mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-01 07:30:17 +01:00
1d656d6360
* ggml_graph_compute: deprecate using ggml_context, try resolve issue #287 * rewrite: no longer consider backward compitability; plan and make_plan * minor: rename ctx as plan; const * remove ggml_graph_compute from tests/test-grad0.c, but current change breaks backward * add static ggml_graph_compute_sugar() * minor: update comments * reusable buffers * ggml : more consistent naming + metal fixes * ggml : fix docs * tests : disable grad / opt + minor naming changes * ggml : add ggml_graph_compute_with_ctx() - backwards compatible API - deduplicates a lot of copy-paste * ci : enable test-grad0 * examples : factor out plan allocation into a helper function * llama : factor out plan stuff into a helper function * ci : fix env * llama : fix duplicate symbols + refactor example benchmark * ggml : remove obsolete assert + refactor n_tasks section * ggml : fix indentation in switch * llama : avoid unnecessary bool * ggml : remove comments from source file and match order in header --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
1709 lines
65 KiB
C++
1709 lines
65 KiB
C++
#include "ggml.h"
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#include <vector>
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#include <cassert>
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#include <random>
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#include <cstring>
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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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float frand() {
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return (float)rand()/(float)RAND_MAX;
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}
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struct random_normal_distribution {
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std::mt19937 gen;
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std::normal_distribution<float> nd;
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float min;
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float max;
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};
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void init_random_normal_distribution(struct random_normal_distribution * rnd, int seed, float mean, float std, float min, float max) {
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rnd->gen = std::mt19937(seed);
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rnd->nd = std::normal_distribution<float>{mean, std};
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rnd->min = min;
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rnd->max = max;
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}
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float frand_normal(struct random_normal_distribution * rnd) {
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const float r = rnd->nd(rnd->gen);
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return ((r < rnd->min) ? (rnd->min) : (r > rnd->max) ? (rnd->max) : r);
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}
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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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struct ggml_tensor * randomize_tensor(
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struct ggml_tensor * tensor,
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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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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 *)tensor->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 *)tensor->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 *)tensor->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 *)tensor->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 tensor;
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}
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struct ggml_tensor * randomize_tensor_normal(
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struct ggml_tensor * tensor,
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int ndims,
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const int64_t ne[],
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struct random_normal_distribution * rnd) {
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float scale = 1.0; // xavier
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switch (ndims) {
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case 1:
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scale /= sqrtf(ne[0]);
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)tensor->data)[i0] = scale * frand_normal(rnd);
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}
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break;
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case 2:
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scale /= sqrtf(ne[0]+ne[1]);
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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 *)tensor->data)[i1*ne[0] + i0] = scale * frand_normal(rnd);
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}
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}
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break;
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case 3:
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scale /= sqrtf(ne[0]+ne[1]);
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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 *)tensor->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = scale * frand_normal(rnd);
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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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scale /= sqrtf(ne[0]+ne[1]);
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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 *)tensor->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = scale * frand_normal(rnd);
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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 tensor;
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}
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struct llama_hparams {
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uint32_t n_vocab = 32000;
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uint32_t n_ctx = 512; // this is provided as user input?
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uint32_t n_embd = 4096;
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uint32_t n_mult = 4;
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uint32_t n_head = 32;
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uint32_t n_layer = 32;
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uint32_t n_rot = 64;
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bool operator!=(const llama_hparams & other) const {
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return memcmp(this, &other, sizeof(llama_hparams));
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}
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};
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uint32_t get_n_ff(const struct llama_hparams* hparams) {
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const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult;
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return n_ff;
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}
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struct llama_hparams_lora {
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uint32_t n_vocab = 32000;
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uint32_t n_ctx = 512; // this is provided as user input?
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uint32_t n_embd = 4096;
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uint32_t n_mult = 4;
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uint32_t n_head = 32;
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uint32_t n_layer = 32;
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uint32_t n_rot = 64;
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uint32_t n_lora = 64;
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bool operator!=(const llama_hparams_lora & other) const {
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return memcmp(this, &other, sizeof(llama_hparams_lora)) != 0;
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}
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};
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struct llama_layer {
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// normalization
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struct ggml_tensor * attention_norm;
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// attention
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struct ggml_tensor * wq;
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struct ggml_tensor * wk;
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struct ggml_tensor * wv;
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struct ggml_tensor * wo;
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// normalization
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struct ggml_tensor * ffn_norm;
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// ff
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struct ggml_tensor * w1;
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struct ggml_tensor * w2;
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struct ggml_tensor * w3;
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};
