Tests for min_p, sampling queue (#5147)

diff --git a/llama.cpp b/llama.cpp
index 4cd0f16ebc1d7b0aaf51ef0694cb6742f94b979d..391c956ec19a6870261513b80dd76ec227f04746 100644 (file)
--- a/llama.cpp
+++ b/llama.cpp
@@ -8133,6 +8133,11 @@ void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * c
 }
 
 void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
+    // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
+    // if (k >= (int32_t)candidates->size) {
+    //     return;
+    // }
+
     const int64_t t_start_sample_us = ggml_time_us();
 
     k = std::max(k, (int) min_keep);

diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp
index 32e58941c0ee08cecbb21defb2e21c1e58e8153e..c3b3d6629d4ba2366049152f8ce7dc48aa5ad8cc 100644 (file)
--- a/tests/test-sampling.cpp
+++ b/tests/test-sampling.cpp
@@ -5,11 +5,10 @@
 #undef NDEBUG
 #endif
 
+#include <algorithm>
 #include <cmath>
-#include <numeric>
-#include <cassert>
+#include <string>
 #include <vector>
-#include <algorithm>
 
 static void dump(const llama_token_data_array * candidates) {
     for (size_t i = 0; i < candidates->size; i++) {
@@ -20,11 +19,11 @@ static void dump(const llama_token_data_array * candidates) {
 #define DUMP(__candidates) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__candidates)); printf("-\n"); } while(0)
 
 static void test_top_k(const std::vector<float> & probs, const std::vector<float> & expected_probs, int k) {
-    size_t n_vocab = probs.size();
+    const size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
     for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
-        float logit = log(probs[token_id]);
+        const float logit = logf(probs[token_id]);
         candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
     }
 
@@ -41,11 +40,11 @@ static void test_top_k(const std::vector<float> & probs, const std::vector<float
 }
 
 static void test_top_p(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
-    size_t n_vocab = probs.size();
+    const size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
     for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
-        float logit = log(probs[token_id]);
+        const float logit = logf(probs[token_id]);
         candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
     }
 
@@ -62,11 +61,11 @@ static void test_top_p(const std::vector<float> & probs, const std::vector<float
 }
 
 static void test_tfs(const std::vector<float> & probs, const std::vector<float> & expected_probs, float z) {
-    size_t n_vocab = probs.size();
+    const size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
     for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
-        float logit = log(probs[token_id]);
+        const float logit = logf(probs[token_id]);
         candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
     }
 
@@ -81,12 +80,33 @@ static void test_tfs(const std::vector<float> & probs, const std::vector<float>
     }
 }
 
+static void test_min_p(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
+    const size_t n_vocab = probs.size();
+    std::vector<llama_token_data> candidates;
+    candidates.reserve(n_vocab);
+    for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
+        const float logit = logf(probs[token_id]);
+        candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
+    }
+
+    llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+    DUMP(&candidates_p);
+    llama_sample_min_p(nullptr, &candidates_p, p, 1);
+    DUMP(&candidates_p);
+    llama_sample_softmax(nullptr, &candidates_p);
+
+    GGML_ASSERT(candidates_p.size == expected_probs.size());
+    for (size_t i = 0; i < candidates_p.size; i++) {
+        GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
+    }
+}
+
 static void test_typical(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
-    size_t n_vocab = probs.size();
+    const size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
     for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
-        float logit = log(probs[token_id]);
+        const float logit = logf(probs[token_id]);
         candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
     }
 
@@ -107,11 +127,11 @@ static void test_repetition_penalties(
 ) {
     GGML_ASSERT(probs.size() == expected_probs.size());
 
-    size_t n_vocab = probs.size();
+    const size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
     candidates.reserve(n_vocab);
     for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
-        float logit = log(probs[token_id]);
+        const float logit = logf(probs[token_id]);
         candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
     }
 
