def get_common_kwds(dtype, device_params):
return dict(
dtype=dtype,
min_mem_coalesce_width=device_params.min_mem_coalesce_width[dtype.itemsize],
local_mem_banks=device_params.local_mem_banks,
get_padding=get_padding,
wrap_const=lambda x: dtypes.c_constant(x, dtypes.real_for(dtype)),
min_blocks=helpers.min_blocks,
mul=functions.mul(dtype, dtype),
polar_unit=functions.polar_unit(dtypes.real_for(dtype)),
cdivs=functions.div(dtype, numpy.uint32, out_dtype=dtype))
def fft512(use_constant_memory=False):
module = Module(TEMPLATE.get_def('fft512'),
render_kwds=dict(
elem_ctype=dtypes.ctype(numpy.complex128),
temp_ctype=dtypes.ctype(numpy.float64),
cdata_ctype=dtypes.ctype(numpy.complex128),
polar_unit=functions.polar_unit(numpy.float64),
mul=functions.mul(numpy.complex128, numpy.complex128),
use_constant_memory=use_constant_memory,
))
return FFT512(module, use_constant_memory)
def get_common_kwds(dtype, device_params):
return dict(
dtype=dtype,
min_mem_coalesce_width=device_params.min_mem_coalesce_width[dtype.itemsize],
local_mem_banks=device_params.local_mem_banks,
get_padding=get_padding,
wrap_const=lambda x: dtypes.c_constant(x, dtypes.real_for(dtype)),
min_blocks=helpers.min_blocks,
mul=functions.mul(dtype, dtype),
polar_unit=functions.polar_unit(dtypes.real_for(dtype)),
cdivs=functions.div(dtype, numpy.uint32, out_dtype=dtype))
def unimod_gen(size, single=True):
if single:
dtype = np.complex64
else:
dtype = np.complex128
unimod = Transformation([
Parameter('output', Annotation(Type(dtype, size), 'o')),
Parameter('input', Annotation(Type(dtype, size), 'i'))
],
'''
${input.ctype} val = ${input.load_same};
${output.store_same}(${polar_unit}(atan2(val.y, val.x)));
''',
render_kwds=dict(polar_unit=functions.polar_unit(
dtype=np.float32 if single else np.double)))
return unimod
def unimod_gen(size, single=True):
if single:
dtype = np.complex64
else:
dtype = np.complex128
unimod = Transformation(
[
Parameter('output', Annotation(Type(dtype, size), 'o')),
Parameter('input', Annotation(Type(dtype, size), 'i'))
],
'''
${input.ctype} val = ${input.load_same};
${output.store_same}(${polar_unit}(atan2(val.y, val.x)));
''',
render_kwds=dict(polar_unit=functions.polar_unit(dtype=np.float32 if single else np.double))
)
return unimod
def normal_bm(bijection, dtype, mean=0, std=1):
"""
Generates normally distributed random numbers with the mean ``mean`` and
the standard deviation ``std`` using Box-Muller transform.
Supported dtypes: ``float(32/64)``, ``complex(64/128)``.
Produces two random numbers per call for real types and one number for complex types.
Returns a :py:class:`~reikna.cbrng.samplers.Sampler` object.
.. note::
In case of a complex ``dtype``, ``std`` refers to the standard deviation of the
complex numbers (same as ``numpy.std()`` returns), not real and imaginary components
(which will be normally distributed with the standard deviation ``std / sqrt(2)``).
Consequently, while ``mean`` is of type ``dtype``, ``std`` must be real.
"""
if dtypes.is_complex(dtype):
r_dtype = dtypes.real_for(dtype)
c_dtype = dtype
else:
r_dtype = dtype
c_dtype = dtypes.complex_for(dtype)
uf = uniform_float(bijection, r_dtype, low=0, high=1)
module = Module(TEMPLATE.get_def("normal_bm"),
render_kwds=dict(complex_res=dtypes.is_complex(dtype),
r_dtype=r_dtype,
r_ctype=dtypes.ctype(r_dtype),
c_dtype=c_dtype,
c_ctype=dtypes.ctype(c_dtype),
polar_unit=functions.polar_unit(r_dtype),
bijection=bijection,
mean=mean,
std=std,
uf=uf))
return Sampler(bijection,
module,
dtype,
deterministic=uf.deterministic,
randoms_per_call=1 if dtypes.is_complex(dtype) else 2)
def normal_bm(bijection, dtype, mean=0, std=1):
"""
Generates normally distributed random numbers with the mean ``mean`` and
the standard deviation ``std`` using Box-Muller transform.
Supported dtypes: ``float(32/64)``, ``complex(64/128)``.
Produces two random numbers per call for real types and one number for complex types.
Returns a :py:class:`~reikna.cbrng.samplers.Sampler` object.
.. note::
In case of a complex ``dtype``, ``std`` refers to the standard deviation of the
complex numbers (same as ``numpy.std()`` returns), not real and imaginary components
(which will be normally distributed with the standard deviation ``std / sqrt(2)``).
