Add custom_gradient (a pass-through) for the numpy backend

keras/src/backend/numpy/core.py

@@ -1,3 +1,5 @@
+import warnings
+
 import numpy as np
 
 from keras.src import tree
@@ -285,7 +287,21 @@ def unstack(x, num=None, axis=0):
     return [x[i] for i in range(x.shape[0])]
 
 
-def custom_gradient(fun):
-    raise NotImplementedError(
-        "`custom_gradient` is not supported with numpy backend"
-    )
+class custom_gradient:
+    """Decorator for custom gradients.
+
+    Args:
+        fun: Forward pass function.
+    """
+
+    def __init__(self, fun):
+        warnings.warn(
+            "`custom_gradient` for the numpy backend acts as a pass-through to "
+            "support the forward pass. No gradient computation or modification "
+            "takes place."
+        )
+        self.fun = fun
+
+    def __call__(self, *args, **kwargs):
+        outputs, _ = self.fun(*args, **kwargs)
+        return outputs

keras/src/backend/numpy/numpy.py

@@ -65,6 +65,8 @@ def matmul(x1, x2):
         dtype = "int32"
     else:
         dtype = dtypes.result_type(x1.dtype, x2.dtype)
+    x1 = x1.astype(dtype)
+    x2 = x2.astype(dtype)
     return np.matmul(x1, x2).astype(dtype)
 
 

keras/src/layers/core/dense_test.py

@@ -334,7 +334,6 @@ def test_enable_lora_when_already_enabled(self):
 
     # Test quantization-related (int8 and float8) methods
 
-    @pytest.mark.requires_trainable_backend
     def test_quantize_int8(self):
         layer = layers.Dense(units=16)
         layer.build((None, 8))
@@ -764,7 +763,6 @@ def test_quantize_float8_fitting(self):
                 len(model.non_trainable_weights),
             )
 
-    @pytest.mark.requires_trainable_backend
     def test_quantize_float8_inference(self):
         config = dict(units=16)
         layer = layers.Dense(**config)

keras/src/layers/core/einsum_dense_test.py

@@ -382,7 +382,6 @@ def test_lora_rank_argument(self):
 
     # Test quantization-related (int8 and float8) methods
 
-    @pytest.mark.requires_trainable_backend
     def test_quantize_int8(self):
         layer = layers.EinsumDense(
             equation="ab,bcd->acd",
@@ -471,7 +470,6 @@ def test_quantize_int8(self):
         ("btd,ndh->btnh", "btd,ndh->btnh", (None, 2, 8), (1, 2, 4)),
         ("btd,df->btf", "btd,df->btf", (None, 4), (1, 2, 4)),
     )
-    @pytest.mark.requires_trainable_backend
     def test_quantize_int8_with_specific_equations(
         self, equation, output_shape, input_shape
     ):
@@ -903,7 +901,6 @@ def test_quantize_float8_fitting(self):
                 len(model.non_trainable_weights),
             )
 
-    @pytest.mark.requires_trainable_backend
     def test_quantize_float8_inference(self):
         config = dict(
             equation="ab,bcd->acd",