def test_dc_get_symbol_called_twice():
a = mx.np.arange(10)
dc.set_variable(a, mx.sym.var('a'))
with dc.context():
b = a + 1
sym1 = dc.get_symbol(b)
sym2 = dc.get_symbol(b)
assert sym1.list_inputs() == ['a']
assert sym2.list_inputs() == ['a']
def _assert_dc(setup, compute, mode='all', setup_is_deterministic=True, numpy=True):
"""Compare results of deferred compute and normal imperative mode.
Parameters
----------
setup : callable
Setup function computing inputs for compute function. Always called
outside of deferred compute.
compute : callable
Compute function. We compare the output between normal computation and
deferred compute.
mode : {'all', 'symbolic', 'imperative', 'imperativewithnondccompute'}
Compare deferred compute outputs triggered via imperative computation
(eg. asnumpy() conversion) or obtained from the exported symbol or
both.
setup_is_deterministic : bool
If True, setup function may be called multiple times. If False, will
only be called once.
numpy : bool
If True, use mx.np. Otherwise mx.nd.
"""
try:
nd = mx.np if numpy else mx.nd
if numpy:
mx.npx.set_np()
xs = setup(nd=nd)
ys = compute(*xs, nd=nd)
ys_np = [y.asnumpy() for y in ys]
if setup_is_deterministic:
xs = setup(nd=nd)
xs_names = list(map(str, range(len(xs))))
symbol_inputs = [
mx.symbol.var(name).as_np_ndarray()
if numpy else mx.symbol.var(name)
for arg, name in zip(xs, xs_names)
]
dc.set_variable(xs, symbol_inputs)
with dc.context():
ys_dc = compute(*xs, nd=nd)
assert mode in ('all', 'symbolic', 'imperative', 'imperativewithnondccompute')
if mode in ('all', 'imperativewithnondccompute'):
ys_dc_np = [(y + 0).asnumpy() for y in ys_dc]
_all_same(ys_np, ys_dc_np)
if mode in ('all', 'imperative'):
ys_dc_np = [y.asnumpy() for y in ys_dc]
_all_same(ys_np, ys_dc_np)
if mode in ('all', 'symbolic'):
sym = dc.get_symbol(ys_dc, sym_cls=mx.sym.np._Symbol if numpy else mx.sym.Symbol)
if setup_is_deterministic:
xs = setup(nd=nd)
args = {name: x for name, x in zip(xs_names, xs)}
ys_sym = sym._bind(mx.context.current_context(), args=args).forward()
ys_sym_np = [y.asnumpy() for y in ys_sym]
_all_same(ys_np, ys_sym_np)
finally:
if numpy:
mx.npx.reset_np()
def test_dc_set_variable_called_twice_error():
a = mx.np.arange(10)
dc.set_variable(a, mx.sym.var('a'))
with pytest.raises(MXNetError):
# TODO(leezu): Should raise ValueError https://github.com/apache/incubator-mxnet/issues/17522
dc.set_variable(a, mx.sym.var('b'))
def test_dc_input_part_of_output():
a = mx.np.arange(10)
dc.set_variable(a, mx.sym.var('a'))
with dc.context():
b = a + 1
dc.get_symbol([a, b])
def test_dc_set_variable_called_twice():
a = mx.np.arange(10)
dc.set_variable(a, mx.sym.var('a'))
dc.set_variable(a, mx.sym.var('b'))
