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import torch
from torch import nn
from algorithms.dememwm.models.attention import TemporalAxialAttention
from algorithms.dememwm.models.dit import DiT, FrameMemoryReferenceAttention, SpatioTemporalDiTBlock
class IdentityRotary:
freqs = None
def rotate_queries_or_keys(self, x, freqs):
return x
def _averaging_temporal_attention(reference_length=0):
attn = TemporalAxialAttention(
dim=1,
heads=1,
dim_head=1,
reference_length=reference_length,
rotary_emb=IdentityRotary(),
)
with torch.no_grad():
attn.to_qkv.weight.zero_()
attn.to_qkv.weight[2, 0] = 1.0
attn.to_out.weight.fill_(1.0)
attn.to_out.bias.zero_()
return attn
class DeMemWMTemporalAttentionTests(unittest.TestCase):
def test_without_frame_memory_keeps_causal_reference_mask(self):
attn = _averaging_temporal_attention(reference_length=2)
x = torch.tensor([10.0, 20.0, 30.0, 100.0, 200.0]).view(1, 5, 1, 1, 1)
out = attn(x)
expected = torch.tensor([10.0, 15.0, 20.0, 100.0, 200.0])
self.assertTrue(torch.allclose(out.flatten(), expected))
def test_frame_memory_attention_bias_keeps_target_causal_and_streams_self_only(self):
attn = _averaging_temporal_attention()
segments = {"target": 3, "anchor": 2, "dynamic": 2, "revisit": 2}
total_frames = sum(segments.values())
bias = attn._frame_memory_attn_bias(
B=1,
T=total_frames,
H=1,
W=1,
dtype=torch.float32,
device=torch.device("cpu"),
frame_memory_segments=segments,
frame_memory_masks=None,
)[0, 0]
allow = torch.isfinite(bias)
target_expected = torch.tril(torch.ones((3, 3), dtype=torch.bool))
self.assertTrue(torch.equal(allow[:3, :3], target_expected))
self.assertFalse(allow[:3, 3:].any().item())
cursor = segments["target"]
for stream in ("anchor", "dynamic", "revisit"):
length = segments[stream]
rows = slice(cursor, cursor + length)
self.assertTrue(torch.equal(allow[rows, rows], torch.eye(length, dtype=torch.bool)))
self.assertFalse(allow[rows, :cursor].any().item())
self.assertFalse(allow[rows, cursor + length :].any().item())
cursor += length
def test_frame_memory_attention_bias_restores_invalid_memory_row_diagonal(self):
attn = _averaging_temporal_attention()
segments = {"target": 2, "anchor": 2, "dynamic": 2, "revisit": 2}
total_frames = sum(segments.values())
masks = {
"target": torch.ones((1, 2), dtype=torch.bool),
"anchor": torch.tensor([[False, True]]),
"dynamic": torch.tensor([[True, False]]),
"revisit": torch.tensor([[False, False]]),
}
bias = attn._frame_memory_attn_bias(
B=1,
T=total_frames,
H=1,
W=1,
dtype=torch.float32,
device=torch.device("cpu"),
frame_memory_segments=segments,
frame_memory_masks=masks,
)[0, 0]
allow = torch.isfinite(bias)
self.assertTrue(allow.any(dim=-1).all().item())
for row in (2, 5, 6, 7):
row_expected = torch.zeros(total_frames, dtype=torch.bool)
row_expected[row] = True
self.assertTrue(torch.equal(allow[row], row_expected))
def test_frame_memory_segments_mask_temporal_streams(self):
attn = _averaging_temporal_attention()
x = torch.tensor([10.0, 20.0, 30.0, 100.0, 1.0, 3.0, 200.0]).view(1, 7, 1, 1, 1)
segments = {"target": 3, "anchor": 1, "dynamic": 2, "revisit": 1}
out = attn(x, frame_memory_segments=segments)
expected = torch.tensor([10.0, 15.0, 20.0, 100.0, 1.0, 3.0, 200.0])
self.assertTrue(torch.allclose(out.flatten(), expected))
masks = {
"target": torch.ones((1, 3), dtype=torch.bool),
"anchor": torch.tensor([[False]]),
"dynamic": torch.tensor([[True, False]]),
"revisit": torch.ones((1, 1), dtype=torch.bool),
}
out = attn(x, frame_memory_segments=segments, frame_memory_masks=masks)
expected = torch.tensor([10.0, 15.0, 20.0, 100.0, 1.0, 3.0, 200.0])
self.assertTrue(torch.allclose(out.flatten(), expected))
def test_frame_memory_temporal_targets_do_not_depend_on_appended_memory(self):
attn = _averaging_temporal_attention()
segments = {"target": 3, "anchor": 1, "dynamic": 2, "revisit": 1}
x = torch.tensor([10.0, 20.0, 30.0, 100.0, 1.0, 3.0, 200.0]).view(1, 7, 1, 1, 1)
changed_memory = torch.tensor([10.0, 20.0, 30.0, -500.0, 700.0, 900.0, -300.0]).view(1, 7, 1, 1, 1)
out = attn(x, frame_memory_segments=segments)
changed_out = attn(changed_memory, frame_memory_segments=segments)
expected_target = torch.tensor([10.0, 15.0, 20.0]).view(1, 3, 1, 1, 1)
self.assertTrue(torch.allclose(out[:, :3], expected_target))
self.assertTrue(torch.allclose(out[:, :3], changed_out[:, :3]))
def test_block_threads_frame_memory_metadata_to_temporal_attention(self):
class ZeroAttention(nn.Module):
def forward(self, x):
return torch.zeros_like(x)
class SpyTemporalAttention(nn.Module):
def __init__(self):
super().__init__()
self.calls = []
def forward(self, x, frame_memory_segments=None, frame_memory_masks=None):
self.calls.append((frame_memory_segments, frame_memory_masks))
return torch.zeros_like(x)
block = SpatioTemporalDiTBlock(
hidden_size=4,
num_heads=1,
reference_length=0,
spatial_rotary_emb=None,
temporal_rotary_emb=None,
)
spy = SpyTemporalAttention()
block.s_attn = ZeroAttention()
block.t_attn = spy
x = torch.zeros((1, 5, 1, 1, 4))
c = torch.zeros((1, 5, 4))
segments = {"target": 2, "anchor": 1, "dynamic": 1, "revisit": 1}
masks = {"dynamic": torch.ones((1, 1), dtype=torch.bool)}
block(x, c, frame_memory_segments=segments, frame_memory_masks=masks)
self.assertIs(spy.calls[0][0], segments)
self.assertIs(spy.calls[0][1], masks)
def test_block_rejects_bad_frame_memory_segment_lengths_before_temporal_attention(self):
class ZeroAttention(nn.Module):
def forward(self, x):
return torch.zeros_like(x)
class UnexpectedTemporalAttention(nn.Module):
def forward(self, x, frame_memory_segments=None, frame_memory_masks=None):
raise AssertionError("temporal attention should not run before frame-memory validation")
block = SpatioTemporalDiTBlock(
hidden_size=4,
num_heads=1,
reference_length=0,
spatial_rotary_emb=None,
temporal_rotary_emb=None,
)
block.s_attn = ZeroAttention()
block.t_attn = UnexpectedTemporalAttention()
x = torch.zeros((1, 5, 1, 1, 4))
c = torch.zeros((1, 5, 4))
with self.assertRaisesRegex(ValueError, "lengths must be nonnegative"):
block(x, c, frame_memory_segments={"target": 2, "anchor": -1, "dynamic": 2, "revisit": 2})
with self.assertRaisesRegex(ValueError, r"sum to 6, expected x\.shape\[1\]=5"):
block(x, c, frame_memory_segments={"target": 2, "anchor": 1, "dynamic": 2, "revisit": 1})
def test_block_rejects_bad_frame_memory_stream_mask_shape_before_temporal_attention(self):
class ZeroAttention(nn.Module):
def forward(self, x):
return torch.zeros_like(x)
class UnexpectedTemporalAttention(nn.Module):
def forward(self, x, frame_memory_segments=None, frame_memory_masks=None):
raise AssertionError("temporal attention should not run before frame-memory validation")
block = SpatioTemporalDiTBlock(
hidden_size=4,
num_heads=1,
reference_length=0,
spatial_rotary_emb=None,
temporal_rotary_emb=None,
)
block.s_attn = ZeroAttention()
block.t_attn = UnexpectedTemporalAttention()
x = torch.zeros((1, 5, 1, 1, 4))
c = torch.zeros((1, 5, 4))
segments = {"target": 2, "anchor": 1, "dynamic": 1, "revisit": 1}
masks = {"dynamic": torch.ones((1, 2), dtype=torch.bool)}
with self.assertRaisesRegex(
ValueError,
r"frame_memory_masks\[\'dynamic\'\] shape \(1, 2\) must match \(1, 1\)",
):
block(x, c, frame_memory_segments=segments, frame_memory_masks=masks)
zero_dynamic_segments = {"target": 2, "anchor": 1, "dynamic": 0, "revisit": 2}
zero_dynamic_masks = {"dynamic": torch.ones((1, 1), dtype=torch.bool)}
with self.assertRaisesRegex(
ValueError,
r"frame_memory_masks\[\'dynamic\'\] shape \(1, 1\) must match \(1, 0\)",
):
block(x, c, frame_memory_segments=zero_dynamic_segments, frame_memory_masks=zero_dynamic_masks)
def test_dit_threads_frame_memory_metadata_to_blocks(self):
class SpyBlock(nn.Module):
def __init__(self):
super().__init__()
self.calls = []
def forward(self, x, c, **kwargs):
self.calls.append(kwargs)
return x
model = DiT(
input_h=2,
input_w=2,
patch_size=1,
in_channels=1,
hidden_size=8,
depth=1,
num_heads=1,
mlp_ratio=1.0,
action_cond_dim=3,
pose_cond_dim=0,
reference_length=0,
use_memory_attention=False,
)
spy = SpyBlock()
model.blocks[0] = spy
model.eval()
x = torch.zeros((1, 5, 1, 2, 2))
t = torch.zeros((1, 5), dtype=torch.long)
action_cond = torch.zeros((1, 5, 3))
segments = {"target": 2, "anchor": 1, "dynamic": 1, "revisit": 1}
masks = {"dynamic": torch.ones((1, 1), dtype=torch.bool)}
with torch.no_grad():
out = model(x, t, action_cond, frame_memory_segments=segments, frame_memory_masks=masks)
self.assertEqual(tuple(out.shape), (1, 2, 1, 2, 2))
self.assertIs(spy.calls[0]["frame_memory_segments"], segments)
self.assertIs(spy.calls[0]["frame_memory_masks"], masks)
def test_dit_baseline_and_packed_frame_memory_output_lengths_with_real_blocks(self):
torch.manual_seed(0)
model = DiT(
input_h=2,
input_w=2,
patch_size=1,
in_channels=1,
hidden_size=8,
depth=1,
num_heads=1,
mlp_ratio=1.0,
action_cond_dim=3,
pose_cond_dim=0,
reference_length=1,
use_memory_attention=True,
)
model.eval()
x = torch.randn((1, 3, 1, 2, 2))
t = torch.zeros((1, 3), dtype=torch.long)
action_cond = torch.zeros((1, 3, 3))
with torch.no_grad():
baseline = model(x, t, action_cond, reference_length=1)
self.assertEqual(tuple(baseline.shape), (1, 3, 1, 2, 2))
packed = torch.randn((1, 5, 1, 2, 2))
packed_t = torch.zeros((1, 5), dtype=torch.long)
packed_action = torch.zeros((1, 5, 3))
segments = {"target": 2, "anchor": 1, "dynamic": 1, "revisit": 1}
masks = {
"target": torch.ones((1, 2), dtype=torch.bool),
"anchor": torch.ones((1, 1), dtype=torch.bool),
"dynamic": torch.zeros((1, 1), dtype=torch.bool),
"revisit": torch.ones((1, 1), dtype=torch.bool),
}
with torch.no_grad():
packed_out = model(
packed,
packed_t,
packed_action,
reference_length=1,
frame_memory_segments=segments,
frame_memory_masks=masks,
)
self.assertEqual(tuple(packed_out.shape), (1, 2, 1, 2, 2))
def test_dit_has_no_worldmem_reference_attention_fallback(self):
model = DiT(
input_h=2,
input_w=2,
patch_size=1,
in_channels=1,
hidden_size=8,
depth=1,
num_heads=1,
mlp_ratio=1.0,
action_cond_dim=3,
pose_cond_dim=0,
reference_length=1,
use_memory_attention=True,
)
block = model.blocks[0]
self.assertFalse(hasattr(block, "r_attn"))
self.assertFalse(hasattr(block, "pose_cond_mlp"))
calls = []
class SpyReferenceAttention(nn.Module):
def forward(self, target_hidden, memory_hidden, memory_mask=None, geometry_cache=None):
calls.append(memory_hidden)
return torch.zeros_like(target_hidden)
block.r_attn_anchor = SpyReferenceAttention()
block.r_attn_dynamic = SpyReferenceAttention()
block.r_attn_revisit = SpyReferenceAttention()
x = torch.randn((1, 3, 1, 2, 2))
t = torch.zeros((1, 3), dtype=torch.long)
action_cond = torch.zeros((1, 3, 3))
with torch.no_grad():
out = model(x, t, action_cond, reference_length=1)
self.assertEqual(tuple(out.shape), (1, 3, 1, 2, 2))
self.assertEqual(calls, [])
def test_dit_builds_geometry_cache_once_and_threads_to_reference_attention(self):
class ZeroAttention(nn.Module):
def forward(self, x):
return torch.zeros_like(x)
class ZeroTemporalAttention(nn.Module):
def forward(self, x, frame_memory_segments=None, frame_memory_masks=None):
return torch.zeros_like(x)
class SpyReferenceAttention(nn.Module):
def __init__(self):
super().__init__()
self.geometry = []
def forward(self, target_hidden, memory_hidden, memory_mask=None, geometry_cache=None):
self.geometry.append(geometry_cache)
return torch.zeros_like(target_hidden)
model = DiT(
input_h=2,
input_w=2,
patch_size=1,
in_channels=1,
hidden_size=8,
depth=2,
num_heads=1,
mlp_ratio=1.0,
action_cond_dim=3,
pose_cond_dim=0,
reference_length=0,
use_plucker=True,
use_memory_attention=True,
)
spies = []
for block in model.blocks:
block.s_attn = ZeroAttention()
block.t_attn = ZeroTemporalAttention()
anchor = SpyReferenceAttention()
dynamic = SpyReferenceAttention()
revisit = SpyReferenceAttention()
block.r_attn_anchor = anchor
block.r_attn_dynamic = dynamic
block.r_attn_revisit = revisit
spies.append((anchor, dynamic, revisit))
model.eval()
x = torch.zeros((1, 5, 1, 2, 2))
t = torch.zeros((1, 5), dtype=torch.long)
action_cond = torch.zeros((1, 5, 3))
frame_memory_pose = torch.zeros((1, 5, 5))
frame_memory_pose[0, :, 0] = torch.arange(5, dtype=torch.float32)
image_hw = torch.tensor([[4, 8]], dtype=torch.long)
segments = {"target": 2, "anchor": 1, "dynamic": 1, "revisit": 1}
masks = {"dynamic": torch.tensor([[False]])}
with torch.no_grad():
model(
x,
t,
action_cond,
frame_memory_segments=segments,
frame_memory_masks=masks,
frame_memory_pose=frame_memory_pose,
image_hw=image_hw,
)
anchor_cache = spies[0][0].geometry[0]
self.assertIs(anchor_cache, spies[1][0].geometry[0])
self.assertEqual(tuple(anchor_cache["query_rays"].shape), (1, 2, 2, 2, 6))
self.assertEqual(tuple(anchor_cache["relative_rays"].shape), (1, 2, 1, 2, 2, 6))
self.assertTrue(torch.equal(anchor_cache["image_hw"], torch.tensor([[4.0, 8.0]])))
self.assertTrue(torch.allclose(anchor_cache["intrinsics"], torch.tensor([[2.8, 1.4, 4.0, 2.0]])))
self.assertTrue(torch.equal(anchor_cache["relative_pose"][0, :, 0, 0], torch.tensor([2.0, 1.0])))
self.assertTrue(torch.equal(spies[0][1].geometry[0]["mask"], masks["dynamic"]))
def test_frame_memory_geometry_uses_fp32_math_then_returns_activation_dtype(self):
model = DiT(
input_h=2,
input_w=2,
patch_size=1,
in_channels=1,
hidden_size=8,
depth=1,
num_heads=1,
mlp_ratio=1.0,
action_cond_dim=3,
pose_cond_dim=0,
reference_length=0,
use_plucker=True,
use_memory_attention=True,
)
device = torch.device("cpu")
segments = {"target": 1, "anchor": 1, "dynamic": 0, "revisit": 0}
frame_memory_pose = torch.zeros((1, 2, 5), device=device, dtype=torch.float32)
frame_memory_pose[0, 0, 0] = 10000.0
frame_memory_pose[0, 1, 0] = 10001.0
image_hw = torch.tensor([[4, 8]], device=device, dtype=torch.long)
with torch.autocast(device_type=device.type, dtype=torch.bfloat16):
cache = model._build_frame_memory_geometry(
segments,
None,
frame_memory_pose,
image_hw,
grid_h=2,
grid_w=2,
device=device,
dtype=torch.float16,
)
anchor_cache = cache["anchor"]
for tensor in (
cache["query_rays"],
cache["target_pose"],
cache["image_hw"],
cache["intrinsics"],
anchor_cache["relative_pose"],
anchor_cache["relative_c2w"],
anchor_cache["relative_rays"],
):
self.assertEqual(tensor.dtype, torch.float16)
self.assertTrue(torch.isfinite(tensor).all().item())
self.assertEqual(anchor_cache["relative_pose"][0, 0, 0, 0].item(), 1.0)
self.assertEqual(anchor_cache["relative_c2w"][0, 0, 0, 0, 3].item(), 1.0)
def test_frame_memory_reference_attention_masks_padded_keys(self):
attn = FrameMemoryReferenceAttention(hidden_size=1, num_heads=1)
with torch.no_grad():
attn.to_q.weight.zero_()
attn.to_k.weight.zero_()
attn.to_v.weight.fill_(1.0)
attn.out_proj.weight.fill_(1.0)
attn.out_proj.bias.zero_()
target = torch.zeros((1, 1, 1, 1, 1))
memory = torch.tensor([1.0, 10.0]).view(1, 2, 1, 1, 1)
first_only = attn(target, memory, torch.tensor([[True, False]]))
second_only = attn(target, memory, torch.tensor([[False, True]]))
empty = attn(target, memory, torch.zeros((1, 2), dtype=torch.bool))
self.assertTrue(torch.allclose(first_only, torch.ones_like(first_only)))
self.assertTrue(torch.allclose(second_only, torch.full_like(second_only, 10.0)))
self.assertTrue(torch.equal(empty, torch.zeros_like(empty)))
def test_frame_memory_reference_attention_uses_key_geometry_only(self):
attn = FrameMemoryReferenceAttention(hidden_size=2, num_heads=1)
with torch.no_grad():
attn.to_q.weight.zero_()
attn.to_q.weight[0, 0] = 1.0
attn.to_k.weight.zero_()
attn.to_k.weight[0, 0] = 1.0
attn.to_v.weight.zero_()
attn.to_v.weight[0, 1] = 1.0
attn.query_pose_proj.weight.zero_()
attn.query_pose_proj.bias.zero_()
attn.query_pose_proj.weight[0, 5] = 100.0
attn.key_pose_proj.weight.zero_()
attn.key_pose_proj.bias.zero_()
attn.key_pose_proj.weight[0, 5] = 1.0
attn.out_proj.weight.zero_()
attn.out_proj.weight[0, 0] = 1.0
attn.out_proj.bias.zero_()
target = torch.zeros((1, 1, 1, 1, 2))
target[0, 0, 0, 0, 0] = 1.0
memory = torch.zeros((1, 2, 1, 1, 2))
memory[0, :, 0, 0, 1] = torch.tensor([1.0, 10.0])
query_rays = torch.zeros((1, 1, 1, 1, 6))
query_rays_changed = query_rays.clone()
query_rays_changed[..., 5] = 10.0
relative_a = torch.zeros((1, 1, 2, 1, 1, 6))
relative_b = torch.zeros_like(relative_a)
relative_a[0, 0, 0, 0, 0, 5] = 2.0
relative_b[0, 0, 1, 0, 0, 5] = 2.0
out_a = attn(
target,
memory,
geometry_cache={"query_rays": query_rays, "relative_rays": relative_a},
)
out_a_query_changed = attn(
target,
memory,
geometry_cache={"query_rays": query_rays_changed, "relative_rays": relative_a},
)
out_b = attn(
target,
memory,
geometry_cache={"query_rays": query_rays, "relative_rays": relative_b},
)
self.assertTrue(torch.allclose(out_a, out_a_query_changed))
self.assertLess(out_a[0, 0, 0, 0, 0].item(), out_b[0, 0, 0, 0, 0].item())
self.assertFalse(torch.allclose(out_a, out_b))
if __name__ == "__main__":
unittest.main()
|