| """ |
| tests/test_model.py |
| |
| Automated tests for BrainConfig, RMSNorm, embeddings, and the assembled |
| BrainForCausalLM: shape, forward, backward, parameter count, weight tying, |
| and checkpoint save/load. Fixes audit Problem #4 (zero automated tests) at |
| the model level. |
| """ |
|
|
| import math |
|
|
| import pytest |
| import torch |
| import torch.nn as nn |
|
|
| from brain.core.config import BrainConfig |
| from brain.model.brain_model import BrainForCausalLM |
| from brain.model.embeddings import TokenEmbedding |
| from brain.model.transformer.normalization import RMSNorm, _ManualRMSNorm |
|
|
|
|
| @pytest.fixture |
| def tiny_config(): |
| return BrainConfig( |
| vocab_size=256, hidden_size=32, num_layers=2, num_heads=4, |
| num_kv_heads=2, intermediate_size=64, max_position_embeddings=64, |
| ) |
|
|
|
|
| |
| |
| |
| def test_config_rejects_indivisible_hidden_size(): |
| with pytest.raises(ValueError): |
| BrainConfig(hidden_size=385, num_heads=6) |
|
|
|
|
| def test_config_rejects_odd_head_dim(): |
| |
| with pytest.raises(ValueError): |
| BrainConfig(hidden_size=48, num_heads=16, num_kv_heads=16) |
|
|
|
|
| def test_config_rejects_kv_heads_not_dividing_heads(): |
| with pytest.raises(ValueError): |
| BrainConfig(hidden_size=384, num_heads=6, num_kv_heads=4) |
|
|
|
|
| def test_config_yaml_roundtrip(tmp_path): |
| cfg = BrainConfig(hidden_size=64, num_heads=4, num_kv_heads=2) |
| path = tmp_path / "cfg.yaml" |
| cfg.to_yaml(path) |
| reloaded = BrainConfig.from_yaml(path) |
| assert reloaded == cfg |
|
|
|
|
| def test_real_brain_0_2_config_param_count(): |
| cfg = BrainConfig.from_yaml("configs/model/brain_0_2_30m.yaml") |
| model = BrainForCausalLM(cfg) |
| total = model.count_parameters()["total"] |
| |
| |
| assert total == 26_944_896 |
|
|
|
|
| |
| |
| |
| def test_rmsnorm_native_matches_manual_fallback(): |
| torch.manual_seed(0) |
| x = torch.randn(2, 5, 16) |
| native = nn.RMSNorm(16, eps=1e-5) |
| manual = _ManualRMSNorm(16, eps=1e-5) |
| manual.weight.data.copy_(native.weight.data) |
| assert torch.allclose(native(x), manual(x), atol=1e-6) |
|
|
|
|
| def test_rmsnorm_output_rms_is_one_with_unit_weight(): |
| norm = RMSNorm(16) |
| with torch.no_grad(): |
| norm.weight.fill_(1.0) |
| x = torch.randn(4, 16) * 5 |
| out = norm(x) |
| rms = out.pow(2).mean(dim=-1).sqrt() |
| assert torch.allclose(rms, torch.ones_like(rms), atol=1e-4) |
|
|
|
|
| |
| |
| |
| def test_embedding_padding_idx_stays_zero(): |
| emb = TokenEmbedding(vocab_size=50, hidden_size=8, pad_id=0) |
| assert torch.all(emb.embedding.weight[0] == 0) |
|
|
|
|
| |
| |
| |
| def test_model_forward_shape(tiny_config): |
| model = BrainForCausalLM(tiny_config) |
| input_ids = torch.randint(0, tiny_config.vocab_size, (2, 10)) |
| out = model(input_ids) |
| assert out["logits"].shape == (2, 10, tiny_config.vocab_size) |
|
|
|
|
| def test_model_backward_produces_gradients(tiny_config): |
| model = BrainForCausalLM(tiny_config) |
| input_ids = torch.randint(0, tiny_config.vocab_size, (2, 10)) |
| out = model(input_ids, labels=input_ids.clone()) |
| out["loss"].backward() |
| assert all(p.grad is not None for p in model.parameters() if p.requires_grad) |
|
|
|
|
| def test_weight_tying_shares_storage(tiny_config): |
| model = BrainForCausalLM(tiny_config) |
| assert model.lm_head.weight.data_ptr() == model.token_embedding.embedding.weight.data_ptr() |
|
|
|
|
| def test_untied_embeddings_do_not_share_storage(): |
| cfg = BrainConfig(vocab_size=256, hidden_size=32, num_layers=1, num_heads=4, |
| num_kv_heads=2, intermediate_size=64, tie_word_embeddings=False) |
| model = BrainForCausalLM(cfg) |
| assert model.lm_head.weight.data_ptr() != model.token_embedding.embedding.weight.data_ptr() |
|
|
|
|
| def test_checkpoint_save_load_identical_output(tiny_config, tmp_path): |
| torch.manual_seed(0) |
| model = BrainForCausalLM(tiny_config) |
| model.eval() |
| input_ids = torch.randint(0, tiny_config.vocab_size, (1, 6)) |
|
|
| ckpt_path = tmp_path / "model.pt" |
| model.save_checkpoint(ckpt_path) |
| reloaded = BrainForCausalLM.load_checkpoint(ckpt_path) |
| reloaded.eval() |
|
|
| with torch.no_grad(): |
| out1 = model(input_ids)["logits"] |
| out2 = reloaded(input_ids)["logits"] |
| assert torch.allclose(out1, out2, atol=1e-6) |
| assert reloaded.config == tiny_config |
|
|
|
|
| def test_residual_write_projections_have_scaled_down_init(tiny_config): |
| model = BrainForCausalLM(tiny_config) |
| o_proj_std = model.blocks[0].attention.o_proj.weight.std().item() |
| q_proj_std = model.blocks[0].attention.q_proj.weight.std().item() |
| assert o_proj_std < q_proj_std |
|
|
|
|
| def test_real_brain_0_3_config_param_count(): |
| cfg = BrainConfig.from_yaml("configs/model/brain_0_3_35m.yaml") |
| model = BrainForCausalLM(cfg) |
| total = model.count_parameters()["total"] |
| assert total == 33_236_352, f"unexpected v0.3 param count: {total}" |
|
|
