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from __future__ import print_function
import os
import sys
import unittest
from six.moves import range
from six import PY3
import pickle
import numpy as np
from pygpu import gpuarray
from pygpu.collectives import COMM_ID_BYTES, GpuCommCliqueId, GpuComm
from pygpu.tests.support import (check_all, gen_gpuarray, context as ctx)
def get_user_gpu_rank():
for name in ['GPUARRAY_TEST_DEVICE', 'DEVICE']:
if name in os.environ:
devname = os.environ[name]
if devname.startswith("opencl"):
return -1
if devname[-1] == 'a':
return 0
return int(devname[-1])
return -1
try:
from mpi4py import MPI
MPI_IMPORTED = True
except:
MPI_IMPORTED = False
print("mpi4py found: " + str(MPI_IMPORTED), file=sys.stderr)
@unittest.skipIf(get_user_gpu_rank() == -1, "Collective operations supported on CUDA devices only.")
class TestGpuCommCliqueId(unittest.TestCase):
def setUp(self):
self.cid = GpuCommCliqueId(context=ctx)
def _create_in_scope_from_string(self):
comm_id = bytearray(b'pipes' * (COMM_ID_BYTES // 5 + 1))
return GpuCommCliqueId(context=ctx, comm_id=comm_id)
def test_create_from_string_id(self):
cid2 = self._create_in_scope_from_string()
a = bytearray(b'pipes' * (COMM_ID_BYTES // 5 + 1))
assert cid2.comm_id == a[:COMM_ID_BYTES], (cid2.comm_id, a[:COMM_ID_BYTES])
b = bytearray(b'mlkies' * (COMM_ID_BYTES // 6 + 1))
cid2.comm_id = b
assert cid2.comm_id == b[:COMM_ID_BYTES], (cid2.comm_id, b[:COMM_ID_BYTES])
with self.assertRaises(ValueError):
cid2.comm_id = bytearray(b'testestestest')
def test_pickle(self):
with self.assertRaises(RuntimeError):
pickle.dumps(self.cid)
with self.assertRaises(RuntimeError):
pickle.dumps(self.cid, protocol=0)
with self.assertRaises(RuntimeError):
pickle.dumps(self.cid, protocol=1)
with self.assertRaises(RuntimeError):
pickle.dumps(self.cid, protocol=2)
if PY3:
with self.assertRaises(RuntimeError):
pickle.dumps(self.cid, protocol=3)
with self.assertRaises(RuntimeError):
pickle.dumps(self.cid, protocol=-1)
def test_create_from_previous(self):
cid2 = GpuCommCliqueId(context=ctx, comm_id=bytearray(b'y' * COMM_ID_BYTES))
cid3 = GpuCommCliqueId(context=ctx, comm_id=cid2.comm_id)
assert cid2.comm_id == cid3.comm_id
def test_richcmp(self):
cid1 = GpuCommCliqueId(context=ctx, comm_id=bytearray(b'y' * COMM_ID_BYTES))
cid2 = GpuCommCliqueId(context=ctx, comm_id=cid1.comm_id)
cid3 = GpuCommCliqueId(context=ctx, comm_id=bytearray(b'z' * COMM_ID_BYTES))
assert cid1 == cid2
assert cid1 != cid3
assert cid3 > cid2
assert cid3 >= cid2
assert cid1 >= cid2
assert cid2 < cid3
assert cid2 <= cid3
assert cid2 <= cid1
with self.assertRaises(TypeError):
a = cid2 > "asdfasfa"
@unittest.skipUnless(MPI_IMPORTED, "Needs mpi4py module")
@unittest.skipIf(get_user_gpu_rank() == -1, "Collective operations supported on CUDA devices only")
class TestGpuComm(unittest.TestCase):
@classmethod
def setUpClass(cls):
