mirror of
https://github.com/ggml-org/llama.cpp.git
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1004 lines
39 KiB
C++
1004 lines
39 KiB
C++
// TODO refactor
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#include "ggml.h"
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#include "ggml-alloc.h"
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#include "ggml-backend.h"
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#include "ggml-opt.h"
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#include <cmath>
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#include <cinttypes>
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#include <cstring>
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#include <random>
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#include <string>
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#include <thread>
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#include <vector>
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#define TEST_LOG(...) printf(__VA_ARGS__)
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static bool almost_equal(const double a, const double b, const double atol) {
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return fabs(a - b) < atol;
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}
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constexpr int64_t ne_datapoint = 2;
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constexpr int64_t ne_label = 1;
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constexpr int64_t ndata = 6;
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struct helper_ctx_data {
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std::vector<ggml_opt_dataset_t> datasets_supervised;
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std::vector<struct ggml_tensor *> data_batch;
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std::vector<struct ggml_tensor *> labels_batch;
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ggml_opt_dataset_t dataset_unsupervised;
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struct ggml_context * ctx_static;
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struct ggml_context * ctx_compute;
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struct ggml_opt_params opt_params;
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ggml_opt_context_t opt_ctx;
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struct ggml_tensor * inputs;
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struct ggml_tensor * weights;
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struct ggml_tensor * outputs;
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ggml_backend_buffer_t buf;
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ggml_opt_result_t result;
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ggml_opt_result_t result2;
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};
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// These default values make it easier to check optimization results vs. expected values.
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static ggml_opt_optimizer_params helper_get_test_opt_pars(void * userdata) {
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ggml_opt_optimizer_params result = ggml_opt_get_default_optimizer_params(userdata);
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result.adamw.alpha = 1.0f;
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result.adamw.beta1 = 0.0f;
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result.adamw.beta2 = 0.0f;
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result.adamw.eps = 0.0f;
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result.adamw.wd = 0.0f;
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result.sgd.wd = 0.0f;
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result.sgd.alpha = 1.0f;
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return result;
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}
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static helper_ctx_data helper_get_ctx_data(
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enum ggml_opt_optimizer_type optim,
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ggml_backend_sched_t backend_sched,
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ggml_backend_t backend,
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const bool init_opt_ctx = true,
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const bool optimizer_defaults = true,
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int64_t nbatch_logical = 1,
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int64_t nbatch_physical = 1,
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enum ggml_opt_loss_type loss_type = GGML_OPT_LOSS_TYPE_SUM) {
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std::vector<ggml_opt_dataset_t> datasets(ndata);
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for (int64_t ndata_shard = 1; ndata_shard <= ndata; ++ndata_shard) {
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ggml_opt_dataset_t dataset = ggml_opt_dataset_init(
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GGML_TYPE_F32, GGML_TYPE_F32, ne_datapoint, ne_label, ndata, ndata_shard);
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float * data = ggml_get_data_f32(ggml_opt_dataset_data( dataset));
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float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset));
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for (int64_t idata = 0; idata < ndata; ++idata) {
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for (int64_t id = 0; id < ne_datapoint; ++id) {
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data[ idata*ne_datapoint + id] = 16*idata + id;
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}
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for (int64_t il = 0; il < ne_label; ++il) {
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labels[idata*ne_label + il] = 16*(16*idata + il);
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}
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}
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datasets[ndata_shard-1] = dataset;
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}
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ggml_opt_dataset_t dataset_unsupervised = ggml_opt_dataset_init(
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GGML_TYPE_F32, GGML_TYPE_F32, 1, 0, ndata, /*ndata_shard =*/ 1);
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float * data = ggml_get_data_f32(ggml_opt_dataset_data(dataset_unsupervised));
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for (int64_t idata = 0; idata < ndata; ++idata) {
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data[idata] = idata;
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}
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struct ggml_context * ctx_static;
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struct ggml_context * ctx_compute;
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{
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struct ggml_init_params params = {
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/*.mem_size =*/ (2*ndata + 2)*ggml_tensor_overhead(),
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/*.mem_buffer =*/ nullptr,
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/*.no_alloc =*/ true,
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};
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ctx_static = ggml_init(params);
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}
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{
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struct ggml_init_params params = {
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/*.mem_size =*/ GGML_DEFAULT_GRAPH_SIZE*ggml_tensor_overhead() + 3*ggml_graph_overhead(),
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/*.mem_buffer =*/ nullptr,
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/*.no_alloc =*/ true,
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};
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ctx_compute = ggml_init(params);
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}
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std::vector<struct ggml_tensor *> data_batch(ndata);
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std::vector<struct ggml_tensor *> labels_batch(ndata);
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for (int64_t ndata_batch = 1; ndata_batch <= ndata; ++ndata_batch) {
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data_batch[ndata_batch-1] = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, ndata_batch*ne_datapoint);
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labels_batch[ndata_batch-1] = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, ndata_batch*ne_label);
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}
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struct ggml_tensor * inputs = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, nbatch_physical);
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ggml_set_name(inputs, "inputs");
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struct ggml_tensor * weights = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1);
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ggml_set_name(weights, "weights");
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ggml_set_param(weights);
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struct ggml_tensor * intermediary = ggml_add(ctx_compute, inputs, weights);
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struct ggml_tensor * outputs = ggml_scale(ctx_compute, intermediary, 1.0f);
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ggml_set_name(outputs, "outputs");
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ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx_static, backend);
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const float w0 = float(ndata)/2;
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ggml_backend_tensor_set(weights, &w0, 0, sizeof(float));
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GGML_ASSERT(nbatch_logical % nbatch_physical == 0);
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const int32_t opt_period = nbatch_logical / nbatch_physical;
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struct ggml_opt_params opt_params = ggml_opt_default_params(backend_sched, loss_type);
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opt_params.ctx_compute = ctx_compute;
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opt_params.inputs = inputs;
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opt_params.outputs = outputs;
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opt_params.opt_period = opt_period;
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opt_params.optimizer = optim;
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if (!optimizer_defaults) {
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opt_params.get_opt_pars = helper_get_test_opt_pars;
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}
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GGML_ASSERT(opt_params.get_opt_pars);
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ggml_opt_context_t opt_ctx = init_opt_ctx ? ggml_opt_init(opt_params) : nullptr;
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GGML_ASSERT(!opt_ctx || ggml_opt_context_optimizer_type(opt_ctx) == opt_params.optimizer);
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ggml_opt_result_t result = ggml_opt_result_init();
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ggml_opt_result_t result2 = ggml_opt_result_init();
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return {datasets, data_batch, labels_batch, dataset_unsupervised, ctx_static, ctx_compute, opt_params, opt_ctx, inputs, weights, outputs, buf, result, result2};
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}
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static void helper_free_ctx_data(struct helper_ctx_data ctx_data) {
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ggml_opt_result_free(ctx_data.result);
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ggml_opt_result_free(ctx_data.result2);
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ggml_opt_free(ctx_data.opt_ctx);
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ggml_backend_buffer_free(ctx_data.buf);
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ggml_free(ctx_data.ctx_static);
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ggml_free(ctx_data.ctx_compute);
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for (ggml_opt_dataset_t dataset : ctx_data.datasets_supervised) {
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ggml_opt_dataset_free(dataset);
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}
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ggml_opt_dataset_free(ctx_data.dataset_unsupervised);
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}
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static void print_ok(bool subtest_ok) {
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printf(subtest_ok ? "\033[1;32mOK\033[0m\n" : "\033[1;31mFAIL\033[0m\n");
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}
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static void helper_after_test(
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enum ggml_opt_optimizer_type optim,
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const char * func, const bool high_level, const std::string options,
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const std::string subtest, const bool subtest_ok, int & ntest, int & npass) {
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printf(" %s(high_level=%s%s, subtest=%s, optimizer=%s): ",
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func, high_level ? "yes" : "no", options.c_str(), subtest.c_str(), ggml_opt_optimizer_name(optim));
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print_ok(subtest_ok);
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if (subtest_ok)
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npass++;
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ntest++;
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}
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static void print_ok(const char * func, bool subtest_ok, int & npass, int & ntest, const char * args = "") {
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printf(" %s(%s): ", func, args);
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print_ok(subtest_ok);
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if (subtest_ok)
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npass++;
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++ntest;
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}
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static std::pair<int, int> test_dataset(
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enum ggml_opt_optimizer_type optim,
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ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool shuffle) {
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int ntest = 0;
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int npass = 0;
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struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend);
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for (int64_t ndata_shard = 1; ndata_shard <= ndata; ++ndata_shard) {
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ggml_opt_dataset_t dataset = cd.datasets_supervised[ndata_shard-1];
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if (shuffle) {
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ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1);
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}
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for (int64_t ndata_batch = 1; ndata_batch <= ndata; ++ndata_batch) {
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if (ndata_batch % ndata_shard != 0) {
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continue;
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}
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bool subtest_ok = true;
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struct ggml_tensor * data_batch = cd.data_batch[ndata_batch-1];
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struct ggml_tensor * labels_batch = cd.labels_batch[ndata_batch-1];
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std::vector<float> data(ggml_nelements( data_batch));
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std::vector<float> labels(ggml_nelements(labels_batch));
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std::vector<int64_t> idata_shuffled;
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const int64_t nbatches = ndata / ndata_batch;
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for (int64_t ibatch = 0; ibatch < nbatches; ++ibatch) {
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ggml_opt_dataset_get_batch(dataset, data_batch, labels_batch, ibatch);
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ggml_backend_tensor_get( data_batch, data.data(), 0, ggml_nbytes( data_batch));
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ggml_backend_tensor_get(labels_batch, labels.data(), 0, ggml_nbytes(labels_batch));
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for (int64_t idata_batch = 0; idata_batch < ndata_batch; ++idata_batch) {
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const int64_t idata = ibatch*ndata_batch + idata_batch;
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const int64_t idata_found = data[idata_batch*ne_datapoint] / 16;
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subtest_ok = subtest_ok && (shuffle || idata_found == idata);
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idata_shuffled.push_back(idata_found);
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for (int64_t id = 0; id < ne_datapoint; ++id) {
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if (data[ idata_batch*ne_datapoint + id] != 16*idata_found + id) {
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subtest_ok = false;
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}
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}
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for (int64_t il = 0; il < ne_label; ++il) {
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if (labels[idata_batch*ne_label + il] != 16*(16*idata_found + il)) {
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subtest_ok = false;
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}
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}
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}
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}
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if (!shuffle || ndata % ndata_batch == 0) {
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const int ndata_max = (ndata / ndata_batch) * ndata_batch;
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for (int64_t idata = 0; subtest_ok && idata < ndata_max; ++idata) {
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int ninstances = 0;
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for (int64_t id : idata_shuffled) {
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ninstances += id == idata;
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}
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if (ninstances != 1) {
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subtest_ok = false;
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}
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}
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}
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printf(" %s(shuffle=%s, ndata_shard=%" PRId64 ", ndata_batch=%" PRId64 "): ",
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__func__, shuffle ? "yes" : "no", ndata_shard, ndata_batch);
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if (subtest_ok) {
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printf("\033[1;32mOK\033[0m\n");
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npass++;
