Felipe97/llama-cpp-compiled

1#include "gguf-model-data.h"2 3#include <cstdio>4 5#define TEST_ASSERT(cond, msg) \6    do { \7        if (!(cond)) { \8            fprintf(stderr, "FAIL: %s (line %d): %s\n", #cond, __LINE__, msg); \9            return 1; \10        } \11    } while (0)12 13int main() {14    fprintf(stderr, "=== test-gguf-model-data ===\n");15 16    // Fetch Qwen3-0.6B Q8_0 metadata17    auto result = gguf_fetch_model_meta("ggml-org/Qwen3-0.6B-GGUF", "Q8_0");18 19    if (!result.has_value()) {20        fprintf(stderr, "SKIP: could not fetch model metadata (no network or HTTP disabled)\n");21        return 0;22    }23 24    const auto & model = result.value();25 26    fprintf(stderr, "Architecture:  %s\n", model.architecture.c_str());27    fprintf(stderr, "n_embd:        %u\n", model.n_embd);28    fprintf(stderr, "n_ff:          %u\n", model.n_ff);29    fprintf(stderr, "n_vocab:       %u\n", model.n_vocab);30    fprintf(stderr, "n_layer:       %u\n", model.n_layer);31    fprintf(stderr, "n_head:        %u\n", model.n_head);32    fprintf(stderr, "n_head_kv:     %u\n", model.n_head_kv);33    fprintf(stderr, "n_expert:      %u\n", model.n_expert);34    fprintf(stderr, "n_embd_head_k: %u\n", model.n_embd_head_k);35    fprintf(stderr, "n_embd_head_v: %u\n", model.n_embd_head_v);36    fprintf(stderr, "tensors:       %zu\n", model.tensors.size());37 38    // Verify architecture39    TEST_ASSERT(model.architecture == "qwen3", "expected architecture 'qwen3'");40 41    // Verify key dimensions (Qwen3-0.6B)42    TEST_ASSERT(model.n_layer == 28, "expected n_layer == 28");43    TEST_ASSERT(model.n_embd == 1024, "expected n_embd == 1024");44    TEST_ASSERT(model.n_head == 16, "expected n_head == 16");45    TEST_ASSERT(model.n_head_kv == 8, "expected n_head_kv == 8");46    TEST_ASSERT(model.n_expert == 0, "expected n_expert == 0 (not MoE)");47    TEST_ASSERT(model.n_vocab == 151936, "expected n_vocab == 151936");48 49    // Verify tensor count50    TEST_ASSERT(model.tensors.size() == 311, "expected tensor count == 311");51 52    // Verify known tensor names exist53    bool found_attn_q = false;54    bool found_token_embd = false;55    bool found_output_norm = false;56    for (const auto & t : model.tensors) {57        if (t.name == "blk.0.attn_q.weight") {58            found_attn_q = true;59        }60        if (t.name == "token_embd.weight") {61            found_token_embd = true;62        }63        if (t.name == "output_norm.weight") {64            found_output_norm = true;65        }66    }67    TEST_ASSERT(found_attn_q, "expected tensor 'blk.0.attn_q.weight'");68    TEST_ASSERT(found_token_embd, "expected tensor 'token_embd.weight'");69    TEST_ASSERT(found_output_norm, "expected tensor 'output_norm.weight'");70 71    // Verify token_embd.weight shape72    for (const auto & t : model.tensors) {73        if (t.name == "token_embd.weight") {74            TEST_ASSERT(t.ne[0] == 1024, "expected token_embd.weight ne[0] == 1024");75            TEST_ASSERT(t.n_dims == 2, "expected token_embd.weight to be 2D");76            break;77        }78    }79 80    // Test that second call uses cache (just call again, it should work)81    auto result2 = gguf_fetch_model_meta("ggml-org/Qwen3-0.6B-GGUF", "Q8_0");82    TEST_ASSERT(result2.has_value(), "cached fetch should succeed");83    TEST_ASSERT(result2->tensors.size() == model.tensors.size(), "cached result should match");84 85    // Test a split MoE model without specifying quant (should default to Q8_0)86    auto result3 = gguf_fetch_model_meta("ggml-org/GLM-4.6V-GGUF");87    if (!result3.has_value()) {88        fprintf(stderr, "SKIP: could not fetch GLM-4.6V metadata (no network?)