def set_numba_threads(n):
numba_threads = numba.get_num_threads()
try:
numba.set_num_threads(n)
yield
finally:
numba.set_num_threads(numba_threads)
def _test_func(acc, buf, local_mask):
set_num_threads(nthreads)
# set threads in parent function
set_num_threads(local_mask[0])
if local_mask[0] < N:
child_func(buf, local_mask[0])
acc[0] += get_num_threads()
def test_func(nthreads):
x = 5
buf = np.empty((x, ))
set_num_threads(nthreads)
for i in prange(x):
buf[i] = get_num_threads()
return buf
def test_func():
set_num_threads(mask)
x = 5000000
buf = np.empty((x, ))
for i in prange(x):
buf[i] = get_thread_id()
return len(np.unique(buf)), get_num_threads()
def _transform(self, X, y=None):
"""Transform input time series.
Parameters
----------
X : 3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
panel of time series to transform
y : ignored argument for interface compatibility
Returns
-------
pandas DataFrame, transformed features
"""
X = X[:, 0, :].astype(np.float32)
# change n_jobs dependend on value and existing cores
prev_threads = get_num_threads()
if self.n_jobs < 1 or self.n_jobs > multiprocessing.cpu_count():
n_jobs = multiprocessing.cpu_count()
else:
n_jobs = self.n_jobs
set_num_threads(n_jobs)
X_ = _transform(X, self.parameters)
set_num_threads(prev_threads)
return pd.DataFrame(X_)
def _test_func(buf, local_mask):
set_num_threads(nthreads)
# when the threads exit the child functions they should
# have a TLS slot value of the local mask as it was set
# in child
if local_mask[0] < config.NUMBA_NUM_THREADS:
child(buf, local_mask[0])
assert get_num_threads() == local_mask[0]
def test_numba_info():
ni = d.numba_info()
if numba.config.DISABLE_JIT:
assert ni is None
else:
assert ni is not None
assert ni.threading == numba.threading_layer()
assert ni.threads == numba.get_num_threads()
def nn_descent_internal_high_memory_parallel(
current_graph,
inds,
indptr,
data,
n_neighbors,
rng_state,
max_candidates=50,
dist=sparse_euclidean,
n_iters=10,
delta=0.001,
verbose=False,
):
n_vertices = indptr.shape[0] - 1
block_size = 16384
n_blocks = n_vertices // block_size
n_threads = numba.get_num_threads()
in_graph = [
set(current_graph[0][i].astype(np.int64))
for i in range(current_graph[0].shape[0])
]
for n in range(n_iters):
if verbose:
print("\t", n + 1, " / ", n_iters)
(new_candidate_neighbors, old_candidate_neighbors) = new_build_candidates(
current_graph, max_candidates, rng_state, n_threads
)
c = 0
for i in range(n_blocks + 1):
block_start = i * block_size
block_end = min(n_vertices, (i + 1) * block_size)
new_candidate_block = new_candidate_neighbors[block_start:block_end]
old_candidate_block = old_candidate_neighbors[block_start:block_end]
dist_thresholds = current_graph[1][:, 0]
updates = generate_graph_updates(
new_candidate_block,
old_candidate_block,
dist_thresholds,
inds,
indptr,
data,
dist,
)
c += apply_graph_updates_high_memory(current_graph, updates, in_graph)
if c <= delta * n_neighbors * n_vertices:
if verbose:
print("\tStopping threshold met -- exiting after", n + 1, "iterations")
return
def filter_genes(adata, minc):
t0 = time.time()
cols_to_keep = 0
cols = 0
if use_fastpp:
@numba.njit(cache=True, parallel=True)
def get_cols_to_keep(indices, data, minc, colcount, nthr):
counts = np.zeros((nthr, colcount), dtype=np.int32)
for i in numba.prange(nthr):
start = i * indices.shape[0] // nthr
end = (i + 1) * indices.shape[0] // nthr
for j in range(start, end):
if data[j] != 0 and indices[j] < colcount:
#if indices[j]<colcount:
counts[i, indices[j]] += 1
counts = np.sum(counts, axis=0)
keep_cols = counts >= minc
return counts, keep_cols
ncols = adata.X.shape[1]
nthr = numba.get_num_threads()
print("filter_genes: prep ", time.time() - t0)
counts, cols_to_keep = get_cols_to_keep(adata.X.indices, adata.X.data,
minc, ncols, nthr)
print("filter_genes: compute ", time.time() - t0)
adata.var['n_cells'] = counts
print("filter_genes: set metadata ", time.time() - t0)
if strategy == 1: adata = adata[:, cols_to_keep]