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} else {
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printf("\033[1;31mFAIL\033[0m\n");
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}
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ntest++;
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}
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}
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helper_free_ctx_data(cd);
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return std::make_pair(npass, ntest);
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}
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static std::pair<int, int> test_grad(
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enum ggml_opt_optimizer_type optim,
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ggml_backend_sched_t backend_sched, ggml_backend_t backend) {
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int ntest = 0;
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int npass = 0;
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struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false,
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/*nbatch_logical =*/ 999999, /*nbatch_physical =*/ 1);
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std::vector<float> grad_history(ndata);
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for (int64_t idata = 0; idata < ndata; ++idata) {
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grad_history[idata] = NAN;
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}
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for (int idata = 0; idata < ndata; ++idata) {
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const float idataf = idata;
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ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
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// leaked
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ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
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ggml_opt_eval(cd.opt_ctx, cd.result);
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ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, sizeof(float));
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}
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{
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bool subtest_ok = true;
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for (int idata = 0; idata < ndata; ++idata) {
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if (grad_history[idata] != idata + 1) {
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subtest_ok = false;
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}
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}
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printf(" %s(): ", __func__);
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if (subtest_ok) {
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printf("\033[1;32mOK\033[0m\n");
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npass++;
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} else {
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printf("\033[1;31mFAIL\033[0m\n");
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}
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ntest++;
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}
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helper_free_ctx_data(cd);
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return std::make_pair(npass, ntest);
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}
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static void helper_after_test_forward_backward(
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enum ggml_opt_optimizer_type optim,
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const char * func, const bool high_level, const bool shuffle,
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const std::string subtest, const bool subtest_ok, int & ntest, int & npass) {
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std::string options = ", shuffle=";
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options += shuffle ? "yes" : "no";
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helper_after_test(optim, func, high_level, options, subtest, subtest_ok, ntest, npass);
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}
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static std::pair<int, int> test_forward_backward(
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enum ggml_opt_optimizer_type optim,
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ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool high_level, const bool shuffle) {
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int ntest = 0;
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int npass = 0;
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struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false);
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struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx);
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std::vector<float> loss_history(ndata);
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for (int64_t idata = 0; idata < ndata; ++idata) {
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loss_history[idata] = NAN;
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}
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{
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int64_t ndata;
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ggml_opt_result_ndata(cd.result, &ndata);
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double loss;
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double loss_unc;
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ggml_opt_result_loss(cd.result, &loss, &loss_unc);
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double accuracy;
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double accuracy_unc;
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ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc);
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const bool subtest_ok = ndata == 0 && almost_equal(loss, 0.0, 1e-6) && std::isnan(loss_unc) && std::isnan(accuracy) && std::isnan(accuracy_unc);
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helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "results_initial", subtest_ok, ntest, npass);
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}
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if (high_level) {
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ggml_opt_dataset_t dataset = cd.dataset_unsupervised;
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if (shuffle) {
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ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1);
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}
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ggml_opt_epoch(cd.opt_ctx, dataset, nullptr, cd.result, 0, nullptr, nullptr);
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} else {
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for (int idata = 0; idata < ndata; ++idata) {
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const float idataf = idata;
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ggml_opt_alloc(cd.opt_ctx, /*backward =*/ false);
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ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
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ggml_opt_eval(cd.opt_ctx, cd.result);
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ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
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}
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}
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{
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float weights;
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ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
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const bool subtest_ok = almost_equal(weights, ndata/2, 1e-10);
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helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "weights_after_forward", subtest_ok, ntest, npass);
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}
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{
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constexpr double atol = 1e-10;
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int64_t ndata;
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ggml_opt_result_ndata(cd.result, &ndata);
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bool subtest_ok = ndata == 6;
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double loss;
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double loss_unc;
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ggml_opt_result_loss(cd.result, &loss, &loss_unc);
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subtest_ok = subtest_ok && almost_equal(loss, 33.0, atol) && almost_equal(loss_unc, sqrt(3.5), atol);
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double accuracy;
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double accuracy_unc;
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ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc);
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subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc);
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helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "results_after_forward", subtest_ok, ntest, npass);
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}
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float w0;
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ggml_backend_tensor_get(cd.weights, &w0, 0, sizeof(float));
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for (int i = 0; i < 10; ++i) {
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ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
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// leaked.
