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tests : add unit test coverage for llama_tensor_get_type (#20112)

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* Add unit test coverage for llama_tensor_get_type

* Fix merge conflicts, add more schemas

* clang formatter changes

* Trailing whitespace

* Update name

* Start rebase

* Updating files with upstream changes prior to rebase

* Changes needed from rebase

* Update attn_qkv schema, change throw behaviour

* Fix merge conflicts

* White space

* Update with latest changes to state counters

* Revert accidental personal CLAUDE.md changes

* Change quotation mark

* Reuse metadata.name since we have it

* Move test-only stuff out of llama-quant.cpp

* Hide the regex functionality back in llama-quant.cpp, use a unique pointer to a new struct 'compiled_tensor_type_patterns' which contains the patterns

* cont : inital deslop guidelines

* Cleanup based on review comments

* Continue cleanup

* Small cleanup

* Manually set proper ordering of tensors, mostly applies to gemma

* Formatting

* Update tests/test-quant-type-selection.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Fix merge conflicts

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
Bartowski
2026-04-02 16:53:58 -04:00
committed by GitHub
parent a1cfb64530
commit 7992aa7c8e
20 changed files with 35301 additions and 51 deletions
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tests/test-gguf-model-data.cpp
+33
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@@ -116,6 +116,39 @@ int main() {
// Verify tensor count
TEST_ASSERT(model3.tensors.size() == 780, "expected tensor count == 780");
// Test a hybrid-attention model with array-valued head counts
auto result4 = gguf_fetch_model_meta("ggml-org/Step-3.5-Flash-GGUF", "Q4_K");
if (!result4.has_value()) {
fprintf(stderr, "FAIL: could not fetch Step-3.5-Flash metadata\n");
return 1;
}
const auto & model4 = result4.value();
fprintf(stderr, "Architecture: %s\n", model4.architecture.c_str());
fprintf(stderr, "n_embd: %u\n", model4.n_embd);
fprintf(stderr, "n_ff: %u\n", model4.n_ff);
fprintf(stderr, "n_vocab: %u\n", model4.n_vocab);
fprintf(stderr, "n_layer: %u\n", model4.n_layer);
fprintf(stderr, "n_head: %u\n", model4.n_head);
fprintf(stderr, "n_head_kv: %u\n", model4.n_head_kv);
fprintf(stderr, "n_expert: %u\n", model4.n_expert);
fprintf(stderr, "n_embd_head_k: %u\n", model4.n_embd_head_k);
fprintf(stderr, "n_embd_head_v: %u\n", model4.n_embd_head_v);
fprintf(stderr, "tensors: %zu\n", model4.tensors.size());
TEST_ASSERT(model4.architecture == "step35", "expected architecture 'step35'");
TEST_ASSERT(model4.n_layer == 45, "expected n_layer == 45");
TEST_ASSERT(model4.n_embd == 4096, "expected n_embd == 4096");
TEST_ASSERT(model4.n_ff == 11264, "expected n_ff == 11264");
TEST_ASSERT(model4.n_head == 64, "expected n_head == 64 (first element of per-layer array)");
TEST_ASSERT(model4.n_head_kv == 8, "expected n_head_kv == 8 (first element of per-layer array)");
TEST_ASSERT(model4.n_expert == 288, "expected n_expert == 288");
TEST_ASSERT(model4.n_embd_head_k == 128, "expected n_embd_head_k == 128");
TEST_ASSERT(model4.n_embd_head_v == 128, "expected n_embd_head_v == 128");
TEST_ASSERT(model4.n_vocab == 128896, "expected n_vocab == 128896");
TEST_ASSERT(model4.tensors.size() == 754, "expected tensor count == 754");
fprintf(stderr, "=== ALL TESTS PASSED ===\n");
return 0;
}