if strategy == 2: adata._inplace_subset_var(cols_to_keep)
if strategy == 3: adata = adata[:, cols_to_keep].copy()
if strategy == 4:
adata = anndata.AnnData(adata.X[:, cols_to_keep], adata.obs,
adata.var.iloc[cols_to_keep, :])
if strategy == 5:
adata = anndata.AnnData(
adata.X[:, cols_to_keep], adata.obs,
adata.var.drop(adata.var.iloc[np.logical_not(cols_to_keep), :],
inplace=True))
if strategy == 6: adata._inplace_subset_var(cols_to_keep)
if strategy == 7:
adata = anndata.AnnData(csr_subset(adata.X, None, cols_to_keep),
adata.obs, adata.var.iloc[cols_to_keep, :])
if strategy == 8: cols = csr_col_subset(adata.X, cols_to_keep)
if strategy == 9: adata._inplace_subset_var(cols_to_keep)
if strategy == 10:
adata = anndata.AnnData(adata.X[:, cols_to_keep], adata.obs,
adata.var.iloc[cols_to_keep, :])
if strategy == 11: cols = csr_col_subset(adata.X, cols_to_keep)
else:
sc.pp.filter_genes(adata, min_cells=minc)
print("filter_genes: filter total", time.time() - t0)
if cols == 0: cols = adata.shape[1]
return adata, cols_to_keep, cols
def __call__(self, *, n_steps, mu_coeff, post_step, post_iter, fields):
assert self.n_threads == 1 or numba.get_num_threads() == self.n_threads
with warnings.catch_warnings():
warnings.simplefilter('ignore',
category=NumbaExperimentalFeatureWarning)
wall_time_per_timestep = self.__call(
n_steps, mu_coeff, post_step, post_iter,
*(_Impl(field=v.impl[IMPL_META_AND_DATA], bc=v.impl[IMPL_BC])
for v in fields.values()), self.traversals.null_impl)
return wall_time_per_timestep
def klip_chunk_svd(image_vecs_meansub, n_images, mtx_u0, diag_s0, mtx_v0,
k_klip, reuse, strategy, exclusion_values, exclusion_deltas,
signal_vecs):
n_frames = image_vecs_meansub.shape[1]
output = np.zeros_like(image_vecs_meansub)
if signal_vecs is not None:
output_model = np.zeros_like(signal_vecs)
else:
output_model = None
print('klip_chunk_svd running with', numba.get_num_threads(), 'threads on',
n_frames, 'frames')
for i in numba.prange(n_frames):
if not reuse:
min_excluded_idx, max_excluded_idx = exclusions_to_range(
n_images=n_images,
current_idx=i,
exclusion_values=exclusion_values,
exclusion_deltas=exclusion_deltas,
)
n_excluded = max_excluded_idx - min_excluded_idx + 1
print('processing frame', i, ', excluding', n_excluded,
' frames (from frame', min_excluded_idx, 'to',
max_excluded_idx, ")")
if strategy == constants.KlipStrategy.DOWNDATE_SVD:
assert mtx_u0 is not None
assert diag_s0 is not None
assert mtx_v0 is not None
subset_mtx_u0 = np.ascontiguousarray(mtx_u0[:, :k_klip +
n_excluded])
subset_diag_s = diag_s0[:k_klip + n_excluded]
subset_mtx_v0 = np.ascontiguousarray(mtx_v0[:, :k_klip +
n_excluded])
new_u, _, _ = learning.minimal_downdate(
subset_mtx_u0,
subset_diag_s,
subset_mtx_v0,
min_col_to_remove=min_excluded_idx,
max_col_to_remove=max_excluded_idx + 1,
)
eigenimages = new_u[:, :k_klip]
else:
subset_image_vecs = utils.drop_idx_range_cols(
image_vecs_meansub, min_excluded_idx, max_excluded_idx + 1)
eigenimages, _, _ = learning._numba_svd_wrap(
subset_image_vecs, k_klip)
else:
assert mtx_u0 is not None
eigenimages = mtx_u0[:, :k_klip]
meansub_target = image_vecs_meansub[:, i]
# Since we may have truncated by columns above, this re-contiguou-fies
# and silences the NumbaPerformanceWarning
eigenimages = np.ascontiguousarray(eigenimages)
output[:, i] = meansub_target - eigenimages @ (
eigenimages.T @ meansub_target)
return output, output_model
def condensation(solver, n_cell, cell_start_arg, v, particle_temperatures,
r_cr, n, vdry, idx, rhod, thd, qv, dv, prhod, pthd, pqv,
kappa, rtol_x, rtol_thd, dt, substeps, cell_order,
ripening_flags):
n_threads = min(numba.get_num_threads(), n_cell)
AlgorithmicMethods._condensation(
solver, n_threads, n_cell, cell_start_arg.data, v.data,
particle_temperatures.data, r_cr.data, n.data, vdry.data, idx.data,
rhod.data, thd.data, qv.data, dv, prhod.data, pthd.data, pqv.data,
kappa, rtol_x, rtol_thd, dt, substeps.data, cell_order,
ripening_flags.data)
def get_num_threads():
"""
Get current number of threads.
