Remove _asarray

pytensor/scalar/basic.py

@@ -32,7 +32,6 @@
 from pytensor.graph.utils import MetaObject, MethodNotDefined
 from pytensor.link.c.op import COp
 from pytensor.link.c.type import CType
-from pytensor.misc.safe_asarray import _asarray
 from pytensor.printing import pprint
 from pytensor.utils import (
     apply_across_args,
@@ -150,7 +149,7 @@ def __call__(self, x):
                 and rval.dtype in ("float64", "float32")
                 and rval.dtype != config.floatX
             ):
-                rval = _asarray(rval, dtype=config.floatX)
+                rval = rval.astype(config.floatX)
             return rval
 
         # The following is the original code, corresponding to the 'custom'
@@ -176,15 +175,15 @@ def __call__(self, x):
             and config.floatX in self.dtypes
             and config.floatX != "float64"
         ):
-            return _asarray(x, dtype=config.floatX)
+            return np.asarray(x, dtype=config.floatX)
 
         # Don't autocast to float16 unless config.floatX is float16
         try_dtypes = [
             d for d in self.dtypes if config.floatX == "float16" or d != "float16"
         ]
 
         for dtype in try_dtypes:
-            x_ = _asarray(x, dtype=dtype)
+            x_ = np.asarray(x).astype(dtype=dtype)
             if np.all(x == x_):
                 break
         # returns either an exact x_==x, or the last cast x_
@@ -245,7 +244,9 @@ def convert(x, dtype=None):
 
     if dtype is not None:
         # in this case, the semantics are that the caller is forcing the dtype
-        x_ = _asarray(x, dtype=dtype)
+        if dtype == "floatX":
+            dtype = config.floatX
+        x_ = np.asarray(x).astype(dtype)
     else:
         # In this case, this function should infer the dtype according to the
         # autocasting rules. See autocasting above.
@@ -256,7 +257,7 @@ def convert(x, dtype=None):
             except OverflowError:
                 # This is to imitate numpy behavior which tries to fit
                 # bigger numbers into a uint64.
-                x_ = _asarray(x, dtype="uint64")
+                x_ = np.asarray(x, dtype="uint64")
         elif isinstance(x, builtins.float):
             x_ = autocast_float(x)
         elif isinstance(x, np.ndarray):

pytensor/sparse/basic.py

@@ -24,7 +24,6 @@
 from pytensor.graph.op import Op
 from pytensor.link.c.op import COp
 from pytensor.link.c.type import generic
-from pytensor.misc.safe_asarray import _asarray
 from pytensor.sparse.type import SparseTensorType, _is_sparse
 from pytensor.sparse.utils import hash_from_sparse
 from pytensor.tensor import basic as ptb
@@ -595,11 +594,11 @@
         (csm,) = inputs
         out[0][0] = csm.data
         if str(csm.data.dtype) == "int32":
-            out[0][0] = _asarray(out[0][0], dtype="int32")
+            out[0][0] = np.asarray(out[0][0], dtype="int32")
         # backport
-        out[1][0] = _asarray(csm.indices, dtype="int32")
-        out[2][0] = _asarray(csm.indptr, dtype="int32")
-        out[3][0] = _asarray(csm.shape, dtype="int32")
+        out[1][0] = np.asarray(csm.indices, dtype="int32")
+        out[2][0] = np.asarray(csm.indptr, dtype="int32")
+        out[3][0] = np.asarray(csm.shape, dtype="int32")
 
     def grad(self, inputs, g):
         # g[1:] is all integers, so their Jacobian in this op
@@ -698,17 +697,17 @@
 
         if not isinstance(indices, Variable):
             indices_ = np.asarray(indices)
-            indices_32 = _asarray(indices, dtype="int32")
+            indices_32 = np.asarray(indices, dtype="int32")
             assert (indices_ == indices_32).all()
             indices = indices_32
         if not isinstance(indptr, Variable):
             indptr_ = np.asarray(indptr)
-            indptr_32 = _asarray(indptr, dtype="int32")
+            indptr_32 = np.asarray(indptr, dtype="int32")
             assert (indptr_ == indptr_32).all()
             indptr = indptr_32
         if not isinstance(shape, Variable):
             shape_ = np.asarray(shape)
-            shape_32 = _asarray(shape, dtype="int32")
+            shape_32 = np.asarray(shape, dtype="int32")
             assert (shape_ == shape_32).all()
             shape = shape_32
 
