wehub-resource-sync
249 lines
8.3 KiB
C
249 lines
8.3 KiB
C
#include "ggml.h"
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#include "ggml-cpu.h"
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#include <string.h>
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#include <stdio.h>
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#include <stdlib.h>
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struct ggml_context* make_ctx(void) {
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struct ggml_init_params params = {
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.mem_size = 2 * 1024 * 1024,
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};
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return ggml_init(params);
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}
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void printf_tensor(struct ggml_tensor * t) {
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if (t->type == GGML_TYPE_F32) {
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const float * t_d = ggml_get_data_f32(t);
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for (int i = 0; i < t->ne[2]; ++i) {
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for (int j = 0; j < t->ne[1]; ++j) {
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for (int k = 0; k < t->ne[0]; ++k) {
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printf("%.1f ", t_d[i * t->ne[1] * t->ne[0] + j * t->ne[0] + k]);
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}
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printf("\n");
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}
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printf("---\n");
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}
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}
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else if (t->type == GGML_TYPE_F16) {
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const ggml_fp16_t * t_d = ggml_get_data(t);
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for (int i = 0; i < t->ne[2]; ++i) {
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for (int j = 0; j < t->ne[1]; ++j) {
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for (int k = 0; k < t->ne[0]; ++k) {
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printf("%.1f ", ggml_fp16_to_fp32(t_d[i * t->ne[1] * t->ne[0] + j * t->ne[0] + k]));
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}
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printf("\n");
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}
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printf("---\n");
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}
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}
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else {
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printf("unknown type\n");
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}
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}
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void check_tensor(struct ggml_tensor * t, float * expected_t_d, int ne0, int ne1, int ne2) {
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GGML_ASSERT(t->type == GGML_TYPE_F32);
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GGML_ASSERT(t->ne[0] == ne0);
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GGML_ASSERT(t->ne[1] == ne1);
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GGML_ASSERT(t->ne[2] == ne2);
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for (int i2 = 0; i2 < ne2; ++i2) {
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for (int i1 = 0; i1 < ne1; ++i1) {
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for (int i0 = 0; i0 < ne0; ++i0) {
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float expected = *(expected_t_d + i2 * ne1 * ne0 + i1 * ne0 + i0);
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float actual = ggml_get_data_f32(t)[i2 * ne1 * ne0 + i1 * ne0 + i0];
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if (expected != actual) {
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printf("expected %.1f, got %.1f\n", expected, actual);
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}
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GGML_ASSERT(expected == actual);
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}
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}
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}
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}
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void test_conv_transpose_1d(void) {
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float buf_f32[1024];
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for (int i = 0; i < 1024; ++i) {
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buf_f32[i] = (float)i;
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}
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ggml_fp16_t buf_f16[1024];
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for (int i = 0; i < 1024; ++i) {
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buf_f16[i] = ggml_fp32_to_fp16((float)i);
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}
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float expected_out_1[3][4] = {
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{18.0, 45.0, 59.0, 37.0},
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{24.0, 61.0, 83.0, 51.0},
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{30.0, 77.0, 107.0, 65.0},
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};
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float expected_out_2[3][6] = {
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{18.0, 21.0, 24.0, 29.0, 30.0, 37.0},
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{24.0, 27.0, 34.0, 39.0, 44.0, 51.0},
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{30.0, 33.0, 44.0, 49.0, 58.0, 65.0},
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};
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float expected_out_3[3][8] = {
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{18.0, 21.0, 0.0, 24.0, 29.0, 0.0, 30.0, 37.0},
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{24.0, 27.0, 0.0, 34.0, 39.0, 0.0, 44.0, 51.0},
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{30.0, 33.0, 0.0, 44.0, 49.0, 0.0, 58.0, 65.0},
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};
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// conv transpose 1d with stride 1, 2 & 3
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{
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struct ggml_context * ctx = make_ctx();
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struct ggml_tensor * t = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 3, 2); // l x cin
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memcpy(t->data, buf_f32, ggml_nbytes(t));
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struct ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F16, 2, 3, 2); // k x cout x cin
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memcpy(k->data, buf_f16, ggml_nbytes(k));
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struct ggml_tensor * out_1 = ggml_conv_transpose_1d(ctx, k, t, 1 /* s0 */, 0 /* p0 */, 1 /* d0 */);
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struct ggml_tensor * out_2 = ggml_conv_transpose_1d(ctx, k, t, 2 /* s0 */, 0 /* p0 */, 1 /* d0 */);
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struct ggml_tensor * out_3 = ggml_conv_transpose_1d(ctx, k, t, 3 /* s0 */, 0 /* p0 */, 1 /* d0 */);
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struct ggml_cgraph * gf_1 = ggml_new_graph(ctx);
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struct ggml_cgraph * gf_2 = ggml_new_graph(ctx);
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struct ggml_cgraph * gf_3 = ggml_new_graph(ctx);
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ggml_build_forward_expand(gf_1, out_1);
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ggml_build_forward_expand(gf_2, out_2);
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ggml_build_forward_expand(gf_3, out_3);
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ggml_graph_compute_with_ctx(ctx, gf_1, 1);
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ggml_graph_compute_with_ctx(ctx, gf_2, 1);
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ggml_graph_compute_with_ctx(ctx, gf_3, 1);
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check_tensor(out_1, (float*)expected_out_1, 4, 3, 1);
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check_tensor(out_2, (float*)expected_out_2, 6, 3, 1);
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check_tensor(out_3, (float*)expected_out_3, 8, 3, 1);
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}
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}
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void test_conv_transpose_2d(void) {