Returns
-------
int
Number of threads.
"""
return numba.get_num_threads()
def condensation(solver, n_cell, cell_start_arg, v, v_cr, n, vdry, idx,
rhod, thd, qv, dv, prhod, pthd, pqv, kappa, rtol_x,
rtol_thd, dt, counters, cell_order, RH_max, success):
n_threads = min(numba.get_num_threads(), n_cell)
AlgorithmicMethods._condensation(
solver, n_threads, n_cell, cell_start_arg.data, v.data, v_cr.data,
n.data, vdry.data, idx.data, rhod.data, thd.data, qv.data, dv,
prhod.data, pthd.data, pqv.data, kappa, rtol_x, rtol_thd, dt,
counters['n_substeps'].data, counters['n_activating'].data,
counters['n_deactivating'].data, counters['n_ripening'].data,
cell_order, RH_max.data, success.data)
def test_func(nthreads):
buf = np.zeros((M, N))
set_num_threads(nthreads)
for i in prange(M):
local_mask = 1 + i % mask
# when the threads exit the child functions they should
# have a TLS slot value of the local mask as it was set
# in child
if local_mask < config.NUMBA_NUM_THREADS:
child(buf, local_mask)
assert get_num_threads() == local_mask
return buf
def _test_func(nthreads):
acc = 0
buf = np.zeros((M, N))
set_num_threads(nthreads)
for i in prange(M):
local_mask = 1 + i % mask
# set threads in parent function
set_num_threads(local_mask)
if local_mask < N:
child_func(buf, local_mask)
acc += get_num_threads()
return acc, buf
def __call__(self, nt, mu_coeff, advectee, advectee_bc, advector,
advector_bc, g_factor, g_factor_bc, vectmp_a, vectmp_a_bc,
vectmp_b, vectmp_b_bc, vectmp_c, vectmp_c_bc, psi_min,
psi_min_bc, psi_max, psi_max_bc, beta_up, beta_up_bc,
beta_down, beta_down_bc):
assert self.n_threads == 1 or numba.get_num_threads() == self.n_threads
return self.__call(nt, mu_coeff, advectee, advectee_bc, advector,
advector_bc, g_factor, g_factor_bc, vectmp_a,
vectmp_a_bc, vectmp_b, vectmp_b_bc, vectmp_c,
vectmp_c_bc, psi_min, psi_min_bc, psi_max,
psi_max_bc, beta_up, beta_up_bc, beta_down,
beta_down_bc)
def numba_info():
x = _par_test(100)
_log.debug('sum: %d', x)
try:
layer = numba.threading_layer()
except ValueError:
_log.info('Numba threading not initialized')
return None
_log.info('numba threading layer: %s', layer)
nth = numba.get_num_threads()
return NumbaInfo(layer, nth)
def filter_cells(adata, ming, maxg):
t0 = time.time()
rows_to_keep = 0
rows = 0
if use_fastpp:
@numba.njit(cache=True, parallel=True)
def get_rows_to_keep(indptr, ming, maxg):
lens = indptr[1:] - indptr[:-1]
keep_rows = np.logical_and(lens >= ming, lens <= maxg)
return lens, keep_rows
nrows = adata.X.shape[0]
nelems = adata.X.data.shape[0]
nthr = numba.get_num_threads()
print("filter_cells: prep ", time.time() - t0)
row_lengths, rows_to_keep = get_rows_to_keep(adata.X.indptr, ming,
maxg)
print("filter_cells: compute ", time.time() - t0)
adata.obs['n_genes'] = row_lengths
print("filter_cells: set metadata ", time.time() - t0)
if strategy == 1: adata = adata[rows_to_keep]
if strategy == 2: adata._inplace_subset_obs(rows_to_keep)
if strategy == 3: adata = adata[rows_to_keep].copy()
if strategy == 4:
adata = anndata.AnnData(adata.X[rows_to_keep],
adata.obs.iloc[rows_to_keep, :], adata.var)
if strategy == 5:
adata = anndata.AnnData(
adata.X[rows_to_keep],
adata.obs.drop(adata.obs.iloc[np.logical_not(rows_to_keep), :],