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struct llama_layer_lora {
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// normalization
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struct ggml_tensor * attention_norm;
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// attention
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struct ggml_tensor * wqa;
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struct ggml_tensor * wqb;
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struct ggml_tensor * wka;
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struct ggml_tensor * wkb;
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struct ggml_tensor * wva;
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struct ggml_tensor * wvb;
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struct ggml_tensor * woa;
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struct ggml_tensor * wob;
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// normalization
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struct ggml_tensor * ffn_norm;
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// ff
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struct ggml_tensor * w1;
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struct ggml_tensor * w2;
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struct ggml_tensor * w3;
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};
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struct llama_kv_cache {
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struct ggml_context * ctx = NULL;
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struct ggml_tensor * k;
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struct ggml_tensor * v;
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// llama_ctx_buffer buf;
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int n; // number of tokens currently in the cache
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};
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struct llama_model {
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struct ggml_context * ctx = NULL;
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llama_hparams hparams;
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struct ggml_tensor * tok_embeddings;
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struct ggml_tensor * norm;
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struct ggml_tensor * output;
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std::vector<llama_layer> layers;
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};
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struct llama_model_lora {
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struct ggml_context * ctx = NULL;
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llama_hparams_lora hparams;
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struct ggml_tensor * tok_embeddings;
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struct ggml_tensor * norm;
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struct ggml_tensor * outputa;
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struct ggml_tensor * outputb;
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std::vector<llama_layer_lora> layers;
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};
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void init_model(struct llama_model * model) {
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const auto & hparams = model->hparams;
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const uint32_t n_embd = hparams.n_embd;
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const uint32_t n_layer = hparams.n_layer;
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const uint32_t n_vocab = hparams.n_vocab;
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const uint32_t n_ff = get_n_ff(&hparams);
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struct ggml_context * ctx = model->ctx;
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model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("tok_embeddings.weight", {n_embd, n_vocab});
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model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // ("norm.weight", {n_embd});
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model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("output.weight", {n_embd, n_vocab});
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model->layers.resize(n_layer);
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for (uint32_t i = 0; i < n_layer; ++i) {
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auto & layer = model->layers[i];
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// std::string layers_i = "layers." + std::to_string(i);
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layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".attention_norm.weight", {n_embd});
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layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wq.weight", {n_embd, n_embd});
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layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wk.weight", {n_embd, n_embd});
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layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wv.weight", {n_embd, n_embd});
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layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wo.weight", {n_embd, n_embd});
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layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".ffn_norm.weight", {n_embd});
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layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w1.weight", {n_embd, n_ff});
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layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); // (layers_i + ".feed_forward.w2.weight", { n_ff, n_embd});
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layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w3.weight", {n_embd, n_ff});
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}
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}
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void init_model_lora(struct llama_model_lora * model) {
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const auto & hparams = model->hparams;
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const uint32_t n_embd = hparams.n_embd;
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const uint32_t n_mult = hparams.n_mult;
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const uint32_t n_layer = hparams.n_layer;
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const uint32_t n_vocab = hparams.n_vocab;
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const uint32_t n_lora = hparams.n_lora;
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const uint32_t n_ff = ((2*(4*n_embd)/3 + n_mult - 1)/n_mult)*n_mult;
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struct ggml_context * ctx = model->ctx;
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model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("tok_embeddings.weight", {n_embd, n_vocab});
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model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // ("norm.weight", {n_embd});
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model->outputa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_vocab); // ("output.weight", {n_embd, n_vocab});
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model->outputb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // ("output.weight", {n_embd, n_vocab});
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model->layers.resize(n_layer);
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for (uint32_t i = 0; i < n_layer; ++i) {
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auto & layer = model->layers[i];
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// std::string layers_i = "layers." + std::to_string(i);
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layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".attention_norm.weight", {n_embd});
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layer.wqa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wq.weight", {n_embd, n_embd});
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layer.wqb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wq.weight", {n_embd, n_embd});
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layer.wka = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wk.weight", {n_embd, n_embd});
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layer.wkb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wk.weight", {n_embd, n_embd});
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layer.wva = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wv.weight", {n_embd, n_embd});
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layer.wvb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wv.weight", {n_embd, n_embd});
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layer.woa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wo.weight", {n_embd, n_embd});
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layer.wob = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wo.weight", {n_embd, n_embd});
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layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".ffn_norm.weight", {n_embd});
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layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w1.weight", {n_embd, n_ff});
