Fix negative index handling in MultiHeadAttention attention_axes

.gitignore

@@ -6,6 +6,7 @@ __pycache__
 **/.vscode test/**
 **/.vscode-smoke/**
 **/.venv*/
+venv
 bin/**
 build/**
 obj/**

integration_tests/test_multi_head_attention_negative_axis.py

@@ -0,0 +1,28 @@
+import numpy as np
+
+import keras
+
+
+def test_attention_axes_negative_indexing_matches_positive():
+    x = np.random.normal(size=(2, 3, 8, 4))
+
+    mha_pos = keras.layers.MultiHeadAttention(
+        num_heads=2, key_dim=4, attention_axes=2
+    )
+    mha_neg = keras.layers.MultiHeadAttention(
+        num_heads=2, key_dim=4, attention_axes=-2
+    )
+
+    _ = mha_pos(x, x)
+    _ = mha_neg(x, x)
+
+    mha_neg.set_weights(mha_pos.get_weights())
+
+    z_pos, a_pos = mha_pos(x, x, return_attention_scores=True)
+    z_neg, a_neg = mha_neg(x, x, return_attention_scores=True)
+
+    assert z_pos.shape == z_neg.shape
+    assert a_pos.shape == a_neg.shape
+
+    np.testing.assert_allclose(z_pos, z_neg, rtol=1e-5, atol=1e-5)
+    np.testing.assert_allclose(a_pos, a_neg, rtol=1e-5, atol=1e-5)

integration_tests/test_save_img.py

@@ -0,0 +1,27 @@
+import os
+
+import numpy as np
+import pytest
+
+from keras.utils import img_to_array
+from keras.utils import load_img
+from keras.utils import save_img
+
+
+@pytest.mark.parametrize(
+    "shape, name",
+    [
+        ((50, 50, 3), "rgb.jpg"),
+        ((50, 50, 4), "rgba.jpg"),
+    ],
+)
+def test_save_jpg(tmp_path, shape, name):
+    img = np.random.randint(0, 256, size=shape, dtype=np.uint8)
+    path = tmp_path / name
+    save_img(path, img, file_format="jpg")
+    assert os.path.exists(path)
+
+    # Check that the image was saved correctly and converted to RGB if needed.
+    loaded_img = load_img(path)
+    loaded_array = img_to_array(loaded_img)
+    assert loaded_array.shape == (50, 50, 3)

keras/src/layers/attention/multi_head_attention.py

@@ -378,7 +378,17 @@ def _build_attention(self, rank):
         if self._attention_axes is None:
             self._attention_axes = tuple(range(1, rank - 2))
         else:
-            self._attention_axes = tuple(self._attention_axes)
+            # Normalize negative indices relative to INPUT rank (rank - 1)
+            input_rank = rank - 1
+            normalized_axes = []
+            for ax in self._attention_axes:
+                if ax < 0:
+                    # Normalize relative to input rank
+                    normalized_ax = input_rank + ax
+                else:
+                    normalized_ax = ax
+                normalized_axes.append(normalized_ax)
+            self._attention_axes = tuple(normalized_axes)
         (
             self._dot_product_equation,
             self._combine_equation,
@@ -760,6 +770,12 @@ def _build_attention_equation(rank, attn_axes):
     Returns:
         Einsum equations.
     """
+    # Normalize negative indices to positive indices
+    if isinstance(attn_axes, (list, tuple)):
+        attn_axes = tuple(ax % rank if ax < 0 else ax for ax in attn_axes)
+    else:
+        attn_axes = (attn_axes % rank if attn_axes < 0 else attn_axes,)
+
     target_notation = ""
     for i in range(rank):
         target_notation += _index_to_einsum_variable(i)

keras/src/utils/image_utils.py

@@ -175,10 +175,13 @@ def save_img(path, x, data_format=None, file_format=None, scale=True, **kwargs):
         **kwargs: Additional keyword arguments passed to `PIL.Image.save()`.
     """
     data_format = backend.standardize_data_format(data_format)
+    # Normalize jpg → jpeg
+    if file_format is not None and file_format.lower() == "jpg":
+        file_format = "jpeg"
     img = array_to_img(x, data_format=data_format, scale=scale)
-    if img.mode == "RGBA" and (file_format == "jpg" or file_format == "jpeg"):
+    if img.mode == "RGBA" and file_format == "jpeg":
         warnings.warn(
-            "The JPG format does not support RGBA images, converting to RGB."
+            "The JPEG format does not support RGBA images, converting to RGB."
         )
         img = img.convert("RGB")
     img.save(path, format=file_format, **kwargs)