def test_outer_vs_backend(dtype, backend):
shape = (4, 3)
dtype = testing_utils.np_dtype_to_backend(backend, dtype)
testing_utils.check_contraction_dtype(backend, dtype)
tensor1 = tensornetwork.ones(shape, backend=backend, dtype=dtype)
tensor2 = tensornetwork.ones(shape, backend=backend, dtype=dtype)
result = tensornetwork.outer(tensor1, tensor2)
backend_obj = backends.backend_factory.get_backend(backend)
arrays = [t.array for t in [tensor1, tensor2]]
backend_result = backend_obj.outer_product(*arrays)
np.testing.assert_allclose(backend_result, result.array)
def test_ncon_vs_backend(dtype, backend):
shape = (4, 3)
dtype = testing_utils.np_dtype_to_backend(backend, dtype)
testing_utils.check_contraction_dtype(backend, dtype)
tensor1 = tensornetwork.ones(shape, backend=backend, dtype=dtype)
tensor2 = tensornetwork.ones(shape, backend=backend, dtype=dtype)
tensor3 = tensornetwork.ones(shape, backend=backend, dtype=dtype)
tensors = [tensor1, tensor2, tensor3]
arrays = [tensor1.array, tensor2.array, tensor3.array]
idxs = [[1, -1], [1, 2], [-2, 2]]
result = ncon(tensors, idxs, backend=backend)
old_result = tensornetwork.ncon(arrays, idxs, backend=backend)
np.testing.assert_allclose(old_result, result.array)
def test_tensordot_int_vs_backend(backend, dtype):
"""
Tests that tensordot yields the same result as the backend equivalent.
"""
shape = (4, 4, 4)
dtype = testing_utils.np_dtype_to_backend(backend, dtype)
testing_utils.check_contraction_dtype(backend, dtype)
tensor1 = tensornetwork.ones(shape, backend=backend, dtype=dtype)
tensor2 = tensornetwork.ones(shape, backend=backend, dtype=dtype)
tensors = [tensor1, tensor2]
dim = 1
result = tensornetwork.tensordot(*tensors, dim)
backend_obj = backends.backend_factory.get_backend(backend)
arrays = [t.array for t in tensors]
backend_result = backend_obj.tensordot(*arrays, axes=dim)
np.testing.assert_allclose(backend_result, result.array)
def test_einsum_vs_backend(dtype, backend):
shape = (4, 3)
dtype = testing_utils.np_dtype_to_backend(backend, dtype)
testing_utils.check_contraction_dtype(backend, dtype)
tensor1 = tensornetwork.ones(shape, backend=backend, dtype=dtype)
tensor2 = tensornetwork.ones(shape, backend=backend, dtype=dtype)
tensor3 = tensornetwork.ones(shape, backend=backend, dtype=dtype)
result = tensornetwork.einsum("ba, bc, dc",
tensor1,
tensor2,
tensor3,
optimize=True)
backend_obj = backends.backend_factory.get_backend(backend)
arrays = [t.array for t in [tensor1, tensor2, tensor3]]
backend_result = backend_obj.einsum("ba, bc, dc", *arrays, optimize=True)
np.testing.assert_allclose(backend_result, result.array)
def test_tensordot_invalid_backend_raises_value_error(backend, dtype):
"""
Tests that tensordot raises ValueError when fed Tensors with different
backends. Other failure modes are tested at the backend level.
"""
backend_names = set(["jax", "numpy", "tensorflow", "pytorch"])
this_name = set([backend])
other_backend_names = list(backend_names - this_name)
shape = (4, 4, 4)
dtype1 = testing_utils.np_dtype_to_backend(backend, dtype)
testing_utils.check_contraction_dtype(backend, dtype1)
tensor1 = tensornetwork.ones(shape, backend=backend, dtype=dtype1)
for other_backend in other_backend_names:
dtype2 = testing_utils.np_dtype_to_backend(other_backend, dtype)
testing_utils.check_contraction_dtype(other_backend, dtype2)
tensor2 = tensornetwork.ones(shape,
backend=other_backend,
dtype=dtype2)
with pytest.raises(ValueError):
_ = tensornetwork.tensordot(tensor1, tensor2,
[[2, 0, 1], [1, 2, 0]])