mlx - test updates and signbit implementation

.github/workflows/actions.yml

@@ -15,7 +15,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [3.10]
+        python-version: ['3.10']
         backend: [tensorflow, jax, torch, numpy, openvino, mlx]
     name: Run tests
     runs-on: ubuntu-latest

keras/api/__init__.py

@@ -41,6 +41,9 @@
 from keras.src.backend.common.remat import remat
 from keras.src.backend.common.stateless_scope import StatelessScope
 from keras.src.backend.common.symbolic_scope import SymbolicScope
+from keras.src.backend.exports import Variable
+from keras.src.backend.exports import device
+from keras.src.backend.exports import name_scope
 from keras.src.dtype_policies.dtype_policy import DTypePolicy
 from keras.src.dtype_policies.dtype_policy import FloatDTypePolicy
 from keras.src.initializers.initializer import Initializer

keras/api/_tf_keras/keras/__init__.py

@@ -39,6 +39,9 @@
 from keras.src.backend.common.remat import remat
 from keras.src.backend.common.stateless_scope import StatelessScope
 from keras.src.backend.common.symbolic_scope import SymbolicScope
+from keras.src.backend.exports import Variable
+from keras.src.backend.exports import device
+from keras.src.backend.exports import name_scope
 from keras.src.dtype_policies.dtype_policy import DTypePolicy
 from keras.src.dtype_policies.dtype_policy import FloatDTypePolicy
 from keras.src.initializers.initializer import Initializer

keras/src/backend/common/variables_test.py

@@ -943,6 +943,11 @@ def test_add(self, dtypes):
         x2_jax = jnp.ones((1,), dtype=dtype2)
         expected_dtype = standardize_dtype(jnp.add(x1_jax, x2_jax).dtype)
 
+        if backend.backend() == "mlx":
+            if expected_dtype == "complex128":
+                # mlx backend does not support complex128
+                expected_dtype = "complex64"
+
         self.assertDType(x1 + x2, expected_dtype)
         self.assertDType(x1.__radd__(x2), expected_dtype)
 
@@ -959,6 +964,11 @@ def test_sub(self, dtypes):
         x2_jax = jnp.ones((1,), dtype=dtype2)
         expected_dtype = standardize_dtype(jnp.add(x1_jax, x2_jax).dtype)
 
+        if backend.backend() == "mlx":
+            if expected_dtype == "complex128":
+                # mlx backend does not support complex128
+                expected_dtype = "complex64"
+
         self.assertDType(x1 - x2, expected_dtype)
         self.assertDType(x1.__rsub__(x2), expected_dtype)
 
@@ -975,6 +985,11 @@ def test_mul(self, dtypes):
         x2_jax = jnp.ones((1,), dtype=dtype2)
         expected_dtype = standardize_dtype(jnp.add(x1_jax, x2_jax).dtype)
 
+        if backend.backend() == "mlx":
+            if expected_dtype == "complex128":
+                # mlx backend does not have complex128
+                expected_dtype = "complex64"
+
         self.assertDType(x1 * x2, expected_dtype)
         self.assertDType(x1.__rmul__(x2), expected_dtype)
 
@@ -1059,13 +1074,26 @@ def test_pow(self, dtypes):
         x2_jax = jnp.ones((1,), dtype=dtype2)
         expected_dtype = standardize_dtype(jnp.power(x1_jax, x2_jax).dtype)
 
+        if backend.backend() == "mlx":
+            if expected_dtype == "complex128":
+                # mlx backend does not support complex128
+                expected_dtype = "complex64"
+
         self.assertDType(x1**x2, expected_dtype)
         self.assertDType(x1.__rpow__(x2), expected_dtype)
 
     @parameterized.named_parameters(
         named_product(dtypes=itertools.combinations(ALL_DTYPES, 2))
     )
     def test_matmul(self, dtypes):
+        if backend.backend() == "mlx":
+            result_dtype = backend.result_type(*dtypes)
+            if "float" not in result_dtype:
+                self.skipTest(
+                    "mlx backend only supports matmul for real floating point "
+                    "types"
+                )
+
         import jax.numpy as jnp
 
         dtype1, dtype2 = dtypes

keras/src/backend/mlx/core.py

@@ -2,6 +2,7 @@
 import functools
 import warnings
 
+import ml_dtypes
 import mlx.core as mx
 import numpy as np
 
@@ -97,10 +98,14 @@ def convert_to_tensor(x, dtype=None, sparse=None, ragged=None):
         if x.dtype == np.float64:
             # mlx backend does not support float64
             x = x.astype(np.float32)
-        if standardize_dtype(x.dtype) == "bfloat16" and mlx_dtype is None:
+        if standardize_dtype(x.dtype) == "bfloat16":
+            # mlx currently fails to load a numpy array with dtype=bfloat16
+            # upcast to float32 to avoid error
+            x = x.astype(np.float32)
             # if a bfloat16 np.ndarray is passed to mx.array with dtype=None
             # it casts the output to complex64, so we force cast to bfloat16
-            mlx_dtype = mx.bfloat16
+            # (but by upcasting we avoid x.dtype=bfloat16 and mlx_dtype=None)
+            mlx_dtype = mx.bfloat16 if mlx_dtype is None else mlx_dtype
         return mx.array(x, dtype=mlx_dtype)
 