inplace=True), adata.var)
if strategy == 6: adata._inplace_subset_obs(rows_to_keep)
if strategy == 7:
adata = anndata.AnnData(csr_subset(adata.X, rows_to_keep),
adata.obs.iloc[rows_to_keep, :], adata.var)
if strategy == 8: rows = csr_row_subset(adata.X, rows_to_keep)
if strategy == 9: adata._inplace_subset_obs(rows_to_keep)
if strategy == 10:
adata = anndata.AnnData(adata.X[rows_to_keep],
adata.obs.iloc[rows_to_keep, :], adata.var)
if strategy == 11: rows = csr_row_subset2(adata.X, rows_to_keep)
#if strategy==11: adata = anndata.AnnData(adata.X[rows_to_keep],adata.obs.iloc[rows_to_keep,:],adata.var)
else:
sc.pp.filter_cells(adata, min_genes=ming)
print("filter_cells: first call ", time.time() - t0)
sc.pp.filter_cells(adata, max_genes=maxg)
print("filter_cells: filter total", time.time() - t0)
if rows == 0: rows = adata.shape[0]
return adata, rows_to_keep, rows
def set_threads(num: int = -1) -> int:
"""Set the number of numba threads
Args:
num (int, optional): The number of threads. Defaults to -1.
Returns:
int: The old number of theads (or -1 if unchanged).
"""
if num > 0:
old = get_num_threads()
if old != num:
set_num_threads(num)
return old
return -1
def __init__(
self,
minibatch: int,
maxT: int,
maxU: int,
alphabet_size: int,
workspace,
blank: int,
fastemit_lambda: float,
clamp: float,
num_threads: int,
stream,
):
"""
Helper class to launch the CUDA Kernels to compute the Transducer Loss.
Args:
minibatch: Int representing the batch size.
maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor.
maxU: The maximum possible target sequence length. Represents U in the logprobs tensor.
alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank).
workspace: An allocated chunk of memory that will be sliced off and reshaped into required
blocks used as working memory.
blank: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab.
fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to
FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization.
clamp: Float value. When set to value >= 0.0, will clamp the gradient to [-clamp, clamp].
num_threads: Number of OMP threads to launch.
stream: Numba Cuda Stream.
"""
self.minibatch_ = minibatch
self.maxT_ = maxT
self.maxU_ = maxU
self.alphabet_size_ = alphabet_size
self.gpu_workspace = cuda.as_cuda_array(
workspace
) # a flat vector of floatX numbers that represents allocated memory slices
self.blank_ = blank
self.fastemit_lambda_ = fastemit_lambda
self.clamp_ = abs(clamp)
self.num_threads_ = num_threads
self.stream_ = stream # type: cuda.cudadrv.driver.Stream
if num_threads > 0:
numba.set_num_threads(min(multiprocessing.cpu_count(),
num_threads))
else:
self.num_threads_ = numba.get_num_threads()
def set_numba_threads(n):
import numba
from numba.core.config import NUMBA_NUM_THREADS
numba_threads = numba.get_num_threads()
try:
if n > NUMBA_NUM_THREADS:
warnings.warn(
f"Attempting to set threads to {n}, which is larger than "
f"NUMBA_NUM_THREADS={NUMBA_NUM_THREADS}. "
f"Setting to allowed maximum NUMBA_NUM_THREADS instead.")
n = min(n, NUMBA_NUM_THREADS)
numba.set_num_threads(n)
yield
finally:
numba.set_num_threads(numba_threads)
def _transform(self, X, y=None):
"""Transform input time series using random convolutional kernels.