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layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); // (layers_i + ".feed_forward.w2.weight", { n_ff, n_embd});
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layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w3.weight", {n_embd, n_ff});
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}
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}
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void set_param_model(struct llama_model * model) {
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const auto& hparams = model->hparams;
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const uint32_t n_layer = hparams.n_layer;
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struct ggml_context* ctx = model->ctx;
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ggml_set_param(ctx, model->tok_embeddings);
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ggml_set_param(ctx, model->norm);
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ggml_set_param(ctx, model->output);
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for (uint32_t i = 0; i < n_layer; ++i) {
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auto & layer = model->layers[i];
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ggml_set_param(ctx, layer.attention_norm);
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ggml_set_param(ctx, layer.wq);
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ggml_set_param(ctx, layer.wk);
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ggml_set_param(ctx, layer.wv);
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ggml_set_param(ctx, layer.wo);
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ggml_set_param(ctx, layer.ffn_norm);
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ggml_set_param(ctx, layer.w1);
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ggml_set_param(ctx, layer.w2);
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ggml_set_param(ctx, layer.w3);
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}
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}
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void set_param_model_lora(struct llama_model_lora * model) {
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const auto& hparams = model->hparams;
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const uint32_t n_layer = hparams.n_layer;
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struct ggml_context* ctx = model->ctx;
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ggml_set_param(ctx, model->tok_embeddings);
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ggml_set_param(ctx, model->norm);
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ggml_set_param(ctx, model->outputa);
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ggml_set_param(ctx, model->outputb);
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for (uint32_t i = 0; i < n_layer; ++i) {
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auto & layer = model->layers[i];
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ggml_set_param(ctx, layer.attention_norm);
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ggml_set_param(ctx, layer.wqa);
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ggml_set_param(ctx, layer.wqb);
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ggml_set_param(ctx, layer.wka);
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ggml_set_param(ctx, layer.wkb);
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ggml_set_param(ctx, layer.wva);
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ggml_set_param(ctx, layer.wvb);
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ggml_set_param(ctx, layer.woa);
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ggml_set_param(ctx, layer.wob);
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ggml_set_param(ctx, layer.ffn_norm);
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ggml_set_param(ctx, layer.w1);
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ggml_set_param(ctx, layer.w2);
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ggml_set_param(ctx, layer.w3);
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}
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}
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void randomize_model(struct llama_model * model, int seed, float mean, float std, float min, float max) {
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const auto & hparams = model->hparams;
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const uint32_t n_layer = hparams.n_layer;
|
|
|
|
struct random_normal_distribution rnd;
|
|
init_random_normal_distribution(&rnd, seed, mean, std, min, max);
|
|
randomize_tensor_normal(model->tok_embeddings, model->tok_embeddings->n_dims, model->tok_embeddings->ne, &rnd);
|
|
randomize_tensor_normal(model->norm, model->norm->n_dims, model->norm->ne, &rnd);
|
|
randomize_tensor_normal(model->output, model->output->n_dims, model->output->ne, &rnd);
|
|
|
|
for (uint32_t i = 0; i < n_layer; ++i) {
|
|
auto & layer = model->layers[i];
|
|
randomize_tensor_normal(layer.attention_norm, layer.attention_norm->n_dims, layer.attention_norm->ne, &rnd);
|
|
|
|
randomize_tensor_normal(layer.wq, layer.wq->n_dims, layer.wq->ne, &rnd);
|
|
randomize_tensor_normal(layer.wk, layer.wk->n_dims, layer.wk->ne, &rnd);
|
|
randomize_tensor_normal(layer.wv, layer.wv->n_dims, layer.wv->ne, &rnd);
|
|
randomize_tensor_normal(layer.wo, layer.wo->n_dims, layer.wo->ne, &rnd);
|
|
|
|
randomize_tensor_normal(layer.ffn_norm, layer.ffn_norm->n_dims, layer.ffn_norm->ne, &rnd);
|
|
|
|
randomize_tensor_normal(layer.w1, layer.w1->n_dims, layer.w1->ne, &rnd);
|
|
randomize_tensor_normal(layer.w2, layer.w2->n_dims, layer.w2->ne, &rnd);
|
|
randomize_tensor_normal(layer.w3, layer.w3->n_dims, layer.w3->ne, &rnd);
|
|
}
|
|
}
|
|
|
|
|
|
void randomize_model_lora(struct llama_model_lora * model, int seed, float mean, float std, float min, float max) {
|
|
const auto & hparams = model->hparams;
|
|
|
|
const uint32_t n_layer = hparams.n_layer;
|
|
|
|
struct random_normal_distribution rnd;
|
|
init_random_normal_distribution(&rnd, seed, mean, std, min, max);
|
|
randomize_tensor_normal(model->tok_embeddings, model->tok_embeddings->n_dims, model->tok_embeddings->ne, &rnd);
|
|
randomize_tensor_normal(model->norm, model->norm->n_dims, model->norm->ne, &rnd);
|
|
randomize_tensor_normal(model->outputa, model->outputa->n_dims, model->outputa->ne, &rnd);
|
|
randomize_tensor_normal(model->outputb, model->outputb->n_dims, model->outputb->ne, &rnd);
|
|
|
|
for (uint32_t i = 0; i < n_layer; ++i) {
|
|
auto & layer = model->layers[i];
|
|
randomize_tensor_normal(layer.attention_norm, layer.attention_norm->n_dims, layer.attention_norm->ne, &rnd);
|
|
|
|
randomize_tensor_normal(layer.wqa, layer.wqa->n_dims, layer.wqa->ne, &rnd);
|
|
randomize_tensor_normal(layer.wqb, layer.wqb->n_dims, layer.wqb->ne, &rnd);
|
|
randomize_tensor_normal(layer.wka, layer.wka->n_dims, layer.wka->ne, &rnd);
|
|
randomize_tensor_normal(layer.wkb, layer.wkb->n_dims, layer.wkb->ne, &rnd);
|
|
randomize_tensor_normal(layer.wva, layer.wva->n_dims, layer.wva->ne, &rnd);
|
|
randomize_tensor_normal(layer.wvb, layer.wvb->n_dims, layer.wvb->ne, &rnd);
|
|
randomize_tensor_normal(layer.woa, layer.woa->n_dims, layer.woa->ne, &rnd);
|
|
randomize_tensor_normal(layer.wob, layer.wob->n_dims, layer.wob->ne, &rnd);
|
|
|
|
randomize_tensor_normal(layer.ffn_norm, layer.ffn_norm->n_dims, layer.ffn_norm->ne, &rnd);
|
|
|
|
randomize_tensor_normal(layer.w1, layer.w1->n_dims, layer.w1->ne, &rnd);
|
|
randomize_tensor_normal(layer.w2, layer.w2->n_dims, layer.w2->ne, &rnd);
|
|
randomize_tensor_normal(layer.w3, layer.w3->n_dims, layer.w3->ne, &rnd);
|
|
}
|
|
}
|
|
|
|
bool init_kv_cache(struct llama_kv_cache* cache, struct llama_model * model, int n_batch) {
|
|
const auto & hparams = model->hparams;
|
|
|
|
const uint32_t n_ctx = hparams.n_ctx;
|
|
const uint32_t n_embd = hparams.n_embd;
|
|
const uint32_t n_layer = hparams.n_layer;
|
|
|
|
const int64_t n_mem = n_layer*n_ctx*n_batch;
|
|
const int64_t n_elements = n_embd*n_mem;
|
|
|
|
// cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB);
|
|
|
|
// struct ggml_init_params params;
|
|
// params.mem_size = cache.buf.size;
|
|
// params.mem_buffer = cache.buf.addr;
|
|
// params.no_alloc = false;
|
|
if (!cache->ctx) {
|
|
struct ggml_init_params params;
|
|
params.mem_size = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024;
|
|
params.mem_buffer = NULL;
|
|
params.no_alloc = false;
|
|
|
|
cache->ctx = ggml_init(params);
|
|
|
|
if (!cache->ctx) {
|
|
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
cache->k = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);
|
|
cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);
|
|
|
|
return true;
|
|
}
|
|
|
|
bool init_kv_cache_lora(struct llama_kv_cache* cache, struct llama_model_lora * model, int n_batch) {
|
|
const auto & hparams = model->hparams;
|
|
|
|
const uint32_t n_ctx = hparams.n_ctx;
|
|
const uint32_t n_embd = hparams.n_embd;
|
|
const uint32_t n_layer = hparams.n_layer;
|
|
|
|
const int64_t n_mem = n_layer*n_ctx*n_batch;
|
|
const int64_t n_elements = n_embd*n_mem;
|
|
|
|
// cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB);
|
|
|
|
// struct ggml_init_params params;
|
|
// params.mem_size = cache.buf.size;
|
|
// params.mem_buffer = cache.buf.addr;
|
|
// params.no_alloc = false;
|
|
if (!cache->ctx) {
|
|
struct ggml_init_params params;
|
|
params.mem_size = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024;
|
|
params.mem_buffer = NULL;
|
|
params.no_alloc = false;
|
|
|
|
cache->ctx = ggml_init(params);
|
|
|
|
if (!cache->ctx) {
|
|
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
