feat: add lewm-conditioned imf training and sigreg loss

tests/test_imf_vla_agent.py

@@ -376,6 +376,29 @@ class _ForbiddenScheduler:
         raise AssertionError('IMF inference should not use DDIM scheduler step')
 
 
+class _StubFutureTokenPredictor(nn.Module):
+    def __init__(self, num_future_tokens=1):
+        super().__init__()
+        self.num_future_tokens = int(num_future_tokens)
+        self.calls = []
+
+    def forward(self, history_tokens):
+        self.calls.append(history_tokens.detach().clone())
+        summary = history_tokens.mean(dim=1, keepdim=True)
+        return summary.repeat(1, self.num_future_tokens, 1)
+
+
+class _RecordingSigReg(nn.Module):
+    def __init__(self, value=0.5):
+        super().__init__()
+        self.value = float(value)
+        self.calls = []
+
+    def forward(self, embeddings):
+        self.calls.append(embeddings.detach().clone())
+        return embeddings.new_tensor(self.value)
+
+
 def _make_images(batch_size, obs_horizon, per_camera_fill):
     return {
         name: torch.full((batch_size, obs_horizon, 1, 2, 2), fill_value=value, dtype=torch.float32)
@@ -501,6 +524,169 @@ class IMFVLAAgentTest(unittest.TestCase):
         self.assertTrue(torch.allclose(head.calls[0]['t'], torch.ones(2)))
         self.assertTrue(torch.allclose(head.calls[0]['cond'], expected_cond))
 
+    def test_predict_action_appends_lewm_future_tokens_to_history_conditioning(self):
+        agent_cls, agent_module = _load_imf_agent_class()
+        head = _RecordingLinearIMFHead()
+        future_predictor = _StubFutureTokenPredictor(num_future_tokens=1)
+        agent = agent_cls(
+            vision_backbone=_StubVisionBackbone(),
+            state_encoder=nn.Identity(),
+            action_encoder=nn.Identity(),
+            head=head,
+            action_dim=2,
+            obs_dim=1,
+            pred_horizon=3,
+            obs_horizon=2,
+            diffusion_steps=10,
+            inference_steps=1,
+            num_cams=len(_CAMERA_NAMES),
+            camera_names=_CAMERA_NAMES,
+            num_action_steps=2,
+            head_type='transformer',
+            extra_condition_tokens=1,
+            lewm_history_horizon=3,
+            lewm_query_offsets=[8],
+            lewm_predictor=future_predictor,
+            lewm_pred_projector=nn.Identity(),
+            lewm_loss_weight=0.5,
+        )
+        agent.infer_scheduler = _ForbiddenScheduler()
+
+        images = _make_images(
+            batch_size=1,
+            obs_horizon=2,
+            per_camera_fill={'r_vis': 10.0, 'top': 20.0, 'front': 30.0},
+        )
+        qpos = torch.tensor([[[1.0], [2.0]]], dtype=torch.float32)
+        lewm_images = _make_images(
+            batch_size=1,
+            obs_horizon=3,
+            per_camera_fill={'r_vis': 10.0, 'top': 20.0, 'front': 30.0},
+        )
+        lewm_qpos = torch.tensor([[[0.5], [1.5], [2.5]]], dtype=torch.float32)
+        initial_noise = torch.tensor(
+            [[[1.0, -1.0], [0.0, 2.0], [3.0, -2.0]]],
+            dtype=torch.float32,
+        )
+
+        with mock.patch.object(agent_module.torch, 'randn', return_value=initial_noise):
+            _ = agent.predict_action(
+                images,
+                qpos,
+                lewm_images=lewm_images,
+                lewm_proprioception=lewm_qpos,
+            )
+
+        expected_history = torch.tensor(
+            [[[10.0, 20.0, 30.0, 1.0], [10.0, 20.0, 30.0, 2.0]]],
+            dtype=torch.float32,
+        )
+        expected_future = torch.tensor([[[10.0, 20.0, 30.0, 1.5]]], dtype=torch.float32)
+        expected_cond = torch.cat([expected_history, expected_future], dim=1)
+
+        self.assertEqual(agent.condition_sequence_length, 3)
+        self.assertEqual(agent.per_step_cond_dim, 4)
+        self.assertEqual(len(head.calls), 1)
+        self.assertTrue(torch.allclose(head.calls[0]['cond'], expected_cond))
+        self.assertEqual(len(future_predictor.calls), 1)
+
+    def test_compute_loss_tracks_action_and_lewm_loss_breakdown(self):
+        agent_cls, agent_module = _load_imf_agent_class()
+        head = _RecordingLinearIMFHead()
+        future_predictor = _StubFutureTokenPredictor(num_future_tokens=1)
+        sigreg = _RecordingSigReg(value=0.75)
+        agent = agent_cls(
+            vision_backbone=_StubVisionBackbone(),
+            state_encoder=nn.Identity(),
+            action_encoder=nn.Identity(),
+            head=head,
+            action_dim=2,
+            obs_dim=1,
+            pred_horizon=3,
+            obs_horizon=2,
+            diffusion_steps=10,
+            inference_steps=1,
+            num_cams=len(_CAMERA_NAMES),
+            camera_names=_CAMERA_NAMES,
+            num_action_steps=2,
+            head_type='transformer',
+            extra_condition_tokens=1,
+            lewm_history_horizon=3,
+            lewm_query_offsets=[8],
+            lewm_predictor=future_predictor,
+            lewm_pred_projector=nn.Identity(),
+            lewm_sigreg=sigreg,
+            lewm_sigreg_weight=0.09,
+            lewm_loss_weight=0.25,
+        )
+
+        images = _make_images(
+            batch_size=1,
+            obs_horizon=2,
+            per_camera_fill={'r_vis': 1.0, 'top': 2.0, 'front': 3.0},
+        )
+        qpos = torch.tensor([[[0.25], [0.75]]], dtype=torch.float32)
+        actions = torch.tensor(
+            [[[1.0, -1.0], [0.5, 0.25], [-0.5, 1.5]]],
+            dtype=torch.float32,
+        )
+        lewm_images = _make_images(
+            batch_size=1,
+            obs_horizon=3,
+            per_camera_fill={'r_vis': 1.0, 'top': 2.0, 'front': 3.0},
+        )
+        lewm_qpos = torch.tensor([[[0.1], [0.2], [0.3]]], dtype=torch.float32)
+        lewm_future_images = _make_images(
+            batch_size=1,
+            obs_horizon=1,
+            per_camera_fill={'r_vis': 1.0, 'top': 2.0, 'front': 3.0},
+        )
+        lewm_future_qpos = torch.tensor([[[0.4]]], dtype=torch.float32)
+        noise = torch.tensor(
+            [[[0.2, -0.4], [0.1, 0.3], [0.5, -0.2]]],
+            dtype=torch.float32,
+        )
+        t_sample = torch.tensor([0.8], dtype=torch.float32)
+        r_sample = torch.tensor([0.25], dtype=torch.float32)
+
+        with mock.patch.object(agent_module.torch, 'randn_like', return_value=noise), \
+             mock.patch.object(agent_module.torch, 'rand', side_effect=[t_sample, r_sample]):
+            loss = agent.compute_loss(
+                {
+                    'images': images,
+                    'qpos': qpos,
+                    'action': actions,
+                    'lewm_images': lewm_images,
+                    'lewm_qpos': lewm_qpos,
+                    'lewm_future_images': lewm_future_images,
+                    'lewm_future_qpos': lewm_future_qpos,
+                }
+            )
+
+        metrics = agent.get_last_loss_breakdown()
+        self.assertAlmostEqual(loss.item(), metrics['loss'], places=6)
+        self.assertIn('action_loss', metrics)
+        self.assertIn('lewm_pred_loss', metrics)
+        self.assertIn('lewm_sigreg_loss', metrics)
+        self.assertIn('lewm_loss', metrics)
+        self.assertAlmostEqual(metrics['lewm_sigreg_loss'], 0.75, places=6)
+        self.assertAlmostEqual(
+            metrics['lewm_loss'],
+            metrics['lewm_pred_loss'] + 0.09 * metrics['lewm_sigreg_loss'],
+            places=5,
+        )
+        self.assertAlmostEqual(
+            metrics['loss'],
+            metrics['action_loss'] + 0.25 * metrics['lewm_loss'],
+            places=5,
+        )
+        self.assertEqual(len(sigreg.calls), 1)
+        expected_lewm_history = torch.tensor(
+            [[[1.0, 2.0, 3.0, 0.1], [1.0, 2.0, 3.0, 0.2], [1.0, 2.0, 3.0, 0.3]]],
+            dtype=torch.float32,
+        )
+        torch.testing.assert_close(sigreg.calls[0], expected_lewm_history.transpose(0, 1))
+
     def test_select_action_only_regenerates_when_action_queue_is_empty(self):
         agent, _head, _agent_module = self._make_agent(pred_horizon=4, obs_horizon=2, num_action_steps=2)
         observation = {
@@ -851,6 +1037,46 @@ class IMFVLAAgentTest(unittest.TestCase):
         self.assertEqual(agent.vision_encoder.output_dim, 96)
         self.assertEqual(agent.vision_encoder.eval_image_resize_shape, (256, 256))
 