if get_user_gpu_rank() == -1 or not MPI_IMPORTED:
return
cls.mpicomm = MPI.COMM_WORLD
cls.size = cls.mpicomm.Get_size()
cls.rank = cls.mpicomm.Get_rank()
cls.ctx = gpuarray.init("cuda" + str(cls.rank))
print("*** Collectives testing for", cls.ctx.devname, file=sys.stderr)
cls.cid = GpuCommCliqueId(context=cls.ctx)
cls.mpicomm.Bcast(cls.cid.comm_id, root=0)
cls.gpucomm = GpuComm(cls.cid, cls.size, cls.rank)
def test_count(self):
assert self.gpucomm.count == self.size, (self.gpucomm.count, self.size)
def test_rank(self):
assert self.gpucomm.rank == self.rank, (self.gpucomm.rank, self.rank)
def test_reduce(self):
cpu, gpu = gen_gpuarray((3, 4, 5), order='c', incr=self.rank, ctx=self.ctx)
rescpu = np.empty_like(cpu)
resgpu = gpu._empty_like_me()
if self.rank != 0:
self.gpucomm.reduce(gpu, 'sum', resgpu, root=0)
self.mpicomm.Reduce([cpu, MPI.FLOAT], None, op=MPI.SUM, root=0)
else:
self.gpucomm.reduce(gpu, 'sum', resgpu)
self.mpicomm.Reduce([cpu, MPI.FLOAT], [rescpu, MPI.FLOAT], op=MPI.SUM, root=0)
if self.rank == 0:
assert np.allclose(resgpu, rescpu)
resgpu = self.gpucomm.reduce(gpu, 'sum', root=0)
if self.rank == 0:
assert resgpu.shape == gpu.shape, (resgpu.shape, gpu.shape)
assert resgpu.dtype == gpu.dtype, (resgpu.dtype, gpu.dtype)
assert resgpu.flags['C'] == gpu.flags['C']
assert resgpu.flags['F'] == gpu.flags['F']
assert np.allclose(resgpu, rescpu)
else:
assert resgpu is None
if self.rank == 0:
resgpu = self.gpucomm.reduce(gpu, 'sum')
assert resgpu.shape == gpu.shape, (resgpu.shape, gpu.shape)
assert resgpu.dtype == gpu.dtype, (resgpu.dtype, gpu.dtype)
assert resgpu.flags['C'] == gpu.flags['C']
assert resgpu.flags['F'] == gpu.flags['F']
assert np.allclose(resgpu, rescpu)
else:
resgpu = self.gpucomm.reduce(gpu, 'sum', root=0)
assert resgpu is None
def test_all_reduce(self):
cpu, gpu = gen_gpuarray((3, 4, 5), order='c', incr=self.rank, ctx=self.ctx)
rescpu = np.empty_like(cpu)
resgpu = gpu._empty_like_me()
self.gpucomm.all_reduce(gpu, 'sum', resgpu)
self.mpicomm.Allreduce([cpu, MPI.FLOAT], [rescpu, MPI.FLOAT], op=MPI.SUM)
assert np.allclose(resgpu, rescpu)
resgpu = self.gpucomm.all_reduce(gpu, 'sum')
assert resgpu.shape == gpu.shape, (resgpu.shape, gpu.shape)
assert resgpu.dtype == gpu.dtype, (resgpu.dtype, gpu.dtype)
assert resgpu.flags['C'] == gpu.flags['C']
assert resgpu.flags['F'] == gpu.flags['F']
assert np.allclose(resgpu, rescpu)
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_broadcast(self):
if self.rank == 0:
cpu, gpu = gen_gpuarray((3, 4, 5), order='c', incr=self.rank, ctx=self.ctx)
else:
cpu = np.zeros((3, 4, 5), dtype='float32')
gpu = gpuarray.asarray(cpu, context=self.ctx)
if self.rank == 0:
self.gpucomm.broadcast(gpu)
else:
self.gpucomm.broadcast(gpu, root=0)
self.mpicomm.Bcast(cpu, root=0)
assert np.allclose(gpu, cpu)
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)
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