def apply_backwards(arrow: Arrow,
outputs: List[np.ndarray],
port_attr=None) -> List[np.ndarray]:
"""
Takes out_port vals (excluding errors) and returns in_port vals (including params).
FIXME: Mutates port_attr
"""
out_ports = [
out_port for out_port in arrow.out_ports()
if not is_error_port(out_port)
]
if port_attr is None:
port_attr = propagate(arrow)
for i, out_port in enumerate(out_ports):
if out_port not in port_attr:
port_attr[out_port] = {}
port_attr[out_port]['value'] = outputs[i]
for out_port in arrow.out_ports():
if is_error_port(out_port):
if out_port not in port_attr:
port_attr[out_port] = {}
if 'shape' in port_attr[out_port]:
port_attr[out_port]['value'] = np.zeros(
port_attr[out_port]['shape'])
else:
# FIXME: there has to be a better way to do this
print("WARNING: shape of error port unknown: %s" % (out_port))
port_attr[out_port]['value'] = 0
port_attr = propagate(arrow, port_attr) #, only_prop=set(['value']))
vals = extract_attribute('value', port_attr)
in_vals = {port: vals[port] for port in arrow.in_ports() if port in vals}
return in_vals
def constant_dispatch(arr, port_attr: PortAttributes, state=None):
ptc = extract_attribute('constant', port_attr)
# All the outputs are constant if and only if all the inputs are constant
if all((value == CONST for value in ptc.values())):
return {port: {'constant': CONST} for port in arr.ports()}
else:
return {port: {'constant': ptc[port]} if port in ptc \
else {'constant': VAR} for port in arr.ports()}
def mul_symbt_disp(arr: "MulArrow", port_attr: PortAttributes):
ptv = extract_attribute('symbolic_tensor', port_attr)
if arr.in_port(0) in ptv and arr.in_port(1) in ptv:
assert False, "Figure this out"
elif arr.in_port(0) in ptv:
# probably wrong as nothing is being multiplied
return {arr.out_port(0): {'symbolic_tensor': ptv[arr.in_port(0)]}}
elif arr.in_port(1) in ptv:
return {arr.out_port(0): {'symbolic_tensor': ptv[arr.in_port(1)]}}
else:
assert False, "why am i here"
def add_symbt_disp(arr: "AddArrow", port_attr: PortAttributes):
ptv = extract_attribute('symbolic_tensor', port_attr)
if arr.in_port(0) in ptv and arr.in_port(1) in ptv:
assert False, "Figure this out"
elif arr.in_port(0) in ptv:
# seems wrong tbh, nothing is being added?
return {arr.out_port(0): {'symbolic_tensor': ptv[arr.in_port(0)]}}
elif arr.in_port(1) in ptv:
return {arr.out_port(0): {'symbolic_tensor': ptv[arr.in_port(1)]}}
else:
assert False, "why am i here"
def broadcast_dispatch(arr: Arrow, port_attr: PortAttributes):
"""Decide output shape."""
pts = extract_attribute('shape', port_attr)
in_pts = extract(arr.in_ports(), pts)
shapes = list(in_pts.values())
shape = ()
# FIXME: Broadcasting rule are complex, lets cheat
g = tf.Graph()
with g.as_default():
phs = [
tf.placeholder(dtype='float32', shape=shape) for shape in shapes
]
# import pdb; pdb.set_trace()
z = tf.add(*phs)
shape = tuple(z.get_shape().as_list())
# for s in shapes:
# if len(s) >= len(shape):
# if len(shape) > 0:
# assert s[-len(shape):] == shape, "Shapes incompatible %s %s %s" % (s, s[-len(shape):], shape)
# shape = s
# print("Broadcasting %s" % pts)
return {port: {'shape': shape} for port in arr.out_ports()}
def pow_dispatch2(arr: "PowArrow", port_attr: PortAttributes):
ptv = extract_attribute('value', port_attr)
i = arr.in_ports()
o = arr.out_ports()
return {i[1]: {'value': np.log(ptv[i[0]]) / np.log(ptv[o[0]])}}
def sub_dispatch1(arr: "SubArrow", port_attr: PortAttributes):
ptv = extract_attribute('value', port_attr)
i = arr.in_ports()
o = arr.out_ports()
return {o[0]: {'value': ptv[i[0]] - ptv[i[1]]}}
def add_dispatch3(arr: "AddArrow", port_attr: PortAttributes):
ptv = extract_attribute('value', port_attr)
i = arr.in_ports()
o = arr.out_ports()
return {i[0]: {'value': ptv[o[0]] - ptv[i[1]]}}
def neg_fwd_disp(arr: "NegArrow", port_attr: PortAttributes):
ptv = extract_attribute('value', port_attr)
in_val = port_attr[arr.in_port(0)]['value']
return {arr.out_port(0): {'value': -in_val}}
def log_fwd_disp(arr: "LogArrow", port_attr: PortAttributes):
ptv = extract_attribute('value', port_attr)
in_val = port_attr[arr.in_port(0)]['value']
return {arr.out_port(0): {'value': np.log(in_val)}}
def pow_dispatch3(arr: "PowArrow", port_attr: PortAttributes):
ptv = extract_attribute('value', port_attr)
i = arr.in_ports()
o = arr.out_ports()
return {i[0]: {'value': ptv[o[0]]**(1.0 / ptv[i[1]])}}
def gather_shape_dispatch(arr: "GatherArrow", port_attr: PortAttributes):
# Produces an output tensor with shape `indices.shape + params.shape[1:]`
pts = extract_attribute('shape', port_attr)
indices_shape = const_to_tuple(pts[arr.in_ports()[1]])
param_shape = const_to_tuple(pts[arr.in_ports()[0]])
return {arr.out_ports()[0]: {'shape': indices_shape + param_shape[1:]}}
def div_dispatch2(arr: "DivArrow", port_attr: PortAttributes):
ptv = extract_attribute('value', port_attr)
i = arr.in_ports()
o = arr.out_ports()
return {i[1]: {'value': ptv[i[0]] / ptv[o[0]]}}
def gathernd_shape_dispatch(arr: "GatherArrow", port_attr: PortAttributes):
# [d_0, ..., d_{Q-2}, params.shape[K], ..., params.shape[P-1]].
pts = extract_attribute('shape', port_attr)
indices_shape = const_to_tuple(pts[arr.in_ports()[1]])
param_shape = const_to_tuple(pts[arr.in_ports()[0]])
return {arr.out_ports()[0]: {'shape': indices_shape[:-1] + param_shape[indices_shape[-1]:]}}
def stack_shape_disp(arr: "StackArrow", port_attr: PortAttributes):
ptv = extract_attribute('shape', port_attr)
import pdb; pdb.set_trace()
o = port_attr[arr.out_port(0)]
return {arr.out_port(0): {'shape': new_shape}}
def fwd_disp(arr: "SelectArrow", port_attr: PortAttributes):
ptv = extract_attribute('value', port_attr)
i = arr.in_ports()
o = arr.out_ports()
return {o[0] : {'value': np.where(ptv[i[0]], ptv[i[1]], ptv[i[2]])}}
def reshape_eval_dispatch(arr: "ReshapeArrow", port_attr: PortAttributes):
ptv = extract_attribute('value', port_attr)
i = arr.in_ports()
o = arr.out_ports()
res = np.reshape(ptv[i[0]], ptv[i[1]])
return {o[0]: {'value': res}}
