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-rw-r--r--tests/test-sampling.cpp75
1 files changed, 20 insertions, 55 deletions
diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp
index 019c0d46..32e58941 100644
--- a/tests/test-sampling.cpp
+++ b/tests/test-sampling.cpp
@@ -8,11 +8,9 @@
#include <cmath>
#include <numeric>
#include <cassert>
-#include <iostream>
#include <vector>
#include <algorithm>
-
static 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);
@@ -21,7 +19,6 @@ static void dump(const llama_token_data_array * candidates) {
#define DUMP(__candidates) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__candidates)); printf("-\n"); } while(0)
-
static 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;
@@ -37,13 +34,12 @@ static void test_top_k(const std::vector<float> & probs, const std::vector<float
llama_sample_top_k(nullptr, &candidates_p, k, 1);
DUMP(&candidates_p);
- assert(candidates_p.size == expected_probs.size());
+ GGML_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);
+ GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-5);
}
}
-
static 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;
@@ -59,13 +55,12 @@ static void test_top_p(const std::vector<float> & probs, const std::vector<float
llama_sample_top_p(nullptr, &candidates_p, p, 1);
DUMP(&candidates_p);
- assert(candidates_p.size == expected_probs.size());
+ GGML_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);
+ GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
}
}
-
static 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;
@@ -80,13 +75,12 @@ static void test_tfs(const std::vector<float> & probs, const std::vector<float>
llama_sample_tail_free(nullptr, &candidates_p, z, 1);
DUMP(&candidates_p);
- assert(candidates_p.size == expected_probs.size());
+ GGML_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);
+ GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
}
}
-
static 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;
@@ -101,18 +95,17 @@ static void test_typical(const std::vector<float> & probs, const std::vector<flo
llama_sample_typical(nullptr, &candidates_p, p, 1);
DUMP(&candidates_p);
- assert(candidates_p.size == expected_probs.size());
+ GGML_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);
+ GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
}
}
-
-static void test_repetition_penalty(
+static void test_repetition_penalties(
const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
- const std::vector<float> & expected_probs, float penalty
+ const std::vector<float> & expected_probs, float repeat_penalty, float alpha_frequency, float alpha_presence
) {
- assert(probs.size() == expected_probs.size());
+ GGML_ASSERT(probs.size() == expected_probs.size());
size_t n_vocab = probs.size();
std::vector<llama_token_data> candidates;
@@ -125,41 +118,13 @@ static void test_repetition_penalty(
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, (const llama_token *) last_tokens.data(), last_tokens.size(), penalty);
+ llama_sample_repetition_penalties(nullptr, &candidates_p, (const llama_token *) last_tokens.data(), last_tokens.size(), repeat_penalty, 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-6);
- }
-}
-
-
-static 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, (const 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());
+ GGML_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);
+ GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
}
}
@@ -181,13 +146,13 @@ int main(void) {
test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
- test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.25f, 0.25f, 0.25f, 0.25f, 0}, 50.0f);
- test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0}, 50.0f);
- test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.5f, 0.5f, 0, 0, 0}, 50.0f);
+ test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.25f, 0.25f, 0.25f, 0.25f, 0}, 50.0f, 0.0f, 0.0f);
+ test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0}, 50.0f, 0.0f, 0.0f);
+ test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.5f, 0.5f, 0, 0, 0}, 50.0f, 0.0f, 0.0f);
- test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.249997f, 0.249997f, 0.249997f, 0.249997f, 0.000011f}, 5.0f, 5.0f);
- test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 5.0f, 5.0f);
- test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 5.0f, 5.0f);
+ test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.249997f, 0.249997f, 0.249997f, 0.249997f, 0.000011f}, 1.0f, 5.0f, 5.0f);
+ test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 1.0f, 5.0f, 5.0f);
+ test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 1.0f, 5.0f, 5.0f);
printf("OK\n");
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-18 22:43:01 +0000