def omnistaging_disabler() -> None:
global axis_index
psum_p.bind = partial(core.Primitive.bind, psum_p) # type: ignore
psum_p.def_impl(partial(pxla.apply_parallel_primitive, psum_p)) # type: ignore
pxla.parallel_pure_rules[psum_p] = lambda *args, shape: (x * prod(shape) for x in args) # type: ignore
def _axis_index_bind(*, axis_name):
dynamic_axis_env = pxla._thread_local_state.dynamic_axis_env
frame = dynamic_axis_env[axis_name]
sizes = dynamic_axis_env.sizes[:dynamic_axis_env.index(frame)+1]
nreps = dynamic_axis_env.nreps
trace = frame.pmap_trace
out_aval = ShapedArray((), np.int32)
out_tracer = pe.JaxprTracer(trace, pe.PartialVal.unknown(out_aval), None)
eqn = pe.new_eqn_recipe([], [out_tracer], axis_index_p,
dict(nreps=nreps, sizes=sizes, axis_name=axis_name),
source_info_util.current())
out_tracer.recipe = eqn
return out_tracer
def _axis_index_translation_rule(c, nreps, sizes, axis_name):
div = xb.constant(c, np.array(nreps // prod(sizes), dtype=np.uint32))
mod = xb.constant(c, np.array(sizes[-1], dtype=np.uint32))
unsigned_index = xops.Rem(xops.Div(xops.ReplicaId(c), div), mod)
return xops.ConvertElementType(unsigned_index, xb.dtype_to_etype(np.int32))
axis_index_p.def_custom_bind(_axis_index_bind)
axis_index_p.def_abstract_eval(
lambda *args, **params: ShapedArray((), np.int32))
xla.translations[axis_index_p] = _axis_index_translation_rule
def _all_to_all_translation_rule(c, x, *, split_axis, concat_axis, axis_name,
axis_index_groups, axis_env, platform):
# Workaround for AllToAll not being implemented on CPU.
replica_groups = _replica_groups(axis_env, axis_name, axis_index_groups)
if len(replica_groups[0]) == 1:
return x
elif platform != 'tpu':
warnings.warn("all_to_all (and pswapaxes) are only implemented properly for TPUs. All other "
"backends emulate it using a very slow and memory intensive algorithm, so expect "
"significant slowdowns.")
lowering = xla.lower_fun(_all_to_all_via_all_gather, multiple_results=False, parallel=True)
return lowering(c, x,
split_axis=split_axis, concat_axis=concat_axis, axis_name=axis_name,
axis_index_groups=axis_index_groups, axis_env=axis_env, platform=platform)
else:
split_count = len(replica_groups[0])
if not all(split_count == len(g) for g in replica_groups):
raise ValueError('Replica groups must be equally sized')
replica_groups_protos = xc.make_replica_groups(replica_groups)
if concat_axis == split_axis:
return xops.AllToAll(x, split_axis, concat_axis, split_count,
replica_groups_protos)
else:
if concat_axis < split_axis:
split_axis += 1
elif split_axis < concat_axis:
concat_axis += 1
x = xla.lower_fun(partial(lax.expand_dims, dimensions=(concat_axis,)), multiple_results=False)(c, x)
x = xops.AllToAll(x, split_axis, concat_axis, split_count, replica_groups_protos)
x = xla.lower_fun(partial(lax.squeeze, dimensions=(split_axis,)), multiple_results=False)(c, x)
return x
def testSumPool(self):
W = jnp.array(np.random.randn(3, 3, 1, 5), dtype=np.float32)
X = jnp.array(np.random.randn(10, 5, 5, 1), dtype=np.float32)
def f(params, x):
one = (1, 1)
dimension_numbers = ('NHWC', 'HWIO', 'NHWC')
y = lax.conv_general_dilated(
x, params, one, 'SAME', one, one, dimension_numbers)
y = lax.reduce_window(
y, 0.0, lax.add, (1, 2, 2, 1), (1, 1, 1, 1), 'SAME')
return y
grad_loss = grad(lambda params, x: jnp.mean(f(params, x) ** 2))