cache->k = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);
|
|
cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements);
|
|
|
|
return true;
|
|
}
|
|
|
|
struct ggml_tensor * forward(
|
|
struct llama_model * model,
|
|
struct llama_kv_cache * cache,
|
|
struct ggml_context * ctx0,
|
|
struct ggml_cgraph * gf,
|
|
struct ggml_tensor * tokens_input,
|
|
const int n_tokens,
|
|
const int n_past) {
|
|
|
|
const int N = n_tokens;
|
|
|
|
struct llama_kv_cache& kv_self = *cache;
|
|
const auto & hparams = model->hparams;
|
|
const int n_ctx = hparams.n_ctx;
|
|
const int n_embd = hparams.n_embd;
|
|
const int n_layer = hparams.n_layer;
|
|
const int n_head = hparams.n_head;
|
|
const int n_rot = hparams.n_rot;
|
|
|
|
struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
|
memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens));
|
|
|
|
struct ggml_tensor * kc = kv_self.k;
|
|
struct ggml_tensor * vc = kv_self.v;
|
|
|
|
// inpL shape [n_embd,N,1,1]
|
|
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
struct ggml_tensor * cur;
|
|
|
|
// lctx.use_buf(ctx0, 0);
|
|
|
|
// norm
|
|
{
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_rms_norm(ctx0, inpL);
|
|
|
|
// cur = attention_norm*cur
|
|
cur = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model->layers[il].attention_norm, cur),
|
|
cur);
|
|
}
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
// wq shape [n_embd, n_embd, 1, 1]
|
|
// wk shape [n_embd, n_embd, 1, 1]
|
|
// Qcur shape [n_embd/n_head, n_head, N, 1]
|
|
// Kcur shape [n_embd/n_head, n_head, N, 1]
|
|
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
|
|
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
|
|
|
|
// store key and value to memory
|
|
{
|
|
// compute the transposed [N, n_embd] V matrix
|
|
// wv shape [n_embd, n_embd, 1, 1]
|
|
// Vcur shape [n_embd, N, 1, 1]
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wv, cur), n_embd, N)));
|
|
|
|
// kv_self.k shape [n_embd * n_ctx * n_layer, 1]
|
|
// kv_self.v shape [n_embd * n_ctx * n_layer, 1]
|
|
// k shape [n_embd * N, 1] == kv_self.k[:,n_past:n_past+N,il,0]
|
|
// v shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0]
|
|
|
|
/* {
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
|
|
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
|
|
( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
|
|
|
|
// important: storing RoPE-ed version of K in the KV cache!
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
|
} //*/
|
|
|
|
kc = ggml_set_1d(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
|
|
vc = ggml_set_2d(ctx0, vc, Vcur, ( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
|
|
}
|
|
|
|
// Qcur shape [n_embd/n_head, n_head, N, 1]
|
|
// Q shape [n_embd/n_head, N, n_head, 1]
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
Qcur,
|
|
0, 2, 1, 3);
|
|
|
|
// kv_self.k shape [n_embd * n_ctx * n_layer, 1]
|
|
// K shape [n_embd/n_head, n_past + N, n_head, 1]
|
|
struct ggml_tensor * K =
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd),
|
|
n_embd/n_head, n_head, n_past + N),
|
|
0, 2, 1, 3);
|
|
|
|
// K * Q
|
|
// KQ shape [n_past + N, N, n_head, 1]
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd/n_head)
|
|
// KQ_scaled shape [n_past + N, N, n_head, 1]
|
|
struct ggml_tensor * KQ_scaled =
|
|
ggml_scale(ctx0,
|
|
KQ,
|
|
ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
// KQ_masked shape [n_past + N, N, n_head, 1]
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
// KQ_soft_max shape [n_past + N, N, n_head, 1]
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
|
|
// split cached V into n_head heads
|
|
//// V shape [n_past + N, n_embd/n_head, n_head, 1]
|
|
// V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1]
|
|
struct ggml_tensor * V =
|
|
ggml_view_3d(ctx0, vc,
|
|
n_past + N, n_embd/n_head, n_head,
|
|
n_ctx*ggml_element_size(vc),
|
|
n_ctx*ggml_element_size(vc)*n_embd/n_head,
|
|
il*n_ctx*ggml_element_size(vc)*n_embd);
|
|
|
|
// KQV shape [n_embd/n_head, N, n_head, 1]
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
// KQV_merged shape [n_embd/n_head, n_head, N, 1]
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
// KQV_merged shape
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N);
|
|
// cur = ggml_cpy(ctx0,
|
|
// KQV_merged,
|
|
// ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
|
|
|
// projection (no bias)
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_mul_mat(ctx0,
|
|
model->layers[il].wo,
|
|
cur);
|
|
}
|
|
|
|
// lctx.use_buf(ctx0, 1);
|
|
|
|
// inpFF shape [n_embd,N,1,1]
|
|
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
|
|
|
// feed-forward network
|
|
{
|
|
// norm
|
|
{
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_rms_norm(ctx0, inpFF);
|
|
|
|
// cur = ffn_norm*cur
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),
|
|
cur);
|
|
}
|
|
|
|
// tmp shape [n_ff,N,1,1]
|
|
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
|
model->layers[il].w3,
|
|
cur);
|
|
|
|
// cur shape [n_ff,N,1,1]
|
|
cur = ggml_mul_mat(ctx0,
|
|
model->layers[il].w1,
|
|
cur);
|
|
|
|
// SILU activation
|
|
// cur shape [n_ff,N,1,1]
|
|
cur = ggml_silu(ctx0, cur);
|
|
|
|
// cur shape [n_ff,N,1,1]
|
|
cur = ggml_mul(ctx0, cur, tmp);
|
|
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_mul_mat(ctx0,
|
|
model->layers[il].w2,
|
|
cur);
|
|
}
|
|
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
|
|
// input for next layer
|
|
// inpL shape [n_embd,N,1,1]
|
|
inpL = cur;
|
|
}
|
|
|
|
// norm
|
|
{
|
|
|
|
// inpL shape [n_embd,N,1,1]
|
|
inpL = ggml_rms_norm(ctx0, inpL);
|
|
|
|
// inpL = norm*inpL
|
|
// inpL shape [n_embd,N,1,1]
|
|
inpL = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model->norm, inpL),
|
|
inpL);
|
|
|
|
//embeddings = inpL;
|
|
}
|
|
|
|
// lm_head
|
|
// inpL shape [n_vocab,N,1,1]
|
|
inpL = ggml_mul_mat(ctx0, model->output, inpL);
|
|
|
|
// run the computation
|
|
ggml_build_forward_expand(gf, inpL);
|
|
|
|
return inpL;
|
|
}
|
|
|
|
void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) {
|
|
GGML_ASSERT(tensor->n_dims == 1);
|
|
GGML_ASSERT(tensor->ne[0] == ne0);
|
|
}
|
|
|
|
void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1) {
|
|
GGML_ASSERT(tensor->n_dims == 2);
|
|
GGML_ASSERT(tensor->ne[0] == ne0);
|
|
GGML_ASSERT(tensor->ne[1] == ne1);
|
|
}
|
|
|
|
void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2) {
|
|
GGML_ASSERT(tensor->n_dims == 3);
|
|
GGML_ASSERT(tensor->ne[0] == ne0);
|
|
GGML_ASSERT(tensor->ne[1] == ne1);
|
|
GGML_ASSERT(tensor->ne[2] == ne2);
|
|
}
|
|
|
|
void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) {
|
|
GGML_ASSERT(tensor->n_dims == 4);
|
|
GGML_ASSERT(tensor->ne[0] == ne0);
|
|
GGML_ASSERT(tensor->ne[1] == ne1);
|
|
GGML_ASSERT(tensor->ne[2] == ne2);
|
|
GGML_ASSERT(tensor->ne[3] == ne3);
|
|
}
|
|
|
|
struct ggml_tensor * forward_batch(
|
|
struct llama_model * model,
|
|
struct llama_kv_cache * cache,
|
|
struct ggml_context * ctx0,
|
|
struct ggml_cgraph * gf,
|
|
struct ggml_tensor * tokens_input,
|
|
const int n_tokens,
|
|
const int n_past,
|
|
const int n_batch) {
|
|
|
|
const int N = n_tokens;
|
|
|
|
struct llama_kv_cache& kv_self = *cache;
|
|
const auto & hparams = model->hparams;
|
|
const int n_ctx = hparams.n_ctx;
|
|
const int n_vocab = hparams.n_vocab;
|
|
const int n_embd = hparams.n_embd;
|
|
const int n_layer = hparams.n_layer;
|
|
const int n_head = hparams.n_head;
|
|
const int n_rot = hparams.n_rot;
|
|
const int n_ff = get_n_ff(&hparams);
|
|
|
|
struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch);
|
|
memcpy(tokens->data, tokens_input->data, ggml_element_size(tokens)*N*n_batch);
|
|
|
|
struct ggml_tensor * kc = kv_self.k;
|
|
struct ggml_tensor * vc = kv_self.v;
|
|
|
|
// inpL shape [n_embd,N*n_batch,1]
|
|
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
|
|
assert_shape_2d(inpL, n_embd, N*n_batch);
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
struct ggml_tensor * cur;
|
|
|
|
// lctx.use_buf(ctx0, 0);
|
|
|
|
// norm
|
|
{
|
|
// cur shape [n_embd,N*n_batch,1,1]
|
|
cur = ggml_rms_norm(ctx0, inpL);
|
|
assert_shape_2d(cur, n_embd, N*n_batch);
|
|
|
|
// cur = attention_norm*cur
|
|
cur = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model->layers[il].attention_norm, cur),
|
|
cur);
|
|
assert_shape_2d(cur, n_embd, N*n_batch);
|
|
}
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
// wq shape [n_embd, n_embd, 1, 1]
|
|
// wk shape [n_embd, n_embd, 1, 1]
|
|
// Qcur shape [n_embd/n_head, n_head, N, n_batch]
|
|
// Kcur shape [n_embd/n_head, n_head, N, n_batch]
|
|
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
|
|
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
|
|
assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
|
|
assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
|
|
|
|
// store key and value to memory
|
|
{
|
|
// compute the transposed [N, n_embd] V matrix
|
|
// wv shape [n_embd, n_embd, 1, 1]
|
|
// Vcur shape [N, n_embd, n_batch, 1]
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx0,
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_mul_mat(ctx0,
|
|
model->layers[il].wv,
|
|
cur),
|
|
n_embd, N, n_batch),
|
|
1, 0, 2, 3));
|
|
|
|
assert_shape_3d(Vcur, N, n_embd, n_batch);
|
|
|
|
// kv_self.k shape [n_embd * n_ctx * n_batch * n_layer]
|
|
// kv_self.v shape [n_ctx * n_embd * n_batch * n_layer]
|
|
// k shape [n_embd * N, n_batch] == kv_self.k[:,n_past:n_past+N,:,il]
|
|
// v shape [N, n_embd, n_batch, 1] == kv_self.v[:,n_past:n_past+N,:,il]
|
|
|
|
/* {
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
|
|
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
|
|
( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
|
|
|
|
// important: storing RoPE-ed version of K in the KV cache!
