Initial pi0.7 minimal validation

tests/test_memory.py

@@ -0,0 +1,63 @@
+import torch
+
+from pi07mini.memory import MEMConfig, MEMVideoEncoder
+
+
+def tiny_encoder() -> MEMVideoEncoder:
+    return MEMVideoEncoder(
+        MEMConfig(
+            image_size=16,
+            patch_size=8,
+            d_model=16,
+            n_heads=4,
+            n_layers=2,
+            max_history_frames=6,
+        )
+    ).eval()
+
+
+def test_mem_output_token_count_is_independent_of_history_length() -> None:
+    encoder = tiny_encoder()
+    current = torch.randn(2, 3, 16, 16)
+    with torch.no_grad():
+        no_history = encoder(current)
+        long_history = encoder(current, torch.randn(2, 6, 3, 16, 16))
+    assert no_history.shape == (2, 4, 16)
+    assert long_history.shape == no_history.shape
+
+
+def test_mem_is_sensitive_to_temporal_order() -> None:
+    torch.manual_seed(7)
+    encoder = tiny_encoder()
+    current = torch.randn(1, 3, 16, 16)
+    history = torch.stack(
+        (
+            torch.zeros(3, 16, 16),
+            torch.ones(3, 16, 16),
+            torch.full((3, 16, 16), 2.0),
+        )
+    )[None]
+    with torch.no_grad():
+        chronological = encoder(current, history)
+        reversed_history = encoder(current, history.flip(1))
+    assert not torch.allclose(chronological, reversed_history)
+
+
+def test_masked_history_frame_cannot_change_memory() -> None:
+    torch.manual_seed(9)
+    encoder = tiny_encoder()
+    current = torch.randn(1, 3, 16, 16)
+    history = torch.randn(1, 2, 3, 16, 16)
+    changed = history.clone()
+    changed[:, 0] = 1000.0
+    mask = torch.tensor([[False, True]])
+    with torch.no_grad():
+        first = encoder(current, history, mask)
+        second = encoder(current, changed, mask)
+    torch.testing.assert_close(first, second)
+
+
+def test_current_frame_temporal_position_is_exactly_zero() -> None:
+    encoding = MEMVideoEncoder.temporal_position(torch.tensor([3, 2, 1, 0]), 15)
+    assert torch.count_nonzero(encoding[-1]) == 0
+