@@ -1461,7 +1460,7 @@
         (x, ind1, ind2) = inputs
         (out,) = outputs
         assert _is_sparse(x)
-        out[0] = _asarray(x[ind1, ind2], x.dtype)
+        out[0] = np.asarray(x[ind1, ind2], x.dtype)
 
 
 get_item_scalar = GetItemScalar()
@@ -2142,7 +2141,7 @@
 
         # The asarray is needed as in some case, this return a
         # numpy.matrixlib.defmatrix.matrix object and not an ndarray.
-        out[0] = _asarray(x + y, dtype=node.outputs[0].type.dtype)
+        out[0] = np.asarray(x + y, dtype=node.outputs[0].type.dtype)
 
     def grad(self, inputs, gout):
         (x, y) = inputs
@@ -3497,7 +3496,7 @@
 
         # The cast is needed as otherwise we hit the bug mentioned into
         # _asarray function documentation.
-        out[0] = _asarray(variable, str(variable.dtype))
+        out[0] = np.asarray(variable, str(variable.dtype))
 
     def grad(self, inputs, gout):
         # a is sparse, b is dense, g_out is dense
@@ -4012,7 +4011,7 @@
         if x_is_sparse and y_is_sparse:
             rval = rval.toarray()
 
-        out[0] = _asarray(rval, dtype=node.outputs[0].dtype)
+        out[0] = np.asarray(rval, dtype=node.outputs[0].dtype)
 
     def grad(self, inputs, gout):
         (x, y) = inputs

pytensor/sparse/rewriting.py

@@ -1,3 +1,4 @@
+import numpy as np
 import scipy
 
 import pytensor
@@ -10,7 +11,6 @@
     node_rewriter,
 )
 from pytensor.link.c.op import COp, _NoPythonCOp
-from pytensor.misc.safe_asarray import _asarray
 from pytensor.sparse import basic as sparse
 from pytensor.sparse.basic import (
     CSC,
@@ -283,7 +283,7 @@
             (a_val, a_ind, a_ptr), (a_nrows, b.shape[0]), copy=False
         )
         # out[0] = a.dot(b)
-        out[0] = _asarray(a * b, dtype=node.outputs[0].type.dtype)
+        out[0] = np.asarray(a * b, dtype=node.outputs[0].type.dtype)
         assert _is_dense(out[0])  # scipy 0.7 automatically converts to dense
 
     def c_code(self, node, name, inputs, outputs, sub):

pytensor/tensor/basic.py

@@ -32,7 +32,6 @@
 from pytensor.graph.type import HasShape, Type
 from pytensor.link.c.op import COp
 from pytensor.link.c.params_type import ParamsType
-from pytensor.misc.safe_asarray import _asarray
 from pytensor.printing import Printer, min_informative_str, pprint, set_precedence
 from pytensor.raise_op import CheckAndRaise, assert_op
 from pytensor.scalar import int32
@@ -512,7 +511,7 @@ def get_underlying_scalar_constant_value(
                     ret = v.owner.inputs[0].owner.inputs[idx]
                     ret = get_underlying_scalar_constant_value(ret, max_recur=max_recur)
                     # MakeVector can cast implicitly its input in some case.
-                    return _asarray(ret, dtype=v.type.dtype)
+                    return np.asarray(ret, dtype=v.type.dtype)
 
