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test_backends.py
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# pylint: disable=invalid-name
import sys
import os
from functools import partial
os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
import numpy as np
import pytest
from pytest_lazyfixture import lazy_fixture as lf
import tensorflow as tf
thisfile = os.path.abspath(__file__)
modulepath = os.path.dirname(os.path.dirname(thisfile))
sys.path.insert(0, modulepath)
import tensorcircuit as tc
dtype = np.complex64
def universal_vmap():
def sum_real(x, y):
return tc.backend.real(x + y)
vop = tc.backend.vmap(sum_real, vectorized_argnums=(0, 1))
t = tc.gates.array_to_tensor(np.ones([20, 1]))
return vop(t, 2.0 * t)
def test_vmap_np():
r = universal_vmap()
assert r.shape == (20, 1)
def test_vmap_jax(jaxb):
r = universal_vmap()
assert r.shape == (20, 1)
def test_vmap_tf(tfb):
r = universal_vmap()
assert r.numpy()[0, 0] == 3.0
# @pytest.mark.skip(
# reason="pytorch backend to be fixed with newly added complex dtype support"
# )
def test_vmap_torch(torchb):
r = universal_vmap()
assert r.numpy()[0, 0] == 3.0
def test_grad_torch(torchb):
a = tc.backend.ones([2], dtype="float32")
# @partial(tc.backend.jit, jit_compile=True)
@tc.backend.grad
def f(x):
return tc.backend.sum(x)
np.testing.assert_allclose(f(a), np.ones([2]), atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_backend_scatter(backend):
np.testing.assert_allclose(
tc.backend.scatter(
tc.array_to_tensor(np.arange(8), dtype="int32"),
tc.array_to_tensor(np.array([[1], [4]]), dtype="int32"),
tc.array_to_tensor(np.array([0, 0]), dtype="int32"),
),
np.array([0, 0, 2, 3, 0, 5, 6, 7]),
atol=1e-4,
)
np.testing.assert_allclose(
tc.backend.scatter(
tc.array_to_tensor(np.arange(8).reshape([2, 4]), dtype="int32"),
tc.array_to_tensor(np.array([[0, 2], [1, 2], [1, 3]]), dtype="int32"),
tc.array_to_tensor(np.array([0, 99, 0]), dtype="int32"),
),
np.array([[0, 1, 0, 3], [4, 5, 99, 0]]),
atol=1e-4,
)
answer = np.arange(8).reshape([2, 2, 2])
answer[0, 1, 0] = 99
np.testing.assert_allclose(
tc.backend.scatter(
tc.array_to_tensor(np.arange(8).reshape([2, 2, 2]), dtype="int32"),
tc.array_to_tensor(np.array([[0, 1, 0]]), dtype="int32"),
tc.array_to_tensor(np.array([99]), dtype="int32"),
),
answer,
atol=1e-4,
)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_backend_methods(backend):
# TODO(@refraction-ray): add more methods
np.testing.assert_allclose(
tc.backend.softmax(tc.array_to_tensor(np.ones([3, 2]), dtype="float32")),
np.ones([3, 2]) / 6.0,
atol=1e-4,
)
arr = np.random.normal(size=(6, 6))
np.testing.assert_allclose(
tc.backend.adjoint(tc.array_to_tensor(arr + 1.0j * arr)),
arr.T - 1.0j * arr.T,
atol=1e-4,
)
arr = tc.backend.zeros([5], dtype="float32")
np.testing.assert_allclose(
tc.backend.sigmoid(arr),
tc.backend.ones([5]) * 0.5,
atol=1e-4,
)
ans = np.array([[1, 0.5j], [-0.5j, 1]])
ans2 = ans @ ans
ansp = tc.backend.sqrtmh(tc.array_to_tensor(ans2))
print(ansp @ ansp, ans @ ans)
np.testing.assert_allclose(ansp @ ansp, ans @ ans, atol=1e-4)
np.testing.assert_allclose(
tc.backend.sum(tc.array_to_tensor(np.arange(4))), 6, atol=1e-4
)
indices = np.array([[1, 2], [0, 1]])
ans = np.array([[[0, 1, 0], [0, 0, 1]], [[1, 0, 0], [0, 1, 0]]])
np.testing.assert_allclose(tc.backend.one_hot(indices, 3), ans, atol=1e-4)
a = tc.array_to_tensor(np.array([1, 1, 3, 2, 2, 1]), dtype="int32")
np.testing.assert_allclose(tc.backend.unique_with_counts(a)[0].shape[0], 3)
np.testing.assert_allclose(
tc.backend.cumsum(tc.array_to_tensor(np.array([[0.2, 0.2], [0.2, 0.4]]))),
np.array([0.2, 0.4, 0.6, 1.0]),
atol=1e-4,
)
np.testing.assert_allclose(
