def test_all_gather(self):
texp = np.arange(self.size * 10, dtype='int32')
cpu = np.arange(self.rank * 10, self.rank * 10 + 10, dtype='int32')
a = cpu
gpu = gpuarray.asarray(a, context=self.ctx)
resgpu = self.gpucomm.all_gather(gpu, nd_up=0)
check_all(resgpu, texp)
a = cpu.reshape((2, 5), order='C')
exp = texp.reshape((2 * self.size, 5), order='C')
gpu = gpuarray.asarray(a, context=self.ctx)
resgpu = self.gpucomm.all_gather(gpu, nd_up=0)
check_all(resgpu, exp)
a = cpu.reshape((2, 5), order='C')
exp = texp.reshape((self.size, 2, 5), order='C')
gpu = gpuarray.asarray(a, context=self.ctx)
resgpu = self.gpucomm.all_gather(gpu, nd_up=1)
check_all(resgpu, exp)
a = cpu.reshape((2, 5), order='C')
exp = texp.reshape((self.size, 1, 1, 2, 5), order='C')
gpu = gpuarray.asarray(a, context=self.ctx)
resgpu = self.gpucomm.all_gather(gpu, nd_up=3)
check_all(resgpu, exp)
a = cpu.reshape((5, 2), order='F')
exp = texp.reshape((5, 2 * self.size), order='F')
gpu = gpuarray.asarray(a, context=self.ctx)
resgpu = self.gpucomm.all_gather(gpu, nd_up=0)
check_all(resgpu, exp)
a = cpu.reshape((5, 2), order='F')
exp = texp.reshape((5, 2, self.size), order='F')
gpu = gpuarray.asarray(a, context=self.ctx)
resgpu = self.gpucomm.all_gather(gpu, nd_up=1)
check_all(resgpu, exp)
a = cpu.reshape((5, 2), order='F')
exp = texp.reshape((5, 2, 1, 1, self.size), order='F')
gpu = gpuarray.asarray(a, context=self.ctx)
resgpu = self.gpucomm.all_gather(gpu, nd_up=3)
check_all(resgpu, exp)
with self.assertRaises(Exception):
resgpu = self.gpucomm.all_gather(gpu, nd_up=-2)
def test_all_gather(self):
texp = np.arange(self.size * 10, dtype='int32')
cpu = np.arange(self.rank * 10, self.rank * 10 + 10, dtype='int32')
a = cpu
gpu = gpuarray.asarray(a, context=self.ctx)
resgpu = self.gpucomm.all_gather(gpu, nd_up=0)
check_all(resgpu, texp)
a = cpu.reshape((2, 5), order='C')
exp = texp.reshape((2 * self.size, 5), order='C')
gpu = gpuarray.asarray(a, context=self.ctx)
resgpu = self.gpucomm.all_gather(gpu, nd_up=0)
check_all(resgpu, exp)
a = cpu.reshape((2, 5), order='C')
exp = texp.reshape((self.size, 2, 5), order='C')
gpu = gpuarray.asarray(a, context=self.ctx)
resgpu = self.gpucomm.all_gather(gpu, nd_up=1)
check_all(resgpu, exp)
a = cpu.reshape((2, 5), order='C')
exp = texp.reshape((self.size, 1, 1, 2, 5), order='C')
gpu = gpuarray.asarray(a, context=self.ctx)
resgpu = self.gpucomm.all_gather(gpu, nd_up=3)
check_all(resgpu, exp)
a = cpu.reshape((5, 2), order='F')
exp = texp.reshape((5, 2 * self.size), order='F')
gpu = gpuarray.asarray(a, context=self.ctx, order='F')
resgpu = self.gpucomm.all_gather(gpu, nd_up=0)
check_all(resgpu, exp)
a = cpu.reshape((5, 2), order='F')
exp = texp.reshape((5, 2, self.size), order='F')
gpu = gpuarray.asarray(a, context=self.ctx, order='F')
resgpu = self.gpucomm.all_gather(gpu, nd_up=1)
check_all(resgpu, exp)
a = cpu.reshape((5, 2), order='F')
exp = texp.reshape((5, 2, 1, 1, self.size), order='F')
gpu = gpuarray.asarray(a, context=self.ctx, order='F')
