def test_engine_openmp_after_fork():
"""
Test that the number of max threads in the child is 1. After forking we should not use a bigger
OMP thread pool.
With GOMP the child always has the same number when calling omp_get_max_threads, with LLVM OMP
the child respects the number of max threads set in the parent.
"""
with EnvManager('OMP_NUM_THREADS', '42'):
r, w = os.pipe()
pid = os.fork()
if pid:
os.close(r)
wfd = os.fdopen(w, 'w')
wfd.write('a')
omp_max_threads = mx.base._LIB.omp_get_max_threads()
print("Parent omp max threads: {}".format(omp_max_threads))
try:
wfd.close()
except:
pass
try:
(cpid, status) = os.waitpid(pid, 0)
assert cpid == pid
exit_status = status >> 8
assert exit_status == 0
except:
pass
else:
os.close(w)
rfd = os.fdopen(r, 'r')
rfd.read(1)
omp_max_threads = mx.base._LIB.omp_get_max_threads()
print("Child omp max threads: {}".format(omp_max_threads))
assert omp_max_threads == 1
def test_device_pushpull():
def check_dense_pushpull(kv_type):
for shape, key in zip(shapes, keys):
for n_gpus in gpus:
kv_device = mx.kv.create(kv_type)
a = mx.nd.ones(shape, mx.gpu(0))
cur_key = str(key * max(gpus) + n_gpus)
kv_device.init(cur_key, a)
arr_list = [
mx.nd.ones(shape, mx.gpu(x)) for x in range(n_gpus)
]
res = [mx.nd.zeros(shape, mx.gpu(x)) for x in range(n_gpus)]
kv_device.push(cur_key, arr_list)
kv_device.pull(cur_key, res)
for x in range(n_gpus):
assert (np.sum(np.abs((res[x] - n_gpus).asnumpy())) == 0)
kvstore_tree_array_bound = 'MXNET_KVSTORE_TREE_ARRAY_BOUND'
kvstore_usetree_values = ['', '1']
kvstore_usetree = 'MXNET_KVSTORE_USETREE'
for _ in range(2):
for x in kvstore_usetree_values:
with EnvManager(kvstore_usetree, x):
check_dense_pushpull('local')
check_dense_pushpull('device')
os.environ[kvstore_tree_array_bound] = '1'
del os.environ[kvstore_tree_array_bound]
def test_unary_func():
def check_unary_func(x):
f_exp = lambda x: nd.exp(x)
f_exp_grad = lambda x: [nd.exp(x)]
autograd_assert(x, func=f_exp, grad_func=f_exp_grad)
f_half = lambda x: x/2
f_half_grad = lambda x: [nd.ones(x.shape) * 0.5]
autograd_assert(x, func=f_half, grad_func=f_half_grad)
f_square = lambda x: x**2
f_square_grad = lambda x: [2*x]
autograd_assert(x, func=f_square, grad_func=f_square_grad)
uniform = nd.uniform(shape=(4, 5))
stypes = ['default', 'row_sparse', 'csr']
with EnvManager('MXNET_STORAGE_FALLBACK_LOG_VERBOSE', '0'):
for stype in stypes:
check_unary_func(uniform.tostype(stype))
def test_binary_func():
def check_binary_func(x, y):
f_add = lambda x, y: x+y
f_add_grad = lambda x, y: [nd.ones(x.shape), nd.ones(y.shape)]
autograd_assert(x, y, func=f_add, grad_func=f_add_grad)
f_mul = lambda x, y: x*y
f_mul_grad = lambda x, y: [y, x]
autograd_assert(x, y, func=f_mul, grad_func=f_mul_grad)
f_compose = lambda x, y: x+x*y
f_compose_grad = lambda x, y: [nd.ones(x.shape) + y, x]
autograd_assert(x, y, func=f_compose, grad_func=f_compose_grad)
