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-rw-r--r--tests/CMakeLists.txt1
-rw-r--r--tests/test-sampling.cpp199
2 files changed, 200 insertions, 0 deletions
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 81eadbc..6456485 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -8,4 +8,5 @@ endfunction()
# llama_add_test(test-double-float.c) # SLOW
llama_add_test(test-quantize-fns.cpp)
llama_add_test(test-quantize-perf.cpp)
+llama_add_test(test-sampling.cpp)
llama_add_test(test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab.bin)
diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp
new file mode 100644
index 0000000..7eee4f6
--- /dev/null
+++ b/tests/test-sampling.cpp
@@ -0,0 +1,199 @@
+#include "llama.h"
+#include "ggml.h"
+#include <cassert>
+#include <cmath>
+#include <numeric>
+#include <cassert>
+#include <iostream>
+#include <vector>
+#include <algorithm>
+
+
+void dump(const llama_token_data_array * candidates) {
+ for (size_t i = 0; i < candidates->size; i++) {
+ printf("%d: %f (%f)\n", candidates->data[i].id, candidates->data[i].p, candidates->data[i].logit);
+ }
+}
+
+#define DUMP(__candidates) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__candidates)); printf("-\n"); } while(0)
+
+
+void test_top_k(const std::vector<float> & probs,
+ const std::vector<float> & expected_probs,
+ int k) {
+ size_t n_vocab = probs.size();
+ std::vector<llama_token_data> candidates;
+ candidates.reserve(n_vocab);
+ for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
+ float logit = log(probs[token_id]);
+ candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
+ }
+
+ llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+ llama_sample_softmax(nullptr, &candidates_p);
+ DUMP(&candidates_p);
+ llama_sample_top_k(nullptr, &candidates_p, k);
+ DUMP(&candidates_p);
+
+ assert(candidates_p.size == expected_probs.size());
+ for (size_t i = 0; i < candidates_p.size; i++) {
+ assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-5);
+ }
+}
+
+
+void test_top_p(const std::vector<float> & probs,
+ const std::vector<float> & expected_probs,
+ float p) {
+
+ size_t n_vocab = probs.size();
+ std::vector<llama_token_data> candidates;
+ candidates.reserve(n_vocab);
+ for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
+ float logit = log(probs[token_id]);
+ candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
+ }
+
+ llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+ llama_sample_softmax(nullptr, &candidates_p);
+ DUMP(&candidates_p);
+ llama_sample_top_p(nullptr, &candidates_p, p);
+ DUMP(&candidates_p);
+
+ assert(candidates_p.size == expected_probs.size());
+ for (size_t i = 0; i < candidates_p.size; i++) {
+ assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
+ }
+}
+
+
+void test_tfs(const std::vector<float> & probs,
+ const std::vector<float> & expected_probs,
+ float z) {
+ size_t n_vocab = probs.size();
+ std::vector<llama_token_data> candidates;
+ candidates.reserve(n_vocab);
+ for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
+ float logit = log(probs[token_id]);
+ candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
+ }
+
+ llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+ DUMP(&candidates_p);
+ llama_sample_tail_free(nullptr, &candidates_p, z);
+ DUMP(&candidates_p);
+
+ assert(candidates_p.size == expected_probs.size());
+ for (size_t i = 0; i < candidates_p.size; i++) {
+ assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
+ }
+}
+
+
+void test_typical(const std::vector<float> & probs,
+ const std::vector<float> & expected_probs,
+ float p) {
+ size_t n_vocab = probs.size();
+ std::vector<llama_token_data> candidates;
+ candidates.reserve(n_vocab);
+ for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
+ float logit = log(probs[token_id]);
+ candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
+ }
+
+ llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+ DUMP(&candidates_p);
+ llama_sample_typical(nullptr, &candidates_p, p);
+ DUMP(&candidates_p);
+
+ assert(candidates_p.size == expected_probs.size());
+ for (size_t i = 0; i < candidates_p.size; i++) {
+ assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
+ }
+}
+
+
+void test_repetition_penalty(
+ const std::vector<float> & probs,
+ const std::vector<llama_token> & last_tokens,
+ const std::vector<float> & expected_probs,
+ float penalty) {
+ assert(probs.size() == expected_probs.size());
+
+ size_t n_vocab = probs.size();
+ std::vector<llama_token_data> candidates;
+ candidates.reserve(n_vocab);
+ for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
+ float logit = log(probs[token_id]);
+ candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
+ }
+
+ llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+ llama_sample_softmax(nullptr, &candidates_p);
+ DUMP(&candidates_p);
+ llama_sample_repetition_penalty(nullptr, &candidates_p, (llama_token *)last_tokens.data(), last_tokens.size(), penalty);
+ llama_sample_softmax(nullptr, &candidates_p);
+ DUMP(&candidates_p);
+
+ assert(candidates_p.size == expected_probs.size());
+ for (size_t i = 0; i < candidates_p.size; i++) {
+ assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-6);
+ }
+}
+
+
+void test_frequency_presence_penalty(
+ const std::vector<float> & probs,
+ const std::vector<llama_token> & last_tokens,
+ const std::vector<float> & expected_probs,
+ float alpha_frequency, float alpha_presence) {
+ assert(probs.size() == expected_probs.size());
+
+ size_t n_vocab = probs.size();
+ std::vector<llama_token_data> candidates;
+ candidates.reserve(n_vocab);
+ for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
+ float logit = log(probs[token_id]);
+ candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
+ }
+
+ llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+ llama_sample_softmax(nullptr, &candidates_p);
+ // DUMP(&candidates_p);
+ llama_sample_frequency_and_presence_penalties(nullptr, &candidates_p, (llama_token *)last_tokens.data(), last_tokens.size(), alpha_frequency, alpha_presence);
+ llama_sample_softmax(nullptr, &candidates_p);
+ // DUMP(&candidates_p);
+
+ assert(candidates_p.size == expected_probs.size());
+ for (size_t i = 0; i < candidates_p.size; i++) {
+ assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
+ }
+}
+
+int main(void) {
+ ggml_time_init();
+
+ test_top_k({0.1, 0.2, 0.3, 0.4}, {0.4}, 1);
+ test_top_k({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3, 0.2}, 3);
+
+ test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4}, 0);
+ test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3}, 0.7);
+ test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3, 0.2, 0.1}, 1);
+
+ test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3}, 0.25);
+ test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3, 0.25}, 0.75);
+ test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3, 0.25}, 0.99);
+
+ test_typical({0.97, 0.01, 0.01, 0.01}, {0.97}, 0.5);
+ test_typical({0.4, 0.2, 0.2, 0.2}, {0.2, 0.2, 0.2}, 0.5);
+
+ test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0}, {0.25, 0.25, 0.25, 0.25, 0}, 50.0);
+ test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2}, {0.5, 0.5, 0, 0, 0}, 50.0);
+ test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2, 0, 0}, {0.5, 0.5, 0, 0, 0}, 50.0);
+
+ test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0}, {0.249997, 0.249997, 0.249997, 0.249997, 0.000011}, 5.0, 5.0);
+ test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2}, {0.499966, 0.499966, 0.000023, 0.000023, 0.000023}, 5.0, 5.0);
+ test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2, 0, 0}, {0.499977, 0.499977, 0.000023, 0.000023, 0.000000}, 5.0, 5.0);
+
+ printf("OK\n");
+}
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