def compress(self, tensor, name):
shape = tensor.size()
tensor_flatten = tensor.flatten()
cupy_tensor = cupy.fromDlpack(to_dlpack(tensor_flatten))
tensor_cast = cupy_tensor.view(cupy.int32)
sign = tensor_cast & cupy.int32(0b10000000000000000000000000000000)
exp = tensor_cast & cupy.int32(0b01111111100000000000000000000000)
mantissa = tensor_cast & cupy.int32(0b00000000011111111111111111111111)
exp_add_one = mantissa > cupy.random.randint(low=0, high=0b00000000011111111111111111111111,
size=cupy_tensor.shape,
dtype=cupy.int32)
exponent = cupy.where(exp_add_one, exp + 0b00000000100000000000000000000000, exp)
exp_shift = cupy.clip(exponent, a_min=0b00001001000000000000000000000000, a_max=0b01001000100000000000000000000000)
exps = cupy.right_shift(exp_shift, 23)
exps = cupy.bitwise_or(cupy.right_shift(sign, 24), exps - 18)
tensor_compressed = exps.astype(cupy.uint8)
return [from_dlpack(tensor_compressed.toDlpack())], shape
def int_value_noise_3d(x, y, z, seed):
x = x.astype(int)
y = y.astype(int)
z = z.astype(int)
c = (__BN_X_NOISE_GEN * x) + (__BN_Y_NOISE_GEN * y) + (
__BN_Z_NOISE_GEN * z) + __BN_SEED_NOISE_GEN * seed
c = cp.bitwise_and(c, 0x7fffffff)
c = cp.bitwise_xor(cp.right_shift(c, 13), c)
c = cp.bitwise_and((c * (c * c * 60493 + 19990303) + 1376312589),
0x7fffffff)
return c
def bitwise_right_shift(x1: Array, x2: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.right_shift <numpy.right_shift>`.
See its docstring for more information.
"""
if x1.dtype not in _integer_dtypes or x2.dtype not in _integer_dtypes:
raise TypeError(
"Only integer dtypes are allowed in bitwise_right_shift")
# Call result type here just to raise on disallowed type combinations
_result_type(x1.dtype, x2.dtype)
x1, x2 = Array._normalize_two_args(x1, x2)
# Note: bitwise_right_shift is only defined for x2 nonnegative.
if np.any(x2._array < 0):
raise ValueError(
"bitwise_right_shift(x1, x2) is only defined for x2 >= 0")
return Array._new(np.right_shift(x1._array, x2._array))
def gradient_noise_3d(fx, fy, fz, ix, iy, iz, seed):
vi = (__BN_X_NOISE_GEN * ix) + (__BN_Y_NOISE_GEN * iy) + (
__BN_Z_NOISE_GEN * iz) + __BN_SEED_NOISE_GEN * seed
vi = cp.bitwise_and(vi, 0xffffffff)
vi = cp.bitwise_xor(vi, cp.right_shift(vi, __BN_SHIFT_NOISE_GEN))
vi = cp.bitwise_and(vi, 0xff)
vi_l2 = cp.left_shift(vi, 2)
xvGrad = g_randomVectors[vi_l2]
yvGrad = g_randomVectors[vi_l2 + 1]
zvGrad = g_randomVectors[vi_l2 + 2]
xvPoint = fx - ix
yvPoint = fy - iy
zvPoint = fz - iz
return ((xvGrad * xvPoint) + (yvGrad * yvPoint) +
(zvGrad * zvPoint)) * 2.12
def __rrshift__(self, other):
return cupy.right_shift(other, self)
