def function_tester(rng,
func,
ref_func,
inputs,
func_args=[],
func_kwargs={},
atol_f=1e-6,
atol_b=1e-3,
atol_accum=1e-6,
dstep=1e-3,
backward=None,
ctx=None,
func_name=None,
ref_grad=None,
disable_half_test=False,
atol_half=1e-1,
insert_identity=[],
disable_clear_no_need_grad_test=False,
auto_forward=False):
""" Automatic testing of forward/backward pass of `func` by comparing it
to the reference implementation in `ref_func`.
Syntax of `ref_func`: inputs, parameters
Syntax of `ref_grad`: inputs, output grads, parameters
"""
if ctx is None:
ctx = nn.Context()
if backward is None:
backward = [True for _ in inputs]
# Create Variables
# print('create_variable')
def create_variables(inputs, backward):
vinputs = []
for i, b in zip(inputs, backward):
if i is None:
vinputs += [None]
continue
vinputs += [nn.Variable(i.shape, need_grad=b)]
vinputs[-1].data.cast(i.dtype)[...] = i
return vinputs
# Half test
if not disable_half_test:
finputs = create_variables(inputs, backward)
hinputs = create_variables(inputs, backward)
half_test(rng,
func,
finputs,
hinputs,
func_args,
func_kwargs,
backward,
ctx,
func_name,
atol=atol_half)
vinputs = create_variables(inputs, backward)
# Checking forward
# print('checking forward')
with nn.context_scope(ctx), nn.auto_forward():
o = func(*(vinputs + func_args), **func_kwargs)
rinputs = copy.deepcopy(inputs) # inputs for ref_func
refs = ref_func(*(rinputs + func_args), **func_kwargs)
refs = force_tuple(refs)
o = force_tuple(o)
assert len(o) == len(refs)
for i, ref in enumerate(refs):
res = o[i].d
assert_allclose(ref,
res,
atol=atol_f,
err_msg="{} forward test fails".format(func_name))
# Checking recomputation
vinputs = create_variables(inputs, backward)
recomputation_test(rng, func, vinputs, func_args, func_kwargs, ctx)
# Checking forward(clear_no_need_grad=True)
if not disable_clear_no_need_grad_test:
clear_no_need_grad_tester(rng, func, inputs, func_args, func_kwargs,
backward, atol_f, ctx, func_name,
insert_identity, auto_forward)
# Checking function name
try:
import function_test_callback
result = create_function_nnp(vinputs, o, func_name, func_args,
func_kwargs)
if result is not None:
function_test_callback.callback(func_name, *result)
except UnboundLocalError:
pass
except IndexError:
pass
except ImportError:
pass
# print('checking function name')
if func_name is not None:
assert o[0].parent.name == func_name
# Checking backward
# print('checking backward')
if not True in backward:
return
# NNabla backward
for v in vinputs:
if v is None:
continue
if len(v.shape) == 0:
v.g = randn(rng)
continue
v.g = randn(rng, *v.shape)
# Verify grad
vinputs = create_variables(inputs, backward)
rinputs = copy.deepcopy(inputs)
rinputs = [
rinput if test else None for rinput, test in zip(rinputs, backward)
]
vgrads = [randn(rng, *o_.shape) for o_ in o]
def reset_ograds():
'''
Reset output grads everytime we call backward.
This is required because the output grad might
be inplaced and modified during backward operation.
'''
for ovar, g in zip(o, vgrads):
ovar.g = g
agrads, ngrads = compute_analytical_and_numerical_grad(o[0].parent,
vinputs,
o,
rinputs,
vgrads,
epsilon=dstep,
rng=rng,
ref_grad=ref_grad)
if ref_grad is not None:
rinputs = copy.deepcopy(inputs)
doutputs = copy.deepcopy(vgrads)
ngrads = ref_grad(*(rinputs + doutputs + func_args),
**func_kwargs,
need_grad_flags=backward)
assert_allclose(
ngrads,
agrads,
atol=atol_b,
err_msg="{} backward w/o accumulation test fails".format(func_name))
# Check if need_grad works
for v, b in zip(vinputs, backward):
if not b or v is None:
continue
v.grad.zero()
v.need_grad = False
reset_ograds()
try:
o[0].parent.forward(list(filter(lambda x: x is not None, vinputs)),
o)
o[0].parent.backward(
list(filter(lambda x: x is not None, vinputs)), o)
except RuntimeError as e:
continue # TODO
assert np.all(v.g == 0)
# test accum=False
for i in range(len(vinputs)):
if vinputs[i] is None:
continue
v = vinputs[i]
v.need_grad = backward[i]
for i in range(len(vinputs)):
if vinputs[i] is None:
continue
v = vinputs[i]
if not backward[i]:
continue
f = o[0].parent
# Prepare function inputs
finputs = list(filter(lambda x: x is not None, vinputs))
# Save accum gradient result
g = randn(rng, *v.shape)
v.g = g
reset_ograds()
f.forward(finputs, o)
f.backward(finputs, o)
true_g = v.g - g
# Check accum=False
accum = [j != i for j, vv in enumerate(vinputs) if vv is not None]
v.g = randn(rng, *v.shape)
reset_ograds()
f.forward(finputs, o)
f.backward(finputs, o, accum)
assert_allclose(
v.g,
true_g,
atol=atol_accum,
err_msg="{} backward w/ accumulation test fails.".format(
func_name))
# Check accum=False with NaN gradient
v.g = np.float32('nan')
reset_ograds()
f.forward(finputs, o)
f.backward(finputs, o, accum)
assert not np.any(np.isnan(v.g))
def function_tester(rng,
func,
ref_func,
inputs,
func_args=[],
func_kwargs={},
atol_f=1e-6,
atol_b=1e-3,
atol_accum=1e-6,
dstep=1e-3,
backward=None,
ctx=None,
func_name=None,
ref_grad=None,
disable_half_test=False,
atol_half=1e-1):
""" Automatic testing of forward/backward pass of `func` by comparing it
to the reference implementation in `ref_func`.