tc.backend.max(tc.backend.ones([2, 2], "float32")), 1.0, atol=1e-4
)
np.testing.assert_allclose(
tc.backend.min(
tc.backend.cast(
tc.backend.convert_to_tensor(np.array([[1.0, 2.0], [2.0, 3.0]])),
"float64",
),
axis=1,
),
np.array([1.0, 2.0]),
atol=1e-4,
) # by default no keepdim
np.testing.assert_allclose(
tc.backend.concat([tc.backend.ones([2, 2]), tc.backend.ones([1, 2])]),
tc.backend.ones([3, 2]),
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.gather1d(
tc.array_to_tensor(np.array([0, 1, 2])),
tc.array_to_tensor(np.array([2, 1, 0]), dtype="int32"),
),
np.array([2, 1, 0]),
atol=1e-5,
)
def sum_(carry, x):
return carry + x
r = tc.backend.scan(sum_, tc.backend.ones([10, 2]), tc.backend.zeros([2]))
np.testing.assert_allclose(r, 10 * np.ones([2]), atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb"), lf("torchb")])
def test_backend_methods_2(backend):
np.testing.assert_allclose(tc.backend.mean(tc.backend.ones([10])), 1.0, atol=1e-5)
# acos acosh asin asinh atan atan2 atanh cosh (cos) tan tanh sinh (sin)
np.testing.assert_allclose(
tc.backend.acos(tc.backend.ones([2], dtype="float32")),
np.arccos(tc.backend.ones([2])),
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.acosh(tc.backend.ones([2], dtype="float32")),
np.arccosh(tc.backend.ones([2])),
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.asin(tc.backend.ones([2], dtype="float32")),
np.arcsin(tc.backend.ones([2])),
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.asinh(tc.backend.ones([2], dtype="float32")),
np.arcsinh(tc.backend.ones([2])),
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.atan(0.5 * tc.backend.ones([2], dtype="float32")),
np.arctan(0.5 * tc.backend.ones([2])),
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.atan2(
tc.backend.ones([1], dtype="float32"), tc.backend.ones([1], dtype="float32")
),
np.arctan2(
tc.backend.ones([1], dtype="float32"), tc.backend.ones([1], dtype="float32")
),
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.atanh(0.5 * tc.backend.ones([2], dtype="float32")),
np.arctanh(0.5 * tc.backend.ones([2])),
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.cosh(tc.backend.ones([2], dtype="float32")),
np.cosh(tc.backend.ones([2])),
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.tan(tc.backend.ones([2], dtype="float32")),
np.tan(tc.backend.ones([2])),
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.tanh(tc.backend.ones([2], dtype="float32")),
np.tanh(tc.backend.ones([2])),
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.sinh(0.5 * tc.backend.ones([2], dtype="float32")),
np.sinh(0.5 * tc.backend.ones([2])),
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.eigvalsh(tc.backend.ones([2, 2])), np.array([0, 2]), atol=1e-5
)
np.testing.assert_allclose(
tc.backend.left_shift(
tc.backend.convert_to_tensor(np.array([4, 3])),
tc.backend.convert_to_tensor(np.array([1, 1])),
),
np.array([8, 6]),
)
np.testing.assert_allclose(
tc.backend.right_shift(
tc.backend.convert_to_tensor(np.array([4, 3])),
tc.backend.convert_to_tensor(np.array([1, 1])),
),
np.array([2, 1]),
)
np.testing.assert_allclose(
tc.backend.mod(
tc.backend.convert_to_tensor(np.array([4, 3])),
tc.backend.convert_to_tensor(np.array([2, 2])),
),
np.array([0, 1]),
)
np.testing.assert_allclose(
tc.backend.arange(3),
np.array([0, 1, 2]),
)
np.testing.assert_allclose(
tc.backend.arange(1, 5, 2),
np.array([1, 3]),
)
assert tc.backend.dtype(tc.backend.ones([])) == "complex64"
edges = [-1, 3.3, 9.1, 10.0]
values = tc.backend.convert_to_tensor(np.array([0.0, 4.1, 12.0], dtype=np.float32))
r = tc.backend.numpy(tc.backend.searchsorted(edges, values))
np.testing.assert_allclose(r, np.array([1, 2, 4]))
p = tc.backend.convert_to_tensor(
np.array(