@@ -128,6 +148,88 @@ static void test_repetition_penalties(
     }
 }
 
+static void test_sampler_queue(
+    const size_t n_vocab, const std::string samplers_sequence, const int top_k, const float top_p, const float min_p
+) {
+    std::vector<llama_token_data> candidates;
+    candidates.reserve(n_vocab);
+    for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
+        const float logit = logf(token_id);
+        candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
+    }
+
+    llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+
+          llama_token min_token_id = 0;
+    const llama_token max_token_id = n_vocab-1;
+
+    for (auto s : samplers_sequence) {
+        switch (s){
+            case 'k': llama_sample_top_k    (nullptr, &candidates_p, top_k, 1); break;
+            case 'f': GGML_ASSERT(false && "tail_free test not implemented");   break;
+            case 'y': GGML_ASSERT(false && "typical test not implemented");     break;
+            case 'p': llama_sample_top_p    (nullptr, &candidates_p, top_p, 1); break;
+            case 'm': llama_sample_min_p    (nullptr, &candidates_p, min_p, 1); break;
+            case 't': GGML_ASSERT(false && "temperature test not implemented"); break;
+            default : GGML_ASSERT(false && "Unknown sampler");                  break;
+        }
+
+        llama_sample_softmax(nullptr, &candidates_p); // make sure tokens are sorted for tests
+
+        const int size = candidates_p.size;
+
+        if (s == 'k') {
+            const int expected_size = std::min(size, top_k);
+            min_token_id = std::max(min_token_id, (llama_token)(n_vocab - top_k));
+
+            GGML_ASSERT(size == expected_size);
+            GGML_ASSERT(candidates_p.data[0].id == max_token_id);
+            GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id);
+        } else if (s == 'p') {
+            const int softmax_divisor = n_vocab * (n_vocab-1) / 2 - min_token_id * (min_token_id-1) / 2;
+            const int softmax_numerator_target = ceilf(top_p * softmax_divisor);
+
+                min_token_id  = n_vocab;
+            int expected_size = 0;
+            int cumsum        = 0;
+            do { // do-while because always at least one token is sampled
+                min_token_id--;
+                expected_size++;
+
+                cumsum += min_token_id;
+            } while (cumsum < softmax_numerator_target);
+
+            // token 0 has p == 0, need special consideration for cumsum because top_p immediately returns
+            if (min_token_id == 1) {
+                min_token_id--;
+                expected_size += 1;
+            }
+
+            GGML_ASSERT(size == expected_size);
+            GGML_ASSERT(candidates_p.data[0].id == max_token_id);
+            GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id);
+        } else if (s == 'm') {
+            int expected_size = ceilf((1.0f-min_p) * n_vocab);
+            expected_size = std::max(expected_size, 1);
+            expected_size = std::min(expected_size, size);
+
+            min_token_id = floorf(min_p * n_vocab);
+            min_token_id = std::max(min_token_id, 1);
+            min_token_id = std::max(min_token_id, (llama_token)(n_vocab - size));
+            min_token_id = std::min(min_token_id, (llama_token)(n_vocab - 1));
+
+            GGML_ASSERT(size == expected_size);
+            GGML_ASSERT(candidates_p.data[0].id == max_token_id);
+            GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id);
+        } else {
+            GGML_ASSERT(false);
+        }
+    }
+
+    printf("Sampler queue %3s OK with n_vocab=%05ld top_k=%05d top_p=%f min_p=%f\n",
+           samplers_sequence.c_str(), n_vocab, top_k, top_p, min_p);
+}
+
 int main(void) {
     ggml_time_init();
 
@@ -139,6 +241,15 @@ int main(void) {
     test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 0.8f);
     test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1);
 
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.00f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.24f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.9f, 0.3f/0.9f, 0.2f/0.9f},            0.26f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.9f, 0.3f/0.9f, 0.2f/0.9f},            0.49f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.7f, 0.3f/0.7f},                       0.51f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.7f, 0.3f/0.7f},                       0.74f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f},                                  0.76f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f},                                  1.00f);
+
     test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f);
     test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f);
     test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f);
@@ -154,6 +265,34 @@ int main(void) {
     test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2},       {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 1.0f, 5.0f, 5.0f);
     test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 1.0f, 5.0f, 5.0f);
 
+    test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f);
+    test_sampler_queue(10000, "k",     1, 1.0f, 1.0f);
+    test_sampler_queue(10000, "p", 10000, 1.0f, 1.0f);
+    test_sampler_queue(10000, "p", 10000, 0.0f, 1.0f);
+    test_sampler_queue(10000, "m", 10000, 1.0f, 1.0f);
+    test_sampler_queue(10000, "m", 10000, 1.0f, 1e-12);
+
+    test_sampler_queue(10000, "k",   100, 1.0000f, 1.0f);
+    test_sampler_queue(10000, "p", 10000, 0.0002f, 1.0f);
+    test_sampler_queue(10000, "p", 10000, 0.8000f, 1.0f);
+    test_sampler_queue(10000, "m", 10000, 1.0000f, 9997.9f/9999.0f);
+    test_sampler_queue(10000, "m", 10000, 1.0000f, 0.1f);
+
+    test_sampler_queue(10000, "kp", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "km", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "pk", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "pm", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "mk", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "mp", 100, 0.8f, 9997.9f/9999.0f);
+    test_sampler_queue(10000, "mp", 100, 0.8f, 0.1f);
+
+    test_sampler_queue(10000, "kpm", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "kmp", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "pkm", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "pmk", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "mkp", 100, 0.8f, 0.1f);
+    test_sampler_queue(10000, "mpk", 100, 0.8f, 0.1f);
+
     printf("OK\n");
 
     return 0;