uniform_x = nd.uniform(shape=(4, 5))
uniform_y = nd.uniform(shape=(4, 5))
stypes = ['default', 'row_sparse', 'csr']
with EnvManager('MXNET_STORAGE_FALLBACK_LOG_VERBOSE', '0'):
for stype_x in stypes:
for stype_y in stypes:
x = uniform_x.tostype(stype_x)
y = uniform_y.tostype(stype_y)
check_binary_func(x, y)
def test_rsp_push_pull():
def check_rsp_push_pull(kv_type, sparse_pull, is_push_cpu=True):
kv = init_kv_with_str('row_sparse', kv_type)
kv.init('e', mx.nd.ones(shape).tostype('row_sparse'))
push_ctxs = [mx.cpu(i) if is_push_cpu else mx.gpu(i) for i in range(2)]
kv.push('e', [
mx.nd.ones(shape, ctx=context).tostype('row_sparse')
for context in push_ctxs
])
def check_rsp_pull(kv,
ctxs,
sparse_pull,
is_same_rowid=False,
use_slice=False):
count = len(ctxs)
num_rows = shape[0]
row_ids = []
all_row_ids = np.arange(num_rows)
vals = [
mx.nd.sparse.zeros(shape=shape,
ctx=ctxs[i],
stype='row_sparse') for i in range(count)
]
if is_same_rowid:
row_id = np.random.randint(num_rows, size=num_rows)
row_ids = [mx.nd.array(row_id)] * count
elif use_slice:
total_row_ids = mx.nd.array(
np.random.randint(num_rows, size=count * num_rows))
row_ids = [
total_row_ids[i * num_rows:(i + 1) * num_rows]
for i in range(count)
]
else:
for i in range(count):
row_id = np.random.randint(num_rows, size=num_rows)
row_ids.append(mx.nd.array(row_id))
row_ids_to_pull = row_ids[0] if (len(row_ids) == 1
or is_same_rowid) else row_ids
vals_to_pull = vals[0] if len(vals) == 1 else vals
kv.row_sparse_pull('e', out=vals_to_pull, row_ids=row_ids_to_pull)
for val, row_id in zip(vals, row_ids):
retained = val.asnumpy()
excluded_row_ids = np.setdiff1d(all_row_ids, row_id.asnumpy())
for row in range(num_rows):
expected_val = np.zeros_like(retained[row])
expected_val += 0 if row in excluded_row_ids else 2
assert_almost_equal(retained[row], expected_val)
if sparse_pull is True:
kv.pull('e', out=vals_to_pull, ignore_sparse=False)
for val in vals:
retained = val.asnumpy()
expected_val = np.zeros_like(retained)
expected_val[:] = 2
assert_almost_equal(retained, expected_val)
check_rsp_pull(kv, [mx.gpu(0)], sparse_pull)
check_rsp_pull(kv, [mx.cpu(0)], sparse_pull)
check_rsp_pull(kv, [mx.gpu(i // 2) for i in range(4)], sparse_pull)
check_rsp_pull(kv, [mx.gpu(i // 2) for i in range(4)],
sparse_pull,
is_same_rowid=True)
check_rsp_pull(kv, [mx.cpu(i) for i in range(4)], sparse_pull)
check_rsp_pull(kv, [mx.cpu(i) for i in range(4)],
sparse_pull,
is_same_rowid=True)
check_rsp_pull(kv, [mx.gpu(i // 2) for i in range(4)],
sparse_pull,
use_slice=True)
check_rsp_pull(kv, [mx.cpu(i) for i in range(4)],
sparse_pull,
use_slice=True)
envs = ["", "1"]
key = "MXNET_KVSTORE_USETREE"
for val in envs:
with EnvManager(key, val):
if val is "1":
sparse_pull = False
else:
sparse_pull = True
check_rsp_push_pull('local', sparse_pull)
check_rsp_push_pull('device', sparse_pull)
check_rsp_push_pull('device', sparse_pull, is_push_cpu=False)
