File size: 5,719 Bytes
e2c008f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | import numpy as np
import coremltools as ct
from coremltools.converters.mil import Builder as mb
from coremltools.converters.mil.mil import types
def bit_const(v):
b = np.array([(v >> i) & 1 for i in range(32)], dtype=np.float16).reshape(1,32,1,1)
return mb.const(val=b)
def band(a,b):
return mb.mul(x=a, y=b)
def bxor(a,b):
return mb.abs(x=mb.sub(x=a, y=b))
def bor(a,b):
return mb.maximum(x=a, y=b)
def xor3(a,b,c):
return mb.abs(x=mb.sub(x=mb.abs(x=mb.sub(x=a, y=b)), y=c))
def maj(a,b,c):
return mb.maximum(
x=mb.maximum(x=mb.minimum(x=a, y=b), y=mb.minimum(x=a, y=c)),
y=mb.minimum(x=b, y=c)
)
def ch(e,f,g):
return bxor(g, band(e, bxor(f, g)))
_W_ROTR = {}
_W_SHL = {}
_W_SHR = {}
def _w_rotr(k):
W = np.zeros((32,32,1,1), dtype=np.float16)
for o in range(32):
i = (o + k) % 32
W[o, i, 0, 0] = np.float16(1.0)
return mb.const(val=W)
def _w_shl(k):
W = np.zeros((32,32,1,1), dtype=np.float16)
for o in range(32):
i = o - k
if i >= 0:
W[o, i, 0, 0] = np.float16(1.0)
return mb.const(val=W)
def _w_shr(k):
W = np.zeros((32,32,1,1), dtype=np.float16)
for o in range(32):
i = o + k
if i < 32:
W[o, i, 0, 0] = np.float16(1.0)
return mb.const(val=W)
def rotr(x,k):
k %= 32
if k == 0: return x
if k not in _W_ROTR:
_W_ROTR[k] = _w_rotr(k)
return mb.conv(x=x, weight=_W_ROTR[k], pad_type="valid", groups=1)
def shl(x,k):
k = 0 if k < 0 else (31 if k > 31 else k)
if k == 0: return x
if k not in _W_SHL:
_W_SHL[k] = _w_shl(k)
return mb.conv(x=x, weight=_W_SHL[k], pad_type="valid", groups=1)
def shr(x,k):
k = 0 if k < 0 else (31 if k > 31 else k)
if k == 0: return x
if k not in _W_SHR:
_W_SHR[k] = _w_shr(k)
return mb.conv(x=x, weight=_W_SHR[k], pad_type="valid", groups=1)
def Sigma0(x):
return xor3(rotr(x,2), rotr(x,13), rotr(x,22))
def Sigma1(x):
return xor3(rotr(x,6), rotr(x,11), rotr(x,25))
def sigma0(x):
return xor3(rotr(x,7), rotr(x,18), shr(x,3))
def sigma1(x):
return xor3(rotr(x,17), rotr(x,19), shr(x,10))
def csa(a,b,c):
return xor3(a,b,c), maj(a,b,c)
def cpa(a,b):
p0 = bxor(a,b)
p = p0
g = band(a,b)
for d in [1,2,4,8,16]:
g = bor(g, band(p, shl(g, d)))
p = band(p, shl(p, d))
return bxor(p0, shl(g, 1))
def add2(a,b):
return cpa(a,b)
def add3(a,b,c):
s1,c1 = csa(a,b,c)
return cpa(s1, shl(c1,1))
def add4(a,b,c,d):
z = mb.const(val=np.zeros((1,32,1,1), dtype=np.float16))
s1,c1 = csa(a,b,c)
s2,c2 = csa(s1,d,z)
s3,c3 = csa(s2, shl(c1,1), shl(c2,1))
return cpa(s3, shl(c3,1))
def add5(a,b,c,d,e):
s1,c1 = csa(a,b,c)
s2,c2 = csa(d,e,s1)
s3,c3 = csa(s2, shl(c1,1), shl(c2,1))
return cpa(s3, shl(c3,1))
