from nengo.builder.processes import SimProcess
from nengo.synapses import Lowpass
from nengo.utils.filter_design import cont2discrete
import numpy as np
import tensorflow as tf
from nengo_dl import utils, DEBUG
from nengo_dl.builder import Builder, OpBuilder
[docs]@Builder.register(SimProcess)
class SimProcessBuilder(OpBuilder):
"""Builds a group of :class:`~nengo:nengo.builder.processes.SimProcess`
operators.
Calls the appropriate sub-build class for the different process types.
Attributes
----------
TF_PROCESS_IMPL : list of :class:`~nengo:nengo.Process`
the process types that have a custom implementation
"""
TF_PROCESS_IMPL = (Lowpass,)
pass_rng = True
def __init__(self, ops, signals, rng):
if DEBUG:
print("sim_process")
print([op for op in ops])
print("process", [op.process for op in ops])
print("input", [op.input for op in ops])
print("output", [op.output for op in ops])
print("t", [op.t for op in ops])
process_type = type(ops[0].process)
# if we have a custom tensorflow implementation for this process type,
# then we build that. otherwise we'll just execute the process step
# function externally (using `tf.py_func`), so we just need to set up
# the inputs/outputs for that.
if process_type in self.TF_PROCESS_IMPL:
# note: we do this two-step check (even though it's redundant) to
# make sure that TF_PROCESS_IMPL is kept up to date
if process_type == Lowpass:
self.built_process = LinearFilter(ops, signals)
else:
self.built_process = GenericProcessBuilder(ops, signals, rng)
[docs] def build_step(self, signals):
self.built_process.build_step(signals)
[docs]class GenericProcessBuilder(object):
"""Builds all process types for which there is no custom Tensorflow
implementation.
Notes
-----
These will be executed as native Python functions, requiring execution to
move in and out of Tensorflow. This can significantly slow down the
simulation, so any performance-critical processes should consider
adding a custom Tensorflow implementation for their type instead.
"""
def __init__(self, ops, signals, rng):
self.input_data = (None if ops[0].input is None else
signals.combine([op.input for op in ops]))
self.output_data = signals.combine([op.output for op in ops])
self.output_shape = self.output_data.shape + (signals.minibatch_size,)
self.mode = "inc" if ops[0].mode == "inc" else "update"
self.prev_result = []
# build the step function for each process
step_fs = [
[op.process.make_step(
op.input.shape if op.input is not None else (0,),
op.output.shape, signals.dt_val,
op.process.get_rng(rng))
for _ in range(signals.minibatch_size)] for op in ops]
# `merged_func` calls the step function for each process and
# combines the result
@utils.align_func(self.output_shape, self.output_data.dtype)
def merged_func(time, input):
input_offset = 0
func_output = []
for i, op in enumerate(ops):
if op.input is not None:
input_shape = op.input.shape[0]
func_input = input[input_offset:input_offset + input_shape]
input_offset += input_shape
mini_out = []
for j in range(signals.minibatch_size):
x = [] if op.input is None else [func_input[..., j]]
mini_out += [step_fs[i][j](*([time] + x))]
func_output += [np.stack(mini_out, axis=-1)]
return np.concatenate(func_output, axis=0)
self.merged_func = merged_func
self.merged_func.__name__ = utils.sanitize_name(
"_".join([type(op.process).__name__ for op in ops]))
def build_step(self, signals):
input = ([] if self.input_data is None
else signals.gather(self.input_data))
# note: we need to make sure that the previous call to this function
# has completed before the next starts, since we don't know that the
# functions are thread safe
with tf.control_dependencies(self.prev_result):
result = tf.py_func(
self.merged_func, [signals.time, input],
self.output_data.dtype, name=self.merged_func.__name__)
result.set_shape(self.output_shape)
self.prev_result = [result]
signals.scatter(self.output_data, result, mode=self.mode)
[docs]class LinearFilter(object):
"""Build a group of :class:`~nengo:nengo.LinearFilter`
neuron operators."""
def __init__(self, ops, signals):
# TODO: implement general linear filter (using tensorarrays?)
self.input_data = (None if ops[0].input is None else
signals.combine([op.input for op in ops]))
self.output_data = signals.combine([op.output for op in ops])
nums = []
dens = []
for op in ops:
if op.process.tau <= 0.03 * signals.dt_val:
num = 1
den = 0
else:
num, den, _ = cont2discrete((op.process.num, op.process.den),
signals.dt_val, method="zoh")
num = num.flatten()
num = num[1:] if num[0] == 0 else num
assert len(num) == 1
num = num[0]
den = den[1:] # drop first element (equal to 1)
if len(den) == 0:
den = 0
else:
assert len(den) == 1
den = den[0]
nums += [num] * op.input.shape[0]
dens += [den] * op.input.shape[0]
nums = np.asarray(nums)[:, None]
# note: applying the negative here
dens = -np.asarray(dens)[:, None]
# need to manually broadcast for scatter_mul
dens = np.tile(dens, (1, signals.minibatch_size))
self.nums = tf.constant(nums, dtype=self.output_data.dtype)
self.dens = tf.constant(dens, dtype=self.output_data.dtype)
def build_step(self, signals):
input = signals.gather(self.input_data)
# signals.scatter(self.output_data, self.dens, mode="mul")
# signals.scatter(self.output_data, self.nums * input, mode="inc")
output = signals.gather(self.output_data)
signals.scatter(self.output_data,
self.dens * output + self.nums * input)