? src0->nb[0] : 0; |
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const uint64_t nb01 = src0 ? src0->nb[1] : 0; |
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const uint64_t nb02 = src0 ? src0->nb[2] : 0; |
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const uint64_t nb03 = src0 ? src0->nb[3] : 0; |
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const int64_t ne10 = src1 ? src1->ne[0] : 0; |
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const int64_t ne11 = src1 ? src1->ne[1] : 0; |
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const int64_t ne12 = src1 ? src1->ne[2] : 0; |
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const int64_t ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13); |
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const uint64_t nb10 = src1 ? src1->nb[0] : 0; |
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const uint64_t nb11 = src1 ? src1->nb[1] : 0; |
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const uint64_t nb12 = src1 ? src1->nb[2] : 0; |
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const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13); |
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const int64_t ne0 = dst ? dst->ne[0] : 0; |
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const int64_t ne1 = dst ? dst->ne[1] : 0; |
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const int64_t ne2 = dst ? dst->ne[2] : 0; |
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const int64_t ne3 = dst ? dst->ne[3] : 0; |
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const uint64_t nb0 = dst ? dst->nb[0] : 0; |
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const uint64_t nb1 = dst ? dst->nb[1] : 0; |
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const uint64_t nb2 = dst ? dst->nb[2] : 0; |
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const uint64_t nb3 = dst ? dst->nb[3] : 0; |
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const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; |
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const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; |
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const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT; |
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id<MTLBuffer> id_src0 = src0 ? ggml_metal_get_buffer(ctx, src0, &offs_src0) : nil; |
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id<MTLBuffer> id_src1 = src1 ? ggml_metal_get_buffer(ctx, src1, &offs_src1) : nil; |
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id<MTLBuffer> id_dst = dst ? ggml_metal_get_buffer(ctx, dst, &offs_dst) : nil; |
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//metal_printf("%s: op - %s\n", __func__, ggml_op_name(dst->op)); |
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//if (src0) { |
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// metal_printf("%s: src0 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src0t), ne00, ne01, ne02, |
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// ggml_is_contiguous(src0), src0->name); |
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//} |
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//if (src1) { |
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// metal_printf("%s: src1 - %4s [%5lld, %5lld, %5lld], %d, %s\n", __func__, ggml_type_name(src1t), ne10, ne11, ne12, |
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// ggml_is_contiguous(src1), src1->name); |
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//} |
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//if (dst) { |
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// metal_printf("%s: dst - %4s [%5lld, %5lld, %5lld], 1, %s\n", __func__, ggml_type_name(dstt), ne0, ne1, ne2, |
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// dst->name); |
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//} |
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switch (dst->op) { |
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case GGML_OP_RESHAPE: |
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case GGML_OP_VIEW: |
