def test_gradients(backend_tests, custom_args):
test_idx, f, flag, dim = custom_args
# backend_tests fixture will parameterize over cpu and gpu
# backends as well as float16 and float32
# pull the be and dtype from the actions of the fixture
be = NervanaObject.be
dtype = be.default_dtype
# tensors
tensors = gen_backend_tensors([np, be],
5, [dim] * 5, [flag] * 5,
dtype=dtype)
# compare function value and gradient
numpy_func_val = call_func(f, np, tensors[0])
backend_func_val = call_func(f, be, tensors[1])
numerical_gradient = get_numerical_gradient(f, tensors[0])
ad = get_audiff_gradient(f, be, tensors[1])
autodiff_gradient = ad.get_grad_asnumpyarray(tensors[1])
# TODO: stricter test to fix numerical issues
assert_tensors_allclose(numpy_func_val,
backend_func_val,
rtol=1e-2,
atol=1e-2)
assert_tensors_allclose(numerical_gradient,
autodiff_gradient,
rtol=1e-02,
atol=1e-3)
# cleanup diff tree
ad.cleanup()
dtype = None
be = None
def test_gradients(backend_tests, custom_args):
test_idx, f, flag, dim = custom_args
# backend_tests fixture will parameterize over cpu and gpu
# backends as well as float16 and float32
# pull the be and dtype from the actions of the fixture
be = NervanaObject.be
dtype = be.default_dtype
# tensors
tensors = gen_backend_tensors([np, be], [dim] * 5, [flag] * 5, dtype=dtype)
# compare function value and gradient
numpy_func_val = call_func(f, np, tensors[0])
backend_func_val = call_func(f, be, tensors[1])
numerical_gradient = get_numerical_gradient(f, tensors[0])
ad = get_audiff_gradient(f, be, tensors[1])
autodiff_gradient = ad.get_grad_asnumpyarray(tensors[1])
# TODO: stricter test to fix numerical issues
assert tensors_allclose(numpy_func_val, backend_func_val, rtol=1e-2, atol=1e-2)
assert tensors_allclose(numerical_gradient, autodiff_gradient, rtol=1e-02, atol=1e-3)
# cleanup diff tree
ad.cleanup()
dtype = None
be = None
def test_gradients(custom_args):
test_idx, f, flag, dim, dtype, backend_type = custom_args
be = backend_type(default_dtype=dtype)
# tensors
tensors = gen_backend_tensors([np, be],
5, [dim] * 5, [flag] * 5,
dtype=dtype)
# compare function value and gradient
numpy_func_val = call_func(f, np, tensors[0])
backend_func_val = call_func(f, be, tensors[1])
numerical_gradient = get_numerical_gradient(f, tensors[0])
autodiff_gradient = get_audiff_gradient(f, be, tensors[1])
# TODO: stricter test to fix numerical issues
assert_tensors_allclose(numpy_func_val,
backend_func_val,
rtol=0.,
atol=1e-2)
assert_tensors_allclose(numerical_gradient,
autodiff_gradient,
rtol=1e-02,
atol=1e-3)
if backend_type is NervanaGPU:
be.ctx.detach()
del (be)
def test_vs_numpy(backend_tests, custom_args):
test_idx, f, flag, dim = custom_args
# backend
be = NervanaObject.be
dtype = be.default_dtype
# tensors
tensors = gen_backend_tensors([np, be], [dim] * 4, [flag] * 4, dtype=dtype)
# compare function values
numpy_func_val = call_func(f, np, tensors[0])
backend_func_val = call_func(f, be, tensors[1])
assert tensors_allclose(numpy_func_val, backend_func_val, rtol=1e-2, atol=1e-2)
def test_vs_numpy(custom_args):
test_idx, f, flag, dim, dtype, backend_type = custom_args
# backend
be = gen_backend(backend_type, default_dtype=dtype)
# tensors
tensors = gen_backend_tensors(
[np, be], 5, [dim] * 5, [flag] * 5, dtype=dtype)
# compare function value and gradient
numpy_func_val = call_func(f, np, tensors[0])
backend_func_val = call_func(f, be, tensors[1])
assert_tensors_allclose(
numpy_func_val, backend_func_val, rtol=1e-2, atol=1e-2)
def test_gradients(custom_args):
test_idx, f, flag, dim, dtype, backend_type = custom_args
be = gen_backend(backend_type, default_dtype=dtype)
# tensors
tensors = gen_backend_tensors(
[np, be], 5, [dim] * 5, [flag] * 5, dtype=dtype)
# compare function value and gradient
numpy_func_val = call_func(f, np, tensors[0])
backend_func_val = call_func(f, be, tensors[1])
numerical_gradient = get_numerical_gradient(f, tensors[0])
autodiff_gradient = get_audiff_gradient(f, be, tensors[1])
# TODO: stricter test to fix numerical issues
assert_tensors_allclose(
numpy_func_val, backend_func_val, rtol=1e-2, atol=1e-2)
assert_tensors_allclose(
numerical_gradient, autodiff_gradient, rtol=1e-02, atol=1e-3)