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ggml_opt_eval(cd.opt_ctx, cd.result);
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}
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ggml_backend_tensor_set(cd.weights, &w0, 0, sizeof(float));
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ggml_opt_reset(cd.opt_ctx, /*optimizer =*/ false);
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ggml_opt_result_reset(cd.result);
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for (int64_t idata = 0; idata < ndata; ++idata) {
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loss_history[idata] = NAN;
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}
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if (high_level) {
|
|
ggml_opt_dataset_t dataset = cd.dataset_unsupervised;
|
|
if (shuffle) {
|
|
ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1);
|
|
}
|
|
ggml_opt_epoch(cd.opt_ctx, dataset, cd.result, nullptr, ndata, nullptr, nullptr);
|
|
} else {
|
|
for (int idata = 0; idata < ndata; ++idata) {
|
|
const float idataf = idata;
|
|
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
|
|
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
|
|
ggml_opt_eval(cd.opt_ctx, cd.result);
|
|
ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
|
|
}
|
|
}
|
|
|
|
{
|
|
float weights;
|
|
ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
|
|
const bool subtest_ok = almost_equal(weights, -ndata * 0.5, 1e-10);
|
|
helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "weights_after_forward_backward", subtest_ok, ntest, npass);
|
|
}
|
|
{
|
|
int64_t ndata;
|
|
ggml_opt_result_ndata(cd.result, &ndata);
|
|
bool subtest_ok = ndata == 6;
|
|
|
|
double loss;
|
|
double loss_unc;
|
|
ggml_opt_result_loss(cd.result, &loss, &loss_unc);
|
|
subtest_ok = subtest_ok && almost_equal(loss, 18.0, 1e-10) && (shuffle || loss_unc == 0.0);
|
|
|
|
double accuracy;
|
|
double accuracy_unc;
|
|
ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc);
|
|
subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc);
|
|
|
|
helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "result_after_forward_backward", subtest_ok, ntest, npass);
|
|
}
|
|
|
|
helper_free_ctx_data(cd);
|
|
|
|
return std::make_pair(npass, ntest);
|
|
}
|
|
|
|
static std::pair<int, int> test_epoch_vs_fit(
|
|
enum ggml_opt_optimizer_type optim,
|
|
ggml_backend_sched_t backend_sched, ggml_backend_t backend) {
|
|
int ntest = 0;
|
|
int npass = 0;
|
|
|
|
float weights_epoch;
|
|
float weights_fit;
|
|
|
|
{
|
|
struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend, /*init_opt_ctx =*/ true);
|
|
ggml_opt_dataset_t dataset = cd.dataset_unsupervised;
|
|
|
|
ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1);
|
|
ggml_opt_epoch(cd.opt_ctx, dataset, cd.result, nullptr, ndata, nullptr, nullptr);
|
|
// leaked.
|
|
|
|
ggml_backend_tensor_get(cd.weights, &weights_epoch, 0, ggml_nbytes(cd.weights));
|
|
helper_free_ctx_data(cd);
|
|
}
|
|
{
|
|
struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend, /*init_opt_ctx =*/ false);
|
|
ggml_opt_dataset_t dataset = cd.dataset_unsupervised;
|
|
|
|
ggml_opt_fit(backend_sched, cd.ctx_compute, cd.inputs, cd.outputs, dataset, GGML_OPT_LOSS_TYPE_SUM,
|
|
optim, ggml_opt_get_default_optimizer_params, 1, 1, 0.0f, true);
|
|
|
|
ggml_backend_tensor_get(cd.weights, &weights_fit, 0, ggml_nbytes(cd.weights));
|
|
helper_free_ctx_data(cd);
|
|
}
|
|
|
|
const bool subtest_ok = weights_epoch == weights_fit;
|
|
|
|
print_ok(__func__, subtest_ok, npass, ntest);
|
|
|
|
return std::make_pair(npass, ntest);
|
|
}
|
|
|
|
static void helper_after_test_idata_split(
|
|
enum ggml_opt_optimizer_type optim,
|
|
const char * func, const bool high_level, const int epoch,
|
|
const std::string subtest, const bool subtest_ok, int & ntest, int & npass) {
|
|
std::string options = ", epoch=";
|
|
options += std::to_string(epoch);
|
|
helper_after_test(optim, func, high_level, options, subtest, subtest_ok, ntest, npass);
|
|
}
|
|
|
|
static std::pair<int, int> test_idata_split(
|
|
enum ggml_opt_optimizer_type optim,
|
|
ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool high_level) {
|
|
int ntest = 0;
|
|
int npass = 0;
|
|
|
|
struct helper_ctx_data cd = helper_get_ctx_data(optim, backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false);
|
|
struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx);
|
|
const int idata_split = ndata * 2/3;
|
|
|
|
std::vector<float> loss_history(ndata);
|
|
for (int64_t idata = 0; idata < ndata; ++idata) {
|
|
loss_history[idata] = NAN;
|
|
}
|
|
|
|
bool const adamw = optim == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
|
|
for (int epoch = 1; epoch <= 4; ++epoch) {
|
|
if (high_level) {
|
|
ggml_opt_epoch(cd.opt_ctx, cd.dataset_unsupervised, cd.result, cd.result2, idata_split, nullptr, nullptr);
|
|
} else {
|
|
int idata = 0;
|
|
for (; idata < idata_split; ++idata) {
|
|
const float idataf = idata;
|
|
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
|
|
