mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 21:39:52 +00:00
2b3389677a
* ggml : unify rope norm/neox (CPU) * ggml : fix compile warning * ggml : remove GLM rope mode ggml-ci * metal : better rope implementation ggml-ci * cuda : better rope implementation ggml-ci * naming : n_orig_ctx -> n_ctx_orig ggml-ci * dev : add reminders to update backends ggml-ci * vulkan : fix ggml_rope_ext() usage * cuda : fix array size + indents ggml-ci
222 lines
6.1 KiB
C++
222 lines
6.1 KiB
C++
#include "ggml.h"
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#include <cmath>
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#include <cstdio>
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#include <cstdlib>
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#include <cassert>
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#include <vector>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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#if defined(__GNUC__)
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#pragma GCC diagnostic ignored "-Wdouble-promotion"
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#endif
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#define MAX_NARGS 3
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#undef MIN
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#undef MAX
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#define MIN(a, b) ((a) < (b) ? (a) : (b))
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#define MAX(a, b) ((a) > (b) ? (a) : (b))
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#define GGML_SILU_FP16
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//
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// logging
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//
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#if (GGML_DEBUG >= 1)
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#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
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#else
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#define GGML_PRINT_DEBUG(...)
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#endif
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#if (GGML_DEBUG >= 5)
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#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
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#else
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#define GGML_PRINT_DEBUG_5(...)
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#endif
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#if (GGML_DEBUG >= 10)
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#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
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#else
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#define GGML_PRINT_DEBUG_10(...)
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#endif
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#define GGML_PRINT(...) printf(__VA_ARGS__)
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static float frand(void) {
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return (float)rand()/(float)RAND_MAX;
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}
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static int irand(int n) {
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if (n == 0) return 0;
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return rand()%n;
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}
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static void get_random_dims(int64_t * dims, int ndims) {
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dims[0] = dims[1] = dims[2] = dims[3] = 1;
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for (int i = 0; i < ndims; i++) {
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dims[i] = 1 + irand(4);
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}
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}
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static struct ggml_tensor * get_random_tensor_f32(
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struct ggml_context * ctx0,
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int ndims,
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const int64_t ne[],
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float fmin,
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float fmax) {
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struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F32, ndims, ne);
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switch (ndims) {
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case 1:
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)result->data)[i0] = frand()*(fmax - fmin) + fmin;
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}
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break;
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case 2:
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for (int i1 = 0; i1 < ne[1]; i1++) {
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)result->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
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}
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}
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break;
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case 3:
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for (int i2 = 0; i2 < ne[2]; i2++) {
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for (int i1 = 0; i1 < ne[1]; i1++) {
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
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}
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}
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}
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break;
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case 4:
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for (int i3 = 0; i3 < ne[3]; i3++) {
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for (int i2 = 0; i2 < ne[2]; i2++) {
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for (int i1 = 0; i1 < ne[1]; i1++) {
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
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}
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}
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}
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}
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break;
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default:
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assert(false);
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};
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return result;
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}
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static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
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struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
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if (plan.work_size > 0) {
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buf.resize(plan.work_size);
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plan.work_data = buf.data();
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}
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ggml_graph_compute(graph, &plan);
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}
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int main(int /*argc*/, const char ** /*argv*/) {
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struct ggml_init_params params = {
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/* .mem_size = */ 128*1024*1024,
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/* .mem_buffer = */ NULL,
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/* .no_alloc = */ false,
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};
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std::vector<uint8_t> work_buffer;
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struct ggml_context * ctx0 = ggml_init(params);
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struct ggml_tensor * x;
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// rope f32
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for (int m = 0; m < 3; ++m) {
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const int ndims = 4;
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const int64_t n_rot = 128;
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const int64_t ne[4] = { 2*n_rot, 32, 73, 1 };
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const int n_past_0 = 100;
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const int n_past_2 = 33;
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struct ggml_tensor * p0 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
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struct ggml_tensor * p1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
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struct ggml_tensor * p2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]);
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for (int i = 0; i < ne[2]; ++i) {
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((int32_t *) p0->data)[i] = n_past_0 + i;
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((int32_t *) p1->data)[i] = n_past_2 - n_past_0;
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((int32_t *) p2->data)[i] = n_past_2 + i;
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}
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// test mode 0, 2, 4 (standard, GPT-NeoX, GLM)
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const int mode = m == 0 ? 0 : m == 1 ? 2 : 4;
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x = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
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// 100, 101, 102, ..., 172
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struct ggml_tensor * r0 = ggml_rope(ctx0, x, p0, n_rot, mode);
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// -67, -67, -67, ..., -67
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struct ggml_tensor * r1 = ggml_rope(ctx0, r0, p1, n_rot, mode); // "context swap", i.e. forget n_past_0 - n_past_2 tokens
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// 33, 34, 35, ..., 105
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struct ggml_tensor * r2 = ggml_rope(ctx0, x, p2, n_rot, mode);
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ggml_cgraph * gf = ggml_new_graph(ctx0);
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ggml_build_forward_expand(gf, r0);
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ggml_build_forward_expand(gf, r1);
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ggml_build_forward_expand(gf, r2);
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ggml_graph_compute_helper(work_buffer, gf, 4);
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// check that r1 and r2 are the same
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{
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double sum0 = 0.0f;
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double sum1 = 0.0f;
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double diff = 0.0f;
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const float * r1_data = (float *) r1->data;
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const float * r2_data = (float *) r2->data;
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const int n_elements = ggml_nelements(r1);
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for (int i = 0; i < n_elements; ++i) {
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sum0 += fabs(r1_data[i]);
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sum1 += fabs(r2_data[i]);
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diff += fabs(r1_data[i] - r2_data[i]);
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//if (fabs(r1_data[i] - r2_data[i]) > 0.0001f) {
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// printf("%d: %f %f\n", i, r1_data[i], r2_data[i]);
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// printf("diff: %f\n", fabs(r1_data[i] - r2_data[i]));
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//}
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}
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//for (int i = 4096; i < 4096 + 128; ++i) {
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// printf("%f %f\n", r1_data[i], r2_data[i]);
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//}
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printf("mode: %d\n", mode);
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printf("sum0: %f\n", sum0);
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printf("sum1: %f\n", sum1);
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printf("diff: %f\n", diff);
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printf("rel err: %f\n", diff / sum0);
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printf("rel err: %f\n", diff / sum1);
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GGML_ASSERT(diff / sum0 < 0.0001f);
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GGML_ASSERT(diff / sum1 < 0.0001f);
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}
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}
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ggml_free(ctx0);
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return 0;
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}
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