     if isinstance(x, list):
@@ -154,8 +159,9 @@ def convert_to_tensors(*xs):
 def convert_to_numpy(x):
     # Performs a copy. If we want 0-copy we can pass copy=False
     if isinstance(x, mx.array) and x.dtype == mx.bfloat16:
-        # mlx currently has an error passing bloat16 array to numpy
-        return np.array(x.astype(mx.float32))
+        # mlx currently has an error passing bfloat16 array to numpy
+        # upcast to float32 then downcast to bfloat16
+        return np.array(x.astype(mx.float32)).astype(ml_dtypes.bfloat16)
     return np.array(x)
 
 

keras/src/backend/mlx/image.py

@@ -655,3 +655,35 @@ def _compute_weight_mat(
         weights,
         0,
     )
+
+
+def elastic_transform(
+    images,
+    alpha=20.0,
+    sigma=5.0,
+    interpolation="bilinear",
+    fill_mode="reflect",
+    fill_value=0.0,
+    seed=None,
+    data_format=None,
+):
+    raise NotImplementedError("elastic_transform not yet implemented in mlx.")
+
+
+def perspective_transform(
+    images,
+    start_points,
+    end_points,
+    interpolation="bilinear",
+    fill_value=0,
+    data_format=None,
+):
+    raise NotImplementedError(
+        "perspective_transform not yet implemented in mlx."
+    )
+
+
+def gaussian_blur(
+    images, kernel_size=(3, 3), sigma=(1.0, 1.0), data_format=None
+):
+    raise NotImplementedError("gaussian_blur not yet implemented in mlx.")

keras/src/backend/mlx/math.py

@@ -60,17 +60,24 @@ def segment_max(data, segment_ids, num_segments=None, sorted=False):
 
 
 def top_k(x, k, sorted=True):
+    # default to sorted=True to match other backends
     x = convert_to_tensor(x)
     indices = mx.argpartition(mx.negative(x), k, axis=-1)[..., :k]
     values = mx.take_along_axis(x, indices, axis=-1)
+
+    if sorted:
+        sort_indices = mx.argsort(mx.negative(values), axis=-1)
+        values = mx.take_along_axis(values, sort_indices, axis=-1)
+        indices = mx.take_along_axis(indices, sort_indices, axis=-1)
+
     return values, indices
 
 
 def in_top_k(targets, predictions, k):
     targets = convert_to_tensor(targets)
     predictions = convert_to_tensor(predictions)
     targets = targets[..., None]
-    topk_values = top_k(predictions, k)[0]
+    topk_values = top_k(predictions, k, sorted=False)[0]
     targets_values = mx.take_along_axis(predictions, targets, axis=-1)
     mask = targets_values >= topk_values
     return mx.any(mask, axis=-1)

keras/src/backend/mlx/nn.py

@@ -1285,6 +1285,7 @@ def dot_product_attention(
     scale=None,
     is_causal=False,
     flash_attention=None,
+    attn_logits_soft_cap=None,
 ):
     if flash_attention is None:
         flash_attention = False

keras/src/backend/mlx/numpy.py

@@ -12,6 +12,7 @@
 from keras.src.backend.mlx.core import cast
 from keras.src.backend.mlx.core import convert_to_tensor
 from keras.src.backend.mlx.core import convert_to_tensors
+from keras.src.backend.mlx.core import is_tensor
 from keras.src.backend.mlx.core import slice
 from keras.src.backend.mlx.core import to_mlx_dtype
 
@@ -272,8 +273,20 @@ def bitwise_xor(x, y):
 
 def bitwise_left_shift(x, y):
     x = convert_to_tensor(x)
-    y = convert_to_tensor(y)
-    return mx.left_shift(x, y)
+    if not isinstance(y, int):
+        y = convert_to_tensor(y)
+
+    # handle result dtype to match other backends
+    types = [x.dtype]
+    if is_tensor(y):
+        types.append(y.dtype)
+    result_dtype = result_type(*types)
+    mlx_result_dtype = to_mlx_dtype(result_dtype)
+
+    result = mx.left_shift(x, y)
+    if result.dtype != mlx_result_dtype:
+        return result.astype(mlx_result_dtype)
+    return result
 
 
 def left_shift(x, y):
@@ -282,8 +295,20 @@ def left_shift(x, y):
 
 def bitwise_right_shift(x, y):
     x = convert_to_tensor(x)
-    y = convert_to_tensor(y)
-    return mx.right_shift(x, y)
+    if not isinstance(y, int):
+        y = convert_to_tensor(y)
+
+    # handle result dtype to match other backends
+    types = [x.dtype]
+    if is_tensor(y):
+        types.append(y.dtype)
+    result_dtype = result_type(*types)
+    mlx_result_dtype = to_mlx_dtype(result_dtype)
+
+    result = mx.right_shift(x, y)
+    if result.dtype != mlx_result_dtype:
+        return result.astype(mlx_result_dtype)
+    return result
 
