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test_backends.py
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import sys
import os
from functools import partial
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
ii = np.eye(4, dtype=dtype)
iir = ii.reshape([2, 2, 2, 2])
ym = np.array([[0, -1.0j], [1.0j, 0]], dtype=dtype)
zm = np.array([[1.0, 0.0], [0.0, -1.0]], dtype=dtype)
yz = np.kron(ym, zm)
yzr = yz.reshape([2, 2, 2, 2])
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
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
def test_backend_scatter(backend):
assert np.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,
)
assert np.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
assert np.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
assert np.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))
assert np.allclose(
tc.backend.relu(tc.array_to_tensor(arr, dtype="float32")),
np.maximum(arr, 0),
atol=1e-4,
)
assert np.allclose(
tc.backend.adjoint(tc.array_to_tensor(arr + 1.0j * arr)),
arr.T - 1.0j * arr.T,
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)
assert np.allclose(ansp @ ansp, ans @ ans, atol=1e-4)
assert np.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]]])
assert np.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")
assert np.allclose(tc.backend.unique_with_counts(a)[0].shape[0], 3)
assert np.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,
)
@pytest.mark.parametrize("backend", [lf("npb"), lf("tfb"), lf("jaxb")])
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])}
)
assert np.allclose(r["a"], 3 * np.ones([2]), atol=1e-4)
@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)
assert np.allclose(r11, r21, atol=1e-4)
assert np.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)
assert np.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)
assert np.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.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")])
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)
vag = tc.backend.value_and_grad(vqe_energy_p, argnums=(0, 1))
v0, (g00, g01) = vag(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)
assert np.allclose(v1[0], v0, atol=1e-4)
assert np.allclose(g10[0], g00, atol=1e-4)
assert np.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 (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)
assert np.allclose(v.shape, [20])
assert np.allclose(g["a"], 20.0, atol=1e-4)