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
|
} //*/
|
|
|
|
kc = ggml_set_2d(ctx0, kc,
|
|
ggml_reshape_2d(ctx0, Kcur, n_embd*N, n_batch),
|
|
ggml_element_size(kc)*n_embd*n_ctx,
|
|
(ggml_element_size(kc)*n_embd)*(il*n_batch*n_ctx + n_past));
|
|
vc = ggml_set_2d(ctx0, vc,
|
|
ggml_reshape_2d(ctx0, Vcur, N*n_embd, n_batch),
|
|
ggml_element_size(vc)*n_ctx*n_embd,
|
|
ggml_element_size(vc)*(n_past + il*n_embd*n_batch*n_ctx));
|
|
|
|
assert_shape_1d(kc, n_embd * n_ctx * n_batch * n_layer);
|
|
assert_shape_1d(vc, n_embd * n_ctx * n_batch * n_layer);
|
|
}
|
|
|
|
// Qcur shape [n_embd/n_head, n_head, N, n_batch]
|
|
// Q shape [n_embd/n_head, N, n_head, n_batch]
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
Qcur,
|
|
0, 2, 1, 3);
|
|
assert_shape_4d(Q, n_embd/n_head, N, n_head, n_batch);
|
|
|
|
// kv_self.k shape [n_embd * n_ctx * n_batch * n_layer]
|
|
// K shape [n_embd/n_head, n_past + N, n_head, n_batch]
|
|
struct ggml_tensor * K =
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_4d(ctx0,
|
|
ggml_view_3d(ctx0,
|
|
kc,
|
|
n_embd,
|
|
(n_past + N),
|
|
n_batch,
|
|
n_embd*ggml_element_size(kc),
|
|
n_ctx*n_embd*ggml_element_size(kc),
|
|
il*n_batch*n_ctx*n_embd*ggml_element_size(kc)),
|
|
n_embd/n_head, n_head, n_past + N, n_batch),
|
|
0, 2, 1, 3);
|
|
assert_shape_4d(K, n_embd/n_head, n_past + N, n_head, n_batch);
|
|
|
|
// K * Q
|
|
// KQ shape [n_past + N, N, n_head, n_batch]
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
assert_shape_4d(KQ, n_past + N, N, n_head, n_batch);
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd/n_head)
|
|
// KQ_scaled shape [n_past + N, N, n_head, n_batch]
|
|
struct ggml_tensor * KQ_scaled =
|
|
ggml_scale(ctx0,
|
|
KQ,
|
|
ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
|
|
assert_shape_4d(KQ_scaled, n_past + N, N, n_head, n_batch);
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
// KQ_masked shape [n_past + N, N, n_head, n_batch]
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
|
|
assert_shape_4d(KQ_masked, n_past + N, N, n_head, n_batch);
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
// KQ_soft_max shape [n_past + N, N, n_head, n_batch]
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
assert_shape_4d(KQ_soft_max, n_past + N, N, n_head, n_batch);
|
|
|
|
// split cached V into n_head heads
|
|
// kv_self.v shape [n_ctx * n_embd * n_batch * n_layer]
|
|
// V shape [n_past + N, n_embd/n_head, n_head, n_batch] == kv_self.v[:(n_past+N),:,:,il]
|
|
struct ggml_tensor * V =
|
|
ggml_view_4d(ctx0, vc,
|
|
n_past + N, n_embd/n_head, n_head, n_batch,
|
|
ggml_element_size(vc)*n_ctx,
|
|
ggml_element_size(vc)*n_ctx*n_embd/n_head,
|
|
ggml_element_size(vc)*n_ctx*n_embd,
|
|
il*n_batch*n_ctx*n_embd*ggml_element_size(vc));
|
|
assert_shape_4d(V, n_past + N, n_embd/n_head, n_head, n_batch);
|
|
|
|
// KQV shape [n_embd/n_head, N, n_head, n_batch]
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
assert_shape_4d(KQV, n_embd/n_head, N, n_head, n_batch);
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
// KQV_merged shape [n_embd/n_head, n_head, N, n_batch]
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
assert_shape_4d(KQV_merged, n_embd/n_head, n_head, N, n_batch);
|
|
// KQV_merged shape
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
|
// cur shape [n_embd,N*n_batch,1,1]
|
|
cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N*n_batch);
|
|
assert_shape_2d(cur, n_embd, N*n_batch);
|
|
// cur = ggml_cpy(ctx0,
|
|
// KQV_merged,
|
|
// ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
|
|
|
// projection (no bias)
|
|
// cur shape [n_embd,N*n_batch,1,1]
|
|
cur = ggml_mul_mat(ctx0,
|
|
model->layers[il].wo,
|
|
cur);
|
|
assert_shape_2d(cur, n_embd, N*n_batch);
|
|
}
|
|
|
|
// lctx.use_buf(ctx0, 1);
|
|
|
|
// inpFF shape [n_embd,N*n_batch,1,1]
|
|
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
|
assert_shape_2d(inpFF, n_embd, N*n_batch);
|
|
|
|
// feed-forward network
|
|
{
|
|
// norm
|
|
{
|
|
// cur shape [n_embd,N*n_batch,1,1]
|
|
cur = ggml_rms_norm(ctx0, inpFF);
|
|
assert_shape_2d(cur, n_embd, N*n_batch);
|
|
|
|
// cur = ffn_norm*cur
|
|
// cur shape [n_embd,N*n_batch,1,1]
|
|
cur = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),
|
|
cur);
|
|
assert_shape_2d(cur, n_embd, N*n_batch);
|
|
}
|
|
|
|
// tmp shape [n_ff,N*n_batch,1,1]
|
|
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
|
model->layers[il].w3,
|
|
cur);
|
|
assert_shape_2d(tmp, n_ff, N*n_batch);
|
|
|
|
// cur shape [n_ff,N*n_batch,1,1]
|
|
cur = ggml_mul_mat(ctx0,
|
|
model->layers[il].w1,
|
|
cur);
|
|
assert_shape_2d(cur, n_ff, N*n_batch);
|
|
|
|
// SILU activation
|
|
// cur shape [n_ff,N*n_batch,1,1]
|
|
cur = ggml_silu(ctx0, cur);
|
|
assert_shape_2d(cur, n_ff, N*n_batch);
|
|
|
|
// cur shape [n_ff,N*n_batch,1,1]
|
|
cur = ggml_mul(ctx0, cur, tmp);
|
|
assert_shape_2d(cur, n_ff, N*n_batch);
|
|
|
|
// cur shape [n_embd,N*n_batch,1,1]
|
|
cur = ggml_mul_mat(ctx0,
|
|
model->layers[il].w2,
|
|
cur);
|
|
assert_shape_2d(cur, n_embd, N*n_batch);
|
|
}
|
|
|
|
// cur shape [n_embd,N*n_batch,1,1]
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
assert_shape_2d(cur, n_embd, N*n_batch);
|
|
|
|
// input for next layer
|
|
// inpL shape [n_embd,N*n_batch,1,1]
|
|
inpL = cur;
|
|
assert_shape_2d(inpL, n_embd, N*n_batch);
|
|
}
|
|
|
|
// norm
|
|
{
|
|
|
|
// inpL shape [n_embd,N*n_batch,1,1]
|
|
inpL = ggml_rms_norm(ctx0, inpL);
|
|
assert_shape_2d(inpL, n_embd, N*n_batch);
|
|
|
|
// inpL = norm*inpL
|
|
// inpL shape [n_embd,N*n_batch,1,1]
|
|
inpL = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model->norm, inpL),
|
|
inpL);
|
|
|
|
assert_shape_2d(inpL, n_embd, N*n_batch);
|
|
|
|
//embeddings = inpL;
|
|
}
|
|
|
|
// lm_head
|
|
// inpL shape [n_vocab,N*n_batch,1,1]
|
|
inpL = ggml_mul_mat(ctx0, model->output, inpL);
|
|
assert_shape_2d(inpL, n_vocab, N*n_batch);
|
|
|
|
{
|
|
// inpL shape [n_vocab,N,n_batch,1]
|
|
inpL = ggml_reshape_3d(ctx0,
|
|
inpL,
|
|
n_vocab, N, n_batch);
|
|
assert_shape_3d(inpL, n_vocab, N, n_batch);
|
|
}
|
|
|
|
// run the computation
|
|
ggml_build_forward_expand(gf, inpL);
|
|
|
|
return inpL;
|
|
}
|
|
|
|
|
|
struct ggml_tensor * forward_lora(
|
|
struct llama_model_lora * model,
|
|
struct llama_kv_cache * cache,
|
|
struct ggml_context * ctx0,
|
|
struct ggml_cgraph * gf,
|
|
struct ggml_tensor * tokens_input,
|
|
const int n_tokens,
|
|
const int n_past) {
|
|
|
|
const int N = n_tokens;
|
|
|
|
struct llama_kv_cache& kv_self = *cache;
|
|
const auto & hparams = model->hparams;
|
|
|
|
const int n_ctx = hparams.n_ctx;
|
|
const int n_embd = hparams.n_embd;
|
|
const int n_layer = hparams.n_layer;
|
|
const int n_head = hparams.n_head;
|
|
const int n_rot = hparams.n_rot;
|
|
|
|
struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
|
memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens));
|
|
|
|
struct ggml_tensor * kc = kv_self.k;
|
|
struct ggml_tensor * vc = kv_self.v;
|
|
|
|
// inpL shape [n_embd,N,1,1]
|
|
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens);
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
struct ggml_tensor * cur;
|
|
|
|
// norm
|
|
{
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_rms_norm(ctx0, inpL);
|
|
|
|
// cur = attention_norm*cur
|
|
cur = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model->layers[il].attention_norm, cur),
|
|
cur);
|
|
}
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
// wq shape [n_embd, n_embd, 1, 1]
|
|
// wk shape [n_embd, n_embd, 1, 1]
|
|
// Qcur shape [n_embd/n_head, n_head, N, 1]
|
|
// Kcur shape [n_embd/n_head, n_head, N, 1]
|
|
struct ggml_tensor * Qcur = ggml_rope(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_mul_mat(ctx0,
|
|
model->layers[il].wqa,
|
|
ggml_mul_mat(ctx0,
|
|
model->layers[il].wqb,
|
|
cur)),
|
|
n_embd/n_head, n_head, N),
|
|
n_past, n_rot, 0, 0);
|
|
struct ggml_tensor * Kcur = ggml_rope(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_mul_mat(ctx0,
|
|
model->layers[il].wka,
|
|
ggml_mul_mat(ctx0,
|
|
model->layers[il].wkb,
|
|
cur)),
|
|
n_embd/n_head, n_head, N),
|
|
n_past, n_rot, 0, 0);
|
|
|
|
// store key and value to memory
|
|
{
|
|
// compute the transposed [N, n_embd] V matrix
|
|
// wv shape [n_embd, n_embd, 1, 1]
|
|
// Vcur shape [n_embd, N, 1, 1]
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx0,
|
|
ggml_transpose(ctx0,
|
|
ggml_reshape_2d(ctx0,
|
|
ggml_mul_mat(ctx0,
|
|
model->layers[il].wva,
|
|
ggml_mul_mat(ctx0,
|
|
model->layers[il].wvb,
|
|
cur)),
|
|
n_embd, N)));
|
|
|
|
// kv_self.k shape [n_embd * n_ctx * n_layer, 1]
|
|
// kv_self.v shape [n_embd * n_ctx * n_layer, 1]
|
|
// k shape [n_embd * N, 1] == kv_self.k[:,n_past:n_past+N,il,0]
|
|
// v shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0]
|
|
|
|
/* {
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
|
|
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
|
|
( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
|
|
|
|
// important: storing RoPE-ed version of K in the KV cache!