 
 def right_shift(x, y):
@@ -1567,3 +1592,34 @@ def rot90(array, k=1, axes=(0, 1)):
         array = array[tuple(slices)]
 
     return array
+
+
+def signbit(x):
+    x = convert_to_tensor(x)
+
+    if x.dtype in (
+        mx.float16,
+        mx.float32,
+        mx.float64,
+        mx.bfloat16,
+        mx.complex64,
+    ):
+        if x.dtype == mx.complex64:
+            # check sign of real part for complex numbers
+            real_part = mx.real(x)
+            return signbit(real_part)
+        zeros = x == 0
+        # this works because in mlx 1/0=inf and 1/-0=-inf
+        neg_zeros = (1 / x == mx.array(float("-inf"))) & zeros
+        return mx.where(zeros, neg_zeros, x < 0)
+    elif x.dtype in (mx.uint8, mx.uint16, mx.uint32, mx.uint64):
+        # unsigned integers never negative
+        return mx.zeros_like(x).astype(mx.bool_)
+    elif x.dtype in (mx.int8, mx.int16, mx.int32, mx.int64):
+        # for integers, simple negative check
+        return x < 0
+    elif x.dtype == mx.bool_:
+        # for boolean array, return false
+        return mx.zeros_like(x).astype(mx.bool_)
+    else:
+        raise ValueError(f"Unsupported dtype in `signbit`: {x.dtype}")

keras/src/initializers/constant_initializers_test.py

@@ -84,9 +84,10 @@ def test_stft_initializer(self):
         args = -2 * pi * time_range * freq_range
 
         tol_kwargs = {}
-        if backend.backend() == "jax":
+        if backend.backend() == "jax" or backend.backend() == "mlx":
             # TODO(mostafa-mahmoud): investigate the cases
             # of non-small error in jax and torch
+            # for mlx, minor precision differences with float64 on linux
             tol_kwargs = {"atol": 1e-4, "rtol": 1e-6}
 
         initializer = initializers.STFT("real", None)

keras/src/layers/activations/activation.py

@@ -26,7 +26,6 @@ def __init__(self, activation, **kwargs):
         super().__init__(**kwargs)
         self.supports_masking = True
         self.activation = activations.get(activation)
-        self.built = True
 
         self._build_at_init()
 

keras/src/layers/activations/elu.py

@@ -23,7 +23,6 @@ def __init__(self, alpha=1.0, **kwargs):
         super().__init__(**kwargs)
         self.alpha = alpha
         self.supports_masking = True
-        self.built = True
 
         self._build_at_init()
 

keras/src/layers/core/identity.py

@@ -15,7 +15,6 @@ class Identity(Layer):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self.supports_masking = True
-        self.built = True
 
         self._build_at_init()
 

keras/src/layers/pooling/base_global_pooling.py

@@ -14,7 +14,6 @@ def __init__(
         self.data_format = backend.standardize_data_format(data_format)
         self.keepdims = keepdims
         self.input_spec = InputSpec(ndim=pool_dimensions + 2)
-        self.built = True
 
         self._build_at_init()
 

keras/src/layers/pooling/base_pooling.py

@@ -34,7 +34,6 @@ def __init__(
         self.data_format = backend.standardize_data_format(data_format)
 
         self.input_spec = InputSpec(ndim=pool_dimensions + 2)
-        self.built = True
 
         self._build_at_init()
 

keras/src/layers/regularization/activity_regularization.py

@@ -27,7 +27,6 @@ def __init__(self, l1=0.0, l2=0.0, **kwargs):
         self.supports_masking = True
         self.l1 = l1
         self.l2 = l2
-        self.built = True
 
         self._build_at_init()
 

keras/src/layers/regularization/alpha_dropout.py

@@ -46,7 +46,6 @@ def __init__(self, rate, noise_shape=None, seed=None, **kwargs):
         if rate > 0:
             self.seed_generator = backend.random.SeedGenerator(seed)
         self.supports_masking = True
-        self.built = True
 
         self._build_at_init()
 

keras/src/layers/regularization/dropout.py

@@ -52,7 +52,6 @@ def __init__(self, rate, noise_shape=None, seed=None, **kwargs):
         if rate > 0:
             self.seed_generator = backend.random.SeedGenerator(seed)
         self.supports_masking = True
-        self.built = True
 
         self._build_at_init()
 

keras/src/layers/regularization/gaussian_dropout.py

@@ -37,7 +37,6 @@ def __init__(self, rate, seed=None, **kwargs):
         if rate > 0:
             self.seed_generator = backend.random.SeedGenerator(seed)
         self.supports_masking = True
-        self.built = True
 
         self._build_at_init()
 

keras/src/layers/regularization/gaussian_noise.py

@@ -38,7 +38,6 @@ def __init__(self, stddev, seed=None, **kwargs):
         if stddev > 0:
             self.seed_generator = backend.random.SeedGenerator(seed)
         self.supports_masking = True
-        self.built = True
 
         self._build_at_init()