From 5c1d1177d30673cfac46b1b5879808a6ddcb70e0 Mon Sep 17 00:00:00 2001 From: Lucas Nogueira Date: Fri, 15 Nov 2024 14:32:27 -0300 Subject: [PATCH] Add complete implementation of the classical PCA algorithm with covariance matrix and power iteration with a very simple test file --- Makefile | 7 + .../cvector-generator/cvector-generator.cpp | 3 +- .../mini-tests/test-vanilla-pca.cpp | 116 +++++++ examples/cvector-generator/vanilla_pca.hpp | 314 ++++++++++++++++++ 4 files changed, 438 insertions(+), 2 deletions(-) create mode 100644 examples/cvector-generator/mini-tests/test-vanilla-pca.cpp create mode 100644 examples/cvector-generator/vanilla_pca.hpp diff --git a/Makefile b/Makefile index 87fe795aa..d00f3b057 100644 --- a/Makefile +++ b/Makefile @@ -38,6 +38,7 @@ BUILD_TARGETS = \ llama-tokenize \ llama-vdot \ llama-cvector-generator \ + llama-test-vanilla-pca \ llama-gen-docs \ tests/test-c.o @@ -1479,6 +1480,12 @@ llama-cvector-generator: examples/cvector-generator/cvector-generator.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +# TODO: Move to tests +llama-test-vanilla-pca: examples/cvector-generator/mini-tests/test-vanilla-pca.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + llama-convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp index d1731bba6..e5804be28 100644 --- a/examples/cvector-generator/cvector-generator.cpp +++ b/examples/cvector-generator/cvector-generator.cpp @@ -2,8 +2,7 @@ #include "common.h" #include "llama.h" #include "ggml.h" -#include "pca.hpp" -#include "mean.hpp" +#include "vanilla_pca.hpp" #ifdef GGML_USE_CUDA #include "ggml-cuda.h" diff --git a/examples/cvector-generator/mini-tests/test-vanilla-pca.cpp b/examples/cvector-generator/mini-tests/test-vanilla-pca.cpp new file mode 100644 index 000000000..6744b623e --- /dev/null +++ b/examples/cvector-generator/mini-tests/test-vanilla-pca.cpp @@ -0,0 +1,116 @@ + +#include "common.h" +#include "llama.h" +#include "ggml.h" +#include "../vanilla_pca.hpp" + +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif + +#include +#include + +// Function to initialize ggml with optional GPU backend support +struct ggml_context *initialize_ggml_context() { +#ifdef GGML_USE_CUDA + struct ggml_init_params params = { .mem_size = 1024 * 1024, .mem_buffer = NULL, .use_gpu = true }; + printf("Initializing with GPU backend...\n"); +#else + struct ggml_init_params params = { .mem_size = 1024 * 1024, .mem_buffer = NULL }; + printf("Initializing with CPU backend...\n"); +#endif + return ggml_init(params); +} + +// Helper function to create a tensor from a matrix +struct ggml_tensor *create_tensor(struct ggml_context *ctx, float *data, int rows, int cols) { + struct ggml_tensor *tensor = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, cols, rows); + memcpy(tensor->data, data, ggml_nbytes(tensor)); + return tensor; +} + +// Function to run PCA and print results +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); + + PCA::pca_params pca_params; + pca_params.n_threads = 8; + pca_params.n_batch = 20; + pca_params.n_iterations = 1000; + pca_params.tolerance = 1e-5; + + PCA::pca_result result; + PCA::run_single_pca(pca_params, input_tensor, result); + + printf("\nPrincipal components:\n"); + float *b = (float *)result.principal_component->data; + for (int i = 0; i < result.principal_component->ne[0]; i++) { + printf("%f ", b[i]); + } + printf("\nEigenvalue: %f\n", result.explained_variance); +} + +int main() { + // Initialize ggml context + struct ggml_context *ctx = initialize_ggml_context(); + 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, + 1.045571, -0.342142, -0.926047, -0.512965, + 0.710109, 0.092479, 0.630075, 1.762937, + 0.230954, -0.808937, 1.057424, 0.051361 + }; + + float input_matrix2[100] = { + 440152.493740, 122038.234845, 495176.910111, 34388.521115, 909320.402079, 258779.981600, 662522.284354, 311711.076089, 520068.021178, 546710.279343, + 184854.455526, 969584.627765, 775132.823361, 939498.941564, 894827.350428, 597899.978811, 921874.235023, 88492.502052, 195982.862419, 45227.288911, + 325330.330763, 388677.289689, 271349.031774, 828737.509152, 356753.326694, 280934.509687, 542696.083158, 140924.224975, 802196.980754, 74550.643680, + 986886.936601, 772244.769297, 198715.681534, 5522.117124, 815461.428455, 706857.343848, 729007.168041, 771270.346686, 74044.651734, 358465.728544, + 115869.059525, 863103.425876, 623298.126828, 330898.024853, 63558.350286, 310982.321716, 325183.322027, 729606.178338, 637557.471355, 887212.742576, + 472214.925162, 119594.245938, 713244.787223, 760785.048617, 561277.197569, 770967.179955, 493795.596364, 522732.829382, 427541.018359, 25419.126744, + 107891.426993, 31429.185687, 636410.411264, 314355.981076, 508570.691165, 907566.473926, 249292.229149, 410382.923036, 755551.138543, 228798.165492, + 76979.909829, 289751.452914, 161221.287254, 929697.652343, 808120.379564, 633403.756510, 871460.590188, 803672.076899, 186570.058886, 892558.998490, + 539342.241916, 807440.155164, 896091.299923, 318003.474972, 110051.924528, 227935.162542, 427107.788626, 818014.765922, 860730.583256, 6952.130531, + 510747.302578, 417411.003149, 222107.810471, 119865.367334, 337615.171404, 942909.703913, 323202.932021, 518790.621743, 703018.958895, 363629.602379 + }; + + float input_matrix3[9] = { + 0.374540, 0.950714, 0.731994, + 0.598658, 0.156019, 0.155995, + 0.058084, 0.866176, 0.601115 + }; + + float input_matrix4[9] = { + 10.000000, 0.000000, 0.000000, + 0.000000, 5.000000, 0.000000, + 0.000000, 0.000000, 1.000000 + }; + + // Run PCA for each matrix + printf("Testing Matrix 1:\n"); + run_pca_test(ctx, input_matrix1, 4, 4); + + printf("\nTesting Matrix 2:\n"); + run_pca_test(ctx, input_matrix2, 10, 10); + + printf("\nTesting Matrix 3:\n"); + run_pca_test(ctx, input_matrix3, 3, 3); + + printf("\nTesting Matrix 4:\n"); + run_pca_test(ctx, input_matrix4, 3, 3); + + // Cleanup + ggml_free(ctx); + return 0; +} + diff --git a/examples/cvector-generator/vanilla_pca.hpp b/examples/cvector-generator/vanilla_pca.hpp new file mode 100644 index 000000000..b4350db82 --- /dev/null +++ b/examples/cvector-generator/vanilla_pca.hpp @@ -0,0 +1,314 @@ +#include "common.h" +#include "llama.h" +#include "ggml.h" + +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#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 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 & v_input, // shape of v_input[0]: [n_samples, n_embd] + const std::vector & 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 +}