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# BSD 3-Clause License; see https://github.com/scikit-hep/awkward/blob/main/LICENSE
from __future__ import annotations
import numpy as np
import numpy.testing
import pytest
import awkward as ak
jax = pytest.importorskip("jax")
jax.config.update("jax_platform_name", "cpu")
ak.jax.register_and_check()
# #### ak.contents.NumpyArray ####
test_regulararray = ak.Array(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], backend="jax"
)
test_regulararray_tangent = ak.Array(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], backend="jax"
)
test_regulararray_jax = jax.numpy.array(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], dtype=np.float64
)
test_regulararray_tangent_jax = jax.numpy.array(
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], dtype=np.float64
)
@pytest.mark.parametrize("axis", [0, 1, None])
@pytest.mark.parametrize(
"func_ak", [ak.sum, ak.prod, ak.min, ak.max, ak.mean, ak.prod, ak.ptp, ak.std]
)
def test_reducer(func_ak, axis):
func_jax = getattr(jax.numpy, func_ak.__name__)
def func_ak_with_axis(x):
return func_ak(x, axis=axis)
def func_jax_with_axis(x):
return func_jax(x, axis=axis)
value_jvp, jvp_grad = jax.jvp(
func_ak_with_axis, (test_regulararray,), (test_regulararray_tangent,)
)
value_jvp_jax, jvp_grad_jax = jax.jvp(
func_jax_with_axis, (test_regulararray_jax,), (test_regulararray_tangent_jax,)
)
value_vjp, vjp_func = jax.vjp(func_ak_with_axis, test_regulararray)
value_vjp_jax, vjp_func_jax = jax.vjp(func_jax_with_axis, test_regulararray_jax)
numpy.testing.assert_allclose(
ak.to_list(value_jvp), value_jvp_jax.tolist(), rtol=1e-9, atol=1e-9
)
numpy.testing.assert_allclose(
ak.to_list(value_vjp), value_vjp_jax.tolist(), rtol=1e-9, atol=1e-9
)
numpy.testing.assert_allclose(
ak.to_list(jvp_grad), jvp_grad_jax.tolist(), rtol=1e-9, atol=1e-9
)
numpy.testing.assert_allclose(
ak.to_list(vjp_func(value_vjp)[0]),
(vjp_func_jax(value_vjp_jax)[0]).tolist(),
rtol=1e-9,
atol=1e-9,
)
@pytest.mark.parametrize("axis", [0, 1])
@pytest.mark.parametrize("func_ak", [ak.sort])
def test_sort(func_ak, axis):
def func_ak_with_axis(x):
return func_ak(x, axis=axis)
match = r".*This kernel is not differentiable by the JAX backend.*"
with pytest.raises(ValueError, match=match):
value_jvp, jvp_grad = jax.jvp(
func_ak_with_axis, (test_regulararray,), (test_regulararray_tangent,)
)
with pytest.raises(ValueError, match=match):
value_vjp, vjp_func = jax.vjp(func_ak_with_axis, test_regulararray)
@pytest.mark.parametrize("func_ak", [ak.ravel])
def test_ravel(func_ak):
func_jax = getattr(jax.numpy, func_ak.__name__)
def func_ak_no_axis(x):
return func_ak(x)
def func_jax_no_axis(x):
return func_jax(x)
value_jvp, jvp_grad = jax.jvp(
func_ak_no_axis, (test_regulararray,), (test_regulararray_tangent,)
)
value_jvp_jax, jvp_grad_jax = jax.jvp(
func_jax_no_axis, (test_regulararray_jax,), (test_regulararray_tangent_jax,)
)
value_vjp, vjp_func = jax.vjp(func_ak_no_axis, test_regulararray)
value_vjp_jax, vjp_func_jax = jax.vjp(func_jax_no_axis, test_regulararray_jax)
numpy.testing.assert_allclose(
ak.to_list(value_jvp), value_jvp_jax.tolist(), rtol=1e-9, atol=1e-9
)
numpy.testing.assert_allclose(
ak.to_list(value_vjp), value_vjp_jax.tolist(), rtol=1e-9, atol=1e-9
)
numpy.testing.assert_allclose(
ak.to_list(jvp_grad), jvp_grad_jax.tolist(), rtol=1e-9, atol=1e-9
)
numpy.testing.assert_allclose(
ak.to_list(vjp_func(value_vjp)[0]),
(vjp_func_jax(value_vjp_jax)[0]).tolist(),
rtol=1e-9,
atol=1e-9,
)
@pytest.mark.parametrize("axis", [None])
@pytest.mark.parametrize("func_ak", [ak.any, ak.all])
def test_bool_returns(func_ak, axis):
func_jax = getattr(jax.numpy, func_ak.__name__)
def func_ak_with_axis(x):
return func_ak(x, axis=axis)
def func_jax_with_axis(x):
return func_jax(x, axis=axis)
value_jvp, jvp_grad = jax.jvp(
func_ak_with_axis, (test_regulararray,), (test_regulararray_tangent,)
)
value_jvp_jax, jvp_grad_jax = jax.jvp(
func_jax_with_axis, (test_regulararray_jax,), (test_regulararray_tangent_jax,)
)
value_vjp, vjp_func = jax.vjp(func_ak_with_axis, test_regulararray)
value_vjp_jax, vjp_func_jax = jax.vjp(func_jax_with_axis, test_regulararray_jax)
assert jvp_grad.dtype == jvp_grad_jax.dtype
assert value_jvp.tolist() == value_jvp_jax.tolist()
assert value_vjp.tolist() == value_vjp_jax.tolist()
numpy.testing.assert_allclose(
ak.to_list(vjp_func(value_vjp)[0]),
(vjp_func_jax(value_vjp_jax)[0]).tolist(),
rtol=1e-9,
atol=1e-9,
)
@pytest.mark.parametrize("axis", [0, 1, -1])
@pytest.mark.parametrize("func_ak", [ak.any, ak.all])
def test_bool_raises(func_ak, axis):
def func_with_axis(x):
return func_ak(x, axis=axis)
with pytest.raises(
TypeError, match=".*Make sure that you are not computing the derivative.*"
):
jax.jvp(func_with_axis, (test_regulararray,), (test_regulararray_tangent,))
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