# Test forward prop.
per_example = vmap(partial(f, W))(jnp.reshape(X, (10, 1, 5, 5, 1)))
per_example = jnp.reshape(per_example, (10, 5, 5, 5))
per_example_direct = f(W, X)
self.assertAllClose(per_example, per_example_direct)
# Test gradients.
per_example = vmap(partial(grad_loss, W))(jnp.reshape(X, (10, 1, 5, 5, 1)))
per_example_direct = []
for i in range(10):
g = grad_loss(W, jnp.reshape(X[i], (1, 5, 5, 1)))
per_example_direct += [
jnp.reshape(g, (1,) + g.shape)]
per_example_direct = jnp.concatenate(per_example_direct, axis=0)
self.assertAllClose(per_example, per_example_direct,
rtol=3e-2, atol=1e-3)
def value_and_jacfwd_f(*args, **kwargs):
f = lu.wrap_init(fun, kwargs)
f_partial, dyn_args = argnums_partial(
f, argnums, args, require_static_args_hashable=False)
tree_map(partial(_check_input_dtype_jacfwd, holomorphic), dyn_args)
pushfwd = partial(_jvp, f_partial, dyn_args)
y, jac = vmap(pushfwd, out_axes=(None, -1))(_std_basis(dyn_args))
tree_map(partial(_check_output_dtype_jacfwd, holomorphic), y)
example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args
return y, tree_map(partial(_jacfwd_unravel, example_args), y, jac)
def testSort(self):
v = np.arange(12)[::-1].reshape(3, 4)
sv = vmap(partial(lax.sort, dimension=0), (0,))(v)
self.assertAllClose(sv, v[:, ::-1])
sv = vmap(partial(lax.sort, dimension=-1), (0,))(v)
self.assertAllClose(sv, v[:, ::-1])
sv = vmap(partial(lax.sort, dimension=0), (1,))(v)
self.assertAllClose(sv, v[::-1, :].T)
sv = vmap(partial(lax.sort, dimension=0), (1,), 1)(v)
self.assertAllClose(sv, v[::-1, :])
def triangular_solve_jvp_rule_a(
g_a, ans, a, b, left_side, lower, transpose_a, conjugate_a, unit_diagonal):
m, n = b.shape[-2:]
k = 1 if unit_diagonal else 0
g_a = jnp.tril(g_a, k=-k) if lower else jnp.triu(g_a, k=k)
g_a = lax.neg(g_a)
g_a = jnp.swapaxes(g_a, -1, -2) if transpose_a else g_a
g_a = jnp.conj(g_a) if conjugate_a else g_a
dot = partial(lax.dot if g_a.ndim == 2 else lax.batch_matmul,
precision=lax.Precision.HIGHEST)
def a_inverse(rhs):
return triangular_solve(a, rhs, left_side, lower, transpose_a, conjugate_a,
unit_diagonal)
# triangular_solve is about the same cost as matrix multplication (~n^2 FLOPs
# for matrix/vector inputs). Order these operations in whichever order is
# cheaper.
if left_side:
assert g_a.shape[-2:] == a.shape[-2:] == (m, m) and ans.shape[-2:] == (m, n)
if m > n:
return a_inverse(dot(g_a, ans)) # A^{-1} (∂A X)
else:
return dot(a_inverse(g_a), ans) # (A^{-1} ∂A) X
else:
assert g_a.shape[-2:] == a.shape[-2:] == (n, n) and ans.shape[-2:] == (m, n)
if m < n:
return a_inverse(dot(ans, g_a)) # (X ∂A) A^{-1}
else:
return dot(ans, a_inverse(g_a)) # X (∂A A^{-1})
def ppermute(x, axis_name, perm):
"""Perform a collective permutation according to the permutation ``perm``.
