def calc_jacobian_elem(start, end):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
b = anp.ones(start.shape)
n = new_box(b, 0, VJPNode.new_root())
jac = backward_pass(n, end._node)
return jac._value
def grad_fn(*args, **kwds):
"""Computes the gradient of the wrapped function."""
tape.push_new_tape()
end_node = f(*args)
start_node = tape.pop_tape()
ag_core.active_progenitors.remove(start_node)
if not ag_core.isnode(end_node):
raise ValueError(
"Target not part of a computation being traced. %s" % end_node)
if start_node not in end_node.progenitors:
raise ValueError("Target not derived from source. %s %s" %
(end_node.progenitors, repr(start_node)))
output_gradients = kwds.get("output_gradients", None)
if output_gradients is None:
output_gradients = _ones(end_node.shape, end_node.dtype)
grad = ag_core.backward_pass(output_gradients, end_node, start_node)
return end_node.value, _aggregate_grads(grad.gradients)
def grad_fn(*args, **kwds):
"""Computes the gradient of the wrapped function."""
tape.push_new_tape()
end_node = f(*args)
start_node = tape.pop_tape()
ag_core.active_progenitors.remove(start_node)
if not ag_core.isnode(end_node):
raise ValueError(
"Target not part of a computation being traced. %s" % end_node)
if start_node not in end_node.progenitors:
raise ValueError("Target not derived from source. %s %s" %
(end_node.progenitors, repr(start_node)))
output_gradients = kwds.get("output_gradients", None)
if output_gradients is None:
output_gradients = _ones(end_node.shape, end_node.dtype)
grad = ag_core.backward_pass(output_gradients, end_node, start_node)
return end_node.value, _aggregate_grads(grad.gradients)
def calc_jacobian(start, end):
# if the end_box is not a box - autograd can not track back
if not isbox(end):
return vspace(start.shape).zeros()
# the final jacobian matrices
jac = []
# the backward pass is done for each objective function once
for j in range(end.shape[1]):
b = anp.zeros(end.shape)
b[:, j] = 1
n = new_box(b, 0, VJPNode.new_root())
_jac = backward_pass(n, end._node)
jac.append(_jac)
jac = anp.stack(jac, axis=1)
return jac
def time_fan_out_fan_in_backward_pass():
if MASTER_BRANCH:
backward_pass(1., fan_end_node, fan_start_node)
else:
backward_pass(1., fan_end_node)
def time_long_backward_pass():
if MASTER_BRANCH:
backward_pass(1., long_end_node, long_start_node)
else:
backward_pass(1., long_end_node)
def time_short_backward_pass():
if MASTER_BRANCH:
backward_pass(1., short_end_node, short_start_node)
else:
backward_pass(1., short_end_node)
def time_fan_out_fan_in_backward_pass():
core.backward_pass(1., fan_end_node, fan_start_node)
def time_long_backward_pass():
core.backward_pass(1., long_end_node, long_start_node)
def time_short_backward_pass():
core.backward_pass(1., short_end_node, short_start_node)
def time_fan_out_fan_in_backward_pass():
if MASTER_BRANCH:
backward_pass(1., fan_end_node, fan_start_node)
else:
backward_pass(1., fan_end_node)
def time_long_backward_pass():
if MASTER_BRANCH:
backward_pass(1., long_end_node, long_start_node)
else:
backward_pass(1., long_end_node)
def time_short_backward_pass():
if MASTER_BRANCH:
backward_pass(1., short_end_node, short_start_node)
else:
backward_pass(1., short_end_node)
def time_fan_out_fan_in_backward_pass():
core.backward_pass(1., fan_end_node, fan_start_node)
def time_long_backward_pass():
core.backward_pass(1., long_end_node, long_start_node)
def time_short_backward_pass():
core.backward_pass(1., short_end_node, short_start_node)