resgpu = self.gpucomm.all_gather(gpu, nd_up=3)
check_all(resgpu, exp)
with self.assertRaises(Exception):
resgpu = self.gpucomm.all_gather(gpu, nd_up=-2)
def test_reduce_scatter(self):
texp = self.size * np.arange(5 * self.size) + sum(range(self.size))
exp = texp[self.rank * 5:self.rank * 5 + 5]
# order c
cpu = np.arange(5 * self.size) + self.rank
np.reshape(cpu, (self.size, 5), order='C')
gpu = gpuarray.asarray(cpu, context=self.ctx)
resgpu = gpuarray.empty((5, ),
dtype='int64',
order='C',
context=self.ctx)
self.gpucomm.reduce_scatter(gpu, 'sum', resgpu)
assert np.allclose(resgpu, exp)
# order f
cpu = np.arange(5 * self.size) + self.rank
np.reshape(cpu, (5, self.size), order='F')
gpu = gpuarray.asarray(cpu, context=self.ctx)
resgpu = gpuarray.empty((5, ),
dtype='int64',
order='F',
context=self.ctx)
self.gpucomm.reduce_scatter(gpu, 'sum', resgpu)
assert np.allclose(resgpu, exp)
# make result order c (one less dim)
cpu = np.arange(5 * self.size) + self.rank
np.reshape(cpu, (self.size, 5), order='C')
gpu = gpuarray.asarray(cpu, context=self.ctx)
resgpu = self.gpucomm.reduce_scatter(gpu, 'sum')
check_all(resgpu, exp)
assert resgpu.flags['C_CONTIGUOUS'] is True
# c-contiguous split problem (for size == 1, it can always be split)
if self.size != 1:
cpu = np.arange(5 * (self.size + 1), dtype='int32') + self.rank
np.reshape(cpu, (self.size + 1, 5), order='C')
gpu = gpuarray.asarray(cpu, context=self.ctx)
with self.assertRaises(TypeError):
resgpu = self.gpucomm.reduce_scatter(gpu, 'sum')
# make result order f (one less dim)
cpu = np.arange(5 * self.size) + self.rank
np.reshape(cpu, (5, self.size), order='F')
gpu = gpuarray.asarray(cpu, context=self.ctx)
resgpu = self.gpucomm.reduce_scatter(gpu, 'sum')
check_all(resgpu, exp)
assert resgpu.flags['F_CONTIGUOUS'] is True
# f-contiguous split problem (for size == 1, it can always be split)
if self.size != 1:
cpu = np.arange(5 * (self.size + 1), dtype='int32') + self.rank
np.reshape(cpu, (5, self.size + 1), order='F')
gpu = gpuarray.asarray(cpu, context=self.ctx)
with self.assertRaises(TypeError):
resgpu = self.gpucomm.reduce_scatter(gpu, 'sum')
# make result order c (same dim - less size)
texp = self.size * np.arange(5 * self.size * 3) + sum(range(self.size))
exp = texp[self.rank * 15:self.rank * 15 + 15]
np.reshape(exp, (3, 5), order='C')
cpu = np.arange(5 * self.size * 3) + self.rank
np.reshape(cpu, (self.size * 3, 5), order='C')
gpu = gpuarray.asarray(cpu, context=self.ctx)
resgpu = self.gpucomm.reduce_scatter(gpu, 'sum')
check_all(resgpu, exp)
assert resgpu.flags['C_CONTIGUOUS'] is True
# make result order f (same dim - less size)
texp = self.size * np.arange(5 * self.size * 3) + sum(range(self.size))
exp = texp[self.rank * 15:self.rank * 15 + 15]
np.reshape(exp, (5, 3), order='F')
cpu = np.arange(5 * self.size * 3) + self.rank
np.reshape(cpu, (5, self.size * 3), order='F')
gpu = gpuarray.asarray(cpu, context=self.ctx)
resgpu = self.gpucomm.reduce_scatter(gpu, 'sum')