[0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.2, 0.4], dtype=np.float32
)
)
r = tc.backend.probability_sample(10000, p, status=np.random.uniform(size=[10000]))
_, r = np.unique(r, return_counts=True)
np.testing.assert_allclose(
r - tc.backend.numpy(p) * 10000.0, np.zeros([10]), atol=200, rtol=1
)
np.testing.assert_allclose(
tc.backend.std(tc.backend.cast(tc.backend.arange(1, 4), "float32")),
0.81649658,
atol=1e-5,
)
arr = np.random.normal(size=(6, 6))
np.testing.assert_allclose(
tc.backend.relu(tc.array_to_tensor(arr, dtype="float32")),
np.maximum(arr, 0),
atol=1e-4,
)
np.testing.assert_allclose(
tc.backend.det(tc.backend.convert_to_tensor(np.eye(3) * 2)), 8, atol=1e-5
)
# @pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb"), lf("torchb")])
# def test_backend_array(backend):
# a = tc.backend.array([[0, 1], [1, 0]])
# assert tc.interfaces.which_backend(a).name == tc.backend.name
# a = tc.backend.array([[0, 1], [1, 0]], dtype=tc.rdtypestr)
# assert tc.dtype(a) == "float32"
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb"), lf("torchb")])
def test_device_cpu_only(backend):
a = tc.backend.ones([])
dev_str = tc.backend.device(a)
assert dev_str in ["cpu", "gpu:0"]
tc.backend.device_move(a, dev_str)
@pytest.mark.skipif(
len(tf.config.list_physical_devices()) == 1, reason="no GPU detected"
)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb"), lf("torchb")])
def test_device_cpu_gpu(backend):
a = tc.backend.ones([])
a1 = tc.backend.device_move(a, "gpu:0")
dev_str = tc.backend.device(a1)
assert dev_str == "gpu:0"
a2 = tc.backend.device_move(a1, "cpu")
assert tc.backend.device(a2) == "cpu"
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb"), lf("torchb")])
def test_dlpack(backend):
a = tc.backend.ones([2, 2], dtype="float64")
cap = tc.backend.to_dlpack(a)
a1 = tc.backend.from_dlpack(cap)
np.testing.assert_allclose(a, a1, atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb"), lf("torchb")])
def test_arg_cmp(backend):
np.testing.assert_allclose(tc.backend.argmax(tc.backend.ones([3], "float64")), 0)
np.testing.assert_allclose(
tc.backend.argmax(
tc.array_to_tensor(np.array([[1, 2], [3, 4]]), dtype="float64")
),
np.array([1, 1]),
)
np.testing.assert_allclose(
tc.backend.argmin(
tc.array_to_tensor(np.array([[1, 2], [3, 4]]), dtype="float64"), axis=-1
),
np.array([0, 0]),
)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb"), lf("torchb")])
def test_tree_map(backend):
def f(a, b):
return a + b
r = tc.backend.tree_map(
f, {"a": tc.backend.ones([2])}, {"a": 2 * tc.backend.ones([2])}
)
np.testing.assert_allclose(r["a"], 3 * np.ones([2]), atol=1e-4)
def _add(a, b):
return a + b
ans = tc.backend.tree_map(
_add,
{"a": tc.backend.ones([2]), "b": tc.backend.ones([3])},
{"a": tc.backend.ones([2]), "b": tc.backend.ones([3])},
)
np.testing.assert_allclose(ans["a"], 2 * np.ones([2]))
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_backend_randoms(backend):
@partial(tc.backend.jit, static_argnums=0)
def random_matrixn(key):
tc.backend.set_random_state(key)
r1 = tc.backend.implicit_randn(shape=[2, 2], mean=0.5)
r2 = tc.backend.implicit_randn(shape=[2, 2], mean=0.5)
return r1, r2
key = 42
if tc.backend.name == "tensorflow":
key = tf.random.Generator.from_seed(42)
r11, r12 = random_matrixn(key)
if tc.backend.name == "tensorflow":
key = tf.random.Generator.from_seed(42)
r21, r22 = random_matrixn(key)
np.testing.assert_allclose(r11, r21, atol=1e-4)
np.testing.assert_allclose(r12, r22, atol=1e-4)
assert not np.allclose(r11, r12, atol=1e-4)
def random_matrixu(key):
tc.backend.set_random_state(key)
r1 = tc.backend.implicit_randu(shape=[2, 2], high=2)
r2 = tc.backend.implicit_randu(shape=[2, 2], high=1)
