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#include < thread>
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#include < vector>
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-
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static void init_tensor_uniform (ggml_tensor * tensor, float min = -1 .0f , float max = 1 .0f ) {
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size_t size = ggml_nelements (tensor);
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std::vector<float > data (size);
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#if 0
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- std::default_random_engine generator(rd() );
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+ static std::default_random_engine generator(1234 );
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std::uniform_real_distribution<float> distribution(min, max);
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for (size_t i = 0; i < size; i++) {
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data[i] = distribution(generator);
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}
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- #endif
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+ #else
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auto init_thread = [&](size_t start, size_t end) {
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std::random_device rd;
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std::default_random_engine generator (rd ());
@@ -49,6 +48,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
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for (auto & t : threads) {
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t.join ();
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}
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+ #endif
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if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) {
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ggml_backend_tensor_set (tensor, data.data (), 0 , size * sizeof (float ));
@@ -437,7 +437,7 @@ struct test_case {
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double err = nmse (f1.data (), f2.data (), f1.size ());
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if (err > ud->max_err ) {
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printf (" [%s] NMSE = %f " , ggml_op_desc (t1), err);
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- // for (int i = 0; i < f1.size(); i++) {
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+ // for (int i = 0; i < (int) f1.size(); i++) {
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// printf("%5d %9.6f %9.6f, diff = %9.6f\n", i, f1[i], f2[i], f1[i] - f2[i]);
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// }
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// printf("\n");
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