fix python layer in fft module (#1127)

* fix python layer in fft module

Author: KsanaKozlova

dpnp/backend/include/dpnp_iface_fft.hpp

@@ -60,7 +60,7 @@
  * @param[in]  axis            Axis ID to compute by.
  * @param[in]  input_boundarie Limit number of elements for @ref axis.
  * @param[in]  inverse         Using inverse algorithm.
- * @param[in]  norm            Normalization mode. 0 - backward, 1 - forward.
+ * @param[in]  norm            Normalization mode. 0 - backward, 1 - forward, 2 - ortho.
  */
 template <typename _DataType>
 INP_DLLEXPORT void dpnp_fft_fft_c(const void* array_in,

dpnp/fft/dpnp_iface_fft.py

@@ -45,6 +45,7 @@
 
 from dpnp.dpnp_utils import *
 from dpnp.fft.dpnp_algo_fft import *
+from enum import Enum
 
 
 __all__ = [
@@ -69,6 +70,21 @@
 ]
 
 
+class Norm(Enum):
+    backward = 0
+    forward = 1
+    ortho = 2
+
+def get_validated_norm(norm):
+    if norm is None or norm == "backward":
+        return Norm.backward
+    if norm == "forward":
+        return Norm.forward
+    if norm == "ortho":
+        return Norm.ortho
+    raise ValueError("Unknown norm value.")
+
+
 def fft(x1, n=None, axis=-1, norm=None):
     """
     Compute the one-dimensional discrete Fourier Transform.
@@ -86,10 +102,8 @@ def fft(x1, n=None, axis=-1, norm=None):
 
     x1_desc = dpnp.get_dpnp_descriptor(x1)
     if x1_desc:
-        # if norm is None or norm is 'backward':
-        #     norm_val = 0
-        # else:
-        #     norm_val = 1
+        norm_ = get_validated_norm(norm)
+
         if axis is None:
             axis_param = -1      # the most right dimension (default value)
         else:
@@ -108,9 +122,11 @@ def fft(x1, n=None, axis=-1, norm=None):
             pass
         elif axis != -1:
             pass
+        elif x1_desc.dtype not in (numpy.complex128, numpy.complex64):
+            pass
         else:
             output_boundarie = input_boundarie
-            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, False, 0).get_pyobj()
+            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, False, norm_.value).get_pyobj()
     return call_origin(numpy.fft.fft, x1, n, axis, norm)
 
 
@@ -219,6 +235,9 @@ def fftshift(x1, axes=None):
 
     x1_desc = dpnp.get_dpnp_descriptor(x1)
     if x1_desc and 0:
+
+        norm_= Norm.backward
+
         if axis is None:
             axis_param = -1      # the most right dimension (default value)
         else:
@@ -227,7 +246,7 @@ def fftshift(x1, axes=None):
         if x1_desc.size < 1:
             pass                 # let fallback to handle exception
         else:
-            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, False).get_pyobj()
+            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, False, norm_.value).get_pyobj()
 
     return call_origin(numpy.fft.fftshift, x1, axes)
 
@@ -248,6 +267,8 @@ def hfft(x1, n=None, axis=-1, norm=None):
 
     x1_desc = dpnp.get_dpnp_descriptor(x1)
     if x1_desc and 0:
+        norm_ = get_validated_norm(norm)
+
         if axis is None:
             axis_param = -1      # the most right dimension (default value)
         else:
@@ -267,7 +288,7 @@ def hfft(x1, n=None, axis=-1, norm=None):
         else:
             output_boundarie = input_boundarie
 
-            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, False).get_pyobj()
+            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, False, norm_.value).get_pyobj()
 
     return call_origin(numpy.fft.hfft, x1, n, axis, norm)
 
@@ -287,7 +308,9 @@ def ifft(x1, n=None, axis=-1, norm=None):
     """
 
     x1_desc = dpnp.get_dpnp_descriptor(x1)
-    if x1_desc:
+    if x1_desc and 0:
+        norm_ = get_validated_norm(norm)
+
         if axis is None:
             axis_param = -1      # the most right dimension (default value)
         else:
@@ -307,7 +330,7 @@ def ifft(x1, n=None, axis=-1, norm=None):
         else:
             output_boundarie = input_boundarie
 
