def sample_discrete(densities, logged=False, return_gpuarray=False):
"""
Takes a categorical sample from the unnormalized univariate
densities defined in the rows of 'densities'
Parameters
---------
densities : ndarray or gpuarray (n, k)
logged: boolean indicating whether densities is on the
log scale ...
Returns
-------
indices : ndarray or gpuarray (if return_gpuarray=True)
of length n and dtype = int32
"""
from gpustats.util import info
n, k = densities.shape
# prep data
if isinstance(densities, GPUArray):
if densities.flags.f_contiguous:
gpu_densities = util.transpose(densities)
else:
gpu_densities = densities
else:
densities = util.prep_ndarray(densities)
gpu_densities = to_gpu(densities)
# get gpu function
cu_func = cu_module.get_function('sample_discrete')
# setup GPU data
gpu_random = to_gpu(np.asarray(np.random.rand(n), dtype=np.float32))
gpu_dest = gpu_empty(n, dtype=np.int32)
dims = np.array([n, k, logged], dtype=np.int32)
if info.max_block_threads < 1024:
x_block_dim = 16
else:
x_block_dim = 32
y_block_dim = 16
# setup GPU call
block_design = (x_block_dim, y_block_dim, 1)
grid_design = (int(n / y_block_dim) + 1, 1)
shared_mem = 4 * ((x_block_dim + 1) * y_block_dim + 2 * y_block_dim)
cu_func(gpu_densities,
gpu_random,
gpu_dest,
dims[0],
dims[1],
dims[2],
block=block_design,
grid=grid_design,
shared=shared_mem)
gpu_random.gpudata.free()
if return_gpuarray:
return gpu_dest
else:
res = gpu_dest.get()
gpu_dest.gpudata.free()
return res
def sample_discrete(in_densities, logged=False, pad=False,
return_gpuarray=False):
"""
Takes a categorical sample from the unnormalized univariate
densities defined in the rows of 'densities'
Parameters
---------
densities : ndarray or gpuarray (n, k)
logged: boolean indicating whether densities is on the
log scale ...
Returns
-------
indices : ndarray or gpuarray (if return_gpuarray=True)
of length n and dtype = int32
"""
if pad:
if logged:
densities = util.pad_data_mult16(in_densities, fill=1)
else:
densities = util.pad_data_mult16(in_densities, fill=0)
else:
densities = in_densities
n, k = densities.shape
if logged:
cu_func = cu_module.get_function('sample_discrete_logged')
else:
cu_func = cu_module.get_function('sample_discrete')
if isinstance(densities, GPUArray):
if densities.flags.f_contiguous:
densities.reshape(k, n, 'C')
gpu_densities = util.transpose(densities)
else:
gpu_densities = densities
else:
densities = util.prep_ndarray(densities)
gpu_densities = to_gpu(densities)
# setup GPU data
#gpu_random = curand(n)
gpu_random = to_gpu(np.asarray(np.random.rand(n), dtype=np.float32))
#gpu_dest = to_gpu(np.zeros(n, dtype=np.float32))
gpu_dest = gpu_empty(n, dtype=np.float32)
stride = gpu_densities.shape[1]
if stride % 2 == 0:
stride += 1
dims = np.array([n,k, gpu_densities.shape[1], stride],dtype=np.int32)
# optimize design ...
grid_design, block_design = _tune_sfm(n, stride, cu_func.num_regs)
shared_mem = 4 * (block_design[0] * stride +
1 * block_design[0])
cu_func(gpu_densities, gpu_random, gpu_dest,
dims[0], dims[1], dims[2], dims[3],
block=block_design, grid=grid_design, shared=shared_mem)
gpu_random.gpudata.free()
if return_gpuarray:
return gpu_dest
else:
res = gpu_dest.get()
gpu_dest.gpudata.free()
return res
def sample_discrete(densities, logged=False, return_gpuarray=False):
"""
Takes a categorical sample from the unnormalized univariate
densities defined in the rows of 'densities'
Parameters
---------
densities : ndarray or gpuarray (n, k)
logged: boolean indicating whether densities is on the
log scale ...
Returns
-------
indices : ndarray or gpuarray (if return_gpuarray=True)
of length n and dtype = int32
"""
from gpustats.util import info
n, k = densities.shape
# prep data
if isinstance(densities, GPUArray):
if densities.flags.f_contiguous:
gpu_densities = util.transpose(densities)
else:
gpu_densities = densities
else:
densities = util.prep_ndarray(densities)
gpu_densities = to_gpu(densities)
# get gpu function
cu_func = cu_module.get_function("sample_discrete")
# setup GPU data
gpu_random = to_gpu(np.asarray(np.random.rand(n), dtype=np.float32))
gpu_dest = gpu_empty(n, dtype=np.int32)
dims = np.array([n, k, logged], dtype=np.int32)
if info.max_block_threads < 1024:
x_block_dim = 16
else:
x_block_dim = 32
y_block_dim = 16
# setup GPU call
block_design = (x_block_dim, y_block_dim, 1)
grid_design = (int(n / y_block_dim) + 1, 1)
shared_mem = 4 * ((x_block_dim + 1) * y_block_dim + 2 * y_block_dim)
cu_func(
gpu_densities,
gpu_random,
gpu_dest,
dims[0],
dims[1],
dims[2],
block=block_design,
grid=grid_design,
shared=shared_mem,
)
gpu_random.gpudata.free()
if return_gpuarray:
return gpu_dest
else:
res = gpu_dest.get()
gpu_dest.gpudata.free()
return res
