Add default optimizer to match tf.keras.

keras_core/backend/common/variables.py

@@ -375,12 +375,6 @@ PYTHON_DTYPES_MAP = {
     str: "string",
 }
 
-PYTHON_DTYPES_MAP = {
-    bool: "bool",
-    int: "int",  # TBD by backend
-    float: "float32",
-}
-
 
 def standardize_dtype(dtype):
     if dtype is None:

keras_core/backend/torch/numpy.py

@@ -1,16 +1,8 @@
 import torch
 
-from keras_core.backend.torch.core import cast
 from keras_core.backend.torch.core import convert_to_tensor
 from keras_core.backend.torch.core import to_torch_dtype
 
-TORCH_INT_TYPES = (
-    torch.int8,
-    torch.int16,
-    torch.int32,
-    torch.int64,
-)
-
 
 def add(x1, x2):
     x1, x2 = convert_to_tensor(x1), convert_to_tensor(x2)
@@ -39,8 +31,6 @@ def multiply(x1, x2):
 
 def mean(x, axis=None, keepdims=False):
     x = convert_to_tensor(x)
-    # Conversion to float necessary for `torch.mean`
-    x = cast(x, "float32") if x.dtype in TORCH_INT_TYPES else x
     return torch.mean(x, axis=axis, keepdims=keepdims)
 
 
@@ -182,8 +172,6 @@ def array(x, dtype=None):
 
 def average(x, axis=None, weights=None):
     x = convert_to_tensor(x)
-    # Conversion to float necessary for `torch.mean`
-    x = cast(x, "float32") if x.dtype in TORCH_INT_TYPES else x
     if weights is not None:
         weights = convert_to_tensor(weights)
         return torch.sum(torch.mul(x, weights), dim=axis) / torch.sum(
@@ -390,10 +378,6 @@ def imag(x):
 
 def isclose(x1, x2):
     x1, x2 = convert_to_tensor(x1), convert_to_tensor(x2)
-    if torch.is_floating_point(x1) and not torch.is_floating_point(x2):
-        x2 = cast(x2, x1.dtype)
-    if torch.is_floating_point(x2) and not torch.is_floating_point(x1):
-        x1 = cast(x1, x2.dtype)
     return torch.isclose(x1, x2)
 
 
@@ -466,8 +450,6 @@ def log2(x):
 
 def logaddexp(x1, x2):
     x1, x2 = convert_to_tensor(x1), convert_to_tensor(x2)
-    x1 = cast(x1, "float32") if x1.dtype in TORCH_INT_TYPES else x1
-    x2 = cast(x2, "float32") if x2.dtype in TORCH_INT_TYPES else x2
     return torch.logaddexp(x1, x2)
 
 
@@ -603,8 +585,7 @@ def ravel(x):
 
 
 def real(x):
-    if not isinstance(x, torch.Tensor):
-        x = torch.from_numpy(x)  # needed for complex type conversion
+    x = convert_to_tensor(x)
     return torch.real(x)
 
 
@@ -665,8 +646,6 @@ def stack(x, axis=0):
 
 def std(x, axis=None, keepdims=False):
     x = convert_to_tensor(x)
-    # Conversion to float necessary for `torch.std`
-    x = cast(x, "float32") if x.dtype in TORCH_INT_TYPES else x
     # Remove Bessel correction to align with numpy
     return torch.std(x, dim=axis, keepdim=keepdims, unbiased=False)
 
@@ -697,9 +676,6 @@ def tan(x):
 
 def tensordot(x1, x2, axes=2):
     x1, x2 = convert_to_tensor(x1), convert_to_tensor(x2)
-    # Conversion to long necessary for `torch.tensordot`
-    x1 = cast(x1, "int64") if x1.dtype in TORCH_INT_TYPES else x1
-    x2 = cast(x2, "int64") if x2.dtype in TORCH_INT_TYPES else x2
     return torch.tensordot(x1, x2, dims=axes)
 
 
@@ -801,9 +777,7 @@ def transpose(x, axes=None):
 
 
 def var(x, axis=None, keepdims=False):
-    x = convert_to_tensor(x, dtype="float32")
-    # Conversion to float necessary for `torch.var`
-    x = cast(x, "float32") if x.dtype in TORCH_INT_TYPES else x
+    x = convert_to_tensor(x)
     # Bessel correction removed for numpy compatibility
     return torch.var(x, dim=axis, keepdim=keepdims, correction=0)
 

keras_core/trainers/trainer.py

@@ -21,7 +21,7 @@ class Trainer:
     @tracking.no_automatic_dependency_tracking
     def compile(
         self,
-        optimizer,
+        optimizer="rmsprop",
         loss=None,
         loss_weights=None,
         metrics=None,