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tests : fix memory leaks (ggerganov#936)
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It is annoying to run the tests using the sanitizers
because of all the uninteresting reports about the memory
leaked by the tests themselves.

Signed-off-by: Salvatore Mesoraca <[email protected]>
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@smeso
smeso authored Aug 27, 2024
1 parent b1ce8fb commit 2438d62
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Showing 4 changed files with 38 additions and 38 deletions.
32 changes: 16 additions & 16 deletions tests/test-conv-transpose-1d.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -421,9 +421,9 @@ int main(void)
}
}

float* conv1d_transpose_data_0 = new float[ggml_nelements(conv1d_transpose_res_0)];
std::vector<float> conv1d_transpose_data_0(ggml_nelements(conv1d_transpose_res_0));

ggml_backend_tensor_get(conv1d_transpose_res_0, conv1d_transpose_data_0, 0, ggml_nbytes(conv1d_transpose_res_0));
ggml_backend_tensor_get(conv1d_transpose_res_0, conv1d_transpose_data_0.data(), 0, ggml_nbytes(conv1d_transpose_res_0));

const int n_conv_transpose_1d_test_0 = 4;

Expand All @@ -440,9 +440,9 @@ int main(void)
}
}

float* conv1d_transpose_data_1 = new float[ggml_nelements(conv1d_transpose_res_1)];
std::vector<float> conv1d_transpose_data_1(ggml_nelements(conv1d_transpose_res_1));

ggml_backend_tensor_get(conv1d_transpose_res_1, conv1d_transpose_data_1, 0, ggml_nbytes(conv1d_transpose_res_1));
ggml_backend_tensor_get(conv1d_transpose_res_1, conv1d_transpose_data_1.data(), 0, ggml_nbytes(conv1d_transpose_res_1));



Expand All @@ -462,9 +462,9 @@ int main(void)
}
}

float* conv1d_transpose_data_2 = new float[ggml_nelements(conv1d_transpose_res_2)];
std::vector<float> conv1d_transpose_data_2(ggml_nelements(conv1d_transpose_res_2));

ggml_backend_tensor_get(conv1d_transpose_res_2, conv1d_transpose_data_2, 0, ggml_nbytes(conv1d_transpose_res_2));
ggml_backend_tensor_get(conv1d_transpose_res_2, conv1d_transpose_data_2.data(), 0, ggml_nbytes(conv1d_transpose_res_2));


const int n_conv_transpose_1d_test_2 = 10;
Expand All @@ -481,9 +481,9 @@ int main(void)
}
}

float* conv1d_transpose_data_3 = new float[ggml_nelements(conv1d_transpose_res_3)];
std::vector<float> conv1d_transpose_data_3(ggml_nelements(conv1d_transpose_res_3));

ggml_backend_tensor_get(conv1d_transpose_res_3, conv1d_transpose_data_3, 0, ggml_nbytes(conv1d_transpose_res_3));
ggml_backend_tensor_get(conv1d_transpose_res_3, conv1d_transpose_data_3.data(), 0, ggml_nbytes(conv1d_transpose_res_3));


const int n_conv_transpose_1d_test_3 = 14;
Expand All @@ -501,9 +501,9 @@ int main(void)
}
}

float* conv1d_transpose_data_4 = new float[ggml_nelements(conv1d_transpose_res_4)];
std::vector<float> conv1d_transpose_data_4(ggml_nelements(conv1d_transpose_res_4));

ggml_backend_tensor_get(conv1d_transpose_res_4, conv1d_transpose_data_4, 0, ggml_nbytes(conv1d_transpose_res_4));
ggml_backend_tensor_get(conv1d_transpose_res_4, conv1d_transpose_data_4.data(), 0, ggml_nbytes(conv1d_transpose_res_4));


const int n_conv_transpose_1d_test_4 = 12;
Expand All @@ -522,9 +522,9 @@ int main(void)
}
}

float* conv1d_transpose_data_5 = new float[ggml_nelements(conv1d_transpose_res_5)];
std::vector<float> conv1d_transpose_data_5(ggml_nelements(conv1d_transpose_res_5));