\n");89        return 0;90    }91    const auto & model3 = result3.value();92 93    fprintf(stderr, "Architecture:  %s\n", model3.architecture.c_str());94    fprintf(stderr, "n_embd:        %u\n", model3.n_embd);95    fprintf(stderr, "n_ff:          %u\n", model3.n_ff);96    fprintf(stderr, "n_vocab:       %u\n", model3.n_vocab);97    fprintf(stderr, "n_layer:       %u\n", model3.n_layer);98    fprintf(stderr, "n_head:        %u\n", model3.n_head);99    fprintf(stderr, "n_head_kv:     %u\n", model3.n_head_kv);100    fprintf(stderr, "n_expert:      %u\n", model3.n_expert);101    fprintf(stderr, "n_embd_head_k: %u\n", model3.n_embd_head_k);102    fprintf(stderr, "n_embd_head_v: %u\n", model3.n_embd_head_v);103    fprintf(stderr, "tensors:       %zu\n", model3.tensors.size());104 105    // Verify architecture106    TEST_ASSERT(model3.architecture == "glm4moe", "expected architecture 'glm4moe'");107 108    // Verify key dimensions (GLM-4.6V)109    TEST_ASSERT(model3.n_layer == 46, "expected n_layer == 46");110    TEST_ASSERT(model3.n_embd == 4096, "expected n_embd == 4096");111    TEST_ASSERT(model3.n_head == 96, "expected n_head == 96");112    TEST_ASSERT(model3.n_head_kv == 8, "expected n_head_kv == 8");113    TEST_ASSERT(model3.n_expert == 128, "expected n_expert == 128 (MoE)");114    TEST_ASSERT(model3.n_vocab == 151552, "expected n_vocab == 151552");115 116    // Verify tensor count117    TEST_ASSERT(model3.tensors.size() == 780, "expected tensor count == 780");118 119    // Test a hybrid-attention model with array-valued head counts120    auto result4 = gguf_fetch_model_meta("ggml-org/Step-3.5-Flash-GGUF", "Q4_K");121    if (!result4.has_value()) {122        fprintf(stderr, "FAIL: could not fetch Step-3.5-Flash metadata\n");123        return 1;124    }125    const auto & model4 = result4.value();126 127    fprintf(stderr, "Architecture:  %s\n", model4.architecture.c_str());128    fprintf(stderr, "n_embd:        %u\n", model4.n_embd);129    fprintf(stderr, "n_ff:          %u\n", model4.n_ff);130    fprintf(stderr, "n_vocab:       %u\n", model4.n_vocab);131    fprintf(stderr, "n_layer:       %u\n", model4.n_layer);132    fprintf(stderr, "n_head:        %u\n", model4.n_head);133    fprintf(stderr, "n_head_kv:     %u\n", model4.n_head_kv);134    fprintf(stderr, "n_expert:      %u\n", model4.n_expert);135    fprintf(stderr, "n_embd_head_k: %u\n", model4.n_embd_head_k);136    fprintf(stderr, "n_embd_head_v: %u\n", model4.n_embd_head_v);137    fprintf(stderr, "tensors:       %zu\n", model4.tensors.size());138 139    TEST_ASSERT(model4.architecture == "step35", "expected architecture 'step35'");140 141    TEST_ASSERT(model4.n_layer == 45, "expected n_layer == 45");142    TEST_ASSERT(model4.n_embd == 4096, "expected n_embd == 4096");143    TEST_ASSERT(model4.n_ff == 11264, "expected n_ff == 11264");144    TEST_ASSERT(model4.n_head == 64, "expected n_head == 64 (first element of per-layer array)");145    TEST_ASSERT(model4.n_head_kv == 8, "expected n_head_kv == 8 (first element of per-layer array)");146    TEST_ASSERT(model4.n_expert == 288, "expected n_expert == 288");147    TEST_ASSERT(model4.n_embd_head_k == 128, "expected n_embd_head_k == 128");148    TEST_ASSERT(model4.n_embd_head_v == 128, "expected n_embd_head_v == 128");149    TEST_ASSERT(model4.n_vocab == 128896, "expected n_vocab == 128896");150    TEST_ASSERT(model4.tensors.size() == 754, "expected tensor count == 754");151 152    fprintf(stderr, "=== ALL TESTS PASSED ===\n");153    return 0;154}155