CS348Project/llama.cpp
5
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2025-11-03 09:22:01 +00:00
Code Issues Packages Projects Releases Wiki Activity

tests : add LLAMA, LLAMA4, and GEMMA2 to test-model-random

Browse Source
This commit is contained in:
Francis Couture-Harpin
2025-06-13 20:02:29 -04:00
parent 61f6429470
commit dfa3c18266
2 changed files with 177 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
tests/CMakeLists.txt
View File

@@ -145,6 +145,7 @@ if (NOT WIN32 OR NOT BUILD_SHARED_LIBS)
llama_build_and_test(test-grammar-integration.cpp)
llama_build_and_test(test-llama-grammar.cpp)
llama_build_and_test(test-chat.cpp)
llama_build_and_test(test-model-random.cpp)
# TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8
if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
llama_build_and_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})
@@ -193,7 +194,6 @@ endif()
# llama_build_and_test(test-opt.cpp) # SLOW
llama_build_and_test(test-gguf.cpp)
llama_build_and_test(test-backend-ops.cpp)
llama_build_and_test(test-model-random.cpp)
llama_build_and_test(test-model-load-cancel.cpp LABEL "model")
llama_build_and_test(test-autorelease.cpp LABEL "model")

186
tests/test-model-random.cpp
View File

@@ -257,6 +257,7 @@ struct model_variant {
model_variant(llm_arch arch, const std::string & name) : arch(arch), name(name) {
add_kv(LLM_KV_GENERAL_TYPE, "model");
add_kv(LLM_KV_GENERAL_ARCHITECTURE, llm_arch_name(arch));
add_kv(LLM_KV_GENERAL_NAME, name);
}
model_variant(const model_variant & other) :
@@ -359,7 +360,118 @@ struct model_variant {
// TODO: how to make the variants more modular?
switch (arch) {
case LLM_ARCH_LLAMA:
case LLM_ARCH_LLAMA4:
{
variants.push_back(model_variant(arch, "Llama2"));
model_variant & cur = variants.back();
n_embd = 16;
const uint32_t n_head = 4;
const uint32_t n_head_kv = n_head / 2;
const uint32_t n_embd_head_k = n_embd / n_head;
const uint32_t n_embd_k_gqa = n_embd_head_k * n_head_kv;
const uint32_t n_embd_v_gqa = n_embd_k_gqa;
cur.add_kv(LLM_KV_BLOCK_COUNT, n_layer);
cur.add_kv(LLM_KV_CONTEXT_LENGTH, (uint32_t) 4096);
cur.add_kv(LLM_KV_EMBEDDING_LENGTH, n_embd);
cur.add_kv(LLM_KV_FEED_FORWARD_LENGTH, n_ff);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, n_head);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head_kv);
cur.add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
cur.add_kv(LLM_KV_ROPE_DIMENSION_COUNT, n_embd / n_head);
add_tokenizer(cur, n_vocab);
cur.add_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
cur.add_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
// omitting the actual output tensor to leave it use tok_embd
for (uint32_t i = 0; i < n_layer; ++i) {
cur.add_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
cur.add_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
cur.add_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
cur.add_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
cur.add_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_LLAMA4: // has chunked interleaved sliding-window
{
variants.push_back(model_variant(arch, "Llama4"));
model_variant & cur = variants.back();
n_layer = 4; // for the swa pattern
n_embd = 16;
const uint32_t n_head = 4;
const uint32_t n_head_kv = n_head / 2;
const uint32_t n_embd_head_k = n_embd / n_head;
const uint32_t n_embd_k_gqa = n_embd_head_k * n_head_kv;
const uint32_t n_embd_v_gqa = n_embd_k_gqa;
const uint32_t n_moe_layer_step = 2;
const uint32_t n_ff_exp = n_embd * 2;
const uint32_t n_expert = 4;
cur.add_kv(LLM_KV_BLOCK_COUNT, n_layer);
cur.add_kv(LLM_KV_CONTEXT_LENGTH, (uint32_t) 4096);
cur.add_kv(LLM_KV_EMBEDDING_LENGTH, n_embd);
cur.add_kv(LLM_KV_FEED_FORWARD_LENGTH, n_ff);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, n_head);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head_kv);
cur.add_kv(LLM_KV_EXPERT_COUNT, n_expert);
cur.add_kv(LLM_KV_EXPERT_USED_COUNT, (uint32_t) 2);
cur.add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
cur.add_kv(LLM_KV_ROPE_DIMENSION_COUNT, n_embd / n_head);
cur.add_kv(LLM_KV_INTERLEAVE_MOE_LAYER_STEP, n_moe_layer_step);
cur.add_kv(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, n_ff_exp);
// FIXME: this isn't used because the default is 8192
cur.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW, (uint32_t) 389); // prime number
add_tokenizer(cur, n_vocab);
cur.add_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
cur.add_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
// omitting the actual output tensor to leave it use tok_embd
for (uint32_t i = 0; i < n_layer; ++i) {
bool is_moe_layer = (i + 1) % n_moe_layer_step == 0;
cur.add_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
cur.add_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