check_all(resgpu, exp)
assert resgpu.flags['F_CONTIGUOUS'] is True
def test_reduce_scatter(self):
texp = self.size * np.arange(5 * self.size) + sum(range(self.size))
exp = texp[self.rank * 5:self.rank * 5 + 5]
# order c
cpu = np.arange(5 * self.size) + self.rank
np.reshape(cpu, (self.size, 5), order='C')
gpu = gpuarray.asarray(cpu, context=self.ctx)
resgpu = gpuarray.empty((5,), dtype='int64', order='C', context=self.ctx)
self.gpucomm.reduce_scatter(gpu, 'sum', resgpu)
assert np.allclose(resgpu, exp)
# order f
cpu = np.arange(5 * self.size) + self.rank
np.reshape(cpu, (5, self.size), order='F')
gpu = gpuarray.asarray(cpu, context=self.ctx)
resgpu = gpuarray.empty((5,), dtype='int64', order='F', context=self.ctx)
self.gpucomm.reduce_scatter(gpu, 'sum', resgpu)
assert np.allclose(resgpu, exp)
# make result order c (one less dim)
cpu = np.arange(5 * self.size) + self.rank
np.reshape(cpu, (self.size, 5), order='C')
gpu = gpuarray.asarray(cpu, context=self.ctx)
resgpu = self.gpucomm.reduce_scatter(gpu, 'sum')
check_all(resgpu, exp)
assert resgpu.flags['C_CONTIGUOUS'] is True
# c-contiguous split problem (for size == 1, it can always be split)
if self.size != 1:
cpu = np.arange(5 * (self.size + 1), dtype='int32') + self.rank
np.reshape(cpu, (self.size + 1, 5), order='C')
gpu = gpuarray.asarray(cpu, context=self.ctx)
with self.assertRaises(TypeError):
resgpu = self.gpucomm.reduce_scatter(gpu, 'sum')
# make result order f (one less dim)
cpu = np.arange(5 * self.size) + self.rank
np.reshape(cpu, (5, self.size), order='F')
gpu = gpuarray.asarray(cpu, context=self.ctx)
resgpu = self.gpucomm.reduce_scatter(gpu, 'sum')
check_all(resgpu, exp)
assert resgpu.flags['F_CONTIGUOUS'] is True
# f-contiguous split problem (for size == 1, it can always be split)
if self.size != 1:
cpu = np.arange(5 * (self.size + 1), dtype='int32') + self.rank
np.reshape(cpu, (5, self.size + 1), order='F')
gpu = gpuarray.asarray(cpu, context=self.ctx)
with self.assertRaises(TypeError):
resgpu = self.gpucomm.reduce_scatter(gpu, 'sum')
# make result order c (same dim - less size)
texp = self.size * np.arange(5 * self.size * 3) + sum(range(self.size))
exp = texp[self.rank * 15:self.rank * 15 + 15]
np.reshape(exp, (3, 5), order='C')
cpu = np.arange(5 * self.size * 3) + self.rank
np.reshape(cpu, (self.size * 3, 5), order='C')
gpu = gpuarray.asarray(cpu, context=self.ctx)
resgpu = self.gpucomm.reduce_scatter(gpu, 'sum')
check_all(resgpu, exp)
assert resgpu.flags['C_CONTIGUOUS'] is True
# make result order f (same dim - less size)
texp = self.size * np.arange(5 * self.size * 3) + sum(range(self.size))
exp = texp[self.rank * 15:self.rank * 15 + 15]
np.reshape(exp, (5, 3), order='F')
cpu = np.arange(5 * self.size * 3) + self.rank
np.reshape(cpu, (5, self.size * 3), order='F')
gpu = gpuarray.asarray(cpu, context=self.ctx)
resgpu = self.gpucomm.reduce_scatter(gpu, 'sum')
check_all(resgpu, exp)
assert resgpu.flags['F_CONTIGUOUS'] is True
