"""
Build classes for basic Nengo operators.
"""
from collections import defaultdict
from distutils.version import LooseVersion
import logging
import warnings
from nengo.builder.operator import (
Reset, Copy, ElementwiseInc, DotInc, SimPyFunc)
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import gen_sparse_ops
from nengo_dl import utils
from nengo_dl.builder import Builder, OpBuilder
from nengo_dl.compat import tf_compat, SparseDotInc, SparseMatrix
logger = logging.getLogger(__name__)
[docs]class ResetInc(Reset):
"""
A version of Reset that increments the target value rather than setting it.
"""
@property
def dst(self):
"""Overridden to return from incs rather than sets."""
return self.incs[0]
[docs]@Builder.register(Reset)
@Builder.register(ResetInc)
class ResetBuilder(OpBuilder):
"""
Build a group of `~nengo.builder.operator.Reset` operators.
"""
def __init__(self, ops, signals, config):
super(ResetBuilder, self).__init__(ops, signals, config)
logger.debug("val %s", [op.value for op in ops])
logger.debug("dst %s", [op.dst for op in ops])
self.mode = "inc" if type(ops[0]) == ResetInc else "update"
dtype = np.asarray(ops[0].value).dtype
if np.issubdtype(dtype, np.floating):
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[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]),
signals.constant(value))]
logger.debug("scatters")
logger.debug("\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, mode=self.mode)
[docs] @staticmethod
def mergeable(x, y):
return True
[docs]@Builder.register(Copy)
class CopyBuilder(OpBuilder):
"""
Build a group of `~nengo.builder.operator.Copy` operators.
"""
def __init__(self, ops, signals, config):
super(CopyBuilder, self).__init__(ops, signals, config)
logger.debug("src %s", [op.src for op in ops])
logger.debug("src_slice %s", [getattr(op, "src_slice", None)
for op in ops])
logger.debug("dst %s", [op.dst for op in ops])
logger.debug("dst_slice %s", [getattr(op, "dst_slice", None)
for op in ops])
srcs = []
dsts = []
for op in ops:
srcs += [signals[op.src][op.src_slice]]
dsts += [signals[op.dst][op.dst_slice]]
self.mode = "inc" if ops[0].inc else "update"
self.src_data = signals.combine(srcs)
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)
[docs] def build_step(self, signals):
signals.scatter(self.dst_data, signals.gather(self.src_data),
mode=self.mode)
[docs] @staticmethod
def mergeable(x, y):
return True
# class ElementwiseSet(ElementwiseInc):
# @property
# def Y(self):
# return self.sets[0]
[docs]@Builder.register(ElementwiseInc)
# @Builder.register(ElementwiseSet)
class ElementwiseIncBuilder(OpBuilder):
"""
Build a group of `~nengo.builder.operator.ElementwiseInc` operators.
"""
def __init__(self, ops, signals, config):
super(ElementwiseIncBuilder, self).__init__(ops, signals, config)
logger.debug("dst %s", [op.Y for op in ops])
logger.debug("A %s", [op.A for op in ops])
logger.debug("X %s", [op.X for op in ops])
self.mode = "inc" if type(ops[0]) == ElementwiseInc else "update"
self.Y_data = signals.combine([op.Y for op in ops])
# group all the A's and X's
self.A_data = signals.combine([op.A for op in ops])
self.X_data = signals.combine([op.X for op in ops])
# separate data from each op along the first dimension
if self.A_data.shape[0] != self.X_data.shape[0]:
self.A_data = self.A_data.reshape(
(len(ops), -1) + self.A_data.shape[1:])
self.X_data = self.X_data.reshape(
(len(ops), -1) + self.X_data.shape[1:])
# 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,))
# 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,))
[docs] def build_step(self, signals):
A = signals.gather(self.A_data)
X = signals.gather(self.X_data)
result = tf.multiply(A, X)
signals.scatter(self.Y_data, result, mode=self.mode)
[docs] @staticmethod
def mergeable(x, y):
