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Apply suggestions from the PR: refactor test-vanilla-pca and remove unecessary allocations

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This commit is contained in:
Lucas Nogueira 2024-11-16 20:16:05 -03:00
parent 1840df1b58
commit 82efaafe9d
3 changed files with 19 additions and 354 deletions
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56
examples/cvector-generator/mini-tests/test-vanilla-pca.cpp
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@ -4,20 +4,25 @@
#include "ggml.h"
#include "../pca.hpp"
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#include "ggml-cpp.h"
#include "ggml-backend.h"
#include <cstdio>
#include <cstring>
// Function to run PCA and print results
static void run_pca_test(struct ggml_context *ctx, float *matrix, int rows, int cols) {
// struct ggml_tensor *input_tensor = create_tensor(ctx, matrix, rows, cols);
static void run_pca_test(float *matrix, int rows, int cols) {
// Initialize ggml context
size_t ctx_size = 0;
ctx_size += rows * cols * ggml_type_size(GGML_TYPE_F32);
ctx_size += 1 * ggml_tensor_overhead();
struct ggml_init_params ctx_params {
/*.mem_size =*/ ctx_size,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx = ggml_init(ctx_params);
struct ggml_tensor *input_tensor = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, rows, cols);
memcpy(input_tensor->data, matrix, rows * cols * sizeof(float));
@ -37,32 +42,10 @@ static void run_pca_test(struct ggml_context *ctx, float *matrix, int rows, int
printf("\nEigenvalue: %f\n", result.explained_variance);
free(result.principal_component);
ggml_free(ctx);
}
int main() {
// Initialize ggml context
size_t ctx_size = 0;
ctx_size += 4 * 4 * ggml_type_size(GGML_TYPE_F32);
ctx_size += 10 * 10 * ggml_type_size(GGML_TYPE_F32);
ctx_size += 3 * 3 * ggml_type_size(GGML_TYPE_F32);
ctx_size += 3 * 3 * ggml_type_size(GGML_TYPE_F32);
ctx_size += 4 * ggml_tensor_overhead();
ctx_size += 1024;
// Step 2. Initialize GGML Context
struct ggml_init_params ctx_params {
ctx_size, // mem_size
NULL, // mem_buffer
false, // no_alloc
};
struct ggml_context * ctx = ggml_init(ctx_params);
if (ctx == NULL) {
printf("Failed to initialize ggml context\n");
return 1;
}
// Define matrices
float input_matrix1[16] = {
-0.124132, 0.740341, -0.452462, 0.777050,
@ -98,19 +81,18 @@ int main() {
// Run PCA for each matrix
printf("Testing Matrix 1:\n");
run_pca_test(ctx, input_matrix1, 4, 4);
run_pca_test(input_matrix1, 4, 4);
printf("\nTesting Matrix 2:\n");
run_pca_test(ctx, input_matrix2, 10, 10);
run_pca_test(input_matrix2, 10, 10);
printf("\nTesting Matrix 3:\n");
run_pca_test(ctx, input_matrix3, 3, 3);
run_pca_test(input_matrix3, 3, 3);
printf("\nTesting Matrix 4:\n");
run_pca_test(ctx, input_matrix4, 3, 3);
run_pca_test(input_matrix4, 3, 3);
// Cleanup
ggml_free(ctx);
return 0;
}

3
examples/cvector-generator/pca.hpp
View File

@ -51,7 +51,6 @@ static void compute_covariance(struct pca_params &pca_params,
struct ggml_backend * backend) {
size_t ctx_size = 0;
ctx_size += 7 * X->ne[0] * X->ne[1] * ggml_type_size(GGML_TYPE_F32);
ctx_size += 7 * ggml_tensor_overhead();
ctx_size += ggml_graph_overhead();
ctx_size += 1024;
@ -105,7 +104,6 @@ static void compute_cross_covariance(struct pca_params &pca_params,
struct ggml_backend * backend) {
size_t ctx_size = 0;
ctx_size += 9 * A->ne[0] * B->ne[1] * ggml_type_size(GGML_TYPE_F32);
ctx_size += 9 * ggml_tensor_overhead();
ctx_size += ggml_graph_overhead();