return r1, r2
key = 42
r31, r32 = random_matrixu(key)
np.testing.assert_allclose(r31.shape, [2, 2])
assert np.any(r32 > 0)
assert not np.allclose(r31, r32, atol=1e-4)
def random_matrixc(key):
tc.backend.set_random_state(key)
r1 = tc.backend.implicit_randc(a=[1, 2, 3], shape=(2, 2))
r2 = tc.backend.implicit_randc(a=[1, 2, 3], shape=(2, 2), p=[0.1, 0.4, 0.5])
return r1, r2
r41, r42 = random_matrixc(key)
np.testing.assert_allclose(r41.shape, [2, 2])
assert np.any((r42 > 0) & (r42 < 4))
def vqe_energy(inputs, param, n, nlayers):
c = tc.Circuit(n, inputs=inputs)
paramc = tc.backend.cast(param, "complex64")
for i in range(n):
c.H(i)
for j in range(nlayers):
for i in range(n - 1):
c.ryy(i, i + 1, theta=paramc[2 * j, i])
# c.any(
# i,
# i + 1,
# unitary=tc.backend.cos(paramc[2 * j, i]) * iir
# + tc.backend.sin(paramc[2 * j, i]) * 1.0j * yzr,
# )
for i in range(n):
c.rx(i, theta=paramc[2 * j + 1, i])
e = 0.0
for i in range(n):
e += c.expectation((tc.gates.x(), [i]))
for i in range(n - 1): # OBC
e += c.expectation((tc.gates.z(), [i]), (tc.gates.z(), [(i + 1) % n]))
e = tc.backend.real(e)
return e
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb"), lf("torchb")])
def test_vvag(backend):
n = 4
nlayers = 3
inp = tc.backend.ones([2**n]) / 2 ** (n / 2)
param = tc.backend.ones([2 * nlayers, n])
# inp = tc.backend.cast(inp, "complex64")
# param = tc.backend.cast(param, "complex64")
vqe_energy_p = partial(vqe_energy, n=n, nlayers=nlayers)
vg = tc.backend.value_and_grad(vqe_energy_p, argnums=(0, 1))
v0, (g00, g01) = vg(inp, param)
batch = 8
inps = tc.backend.ones([batch, 2**n]) / 2 ** (n / 2)
inps = tc.backend.cast(inps, "complex64")
pvag = tc.backend.vvag(vqe_energy_p, argnums=(0, 1))
v1, (g10, g11) = pvag(inps, param)
print(v1.shape, g10.shape, g11.shape)
np.testing.assert_allclose(v1[0], v0, atol=1e-4)
np.testing.assert_allclose(g10[0], g00, atol=1e-4)
np.testing.assert_allclose(g11 / batch, g01, atol=1e-4)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_vvag_dict(backend):
def dict_plus(x, y):
a = x["a"]
return tc.backend.real((a + y)[0])
dp_vvag = tc.backend.vvag(dict_plus, vectorized_argnums=1, argnums=0)
x = {"a": tc.backend.ones([1])}
y = tc.backend.ones([20, 1])
v, g = dp_vvag(x, y)
np.testing.assert_allclose(v.shape, [20])
np.testing.assert_allclose(g["a"], 20.0, atol=1e-4)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb"), lf("torchb")])
def test_vjp(backend):
def f(x):
return x**2
inputs = tc.backend.ones([2, 2])
v, g = tc.backend.vjp(f, inputs, inputs)
np.testing.assert_allclose(v, inputs, atol=1e-5)
np.testing.assert_allclose(g, 2 * inputs, atol=1e-5)
def f2(x, y):
return x + y, x - y
inputs = [tc.backend.ones([2]), tc.backend.ones([2])]
v = [2.0 * t for t in inputs]
v, g = tc.backend.vjp(f2, inputs, v)
np.testing.assert_allclose(v[1], np.zeros([2]), atol=1e-5)
np.testing.assert_allclose(g[0], 4 * np.ones([2]), atol=1e-5)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb"), lf("torchb")])
def test_vjp_complex(backend):
def f(x):
return tc.backend.conj(x)
inputs = tc.backend.ones([1]) + 1.0j * tc.backend.ones([1])
v = tc.backend.ones([1], dtype="complex64")
v, g = tc.backend.vjp(f, inputs, v)
np.testing.assert_allclose(tc.backend.numpy(g), np.ones([1]), atol=1e-5)
def f2(x):
return x**2
inputs = tc.backend.ones([1]) + 1.0j * tc.backend.ones([1])
v = tc.backend.ones([1], dtype="complex64") # + 1.0j * tc.backend.ones([1])
v, g = tc.backend.vjp(f2, inputs, v)
# note how vjp definition on complex function is different in jax backend
if tc.backend.name == "jax":
np.testing.assert_allclose(tc.backend.numpy(g), 2 + 2j, atol=1e-5)
else:
np.testing.assert_allclose(tc.backend.numpy(g), 2 - 2j, atol=1e-5)
