CS348Project/llama.cpp
5
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2025-10-27 08:21:30 +00:00
Code Issues Packages Projects Releases Wiki Activity

test-opt: allow slight inprecision (#15503)

Browse Source
This commit is contained in:
Johannes Gäßler
2025-08-22 23:47:01 +02:00
committed by GitHub
parent 45363632cb
commit e92734d51b
1 changed files with 20 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files

32
tests/test-opt.cpp
View File

@@ -358,7 +358,7 @@ static std::pair<int, int> test_forward_backward(
double accuracy;
double accuracy_unc;
ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc);
const bool subtest_ok = ndata == 0 && loss == 0.0 && std::isnan(loss_unc) && std::isnan(accuracy) && std::isnan(accuracy_unc);
const bool subtest_ok = ndata == 0 && almost_equal(loss, 0.0, 1e-6) && std::isnan(loss_unc) && std::isnan(accuracy) && std::isnan(accuracy_unc);
helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "results_initial", subtest_ok, ntest, npass);
}
@@ -381,10 +381,12 @@ static std::pair<int, int> test_forward_backward(
{
float weights;
ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
const bool subtest_ok = weights == ndata/2;
const bool subtest_ok = almost_equal(weights, ndata/2, 1e-10);
helper_after_test_forward_backward(optim, __func__, high_level, shuffle, "weights_after_forward", subtest_ok, ntest, npass);
}
{
constexpr double atol = 1e-10;
int64_t ndata;
ggml_opt_result_ndata(cd.result, &ndata);
bool subtest_ok = ndata == 6;
@@ -392,7 +394,7 @@ static std::pair<int, int> test_forward_backward(
double loss;
double loss_unc;
ggml_opt_result_loss(cd.result, &loss, &loss_unc);
subtest_ok = subtest_ok && loss == 33.0 && almost_equal(loss_unc, sqrt(3.5), 1e-10);
subtest_ok = subtest_ok && almost_equal(loss, 33.0, atol) && almost_equal(loss_unc, sqrt(3.5), atol);
double accuracy;
double accuracy_unc;
@@ -437,7 +439,7 @@ static std::pair<int, int> test_forward_backward(
{
float weights;
ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
const bool subtest_ok = weights == -ndata * .5;
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);
}
{
@@ -448,7 +450,7 @@ static std::pair<int, int> test_forward_backward(
double loss;
double loss_unc;
ggml_opt_result_loss(cd.result, &loss, &loss_unc);
subtest_ok = subtest_ok && loss == 18.0 && (shuffle || loss_unc == 0.0);
subtest_ok = subtest_ok && almost_equal(loss, 18.0, 1e-10) && (shuffle || loss_unc == 0.0);
double accuracy;
double accuracy_unc;
@@ -550,10 +552,12 @@ static std::pair<int, int> test_idata_split(
if (adamw) {
float weights;
ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float));
const bool subtest_ok = weights == ndata/2 - epoch*idata_split;
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;
@@ -561,7 +565,7 @@ static std::pair<int, int> test_idata_split(
double loss;
double loss_unc;
ggml_opt_result_loss(cd.result, &loss, &loss_unc);
subtest_ok = subtest_ok && loss == 28.0 - epoch*16.0 && loss_unc == 0.0;
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;
@@ -571,6 +575,8 @@ static std::pair<int, int> test_idata_split(
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;
@@ -578,7 +584,7 @@ static std::pair<int, int> test_idata_split(
double loss;
double loss_unc;
ggml_opt_result_loss(cd.result2, &loss, &loss_unc);
subtest_ok = subtest_ok && loss == 15.0 - epoch*8 && almost_equal(loss_unc, sqrt(0.5), 1e-10);
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;
@@ -687,22 +693,24 @@ static std::pair<int, int> test_gradient_accumulation(
}
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 = weights == (ndata/2) - epoch;
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 = ndata_result == ndata/nbatch_physical;
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 && loss == (39.0 - epoch*6.0);
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, 1e-6);
subtest_ok = subtest_ok && almost_equal(loss, (39.0 - epoch*6.0) / ndata, atol);
} else {
GGML_ASSERT(false);
}