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
|
} //*/
|
|
|
|
kc = ggml_set_1d(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
|
|
vc = ggml_set_2d(ctx0, vc, Vcur, ( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
|
|
}
|
|
|
|
// Qcur shape [n_embd/n_head, n_head, N, 1]
|
|
// Q shape [n_embd/n_head, N, n_head, 1]
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
Qcur,
|
|
0, 2, 1, 3);
|
|
|
|
// kv_self.k shape [n_embd * n_ctx * n_layer, 1]
|
|
// K shape [n_embd/n_head, n_past + N, n_head, 1]
|
|
struct ggml_tensor * K =
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd),
|
|
n_embd/n_head, n_head, n_past + N),
|
|
0, 2, 1, 3);
|
|
|
|
// K * Q
|
|
// KQ shape [n_past + N, N, n_head, 1]
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd/n_head)
|
|
// KQ_scaled shape [n_past + N, N, n_head, 1]
|
|
struct ggml_tensor * KQ_scaled =
|
|
ggml_scale(ctx0,
|
|
KQ,
|
|
ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
// KQ_masked shape [n_past + N, N, n_head, 1]
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
// KQ_soft_max shape [n_past + N, N, n_head, 1]
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
|
|
// split cached V into n_head heads
|
|
//// V shape [n_past + N, n_embd/n_head, n_head, 1]
|
|
// V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1]
|
|
struct ggml_tensor * V =
|
|
ggml_view_3d(ctx0, vc,
|
|
n_past + N, n_embd/n_head, n_head,
|
|
n_ctx*ggml_element_size(vc),
|
|
n_ctx*ggml_element_size(vc)*n_embd/n_head,
|
|
il*n_ctx*ggml_element_size(vc)*n_embd);
|
|
|
|
// KQV shape [n_embd/n_head, N, n_head, 1]
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
// KQV_merged shape [n_embd/n_head, n_head, N, 1]
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
// KQV_merged shape
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N);
|
|
// cur = ggml_cpy(ctx0,
|
|
// KQV_merged,
|
|
// ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
|
|
|
// projection (no bias)
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_mul_mat(ctx0,
|
|
model->layers[il].woa,
|
|
ggml_mul_mat(ctx0,
|
|
model->layers[il].wob,
|
|
cur));
|
|
}
|
|
|
|
// inpFF shape [n_embd,N,1,1]
|
|
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
|
|
|
// feed-forward network
|
|
{
|
|
// norm
|
|
{
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_rms_norm(ctx0, inpFF);
|
|
|
|
// cur = ffn_norm*cur
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model->layers[il].ffn_norm, cur),
|
|
cur);
|
|
}
|
|
|
|
// tmp shape [n_ff,N,1,1]
|
|
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
|
model->layers[il].w3,
|
|
cur);
|
|
|
|
// cur shape [n_ff,N,1,1]
|
|
cur = ggml_mul_mat(ctx0,
|
|
model->layers[il].w1,
|
|
cur);
|
|
|
|
// SILU activation
|
|
// cur shape [n_ff,N,1,1]
|
|
cur = ggml_silu(ctx0, cur);
|
|
|
|
// cur shape [n_ff,N,1,1]
|
|
cur = ggml_mul(ctx0, cur, tmp);
|
|
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_mul_mat(ctx0,
|
|
model->layers[il].w2,
|
|
cur);
|
|
}
|
|
|
|
// cur shape [n_embd,N,1,1]
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
|
|
// input for next layer
|
|
// inpL shape [n_embd,N,1,1]
|
|
inpL = cur;
|
|
}
|
|
|
|
// norm
|
|
{
|
|
|
|
// inpL shape [n_embd,N,1,1]
|
|
inpL = ggml_rms_norm(ctx0, inpL);
|
|
|
|
// inpL = norm*inpL
|
|
// inpL shape [n_embd,N,1,1]
|
|
inpL = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model->norm, inpL),
|
|
inpL);
|
|
|
|
//embeddings = inpL;
|
|
}
|
|
|
|
|
|
// lm_head
|
|
// inpL shape [n_vocab,N,1,1]
|
|
inpL = ggml_mul_mat(ctx0,
|
|
model->outputa,
|
|
ggml_mul_mat(ctx0,
|
|
model->outputb,
|
|
inpL));
|
|
|
|
// ggml_set_scratch(ctx0, { 0, 0, nullptr, });
|
|
// run the computation
|
|
ggml_build_forward_expand(gf, inpL);
|
|
|
|
return inpL;
|
|
}
|
|
|
|
void sample_softmax(struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) {
|
|
assert(logits->n_dims == 2);
|
|
assert(probs->n_dims == 2);
|
|
assert(best_samples->n_dims == 1);
|
|
assert(logits->ne[1] == best_samples->ne[0]);
|
|
assert(logits->ne[0] == probs->ne[0]);
|
|
assert(logits->ne[1] == probs->ne[1]);
|
|
for (int i = 0; i < logits->ne[1]; ++i) {
|
|
float max_logit = ggml_get_f32_1d(logits, i * logits->ne[0]);
|
|
ggml_set_i32_1d(best_samples, i, 0);
|
|
for (int k = 0; k < logits->ne[0]; ++k) {
|
|
float logit = ggml_get_f32_1d(logits, i * logits->ne[0] + k);
|
|
if (logit > max_logit) {
|
|
max_logit = logit;
|
|
ggml_set_i32_1d(best_samples, i, k);
|
|
}
|
|
}
|
|
float psum = 0;
|
|
for (int k = 0; k < logits->ne[0]; ++k) {
|
|
float logit = ggml_get_f32_1d(logits, i * logits->ne[0] + k);
|
|
float p = (logit == -INFINITY) ? 0 : expf(logit - max_logit);
|
|
psum += p;
|
|
ggml_set_f32_1d(probs, i * probs->ne[0] + k, p);
|
|
}
|
|
for (int k = 0; k < logits->ne[0]; ++k) {
|
|
float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);
|
|
ggml_set_f32_1d(probs, i * probs->ne[0] + k, p / psum);
|
|
}
|
|
}
|
|
}
|
|
|
|
void sample_softmax_batch(struct ggml_context * ctx, struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) {
|
|
GGML_ASSERT(best_samples->n_dims == 2);
|
|
GGML_ASSERT(logits->n_dims == 3);
|
|
GGML_ASSERT(probs->n_dims == 3);
|
|
int n_tokens = best_samples->ne[0];
|
|
int n_batch = best_samples->ne[1];
|
|
int n_vocab = logits->ne[0];
|
|
GGML_ASSERT(n_tokens == logits->ne[1]);
|
|
GGML_ASSERT(n_batch == logits->ne[2]);
|
|
GGML_ASSERT(n_vocab == probs->ne[0]);
|
|
GGML_ASSERT(n_tokens == probs->ne[1]);
|
|
GGML_ASSERT(n_batch == probs->ne[2]);
|
|
|
|
for (int k = 0; k < n_batch; ++k) {
|
|
struct ggml_tensor * best_samples_k = ggml_view_1d(ctx,
|
|
best_samples,
|
|
best_samples->ne[0],
|
|
k*best_samples->nb[1]);
|
|
struct ggml_tensor * logits_k = ggml_view_2d(ctx,
|
|
logits,
|
|
logits->ne[0],
|
|
logits->ne[1],
|
|
logits->nb[1],
|
|
k*logits->nb[2]);
|
|
struct ggml_tensor * probs_k = ggml_view_2d(ctx,
|
|
probs,
|
|
probs->ne[0],
|
|
probs->ne[1],
|
|
probs->nb[1],
|
|
k*probs->nb[2]);
|
|
sample_softmax(logits_k, probs_k, best_samples_k);
|
|
}
|
|
}
|
|
|
|
void print_row(struct ggml_tensor * probs, int i) {
|
|
for (int k = 0; k < probs->ne[0]; ++k) {
|
|
float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);
|
|
printf(" %.2f", p);
|
|
}
|
|
printf("\n");
|
|
}
|
|
|
|
void print_matrix(struct ggml_tensor * probs) {
|
|
assert(probs->n_dims == 2);
|
|
for (int i = 0; i < probs->ne[1]; ++i) {
|
|
for (int k = 0; k < probs->ne[0]; ++k) {
|
|