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float buf_f32[1024];
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for (int i = 0; i < 1024; ++i) {
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buf_f32[i] = (float)i;
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}
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ggml_fp16_t buf_f16[1024];
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for (int i = 0; i < 1024; ++i) {
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buf_f16[i] = ggml_fp32_to_fp16((float)i);
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}
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float expected_out_1[3][3][4] = {
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{
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{72.0, 162.0, 188.0, 106.0},
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{192.0, 430.0, 490.0, 274.0},
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{132.0, 292.0, 326.0, 180.0},
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},
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{
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{96.0, 218.0, 260.0, 146.0},
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{264.0, 590.0, 682.0, 378.0},
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{180.0, 396.0, 446.0, 244.0},
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},
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{
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{120.0, 274.0, 332.0, 186.0},
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{336.0, 750.0, 874.0, 482.0},
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{228.0, 500.0, 566.0, 308.0},
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},
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};
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float expected_out_2[3][4][6] = {
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{
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{72.0, 78.0, 84.0, 92.0, 96.0, 106.0},
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{84.0, 90.0, 100.0, 108.0, 116.0, 126.0},
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{108.0, 120.0, 120.0, 134.0, 132.0, 148.0},
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{132.0, 144.0, 148.0, 162.0, 164.0, 180.0},
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},
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{
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{96.0, 102.0, 116.0, 124.0, 136.0, 146.0},
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{108.0, 114.0, 132.0, 140.0, 156.0, 166.0},
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{156.0, 168.0, 176.0, 190.0, 196.0, 212.0},
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{180.0, 192.0, 204.0, 218.0, 228.0, 244.0},
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},
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{
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{120.0, 126.0, 148.0, 156.0, 176.0, 186.0},
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{132.0, 138.0, 164.0, 172.0, 196.0, 206.0},
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{204.0, 216.0, 232.0, 246.0, 260.0, 276.0},
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{228.0, 240.0, 260.0, 274.0, 292.0, 308.0},
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},
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};
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float expected_out_3[3][5][8] = {
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{
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{72.0, 78.0, 0.0, 84.0, 92.0, 0.0, 96.0, 106.0},
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{84.0, 90.0, 0.0, 100.0, 108.0, 0.0, 116.0, 126.0},
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{0.0, 0.0, 0.0, 0.0, 0.0, 0.0},
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{108.0, 120.0, 0.0, 120.0, 134.0, 0.0, 132.0, 148.0},
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{132.0, 144.0, 0.0, 148.0, 162.0, 0.0, 164.0, 180.0},
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},
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{
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{96.0, 102.0, 0.0, 116.0, 124.0, 0.0, 136.0, 146.0},
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{108.0, 114.0, 0.0, 132.0, 140.0, 0.0, 156.0, 166.0},
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{0.0, 0.0, 0.0, 0.0, 0.0, 0.0},
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{156.0, 168.0, 0.0, 176.0, 190.0, 0.0, 196.0, 212.0},
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{180.0, 192.0, 0.0, 204.0, 218.0, 0.0, 228.0, 244.0},
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},
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{
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{120.0, 126.0, 0.0, 148.0, 156.0, 0.0, 176.0, 186.0},
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{132.0, 138.0, 0.0, 164.0, 172.0, 0.0, 196.0, 206.0},
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{0.0, 0.0, 0.0, 0.0, 0.0, 0.0},
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{204.0, 216.0, 0.0, 232.0, 246.0, 0.0, 260.0, 276.0},
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{228.0, 240.0, 0.0, 260.0, 274.0, 0.0, 292.0, 308.0},
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},
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};
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// conv transpose 2d with stride 1, 2 & 3
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{
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struct ggml_context * ctx = make_ctx();
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struct ggml_tensor * t = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 3, 2, 2, 1); // w x h x cin
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memcpy(t->data, buf_f32, ggml_nbytes(t));
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struct ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 2, 2, 3, 2); // w x h cin x cout
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memcpy(k->data, buf_f16, ggml_nbytes(k));
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struct ggml_tensor * out_1 = ggml_conv_transpose_2d_p0(ctx, k, t, 1);
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struct ggml_tensor * out_2 = ggml_conv_transpose_2d_p0(ctx, k, t, 2);
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struct ggml_tensor * out_3 = ggml_conv_transpose_2d_p0(ctx, k, t, 3);
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struct ggml_cgraph * gf_1 = ggml_new_graph(ctx);
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struct ggml_cgraph * gf_2 = ggml_new_graph(ctx);
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struct ggml_cgraph * gf_3 = ggml_new_graph(ctx);
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ggml_build_forward_expand(gf_1, out_1);
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ggml_build_forward_expand(gf_2, out_2);
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ggml_build_forward_expand(gf_3, out_3);
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ggml_graph_compute_with_ctx(ctx, gf_1, 1);
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ggml_graph_compute_with_ctx(ctx, gf_2, 1);
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ggml_graph_compute_with_ctx(ctx, gf_3, 1);
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// printf("in\n");
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// printf_tensor(t);
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// printf("\n\nkernel\n");
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// printf_tensor(k);
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// printf("\n\nout\n");
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// printf_tensor(out);
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// printf("\n\nout_2\n");
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// printf_tensor(out_2);
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// printf("\n\nout_3\n");
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// printf_tensor(out_3);
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check_tensor(out_1, (float*)expected_out_1, 4, 3, 3);
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check_tensor(out_2, (float*)expected_out_2, 6, 4, 3);
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check_tensor(out_3, (float*)expected_out_3, 8, 5, 3);
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}
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}
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int main(int argc, const char * argv[]) {
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test_conv_transpose_1d();
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test_conv_transpose_2d();
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return 0;
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}
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