cur.add_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
cur.add_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
if (is_moe_layer) {
cur.add_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
cur.add_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert});
cur.add_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert});
cur.add_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert});
// Shared expert
const int64_t n_ff_shexp = n_ff_exp;
cur.add_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp});
cur.add_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd });
cur.add_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp});
} else {
cur.add_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
cur.add_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
}
} break;
case LLM_ARCH_DECI:
case LLM_ARCH_FALCON:
case LLM_ARCH_BAICHUAN:
@@ -392,7 +504,54 @@ struct model_variant {
case LLM_ARCH_MINICPM:
case LLM_ARCH_MINICPM3:
case LLM_ARCH_GEMMA:
case LLM_ARCH_GEMMA2:
break;
case LLM_ARCH_GEMMA2: // has standard interleaved sliding-window
{
variants.push_back(model_variant(arch, "Gemma2"));
model_variant & cur = variants.back();
n_layer = 2; // minimum for the swa pattern
n_embd = 16;
const uint32_t n_head = 4;
const uint32_t n_head_kv = n_head / 2;
const uint32_t n_embd_head_k = n_embd / n_head;
const uint32_t n_embd_k_gqa = n_embd_head_k * n_head_kv;
const uint32_t n_embd_v_gqa = n_embd_k_gqa;
cur.add_kv(LLM_KV_CONTEXT_LENGTH, (uint32_t) 4096);
cur.add_kv(LLM_KV_EMBEDDING_LENGTH, n_embd);
cur.add_kv(LLM_KV_BLOCK_COUNT, n_layer);
cur.add_kv(LLM_KV_FEED_FORWARD_LENGTH, n_ff);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, n_head);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head_kv);
cur.add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
cur.add_kv(LLM_KV_ATTN_LOGIT_SOFTCAPPING, 50.0f);
cur.add_kv(LLM_KV_FINAL_LOGIT_SOFTCAPPING, 30.0f);
cur.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW, (uint32_t) 389); // prime number
add_tokenizer(cur, n_vocab);
cur.add_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
cur.add_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
for (uint32_t i = 0; i < n_layer; ++i) {
cur.add_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
cur.add_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
cur.add_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
cur.add_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
cur.add_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
cur.add_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
cur.add_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd});
}
} break;
case LLM_ARCH_GEMMA3:
case LLM_ARCH_STARCODER2:
break;
@@ -679,7 +838,7 @@ struct reference_logits {
// fprintf(stderr, "Potential error in seq_id %i starting from pos %i\n", seq_id, first_pos_error);
}
const float denom = std::sqrt(sumr2 * sumo2);
const float denom = std::sqrt(sumr2) * std::sqrt(sumo2);
return sumerr2 / (denom > 0.0f ? denom : 1.0f);
}
@@ -767,8 +926,8 @@ int main(int argc, char ** argv) {
std::mt19937 rng(42);
// TODO: multiple sequences per token
const int32_t n_batch = 2048;
const int32_t n_seq_len = 1024;
const int32_t n_batch = 3 * 512;
const int32_t n_seq_len = 643; // prime number
llama_batch batch = llama_batch_init(n_batch, 0, 1);
// TODO: batch with embeddings
@@ -798,7 +957,7 @@ int main(int argc, char ** argv) {
// const auto n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
// const auto n_embd = llama_model_n_embd(model);
for (int32_t n_seq_max : { 1, 2, 13 } ) {
for (int32_t n_seq_max : { 1, 2, 5 } ) {
// TODO(later): context shift testing
for (int32_t n_ctx : { n_seq_len * n_seq_max }) {
@@ -811,8 +970,6 @@ int main(int argc, char ** argv) {
ref_params.n_ubatch = 1;
ref_params.n_ctx = n_seq_len;
ref_params.n_seq_max = 1;
ref_params.n_threads = 1;
ref_params.n_threads_batch = 1;
llama_context * ref_ctx = llama_init_from_model(model, ref_params);
@@ -828,6 +985,13 @@ int main(int argc, char ** argv) {
for (bool shuffle : { false, true }) {
// skip shuffling the batch for non-recurrent models
// (simple splits don't handle shuffled batches correctly)
// FIXME: remove this
if (shuffle && !llama_model_is_recurrent(model)) {
continue;
}
for (int32_t n_ubatch : { 1, 2, 512 } ) {
std::vector<bool> valid(n_seq_max, true);
@@ -881,8 +1045,6 @@ int main(int argc, char ** argv) {
GGML_ASSERT(n_seq_max <= n_batch); // not handling splitting this across batches here
// TODO: use seq_rm, seq_cp, etc. to test if they work properly
// cont batching
for (llama_seq_id s : seq_ids_in_batch) {
llama_pos & pos = seq_id_n_past[s];
@@ -909,6 +1071,10 @@ int main(int argc, char ** argv) {
exit(1);
}
// TODO: use seq_rm, seq_cp, etc. to test if they work properly
// TODO: test pooled embeddings
llama_free(ctx);
}
}