Consequently, while ``mean`` is of type ``dtype``, ``std`` must be real.
"""
if dtypes.is_complex(dtype):
r_dtype = dtypes.real_for(dtype)
c_dtype = dtype
else:
r_dtype = dtype
c_dtype = dtypes.complex_for(dtype)
uf = uniform_float(bijection, r_dtype, low=0, high=1)
module = Module(
TEMPLATE.get_def("normal_bm"),
render_kwds=dict(
complex_res=dtypes.is_complex(dtype),
r_dtype=r_dtype, r_ctype=dtypes.ctype(r_dtype),
c_dtype=c_dtype, c_ctype=dtypes.ctype(c_dtype),
polar_unit=functions.polar_unit(r_dtype),
bijection=bijection,
mean=mean,
std=std,
uf=uf))
return Sampler(
bijection, module, dtype,
deterministic=uf.deterministic, randoms_per_call=1 if dtypes.is_complex(dtype) else 2)
def get_procs(thr, N):
fft = FFTFactory.create(thr, (N,), compile_=False)
unimod_trans = Transformation(
[Parameter('output', Annotation(Type(np.complex128, N), 'o')),
Parameter('input', Annotation(Type(np.complex128, N), 'i'))],
"""
VSIZE_T idx = ${idxs[0]};
${input.ctype} val = ${input.load_same};
if (idx>${N}/2){
val.x = 0.0;
val.y = 0.0;
${output.store_same}(val);
}else
${output.store_same}(${polar_unit}(atan2(val.y, val.x)));
""",
render_kwds=dict(polar_unit=functions.polar_unit(dtype=np.float64), N=N)
)
fft.parameter.output.connect(unimod_trans, unimod_trans.input, uni=unimod_trans.output)
fft_unimod = fft.compile(thr)
mag_square = PureParallel(
[Parameter('output', Annotation(Type(np.complex128, N), 'o')),
Parameter('input', Annotation(Type(np.complex128, N), 'i'))],
'''
VSIZE_T idx = ${idxs[0]};
${input.ctype} val = ${input.load_idx}(idx);
val.x = val.x*val.x + val.y*val.y;
val.y = 0;
${output.store_idx}(idx, val);
'''
)
mag_square = mag_square.compile(thr)
apply_mask = PureParallel(
[Parameter('output', Annotation(Type(np.complex128, N), 'o')),
Parameter('origin', Annotation(Type(np.complex128, N), 'i')),
Parameter('mask', Annotation(Type(np.double, N), 'i'))],
'''
VSIZE_T idx = ${idxs[0]};
${output.store_idx}(idx, ${mul}(${origin.load_idx}(idx), ${mask.load_idx}(idx)));
''',
render_kwds=dict(mul=functions.mul(np.complex128, np.double))
)
apply_mask = apply_mask.compile(thr)
combine_mag_phi = PureParallel(
[Parameter('output', Annotation(Type(np.complex128, N), 'o')),
Parameter('mag_square', Annotation(Type(np.complex128, N), 'i')),
Parameter('phase', Annotation(Type(np.complex128, N), 'i'))],
'''
VSIZE_T idx = ${idxs[0]};
double r = ${mag_square.load_idx}(idx).x;
r = r<0.0 ? 0.0 : ${pow}(r, 0.5);
double2 v = ${phase.load_idx}(idx);
double angle = atan2(v.y, v.x);
${output.store_idx}(idx, ${polar}(r, angle));
''',
render_kwds=dict(pow=functions.pow(np.double), polar=functions.polar(np.double))
)
combine_mag_phi = combine_mag_phi.compile(thr)
return fft_unimod, mag_square, apply_mask, combine_mag_phi
def test_polar_unit(thr, out_code, in_codes):
out_dtype, in_dtypes = generate_dtypes(out_code, in_codes)
check_func(thr, functions.polar_unit(in_dtypes[0]),
lambda theta: numpy.exp(1j * theta), out_dtype, in_dtypes)
def test_polar_unit(thr, out_code, in_codes):
out_dtype, in_dtypes = generate_dtypes(out_code, in_codes)
check_func(
thr, functions.polar_unit(in_dtypes[0]),
lambda theta: numpy.exp(1j * theta), out_dtype, in_dtypes)
def _build_plan(self, plan_factory, device_params, output, alpha, beta):
plan = plan_factory()
samples, modes = alpha.shape
for_reduction = Type(alpha.dtype, (samples, self._max_total_clicks + 1))
prepared_state = plan.temp_array_like(alpha)
plan.kernel_call(
TEMPLATE.get_def("compound_click_probability_prepare"),
[prepared_state, alpha, beta],
kernel_name="compound_click_probability_prepare",
global_size=alpha.shape,
render_kwds=dict(
mul_cc=functions.mul(alpha.dtype, alpha.dtype),
exp_c=functions.exp(alpha.dtype),
))