# TODO(@refraction-ray): consistent and unified pytree utils for pytorch backend?
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_vjp_pytree(backend):
def f3(d):
return d["a"] + d["b"], d["a"]
inputs = {"a": tc.backend.ones([2]), "b": tc.backend.ones([1])}
v = (tc.backend.ones([2]), tc.backend.zeros([2]))
v, g = tc.backend.vjp(f3, inputs, v)
np.testing.assert_allclose(v[0], 2 * np.ones([2]), atol=1e-5)
np.testing.assert_allclose(g["a"], np.ones([2]), atol=1e-5)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb"), lf("torchb")])
def test_jvp(backend):
def f(x):
return x**2
inputs = tc.backend.ones([2, 2])
v, g = tc.backend.jvp(f, inputs, inputs)
np.testing.assert_allclose(v, inputs, atol=1e-5)
np.testing.assert_allclose(g, 2 * inputs, atol=1e-5)
def f2(x, y):
return x + y, x - y
inputs = [tc.backend.ones([2]), tc.backend.ones([2])]
v = [2.0 * t for t in inputs]
v, g = tc.backend.jvp(f2, inputs, v)
np.testing.assert_allclose(v[1], np.zeros([2]), atol=1e-5)
np.testing.assert_allclose(g[0], 4 * np.ones([2]), atol=1e-5)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb"), lf("torchb")])
def test_jvp_complex(backend):
def f(x):
return tc.backend.conj(x)
inputs = tc.backend.ones([1]) + 1.0j * tc.backend.ones([1])
v = tc.backend.ones([1], dtype="complex64")
v, g = tc.backend.jvp(f, inputs, v)
# numpy auto numpy doesn't work for torch conjugate tensor
np.testing.assert_allclose(tc.backend.numpy(g), np.ones([1]), atol=1e-5)
def f2(x):
return x**2
inputs = tc.backend.ones([1]) + 1.0j * tc.backend.ones([1])
v = tc.backend.ones([1]) + 1.0j * tc.backend.ones([1])
v, g = tc.backend.jvp(f2, inputs, v)
np.testing.assert_allclose(tc.backend.numpy(g), 4.0j, atol=1e-5)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_jvp_pytree(backend):
def f3(d):
return d["a"] + d["b"], d["a"]
inputs = {"a": tc.backend.ones([2]), "b": tc.backend.ones([1])}
v = (tc.backend.ones([2]), tc.backend.zeros([2]))
v, g = tc.backend.vjp(f3, inputs, v)
np.testing.assert_allclose(v[0], 2 * np.ones([2]), atol=1e-5)
np.testing.assert_allclose(g["a"], np.ones([2]), atol=1e-5)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
@pytest.mark.parametrize("mode", ["jacfwd", "jacrev"])
def test_jac(backend, mode):
# make no sense for torch backend when you have no real vmap interface
backend_jac = getattr(tc.backend, mode)
def f(x):
return x**2
x = tc.backend.ones([3])
jacf = backend_jac(f)
np.testing.assert_allclose(jacf(x), 2 * np.eye(3), atol=1e-5)
def f2(x):
return x**2, x
jacf2 = backend_jac(f2)
np.testing.assert_allclose(jacf2(x)[1], np.eye(3), atol=1e-5)
np.testing.assert_allclose(jacf2(x)[0], 2 * np.eye(3), atol=1e-5)
def f3(x, y):
return x + y**2
jacf3 = backend_jac(f3, argnums=(0, 1))
jacf3jit = tc.backend.jit(backend_jac(f3, argnums=(0, 1)))
np.testing.assert_allclose(jacf3jit(x, x)[1], 2 * np.eye(3), atol=1e-5)
np.testing.assert_allclose(jacf3(x, x)[1], 2 * np.eye(3), atol=1e-5)
def f4(x, y):
return x**2, y
# note the subtle difference of two tuples order in jacrev and jacfwd for current API
# the value happen to be the same here, though
jacf4 = backend_jac(f4, argnums=(0, 1))
jacf4jit = tc.backend.jit(backend_jac(f4, argnums=(0, 1)))
np.testing.assert_allclose(jacf4jit(x, x)[1][1], np.eye(3), atol=1e-5)
np.testing.assert_allclose(jacf4jit(x, x)[0][1], np.zeros([3, 3]), atol=1e-5)