If ``x`` is a pytree then the result is equivalent to mapping this function to
each leaf in the tree.
This function is an analog of the CollectivePermute XLA HLO.
Args:
x: array(s) with a mapped axis named ``axis_name``.
axis_name: hashable Python object used to name a pmapped axis (see the
:func:`jax.pmap` documentation for more details).
perm: list of pairs of ints, representing
``(source_index, destination_index)``
pairs that encode how the mapped axis named ``axis_name`` should be
shuffled. The integer values are treated as indices into the mapped axis
``axis_name``. Any two pairs should not have the same source index or the
same destination index. For each index of the axis ``axis_name`` that does
not correspond to a destination index in ``perm``, the corresponding
values in the result are filled with zeros of the appropriate type.
Returns:
Array(s) with the same shape as ``x`` with slices along the axis
``axis_name`` gathered from ``x`` according to the permutation ``perm``.
"""
return tree_util.tree_map(
partial(ppermute_p.bind, axis_name=axis_name, perm=tuple(perm)), x)
def testRandom(self):
seeds = vmap(random.PRNGKey)(np.arange(10))
ans = vmap(partial(random.normal, shape=(3, 2)))(seeds)
expected = np.stack([random.normal(random.PRNGKey(seed), (3, 2))
for seed in np.arange(10)])
self.assertAllClose(ans, expected, check_dtypes=False)
assert len(np.unique(ans)) == 10 * 3 * 2
def testPerExampleGradients(self):
def predict(params, inputs):
for W, b in params:
outputs = jnp.dot(W, inputs) + b
inputs = jnp.tanh(outputs)
return outputs
def loss(params, data):
inputs, targets = data
predictions = predict(params, inputs)
return jnp.sum((predictions - targets)**2)
batch_size = 5
layer_sizes = [3, 2, 4]
R = np.random.RandomState(0).randn
params = [(R(m, n), R(m))
for m, n in zip(layer_sizes[1:], layer_sizes[:-1])]
input_batch = R(5, 3)
target_batch = R(5, 4)
batch = (input_batch, target_batch)
ans = vmap(partial(grad(loss), params))(batch)
for ans_pair, param_pair in zip(ans, params):
dW, db = ans_pair
W, b = param_pair
self.assertEqual(dW.shape, (batch_size,) + W.shape)
self.assertEqual(db.shape, (batch_size,) + b.shape)
def eigh_jvp_rule(primals, tangents, lower):
# Derivative for eigh in the simplest case of distinct eigenvalues.
# This is classic nondegenerate perurbation theory, but also see
# https://people.maths.ox.ac.uk/gilesm/files/NA-08-01.pdf
# The general solution treating the case of degenerate eigenvalues is
# considerably more complicated. Ambitious readers may refer to the general
# methods below or refer to degenerate perturbation theory in physics.
# https://www.win.tue.nl/analysis/reports/rana06-33.pdf and
# https://people.orie.cornell.edu/aslewis/publications/99-clarke.pdf
a, = primals
a_dot, = tangents
v, w_real = eigh_p.bind(symmetrize(a), lower=lower)
# for complex numbers we need eigenvalues to be full dtype of v, a:
w = w_real.astype(a.dtype)
eye_n = jnp.eye(a.shape[-1], dtype=a.dtype)