-            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, True).get_pyobj()
+            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, True, norm_.value).get_pyobj()
 
     return call_origin(numpy.fft.ifft, x1, n, axis, norm)
 
@@ -354,6 +377,9 @@ def ifftshift(x1, axes=None):
 
     x1_desc = dpnp.get_dpnp_descriptor(x1)
     if x1_desc and 0:
+
+        norm_ = Norm.backward
+
         if axis is None:
             axis_param = -1      # the most right dimension (default value)
         else:
@@ -362,7 +388,7 @@ def ifftshift(x1, axes=None):
         if x1_desc.size < 1:
             pass                 # let fallback to handle exception
         else:
-            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, False).get_pyobj()
+            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, False, norm_.value).get_pyobj()
 
     return call_origin(numpy.fft.ifftshift, x1, axes)
 
@@ -384,7 +410,7 @@ def ifftn(x1, s=None, axes=None, norm=None):
     """
 
     x1_desc = dpnp.get_dpnp_descriptor(x1)
-    if x1_desc:
+    if x1_desc and 0:
         if s is None:
             boundaries = tuple([x1_desc.shape[i] for i in range(x1_desc.ndim)])
         else:
@@ -432,6 +458,8 @@ def ihfft(x1, n=None, axis=-1, norm=None):
 
     x1_desc = dpnp.get_dpnp_descriptor(x1)
     if x1_desc and 0:
+        norm_ = get_validated_norm(norm)
+
         if axis is None:
             axis_param = -1      # the most right dimension (default value)
         else:
@@ -451,7 +479,7 @@ def ihfft(x1, n=None, axis=-1, norm=None):
         else:
             output_boundarie = input_boundarie
 
-            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, False).get_pyobj()
+            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, False, norm_.value).get_pyobj()
 
     return call_origin(numpy.fft.ihfft, x1, n, axis, norm)
 
@@ -472,6 +500,8 @@ def irfft(x1, n=None, axis=-1, norm=None):
 
     x1_desc = dpnp.get_dpnp_descriptor(x1)
     if x1_desc and 0:
+        norm_ = get_validated_norm(norm)
+
         if axis is None:
             axis_param = -1      # the most right dimension (default value)
         else:
@@ -491,7 +521,7 @@ def irfft(x1, n=None, axis=-1, norm=None):
         else:
             output_boundarie = 2 * (input_boundarie - 1)
 
-            result = dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, True).get_pyobj()
+            result = dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, True, norm_.value).get_pyobj()
             # TODO tmp = utils.create_output_array(result_shape, result_c_type, out)
             # tmp = dparray(result.shape, dtype=dpnp.float64)
             # for it in range(tmp.size):
@@ -592,6 +622,8 @@ def rfft(x1, n=None, axis=-1, norm=None):
 
     x1_desc = dpnp.get_dpnp_descriptor(x1)
     if x1_desc:
+        norm_ = get_validated_norm(norm)
+
         if axis is None:
             axis_param = -1                             # the most right dimension (default value)
         else:
@@ -608,10 +640,14 @@ def rfft(x1, n=None, axis=-1, norm=None):
             pass                                        # let fallback to handle exception
         elif norm is not None:
             pass
+        elif x1_desc.ndim > 1:
+            pass
+        elif x1_desc.dtype not in (numpy.complex128, numpy.complex64):
+            pass
         else:
             output_boundarie = input_boundarie // 2 + 1  # rfft specific requirenment
 
-            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, False).get_pyobj()
+            return dpnp_fft(x1_desc, input_boundarie, output_boundarie, axis_param, False, norm_.value).get_pyobj()
 
     return call_origin(numpy.fft.rfft, x1, n, axis, norm)
 
@@ -674,7 +710,7 @@ def rfftn(x1, s=None, axes=None, norm=None):
     """
 
     x1_desc = dpnp.get_dpnp_descriptor(x1)
-    if x1_desc:
+    if x1_desc and 0:
         if s is None:
             boundaries = tuple([x1_desc.shape[i] for i in range(x1_desc.ndim)])
         else: