Remove dead ducc_fft code.

I guess this was omitted when we switched over to using stablehlo.fft since XLA now calls DUCC itself.

PiperOrigin-RevId: 684437739

jax/_src/internal_test_util/export_back_compat_test_util.py

@@ -20,8 +20,7 @@ work as before. We test this here.
 The tests in this file refer to the test data in
 jax/_src/internal_test_util/export_back_compat_test_data.
 
-There is one test for each version of a custom call target, e.g.,
-`test_ducc_fft` tests the FFT custom calls on CPU.
+There is one test for each version of a custom call target.
 Only custom call targets tested here should be listed in
 export._CUSTOM_CALL_TARGETS_GUARANTEED_STABLE. All other custom
 call targets will result in an error when encountered during serialization.

jaxlib/ducc_fft.py

@@ -1,92 +0,0 @@
-# Copyright 2020 The JAX Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import jaxlib.mlir.ir as ir
-import jaxlib.mlir.dialects.stablehlo as hlo
-
-
-from .hlo_helpers import custom_call
-from .cpu import _ducc_fft
-import numpy as np
-
-from jaxlib import xla_client
-
-for _name, _value in _ducc_fft.registrations().items():
-  xla_client.register_custom_call_target(_name, _value, platform="cpu")
-
-FftType = xla_client.FftType
-
-
-_C2C = 0
-_C2R = 1
-_R2C = 2
-
-
-def _dynamic_ducc_fft_descriptor(
-    dtype, ndims: int, fft_type: FftType, fft_lengths: list[int]
-) -> bytes:
-  assert len(fft_lengths) >= 1
-  assert len(fft_lengths) <= ndims, (fft_lengths, ndims)
-
-  forward = fft_type in (FftType.FFT, FftType.RFFT)
-  is_double = np.finfo(dtype).dtype == np.float64
-  if fft_type == FftType.RFFT:
-    ducc_fft_type = _R2C
-  elif fft_type == FftType.IRFFT:
-    ducc_fft_type = _C2R
-  else:
-    ducc_fft_type = _C2C
-
-  # Builds a PocketFftDescriptor flatbuffer. This descriptor is passed to the
-  # C++ kernel to describe the FFT to perform.
-  axes = [ndims - len(fft_lengths) + d for d in range(len(fft_lengths))]
-
-  descriptor = _ducc_fft.dynamic_ducc_fft_descriptor(
-    ndims=ndims,
-    is_double=is_double,
-    fft_type=ducc_fft_type,
-    axes=axes,
-    forward=forward)
-
-  return descriptor
-
-
-def dynamic_ducc_fft_hlo(
-    result_type: ir.Type,
-    input: ir.Value, *,
-    input_dtype: np.dtype, ndims:int, input_shape: ir.Value,
-    strides_in: ir.Value, strides_out: ir.Value, scale: ir.Value,
-    fft_type: FftType, fft_lengths: list[int], result_shape: ir.Value):
-  """DUCC FFT kernel for CPU, with support for dynamic shapes."""
-  a_type = ir.RankedTensorType(input.type)
-
-  fft_lengths = list(fft_lengths)
-  descriptor_bytes = _dynamic_ducc_fft_descriptor(
-      input_dtype, ndims, fft_type, fft_lengths)
-
-  # PocketFft does not allow size 0 dimensions, but we handled this in fft.py
-  assert 0 not in a_type.shape
-
-  u8_type = ir.IntegerType.get_unsigned(8)
-  descriptor = hlo.constant(
-      ir.DenseElementsAttr.get(
-          np.frombuffer(descriptor_bytes, dtype=np.uint8), type=u8_type))
-  layout = tuple(range(ndims - 1, -1, -1))
-  return custom_call(
-      "dynamic_ducc_fft",
-      result_types=[result_type],
-      operands=[descriptor, input, input_shape, strides_in, strides_out, scale],
-      operand_layouts=[[0], layout, [0], [0], [0], [0]],
-      result_layouts=[layout],
-      result_shapes=[result_shape]).results