K_vals = [
0x428a2f98,0x71374491,0xb5c0fbcf,0xe9b5dba5,0x3956c25b,0x59f111f1,0x923f82a4,0xab1c5ed5,
0xd807aa98,0x12835b01,0x243185be,0x550c7dc3,0x72be5d74,0x80deb1fe,0x9bdc06a7,0xc19bf174,
0xe49b69c1,0xefbe4786,0x0fc19dc6,0x240ca1cc,0x2de92c6f,0x4a7484aa,0x5cb0a9dc,0x76f988da,
0x983e5152,0xa831c66d,0xb00327c8,0xbf597fc7,0xc6e00bf3,0xd5a79147,0x06ca6351,0x14292967,
0x27b70a85,0x2e1b2138,0x4d2c6dfc,0x53380d13,0x650a7354,0x766a0abb,0x81c2c92e,0x92722c85,
0xa2bfe8a1,0xa81a664b,0xc24b8b70,0xc76c51a3,0xd192e819,0xd6990624,0xf40e3585,0x106aa070,
0x19a4c116,0x1e376c08,0x2748774c,0x34b0bcb5,0x391c0cb3,0x4ed8aa4a,0x5b9cca4f,0x682e6ff3,
0x748f82ee,0x78a5636f,0x84c87814,0x8cc70208,0x90befffa,0xa4506ceb,0xbef9a3f7,0xc67178f2
]
IV_vals = [
0x6a09e667,0xbb67ae85,0x3c6ef372,0xa54ff53a,0x510e527f,0x9b05688c,0x1f83d9ab,0x5be0cd19
]
flexN = ct.RangeDim(1, 1024, default=1)
N = flexN.symbol
@mb.program(
input_specs=[
mb.TensorSpec(shape=(N, 32, 1, 8), dtype=types.fp16),
mb.TensorSpec(shape=(N, 32, 1, 16), dtype=types.fp16),
],
opset_version=ct.target.iOS18,
)
def prog(midstate, w_init):
K_bits = [bit_const(k) for k in K_vals]
IV_bits = [bit_const(v) for v in IV_vals]
ONEBIT31 = bit_const(0x80000000)
LEN256 = bit_const(256)
H = mb.split(x=midstate, axis=3, num_splits=8)
W = list(mb.split(x=w_init, axis=3, num_splits=16))
for t in range(16,64):
W.append(add4(sigma1(W[t-2]), W[t-7], sigma0(W[t-15]), W[t-16]))
a,b,c,d,e,f,g,h = H
for t in range(64):
T1 = add5(h, Sigma1(e), ch(e,f,g), W[t], K_bits[t])
T2 = add2(Sigma0(a), maj(a,b,c))
a,b,c,d,e,f,g,h = add2(T1,T2), a, b, c, add2(d,T1), e, f, g
H1 = [add2(H[i], [a,b,c,d,e,f,g,h][i]) for i in range(8)]
W2 = list(H1)
Z = mb.const(val=np.zeros((1,32,1,1), dtype=np.float16))
W2.append(ONEBIT31)
W2.extend([Z,Z,Z,Z,Z,Z])
W2.append(LEN256)
for t in range(16,64):
W2.append(add4(sigma1(W2[t-2]), W2[t-7], sigma0(W2[t-15]), W2[t-16]))
a,b,c,d,e,f,g,h = [IV_bits[i] for i in range(8)]
for t in range(64):
T1 = add5(h, Sigma1(e), ch(e,f,g), W2[t], K_bits[t])
T2 = add2(Sigma0(a), maj(a,b,c))
a,b,c,d,e,f,g,h = add2(T1,T2), a, b, c, add2(d,T1), e, f, g
H2 = [add2([a,b,c,d,e,f,g,h][i], IV_bits[i]) for i in range(8)]
return mb.concat(values=H2, axis=3)
mlmodel = ct.convert(
prog,
convert_to="mlprogram",
compute_units=ct.ComputeUnit.CPU_AND_NE,
minimum_deployment_target=ct.target.iOS18,
compute_precision=ct.precision.FLOAT16,
debug=True,
inputs=[
ct.TensorType(name="midstate", shape=(flexN, 32, 1, 8), dtype=np.float16),
ct.TensorType(name="w_init", shape=(flexN, 32, 1, 16), dtype=np.float16),
],
)
mlmodel.save("sha256d.mlpackage") |