tentatively translate the rest
This commit is contained in:
Christian Zhou-Zheng
parent
0e1f9734de
commit
b67ea65983
1 changed files with 200 additions and 79 deletions
|
|
@ -2,6 +2,14 @@
|
|||
#include "llama.h"
|
||||
#include "ggml.h"
|
||||
|
||||
#ifdef GGML_USE_CUDA
|
||||
#include "ggml-cuda.h"
|
||||
#endif
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
#include "ggml-metal.h"
|
||||
#endif
|
||||
|
||||
#include <cstdio>
|
||||
#include <string>
|
||||
#include <tuple>
|
||||
|
|
@ -410,105 +418,224 @@ static void concatenate_diffs(callback_data & cb_data) {
|
|||
}
|
||||
}
|
||||
|
||||
// BEGIN NON-GGML IMPLEMENTATION
|
||||
struct pca_model {
|
||||
struct ggml_tensor * v_diff_original;
|
||||
struct ggml_tensor * square;
|
||||
struct ggml_tensor * eigenvector;
|
||||
|
||||
// TODO translate to ggml
|
||||
// this probably doesn't want to be a separate function - put it into the compute graph as a step in processing each layer
|
||||
static float* square_diff(callback_data & cb_data, size_t idx) {
|
||||
float* result = new float[cb_data.n_embd * cb_data.n_embd];
|
||||
std::memset(result, 0, cb_data.n_embd * cb_data.n_embd * sizeof(float));
|
||||
for (size_t i = 0; i < (size_t) cb_data.n_embd; i++) {
|
||||
for (size_t j = 0; j < (size_t) cb_data.n_embd; j++) {
|
||||
float sum = 0.0f;
|
||||
// watch out for indexing - can't just use cb_data.n_tokens
|
||||
for (size_t k = 0; k < cb_data.v_diff[idx].n_rows; k++) {
|
||||
sum += cb_data.v_diff[idx].diff[i + cb_data.n_embd * k] * cb_data.v_diff[idx].diff[j + cb_data.n_embd * k];
|
||||
ggml_backend_t backend = NULL;
|
||||
ggml_backend_buffer_t buffer;
|
||||
struct ggml_context * ctx;
|
||||
};
|
||||
|
||||
void load_pca_model(pca_model & model, struct ggml_tensor * v_diff_original, const int n_embd) {
|
||||
#ifdef GGML_USE_CUDA
|
||||
fprintf(stderr, "%s: using CUDA backend\n", __func__);
|
||||
model.backend = ggml_backend_cuda_init(0); // init device 0
|
||||
if (!model.backend) {
|
||||
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
|
||||
}
|
||||
result[i * cb_data.n_embd + j] = sum;
|
||||
#endif
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
fprintf(stderr, "%s: using Metal backend\n", __func__);
|
||||
ggml_backend_metal_log_set_callback(ggml_log_callback_default, nullptr);
|
||||
model.backend = ggml_backend_metal_init();
|
||||
if (!model.backend) {
|
||||
fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
#endif
|
||||
|
||||
// if there aren't GPU Backends fallback to CPU backend
|
||||
if (!model.backend) {
|
||||
model.backend = ggml_backend_cpu_init();
|
||||
}
|
||||
|
||||
// TODO translate to ggml (this is a built-in function in ggml)
|
||||
static void normalize_inplace(std::vector<float> & vec) {
|
||||
// inefficient(?) norm computation
|
||||
float norm = 0.0f;
|
||||
for (const float& val : vec) {
|
||||
norm += val * val;
|
||||
}
|
||||
if(norm == 0) throw std::runtime_error("norm is zero");
|
||||
norm = std::sqrt(norm);
|
||||
for (float& val : vec) {
|
||||
val /= norm;
|
||||
}
|
||||
}
|
||||
const int num_tensors = 3;
|
||||
|
||||
// TODO translate to ggml (this is a built-in function in ggml)
|
||||
static std::vector<float> mul_mat(const float * mat, const std::vector<float> & vec, size_t dim) {
|
||||
std::vector<float> result(dim, 0.0f);
|
||||
for (size_t i = 0; i < dim; ++i) {
|
||||
for (size_t j = 0; j < dim; ++j) {
|
||||
result[i] += mat[i * dim + j] * vec[j];
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
struct ggml_init_params params {
|
||||
/*.mem_size =*/ ggml_tensor_overhead() * num_tensors,
|
||||
/*.mem_buffer =*/ NULL,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
|
||||
