Fabio
c36510c21e
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86 lines
4.3 KiB
C++
86 lines
4.3 KiB
C++
#include <ggml.h>
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#include <ggml-backend.h>
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#include <ggml-metal.h>
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#include <cstring>
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#include <iostream>
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#include <stdexcept>
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#include <vector>
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namespace {
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struct Storage {
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ggml_context * ctx = ggml_init({2 * 1024 * 1024, nullptr, true});
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ggml_backend_buffer_t buffer = nullptr;
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~Storage() {
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if (buffer) ggml_backend_buffer_free(buffer);
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ggml_free(ctx);
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}
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};
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}
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int main() {
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auto backend = ggml_backend_metal_init();
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if (!backend) return 77;
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size_t checked = 0;
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try {
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// Cover partial tiles, offset views, batches, dilation, and production channel widths.
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for (int i = 0; i < 56; ++i) for (auto type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
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int w=i<48 ? 7+i%5 : 483, h=i<48 ? 5+i%4 : 9;
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int c=i<48 ? 1+i%3 : 64*(1+(i-48)%4), n=1+i%2;
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int kw=i<48 ? 1+2*(i%3) : 3, kh=1+2*((i/3)%2);
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int sx=1+i%2, sy=1+(i/2)%2, dx=1+(i/5)%2, dy=1+(i/7)%2;
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int px=kw/2, py=kh/2;
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Storage storage;
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auto ctx=storage.ctx;
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const size_t count=size_t(w)*h*c*n;
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auto base=ggml_new_tensor_1d(ctx,GGML_TYPE_F32,count+2);
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auto x=ggml_view_4d(ctx,base,w,h,c,n,w*4,w*h*4,w*h*c*4,4);
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auto weight=ggml_new_tensor_4d(ctx,type,kw,kh,c,1);
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auto old=ggml_im2col(ctx,weight,x,sx,sy,px,py,dx,dy,true,type);
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auto tiled=ggml_im2col(ctx,weight,x,sx,sy,px,py,dx,dy,true,type);
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ggml_im2col_2d_set_lowering(tiled,GGML_IM2COL_2D_LOWERING_METAL_TILED);
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auto graph=ggml_new_graph(ctx);
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ggml_set_output(old); ggml_set_output(tiled);
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ggml_build_forward_expand(graph,old); ggml_build_forward_expand(graph,tiled);
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storage.buffer=ggml_backend_alloc_ctx_tensors(ctx,backend);
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if(!storage.buffer) throw std::runtime_error("allocation failed");
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std::vector<float> input(count+2);
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for (int pattern = 0; pattern < 2; ++pattern) {
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for (size_t j = 0; j < input.size(); ++j) {
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input[j] = float(int((j * 37 + i + pattern * 71) % 401) - 200) / 8;
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}
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ggml_backend_tensor_set(base, input.data(), 0, input.size() * sizeof(float));
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if (ggml_backend_graph_compute(backend, graph) != GGML_STATUS_SUCCESS) {
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throw std::runtime_error("compute failed");
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}
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std::vector<unsigned char> actual(ggml_nbytes(tiled)), legacy(actual.size());
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ggml_backend_tensor_get(tiled, actual.data(), 0, actual.size());
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ggml_backend_tensor_get(old, legacy.data(), 0, legacy.size());
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if (actual != legacy) throw std::runtime_error("legacy GPU mismatch");
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size_t at = 0;
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// Independent coordinate-based oracle, not output from either GPU kernel.
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for (int bn = 0; bn < n; ++bn) for (int oy = 0; oy < tiled->ne[2]; ++oy)
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for (int ox = 0; ox < tiled->ne[1]; ++ox) for (int ch = 0; ch < c; ++ch)
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for (int ky = 0; ky < kh; ++ky) for (int kx = 0; kx < kw; ++kx) {
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const int iy = oy * sy + ky * dy - py, ix = ox * sx + kx * dx - px;
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const float expected = ix >= 0 && ix < w && iy >= 0 && iy < h
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? input[1 + ((bn * c + ch) * h + iy) * w + ix] : 0;
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if (type == GGML_TYPE_F32) {
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float value;
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std::memcpy(&value, actual.data() + at * sizeof(value), sizeof(value));
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if (value != expected) throw std::runtime_error("scalar F32 mismatch");
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} else {
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ggml_fp16_t value;
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std::memcpy(&value, actual.data() + at * sizeof(value), sizeof(value));
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if (value != ggml_fp32_to_fp16(expected)) throw std::runtime_error("scalar F16 mismatch");
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}
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++at;
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}
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checked += at;
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}
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}
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std::cout<<"PASS 112 shapes/types, two input patterns, "<<checked<<" exact values; legacy Metal and independent scalar oracle\n";
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} catch(const std::exception & e) {
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std::cerr<<e.what()<<'\n';ggml_backend_free(backend);return 1;
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}
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ggml_backend_free(backend);
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}
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