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
|
|
ggml_opt_eval(cd.opt_ctx, cd.result);
|
|
ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
|
|
}
|
|
for (; idata < ndata; ++idata) {
|
|
const float idataf = idata;
|
|
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ false);
|
|
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
|
|
ggml_opt_eval(cd.opt_ctx, cd.result2);
|
|
ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
|
|
}
|
|
}
|
|
|
|
if (adamw) {
|
|
float weights;
|
|
ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
|
|
const bool subtest_ok = almost_equal(weights, ndata/2 - epoch*idata_split, 1e-10);
|
|
helper_after_test_idata_split(optim, __func__, high_level, epoch, "weights", subtest_ok, ntest, npass);
|
|
}
|
|
if (adamw) {
|
|
constexpr double atol = 1e-10;
|
|
|
|
int64_t ndata_result;
|
|
ggml_opt_result_ndata(cd.result, &ndata_result);
|
|
bool subtest_ok = ndata_result == idata_split;
|
|
|
|
double loss;
|
|
double loss_unc;
|
|
ggml_opt_result_loss(cd.result, &loss, &loss_unc);
|
|
subtest_ok = subtest_ok && almost_equal(loss, 28.0 - epoch*16.0, atol) && almost_equal(loss_unc, 0.0, atol);
|
|
|
|
double accuracy;
|
|
double accuracy_unc;
|
|
ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc);
|
|
subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc);
|
|
|
|
helper_after_test_idata_split(optim, __func__, high_level, epoch, "results_backward", subtest_ok, ntest, npass);
|
|
}
|
|
if (adamw) {
|
|
constexpr double atol = 1e-10;
|
|
|
|
int64_t ndata_result;
|
|
ggml_opt_result_ndata(cd.result2, &ndata_result);
|
|
bool subtest_ok = ndata_result == ndata - idata_split;
|
|
|
|
double loss;
|
|
double loss_unc;
|
|
ggml_opt_result_loss(cd.result2, &loss, &loss_unc);
|
|
subtest_ok = subtest_ok && almost_equal(loss, 15.0 - epoch*8, atol) && almost_equal(loss_unc, sqrt(0.5), atol);
|
|
|
|
double accuracy;
|
|
double accuracy_unc;
|
|
ggml_opt_result_accuracy(cd.result2, &accuracy, &accuracy_unc);
|
|
subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc);
|
|
|
|
helper_after_test_idata_split(optim, __func__, high_level, epoch, "results_forward", subtest_ok, ntest, npass);
|
|
}
|
|
|
|
ggml_opt_result_reset(cd.result);
|
|
ggml_opt_result_reset(cd.result2);
|
|
}
|
|
|
|
helper_free_ctx_data(cd);
|
|
|
|
return std::make_pair(npass, ntest);
|
|
}
|
|
|
|
static void helper_after_test_gradient_accumulation(
|
|
enum ggml_opt_optimizer_type optim,
|
|
const char * func, const int nbatch_physical, const enum ggml_opt_loss_type loss_type, const int epoch,
|
|
const std::string subtest, const bool subtest_ok, int & ntest, int & npass) {
|
|
std::string options = ", nbatch_physical=";
|
|
options += std::to_string(nbatch_physical);
|
|
options += ", loss_type=";
|
|
options += loss_type == GGML_OPT_LOSS_TYPE_MEAN ? "mean" : "sum";
|
|
options += ", epoch=";
|
|
options += std::to_string(epoch);
|
|
helper_after_test(optim, func, false, options, subtest, subtest_ok, ntest, npass);
|
|
}
|
|
|
|
static std::pair<int, int> test_gradient_accumulation(
|
|
enum ggml_opt_optimizer_type optim,
|
|
ggml_backend_sched_t backend_sched, ggml_backend_t backend, const int32_t nbatch_physical, const enum ggml_opt_loss_type loss_type) {
|
|
int ntest = 0;
|
|
int npass = 0;
|
|
|
|
struct helper_ctx_data cd = helper_get_ctx_data(
|
|
optim,
|
|
backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false, /*nbatch_logical =*/ 6, nbatch_physical, loss_type);
|
|
|
|
std::vector<float> grad_history(ndata);
|
|
for (int64_t idata = 0; idata < ndata; ++idata) {
|
|
grad_history[idata] = NAN;
|
|
}
|
|
|
|
bool const adamw = optim == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
|
|
if (adamw)
|
|
for (int epoch = 1; epoch <= 4; ++epoch) {
|
|
if (nbatch_physical == 1) {
|
|
for (int idata = 0; idata < ndata; ++idata) {
|
|
const float idataf = idata;
|
|
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
|
|
ggml_backend_tensor_set(cd.inputs, &idataf, 0, 1*sizeof(float));
|
|
ggml_opt_eval(cd.opt_ctx, cd.result);
|
|
ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, 1*sizeof(float));
|
|
}
|
|
} else if (nbatch_physical == 2) {
|
|
for (int idata = 0; idata < ndata; idata += 2) {
|
|
const float idataf[2] = {float(idata + 0), float(idata + 1)};
|
|
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
|
|
ggml_backend_tensor_set(cd.inputs, idataf, 0, 2*sizeof(float));
|
|
ggml_opt_eval(cd.opt_ctx, cd.result);
|
|
|
|
grad_history[idata + 0] = 0.0f;
|
|
ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata + 1, 0, 1*sizeof(float));
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
{
|
|
GGML_ASSERT(ndata == 6);
|
|
constexpr double atol = 1e-6;
|
|
bool subtest_ok = true;
|
|
if (loss_type == GGML_OPT_LOSS_TYPE_SUM) {
|
|
if (nbatch_physical == 1) {
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[0], 1.0, atol);
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[2], 3.0, atol);
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[4], 5.0, atol);
|
|
} else {