np.testing.assert_allclose(jacf4(x, x)[1][1], np.eye(3), atol=1e-5)
np.testing.assert_allclose(jacf4(x, x)[0][1], np.zeros([3, 3]), atol=1e-5)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
@pytest.mark.parametrize("mode", ["jacfwd", "jacrev"])
def test_jac_md_input(backend, mode):
backend_jac = getattr(tc.backend, mode)
def f(x):
return x**2
x = tc.backend.ones([2, 3])
jacf = backend_jac(f)
np.testing.assert_allclose(jacf(x).shape, [2, 3, 2, 3], atol=1e-5)
def f2(x):
return tc.backend.sum(x, axis=0)
x = tc.backend.ones([2, 3])
jacf2 = backend_jac(f2)
np.testing.assert_allclose(jacf2(x).shape, [3, 2, 3], atol=1e-5)
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
@pytest.mark.parametrize("mode", ["jacfwd", "jacrev"])
def test_jac_tall(backend, mode):
backend_jac = getattr(tc.backend, mode)
h = tc.backend.ones([5, 3])
def f(x):
x = tc.backend.reshape(x, [-1, 1])
return tc.backend.reshape(h @ x, [-1])
x = tc.backend.ones([3])
jacf = backend_jac(f)
np.testing.assert_allclose(jacf(x), np.ones([5, 3]), atol=1e-5)
@pytest.mark.parametrize("backend", [lf("jaxb"), lf("tfb")])
def test_vvag_has_aux(backend):
def f(x):
y = tc.backend.sum(x)
return tc.backend.real(y**2), y
fvvag = tc.backend.vvag(f, has_aux=True)
(_, v1), _ = fvvag(tc.backend.ones([10, 2]))
np.testing.assert_allclose(v1, 2 * tc.backend.ones([10]))
def test_jax_svd(jaxb, highp):
def l(A):
u, _, v, _ = tc.backend.svd(A)
return tc.backend.real(u[0, 0] * v[0, 0])
def numericald(A):
eps = 1e-6
DA = np.zeros_like(A)
for i in range(A.shape[0]):
for j in range(A.shape[1]):
dA = np.zeros_like(A)
dA[i, j] = 1
DA[i, j] = (l(A + eps * dA) - l(A)) / eps - 1.0j * (
l(A + eps * 1.0j * dA) - l(A)
) / eps
return DA
def analyticald(A):
A = tc.backend.convert_to_tensor(A)
g = tc.backend.grad(l)
return g(A)
for shape in [(2, 2), (3, 3), (2, 3), (4, 2)]:
m = np.random.normal(size=shape).astype(
np.complex128
) + 1.0j * np.random.normal(size=shape).astype(np.complex128)
print(m)
np.testing.assert_allclose(numericald(m), analyticald(m), atol=1e-3)
@pytest.mark.parametrize("backend", [lf("jaxb"), lf("tfb"), lf("torchb")])
def test_qr(backend, highp):
def get_random_complex(shape):
result = np.random.random(shape) + np.random.random(shape) * 1j
return tc.backend.convert_to_tensor(result.astype(dtype))
np.random.seed(0)
A1 = get_random_complex((2, 2))
A2 = tc.backend.convert_to_tensor(np.array([[1.0, 0.0], [0.0, 0.0]]).astype(dtype))
X = get_random_complex((2, 2))
def func(A, x):
x = tc.backend.cast(x, "complex64")
Q, R = tc.backend.qr(A + X * x)
return tc.backend.real(tc.backend.sum(tc.backend.matmul(Q, R)))
def grad(A, x):
return tc.backend.grad(func, argnums=1)(A, x)
for A in [A1, A2]:
epsilon = tc.backend.convert_to_tensor(1e-3)
n_grad = (func(A, epsilon) - func(A, -epsilon)) / (2 * epsilon)
a_grad = grad(A, tc.backend.convert_to_tensor(0.0))
np.testing.assert_allclose(n_grad, a_grad, atol=1e-3)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_sparse_methods(backend):
values = tc.backend.convert_to_tensor(np.array([1.0, 2.0]))
values = tc.backend.cast(values, "complex64")
indices = tc.backend.convert_to_tensor(np.array([[0, 0], [1, 1]]))
indices = tc.backend.cast(indices, "int64")
spa = tc.backend.coo_sparse_matrix(indices, values, shape=[4, 4])