# carefully build reciprocal delta-eigenvalue matrix, avoiding NaNs.
Fmat = jnp.reciprocal(eye_n + w[..., jnp.newaxis, :] - w[..., jnp.newaxis]) - eye_n
# eigh impl doesn't support batch dims, but future-proof the grad.
dot = partial(lax.dot if a.ndim == 2 else lax.batch_matmul,
precision=lax.Precision.HIGHEST)
vdag_adot_v = dot(dot(_H(v), a_dot), v)
dv = dot(v, jnp.multiply(Fmat, vdag_adot_v))
dw = jnp.real(jnp.diagonal(vdag_adot_v, axis1=-2, axis2=-1))
return (v, w_real), (dv, dw)
def _all_gather_via_psum(x, *, all_gather_dimension, axis_name, axis_index_groups, axis_size):
index = axis_index(axis_name)
if axis_index_groups is not None:
indices = np.array(axis_index_groups).flatten()
axis_index_to_group_index = indices.argsort() % len(axis_index_groups[0])
index = lax_numpy.array(axis_index_to_group_index)[index]
outs = tree_util.tree_map(partial(_expand, all_gather_dimension, axis_size, index), x)
return psum(outs, axis_name, axis_index_groups=axis_index_groups)
def testGatherBatchedIndices(self, axis, shape, dtype, idxs, dnums, slice_sizes):
rng = jtu.rand_default(self.rng())
fun = partial(lax.gather, dimension_numbers=dnums, slice_sizes=slice_sizes)
operand = rng(shape, dtype)
ans = vmap(fun, (None, axis))(operand, idxs)
expected = np.stack([fun(operand, idxs[(slice(None),) * axis + (i,)])
for i in range(idxs.shape[axis])])
self.assertAllClose(ans, expected, check_dtypes=False)
def testGatherGradBatchedOperand(self, axis, shape, dtype, idxs, dnums, slice_sizes):
rng = jtu.rand_default(self.rng())
fun = partial(lax.gather, dimension_numbers=dnums, slice_sizes=slice_sizes)
gfun = grad(lambda x, idx: jnp.sum(jnp.sin(fun(x, idx))))
operand = rng(shape, dtype)
ans = vmap(gfun, (axis, None))(operand, idxs)
expected = np.stack([gfun(operand[(slice(None),) * axis + (i,)], idxs)
for i in range(operand.shape[axis])])
self.assertAllClose(ans, expected, check_dtypes=False)
def test_check_jaxpr_scan_correct(self):
def f(c, x):
b = jnp.cos(jnp.sum(jnp.sin(x)) + jnp.sum(jnp.cos(c)))
c = jnp.sin(c * b)
return c, b
xs = jnp.ones((5, 3))
c = jnp.ones(4)
jaxpr = make_jaxpr(partial(lax.scan, f))(c, xs).jaxpr
core.check_jaxpr(jaxpr)
def test_vjp(self, f, args):
jtu.check_vjp(f,
partial(vjp, f),
args,
rtol={
np.float32: 3e-1,
np.float64: 1e-5
},
atol={
np.float32: 1e-2,
np.float64: 1e-5
})
def testSortKeyVal(self):
k = np.arange(12)[::-1].reshape(3, 4)
v = np.random.RandomState(0).permutation(12).reshape(3, 4)
sk, sv = vmap(partial(lax.sort_key_val, dimension=0), (0, 0))(k, v)
self.assertAllClose(sk, k[:, ::-1])
self.assertAllClose(sv, v[:, ::-1])
sk, sv = vmap(partial(lax.sort_key_val, dimension=0), (1, 1), 1)(k, v)
self.assertAllClose(sk, k[::-1, :])
self.assertAllClose(sv, v[::-1, :])
sk, sv = vmap(partial(lax.sort_key_val, dimension=0), (0, 1))(k, v.T)
self.assertAllClose(sk, k[:, ::-1])
self.assertAllClose(sv, v[:, ::-1])
sk, sv = vmap(partial(lax.sort_key_val, dimension=0), (1, 0))(k.T, v)
self.assertAllClose(sk, k[:, ::-1])
self.assertAllClose(sv, v[:, ::-1])
sk, sv = vmap(partial(lax.sort_key_val, dimension=0), (None, 0))(k[0], v)
self.assertAllClose(sk, np.broadcast_to(k[0, ::-1], (3, 4)))
self.assertAllClose(sv, v[:, ::-1])
sk, sv = vmap(partial(lax.sort_key_val, dimension=0), (1, None))(k.T, v[0])
self.assertAllClose(sk, k[:, ::-1])
self.assertAllClose(sv, np.broadcast_to(v[0, ::-1], (3, 4)))
def value_and_jacrev_f(*args, **kwargs):
f = lu.wrap_init(fun, kwargs)
f_partial, dyn_args = argnums_partial(