+    def test_hydra_config_instantiates_lewm_resnet_query_imf_attnres_with_future_tokens(self):
+        cfg = _compose_cfg(
+            overrides=[
+                'agent=lewm_resnet_query_imf_attnres',
+                'agent.head.n_layer=1',
+                'agent.head.n_emb=16',
+                'agent.lewm_query_offsets=[8]',
+            ]
+        )
+
+        self.assertEqual(cfg.agent._target_, 'roboimi.vla.agent_imf.IMFVLAAgent')
+        self.assertEqual(
+            cfg.agent.vision_backbone._target_,
+            'roboimi.vla.models.backbones.lewm_resnet_query_fusion.LeWMMultiViewResNetBackbone',
+        )
+        self.assertEqual(
+            cfg.agent.state_encoder._target_,
+            'roboimi.vla.modules.encoders.LeWMStateEncoder',
+        )
+        self.assertEqual(cfg.agent.head.cond_dim, 288)
+        self.assertEqual(cfg.agent.cond_projector.output_dim, 288)
+        self.assertEqual(cfg.agent.extra_condition_tokens, 1)
+        self.assertEqual(
+            cfg.agent.lewm_sigreg._target_,
+            'roboimi.vla.models.backbones.lewm_resnet_query_fusion.SIGReg',
+        )
+        self.assertAlmostEqual(cfg.agent.lewm_sigreg_weight, 0.09)
+
+        with _stub_optional_modules(include_imf_head=True):
+            agent = instantiate(cfg.agent)
+
+        self.assertEqual(agent.per_step_cond_dim, 288)
+        self.assertEqual(agent.condition_sequence_length, agent.obs_horizon + 1)
+        self.assertEqual(agent.noise_pred_net.constructor_kwargs['cond_dim'], 288)
+        self.assertEqual(
+            agent.noise_pred_net.constructor_kwargs['n_obs_steps'],
+            agent.condition_sequence_length,
+        )
+        self.assertIsNotNone(agent.lewm_sigreg)
+
 
     def test_hydra_config_instantiates_resnet_imf_attnres_multitoken_with_sequence_length_three_times_obs_horizon(self):
         cfg = _compose_cfg(