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case GGML_OP_TRANSPOSE: |
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case GGML_OP_PERMUTE: |
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{ |
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// noop |
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} break; |
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case GGML_OP_ADD: |
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{ |
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if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
[encoder setComputePipelineState:ctx->pipeline_add]; |
|
[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]; |
|
|
|
const int64_t n = ggml_nelements(dst); |
|
|
|
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; |
|
} break; |
|
case GGML_OP_MUL: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
if (ggml_nelements(src1) == ne10) { |
|
// src1 is a row |
|
[encoder setComputePipelineState:ctx->pipeline_mul_row]; |
|
} else { |
|
[encoder setComputePipelineState:ctx->pipeline_mul]; |
|
} |
|
[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]; |
|
|
|
const int64_t n = ggml_nelements(dst); |
|
|
|
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; |
|
} break; |
|
case GGML_OP_SCALE: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
const float scale = *(const float *) src1->data; |
|
|
|
[encoder setComputePipelineState:ctx->pipeline_scale]; |
|
[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]; |
|
|
|
const int64_t n = ggml_nelements(dst); |
|
|
|
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; |
|
} break; |
|
case GGML_OP_SILU: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
[encoder setComputePipelineState:ctx->pipeline_silu]; |
|
[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_RELU: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
[encoder setComputePipelineState:ctx->pipeline_relu]; |
|
[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_GELU: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
[encoder setComputePipelineState:ctx->pipeline_gelu]; |
|
[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_SOFT_MAX: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
const int nth = 32; |
|
|
|
[encoder setComputePipelineState:ctx->pipeline_soft_max]; |
|
[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 setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0]; |
|
|
|
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; |
|
} break; |
|
case GGML_OP_DIAG_MASK_INF: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
const int n_past = ((int32_t *)(src1->data))[0]; |
|
|
|
[encoder setComputePipelineState:ctx->pipeline_diag_mask_inf]; |
|
[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]; |
|
|
|
[encoder dispatchThreadgroups:MTLSizeMake(ne00, ne01, ne02) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; |
|
} break; |
|
case GGML_OP_MUL_MAT: |
|
{ |
|
// TODO: needs to be updated after PR: https://github.com/ggerganov/ggml/pull/224 |
|
|
|
GGML_ASSERT(ne00 == ne10); |
|
GGML_ASSERT(ne02 == ne12); |
|
|
|
if (ggml_is_contiguous(src0) && |
|
ggml_is_contiguous(src1) && |
|
(src0t == GGML_TYPE_F32 || src0t == GGML_TYPE_F16) && ne11 > 1) { |
|
|
|
if (encoder != nil) { |
|
[encoder endEncoding]; |
|
encoder = nil; |
|
} |
|
|
|
MPSDataType src0dt = src0t == GGML_TYPE_F32 ? MPSDataTypeFloat32 : MPSDataTypeFloat16; |
|
MPSDataType src1dt = src1t == GGML_TYPE_F32 ? MPSDataTypeFloat32 : MPSDataTypeFloat16; |
|
|
|
// for F32 x F32 we use MPS |
|
MPSMatrixDescriptor * desc0 = [MPSMatrixDescriptor |
|
matrixDescriptorWithRows:ne01 columns:ne00 rowBytes:src0->nb[1] dataType:src0dt]; |
|
|
|
MPSMatrixDescriptor * desc1 = [MPSMatrixDescriptor |
|
matrixDescriptorWithRows:ne11 columns:ne10 rowBytes:src1->nb[1] dataType:src1dt]; |
|
|
|
MPSMatrixDescriptor * desc = [MPSMatrixDescriptor |
|
matrixDescriptorWithRows:ne1 columns:ne0 rowBytes:dst->nb[1] dataType:MPSDataTypeFloat32]; |
|
|
|
MPSMatrixMultiplication * mul = [[MPSMatrixMultiplication alloc] |
|
initWithDevice:ctx->device transposeLeft:false transposeRight:true |
|
resultRows:ne11 resultColumns:ne01 interiorColumns:ne00 alpha:1.0 beta:0.0]; |
