93935980f8
* metal : init * whisper : factor out graph builds * whisper : allocate encoder and decoder using ggml-alloc * whisper : ggml-alloc is now supported * whisper : CoreML support ggml-alloc * build : fix ggml-alloc * ios : update submodule * extra : update sync-ggml.sh script to also sync ggml-alloc * ci : see if this is causing the crash * whisper : refactor ggml-alloc init * whisper.android : try to fix build * whisper : initial Metal version * ci : try to debug vmem issue * metal : decoder works on GPU! * metal : add multi-decoder support * ggml : fix ggml_nbytes (probably temp solution) * metal : run "cross" step on the GPU * whisper : remove ggml_repeat in the encoder * whisper : offload the Encoder to Metal * ggml : use simpler ggml_bytes() implementation * ggml-alloc : try to make CI happy by reducing vram to 128GB * whisper : add whisper_allocr to wrap ggml_allocr * whisper : factor out alloc init in a function * cmake : update to support Metal build * whisper : add <functional> header * objc : fix build (no Metal yet) * ios : add Metal support * swiftui : fix build * metal : speed-up KQ multiplication * metal : sync latest llama.cpp kernels * readme : add Metal info * ios : update submodule * coreml : add code to toggle Core ML config (CPU, ANE, GPU) * bench : fix timings by running a pre-heat * bench : start benching the decoder * whisper : add ggml_mul_mat_pad * bench : fix uninitialized vars * whisper : add comment for disabling mul-mat padding * whisper : add description of ggml_mul_mat_pad * whisper : clean-up ggml_mul_mat_pad * metal : remove the "concurrent" flag * bench : variable n_past * ios : update SPM package
72 lines
2.1 KiB
Plaintext
72 lines
2.1 KiB
Plaintext
#if !__has_feature(objc_arc)
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#error This file must be compiled with automatic reference counting enabled (-fobjc-arc)
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#endif
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#import "whisper-encoder.h"
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#import "whisper-encoder-impl.h"
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#import <CoreML/CoreML.h>
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#include <stdlib.h>
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#if __cplusplus
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extern "C" {
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#endif
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struct whisper_coreml_context {
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const void * data;
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};
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struct whisper_coreml_context * whisper_coreml_init(const char * path_model) {
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NSString * path_model_str = [[NSString alloc] initWithUTF8String:path_model];
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NSURL * url_model = [NSURL fileURLWithPath: path_model_str];
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// select which device to run the Core ML model on
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MLModelConfiguration *config = [[MLModelConfiguration alloc] init];
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config.computeUnits = MLComputeUnitsCPUAndGPU;
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//config.computeUnits = MLComputeUnitsCPUAndNeuralEngine;
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//config.computeUnits = MLComputeUnitsAll;
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const void * data = CFBridgingRetain([[whisper_encoder_impl alloc] initWithContentsOfURL:url_model configuration:config error:nil]);
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if (data == NULL) {
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return NULL;
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}
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whisper_coreml_context * ctx = new whisper_coreml_context;
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ctx->data = data;
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return ctx;
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}
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void whisper_coreml_free(struct whisper_coreml_context * ctx) {
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CFRelease(ctx->data);
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delete ctx;
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}
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void whisper_coreml_encode(
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const whisper_coreml_context * ctx,
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float * mel,
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float * out) {
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MLMultiArray * inMultiArray = [
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[MLMultiArray alloc] initWithDataPointer: mel
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shape: @[@1, @80, @3000]
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dataType: MLMultiArrayDataTypeFloat32
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strides: @[@(240000), @(3000), @1]
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deallocator: nil
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error: nil
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];
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@autoreleasepool {
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whisper_encoder_implOutput * outCoreML = [(__bridge id) ctx->data predictionFromLogmel_data:inMultiArray error:nil];
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memcpy(out, outCoreML.output.dataPointer, outCoreML.output.count * sizeof(float));
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}
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}
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#if __cplusplus
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}
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#endif
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