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tests : fix overflow and memory leaks in test-model-random

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* tests : fix integer types in test-model-random
This commit is contained in:
Francis Couture-Harpin
2025-06-12 02:23:44 -04:00
parent 9cd402cbe1
commit 7657835b33
2 changed files with 17 additions and 14 deletions
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7
src/llama-model.cpp
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@@ -8847,9 +8847,7 @@ struct llm_build_starcoder2 : public llm_graph_context {
}; };
struct llm_build_mamba : public llm_graph_context { struct llm_build_mamba : public llm_graph_context {
const llama_model & model; llm_build_mamba(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
llm_build_mamba(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params), model(model) {
ggml_tensor * cur; ggml_tensor * cur;
ggml_tensor * inpL; ggml_tensor * inpL;
@@ -8865,7 +8863,7 @@ struct llm_build_mamba : public llm_graph_context {
LLM_NORM_RMS, il); LLM_NORM_RMS, il);
cb(cur, "attn_norm", il); cb(cur, "attn_norm", il);
cur = build_mamba_layer(gf, cur, state_copy, ubatch, il); cur = build_mamba_layer(gf, cur, state_copy, model, ubatch, il);
if (il == n_layer - 1) { if (il == n_layer - 1) {
// skip computing output for unused tokens // skip computing output for unused tokens
@@ -8906,6 +8904,7 @@ struct llm_build_mamba : public llm_graph_context {
ggml_cgraph * gf, ggml_cgraph * gf,
ggml_tensor * cur, ggml_tensor * cur,
ggml_tensor * state_copy, ggml_tensor * state_copy,
const llama_model & model,
const llama_ubatch & ubatch, const llama_ubatch & ubatch,
int il) const { int il) const {
const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate); const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);

24
tests/test-model-random.cpp
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@@ -227,7 +227,7 @@ struct gguf_value {
for (size_t i = 0; i < arr_size; ++i) { for (size_t i = 0; i < arr_size; ++i) {
memcpy(data.data() + type_size * i, &(*value.array)[i].value, type_size); memcpy(data.data() + type_size * i, &(*value.array)[i].value, type_size);
} }
gguf_set_arr_data(ctx, k, arr_type, data.data(), data.size()); gguf_set_arr_data(ctx, k, arr_type, data.data(), data.size() / type_size);
} }
// TODO: handle nested arrays // TODO: handle nested arrays
} }
@@ -317,7 +317,12 @@ struct model_variant {
gguf_add_tensor(ctx_gguf, tensor); gguf_add_tensor(ctx_gguf, tensor);
} }
return gguf_write_to_file(ctx_gguf, fname, false); bool status = gguf_write_to_file(ctx_gguf, fname, false);
ggml_free(ctx);
gguf_free(ctx_gguf);
return status;
} }
static void insert_from_arch(std::vector<model_variant> & variants, llm_arch arch) { static void insert_from_arch(std::vector<model_variant> & variants, llm_arch arch) {
@@ -762,9 +767,8 @@ int main(int argc, char ** argv) {
std::mt19937 rng(42); std::mt19937 rng(42);
// TODO: multiple sequences per token // TODO: multiple sequences per token
const int64_t n_batch = 2048; const int32_t n_batch = 2048;
const int64_t n_seq_len = 1024; const int32_t n_seq_len = 1024;
std::uniform_int_distribution<int64_t> rand_seq_init_len(n_seq_len / 4, 3 * n_seq_len / 4);
llama_batch batch = llama_batch_init(n_batch, 0, 1); llama_batch batch = llama_batch_init(n_batch, 0, 1);
// TODO: batch with embeddings // TODO: batch with embeddings
@@ -794,10 +798,10 @@ int main(int argc, char ** argv) {
// const auto n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model)); // const auto n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
// const auto n_embd = llama_model_n_embd(model); // const auto n_embd = llama_model_n_embd(model);
for (int64_t n_seq_max : { 1, 2, 13 } ) { for (int32_t n_seq_max : { 1, 2, 13 } ) {
// TODO(later): context shift testing // TODO(later): context shift testing
for (int64_t n_ctx : { n_seq_len * n_seq_max }) { for (int32_t n_ctx : { n_seq_len * n_seq_max }) {
std::vector<reference_logits> ref_outputs; std::vector<reference_logits> ref_outputs;
@@ -824,7 +828,7 @@ int main(int argc, char ** argv) {
for (bool shuffle : { false, true }) { for (bool shuffle : { false, true }) {
for (int64_t n_ubatch : { 1, 2, 512 } ) { for (int32_t n_ubatch : { 1, 2, 512 } ) {
std::vector<bool> valid(n_seq_max, true); std::vector<bool> valid(n_seq_max, true);
@@ -852,7 +856,7 @@ int main(int argc, char ** argv) {
if (batch.n_tokens < n_batch) { if (batch.n_tokens < n_batch) {
const int64_t seq_len = const int64_t seq_len =
std::min(n_batch - batch.n_tokens, std::min(n_batch - batch.n_tokens,
(int64_t) ref_outputs[seq_id].prompt_len - seq_id_n_past[seq_id]); ref_outputs[seq_id].prompt_len - seq_id_n_past[seq_id]);
ref_outputs[seq_id].add_to_batch(batch, seq_id_n_past[seq_id], seq_len, seq_id); ref_outputs[seq_id].add_to_batch(batch, seq_id_n_past[seq_id], seq_len, seq_id);
seq_ids_in_batch.insert(seq_id); seq_ids_in_batch.insert(seq_id);
@@ -891,7 +895,7 @@ int main(int argc, char ** argv) {
} }
fprintf(stdout, fprintf(stdout,
"Comparing output for '%s', with shuffle=%i, n_seq_max=%li, n_ctx=%li, n_ubatch=%li: ", "Comparing output for '%s', with shuffle=%i, n_seq_max=%i, n_ctx=%i, n_ubatch=%i: ",
variant.name.c_str(), shuffle, n_seq_max, n_ctx, n_ubatch); variant.name.c_str(), shuffle, n_seq_max, n_ctx, n_ubatch);
if (std::all_of(valid.begin(), valid.end(), [](bool v) { return v; })) { if (std::all_of(valid.begin(), valid.end(), [](bool v) { return v; })) {
fprintf(stdout, "\033[1;32mOK\033[0m\n"); fprintf(stdout, "\033[1;32mOK\033[0m\n");