// TODO translate to ggml
|
||||
static std::vector<float> power_iteration(callback_data & cb_data, const float * matrix, int maxIterations = 1000, float tolerance = 1e-8) {
|
||||
std::vector<float> b_tensor = std::vector<float>();
|
||||
model.ctx = ggml_init(params);
|
||||
|
||||
// random vector gen/norm
|
||||
model.v_diff_original = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, v_diff_original->ne[0], v_diff_original->ne[1]);
|
||||
model.square = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_embd, n_embd);
|
||||
model.eigenvector = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, n_embd);
|
||||
|
||||
model.buffer = ggml_backend_alloc_ctx_tensors(model.ctx, model.backend);
|
||||
|
||||
ggml_backend_tensor_set(model.v_diff_original, v_diff_original->data, 0, ggml_nbytes(v_diff_original));
|
||||
// no need to load anything into square yet
|
||||
|
||||
// initialize model.eigenvector to random vector
|
||||
std::vector<float> random_vec = std::vector<float>();
|
||||
std::default_random_engine generator(static_cast<unsigned int>(std::time(0)));
|
||||
std::uniform_real_distribution<float> distribution(0.0, 1.0);
|
||||
for (int i = 0; i < cb_data.n_embd; ++i) {
|
||||
b_tensor.push_back(distribution(generator));
|
||||
for (int i = 0; i < n_embd; ++i) {
|
||||
random_vec.push_back(distribution(generator));
|
||||
}
|
||||
normalize_inplace(b_tensor);
|
||||
|
||||
// we don't normalize it at first but that shouldn't be a problem
|
||||
ggml_backend_tensor_set(model.eigenvector, random_vec.data(), 0, ggml_nbytes(model.eigenvector));
|
||||
}
|
||||
|
||||
struct ggml_cgraph * square_diff_graph(const pca_model & model) {
|
||||
static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
|
||||
static std::vector<uint8_t> buf(buf_size);
|
||||
|
||||
struct ggml_init_params params0 = {
|
||||
/*.mem_size =*/ buf_size,
|
||||
/*.mem_buffer =*/ buf.data(),
|
||||
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_allocr_alloc_graph()
|
||||
};
|
||||
struct ggml_context * ctx0 = ggml_init(params0);
|
||||
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
|
||||
|
||||
struct ggml_tensor * square = ggml_mul_mat(ctx0, model.v_diff_original, model.v_diff_original);
|
||||
|
||||
ggml_build_forward_expand(gf, square);
|
||||
|
||||
ggml_free(ctx0);
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_tensor * compute_square(const pca_model & model, ggml_gallocr_t allocr, int n_threads) {
|
||||
struct ggml_cgraph * gf = square_diff_graph(model);
|
||||
|
||||
ggml_gallocr_alloc_graph(allocr, gf);
|
||||
|
||||
if (ggml_backend_is_cpu(model.backend)) {
|
||||
ggml_backend_cpu_set_n_threads(model.backend, n_threads);
|
||||
}
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
if (ggml_backend_is_metal(model.backend)) {
|
||||
ggml_backend_metal_set_n_cb(model.backend, n_threads);
|
||||
}
|
||||
#endif
|
||||
|
||||
ggml_backend_graph_compute(model.backend, gf);
|
||||
|
||||
return gf->nodes[gf->n_nodes - 1];
|
||||
}
|
||||
|
||||
struct ggml_cgraph * power_iteration_graph(const pca_model & model, float tolerance) {
|
||||
static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
|
||||
static std::vector<uint8_t> buf(buf_size);
|
||||
|
||||
struct ggml_init_params params0 = {
|
||||
/*.mem_size =*/ buf_size,
|
||||
/*.mem_buffer =*/ buf.data(),
|
||||
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_allocr_alloc_graph()
|
||||
};
|
||||
struct ggml_context * ctx0 = ggml_init(params0);
|
||||
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
|
||||
|
||||
struct ggml_tensor * b_tensor = ggml_mul_mat(ctx0, model.square, model.eigenvector);
|
||||
// TODO difference between ggml_norm and ggml_norm_inplace?
