metal : sync ggml-metal (ref #1047)
Georgi Gerganov
parent
6c25fae1c4
commit
f1c9df5806
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@ -5,6 +5,9 @@ cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h
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cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu
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cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h
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cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp
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cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h
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cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m
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cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal
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cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h
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cp -rpv ../ggml/examples/common.h ./examples/common.h
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cp -rpv ../ggml/examples/common.cpp ./examples/common.cpp
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@ -0,0 +1,67 @@
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// An interface allowing to compute ggml_cgraph with Metal
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//
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// This is a fully functional interface that extends ggml with GPU support for Apple devices.
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// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.)
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//
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// How it works?
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//
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// As long as your program can create and evaluate a ggml_cgraph on the CPU, you can use this
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// interface to evaluate the same graph on the GPU. Instead of using ggml_graph_compute(), you
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// use ggml_metal_graph_compute() (or ggml_vulkan_graph_compute(), etc.)
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//
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// You only need to make sure that all memory buffers that you used during the graph creation
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// are mapped to the device memory with the ggml_metal_add_buffer() function. This mapping is
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// used during the graph evaluation to determine the arguments of the compute kernels.
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//
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// Synchronization between device and host memory (for example for input and output tensors)
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// is done with the ggml_metal_set_tensor() and ggml_metal_get_tensor() functions.
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//
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#pragma once
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#include <stddef.h>
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#include <stdbool.h>
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// max memory buffers that can be mapped to the device
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#define GGML_METAL_MAX_BUFFERS 16
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struct ggml_tensor;
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struct ggml_cgraph;
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#ifdef __cplusplus
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extern "C" {
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#endif
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struct ggml_metal_context;
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struct ggml_metal_context * ggml_metal_init(void);
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void ggml_metal_free(struct ggml_metal_context * ctx);
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// creates a mapping between a host memory buffer and a device memory buffer
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// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
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// - the mapping is used during computation to determine the arguments of the compute kernels
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// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal
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// - max_size specifies the maximum size of a tensor and is used to create shared views such
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// that it is guaranteed that the tensor will fit in at least one of the views
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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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// set data from host memory into the device
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void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
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// get data from the device into host memory
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void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
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// same as ggml_graph_compute but uses Metal
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// creates gf->n_threads command buffers in parallel
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void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
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#ifdef __cplusplus
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}
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#endif
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@ -0,0 +1,972 @@
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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 % size_page) != 0) {
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size_aligned += (size_page - (size_aligned % size_page));
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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(
|
||||
struct ggml_metal_context * ctx,
|
||||
struct ggml_tensor * t) {
|
||||
metal_printf("%s: set input for tensor '%s'\n", __func__, t->name);
|
||||
|
||||
size_t offs;
|
||||
id<MTLBuffer> 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) {
|
||||
metal_printf("%s: extract results for tensor '%s'\n", __func__, t->name);
|
||||
|
||||
size_t offs;
|
||||
id<MTLBuffer> 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_compute(
|
||||
struct ggml_metal_context * ctx,
|
||||
struct ggml_cgraph * gf) {
|
||||
metal_printf("%s: evaluating graph\n", __func__);
|
||||
|
||||
// create multiple command buffers and enqueue them
|
||||
// then, we encode the graph into the command buffers in parallel
|
||||
|
||||
const int n_cb = gf->n_threads;
|
||||
|
||||
NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb];
|
||||
|
||||
for (int i = 0; i < n_cb; ++i) {
|
||||
command_buffers[i] = [ctx->queue commandBuffer];
|
||||
|
||||
// enqueue the command buffers in order to specify their execution order
|
||||
[command_buffers[i] enqueue];
|
||||
}
|
||||
|
||||
// TODO: is this the best way to start threads?
|
||||
dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
|
||||
|
||||
for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
|
||||
const int n_nodes_per_cb = (gf->n_nodes + n_cb - 1) / n_cb;
|
||||
|
||||
dispatch_async(queue, ^{
|
||||
size_t offs_src0 = 0;
|
||||
size_t offs_src1 = 0;
|
||||
size_t offs_dst = 0;
|
||||
|
||||
id<MTLCommandBuffer> command_buffer = command_buffers[cb_idx];
|
||||
|
||||
id<MTLComputeCommandEncoder> encoder = nil;
|
||||
|
||||
const int node_start = (cb_idx + 0) * n_nodes_per_cb;
|
||||
const int node_end = (cb_idx == n_cb - 1) ? gf->n_nodes : (cb_idx + 1) * n_nodes_per_cb;
|
||||
|
||||
for (int i = node_start; i < node_end; ++i) {
|
||||
metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
|
||||
|
||||
struct ggml_tensor * src0 = gf->nodes[i]->src0;
|
||||
struct ggml_tensor * src1 = gf->nodes[i]->src1;
|
||||
struct ggml_tensor * dst = gf->nodes[i];
|
||||
|
||||
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<MTLBuffer> id_src0 = src0 ? ggml_metal_get_buffer(ctx, src0, &offs_src0) : nil;
|
||||
id<MTLBuffer> id_src1 = src1 ? ggml_metal_get_buffer(ctx, src1, &offs_src1) : nil;
|
||||
id<MTLBuffer> id_dst = dst ? ggml_metal_get_buffer(ctx, dst, &offs_dst) : nil;
|
||||
|
||||
//metal_printf("%s: op - %s\n", __func__, ggml_op_name(dst->op));
|
||||
//if (src0) {
|
||||
// metal_printf("%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) {
|
||||
// metal_printf("%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) {
|
||||
// metal_printf("%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_RESHAPE:
|
||||
case GGML_OP_VIEW:
|
||||
case GGML_OP_TRANSPOSE:
|
||||
case GGML_OP_PERMUTE:
|
||||
{
|
||||
// noop
|
||||
} break;
|
||||
case GGML_OP_ADD:
|
||||
{
|
||||
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);
|
||||
}
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
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Reference in New Issue