Implement C code for ExtractDiagonal and ARange

Set view flag of ExtractDiagonal to True and respect by default

Author: ricardoV94

pytensor/tensor/basic.py

@@ -3207,13 +3207,14 @@ def tile(
     return A_replicated.reshape(tiled_shape)
 
 
-class ARange(Op):
+class ARange(COp):
     """Create an array containing evenly spaced values within a given interval.
 
     Parameters and behaviour are the same as numpy.arange().
 
     """
 
+    # TODO: Arange should work with scalars as inputs, not arrays
     __props__ = ("dtype",)
 
     def __init__(self, dtype):
@@ -3293,13 +3294,30 @@ def upcast(var):
                 )
             ]
 
-    def perform(self, node, inp, out_):
-        start, stop, step = inp
-        (out,) = out_
-        start = start.item()
-        stop = stop.item()
-        step = step.item()
-        out[0] = np.arange(start, stop, step, dtype=self.dtype)
+    def perform(self, node, inputs, output_storage):
+        start, stop, step = inputs
+        output_storage[0][0] = np.arange(
+            start.item(), stop.item(), step.item(), dtype=self.dtype
+        )
+
+    def c_code(self, node, nodename, input_names, output_names, sub):
+        [start_name, stop_name, step_name] = input_names
+        [out_name] = output_names
+        typenum = np.dtype(self.dtype).num
+        return f"""
+            double start = ((dtype_{start_name}*)PyArray_DATA({start_name}))[0];
+            double stop = ((dtype_{stop_name}*)PyArray_DATA({stop_name}))[0];
+            double step = ((dtype_{step_name}*)PyArray_DATA({step_name}))[0];
+            //printf("start: %f, stop: %f, step: %f\\n", start, stop, step);
+            Py_XDECREF({out_name});
+            {out_name} = (PyArrayObject*) PyArray_Arange(start, stop, step, {typenum});
+            if (!{out_name}) {{
+                {sub["fail"]}
+            }}
+        """
+
+    def c_code_cache_version(self):
+        return (0,)
 
     def connection_pattern(self, node):
         return [[True], [False], [True]]
@@ -3685,8 +3703,7 @@ def inverse_permutation(perm):
     )
 
 
-# TODO: optimization to insert ExtractDiag with view=True
-class ExtractDiag(Op):
+class ExtractDiag(COp):
     """
     Return specified diagonals.
 
@@ -3742,7 +3759,7 @@ class ExtractDiag(Op):
 
     __props__ = ("offset", "axis1", "axis2", "view")
 
-    def __init__(self, offset=0, axis1=0, axis2=1, view=False):
+    def __init__(self, offset=0, axis1=0, axis2=1, view=True):
         self.view = view
         if self.view:
             self.view_map = {0: [0]}
@@ -3765,24 +3782,74 @@ def make_node(self, x):
         if x.ndim < 2:
             raise ValueError("ExtractDiag needs an input with 2 or more dimensions", x)
 
-        out_shape = [
-            st_dim
-            for i, st_dim in enumerate(x.type.shape)
-            if i not in (self.axis1, self.axis2)
-        ] + [None]
+        if (dim1 := x.type.shape[self.axis1]) is not None and (
+            dim2 := x.type.shape[self.axis2]
+        ) is not None:
+            offset = self.offset
+            if offset > 0:
+                diag_size = int(np.clip(dim2 - offset, 0, dim1))
+            elif offset < 0:
+                diag_size = int(np.clip(dim1 + offset, 0, dim2))
+            else:
+                diag_size = int(np.minimum(dim1, dim2))
+        else:
+            diag_size = None
+
+        out_shape = (
+            *(
+                dim
+                for i, dim in enumerate(x.type.shape)
+                if i not in (self.axis1, self.axis2)
+            ),
+            diag_size,
+        )
 
         return Apply(
             self,
             [x],
-            [x.type.clone(dtype=x.dtype, shape=tuple(out_shape))()],
+            [x.type.clone(dtype=x.dtype, shape=out_shape)()],
         )
 
-    def perform(self, node, inputs, outputs):
+    def perform(self, node, inputs, output_storage):
         (x,) = inputs
-        (z,) = outputs
-        z[0] = x.diagonal(self.offset, self.axis1, self.axis2)
-        if not self.view:
-            z[0] = z[0].copy()
+        out = x.diagonal(self.offset, self.axis1, self.axis2)
+        if self.view:
+            try:
+                out.flags.writeable = True
+            except ValueError:
+                # We can't make this array writable
+                out = out.copy()
+        else:
+            out = out.copy()
+        output_storage[0][0] = out
+
+    def c_code(self, node, nodename, input_names, output_names, sub):
+        [x_name] = input_names
+        [out_name] = output_names
+        return f"""
+        Py_XDECREF({out_name});
+
+        {out_name} = (PyArrayObject*) PyArray_Diagonal({x_name}, {self.offset}, {self.axis1}, {self.axis2});
+        if (!{out_name}) {{
+            {sub["fail"]}  // Error already set by Numpy
+        }}
+
+        if ({int(self.view)} && PyArray_ISWRITEABLE({x_name})) {{
+            // Make output writeable if input was writeable
+            PyArray_ENABLEFLAGS({out_name}, NPY_ARRAY_WRITEABLE);
+        }} else {{
+            // Make a copy
+            PyArrayObject *{out_name}_copy = (PyArrayObject*) PyArray_Copy({out_name});
+            Py_DECREF({out_name});
+            if (!{out_name}_copy) {{
+                {sub['fail']};  // Error already set by Numpy
+            }}
+            {out_name} = {out_name}_copy;
+        }}
+        """
+
+    def c_code_cache_version(self):
+        return (0,)
 
     def grad(self, inputs, gout):
         # Avoid circular import
@@ -3829,19 +3896,6 @@ def infer_shape(self, fgraph, node, shapes):
         out_shape.append(diag_size)
         return [tuple(out_shape)]
 
-    def __setstate__(self, state):
-        self.__dict__.update(state)
-
-        if self.view:
-            self.view_map = {0: [0]}
-
-        if "offset" not in state:
-            self.offset = 0
-        if "axis1" not in state:
-            self.axis1 = 0
-        if "axis2" not in state:
-            self.axis2 = 1
-
 
 def extract_diag(x):
     warnings.warn(