ggml_backend_tensor_get(conv1d_transpose_res_5, conv1d_transpose_data_5, 0, ggml_nbytes(conv1d_transpose_res_5));
ggml_backend_tensor_get(conv1d_transpose_res_5, conv1d_transpose_data_5.data(), 0, ggml_nbytes(conv1d_transpose_res_5));


const int n_conv_transpose_1d_test_5 = 18;
Expand All @@ -543,9 +543,9 @@ int main(void)
}
}

float* conv1d_transpose_data_6 = new float[ggml_nelements(conv1d_transpose_res_6)];
std::vector<float> conv1d_transpose_data_6(ggml_nelements(conv1d_transpose_res_6));

ggml_backend_tensor_get(conv1d_transpose_res_6, conv1d_transpose_data_6, 0, ggml_nbytes(conv1d_transpose_res_6));
ggml_backend_tensor_get(conv1d_transpose_res_6, conv1d_transpose_data_6.data(), 0, ggml_nbytes(conv1d_transpose_res_6));


const int n_conv_transpose_1d_test_6 = 24;
Expand All @@ -565,9 +565,9 @@ int main(void)
}
}

float* conv1d_transpose_data_7 = new float[ggml_nelements(conv1d_transpose_res_7)];
std::vector<float> conv1d_transpose_data_7(ggml_nelements(conv1d_transpose_res_7));

ggml_backend_tensor_get(conv1d_transpose_res_7, conv1d_transpose_data_7, 0, ggml_nbytes(conv1d_transpose_res_7));
ggml_backend_tensor_get(conv1d_transpose_res_7, conv1d_transpose_data_7.data(), 0, ggml_nbytes(conv1d_transpose_res_7));


const int n_conv_transpose_1d_test_7 = 32*1584;
Expand Down
18 changes: 9 additions & 9 deletions tests/test-conv1d.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -40,17 +40,17 @@ void load_model(test_model & model, bool use_gpu = false) {
int IL = 8, N = 1;

// Initialize adata
float * adata = new float[K * IC * OC];
std::vector<float> adata(K * IC * OC);
for (int i = 0; i < K * IC * OC; i++) {
adata[i] = 4.5f;
}

// Convert adata to fp16 format
std::vector<ggml_fp16_t> hadata(K * IC * OC);
ggml_fp32_to_fp16_row(adata, hadata.data(), K * IC * OC);
ggml_fp32_to_fp16_row(adata.data(), hadata.data(), K * IC * OC);

// Initialize bdata
float * bdata = new float[IL * IC * N];
std::vector<float> bdata(IL * IC * N);
for (int i = 0; i < IL * IC * N; i++) {
bdata[i] = 2.5f;
}
Expand Down Expand Up @@ -129,9 +129,9 @@ void load_model(test_model & model, bool use_gpu = false) {
|| ggml_backend_is_metal(model.backend)
#endif
) {
memcpy(model.b->data, bdata, ggml_nbytes(model.b));
memcpy(model.b->data, bdata.data(), ggml_nbytes(model.b));
} else {
ggml_backend_tensor_set(model.b, bdata, 0, ggml_nbytes(model.b));
ggml_backend_tensor_set(model.b, bdata.data(), 0, ggml_nbytes(model.b));
}
}

Expand Down Expand Up @@ -226,11 +226,11 @@ int main(void)
}
}

uint16_t* im2col_data = new uint16_t[ggml_nelements(im2col_res)];
float* conv2d_data = new float[ggml_nelements(conv1d_res)];
std::vector<uint16_t> im2col_data(ggml_nelements(im2col_res));
std::vector<float> conv2d_data(ggml_nelements(conv1d_res));

ggml_backend_tensor_get(im2col_res, im2col_data, 0, ggml_nbytes(im2col_res));
ggml_backend_tensor_get(conv1d_res, conv2d_data, 0, ggml_nbytes(conv1d_res));
ggml_backend_tensor_get(im2col_res, im2col_data.data(), 0, ggml_nbytes(im2col_res));
ggml_backend_tensor_get(conv1d_res, conv2d_data.data(), 0, ggml_nbytes(conv1d_res));

const int n_conv1d_test = 80;
const int n_im2col_test = 240;
Expand Down
18 changes: 9 additions & 9 deletions tests/test-conv2d.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -40,17 +40,17 @@ void load_model(test_model & model, bool use_gpu = false) {
int IW = 8, IH = 6, N = 1;