                 # This is needed when we take the grad as the Shape op
                 # are not already changed into MakeVector
@@ -1834,7 +1833,7 @@ def perform(self, node, inputs, out_):
         (out,) = out_
         # not calling pytensor._asarray as optimization
         if (out[0] is None) or (out[0].size != len(inputs)):
-            out[0] = _asarray(inputs, dtype=node.outputs[0].dtype)
+            out[0] = np.asarray(inputs, dtype=node.outputs[0].dtype)
         else:
             # assume that out has correct dtype. there is no cheap way to check
             out[0][...] = inputs
@@ -2537,7 +2536,7 @@ def perform(self, node, axis_and_tensors, out_):
                     f"Join axis {int(axis)} out of bounds [0, {int(ndim)})"
                 )
 
-            out[0] = _asarray(
+            out[0] = np.asarray(
                 np.concatenate(tens, axis=axis), dtype=node.outputs[0].type.dtype
             )
 

pytensor/tensor/elemwise.py

@@ -17,7 +17,6 @@
 from pytensor.link.c.op import COp, ExternalCOp, OpenMPOp
 from pytensor.link.c.params_type import ParamsType
 from pytensor.misc.frozendict import frozendict
-from pytensor.misc.safe_asarray import _asarray
 from pytensor.printing import Printer, pprint
 from pytensor.scalar import get_scalar_type
 from pytensor.scalar.basic import bool as scalar_bool
@@ -1412,7 +1411,7 @@ def perform(self, node, inp, out):
 
         out = self.ufunc.reduce(input, axis=axis, dtype=acc_dtype)
 
-        output[0] = _asarray(out, dtype=out_dtype)
+        output[0] = np.asarray(out, dtype=out_dtype)
 
     def infer_shape(self, fgraph, node, shapes):
         (ishape,) = shapes

pytensor/tensor/extra_ops.py

@@ -17,7 +17,6 @@
 from pytensor.link.c.op import COp
 from pytensor.link.c.params_type import ParamsType
 from pytensor.link.c.type import EnumList, Generic
-from pytensor.misc.safe_asarray import _asarray
 from pytensor.raise_op import Assert
 from pytensor.scalar import int32 as int_t
 from pytensor.scalar import upcast
@@ -1307,7 +1306,7 @@ def perform(self, node, inp, out):
         res = np.unravel_index(indices, dims, order=self.order)
         assert len(res) == len(out)
         for i in range(len(out)):
-            ret = _asarray(res[i], node.outputs[0].dtype)
+            ret = np.asarray(res[i], node.outputs[0].dtype)
             if ret.base is not None:
                 # NumPy will return a view when it can.
                 # But we don't want that.
@@ -1382,7 +1381,7 @@ def infer_shape(self, fgraph, node, input_shapes):
     def perform(self, node, inp, out):
         multi_index, dims = inp[:-1], inp[-1]
         res = np.ravel_multi_index(multi_index, dims, mode=self.mode, order=self.order)
-        out[0][0] = _asarray(res, node.outputs[0].dtype)
+        out[0][0] = np.asarray(res, node.outputs[0].dtype)
 
 
 def ravel_multi_index(multi_index, dims, mode="raise", order="C"):

pytensor/tensor/math.py

@@ -14,7 +14,6 @@
 from pytensor.graph.replace import _vectorize_node
 from pytensor.link.c.op import COp
 from pytensor.link.c.params_type import ParamsType
-from pytensor.misc.safe_asarray import _asarray
 from pytensor.printing import pprint
 from pytensor.raise_op import Assert
 from pytensor.scalar.basic import BinaryScalarOp
@@ -202,7 +201,7 @@ def perform(self, node, inp, outs):
         new_shape = (*kept_shape, np.prod(reduced_shape, dtype="int64"))
         reshaped_x = transposed_x.reshape(new_shape)
 