ctx_size += 1024;
@ -280,7 +278,6 @@ static void run_single_pca(struct pca_params &pca_params,
// Compute the context size needed
size_t ctx_size = 0;
ctx_size += m * m * ggml_type_size(GGML_TYPE_F32);
ctx_size += 1 * ggml_tensor_overhead();
// Step 2. Initialize GGML Context

314
examples/cvector-generator/vanilla_pca.hpp
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@ -1,314 +0,0 @@
#include "common.h"
#include "llama.h"
#include "ggml.h"
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#include <cstdio>
#include <ctime>
#include <random>
#include <string>
#include <tuple>
#include <vector>
#include <algorithm>
#include <iostream>
#include <fstream>
#define DEBUG_POS 5
static void print_debug_tensor(struct ggml_tensor * t, bool with_data = true) {
printf("%s: %s (%s): [%d, %d]\n", __func__, t->name, ggml_type_name(t->type), (int) t->ne[0], (int) t->ne[1]);
if (!with_data) return;
printf("%s: %s[0] = [", __func__, t->name);
for (size_t i = 0; i <= DEBUG_POS; i++) {
printf(" %f,", ggml_get_f32_nd(t, i, 0, 0, 0));
}
printf(" ... ]\n");
}
// begin vanilla pca namespace
namespace PCA {
// input params for PCA computations
struct pca_params {
int n_threads = 1;
int n_batch = 20; // number of iterations do to in one batch. larger the batch, more memory is used
int n_iterations = 1000;
float tolerance = 1e-7;
};
// result from each iteration
struct pca_result {
struct ggml_tensor * principal_component; // eigenvectors of the covariance matrix
float explained_variance; // eigenvalues of the covariance matrix
};
void compute_covariance(struct pca_params &pca_params,
struct ggml_tensor * X,
struct ggml_tensor * covariance,
struct ggml_backend * backend) {
// Memory allocation
struct ggml_cgraph * gf = NULL;
struct ggml_context * ctx = NULL;
struct ggml_init_params ctx_params = {
ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(),
NULL,
true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
};
ctx = ggml_init(ctx_params);
gf = ggml_new_graph(ctx);
// Step 0: Transpose the input because of row-major
X = ggml_cont(ctx, ggml_transpose(ctx, X));
// Step 1: Compute the mean for each feature
struct ggml_tensor * mean = ggml_repeat(ctx, ggml_mean(ctx, X), X); // mean with trick to make it easier to sub
struct ggml_tensor * centered_data = ggml_sub(ctx, X, mean);
// Step 2: Compute the covariance matrix
struct ggml_tensor * cov = ggml_mul_mat(ctx, centered_data, centered_data); // C = X * X^T
cov = ggml_scale(ctx, cov, 1.0/(X->ne[0]-1));
ggml_build_forward_expand(gf, cov);
// Step 3: Create ggml_gallocr for graph computation
ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
ggml_gallocr_alloc_graph(allocr, gf);
// Step 4: Check if CPU and compute the result of the graph
if (ggml_backend_is_cpu(backend)) {
ggml_backend_cpu_set_n_threads(backend, pca_params.n_threads);
}
ggml_backend_graph_compute(backend, gf);
// Step 5: Store covariance matrix in the data pointer
struct ggml_tensor * result = ggml_graph_node(gf, ggml_graph_n_nodes(gf)-1);
float * result_data = (float*) malloc(ggml_nbytes(result));
ggml_backend_tensor_get(result, result_data, 0, ggml_nbytes(result));
covariance->data = result_data;
// Step 6: Free memory
ggml_gallocr_free(allocr);
ggml_free(ctx);
}
static void compute_cross_covariance(struct pca_params &pca_params,
struct ggml_tensor * A,
struct ggml_tensor * B,
struct ggml_tensor * cross_covariance,
struct ggml_backend * backend) {
// Memory allocation
struct ggml_cgraph * gf = NULL;
struct ggml_context * ctx = NULL;
