upstream-archive/llama.cpp
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Shared default of 64 for history-based samplers, remove context_size

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Oliver Simons
2026-08-04 09:02:53 +03:00
committed by Georgi Gerganov
parent 748eca633a
commit eb75c420f0
15 changed files with 48 additions and 100 deletions
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tests/test-sampling.cpp
+3 -55
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@@ -10,7 +10,7 @@
#include <string>
#include <vector>
extern struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector<std::vector<llama_token>>& seq_breakers);
extern struct llama_sampler * llama_sampler_init_dry_testing(float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector<std::vector<llama_token>>& seq_breakers);
static void dump(const llama_token_data_array * cur_p) {
for (size_t i = 0; i < cur_p->size; i++) {
@@ -158,26 +158,6 @@ static void test_penalties(
tester.check();
}
static void test_penalties_last_n() {
const int32_t n_vocab = 1025;
std::vector<llama_token_data> data;
data.reserve(n_vocab);
auto * sampler = llama_sampler_init_penalties(n_vocab, -1, 2.0f, 0.0f, 0.0f);
for (llama_token token = 0; token < n_vocab; ++token) {
data.push_back({ token, 1.0f, 0.0f });
llama_sampler_accept(sampler, token);
}
llama_token_data_array cur_p = { data.data(), data.size(), -1, false };
llama_sampler_apply(sampler, &cur_p);
llama_sampler_free(sampler);
GGML_ASSERT(data[0].logit == 1.0f);
GGML_ASSERT(data[1].logit == 0.5f);
GGML_ASSERT(data[1024].logit == 0.5f);
}
static void test_dry(
const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
const std::vector<float> & expected_probs, float dry_multiplier, float dry_base,
@@ -188,7 +168,7 @@ static void test_dry(
sampler_tester tester(probs, expected_probs);
auto * sampler = llama_sampler_init_dry_testing(1024, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers);
auto * sampler = llama_sampler_init_dry_testing(dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers);
for (size_t i = 0; i < last_tokens.size(); i++) {
llama_sampler_accept(sampler, last_tokens[i]);
@@ -201,37 +181,6 @@ static void test_dry(
tester.check();
}
static void test_dry_last_n() {
const int32_t n_vocab = 1027;
std::vector<llama_token> last_tokens = { 0, 1, 2 };
for (llama_token token = 3; token < n_vocab; ++token) {
last_tokens.push_back(token);
}
last_tokens.push_back(0);
last_tokens.push_back(1);
const auto apply_dry = [&](int32_t dry_penalty_last_n) {
std::vector<llama_token_data> data;
data.reserve(n_vocab);
for (llama_token token = 0; token < n_vocab; ++token) {
data.push_back({ token, 0.0f, 0.0f });
}
auto * sampler = llama_sampler_init_dry_testing(2048, 1.0f, 2.0f, 1, dry_penalty_last_n, {});
for (llama_token token : last_tokens) {
llama_sampler_accept(sampler, token);
}
llama_token_data_array cur_p = { data.data(), data.size(), -1, false };
llama_sampler_apply(sampler, &cur_p);
llama_sampler_free(sampler);
return data[2].logit;
};
GGML_ASSERT(apply_dry(-1) == 0.0f);
GGML_ASSERT(apply_dry(2048) < 0.0f);
}
static void test_top_n_sigma(const std::vector<float> & probs, const std::vector<float> & probs_expected, int n) {
sampler_tester tester(probs, probs_expected);
@@ -403,14 +352,13 @@ int main(void) {
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.000011f, 0.249997f, 0.249997f, 0.249997f, 0.249997f}, 1.0f, 5.0f, 5.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.000023f, 0.000023f, 0.000023f, 0.499966f, 0.499966f}, 1.0f, 5.0f, 5.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.000000f, 0.000023f, 0.000023f, 0.499977f, 0.499977f}, 1.0f, 5.0f, 5.0f);
test_penalties_last_n();
test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1}, {0.25f, 0.25f, 0.25f, 0.25f}, 1.0f, 1.1f, 2, 4, {});
test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1, 2, 0, 1}, {0.296923f, 0.296923f, 0.109232f, 0.296923f}, 1.0f, 1.1f, 2, 5, {});
test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 2, 6, {{3}});
test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 1}, {0.241818f, 0.241818f, 0.032727f, 0.241818f, 0.241818f}, 2.0f, 1.1f, 2, 5, {});
test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 4, 7, {});
test_dry_last_n();
test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.0f, 0.0f, 0.428571f, 0.571429f}, 1.00f);
test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 0.00f); // top_n_sigma == 0 now represents a no-op rather than greedy decoding as of PR#13345