vec = tc.backend.ones([4, 1])
da = np.array(
[[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=np.complex64
)
assert tc.backend.is_sparse(spa) is True
assert tc.backend.is_sparse(vec) is False
np.testing.assert_allclose(
tc.backend.to_dense(spa),
da,
atol=1e-5,
)
np.testing.assert_allclose(
tc.backend.sparse_dense_matmul(spa, vec),
np.array([[1], [2], [0], [0]], dtype=np.complex64),
atol=1e-5,
)
spa_np = tc.backend.numpy(spa)
np.testing.assert_allclose(spa_np.todense(), da, atol=1e-6)
np.testing.assert_allclose(
tc.backend.to_dense(tc.backend.coo_sparse_matrix_from_numpy(spa_np)),
da,
atol=1e-5,
)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_backend_randoms_v2(backend):
g = tc.backend.get_random_state(42)
for t in tc.backend.stateful_randc(g, 3, [3]):
assert t >= 0
assert t < 3
key = tc.backend.get_random_state(42)
r = []
for _ in range(2):
key, subkey = tc.backend.random_split(key)
r.append(tc.backend.stateful_randc(subkey, 3, [5]))
assert tuple(r[0]) != tuple(r[1])
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_backend_randoms_v3(backend):
tc.backend.set_random_state(42)
for _ in range(2):
r1 = tc.backend.implicit_randu()
key = tc.backend.get_random_state(42)
for _ in range(2):
key, subkey = tc.backend.random_split(key)
r2 = tc.backend.stateful_randu(subkey)
np.testing.assert_allclose(r1, r2, atol=1e-5)
@tc.backend.jit
def f(key):
tc.backend.set_random_state(key)
r = []
for _ in range(3):
r.append(tc.backend.implicit_randu()[0])
return r
@tc.backend.jit
def f2(key):
r = []
for _ in range(3):
key, subkey = tc.backend.random_split(key)
r.append(tc.backend.stateful_randu(subkey)[0])
return r
key = tc.backend.get_random_state(43)
r = f(key)
key = tc.backend.get_random_state(43)
r1 = f2(key)
np.testing.assert_allclose(r[-1], r1[-1], atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_function_level_set(backend):
def f(x):
return tc.backend.ones([x])
f_jax_128 = tc.set_function_backend("jax")(tc.set_function_dtype("complex128")(f))
# note the order to enable complex 128 in jax backend
assert f_jax_128(3).dtype.__str__() == "complex128"
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_function_level_set_contractor(backend):
@tc.set_function_contractor("branch")
def f():
return tc.contractor
print(f())
print(tc.contractor)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_with_level_set(backend):
with tc.runtime_backend("jax"):
with tc.runtime_dtype("complex128"):
with tc.runtime_contractor("branch"):
assert tc.backend.ones([2]).dtype.__str__() == "complex128"
print(tc.contractor)
print(tc.contractor)
print(tc.backend.name)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_with_level_set_return(backend):
with tc.runtime_backend("jax") as K:
assert K.name == "jax"
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb"), lf("torchb")])
def test_grad_has_aux(backend):
def f(x):
return tc.backend.real(x**2), x**3
vg = tc.backend.value_and_grad(f, has_aux=True)
np.testing.assert_allclose(
vg(tc.backend.ones([]))[1], 2 * tc.backend.ones([]), atol=1e-5
)
def f2(x):
return tc.backend.real(x**2), (x**3, tc.backend.ones([3]))
gs = tc.backend.grad(f2, has_aux=True)
np.testing.assert_allclose(gs(tc.backend.ones([]))[0], 2.0, atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("jaxb"), lf("tfb")])
def test_solve(backend):
A = np.array([[2, 1, 0], [1, 2, 0], [0, 0, 1]], dtype=np.float32)
A = tc.backend.convert_to_tensor(A)
x = np.ones([3, 1], dtype=np.float32)
x = tc.backend.convert_to_tensor(x)
b = (A @ x)[:, 0]
print(A.shape, b.shape)
xp = tc.backend.solve(A, b, assume_a="her")
np.testing.assert_allclose(xp, x[:, 0], atol=1e-5)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb"), lf("torchb")])
def test_treeutils(backend):
d0 = {"a": np.ones([2]), "b": [tc.backend.zeros([]), tc.backend.ones([1, 1])]}
leaves, treedef = tc.backend.tree_flatten(d0)
d1 = tc.backend.tree_unflatten(treedef, leaves)
d2 = tc.backend.tree_map(lambda x: 2 * x, d1)
np.testing.assert_allclose(2 * np.ones([1, 1]), d2["b"][1])
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb"), lf("torchb")])
def test_optimizers(backend):
if tc.backend.name == "jax":
try:
import optax
except ImportError:
pytest.skip("optax is not installed")
if tc.backend.name == "pytorch":
try:
import torch
except ImportError:
pytest.skip("torch is not installed")
def f(params, n):
c = tc.Circuit(n)
c = tc.templates.blocks.example_block(c, params["a"])
c = tc.templates.blocks.example_block(c, params["b"])
return tc.backend.real(c.expectation([tc.gates.x(), [n // 2]]))
vgs = tc.backend.jit(tc.backend.value_and_grad(f, argnums=0), static_argnums=1)
def get_opt():
if tc.backend.name == "tensorflow":
optimizer1 = tf.keras.optimizers.Adam(5e-2)
opt = tc.backend.optimizer(optimizer1)
elif tc.backend.name == "jax":
optimizer2 = optax.adam(5e-2)
opt = tc.backend.optimizer(optimizer2)
elif tc.backend.name == "pytorch":
optimizer3 = partial(torch.optim.Adam, lr=5e-2)
opt = tc.backend.optimizer(optimizer3)
else:
raise ValueError("%s doesn't support optimizer interface" % tc.backend.name)
return opt
n = 3
opt = get_opt()
params = {
"a": tc.backend.ones([4, n], dtype="float32"),
"b": tc.backend.ones([4, n], dtype="float32"),
}
for _ in range(20):
loss, grads = vgs(params, n)
params = opt.update(grads, params)
print(loss)
assert loss < -0.7
def f2(params, n):
c = tc.Circuit(n)
c = tc.templates.blocks.example_block(c, params)
return tc.backend.real(c.expectation([tc.gates.x(), [n // 2]]))
vgs2 = tc.backend.jit(tc.backend.value_and_grad(f2, argnums=0), static_argnums=1)
params = tc.backend.ones([4, n], dtype="float32")
opt = get_opt()
for _ in range(20):
loss, grads = vgs2(params, n)
params = opt.update(grads, params)
print(loss)
assert loss < -0.7
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_hessian(backend):
# hessian support is now very fragile and especially has potential issues on tf backend
def f(param):
return tc.backend.sum(param**2)
hf = tc.backend.hessian(f)
param = tc.backend.ones([2])
np.testing.assert_allclose(hf(param), 2 * tc.backend.eye(2), atol=1e-5)
param = tc.backend.ones([2, 2])
assert list(hf(param).shape) == [2, 2, 2, 2] # possible tf retracing?
g = tc.templates.graphs.Line1D(5)
def circuit_f(param):
c = tc.Circuit(5)
c = tc.templates.blocks.example_block(c, param, nlayers=1)
return tc.templates.measurements.heisenberg_measurements(c, g)
param = tc.backend.ones([10])
hf = tc.backend.hessian(circuit_f)
print(hf(param)) # still upto a conjugate for jax and tf backend.
@pytest.mark.parametrize("backend", [lf("tfb"), lf("jaxb")])
def test_nested_vmap(backend):
def f(x, w):
c = tc.Circuit(4)