f, argnums, args, require_static_args_hashable=False)
tree_map(partial(_check_input_dtype_jacrev, holomorphic, allow_int),
dyn_args)
if not has_aux:
y, pullback = _vjp(f_partial, *dyn_args)
else:
y, pullback, aux = _vjp(f_partial, *dyn_args, has_aux=True)
tree_map(partial(_check_output_dtype_jacrev, holomorphic), y)
jac = vmap(pullback)(_std_basis(y))
jac = jac[0] if isinstance(argnums, int) else jac
example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args
jac_tree = tree_map(partial(_jacrev_unravel, y), example_args, jac)
if not has_aux:
return y, tree_transpose(tree_structure(example_args),
tree_structure(y), jac_tree)
else:
return (y, aux), tree_transpose(tree_structure(example_args),
tree_structure(y), jac_tree)
return
def _contains_query(vals, query):
if isinstance(query, tuple):
return map(partial(_contains_query, vals), query)
if jnp.isnan(query):
if jnp.any(jnp.isnan(vals)):
raise FoundValue('NaN')
elif jnp.isinf(query):
if jnp.any(jnp.isinf(vals)):
raise FoundValue('Found Inf')
elif jnp.isscalar(query):
if jnp.any(vals == query):
raise FoundValue(str(query))
else:
raise ValueError('Malformed Query: {}'.format(query))
def test_reference_cycles(self):
gc.collect()
def f(x):
return x.sum()
fn = partial(linearize, f)
params = jnp.zeros([])
debug = gc.get_debug()
try:
fn(params)
gc.set_debug(gc.DEBUG_SAVEALL)
self.assertEqual(gc.collect(), 0, msg=str(gc.garbage))
finally:
gc.set_debug(debug)
def tree_update(i, grad_tree, opt_state):
states_flat, tree, subtrees = opt_state
grad_flat, tree2 = tree_flatten(grad_tree)
if tree2 != tree:
msg = ("optimizer update function was passed a gradient tree that did "
"not match the parameter tree structure with which it was "
"initialized: parameter tree {} and grad tree {}.")
raise TypeError(msg.format(tree, tree2))
states = map(tree_unflatten, subtrees, states_flat)
new_states = map(partial(update, i), grad_flat, states)
new_states_flat, subtrees2 = unzip2(map(tree_flatten, new_states))
for subtree, subtree2 in zip(subtrees, subtrees2):
if subtree2 != subtree:
msg = ("optimizer update function produced an output structure that "
"did not match its input structure: input {} and output {}.")
raise TypeError(msg.format(subtree, subtree2))
return OptimizerState(new_states_flat, tree, subtrees)
def testConvGeneralDilatedBatchNotMajor(self):
W = jnp.array(np.random.randn(3, 3, 1, 4), dtype=np.float32)
x = jnp.array(np.random.randn(3, 5, 7, 5, 1), dtype=np.float32)
def f(params, x):
one = (1, 1)
dimension_numbers = ('HNWC', 'HWIO', 'HWNC')
y = lax.conv_general_dilated(
x, params, one, 'SAME', one, one, dimension_numbers)
return y
per_example = vmap(partial(f, W))(x)
per_example = jnp.reshape(jnp.transpose(per_example, (1, 2, 0, 3, 4)),
(5, 5, 21, 4))
per_example_direct = f(W, jnp.reshape(jnp.transpose(x, (1, 0, 2, 3, 4)),
(5, 21, 5, 1)))
self.assertAllClose(per_example, per_example_direct)
def testNpMaximumPerExampleGrad(self):
R = np.random.RandomState(0).randn
x = R(10, 5)
W = R(5, 5)
fun = lambda W, x: jnp.sum(jnp.maximum(jnp.dot(x, W), 0.0) ** 2)
ans = vmap(partial(grad(fun), W))(x)
W_t = jnp.transpose(W)
for i in range(10):
x_ex = x[i:i + 1]
expected_ans = 2.0 * jnp.dot(
jnp.maximum(jnp.dot(W_t, jnp.transpose(x_ex)), 0.0), x_ex)
expected_ans = jnp.transpose(expected_ans)
self.assertAllClose(
ans[i], expected_ans, check_dtypes=False,
atol={np.float32:5e-2} if jtu.device_under_test() == "tpu" else None)
def _solve(a, b):
_check_solve_shapes(a, b)