float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);
|
|
printf(" %.2f", p);
|
|
}
|
|
printf("\n");
|
|
}
|
|
}
|
|
|
|
void print_token(int token, int n_vocab) {
|
|
for (int k = 0; k < token; ++k) {
|
|
printf(" ");
|
|
}
|
|
printf("X");
|
|
for (int k = token+1; k < n_vocab; ++k) {
|
|
printf(" ");
|
|
}
|
|
printf("\n");
|
|
}
|
|
|
|
void print_tokens(struct ggml_tensor * tokens, int n_vocab) {
|
|
for (int i=0; i<tokens->ne[0]; ++i) {
|
|
int token = ggml_get_i32_1d(tokens, i);
|
|
print_token(token, n_vocab);
|
|
}
|
|
}
|
|
|
|
void get_example_targets(int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets) {
|
|
int n_tokens = tokens_input->ne[0];
|
|
int n_vocab = targets->ne[0];
|
|
float randomness = 0.0f;
|
|
// ggml_set_zero(targets);
|
|
ggml_set_f32(targets, -1.0f);
|
|
ggml_set_i32_1d(tokens_input, 0, 0);
|
|
for (int i=1; i<n_tokens+1; ++i) {
|
|
float x = example_id + i * 3.14159f * 2.0f * 1.0f * 0.5f / n_tokens;
|
|
float y = sinf(x);//*cosf(x*1.1f+1.0f);
|
|
float z = (y+1.0f)*0.5f; // scale to [0..1]
|
|
z += (frand()-0.5f)*(randomness/n_vocab);
|
|
z = (z < 0.0f) ? 0.0f : (z > 1.0f) ? 1.0f : z; // clamp to [0..1]
|
|
int token = std::max(1,std::min(1+(int)(z*(float)(n_vocab-1)), n_vocab-1));
|
|
ggml_set_f32_1d(targets, (i-1)*n_vocab + token, +1.0f);
|
|
if (i<n_tokens) {
|
|
ggml_set_i32_1d(tokens_input, i, token);
|
|
}
|
|
}
|
|
}
|
|
|
|
void get_example_targets_batch(struct ggml_context * ctx, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets) {
|
|
GGML_ASSERT(tokens_input->n_dims == 2);
|
|
GGML_ASSERT( targets->n_dims == 3);
|
|
int n_tokens = tokens_input->ne[0];
|
|
int n_batch = tokens_input->ne[1];
|
|
GGML_ASSERT(n_tokens == targets->ne[1]);
|
|
GGML_ASSERT(n_batch == targets->ne[2]);
|
|
|
|
for (int k=0; k<n_batch; ++k) {
|
|
struct ggml_tensor * tokens_input_k = ggml_view_1d(ctx,
|
|
tokens_input,
|
|
tokens_input->ne[0],
|
|
k*tokens_input->nb[1]);
|
|
struct ggml_tensor * targets_k = ggml_view_2d(ctx,
|
|
targets,
|
|
targets->ne[0],
|
|
targets->ne[1],
|
|
targets->nb[1],
|
|
k*targets->nb[2]);
|
|
get_example_targets(example_id*n_batch + k, tokens_input_k, targets_k);
|
|
}
|
|
}
|
|
|
|
void lshift_examples(struct ggml_tensor * tokens_input, struct ggml_tensor * targets, int n_shift) {
|
|
int n_tokens = tokens_input->ne[0];
|
|
int n_vocab = targets->ne[0];
|
|
for (int i=0; i<n_tokens-n_shift; ++i) {
|
|
ggml_set_i32_1d(tokens_input, i, ggml_get_i32_1d(tokens_input, i + n_shift));
|
|
for (int k=0; k<n_vocab; ++k) {
|
|
ggml_set_f32_1d(targets, i*n_vocab + k, ggml_get_f32_1d(targets, (i + n_shift)*n_vocab + k));
|
|
}
|
|
}
|
|
}
|
|
|
|
struct ggml_tensor * square_error_loss(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
|
|
// todo: instead of a-b: a[1:]-b[:-1]
|
|
return ggml_sum(ctx, ggml_sqr(ctx, ggml_sub(ctx, a, b)));
|
|
}
|
|
|
|
struct ggml_tensor * cross_entropy_loss(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
|
|
const float eps = 1e-3f;
|
|
return
|
|
ggml_sum(ctx,
|
|
ggml_neg(ctx,
|
|
ggml_sum_rows(ctx,
|
|
ggml_mul(ctx,
|
|
ggml_soft_max(ctx, a),
|
|
ggml_log(ctx,
|
|
ggml_add1(ctx,
|
|
ggml_soft_max(ctx, b),
|
|
ggml_new_f32(ctx, eps)))))));
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
if (argc < 1) {
|
|
fprintf(stderr, "usage: %s\n", argv[0]);
|
|
|
|
return 1;
|
|
}
|
|
|
|
struct ggml_init_params lcparams;
|
|
lcparams.mem_size = 1024ll*1024ll*1024ll;
|
|
lcparams.mem_buffer = NULL;
|
|
lcparams.no_alloc = false;
|
|
|
|
struct llama_model model;
|
|
model.hparams.n_vocab = 8;
|
|
model.hparams.n_ctx = 8;
|
|
model.hparams.n_embd = 32;
|
|
model.hparams.n_mult = 2;
|
|
model.hparams.n_head = 8;
|
|
model.hparams.n_layer = 1;
|
|
model.hparams.n_rot = std::min(16u, model.hparams.n_embd / model.hparams.n_head);
|
|
|
|
// model.hparams.n_embd = 32;
|
|
// model.hparams.n_mult = 2;
|
|
// model.hparams.n_head = 4;
|
|
// model.hparams.n_layer = 8;
|
|
// model.hparams.n_rot = 8;
|
|
|
|
model.ctx = ggml_init(lcparams);
|
|
printf("init model\n");
|
|
init_model(&model);
|
|
set_param_model(&model);
|
|
|
|
randomize_model(&model, 1337, 0.0f, 1.0f, -1.0f, +1.0f);
|
|
|
|
/*
|
|
struct llama_model_lora model_lora;
|
|
// model.hparams.n_vocab = 6;
|
|
// model.hparams.n_ctx = 64;
|
|
// model.hparams.n_embd = 128;
|
|
// model.hparams.n_mult = 2;
|
|
// model.hparams.n_head = 8;
|
|
// model.hparams.n_layer = 6;
|
|
// model.hparams.n_rot = model.hparams.n_embd / model.hparams.n_head;
|
|
|
|
model_lora.hparams.n_vocab = 16;
|
|
model_lora.hparams.n_ctx = 32;
|
|
model_lora.hparams.n_embd = 256;
|
|
model_lora.hparams.n_mult = 2;
|
|
model_lora.hparams.n_head = 16;
|
|
model_lora.hparams.n_layer = 1;
|
|
model_lora.hparams.n_lora = 64;
|
|
model_lora.hparams.n_rot = MIN(16, model_lora.hparams.n_embd / model_lora.hparams.n_head);
|
|
// model.hparams.n_rot = (model.hparams.n_embd / model.hparams.n_head) / 2;
|
|
|
|
// model.hparams.n_embd = 32;
|
|
// model.hparams.n_mult = 2;
|
|
// model.hparams.n_head = 4;
|
|
// model.hparams.n_layer = 8;
|
|
// model.hparams.n_rot = 8;
|
|
|
|
model_lora.ctx = ggml_init(lcparams);
|
|
printf("init model_lora\n");
|
|
init_model_lora(&model_lora);
|
|
set_param_model_lora(&model_lora);
|
|
|
|
randomize_model_lora(&model_lora, 1337, 0.0f, 1.0f, -1.0f, +1.0f);
|
|
*/
|
|
int n_batch = 8;
|
|
// key + value cache for the self attention
|
|
struct llama_kv_cache kv_self;
|
|
printf("init_kv_cache\n");
|
|
kv_self.ctx = model.ctx;
|
|
init_kv_cache(&kv_self, &model, n_batch);
|
|
//init_kv_cache_lora(&kv_self, &model_lora);
|
|
|
|
size_t compute_size = 1024ll*1024ll*1024ll;
|
|
uint8_t * compute_addr = new uint8_t[compute_size];
|
|
|
|
int n_examples = 256;
|
|
int n_tokens = model.hparams.n_ctx;
|
|
int n_vocab = model.hparams.n_vocab;
|
|
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std::vector<uint8_t> work_buffer;
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for (int ex=0; ex<n_examples; ++ex) {
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struct ggml_init_params params = {
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/*.mem_size =*/ compute_size,
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/*.mem_buffer =*/ compute_addr,
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/*.no_alloc =*/ false,
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};
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struct ggml_context * ctx0 = ggml_init(params);
