# get indices to iterate over, in the required order
n = SVSHAPE.lims[0]
mode = SVSHAPE.lims[1]
- #print ("inner butterfly", mode)
+ print ("inner butterfly", mode)
# creating lists of indices to iterate over in each dimension
# has to be done dynamically, because it depends on the size
# first, the size-based loop (which can be done statically)
# start an infinite (wrapping) loop
skip = 0
+ k = 0
+ k_start = 0
while True:
for size in x_r: # loop over 3rd order dimension (size)
x_end = size == x_r[-1]
jr = j_r[:hz2]
#print ("xform jr", jr)
# loop over 1st order dimension
+ k = k_start
for ci, (jl, jh) in enumerate(zip(j, jr)):
z_end = jl == j[-1]
# now depending on MODE return the index. inner butterfly
if SVSHAPE.skip == 0b00: # in [0b00, 0b10]:
result = ri[ji[jl]] # lower half
elif SVSHAPE.skip == 0b01: # in [0b01, 0b11]:
- result = ri[ji[jh]] # upper half, reverse order
- elif SVSHAPE.skip == 0b10: #
- result = ci # coefficient helper
- elif SVSHAPE.skip == 0b11: #
- result = size # coefficient helper
+ result = ri[ji[jh]] # upper half
+ elif mode == 4:
+ # COS table pre-generated mode
+ if SVSHAPE.skip == 0b10: #
+ result = k # cos table offset
+ else: # mode 2
+ # COS table generated on-demand ("Vertical-First") mode
+ if SVSHAPE.skip == 0b10: #
+ result = ci # coefficient helper
+ elif SVSHAPE.skip == 0b11: #
+ result = size # coefficient helper
loopends = (z_end |
((y_end and z_end)<<1) |
((y_end and x_end and z_end)<<2))
yield result + SVSHAPE.offset, loopends
+ k += 1
# now in-place swap
if inplace_mode:
tmp1, tmp2 = ji[jlh], ji[jh]
ji[jlh], ji[jh] = tmp2, tmp1
+ # new k_start point for cos tables( runs inside x_r loop NOT i loop)
+ k_start += halfsize
+
# python "yield" can be iterated. use this to make it clear how
# the indices are generated by using natural-looking nested loops
def test_sv_remap_dct_cos_precompute_inner_8(self):
"""pre-computes a DCT COS table, using the shorter costable
- indices schedule
+ indices schedule. turns out, some COS values are repeated
+ in each layer of the DCT butterfly.
the simpler (scalar) version is in test_caller_transcendentals.py
(test_fp_coss_cvt), this is the SVP64 variant. TODO: really
"err", err)
self.assertTrue(err < 1e-6)
+ def test_sv_remap_fpmadds_dct_8_mode_4(self):
+ """>>> lst = ["svremap 31, 1, 0, 2, 0, 1, 1",
+ "svshape 8, 1, 1, 4, 0",
+ "sv.fdmadds 0.v, 0.v, 0.v, 8.v"
+ "svshape 8, 1, 1, 3, 0",
+ "sv.fadds 0.v, 0.v, 0.v"
+ ]
+ runs a full in-place 8-long O(N log2 N) DCT, both
+ inner and outer butterfly "REMAP" schedules.
+ uses shorter tables: FRC also needs to be on a Schedule
+ """
+ lst = SVP64Asm( ["svremap 31, 1, 0, 2, 0, 1, 1",
+ "svshape 8, 1, 1, 4, 0",
+ "sv.fdmadds 0.v, 0.v, 0.v, 8.v",
+ "svshape 8, 1, 1, 3, 0",
+ "sv.fadds 0.v, 0.v, 0.v"
+ ])
+ lst = list(lst)
+
+ # array and coefficients to test
+ avi = [7.0, -9.8, 3.0, -32.3, 2.1, 3.6, 0.7, -0.2]
+ n = len(avi)
+ levels = n.bit_length() - 1
+ ri = list(range(n))
+ ri = [ri[reverse_bits(i, levels)] for i in range(n)]
+ av = halfrev2(avi, False)
+ av = [av[ri[i]] for i in range(n)]
+ ctable = []
+ size = n
+ while size >= 2:
+ halfsize = size // 2
+ for ci in range(halfsize):
+ ctable.append(math.cos((ci + 0.5) * math.pi / size) * 2.0)
+ size //= 2
+
+ # store in regfile
+ fprs = [0] * 32
+ for i, a in enumerate(av):
+ fprs[i+0] = fp64toselectable(a)
+ for i, c in enumerate(ctable):
+ fprs[i+8] = fp64toselectable(1.0 / c) # invert
+
+ with Program(lst, bigendian=False) as program:
+ sim = self.run_tst_program(program, initial_fprs=fprs)
+ print ("spr svshape0", sim.spr['SVSHAPE0'])
+ print (" xdimsz", sim.spr['SVSHAPE0'].xdimsz)
+ print (" ydimsz", sim.spr['SVSHAPE0'].ydimsz)
+ print (" zdimsz", sim.spr['SVSHAPE0'].zdimsz)
+ print ("spr svshape1", sim.spr['SVSHAPE1'])
+ print ("spr svshape2", sim.spr['SVSHAPE2'])
+ print ("spr svshape3", sim.spr['SVSHAPE3'])
+
+ # outer iterative sum
+ res = transform2(avi)
+
+ for i, expected in enumerate(res):
+ print ("i", i, float(sim.fpr(i)), "expected", expected)
+ for i, expected in enumerate(res):
+ # convert to Power single
+ expected = DOUBLE2SINGLE(fp64toselectable(expected))
+ expected = float(expected)
+ actual = float(sim.fpr(i))
+ # approximate error calculation, good enough test
+ # reason: we are comparing FMAC against FMUL-plus-FADD-or-FSUB
+ # and the rounding is different
+ err = abs((actual - expected) / expected)
+ print ("err", i, err)
+ self.assertTrue(err < 1e-5)
+
def run_tst_program(self, prog, initial_regs=None,
svstate=None,
initial_mem=None,
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