from collections import defaultdict
from nengo.builder.operator import (
Reset, Copy, ElementwiseInc, DotInc, SimPyFunc)
import numpy as np
import tensorflow as tf
from nengo_dl import utils, DEBUG
from nengo_dl.builder import Builder, OpBuilder
[docs]@Builder.register(Reset)
class ResetBuilder(OpBuilder):
"""Build a group of :class:`~nengo:nengo.builder.operator.Reset`
operators."""
def __init__(self, ops, signals):
if DEBUG:
print("reset")
print([str(x) for x in ops])
print("val", [op.value for op in ops])
print("dst", [op.dst for op in ops])
dtype = utils.cast_dtype(np.asarray(ops[0].value).dtype,
signals.dtype).as_numpy_dtype
# unlike other ops, Reset signals might be spread across multiple
# bases, which we need to handle
scatters = defaultdict(list)
for op in ops:
scatters[signals.sig_map[op.dst].key] += [op]
self.scatters = []
for group in scatters.values():
value = np.concatenate(
[np.resize(np.asarray(x.value).astype(dtype), x.dst.shape)
for x in group], axis=0)
value = np.tile(
value[..., None],
tuple(1 for _ in value.shape) + (signals.minibatch_size,))
self.scatters += [(signals.combine([x.dst for x in group]),
tf.constant(value))]
if DEBUG:
print("scatters")
print("\n".join([str(x) for x in self.scatters]))
[docs] def build_step(self, signals):
for data, val in self.scatters:
signals.scatter(data, val)
[docs]@Builder.register(Copy)
class CopyBuilder(OpBuilder):
"""Build a group of :class:`~nengo:nengo.builder.operator.Copy`
operators."""
def __init__(self, ops, signals):
if DEBUG:
print("sliced_copy")
print([str(op) for op in ops])
print("src", [op.src for op in ops])
print("src_slice", [getattr(op, "src_slice", None) for op in ops])
print("dst", [op.dst for op in ops])
print("dst_slice", [getattr(op, "dst_slice", None) for op in ops])
srcs = []
dsts = []
for op in ops:
srcs += [signals.sig_map[op.src][op.src_slice]]
dsts += [signals.sig_map[op.dst][op.dst_slice]]
self.mode = "inc" if ops[0].inc else "update"
self.src_data = signals.combine(srcs, load_indices=False)
self.dst_data = signals.combine(dsts)
if not self.src_data.minibatched and self.dst_data.minibatched:
# broadcast indices so that the un-minibatched src data gets
# copied to each minibatch dimension in dst
self.src_data = self.src_data.broadcast(-1, signals.minibatch_size)
self.src_data.load_indices()
[docs] def build_step(self, signals):
signals.scatter(self.dst_data, signals.gather(self.src_data),
mode=self.mode)
[docs]@Builder.register(ElementwiseInc)
# @Builder.register(DotInc)
class ElementwiseIncBuilder(OpBuilder):
"""Build a group of :class:`~nengo:nengo.builder.operator.ElementwiseInc`
operators."""
def __init__(self, ops, signals):
if DEBUG:
print("elementwise_inc"), len(ops)
print("\n".join([str(x) for x in ops]))
print("dst", [op.Y for op in ops])
print("A", [op.A for op in ops])
print("X", [op.X for op in ops])
self.dot_inc = isinstance(ops[0], DotInc)
self.Y_data = signals.combine([op.Y for op in ops])
# group all the A's and X's
A_data = signals.combine([op.A for op in ops], load_indices=False)
X_data = signals.combine([op.X for op in ops], load_indices=False)
# separate data from each op along the first dimension
self.A_data = A_data.reshape((len(ops), -1) + A_data.shape[1:])
self.X_data = X_data.reshape((len(ops), -1) + X_data.shape[1:])
if self.dot_inc:
# add empty dimension to X for broadcasting across rows
self.X_data = self.X_data.reshape((self.X_data.shape[0], 1) +
self.X_data.shape[1:])
else:
# add empty trailing dimensions for elementwise broadcasting
while self.A_data.ndim < self.X_data.ndim:
self.A_data = self.A_data.reshape(self.A_data.shape + (1,))
while self.X_data.ndim < self.A_data.ndim:
self.X_data = self.X_data.reshape(self.X_data.shape + (1,))
# add broadcast dimension for minibatch, if needed
if not self.A_data.minibatched and self.X_data.minibatched:
self.A_data = self.A_data.reshape(self.A_data.shape + (1,))
elif self.A_data.minibatched and not self.X_data.minibatched:
self.X_data = self.X_data.reshape(self.X_data.shape + (1,))
self.A_data.load_indices()
self.X_data.load_indices()
[docs] def build_step(self, signals):
A = signals.gather(self.A_data)
X = signals.gather(self.X_data)
result = tf.multiply(A, X)
if self.dot_inc:
reduce_axis = -1 - (self.A_data.minibatched or
self.X_data.minibatched)
result = tf.reduce_sum(result, axis=reduce_axis)
signals.scatter(self.Y_data, result, mode="inc")
[docs]@Builder.register(DotInc)
class DotIncBuilder(OpBuilder):
"""Build a group of :class:`~nengo:nengo.builder.operator.DotInc`
operators."""
def __init__(self, ops, signals):
if DEBUG:
print("dot_inc"), len(ops)
print("\n".join([str(x) for x in ops]))
print("dst", [op.Y for op in ops])
print("A", [op.A for op in ops])
print("X", [op.X for op in ops])
self.Y_data = signals.combine([op.Y for op in ops])
# group all the A's and X's
A_data = signals.combine([op.A for op in ops], load_indices=False)
X_data = signals.combine([op.X for op in ops], load_indices=False)
# separate data from each op along the first dimension
self.A_data = A_data.reshape((len(ops), -1, A_data.shape[1]))
self.X_data = X_data.reshape((len(ops), -1))
# approach #2
# if self.A_data.minibatched or not self.X_data.minibatched:
# self.X_data = self.X_data.reshape(self.X_data.shape[:2] + (1,) +
# self.X_data.shape[2:])
# if self.A_data.minibatched and not self.X_data.minibatched:
# self.X_data = self.X_data.broadcast(
# -1, signals.minibatch_size)
# approach #3
# TODO: what should the minibatch_size cutoff be?
self.using_matmul = (
signals.minibatch_size >= 32 and
not self.A_data.minibatched and self.X_data.minibatched)
if not self.using_matmul:
# add empty dimension to X for broadcasting (since we'll be doing
# it with the mul->reduce method)
self.X_data = self.X_data.reshape((self.X_data.shape[0], 1) +
self.X_data.shape[1:])
# add empty minibatch dimension if needed
if not self.A_data.minibatched and self.X_data.minibatched:
self.A_data = self.A_data.reshape(self.A_data.shape + (1,))
if self.A_data.minibatched and not self.X_data.minibatched:
self.X_data = self.X_data.reshape(self.X_data.shape + (1,))
self.A_data.load_indices()
self.X_data.load_indices()
[docs] def build_step(self, signals):
A = signals.gather(self.A_data)
X = signals.gather(self.X_data)
# approach #1: using einsum (einsum seems to be super slow)
# if self.A_data.minibatched and self.X_data.minibatched:
# dot = tf.einsum("ijkl,ikl->ijl", A, X)
# elif self.A_data.minibatched and not self.X_data.minibatched:
# dot = tf.einsum("ijkl,ik->ijl", A, X)
# elif not self.A_data.minibatched and self.X_data.minibatched:
# dot = tf.batch_matmul(A, X)
# else:
# dot = tf.einsum("ijk,ik->ij", A, X)
# approach #2: transpose/tile and use batch_matmul for everything
# if not self.A_data.minibatched and self.X_data.minibatched:
# dot = tf.batch_matmul(A, X)
# else:
# minibatched = self.A_data.minibatched or self.X_data.minibatched
#
# A = tf.transpose(A, (0, 3, 1, 2)) if minibatched else A
# X = tf.transpose(X, (0, 3, 1, 2)) if minibatched else X
# dot = tf.batch_matmul(A, X)
#
# if minibatched:
# dot = tf.transpose(dot, (0, 2, 3, 1))
# approach #3: mix of batch_matmul and manual multiply/reduce
if self.using_matmul:
dot = tf.matmul(A, X)
else:
dot = tf.multiply(A, X)
reduce_axis = -1 - (self.A_data.minibatched or
self.X_data.minibatched)
dot = tf.reduce_sum(dot, axis=reduce_axis)
signals.scatter(self.Y_data, dot, mode="inc")
[docs]@Builder.register(SimPyFunc)
class SimPyFuncBuilder(OpBuilder):
"""Build a group of :class:`~nengo:nengo.builder.operator.SimPyFunc`
operators."""
def __init__(self, ops, signals):
if DEBUG:
print("sim_py_func")
print([str(op) for op in ops])
print("t", [op.t for op in ops])
print("x", [op.x for op in ops])
print("fn", [op.fn for op in ops])
self.time_input = ops[0].t is not None
self.input_data = signals.combine([op.x for op in ops])
if ops[0].output is not None:
self.output_data = signals.combine([op.output for op in ops])
self.output_dtype = self.output_data.dtype
else:
self.output_data = None
self.output_dtype = signals.dtype
def merged_func(time, inputs):
outputs = []
offset = 0
for i, op in enumerate(ops):
if op.output is None:
func = op.fn
else:
func = utils.align_func(
op.output.shape, self.output_dtype)(op.fn)
func_input = inputs[offset:offset + op.x.shape[0]]
offset += op.x.shape[0]
mini_out = []
for j in range(signals.minibatch_size):
if op.t is None:
func_out = func(func_input[..., j])
else:
func_out = func(time, func_input[..., j])
if op.output is None:
# just return time as a noop (since we need to
# return something)
func_out = time
mini_out += [func_out]
outputs += [np.stack(mini_out, axis=-1)]
return np.concatenate(outputs, axis=0)
self.merged_func = merged_func
self.merged_func.__name__ == "_".join(
[utils.function_name(op.fn) for op in ops])
self.output_shape = ((len(ops),) if self.output_data is None else
self.output_data.shape)
self.output_shape += (signals.minibatch_size,)
[docs] def build_step(self, signals):
time = signals.time if self.time_input else []
inputs = ([] if self.input_data is None
else signals.gather(self.input_data))
node_outputs = tf.py_func(
self.merged_func, [time, inputs], self.output_dtype,
name=self.merged_func.__name__)
node_outputs.set_shape(self.output_shape)
if self.output_data is not None:
signals.scatter(self.output_data, node_outputs)
# note: we only need to run the node for side effects, not the
# assignment operator. if the result of the assignment is actually
# used anywhere, then it will be run as part of the normal graph.
return node_outputs