|
|
|
// we need to do ne02 multiplications |
|
// TODO: is there a way to do this in parallel - currently very slow .. |
|
// TODO: might be possible to offload part of the computation to ANE using Accelerate's CBLAS |
|
for (int64_t i02 = 0; i02 < ne02; ++i02) { |
|
size_t offs_src0_cur = offs_src0 + i02*nb02; |
|
size_t offs_src1_cur = offs_src1 + i02*nb12; |
|
size_t offs_dst_cur = offs_dst + i02*nb2; |
|
|
|
MPSMatrix * mat_src0 = [[MPSMatrix alloc] initWithBuffer:id_src0 offset:offs_src0_cur descriptor:desc0]; |
|
MPSMatrix * mat_src1 = [[MPSMatrix alloc] initWithBuffer:id_src1 offset:offs_src1_cur descriptor:desc1]; |
|
MPSMatrix * mat_dst = [[MPSMatrix alloc] initWithBuffer:id_dst offset:offs_dst_cur descriptor:desc ]; |
|
|
|
[mul encodeToCommandBuffer:command_buffer leftMatrix:mat_src1 rightMatrix:mat_src0 resultMatrix:mat_dst]; |
|
} |
|
} else { |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
int nth0 = 32; |
|
int nth1 = 1; |
|
|
|
// use custom matrix x vector kernel |
|
switch (src0t) { |
|
case GGML_TYPE_F16: |
|
{ |
|
GGML_ASSERT(ne02 == ne12); |
|
|
|
nth0 = 64; |
|
nth1 = 1; |
|
[encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32]; |
|
} break; |
|
case GGML_TYPE_Q4_0: |
|
{ |
|
GGML_ASSERT(ne02 == 1); |
|
GGML_ASSERT(ne12 == 1); |
|
|
|
nth0 = 8; |
|
nth1 = 8; |
|
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32]; |
|
} break; |
|
case GGML_TYPE_Q4_1: |
|
{ |
|
GGML_ASSERT(ne02 == 1); |
|
GGML_ASSERT(ne12 == 1); |
|
|
|
nth0 = 8; |
|
nth1 = 8; |
|
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_1_f32]; |
|
} break; |
|
case GGML_TYPE_Q2_K: |
|
{ |
|
GGML_ASSERT(ne02 == 1); |
|
GGML_ASSERT(ne12 == 1); |
|
|
|
nth0 = 4; |
|
nth1 = 16; |
|
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_k_f32]; |
|
} break; |
|
case GGML_TYPE_Q3_K: |
|
{ |
|
GGML_ASSERT(ne02 == 1); |
|
GGML_ASSERT(ne12 == 1); |
|
|
|
nth0 = 4; |
|
nth1 = 16; |
|
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q3_k_f32]; |
|
} break; |
|
case GGML_TYPE_Q4_K: |
|
{ |
|
GGML_ASSERT(ne02 == 1); |
|
GGML_ASSERT(ne12 == 1); |
|
|
|
nth0 = 4; |
|
nth1 = 16; |
|
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_k_f32]; |
|
} break; |
|
case GGML_TYPE_Q5_K: |
|
{ |
|
GGML_ASSERT(ne02 == 1); |
|
GGML_ASSERT(ne12 == 1); |
|
|
|
nth0 = 4; |
|
nth1 = 16; |
|
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q5_k_f32]; |
|
} break; |
|
case GGML_TYPE_Q6_K: |
|
{ |
|
GGML_ASSERT(ne02 == 1); |
|
GGML_ASSERT(ne12 == 1); |
|
|
|
nth0 = 4; |
|
nth1 = 16; |
|
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q6_k_f32]; |
|
} break; |
|
default: |
|
{ |
|
fprintf(stderr, "Asserting on type %d\n",(int)src0t); |
|
GGML_ASSERT(false && "not implemented"); |
|
} |
|
}; |
|
|
|
[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:&nb00 length:sizeof(nb00) atIndex:5]; |
|
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:6]; |
|
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:7]; |
|
[encoder setBytes:&ne10 length:sizeof(ne10) atIndex:8]; |
|
[encoder setBytes:&ne11 length:sizeof(ne11) 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]; |
|
|
|
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1) { |
|
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0]; |
|
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; |
|
} |
|
else if (src0t == GGML_TYPE_Q2_K || |
|
src0t == GGML_TYPE_Q3_K || |
|
src0t == GGML_TYPE_Q4_K || |
|
src0t == GGML_TYPE_Q5_K || |
|
src0t == GGML_TYPE_Q6_K) { |
|
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0]; |
|
[encoder dispatchThreadgroups:MTLSizeMake(ne01, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; |
|
} else { |
|
[encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0]; |
|
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; |
|
} |
|
} |
|
} break; |
|
case GGML_OP_GET_ROWS: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
switch (src0->type) { |
|
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break; |
|
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break; |
|
case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break; |
|
case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_k]; break; |
|