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[0], 0.0, atol);
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[2], 0.0, atol);
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[4], 0.0, atol);
|
|
}
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0, atol);
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0, atol);
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[5], 6.0, atol);
|
|
} else if (loss_type == GGML_OPT_LOSS_TYPE_MEAN) {
|
|
if (nbatch_physical == 1) {
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[0], 1.0/ndata, atol);
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[2], 3.0/ndata, atol);
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[4], 5.0/ndata, atol);
|
|
} else {
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[0], 0.0/ndata, atol);
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[2], 0.0/ndata, atol);
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[4], 0.0/ndata, atol);
|
|
}
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0/ndata, atol);
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0/ndata, atol);
|
|
subtest_ok = subtest_ok && almost_equal(grad_history[5], 6.0/ndata, atol);
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
helper_after_test_gradient_accumulation(optim, __func__, nbatch_physical, loss_type, epoch, "grads", subtest_ok, ntest, npass);
|
|
}
|
|
bool const adamw = optim == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
|
|
if (adamw) {
|
|
constexpr double atol = 1e-6;
|
|
float weights;
|
|
ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
|
|
const bool subtest_ok = almost_equal(weights, (ndata/2) - epoch, atol);
|
|
helper_after_test_gradient_accumulation(optim, __func__, nbatch_physical, loss_type, epoch, "weights", subtest_ok, ntest, npass);
|
|
}
|
|
{
|
|
constexpr double atol = 1e-6;
|
|
int64_t ndata_result;
|
|
ggml_opt_result_ndata(cd.result, &ndata_result);
|
|
bool subtest_ok = almost_equal(ndata_result, ndata/nbatch_physical, atol);
|
|
|
|
double loss;
|
|
ggml_opt_result_loss(cd.result, &loss, /*loss_unc =*/ nullptr);
|
|
if (loss_type == GGML_OPT_LOSS_TYPE_SUM) {
|
|
subtest_ok = subtest_ok && almost_equal(loss, (39.0 - epoch*6.0), atol);
|
|
} else if (loss_type == GGML_OPT_LOSS_TYPE_MEAN) {
|
|
subtest_ok = subtest_ok && almost_equal(loss, (39.0 - epoch*6.0) / ndata, atol);
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
double accuracy;
|
|
double accuracy_unc;
|
|
ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc);
|
|
subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc);
|
|
|
|
helper_after_test_gradient_accumulation(optim, __func__, nbatch_physical, loss_type, epoch, "results", subtest_ok, ntest, npass);
|
|
}
|
|
|
|
ggml_opt_result_reset(cd.result);
|
|
}
|
|
|
|
helper_free_ctx_data(cd);
|
|
|
|
return std::make_pair(npass, ntest);
|
|
}
|
|
|
|
float constexpr g_sgd_lr = 1e-4f;
|
|
|
|
int constexpr g_sgd_epochs = 900;
|
|
|
|
static ggml_opt_optimizer_params helper_get_regression_opt_pars(void * userdata) {
|
|
int64_t epoch = *(int64_t*)userdata;
|
|
ggml_opt_optimizer_params result = ggml_opt_get_default_optimizer_params(nullptr);
|
|
result.adamw.alpha = 0.1f;
|
|
result.sgd.alpha = g_sgd_lr * std::pow(.99, 1000 * (double)epoch / g_sgd_epochs);
|
|
result.sgd.wd = 1e-10;
|
|
return result;
|
|
}
|
|
|
|
static std::pair<int, int> test_regression(
|
|
enum ggml_opt_optimizer_type optim,
|
|
ggml_backend_sched_t backend_sched, ggml_backend_t backend) {
|
|
int ntest = 0;
|
|
int npass = 0;
|
|
|
|
// Test for simple regression with f(x) = a*x + b
|
|
|
|
constexpr int64_t ndata_regression = 201;
|
|
constexpr float a_true = 1.2f;
|
|
constexpr float b_true = 3.4f;
|
|
|
|
std::mt19937 gen(12345);
|
|
std::normal_distribution<float> nd{0.0f, 0.1f};
|
|
|
|
ggml_opt_dataset_t dataset = ggml_opt_dataset_init(
|
|
GGML_TYPE_F32, GGML_TYPE_F32, 1, 1, ndata_regression, ndata_regression);
|
|
|
|
float * data = ggml_get_data_f32(ggml_opt_dataset_data( dataset));
|
|
float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset));
|
|
|
|
constexpr float x_min = -100.0f;
|
|
constexpr float x_max = 100.0f;
|
|
|
|
for (int64_t idata = 0; idata < ndata_regression; ++idata) {
|
|
const float x = x_min + (x_max - x_min) * idata/(ndata_regression-1);
|
|
const float y = a_true*x + b_true + nd(gen);
|
|
|
|
data[idata] = x;
|
|
labels[idata] = y;
|
|
}
|
|
|
|
struct ggml_context * ctx_static;
|
|
struct ggml_context * ctx_compute;
|
|
{
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ 3*ggml_tensor_overhead(),
|
|
/*.mem_buffer =*/ nullptr,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
ctx_static = ggml_init(params);
|
|
}
|
|
{
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ GGML_DEFAULT_GRAPH_SIZE*ggml_tensor_overhead() + 3*ggml_graph_overhead(),
|
|
/*.mem_buffer =*/ nullptr,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
ctx_compute = ggml_init(params);
|
|
}
|
|
|
|
// The first dimension is the dimension of the datapoints, the second dimension is the number of datapoints.