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struct ggml_tensor * after_opt_best_samples = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_batch);
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struct ggml_tensor * after_opt_probs = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens, n_batch);
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struct ggml_tensor * tokens_input = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_batch);
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struct ggml_tensor * targets = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens, n_batch);
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int n_past = 0;
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ggml_cgraph gf = {};
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get_example_targets_batch(ctx0, 64*ex+0, tokens_input, targets);
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struct ggml_tensor * logits = forward_batch(&model, &kv_self, ctx0, &gf, tokens_input, n_tokens, n_past, n_batch);
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// struct ggml_tensor * e = cross_entropy_loss(ctx0, targets, logits);
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struct ggml_tensor * e = square_error_loss(ctx0, targets, logits);
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ggml_build_forward_expand(&gf, e);
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ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1);
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float error_before_opt = ggml_get_f32_1d(e, 0);
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struct ggml_opt_params opt_params_adam = ggml_opt_default_params(GGML_OPT_ADAM);
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struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS);
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opt_params_adam.print_forward_graph = false;
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opt_params_adam.print_backward_graph = false;
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opt_params_lbfgs.print_forward_graph = false;
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opt_params_lbfgs.print_backward_graph = false;
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opt_params_adam.adam.n_iter = 16;
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opt_params_lbfgs.lbfgs.n_iter = 16;
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// ggml_opt(ctx0, opt_params_adam, e);
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ggml_opt(ctx0, opt_params_lbfgs, e);
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//
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ggml_build_forward_expand(&gf, e);
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ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1);
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float error_after_opt = ggml_get_f32_1d(e, 0);
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if (ex % 8 == 0) {
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printf("Example %d\n", (ex+1));
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printf("error_before_opt: %.2f\n", error_before_opt);
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printf("error_after_opt: %.2f\n", error_after_opt);
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}
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if (ex % 64 == 0) {
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sample_softmax_batch(ctx0, logits, after_opt_probs, after_opt_best_samples);
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// printf("probabilities after optimization:\n");
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// print_matrix(after_opt_probs);
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printf("best samples after optimization:\n");
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print_tokens(after_opt_best_samples, n_vocab);
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}
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|
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ggml_free(ctx0);
|
|
}
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|
|
|
{
|
|
int n_gen = 128;
|
|
int sample_ctx = n_tokens-n_tokens/8;
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|
|
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printf("Generating %d tokens.\n", n_gen);
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|
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struct ggml_tensor * tokens_input = ggml_new_tensor_1d(model.ctx, GGML_TYPE_I32, n_tokens);
|
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struct ggml_tensor * targets = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_vocab, n_tokens);
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|
|
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get_example_targets(137, tokens_input, targets);
|
|
for (int i=sample_ctx; i<n_tokens; ++i) {
|
|
ggml_set_i32_1d(tokens_input, i, n_vocab/2);
|
|
}
|
|
|
|
for (int i=0; i<sample_ctx-1; ++i) {
|
|
print_token(ggml_get_i32_1d(tokens_input, i), n_vocab);
|
|
}
|
|
printf("---\n");
|
|
for (int i=0; i<n_gen; ++i) {
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ compute_size,
|
|
/*.mem_buffer =*/ compute_addr,
|
|
/*.no_alloc =*/ false,
|
|
};
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
ggml_cgraph gf = {};
|
|
|
|
int n_past = 0;
|
|
struct ggml_tensor * logits = forward(&model, &kv_self, ctx0, &gf, tokens_input, sample_ctx, n_past);
|
|
|
|
ggml_build_forward_expand(&gf, logits);
|
|
ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1);
|
|
|
|
struct ggml_tensor * best_samples = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sample_ctx);
|
|
struct ggml_tensor * probs = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, sample_ctx);
|
|
|
|
sample_softmax(logits, probs, best_samples);
|
|
|
|
// int sample_at = n_tokens-1;
|
|
int token = ggml_get_i32_1d(best_samples, sample_ctx-1);
|
|
|
|
// print_row(probs, sample_at);
|
|
print_token(token, n_vocab);
|
|
|
|
lshift_examples(tokens_input, targets, 1);
|
|
ggml_set_i32_1d(tokens_input, 0, 0);
|
|
ggml_set_i32_1d(tokens_input, sample_ctx-1, token);
|
|
|
|
ggml_free(ctx0);
|
|
}
|
|
}
|
|
|
|
print_matrix(model.tok_embeddings);
|
|
printf("done\n");
|
|
|
|
// ggml_free(kv_self.ctx);
|
|
// ggml_free(model_lora.ctx);
|
|
ggml_free(model.ctx);
|
|
|
|
return 0;
|
|
}
|