case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_k]; break; |
|
case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_k]; break; |
|
case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_k]; break; |
|
case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_k]; break; |
|
default: GGML_ASSERT(false && "not implemented"); |
|
} |
|
|
|
[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:&(src0->ne[0]) length:sizeof( int64_t) atIndex:3]; |
|
[encoder setBytes:&(src0->nb[1]) length:sizeof(uint64_t) atIndex:4]; |
|
[encoder setBytes:&(dst->nb[1]) length:sizeof(uint64_t) atIndex:5]; |
|
|
|
const int64_t n = ggml_nelements(src1); |
|
|
|
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; |
|
} break; |
|
case GGML_OP_RMS_NORM: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
const float eps = 1e-6f; |
|
|
|
const int nth = 256; |
|
|
|
[encoder setComputePipelineState:ctx->pipeline_rms_norm]; |
|
[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:nth*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_NORM: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
const float eps = 1e-5f; |
|
|
|
const int nth = 256; |
|
|
|
[encoder setComputePipelineState:ctx->pipeline_norm]; |
|
[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:nth*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_ALIBI: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
GGML_ASSERT((src0t == GGML_TYPE_F32)); |
|
|
|
const int n_past = ((int32_t *) src1->data)[0]; UNUSED(n_past); |
|
const int n_head = ((int32_t *) src1->data)[1]; |
|
const float max_bias = ((float *) src1->data)[2]; |
|
|
|
if (__builtin_popcount(n_head) != 1) { |
|
GGML_ASSERT(false && "only power-of-two n_head implemented"); |
|
} |
|
|
|
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); |
|
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); |
|
|
|
[encoder setComputePipelineState:ctx->pipeline_alibi_f32]; |
|
[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]; |
|
const int nth = 32; |
|
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; |
|
} break; |
|
case GGML_OP_ROPE: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
const int n_dims = ((int32_t *) src1->data)[1]; |
|
const int mode = ((int32_t *) src1->data)[2]; |
|
|
|
const int n_past = ((int32_t *)(src1->data))[0]; |
|
|
|
[encoder setComputePipelineState:ctx->pipeline_rope]; |
|
[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:&n_past length:sizeof( int) atIndex:18]; |
|
[encoder setBytes:&n_dims length:sizeof( int) atIndex:19]; |
|
[encoder setBytes:&mode length:sizeof( int) atIndex:20]; |
|
|
|
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; |
|
} break; |
|
case GGML_OP_CPY: |
|
{ |
|
if (encoder == nil) { |
|
encoder = [command_buffer computeCommandEncoder]; |
|
} |
|
|
|
const int nth = 32; |
|
|
|
switch (src0t) { |
|
case GGML_TYPE_F32: |
|
{ |
|
switch (dstt) { |
|
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f16]; break; |
|
case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_cpy_f32_f32]; break; |
|
default: GGML_ASSERT(false && "not implemented"); |
|
}; |
|
} break; |
|
case GGML_TYPE_F16: |
|
{ |
|
switch (dstt) { |
|
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f16_f16]; break; |
|
case GGML_TYPE_F32: GGML_ASSERT(false && "cpy_f16_f32 not implemented"); break; |
|
default: GGML_ASSERT(false && "not implemented"); |
|
}; |
|
} break; |
|
default: GGML_ASSERT(false && "not implemented"); |
|
} |
|
|
|
[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: |
|
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); |
|
GGML_ASSERT(false); |
|
} |
|
} |
|
|
|
if (encoder != nil) { |
|
[encoder endEncoding]; |
|
encoder = nil; |
|
} |
|
|
|
[command_buffer commit]; |
|
}); |
|
} |
|
|
|
// wait for all threads to finish |
|
dispatch_barrier_sync(queue, ^{}); |
|
|
|
[command_buffers[n_cb - 1] waitUntilCompleted]; |
|
|
|
// 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++) { |
|
MTLCommandBufferStatus status = (MTLCommandBufferStatus) [command_buffers[i] status]; |
|
if (status != MTLCommandBufferStatusCompleted) { |
|
fprintf(stderr, "%s: command buffer %d failed with status %lu\n", __func__, i, status); |
|
GGML_ASSERT(false); |
|
} |
|
} |
|
} |
|
|