def test_gradients(backend_tests, custom_args):
test_idx, f, flag, dim = custom_args
# backend_tests fixture will parameterize over cpu, gpu, and mkl
# 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(backend_tests, custom_args):
test_idx, f, flag, dim = custom_args
# backend_tests fixture will parameterize over cpu, gpu, and mkl
# 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_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(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(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] * 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])
try:
assert tensors_allclose(numpy_func_val,
backend_func_val,
rtol=1e-2,
atol=1e-2)
except AssertionError:
# xfail for gpu backend on TITAN XP platforms
if isinstance(NervanaObject.be, NervanaGPU):
if os.getenv("PLATFORM"):
platform = os.getenv("PLATFORM")
else:
if os.path.exists("/usr/bin/nvidia-smi"):
cmd = '/usr/bin/nvidia-smi -q | grep "Product Name" | tail -1 | cut -f 2 -d \':\' | \
cut -f 2,3 -d \' \''
gpu_info = subp.check_output(cmd, shell=True)
else:
gpu_info = "unknown"
if gpu_info == 'TITAN Xp\n':
platform = "TITANXP"
if platform == 'TITANXP':
pytest.xfail(reason="xfail issue #854 with {} PLATFORM".format(
platform))
else:
assert tensors_allclose(numpy_func_val,
backend_func_val,
rtol=1e-2,
atol=1e-2)
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] * 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])
try:
assert tensors_allclose(numpy_func_val, backend_func_val, rtol=1e-2, atol=1e-2)
except AssertionError:
# xfail for gpu backend on TITAN XP platforms
if isinstance(NervanaObject.be, NervanaGPU):
if os.getenv("PLATFORM"):
platform = os.getenv("PLATFORM")
else:
if os.path.exists("/usr/bin/nvidia-smi"):
cmd = '/usr/bin/nvidia-smi -q | grep "Product Name" | tail -1 | cut -f 2 -d \':\' | \
cut -f 2,3 -d \' \''
gpu_info = subp.check_output(cmd, shell=True)
else:
gpu_info = "unknown"
if gpu_info == 'TITAN Xp\n':
platform = "TITANXP"
if platform == 'TITANXP':
pytest.xfail(reason="xfail issue #854 with {} PLATFORM".format(platform))
else:
assert tensors_allclose(numpy_func_val, backend_func_val, rtol=1e-2, atol=1e-2)
