import unittest import torch from backbones import GPTBlock, TrajMixer from models import ( TRAJ_MIXER_ARCHITECTURE, validate_traj_mixer_config, validate_traj_mixer_state_dict, ) from train_util import get_model_parameter_counts class TrajMixerTest(unittest.TestCase): def test_default_shape_parameters_and_initialization(self) -> None: mixer = TrajMixer( n_embd=120, n_head=10, dropout=0.0, ) x = torch.randn(2, 7, 120) self.assertEqual(mixer(x).shape, x.shape) self.assertEqual(sum(p.numel() for p in mixer.parameters()), 33_164) expected = torch.eye(12).expand(10, 12, 12) torch.testing.assert_close(mixer.group_align.detach(), expected) self.assertEqual(mixer.intra_hidden, 48) self.assertEqual( tuple(mixer.intra_gate_proj.shape), (10, 12, 48), ) self.assertEqual( tuple(mixer.intra_value_proj.shape), (10, 12, 48), ) self.assertEqual( tuple(mixer.intra_output_proj.shape), (10, 48, 12), ) self.assertEqual(mixer.hidden_group, 40) self.assertEqual(tuple(mixer.gate_proj.shape), (12, 10, 40)) self.assertEqual(tuple(mixer.value_proj.shape), (12, 10, 40)) self.assertEqual(tuple(mixer.output_proj.shape), (12, 40, 10)) self.assertEqual(tuple(mixer.intra_norm.normalized_shape), (12,)) self.assertEqual(tuple(mixer.cross_norm.normalized_shape), (10,)) def test_zero_output_projections_make_both_stages_identity(self) -> None: torch.manual_seed(0) mixer = TrajMixer(120, n_head=10, dropout=0.0) with torch.no_grad(): mixer.intra_output_proj.zero_() mixer.output_proj.zero_() x = torch.randn(2, 5, 120) torch.testing.assert_close(mixer(x), x) def test_intra_stage_is_independent_across_groups(self) -> None: torch.manual_seed(0) mixer = TrajMixer(120, n_head=10, dropout=0.0) mixer.eval() with torch.no_grad(): mixer.output_proj.zero_() grouped = torch.randn(2, 4, 10, 12) changed = grouped.clone() changed[:, :, 3, :] += torch.randn_like(changed[:, :, 3, :]) original_out = mixer(grouped.reshape(2, 4, 120)).reshape( 2, 4, 10, 12 ) changed_out = mixer(changed.reshape(2, 4, 120)).reshape( 2, 4, 10, 12 ) unchanged_groups = torch.tensor([0, 1, 2, 4, 5, 6, 7, 8, 9]) torch.testing.assert_close( original_out.index_select(2, unchanged_groups), changed_out.index_select(2, unchanged_groups), ) def test_cross_stage_mixes_groups_without_mixing_coordinates(self) -> None: mixer = TrajMixer(6, n_head=3, dropout=0.0) mixer.eval() with torch.no_grad(): mixer.intra_output_proj.zero_() mixer.gate_proj.zero_() mixer.value_proj.zero_() mixer.output_proj.zero_() # For coordinate 0 only, read group 0 through hidden unit 0 and # write the resulting gated value into group 1. mixer.gate_proj[0, 0, 0] = 1.0 mixer.value_proj[0, 0, 0] = 1.0 mixer.output_proj[0, 0, 1] = 1.0 grouped = torch.tensor( [[[ [-1.0, 4.0], [0.0, 5.0], [1.0, 6.0], ]]] ) changed = grouped.clone() changed[0, 0, 0, 0] = 2.0 original_out = mixer(grouped.reshape(1, 1, 6)).reshape(1, 1, 3, 2) changed_out = mixer(changed.reshape(1, 1, 6)).reshape(1, 1, 3, 2) self.assertNotEqual( original_out[0, 0, 1, 0].item(), changed_out[0, 0, 1, 0].item(), ) torch.testing.assert_close( original_out[..., 1], changed_out[..., 1], ) def test_mixer_does_not_mix_sequence_positions(self) -> None: torch.manual_seed(0) mixer = TrajMixer(120, n_head=10, dropout=0.0) mixer.eval() x = torch.randn(2, 5, 120) changed = x.clone() changed[:, 3, :] += torch.randn_like(changed[:, 3, :]) original_out = mixer(x) changed_out = mixer(changed) unchanged_positions = torch.tensor([0, 1, 2, 4]) torch.testing.assert_close( original_out.index_select(1, unchanged_positions), changed_out.index_select(1, unchanged_positions), ) def test_gradients_reach_all_projection_families(self) -> None: torch.manual_seed(1) mixer = TrajMixer(120, n_head=10, dropout=0.0) x = torch.randn(2, 4, 120, requires_grad=True) mixer(x).square().mean().backward() self.assertIsNotNone(x.grad) for name, parameter in mixer.named_parameters(): self.assertIsNotNone(parameter.grad, name) self.assertTrue(torch.isfinite(parameter.grad).all(), name) def test_gpt_block_delegates_both_mixer_residuals_to_traj_mixer(self) -> None: block = GPTBlock(n_embd=120, n_head=10) self.assertIsInstance(block.mlp, TrajMixer) self.assertFalse(hasattr(block, "ln2")) self.assertIsInstance(block.mlp.intra_norm, torch.nn.LayerNorm) self.assertIsInstance(block.mlp.cross_norm, torch.nn.LayerNorm) x = torch.randn(2, 6, 120) self.assertEqual(block(x).shape, x.shape) def test_architecture_marker_is_required(self) -> None: validate_traj_mixer_config( {"model_architecture": TRAJ_MIXER_ARCHITECTURE} ) with self.assertRaisesRegex(ValueError, "only accepts models trained"): validate_traj_mixer_config({}) with self.assertRaisesRegex(ValueError, "only accepts models trained"): validate_traj_mixer_config({"model_architecture": "delphi_swiglu"}) with self.assertRaisesRegex(ValueError, "only accepts models trained"): validate_traj_mixer_config( {"model_architecture": "traj_mixer_v2"} ) def test_checkpoint_must_contain_traj_mixer_parameters(self) -> None: block = GPTBlock(n_embd=120, n_head=10) state_dict = { f"blocks.0.{key}": value for key, value in block.state_dict().items() } validate_traj_mixer_state_dict(state_dict) state_dict.pop("blocks.0.mlp.intra_gate_proj") with self.assertRaisesRegex(ValueError, "not a TrajMixer checkpoint"): validate_traj_mixer_state_dict(state_dict) def test_invalid_group_partition_is_rejected(self) -> None: with self.assertRaisesRegex(ValueError, "divisible"): TrajMixer(n_embd=121, n_head=10) def test_parameter_counts_match_traj_mixer_parameters(self) -> None: mixer = TrajMixer(n_embd=120, n_head=10) self.assertEqual( get_model_parameter_counts(mixer), { "model_parameter_count": 33_164, "trainable_parameter_count": 33_164, }, ) if __name__ == "__main__": unittest.main()