|
||||
// also is this the right way to do multi-step graphs?
|
||||
b_tensor = ggml_norm_inplace(ctx0, b_tensor, tolerance);
|
||||
|
||||
ggml_build_forward_expand(gf, b_tensor);
|
||||
|
||||
ggml_free(ctx0);
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_tensor * compute_piter(const pca_model & model, ggml_gallocr_t allocr, int n_threads, float tolerance) {
|
||||
struct ggml_cgraph * gf = power_iteration_graph(model, tolerance);
|
||||
|
||||
ggml_gallocr_alloc_graph(allocr, gf);
|
||||
|
||||
if (ggml_backend_is_cpu(model.backend)) {
|
||||
ggml_backend_cpu_set_n_threads(model.backend, n_threads);
|
||||
}
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
if (ggml_backend_is_metal(model.backend)) {
|
||||
ggml_backend_metal_set_n_cb(model.backend, n_threads);
|
||||
}
|
||||
#endif
|
||||
|
||||
ggml_backend_graph_compute(model.backend, gf);
|
||||
|
||||
return gf->nodes[gf->n_nodes - 1];
|
||||
}
|
||||
|
||||
static void power_iteration(callback_data & cb_data, int idx, int n_threads, int maxIterations = 1000, float tolerance = 1e-8) {
|
||||
pca_model model;
|
||||
load_pca_model(model, cb_data.v_diff[idx], cb_data.n_embd);
|
||||
|
||||
ggml_gallocr_t allocr = NULL;
|
||||
|
||||
{
|
||||
allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend));
|
||||
|
||||
// create the worst case graph for memory usage estimation
|
||||
struct ggml_cgraph * gf = square_diff_graph(model);
|
||||
ggml_gallocr_reserve(allocr, gf);
|
||||
size_t mem_size = ggml_gallocr_get_buffer_size(allocr, 0);
|
||||
|
||||
fprintf(stderr, "%s: square diff, compute buffer size: %.4f KB\n", __func__, mem_size/1024.0);
|
||||
}
|
||||
|
||||
struct ggml_tensor * square = compute_square(model, allocr, n_threads);
|
||||
ggml_backend_tensor_get(square, model.square->data, 0, ggml_nbytes(square));
|
||||
|
||||
// yes?
|
||||
ggml_gallocr_free(allocr);
|
||||
|
||||
for (int iter = 0; iter < maxIterations; ++iter) {
|
||||
|
||||
// store the previous one so we can check for convergence
|
||||
std::vector<float> b_prev_tensor = b_tensor;
|
||||
// TODO do I need to reset it like this every time?