# Block size is limited by the amount of available local memory.
# In some OpenCL implementations the number reported cannot actually be fully used
# (because it's used by kernel arguments), so we're padding it a little.
local_mem_size = device_params.local_mem_size
max_elems = (local_mem_size - 256) // alpha.dtype.itemsize
block_size = 2**helpers.log2(max_elems)
# No reason to have block size larger than the number of modes
block_size = min(block_size, helpers.bounding_power_of_2(modes))
products_gsize = (samples, helpers.min_blocks(self._max_total_clicks + 1, block_size) * block_size)
products = plan.temp_array_like(for_reduction)
read_size = min(block_size, device_params.max_work_group_size)
while read_size > 1:
full_steps = modes // block_size
remainder_size = modes % block_size
try:
plan.kernel_call(
TEMPLATE.get_def("compound_click_probability_aggregate"),
[products, prepared_state],
kernel_name="compound_click_probability_aggregate",
global_size=products_gsize,
local_size=(1, read_size,),
render_kwds=dict(
block_size=block_size,
read_size=read_size,
full_steps=full_steps,
remainder_size=remainder_size,
output_size=self._max_total_clicks + 1,
mul_cc=functions.mul(alpha.dtype, alpha.dtype),
add_cc=functions.add(alpha.dtype, alpha.dtype),
polar_unit=functions.polar_unit(dtypes.real_for(alpha.dtype)),
modes=self._system.modes,
max_total_clicks=self._max_total_clicks,
))
except OutOfResourcesError:
read_size //= 2
break
reduction = Reduce(for_reduction, predicate_sum(alpha.dtype), axes=(0,))
temp = plan.temp_array_like(reduction.parameter.output)
plan.computation_call(reduction, temp, products)
fft = FFT(temp)
real_trf = Transformation([
Parameter('output', Annotation(output, 'o')),
Parameter('input', Annotation(temp, 'i')),
],
"""
${input.ctype} val = ${input.load_same};
${output.store_same}(val.x);
""")
fft.parameter.output.connect(real_trf, real_trf.input, output_p=real_trf.output)
plan.computation_call(fft, output, temp, True)
return plan
def get_procs(thr, N):
fft = FFTFactory.create(thr, (N, ), compile_=False)
unimod_trans = Transformation(
[
Parameter('output', Annotation(Type(np.complex128, N), 'o')),
Parameter('input', Annotation(Type(np.complex128, N), 'i'))
],
"""
VSIZE_T idx = ${idxs[0]};
${input.ctype} val = ${input.load_same};
if (idx>${N}/2){
val.x = 0.0;
val.y = 0.0;
${output.store_same}(val);
}else
${output.store_same}(${polar_unit}(atan2(val.y, val.x)));
""",
render_kwds=dict(polar_unit=functions.polar_unit(dtype=np.float64),
N=N))
fft.parameter.output.connect(unimod_trans,
unimod_trans.input,
uni=unimod_trans.output)
fft_unimod = fft.compile(thr)
mag_square = PureParallel([
Parameter('output', Annotation(Type(np.complex128, N), 'o')),
Parameter('input', Annotation(Type(np.complex128, N), 'i'))
], '''
VSIZE_T idx = ${idxs[0]};
${input.ctype} val = ${input.load_idx}(idx);
val.x = val.x*val.x + val.y*val.y;
val.y = 0;
${output.store_idx}(idx, val);
''')
mag_square = mag_square.compile(thr)
apply_mask = PureParallel(
[
Parameter('output', Annotation(Type(np.complex128, N), 'o')),
Parameter('origin', Annotation(Type(np.complex128, N), 'i')),
Parameter('mask', Annotation(Type(np.double, N), 'i'))
],
'''
VSIZE_T idx = ${idxs[0]};
${output.store_idx}(idx, ${mul}(${origin.load_idx}(idx), ${mask.load_idx}(idx)));
''',
render_kwds=dict(mul=functions.mul(np.complex128, np.double)))
apply_mask = apply_mask.compile(thr)
combine_mag_phi = PureParallel([
Parameter('output', Annotation(Type(np.complex128, N), 'o')),
Parameter('mag_square', Annotation(Type(np.complex128, N), 'i')),
Parameter('phase', Annotation(Type(np.complex128, N), 'i'))
],
'''
VSIZE_T idx = ${idxs[0]};
double r = ${mag_square.load_idx}(idx).x;
r = r<0.0 ? 0.0 : ${pow}(r, 0.5);
double2 v = ${phase.load_idx}(idx);
double angle = atan2(v.y, v.x);
${output.store_idx}(idx, ${polar}(r, angle));
''',
render_kwds=dict(
pow=functions.pow(np.double),
polar=functions.polar(np.double)))
combine_mag_phi = combine_mag_phi.compile(thr)
return fft_unimod, mag_square, apply_mask, combine_mag_phi