Parameters
----------
X : 3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
panel of time series to transform
y : ignored argument for interface compatibility
Returns
-------
pandas DataFrame, transformed features
"""
X = X.astype(np.float64)
X = convert(X,
from_type="numpy3D",
to_type="numpyflat",
as_scitype="Panel")
if self.normalise:
X = (X - X.mean(axis=-1, keepdims=True)) / (
X.std(axis=-1, keepdims=True) + 1e-8)
X1 = np.diff(X, 1)
# change n_jobs dependend on value and existing cores
prev_threads = get_num_threads()
if self.n_jobs < 1 or self.n_jobs > multiprocessing.cpu_count():
n_jobs = multiprocessing.cpu_count()
else:
n_jobs = self.n_jobs
set_num_threads(n_jobs)
X = _transform(
X,
X1,
self.parameter,
self.parameter1,
self.n_features_per_kernel,
)
X = np.nan_to_num(X)
set_num_threads(prev_threads)
# # from_2d_array_to_3d_numpy
# _X = np.reshape(_X, (_X.shape[0], 1, _X.shape[1])).astype(np.float64)
return pd.DataFrame(X)
def transform(self, X, y=None):
"""Transform input time series using random convolutional kernels.
Parameters
----------
X : pandas DataFrame, input time series (sktime format)
y : array_like, target values (optional, ignored as irrelevant)
Returns
-------
pandas DataFrame, transformed features
"""
self.check_is_fitted()
_X = check_X(X, enforce_univariate=True, coerce_to_numpy=True)
_X = _X[:, 0, :].astype(np.float64)
_X = from_3d_numpy_to_2d_array(_X)
if self.normalise:
_X = (_X - _X.mean(axis=-1, keepdims=True)) / (
_X.std(axis=-1, keepdims=True) + 1e-8)
X1 = np.diff(_X, 1)
# change n_jobs dependend on value and existing cores
prev_threads = get_num_threads()
if self.n_jobs < 1 or self.n_jobs > multiprocessing.cpu_count():
n_jobs = multiprocessing.cpu_count()
else:
n_jobs = self.n_jobs
set_num_threads(n_jobs)
_X = _transform(
_X,
X1,
self.parameter,
self.parameter1,
self.n_features_per_kernel,
)
_X = np.nan_to_num(_X)
set_num_threads(prev_threads)
# # from_2d_array_to_3d_numpy
# _X = np.reshape(_X, (_X.shape[0], 1, _X.shape[1])).astype(np.float64)
return pd.DataFrame(_X)
def __init__(
self,
minibatch: int,
maxT: int,
maxU: int,
alphabet_size: int,
workspace: torch.Tensor,
blank: int,
fastemit_lambda: float,
num_threads: int,
batch_first: bool,
):
"""
Helper class to compute the Transducer Loss on CPU.
Args:
minibatch: Size of the minibatch b.
maxT: The maximum possible acoustic sequence length. Represents T in the logprobs tensor.
maxU: The maximum possible target sequence length. Represents U in the logprobs tensor.
alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank).
workspace: An allocated chunk of memory that will be sliced off and reshaped into required
blocks used as working memory.
blank: Index of the RNNT blank token in the vocabulary. Generally the first or last token in the vocab.
fastemit_lambda: Float scaling factor for FastEmit regularization. Refer to
FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization.
num_threads: Number of OMP threads to launch.
batch_first: Bool that decides if batch dimension is first or third.
"""
self.minibatch_ = minibatch
self.maxT_ = maxT
self.maxU_ = maxU
self.alphabet_size_ = alphabet_size
self.workspace = workspace # a flat vector of floatX numbers that represents allocated memory slices
self.blank_ = blank
self.fastemit_lambda_ = fastemit_lambda
self.num_threads_ = num_threads
self.batch_first = batch_first
if num_threads > 0:
numba.set_num_threads(min(multiprocessing.cpu_count(),
num_threads))
else:
self.num_threads_ = numba.get_num_threads()
def VelocityStructFunc_tree(pos,
vel,
weight,
tree,
rbins,
max_bin_size_ratio=100,
theta=0.7,
boxsize=0,
weighted_binning=False):
"""Returns the average mass in radial bins surrounding a point
Arguments:
pos -- shape (N,3) array of particle positions
tree -- Octree instance containing the positions, masses, and softenings of the source particles
Optional arguments:
rbins -- 1D array of radial bin edges - if None will use heuristics to determine sensible bins
