[mlir][python][ctypes] fix ctype python binding complication for complex

[aartbik]

Summary

There is no direct ctypes for MLIR's complex (and thus np.complex128
and np.complex64) yet, causing the mlir python binding methods for
memrefs to crash. This revision fixes this by passing complex arrays
as tuples of floats, correcting at the boundaries for the proper view.

NOTE: some of these changes (4 -> 2) were forced by the new "linting"

[stellaraccident]

I think Mehdi wrote some of this originally. I'll review as well but need to study it...

[mehdi_amini]

Can you add tests for this?

[aartbik]

added tests for c128 and c64, ranked and unranked, memrefs

[aartbik]

In D126422#3541245, @aartbik wrote:

added tests for c128 and c64, ranked and unranked, memrefs

Always good to have tests!

(note that this was fully tested in another context obviously, but this should indeed by in-tree too)

[aartbik]

rebased with main

[aartbik]

@mehdi_amini , good to go?

mlir/python/mlir/runtime/np_to_memref.py

 #  Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 #  See https://llvm.org/LICENSE.txt for license information.
 #  SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 
 #  This file contains functions to convert between Memrefs and NumPy arrays and vice-versa.
 
 import numpy as np
 import ctypes
 
 
+class C128(ctypes.Structure):
+  """A ctype representation for MLIR's Double Complex."""
+  _fields_ = [("real", ctypes.c_double), ("imag", ctypes.c_double)]
+
+
+class C64(ctypes.Structure):
+  """A ctype representation for MLIR's Float Complex."""
+  _fields_ = [("real", ctypes.c_float), ("imag", ctypes.c_float)]
+
+
+def as_ctype(dtp):
+  """Converts dtype to ctype."""
+  if dtp is np.dtype(np.complex128):
+    return C128
+  if dtp is np.dtype(np.complex64):
+    return C64
+  return np.ctypeslib.as_ctypes_type(dtp)
+
+
 def make_nd_memref_descriptor(rank, dtype):
-    class MemRefDescriptor(ctypes.Structure):
-        """
-        Build an empty descriptor for the given rank/dtype, where rank>0.
-        """
+
+    """Builds an empty descriptor for the given rank/dtype, where rank>0."""
 
-        _fields_ = [
-            ("allocated", ctypes.c_longlong),
-            ("aligned", ctypes.POINTER(dtype)),
-            ("offset", ctypes.c_longlong),
-            ("shape", ctypes.c_longlong * rank),
-            ("strides", ctypes.c_longlong * rank),
-        ]
 
-    return MemRefDescriptor
 
 
 def make_zero_d_memref_descriptor(dtype):
-    class MemRefDescriptor(ctypes.Structure):
-        """
-        Build an empty descriptor for the given dtype, where rank=0.
-        """
+
+    """Builds an empty descriptor for the given dtype, where rank=0."""
 
-        _fields_ = [
-            ("allocated", ctypes.c_longlong),
-            ("aligned", ctypes.POINTER(dtype)),
-            ("offset", ctypes.c_longlong),
-        ]
 
-    return MemRefDescriptor
 
 
 class UnrankedMemRefDescriptor(ctypes.Structure):
-    """ Creates a ctype struct for memref descriptor"""
-
-    _fields_ = [("rank", ctypes.c_longlong), ("descriptor", ctypes.c_void_p)]
+  """Creates a ctype struct for memref descriptor"""
 
 
 def get_ranked_memref_descriptor(nparray):
-    """
-    Return a ranked memref descriptor for the given numpy array.
-    """
-    if nparray.ndim == 0:
-        x = make_zero_d_memref_descriptor(np.ctypeslib.as_ctypes_type(nparray.dtype))()
-        x.allocated = nparray.ctypes.data
-        x.aligned = nparray.ctypes.data_as(
-            ctypes.POINTER(np.ctypeslib.as_ctypes_type(nparray.dtype))
-        )
-        x.offset = ctypes.c_longlong(0)
-        return x
+  """Returns a ranked memref descriptor for the given numpy array."""
+  ctp = as_ctype(nparray.dtype)
+    x = make_zero_d_memref_descriptor(ctp)()
+    x.aligned = nparray.ctypes.data_as(ctypes.POINTER(ctp))
 
-    x = make_nd_memref_descriptor(
-        nparray.ndim, np.ctypeslib.as_ctypes_type(nparray.dtype)
-    )()
-    x.allocated = nparray.ctypes.data
-    x.aligned = nparray.ctypes.data_as(
-        ctypes.POINTER(np.ctypeslib.as_ctypes_type(nparray.dtype))
-    )
-    x.offset = ctypes.c_longlong(0)
-    x.shape = nparray.ctypes.shape
+  x = make_nd_memref_descriptor(nparray.ndim, ctp)()
+  x.aligned = nparray.ctypes.data_as(ctypes.POINTER(ctp))
 
-    # Numpy uses byte quantities to express strides, MLIR OTOH uses the
-    # torch abstraction which specifies strides in terms of elements.
-    strides_ctype_t = ctypes.c_longlong * nparray.ndim
-    x.strides = strides_ctype_t(*[x // nparray.itemsize for x in nparray.strides])
-    return x
 
 
 def get_unranked_memref_descriptor(nparray):
-    """
-    Return a generic/unranked memref descriptor for the given numpy array.
-    """
-    d = UnrankedMemRefDescriptor()
-    d.rank = nparray.ndim
-    x = get_ranked_memref_descriptor(nparray)
-    d.descriptor = ctypes.cast(ctypes.pointer(x), ctypes.c_void_p)
-    return d
+  """Returns a generic/unranked memref descriptor for the given numpy array."""
 
 
 def unranked_memref_to_numpy(unranked_memref, np_dtype):
-    """
-    Converts unranked memrefs to numpy arrays.
-    """
-    descriptor = make_nd_memref_descriptor(
-        unranked_memref[0].rank, np.ctypeslib.as_ctypes_type(np_dtype)
-    )
-    val = ctypes.cast(unranked_memref[0].descriptor, ctypes.POINTER(descriptor))
-    np_arr = np.ctypeslib.as_array(val[0].aligned, shape=val[0].shape)
-    strided_arr = np.lib.stride_tricks.as_strided(
-        np_arr,
-        np.ctypeslib.as_array(val[0].shape),
-        np.ctypeslib.as_array(val[0].strides) * np_arr.itemsize,
-    )
-    return strided_arr
+  """Converts unranked memrefs to numpy arrays."""
+  ctp = as_ctype(np_dtype)
+  descriptor = make_nd_memref_descriptor(unranked_memref[0].rank, ctp)
+  if strided_arr.dtype == C128:
+    return strided_arr.view("complex128")
+  if strided_arr.dtype == C64:
+    return strided_arr.view("complex64")
 
 
 def ranked_memref_to_numpy(ranked_memref):
-    """
-    Converts ranked memrefs to numpy arrays.
-    """
-    np_arr = np.ctypeslib.as_array(
-        ranked_memref[0].aligned, shape=ranked_memref[0].shape
-    )
-    strided_arr = np.lib.stride_tricks.as_strided(
-        np_arr,
-        np.ctypeslib.as_array(ranked_memref[0].shape),
-        np.ctypeslib.as_array(ranked_memref[0].strides) * np_arr.itemsize,
-    )
-    return strided_arr
+  """Converts ranked memrefs to numpy arrays."""
+      ranked_memref[0].aligned, shape=ranked_memref[0].shape)
+  if strided_arr.dtype == C128:
+    return strided_arr.view("complex128")
+  if strided_arr.dtype == C64:
+    return strided_arr.view("complex64")