// Initialize adata
float * adata = new float[KW * KH * IC * OC];
std::vector<float> adata(KW * KH * IC * OC);
for (int i = 0; i < KW * KH * IC * OC; i++) {
adata[i] = 2.5f;
}

// Convert adata to fp16 format
std::vector<ggml_fp16_t> hadata(KW * KH * IC * OC);
ggml_fp32_to_fp16_row(adata, hadata.data(), KW * KH * IC * OC);
ggml_fp32_to_fp16_row(adata.data(), hadata.data(), KW * KH * IC * OC);

// Initialize bdata
float * bdata = new float[IW * IH * IC * N];
std::vector<float> bdata(IW * IH * IC * N);
for (int i = 0; i < IW * IH * IC * N; i++) {
bdata[i] = 1.5f;
}
Expand Down Expand Up @@ -129,9 +129,9 @@ void load_model(test_model & model, bool use_gpu = false) {
|| ggml_backend_is_metal(model.backend)
#endif
) {
memcpy(model.b->data, bdata, ggml_nbytes(model.b));
memcpy(model.b->data, bdata.data(), ggml_nbytes(model.b));
} else {
ggml_backend_tensor_set(model.b, bdata, 0, ggml_nbytes(model.b));
ggml_backend_tensor_set(model.b, bdata.data(), 0, ggml_nbytes(model.b));
}
}

Expand Down Expand Up @@ -229,11 +229,11 @@ int main(void)
}
}

uint16_t* im2col_data = new uint16_t[ggml_nelements(im2col_res)];
float* conv2d_data = new float[ggml_nelements(conv2d_res)];
std::vector<uint16_t> im2col_data(ggml_nelements(im2col_res));
std::vector<float> conv2d_data(ggml_nelements(conv2d_res));

ggml_backend_tensor_get(im2col_res, im2col_data, 0, ggml_nbytes(im2col_res));
ggml_backend_tensor_get(conv2d_res, conv2d_data, 0, ggml_nbytes(conv2d_res));
ggml_backend_tensor_get(im2col_res, im2col_data.data(), 0, ggml_nbytes(im2col_res));
ggml_backend_tensor_get(conv2d_res, conv2d_data.data(), 0, ggml_nbytes(conv2d_res));

const int n_conv2d_test = 480;
const int n_im2col_test = 4320;
Expand Down
8 changes: 4 additions & 4 deletions tests/test-mul-mat.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -232,8 +232,8 @@ static void gemm_f16_out_f32(int m, int n, int k,
void perform_gemm_test(float* a, float* b, float* expected, int M, int N, int K) {
printf("\nPerforming gemm_f16_out_f32 test:\n");

float* gemm_out = new float[M * N];
gemm_f16_out_f32(M, N, K, a, b, gemm_out, 0, 1);
std::vector<float> gemm_out(M * N);
gemm_f16_out_f32(M, N, K, a, b, gemm_out.data(), 0, 1);

for (int i = 0; i < M; i++) {
for (int j = 0; j < N; j++) {
Expand Down Expand Up @@ -318,9 +318,9 @@ int main(void)

struct ggml_tensor * result = compute(model, allocr);

float* out_data = new float[ggml_nelements(result)];
std::vector<float> out_data(ggml_nelements(result));

ggml_backend_tensor_get(result, out_data, 0, ggml_nbytes(result));
ggml_backend_tensor_get(result, out_data.data(), 0, ggml_nbytes(result));

printf("\nPerforming ggml_mul_mat test:\n");

Expand Down

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