# Broadcast leading dimensions of b to the shape of a, as is required by
# custom_linear_solve.
out_shape = tuple(d_a if d_b == 1 else d_b
for d_a, d_b in zip(a.shape[:-1] + (1,), b.shape))
b = jnp.broadcast_to(b, out_shape)
# With custom_linear_solve, we can reuse the same factorization when
# computing sensitivities. This is considerably faster.
lu_, _, permutation = lu(lax.stop_gradient(a))
custom_solve = partial(
lax.custom_linear_solve,
lambda x: _matvec_multiply(a, x),
solve=lambda _, x: lu_solve(lu_, permutation, x, trans=0),
transpose_solve=lambda _, x: lu_solve(lu_, permutation, x, trans=1))
if a.ndim == b.ndim + 1:
# b.shape == [..., m]
return custom_solve(b)
else:
# b.shape == [..., m, k]
return api.vmap(custom_solve, b.ndim - 1, max(a.ndim, b.ndim) - 1)(b)
thunk_name = 'fwd_jaxpr_thunk'
params['bwd'] = callback_subtrace(params['bwd'], main)
else:
raise NotImplementedError(primitive)
thunk = params.pop(thunk_name)
@pe._memoize
def new_thunk():
thunk_jaxpr = core.ClosedJaxpr(*thunk())
closed_jaxpr = callback_jaxpr(thunk_jaxpr, main.callback,
main.strip_calls)
return closed_jaxpr.jaxpr, closed_jaxpr.literals
params[thunk_name] = new_thunk
new_fun_jaxpr, new_consts = new_closed_jaxpr.jaxpr, new_closed_jaxpr.literals
closed_fun_jaxpr = core.ClosedJaxpr(
pe.convert_constvars_jaxpr(new_fun_jaxpr), ())
new_num_consts = len(new_consts) + num_consts
out = primitive.bind(*it.chain(new_consts, vals),
fun_jaxpr=closed_fun_jaxpr,
num_consts=new_num_consts,
**params)
return safe_map(trace.pure, out)
custom_callback_rules[cd.custom_jvp_call_jaxpr_p] = partial(
_custom_derivative_call_jaxpr_callback_rule, cd.custom_jvp_call_jaxpr_p)
custom_callback_rules[cd.custom_vjp_call_jaxpr_p] = partial(
_custom_derivative_call_jaxpr_callback_rule, cd.custom_vjp_call_jaxpr_p)
if jnp.issubdtype(dtype, np.complexfloating):
nan = xb.constant(c, np.array(np.nan * (1. + 1j), dtype=dtype))
else:
nan = xb.constant(c, np.array(np.nan, dtype=dtype))
return xops.Broadcast(nan, shape.dimensions())
def _cholesky_cpu_gpu_translation_rule(potrf_impl, c, operand):
shape = c.get_shape(operand)
batch_dims = shape.dimensions()[:-2]
result, info = potrf_impl(c, operand, lower=True)
ok = xops.Eq(info, xops.ConstantLiteral(c, np.array(0, np.int32)))
return _broadcasting_select(c,
xops.Reshape(ok, batch_dims + (1, 1)), result,
_nan_like(c, result))
xla.backend_specific_translations['cpu'][cholesky_p] = partial(
_cholesky_cpu_gpu_translation_rule, lapack.potrf)
if cusolver is not None:
xla.backend_specific_translations['gpu'][cholesky_p] = partial(
_cholesky_cpu_gpu_translation_rule, cusolver.potrf)
if rocsolver is not None:
xla.backend_specific_translations['gpu'][cholesky_p] = partial(
_cholesky_cpu_gpu_translation_rule, rocsolver.potrf)
# Asymmetric eigendecomposition
def eig_impl(operand, *, compute_left_eigenvectors, compute_right_eigenvectors):
return (
xla.apply_primitive(eig_p, operand,
compute_left_eigenvectors=compute_left_eigenvectors,
def test_jvp_linearized(self, f, args):
jtu.check_jvp(f, partial(jvp_unlinearized, f), args,
rtol={np.float32: 3e-2})
def test_jvp(self, f, args):
jtu.check_jvp(f, partial(jvp, f), args, rtol={np.float32: 3e-2})
return xops.Tuple(c, outs)
def _psum_transpose_rule(cts, *args, axis_name, axis_index_groups):
nonzero_out_cts, treedef = tree_util.tree_flatten(cts)
nonzero_in_cts = psum_p.bind(*nonzero_out_cts,
axis_name=axis_name,
axis_index_groups=axis_index_groups)
return tree_util.tree_unflatten(treedef, nonzero_in_cts)
psum_p = core.Primitive('psum')
psum_p.multiple_results = True
psum_p.def_abstract_eval(lambda *args, **params: map(raise_to_shaped, args))
pxla.soft_pmap_rules[psum_p] = \
partial(_allreduce_soft_pmap_rule, psum_p, lax._reduce_sum)
xla.parallel_translations[psum_p] = partial(_allreduce_translation_rule,
lax.add_p) # type: ignore
ad.deflinear2(psum_p, _psum_transpose_rule)
pxla.multi_host_supported_collectives.add(psum_p)
batching.primitive_batchers[psum_p] = partial(_collective_batcher, psum_p)
batching.collective_rules[psum_p] = \
partial(_batched_reduction_collective,
psum_p,
lambda v, d: v.sum(d),
lambda v, axis_size: axis_size * v)
# We set a special bind rule for psum so that psum(1, 'i') can be evaluated at
# tracing time.
@psum_p.def_custom_bind
CallSpec(fun_with_two_calls, (R(3, 2),)),
CallSpec(fun_with_call_closure, (R(3, 2),)),
CallSpec(fun_call_jitted, (R(1,),)),
CallSpec(fun_with_nested_calls, (R(),)),
CallSpec(fun_with_nested_calls, (R(3, 2),)),
CallSpec(fun_with_nested_calls_2, (R(1, 2),)),
]
def jvp_unlinearized(f, primals, tangents):
out, jvp = linearize(f, *primals)
return out, jvp(*tangents)
test_specs = []
for ts in test_specs_base:
test_specs.append(ts)
test_specs.append(CallSpec(partial(jvp, ts.fun), (ts.args, ts.args)))
test_specs.append(CallSpec(jit(ts.fun), ts.args))
test_specs.append(CallSpec(jit(jit(ts.fun)), ts.args))
test_specs.append(CallSpec(partial(jvp_unlinearized, ts.fun),
(ts.args, ts.args)))
def fwd_deriv(f):
def df(x):
return jvp(f, (x,), (1.0,))[1]
return df
class CoreTest(jtu.JaxTestCase):
def _allgather(x, dim, size, index, axis_name, axis_index_groups=None):
outs = tree_util.tree_map(partial(_expand, dim, size, index), x)
return psum(outs, axis_name, axis_index_groups=axis_index_groups)