struct ggml_init_params ctx_params = {
ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(),
NULL,
true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
};
ctx = ggml_init(ctx_params);
gf = ggml_new_graph(ctx);
// Step 1: Compute matrices of cross_covariance
struct ggml_tensor * AT = ggml_cont(ctx, ggml_transpose(ctx, A));
struct ggml_tensor * BT = ggml_cont(ctx, ggml_transpose(ctx, B));
struct ggml_tensor * AT_B = ggml_mul_mat(ctx, AT, BT);
struct ggml_tensor * BT_A = ggml_cont(ctx, ggml_transpose(ctx, AT_B));
// Step 2: Compute the covariance matrix
struct ggml_tensor * cross_cov = ggml_add(ctx, AT_B, BT_A);
cross_cov = ggml_scale(ctx, cross_cov, 0.5);
ggml_build_forward_expand(gf, cross_cov);
// Step 3: Create ggml_gallocr for graph computation
ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
ggml_gallocr_alloc_graph(allocr, gf);
// Step 4: Check if CPU and compute the result of the graph
if (ggml_backend_is_cpu(backend)) {
ggml_backend_cpu_set_n_threads(backend, pca_params.n_threads);
}
ggml_backend_graph_compute(backend, gf);
// Step 5: Store covariance matrix in the data pointer
struct ggml_tensor * result = ggml_graph_node(gf, ggml_graph_n_nodes(gf)-1);
float * result_data = (float*) malloc(ggml_nbytes(result));
ggml_backend_tensor_get(result, result_data, 0, ggml_nbytes(result));
cross_covariance->data = result_data;
// Step 6: Free memory
ggml_gallocr_free(allocr);
ggml_free(ctx);
}
// Find the dominant eigenvector of tensor M
static void power_iteration(struct pca_params &pca_params,
struct ggml_tensor * M,
struct pca_result &result,
struct ggml_backend * backend) {
int m = M->ne[1];
// Initialize random vector
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<float> dist(-1.0f, 1.0f);
float * b = (float*) malloc(m * sizeof(float));
for (int i = 0; i < m; i++) {
b[i] = dist(gen);
};
float eigenvalue = 0;
// Iterate
int n_rounds = pca_params.n_iterations / pca_params.n_batch;
for(int i = 0; i < n_rounds; i++) {
// Memory allocation
struct ggml_cgraph * gf = NULL;
struct ggml_context * ctx = NULL;
struct ggml_init_params params = {
ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(),
NULL,
true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
};
ctx = ggml_init(params);
gf = ggml_new_graph(ctx);
// Fill current eigen vector
struct ggml_tensor * e_curr = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, m);
struct ggml_tensor * e_prev = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, m);
ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
ggml_backend_tensor_set(e_curr, b, 0, ggml_nbytes(e_curr));
ggml_backend_tensor_set(e_prev, b, 0, ggml_nbytes(e_curr));
struct ggml_tensor * e_next = NULL;
struct ggml_tensor * e_norm = NULL;
for(int j = 0; j < pca_params.n_batch; j++) {
// Compute next candidate vector multiplying M with the current vector
e_next = ggml_mul_mat(ctx, M, e_curr);
// Compute the norm of the new vector (and normalize it)
// this will give us the next eigenvector and eigenvalue
e_norm = ggml_sqrt_inplace(ctx, ggml_sum_rows(ctx, ggml_sqr(ctx, e_next)));
e_curr = ggml_div_inplace(ctx, e_next, e_norm);
ggml_format_name(e_norm, "eigenvalue_%d", j);
ggml_format_name(e_curr, "eigenvector_%d", j);
// Update graph
ggml_build_forward_expand(gf, e_curr);
}
// Compute the similarity between the current eigenvector and the previous (dot product)
struct ggml_tensor * similarity = ggml_mul_mat(ctx, e_curr, e_prev);
ggml_build_forward_expand(gf, similarity);
// Create ggml_gallocr for graph computation
ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
ggml_gallocr_alloc_graph(allocr, gf);
// Check if CPU and compute the result of the graph
if (ggml_backend_is_cpu(backend)) {
ggml_backend_cpu_set_n_threads(backend, pca_params.n_threads);
}
ggml_status graph_status = ggml_backend_graph_compute(backend, gf);
// Get graph results (eigenvector and eigenvalue) and store it in b and eigenvalue
if(graph_status == GGML_STATUS_SUCCESS){
// Similarity is the last node in the graph
struct ggml_tensor * similarity_tensor = ggml_graph_node(gf, ggml_graph_n_nodes(gf)-1);
float similarity = (float)((float*) similarity_tensor->data)[0];
// Eigenvector is the second last node in the graph
// struct ggml_tensor * eigenvector_tensor = gf->nodes[gf->n_nodes-2];
struct ggml_tensor * eigenvector_tensor = ggml_graph_node(gf,ggml_graph_n_nodes(gf)-2);
float * eigenvector_data = (float*) malloc(ggml_nbytes(eigenvector_tensor));
ggml_backend_tensor_get(eigenvector_tensor, eigenvector_data, 0, ggml_nbytes(eigenvector_tensor));
b = eigenvector_data;
// Eigenvalue computation is 1 operation before eigenvector computation
// struct ggml_tensor * eigenvalue_tensor = gf->nodes[gf->n_nodes-3];
struct ggml_tensor * eigenvalue_tensor = ggml_graph_node(gf, ggml_graph_n_nodes(gf)-3);
eigenvalue = (float)((float*) eigenvalue_tensor->data)[0];
// Check if the similarity is close enough to 1, if so we converged and should break
if(1 - similarity < pca_params.tolerance)
break;
}
// Free memory
ggml_gallocr_free(allocr);
ggml_free(ctx);
}
// Store result
result.principal_component->data = b;
result.explained_variance = eigenvalue;
return;
}
static void run_single_pca(struct pca_params &pca_params,
struct ggml_tensor * X,
struct pca_result &result
) {
ggml_set_name(X, "input_tensor");
int m = X->ne[1]; // Number of features
// Step 1. Initialize GGML Backend
ggml_backend_t backend = NULL;
#ifdef GGML_USE_CUDA
fprintf(stderr, "%s: using CUDA backend\n", __func__);
backend = ggml_backend_cuda_init(0); // init device 0
if (!backend) { fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); }
#endif
// If there aren't GPU Backends fallback to CPU backend
if (!backend) { backend = ggml_backend_cpu_init(); }
// Compute the context size needed
size_t ctx_size = 2 * ggml_tensor_overhead();
// Step 2. Initialize GGML Context
struct ggml_init_params ctx_params {
ctx_size, // mem_size
NULL, // mem_buffer
true, // no_alloc
};
struct ggml_context * ctx = ggml_init(ctx_params);
ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
// Step 3. Compute the data covariance matrix
struct ggml_tensor * covariance = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, m, m);
ggml_set_name(covariance, "covariance_tensor");
compute_covariance(pca_params, X, covariance, backend);
// Step 4. Power iteration
result.principal_component = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, m);
power_iteration(pca_params, covariance, result, backend);
// Free ggml context and backend
ggml_free(ctx);
ggml_backend_free(backend);
}
static void run_pca(
struct pca_params & params,
const std::vector<struct ggml_tensor *> & v_input, // shape of v_input[0]: [n_samples, n_embd]
const std::vector<struct ggml_tensor *> & v_output) {
for (size_t i = 0; i < v_input.size(); i++) {
struct pca_result result;
run_single_pca(params, v_input[i], result);
ggml_backend_tensor_get(result.principal_component, v_output[i]->data, 0, ggml_nbytes(result.principal_component));
}
}
// end namespace
}