max_bin_size_ratio -- controls the accuracy of the binning - tree nodes are subdivided until their side length is at most this factor * the radial bin width (default 0.5)
Returns:
mbins -- arrays containing total mass in each bin
"""
Nthreads = get_num_threads()
mbin = zeros((Nthreads, rbins.shape[0] - 1))
wtsum = zeros_like(mbin)
# break into chunks for parallelization
for chunk in prange(Nthreads):
for i in range(chunk, pos.shape[0], Nthreads):
dwtsum, dmbin = VelocityStructWalk(
pos[i],
vel[i],
tree,
rbins,
max_bin_size_ratio=max_bin_size_ratio,
theta=theta,
boxsize=boxsize,
weighted_binning=weighted_binning)
for j in range(mbin.shape[1]):
mbin[chunk, j] += dmbin[j] * weight[i]
wtsum[chunk, j] += weight[i] * dwtsum[j]
return mbin.sum(0) / wtsum.sum(0)
def apply_graph_updates_low_memory(current_graph, updates):
n_changes = 0
priorities = current_graph[1]
indices = current_graph[0]
flags = current_graph[2]
n_threads = numba.get_num_threads()
for n in numba.prange(n_threads):
for i in range(len(updates)):
for j in range(len(updates[i])):
p, q, d = updates[i][j]
if p == -1 or q == -1:
continue
if p % n_threads == n:
# added = heap_push(current_graph, p, d, q, 1)
added = checked_flagged_heap_push(
priorities[p],
indices[p],
flags[p],
d,
q,
1,
)
n_changes += added
if q % n_threads == n:
# added = heap_push(current_graph, q, d, p, 1)
added = checked_flagged_heap_push(
priorities[q],
indices[q],
flags[q],
d,
p,
1,
)
n_changes += added
return n_changes
def _transform(self, X, y=None):
"""Transform input time series using random convolutional kernels.
Parameters
----------
X : 3D np.ndarray of shape = [n_instances, n_dimensions, series_length]
panel of time series to transform
y : ignored argument for interface compatibility
Returns
-------
pandas DataFrame, transformed features
"""
if self.normalise:
X = (X - X.mean(axis=-1, keepdims=True)) / (
X.std(axis=-1, keepdims=True) + 1e-8
)
_X1 = np.diff(X, 1)
# change n_jobs dependend on value and existing cores
prev_threads = get_num_threads()
if self.n_jobs < 1 or self.n_jobs > multiprocessing.cpu_count():
n_jobs = multiprocessing.cpu_count()
else:
n_jobs = self.n_jobs
set_num_threads(n_jobs)
X = _transform(
X,
_X1,
self.parameter,
self.parameter1,
self.n_features_per_kernel,
)
X = np.nan_to_num(X)
set_num_threads(prev_threads)
return pd.DataFrame(X)
def sum(X, axis=None):
@numba.njit(cache=True, parallel=True)
def _sum(X):
s = 0
for i in numba.pndindex(X.shape):
s += X[i]
return s
@numba.njit(cache=True, parallel=True)
def _sum0(X, nthr):
s = np.empty((nthr, X.shape[1]), dtype=X.dtype)
for i in numba.prange(nthr):
for r in range(i, X.shape[0], nthr):
s[i] = X[r]
return s.sum(axis=0)
@numba.njit(cache=True, parallel=True)
def _sum1(X):
s = np.empty(X.shape[0], dtype=X.dtype)
for r in numba.prange(X.shape[0]):
s[r] = X[r].sum()
return s
if issparse(X) or not use_fastpp:
if axis is None:
return np.array(X.sum())
return np.array(X.sum(axis=axis))
if axis is None:
return _sum(X)
if X.ndim == 2:
if axis == 0:
nthr = numba.get_num_threads()
return _sum0(X, nthr)
return _sum1(X)
# if axis is None:
# return X.sum()
return X.sum(axis=axis)
def __init__(self,
*,
options: Options,
n_dims: (int, None) = None,
non_unit_g_factor: bool = False,
grid: (tuple, None) = None,
n_threads: (int, None) = None):
if n_dims is not None and grid is not None:
raise ValueError()
if n_dims is None and grid is None:
raise ValueError()
if grid is None:
grid = tuple([-1] * n_dims)
if n_dims is None:
n_dims = len(grid)
if n_dims > 1 and options.DPDC:
raise NotImplementedError()
if n_threads is None:
n_threads = numba.get_num_threads()
self.__options = options
self.__n_threads = 1 if n_dims == 1 else n_threads
if self.__n_threads > 1:
try:
numba.parfors.parfor.ensure_parallel_support()
except numba.core.errors.UnsupportedParforsError:
print(
"Numba ensure_parallel_support() failed, forcing n_threads=1",
file=sys.stderr)
self.__n_threads = 1
self.__n_dims = n_dims
self.__call, self.traversals = make_step_impl(options,
non_unit_g_factor, grid,
self.n_threads)