Syntax of `ref_func`: inputs, parameters
Syntax of `ref_grad`: inputs, output grads, parameters
"""
if ctx is None:
ctx = nn.Context()
if backward is None:
backward = [True for _ in inputs]
# Create Variables
# print('create_variable')
def create_variables(inputs, backward):
vinputs = []
for i, b in zip(inputs, backward):
if i is None:
vinputs += [None]
continue
vinputs += [nn.Variable(i.shape, need_grad=b)]
vinputs[-1].data.cast(i.dtype)[...] = i
return vinputs
# Half test
if not disable_half_test:
finputs = create_variables(inputs, backward)
hinputs = create_variables(inputs, backward)
half_test(rng,
func,
finputs,
hinputs,
func_args,
func_kwargs,
backward,
ctx,
func_name,
atol=atol_half)
vinputs = create_variables(inputs, backward)
# Checking forward
# print('checking forward')
with nn.context_scope(ctx), nn.auto_forward():
o = func(*(vinputs + func_args), **func_kwargs)
rinputs = copy.deepcopy(inputs) # inputs for ref_func
refs = ref_func(*(rinputs + func_args), **func_kwargs)
refs = force_tuple(refs)
o = force_tuple(o)
assert len(o) == len(refs)
for i, ref in enumerate(refs):
res = o[i].d
assert np.allclose(ref, res,
atol=atol_f), str(ArrayDiffStats(ref, res))
# Checking function name
try:
import function_test_callback
result = create_function_nnp(vinputs, o, func_name, func_args,
func_kwargs)
if result is not None:
function_test_callback.callback(func_name, *result)
except UnboundLocalError:
pass
except IndexError:
pass
except ImportError:
pass
# print('checking function name')
if func_name is not None:
assert o[0].parent.name == func_name
# Checking backward
# print('checking backward')
if not True in backward:
return
# NNabla backward
for v in vinputs:
if v is None:
continue
if len(v.shape) == 0:
v.g = rng.randn()
continue
v.g = rng.randn(*v.shape).astype(v.data.dtype)
# Verify grad
vinputs = create_variables(inputs, backward)
rinputs = copy.deepcopy(inputs)
rinputs = [
rinput if test else None for rinput, test in zip(rinputs, backward)
]
vgrads = [rng.randn(*o_.shape) for o_ in o]
agrads, ngrads = compute_analytical_and_numerical_grad(o[0].parent,
vinputs,
o,
rinputs,
vgrads,
epsilon=dstep,
rng=rng,
ref_grad=ref_grad)
if ref_grad is not None:
rinputs = copy.deepcopy(inputs)
doutputs = [o_.g for o_ in o]
ngrads = ref_grad(*(rinputs + doutputs + func_args), **func_kwargs)
assert np.allclose(ngrads, agrads,
atol=atol_b), str(ArrayDiffStats(ngrads, agrads))
# Check if need_grad works
for v, b in zip(vinputs, backward):
if not b or v is None:
continue
v.g = 0
v.need_grad = False
try:
o[0].parent.backward(
list(filter(lambda x: x is not None, vinputs)), o)
except RuntimeError as e:
continue # TODO
assert np.all(v.g == 0)
# test accum=False
for i in range(len(vinputs)):
if vinputs[i] is None:
continue
v = vinputs[i]
v.need_grad = backward[i]
for i in range(len(vinputs)):
if vinputs[i] is None:
continue
v = vinputs[i]
if not backward[i]:
continue
f = o[0].parent
# If input's grad is inplaced, the test doesn't work correctly.
if f.inplace_grad(i):
continue
# Prepare function inputs
finputs = list(filter(lambda x: x is not None, vinputs))
# Save accum gradient result
g = rng.randn(*v.shape)
v.g = g
f.forward(finputs, o)
f.backward(finputs, o)
true_g = v.g - g
# Check accum=False
accum = [j != i for j, vv in enumerate(vinputs) if vv is not None]
v.g = rng.randn(*v.shape)
f.forward(finputs, o)
f.backward(finputs, o, accum)
assert np.allclose(v.g, true_g,
atol=atol_accum), str(ArrayDiffStats(v.g, true_g))
# Check accum=False with NaN gradient
v.g = np.float32('nan')
f.forward(finputs, o)
f.backward(finputs, o, accum)
assert not np.any(np.isnan(v.g))