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#include < vector>
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#include < algorithm>
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+ #undef assert
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+ #define assert (__expr ) do { if (!(__expr)) { printf (" %s:%d (%s) %s\n " , __FILE__, __LINE__, __func__, #__expr); exit (1 ); } } while (0 )
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+
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void dump (const llama_token_data_array * candidates) {
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for (size_t i = 0 ; i < candidates->size ; i++) {
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printf (" %d: %f (%f)\n " , candidates->data [i].id , candidates->data [i].p , candidates->data [i].logit );
@@ -53,13 +56,14 @@ void test_top_p(const std::vector<float> & probs,
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}
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llama_token_data_array candidates_p = { candidates.data (), candidates.size (), false };
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+ llama_sample_softmax (nullptr , &candidates_p);
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// DUMP(&candidates_p);
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llama_sample_top_p (nullptr , &candidates_p, p);
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// DUMP(&candidates_p);
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assert (candidates_p.size == expected_probs.size ());
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for (size_t i = 0 ; i < candidates_p.size ; i++) {
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- assert (fabs (candidates_p.data [i].p - expected_probs[i]) < 1e-5 );
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+ assert (fabs (candidates_p.data [i].p - expected_probs[i]) < 1e-3 );
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}
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}
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@@ -82,7 +86,7 @@ void test_tfs(const std::vector<float> & probs,
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assert (candidates_p.size == expected_probs.size ());
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for (size_t i = 0 ; i < candidates_p.size ; i++) {
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- assert (fabs (candidates_p.data [i].p - expected_probs[i]) < 1e-6 );
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+ assert (fabs (candidates_p.data [i].p - expected_probs[i]) < 1e-3 );
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}
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}
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@@ -105,7 +109,7 @@ void test_typical(const std::vector<float> & probs,
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assert (candidates_p.size == expected_probs.size ());
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for (size_t i = 0 ; i < candidates_p.size ; i++) {
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- assert (fabs (candidates_p.data [i].p - expected_probs[i]) < 1e-6 );
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+ assert (fabs (candidates_p.data [i].p - expected_probs[i]) < 1e-3 );
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}
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}
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@@ -163,7 +167,7 @@ void test_frequency_presence_penalty(
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assert (candidates_p.size == expected_probs.size ());
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for (size_t i = 0 ; i < candidates_p.size ; i++) {
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- assert (fabs (candidates_p.data [i].p - expected_probs[i]) < 1e-6 );
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+ assert (fabs (candidates_p.data [i].p - expected_probs[i]) < 1e-3 );
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}
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}
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@@ -182,9 +186,9 @@ int main(void) {
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test_typical ({0.97 , 0.01 , 0.01 , 0.01 }, {0.97 }, 0.5 );
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test_typical ({0.4 , 0.2 , 0.2 , 0.2 }, {0.2 , 0.2 , 0.2 }, 0.5 );
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- test_repetition_penalty ({0.2 , 0.2 , 0.2 , 0.2 , 0.2 }, {0 }, {0 , 0.25 , 0.25 , 0.25 , 0.25 }, 50.0 );
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- test_repetition_penalty ({0.2 , 0.2 , 0.2 , 0.2 , 0.2 }, {0 , 1 , 2 }, {0 , 0 , 0 , 0.5 , 0.5 }, 50.0 );
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- test_repetition_penalty ({0.2 , 0.2 , 0.2 , 0.2 , 0.2 }, {0 , 1 , 2 , 0 , 0 }, {0 , 0 , 0 , 0.5 , 0.5 }, 50.0 );
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+ test_repetition_penalty ({0.2 , 0.2 , 0.2 , 0.2 , 0.2 }, {0 }, {0.25 , 0.25 , 0.25 , 0.25 , 0 }, 50.0 );
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+ test_repetition_penalty ({0.2 , 0.2 , 0.2 , 0.2 , 0.2 }, {0 , 1 , 2 }, {0.5 , 0.5 , 0 , 0 , 0 }, 50.0 );
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+ test_repetition_penalty ({0.2 , 0.2 , 0.2 , 0.2 , 0.2 }, {0 , 1 , 2 , 0 , 0 }, {0.5 , 0.5 , 0 , 0 , 0 }, 50.0 );
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test_frequency_presence_penalty ({0.2 , 0.2 , 0.2 , 0.2 , 0.2 }, {0 }, {0.249997 , 0.249997 , 0.249997 , 0.249997 , 0.000011 }, 5.0 , 5.0 );
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test_frequency_presence_penalty ({0.2 , 0.2 , 0.2 , 0.2 , 0.2 }, {0 , 1 , 2 }, {0.499966 , 0.499966 , 0.000023 , 0.000023 , 0.000023 }, 5.0 , 5.0 );
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