-        max_idx[0] = _asarray(np.argmax(reshaped_x, axis=-1), dtype="int64")
+        max_idx[0] = np.asarray(np.argmax(reshaped_x, axis=-1), dtype="int64")
 
     def c_code(self, node, name, inp, out, sub):
         (x,) = inp
@@ -730,32 +729,32 @@ def isclose(a, b, rtol=1.0e-5, atol=1.0e-8, equal_nan=False):
     --------
     >>> import pytensor
     >>> import numpy as np
-    >>> a = _asarray([1e10, 1e-7], dtype="float64")
-    >>> b = _asarray([1.00001e10, 1e-8], dtype="float64")
+    >>> a = np.array([1e10, 1e-7], dtype="float64")
+    >>> b = np.array([1.00001e10, 1e-8], dtype="float64")
     >>> pytensor.tensor.isclose(a, b).eval()
     array([ True, False])
-    >>> a = _asarray([1e10, 1e-8], dtype="float64")
-    >>> b = _asarray([1.00001e10, 1e-9], dtype="float64")
+    >>> a = np.array([1e10, 1e-8], dtype="float64")
+    >>> b = np.array([1.00001e10, 1e-9], dtype="float64")
     >>> pytensor.tensor.isclose(a, b).eval()
     array([ True,  True])
-    >>> a = _asarray([1e10, 1e-8], dtype="float64")
-    >>> b = _asarray([1.0001e10, 1e-9], dtype="float64")
+    >>> a = np.array([1e10, 1e-8], dtype="float64")
+    >>> b = np.array([1.0001e10, 1e-9], dtype="float64")
     >>> pytensor.tensor.isclose(a, b).eval()
     array([False,  True])
-    >>> a = _asarray([1.0, np.nan], dtype="float64")
-    >>> b = _asarray([1.0, np.nan], dtype="float64")
+    >>> a = np.array([1.0, np.nan], dtype="float64")
+    >>> b = np.array([1.0, np.nan], dtype="float64")
     >>> pytensor.tensor.isclose(a, b).eval()
     array([ True, False])
-    >>> a = _asarray([1.0, np.nan], dtype="float64")
-    >>> b = _asarray([1.0, np.nan], dtype="float64")
+    >>> a = np.array([1.0, np.nan], dtype="float64")
+    >>> b = np.array([1.0, np.nan], dtype="float64")
     >>> pytensor.tensor.isclose(a, b, equal_nan=True).eval()
     array([ True,  True])
-    >>> a = _asarray([1.0, np.inf], dtype="float64")
-    >>> b = _asarray([1.0, -np.inf], dtype="float64")
+    >>> a = np.array([1.0, np.inf], dtype="float64")
+    >>> b = np.array([1.0, -np.inf], dtype="float64")
     >>> pytensor.tensor.isclose(a, b).eval()
     array([ True, False])
-    >>> a = _asarray([1.0, np.inf], dtype="float64")
-    >>> b = _asarray([1.0, np.inf], dtype="float64")
+    >>> a = np.array([1.0, np.inf], dtype="float64")
+    >>> b = np.array([1.0, np.inf], dtype="float64")
     >>> pytensor.tensor.isclose(a, b).eval()
     array([ True,  True])
 

pytensor/tensor/random/op.py

@@ -10,7 +10,6 @@
 from pytensor.graph.basic import Apply, Variable, equal_computations
 from pytensor.graph.op import Op
 from pytensor.graph.replace import _vectorize_node
-from pytensor.misc.safe_asarray import _asarray
 from pytensor.scalar import ScalarVariable
 from pytensor.tensor.basic import (
     as_tensor_variable,
@@ -403,7 +402,7 @@ def perform(self, node, inputs, outputs):
         smpl_val = self.rng_fn(rng, *([*args, size]))
 
         if not isinstance(smpl_val, np.ndarray) or str(smpl_val.dtype) != self.dtype:
-            smpl_val = _asarray(smpl_val, dtype=self.dtype)
+            smpl_val = np.asarray(smpl_val, dtype=self.dtype)
 
         smpl_out[0] = smpl_val