|
|
struct ggml_tensor * x = ggml_new_tensor_2d(ctx_static, GGML_TYPE_F32, 1, ndata_regression);
|
|
ggml_set_name(x, "x");
|
|
|
|
struct ggml_tensor * a = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1);
|
|
ggml_set_name(a, "a");
|
|
ggml_set_param(a);
|
|
|
|
struct ggml_tensor * b = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1);
|
|
ggml_set_name(b, "b");
|
|
ggml_set_param(b);
|
|
|
|
struct ggml_tensor * f = ggml_add(ctx_compute, ggml_mul(ctx_compute, x, a), b);
|
|
ggml_set_name(f, "f");
|
|
|
|
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx_static, backend);
|
|
const float a0 = 1.0f;
|
|
const float b0 = 3.0f;
|
|
ggml_backend_tensor_set(a, &a0, 0, sizeof(float));
|
|
ggml_backend_tensor_set(b, &b0, 0, sizeof(float));
|
|
|
|
bool const adamw = optim == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
|
|
int64_t const n_epoch = adamw ? 100 : g_sgd_epochs;
|
|
ggml_opt_fit(backend_sched, ctx_compute, x, f, dataset, GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR, optim,
|
|
helper_get_regression_opt_pars, n_epoch, ndata_regression, 0.0f, true);
|
|
|
|
{
|
|
float a_fit;
|
|
ggml_backend_tensor_get(a, &a_fit, 0, sizeof(float));
|
|
float b_fit;
|
|
ggml_backend_tensor_get(b, &b_fit, 0, sizeof(float));
|
|
float tol = adamw ? 1e-2 : 5e-2;
|
|
const bool aok = almost_equal(a_fit, a_true, tol);
|
|
const bool bok = almost_equal(b_fit, b_true, tol);
|
|
const bool subtest_ok = aok && bok;
|
|
print_ok(__func__, adamw ? subtest_ok : true, npass, ntest, "subtest=weights");
|
|
}
|
|
|
|
ggml_backend_buffer_free(buf);
|
|
ggml_free(ctx_static);
|
|
ggml_opt_dataset_free(dataset);
|
|
|
|
return std::make_pair(npass, ntest);
|
|
}
|
|
|
|
static std::pair<int, int> test_backend(
|
|
ggml_backend_sched_t backend_sched, ggml_backend_t backend, enum ggml_opt_optimizer_type optim) {
|
|
int npass = 0;
|
|
int ntest = 0;
|
|
|
|
for (bool shuffle : {false, true}) {
|
|
std::pair<int, int> partial = test_dataset(optim, backend_sched, backend, shuffle);
|
|
npass += partial.first;
|
|
ntest += partial.second;
|
|
}
|
|
{
|
|
std::pair<int, int> partial = test_grad(optim, backend_sched, backend);
|
|
npass += partial.first;
|
|
ntest += partial.second;
|
|
}
|
|
for (bool high_level : {false, true}){
|
|
for (bool shuffle : {false, true}) {
|
|
if (!high_level && shuffle) {
|
|
continue;
|
|
}
|
|
|
|
std::pair<int, int> partial = test_forward_backward(optim, backend_sched, backend, high_level, shuffle);
|
|
npass += partial.first;
|
|
ntest += partial.second;
|
|
}
|
|
}
|
|
{
|
|
std::pair<int, int> partial = test_epoch_vs_fit(optim, backend_sched, backend);
|
|
npass += partial.first;
|
|
ntest += partial.second;
|
|
}
|
|
for (bool high_level : {false, true}){
|
|
std::pair<int, int> partial = test_idata_split(optim, backend_sched, backend, high_level);
|
|
npass += partial.first;
|
|
ntest += partial.second;
|
|
}
|
|
bool const adamw = optim == GGML_OPT_OPTIMIZER_TYPE_ADAMW;
|
|
if (adamw) {
|
|
for (int32_t nbatch_physical : { 2, 1 }) {
|
|
for (enum ggml_opt_loss_type loss_type : { GGML_OPT_LOSS_TYPE_SUM, GGML_OPT_LOSS_TYPE_MEAN }) {
|
|
std::pair<int, int> partial =
|
|
test_gradient_accumulation(optim, backend_sched, backend, nbatch_physical, loss_type);
|
|
npass += partial.first;
|
|
ntest += partial.second;
|
|
}
|
|
}
|
|
}
|
|
{
|
|
std::pair<int, int> partial = test_regression(optim, backend_sched, backend);
|
|
npass += partial.first;
|
|
ntest += partial.second;
|
|
}
|
|
|
|
return std::make_pair(npass, ntest);
|
|
}
|
|
|
|
|
|
int main(void) {
|
|
ggml_log_set(nullptr, nullptr);
|
|
ggml_backend_load_all();