|
||||
allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend));
|
||||
|
||||
// matrix multiplication and renormalize
|
||||
b_tensor = mul_mat(matrix, b_tensor, cb_data.n_embd);
|
||||
normalize_inplace(b_tensor);
|
||||
struct ggml_tensor * host_new_eigenvector = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, cb_data.n_embd);
|
||||
struct ggml_tensor * b_tensor = compute_piter(model, allocr, n_threads, tolerance);
|
||||
ggml_backend_tensor_get(b_tensor, host_new_eigenvector->data, 0, ggml_nbytes(b_tensor));
|
||||
|
||||
// convergence check
|
||||
float diff = 0.0;
|
||||
for (int i = 0; i < cb_data.n_embd; ++i) {
|
||||
diff += std::pow(b_tensor[i] - b_prev_tensor[i], 2);
|
||||
diff += std::pow(((float *)(host_new_eigenvector->data))[i] - ((float *)(model.eigenvector->data))[i], 2);
|
||||
}
|
||||
|
||||
// update eigenvector
|
||||
ggml_backend_tensor_set(model.eigenvector, host_new_eigenvector->data, 0, ggml_nbytes(model.eigenvector));
|
||||
|
||||
try {
|
||||
if (std::sqrt(diff) < tolerance) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
return b_tensor;
|
||||
catch (std::exception & e) {
|
||||
// catch division by zero I guess
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// push back v_final with eigenvector
|
||||
ggml_backend_tensor_get(model.eigenvector, cb_data.v_final[idx]->data, 0, ggml_nbytes(model.eigenvector));
|
||||
}
|
||||
|
||||
// TODO translate to ggml
|
||||
static void pca(callback_data & cb_data, int n_threads) {
|
||||
int n_layers = cb_data.v_diff.size();
|
||||
std::vector<std::thread> threads;
|
||||
cb_data.v_final.reserve(n_layers);
|
||||
auto worker_function = [&](int worker_id) {
|
||||
for (int il = worker_id; il < n_layers; il += n_threads) {
|
||||
float * matrix = square_diff(cb_data, il);
|
||||
std::vector<float> eigenvector = power_iteration(cb_data, matrix);
|
||||
cb_data.v_final[il] = (float *) malloc(eigenvector.size() * sizeof(float));
|
||||
memcpy(cb_data.v_final[il], eigenvector.data(), eigenvector.size() * sizeof(float));
|
||||
printf("Done with layer %d\n", il);
|
||||
printf("il = %d | %f %f \n", il, cb_data.v_final[il][0], cb_data.v_final[il][1]);
|
||||
}
|
||||
};
|
||||
printf("Running PCA...\n");
|
||||
for (int i = 0; i < n_threads; ++i) {
|
||||
threads.emplace_back(worker_function, i);
|
||||
for (int il = 0; il < cb_data.v_diff.size(); ++il) {
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ cb_data.n_embd * sizeof(float),
|
||||
/*.mem_buffer =*/ NULL,
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
struct ggml_context * ctx_data = ggml_init(params);
|
||||
cb_data.v_final.push_back(ggml_new_tensor_1d(ctx_data, GGML_TYPE_F32, cb_data.n_embd));
|
||||
power_iteration(cb_data, il, n_threads);
|
||||
printf("Done with layer %d\n", il);
|
||||
printf("il = %d | %f %f \n", il, ggml_get_f32_1d(cb_data.v_final[il], 0), ggml_get_f32_1d(cb_data.v_final[il], 1));
|
||||
}
|
||||
for (auto & th : threads) th.join();
|
||||
printf("Done with PCA.\n");
|
||||
printf("Done with PCA.\n");
|
||||
}
|
||||
|
||||
|
|
@ -591,12 +718,6 @@ int main(int argc, char ** argv) {
|
|||
cb_data.n_embd = n_embd;
|
||||
int n_prompts = cparams.positive_prompts.size();
|
||||
|
||||
// vector of finished vectors of size [n_embd], we have (n_layers - 1) vectors in total
|
||||
std::vector<float *> v_final(n_layers - 1, NULL);
|
||||
for (size_t i = 0; i < v_final.size(); ++i) {
|
||||
v_final[i] = (float *) calloc(n_embd, sizeof(float));
|
||||
}
|
||||
|
||||
// create templated prompts
|
||||
for (int i = 0; i < n_prompts; ++i) {
|
||||
populate_entries(cparams, cparams.positive_prompts[i], cparams.negative_prompts[i]);
|
||||
|
|
@ -653,7 +774,7 @@ int main(int argc, char ** argv) {
|
|||
|
||||
concatenate_diffs(cb_data);
|
||||
pca(cb_data, cparams.n_threads);
|
||||
printf("v_final %f %f \n", cb_data.v_final[0][0], cb_data.v_final[0][1]);
|
||||
//printf("v_final %f %f \n", cb_data.v_final[0][0], cb_data.v_final[0][1]);
|
||||
|
||||
llama_free(ctx);
|
||||
llama_free_model(model);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue