change default behavior of end to None

src/refiners/fluxion/layers/basics.py

@@ -84,7 +84,7 @@ def forward(self, x: Tensor) -> Tensor:
 
 
 class Slicing(Module):
-    def __init__(self, dim: int = 0, start: int = 0, end: int = -1, step: int = 1) -> None:
+    def __init__(self, dim: int = 0, start: int = 0, end: int | None = None, step: int = 1) -> None:
         super().__init__()
         self.dim = dim
         self.start = start
@@ -94,7 +94,8 @@ def __init__(self, dim: int = 0, start: int = 0, end: int = -1, step: int = 1) -
     def forward(self, x: Tensor) -> Tensor:
         dim_size = x.shape[self.dim]
         start = self.start if self.start >= 0 else dim_size + self.start
-        end = self.end if self.end >= 0 else dim_size + self.end
+        end = self.end or dim_size
+        end = end if end >= 0 else dim_size + end
         start = max(min(start, dim_size), 0)
         end = max(min(end, dim_size), 0)
         if start >= end:

src/refiners/foundationals/latent_diffusion/image_prompt.py

@@ -268,9 +268,7 @@ def __init__(
                             InjectionPoint(),  # Wk
                         ),
                         fl.Chain(
-                            fl.Slicing(
-                                dim=1, start=text_sequence_length, end=text_sequence_length + image_sequence_length
-                            ),
+                            fl.Slicing(dim=1, start=text_sequence_length),
                             fl.Linear(
                                 in_features=self.target.key_embedding_dim,
                                 out_features=self.target.inner_dim,
@@ -286,9 +284,7 @@ def __init__(
                             InjectionPoint(),  # Wv
                         ),
                         fl.Chain(
-                            fl.Slicing(
-                                dim=1, start=text_sequence_length, end=text_sequence_length + image_sequence_length
-                            ),
+                            fl.Slicing(dim=1, start=text_sequence_length),
                             fl.Linear(
                                 in_features=self.target.key_embedding_dim,
                                 out_features=self.target.inner_dim,

src/refiners/foundationals/segment_anything/mask_decoder.py

@@ -156,7 +156,7 @@ def __init__(
                 ),
                 other=DenseEmbeddingUpscaling(embedding_dim=embedding_dim, device=device, dtype=dtype),
             ),
-            fl.Slicing(dim=1, start=1, end=num_mask_tokens + 1),
+            fl.Slicing(dim=1, start=1),
             fl.Reshape(num_mask_tokens, embedding_dim, embedding_dim),
         )
 
@@ -183,7 +183,7 @@ def __init__(
                 device=device,
                 dtype=dtype,
             ),
-            fl.Slicing(dim=-1, start=1, end=num_mask_tokens + 1),
+            fl.Slicing(dim=-1, start=1),
         )
 
 

tests/fluxion/layers/test_basics.py

@@ -19,6 +19,14 @@ def test_slicing_negative_indices() -> None:
     assert torch.equal(sliced, expected)
 
 
+def test_none_end_slicing() -> None:
+    x = torch.randn(2, 1000, 400)
+    slicing = Slicing(dim=1, start=1)
+    sliced = slicing(x)
+    expected = x[:, 1:, :]
+    assert torch.equal(sliced, expected)
+
+
 def test_slicing_step() -> None:
     x = torch.randn(5, 5, 5)
     slicing_layer = Slicing(dim=1, start=0, end=5, step=2)
@@ -27,39 +35,39 @@ def test_slicing_step() -> None:
     assert torch.equal(sliced, expected)
 
 
-def test_slicing_empty_slice():
+def test_slicing_empty_slice() -> None:
     x = torch.randn(5, 5, 5)
     slicing_layer = Slicing(dim=1, start=3, end=3)
     sliced = slicing_layer(x)
     expected = x[:, 3:3]
     assert torch.equal(sliced, expected)
 
 
-def test_slicing_full_dimension():
+def test_slicing_full_dimension() -> None:
     x = torch.randn(5, 5, 5)
     slicing_layer = Slicing(dim=2, start=0, end=5)
     sliced = slicing_layer(x)
     expected = x[:, :, :]
     assert torch.equal(sliced, expected)
 
 
-def test_slicing_step_greater_than_range():
+def test_slicing_step_greater_than_range() -> None:
     x = torch.randn(5, 5, 5)
     slicing_layer = Slicing(dim=1, start=1, end=3, step=4)
     sliced = slicing_layer(x)
     expected = x[:, 1:3:4]
     assert torch.equal(sliced, expected)
 
 
-def test_slicing_reversed_start_end():
+def test_slicing_reversed_start_end() -> None:
     x = torch.randn(5, 5, 5)
     slicing_layer = Slicing(dim=1, start=4, end=2)
     sliced = slicing_layer(x)
     expected = x[:, 4:2]
     assert torch.equal(sliced, expected)
 
 
-def test_slicing_out_of_bounds_indices():
+def test_slicing_out_of_bounds_indices() -> None:
     x = torch.randn(5, 5, 5)
     slicing_layer = Slicing(dim=1, start=-10, end=10)
     sliced = slicing_layer(x)