|
|
const size_t dev_count = ggml_backend_dev_count();
|
|
printf("Testing %zu devices\n\n", dev_count);
|
|
size_t n_ok = 0;
|
|
|
|
std::vector<ggml_backend_dev_t> devs;
|
|
std::vector<ggml_backend_t> backends;
|
|
|
|
for (size_t i = 0; i < dev_count; ++i) {
|
|
devs.push_back(ggml_backend_dev_get(i));
|
|
|
|
ggml_backend_t backend = ggml_backend_dev_init(devs[i], NULL);
|
|
GGML_ASSERT(backend != NULL);
|
|
|
|
auto * reg = ggml_backend_dev_backend_reg(devs[i]);
|
|
auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads");
|
|
if (ggml_backend_set_n_threads_fn) {
|
|
ggml_backend_set_n_threads_fn(backend, std::thread::hardware_concurrency() / 2);
|
|
}
|
|
backends.push_back(backend);
|
|
}
|
|
|
|
size_t n_total = 0;
|
|
for (enum ggml_opt_optimizer_type optim : { GGML_OPT_OPTIMIZER_TYPE_ADAMW, GGML_OPT_OPTIMIZER_TYPE_SGD }) {
|
|
for (size_t i = 0; i < dev_count; ++i) {
|
|
// Put the backend to be tested in front so that it's prioritized:
|
|
std::vector<ggml_backend_t> backends_modded = { backends[i] };
|
|
backends_modded.insert(backends_modded.end(), backends.begin(), backends.end());
|
|
|
|
ggml_backend_sched_t backend_sched = ggml_backend_sched_new(
|
|
backends_modded.data(), nullptr, backends_modded.size(), GGML_DEFAULT_GRAPH_SIZE, false, true);
|
|
|
|
char const* devname = ggml_backend_dev_name(devs[i]);
|
|
printf("Backend %zu/%zu: %s\n", i + 1, dev_count, devname);
|
|
printf(" Device description: %s\n", ggml_backend_dev_description(devs[i]));
|
|
size_t free, total; // NOLINT
|
|
ggml_backend_dev_memory(devs[i], &free, &total);
|
|
printf(" Device memory: %zu MB (%zu MB free)\n", total / 1024 / 1024, free / 1024 / 1024);
|
|
printf("\n");
|
|
|
|
bool skip;
|
|
{
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ 6*ggml_tensor_overhead(),
|
|
/*.mem_buffer =*/ nullptr,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
ggml_context * ctx = ggml_init(params);
|
|
ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
|
|
ggml_set_param(a);
|
|
ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
|
|
ggml_tensor * c = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
|
|
ggml_tensor * d = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
|
|
|
|
ggml_tensor * t = nullptr;
|
|
switch (optim) {
|
|
case GGML_OPT_OPTIMIZER_TYPE_ADAMW: {
|
|
ggml_tensor * p = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 7);
|
|
t = ggml_opt_step_adamw(ctx, a, b, c, d, p);
|
|
} break;
|
|
case GGML_OPT_OPTIMIZER_TYPE_SGD: {
|
|
ggml_tensor * p = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 2);
|
|
t = ggml_opt_step_sgd(ctx, a, b, p);
|
|
} break;
|
|
case GGML_OPT_OPTIMIZER_TYPE_COUNT: {
|
|
GGML_ABORT("fatal error");
|
|
}
|
|
}
|
|
skip = !ggml_backend_supports_op(backends[i], t);
|
|
ggml_free(ctx);
|
|
}
|
|
|
|
std::pair<int, int> result;
|
|
if (!skip) {
|
|
result = test_backend(backend_sched, backends[i], optim);
|
|
printf(" %d/%d tests passed\n", result.first, result.second);
|
|
}
|
|
|
|
printf(" Backend %s %s: ", ggml_backend_name(backends[i]), ggml_opt_optimizer_name(optim));
|
|
if (skip) {
|
|
printf("\033[0;33mSKIPPED\033[0m\n");
|
|
n_ok++;
|
|
} else if (result.first == result.second) {
|
|
printf("\033[1;32mOK\033[0m\n");
|
|
n_ok++;
|
|
} else {
|
|
printf("\033[1;31mFAIL\033[0m\n");
|
|
}
|
|
++n_total;
|
|
printf("\n");
|
|
ggml_backend_sched_free(backend_sched);
|
|
}
|
|
}
|
|
|
|
for (ggml_backend_t backend : backends) {
|
|
ggml_backend_free(backend);
|
|
}
|
|
|
|
printf("%zu/%zu backend*optimizer passed\n", n_ok, n_total);
|
|
bool ok = n_ok == n_total;
|
|
print_ok(ok);
|
|
return ok ? 0 : 1;
|
|
}
|