# for these operations we enforce that the first dimensions
# match (we know all the other dimensions match due to the generic
# checks).
# this allows us to stack all the arguments into continuous array
# blocks, allowing for more efficient multiplication (mainly
# because it allows us to take advantage of broadcasting)
for s0, s1 in zip(x.all_signals, y.all_signals):
shape0 = s0.shape[0] if s0.shape != () else 1
shape1 = s1.shape[0] if s1.shape != () else 1
if shape0 != shape1:
return False
return True
[docs]def sparse_matmul(A_indices, A_data, A_shape, X):
"""
Matrix multiplication between sparse matrix A and dense matrix X
Parameters
----------
A_indices : ``tf.Tensor``
N, 2) rray of [row,col] non-zero entries
A_data : ``tf.Tensor``
(N,) array of data in the nonzero entries specified in ``A_indices``
A_shape : tuple of int
Shape of full A matrix
X : ``tf.Tensor``
Dense matrix being multiplied by A
Returns
-------
dot : ``tf.Tensor``
Result of matrix multiplication between A and X
"""
must_downcast = (
A_data.dtype.base_dtype != tf.float32
and ("gpu" in A_data.device.lower()
or (A_data.device == "" and utils.tf_gpu_installed)))
if must_downcast:
assert A_data.dtype.base_dtype == X.dtype.base_dtype
warnings.warn("Downcasting data to float32 in sparse_matmul, since "
"only float32 is supported on the GPU.")
A = tf.cast(A_data, tf.float32)
X = tf.cast(X, tf.float32)
else:
A = A_data
if LooseVersion(tf.__version__) < LooseVersion("1.7.0"):
mat_mul = gen_sparse_ops._sparse_tensor_dense_mat_mul
else:
mat_mul = gen_sparse_ops.sparse_tensor_dense_mat_mul
dot = mat_mul(A_indices, A, A_shape, X)
if must_downcast:
dot = tf.cast(dot, A_data.dtype.base_dtype)
return dot
# class DotSet(DotInc):
# @property
# def Y(self):
# return self.sets[0]
[docs]@Builder.register(DotInc)
# @Builder.register(DotSet)
class DotIncBuilder(OpBuilder):
"""
Build a group of `~nengo.builder.operator.DotInc` operators.
"""
def __init__(self, ops, signals, config):
# note: bypassing the DotIncBuilder init
# pylint: disable=bad-super-call
super(DotIncBuilder, self).__init__(ops, signals, config)
logger.debug("dst %s", [op.Y for op in ops])
logger.debug("A %s", [op.A for op in ops])
logger.debug("X %s", [op.X for op in ops])
self.mode = "inc" if type(ops[0]) == DotInc else "update"
# check if all the signals have the same size for the first dimension
self.len_match = True
for i, s0 in enumerate(ops[0].all_signals):
shape0 = s0.shape[0] if s0.shape != () else 1
for op in ops:
s1 = op.all_signals[i]
shape1 = s1.shape[0] if s1.shape != () else 1
if shape0 != shape1:
self.len_match = False
break
if not self.len_match:
break
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])
X_data = signals.combine([op.X for op in ops])
if self.len_match:
# if the first dimensions all match, then we can used the
# (batched) matrix multiplication op
# 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))
if self.A_data.minibatched:
# add broadcast dimension to X
self.X_data = self.X_data.reshape(self.X_data.shape + (1,))
# precompute transposition indices
self.perm = tf.constant((0, 3, 1, 2))
self.perm_inv = tf.constant((0, 2, 3, 1))
else:
# if the first dimensions don't match, then we create a block
# diagonal matrix out of all the op matrices, and then multiply
# them using a sparse matrix multiplication
self.A_data = A_data.reshape((-1,))
self.X_data = X_data
assert not self.A_data.minibatched
assert self.X_data.minibatched and self.Y_data.minibatched
sparse_indices = []
corner = np.zeros(2, dtype=np.int64)
for op in ops:
block_shape = (op.A.shape[0], op.A.shape[1])
idxs = np.reshape(np.dstack(np.meshgrid(
np.arange(block_shape[0]), np.arange(block_shape[1]),
indexing="ij")), (-1, 2))
idxs += corner
corner += block_shape
sparse_indices += [idxs]
sparse_indices = np.concatenate(sparse_indices, axis=0)
self.sparse_indices = signals.constant(sparse_indices, dtype=(
tf.int32 if np.all(sparse_indices < np.iinfo(np.int32).max)
else tf.int64))
self.A_shape = tf.constant(corner, dtype=tf.int64)
[docs] def build_step(self, signals):
A = signals.gather(self.A_data)
X = signals.gather(self.X_data)
if self.len_match:
if self.A_data.minibatched and self.X_data.minibatched:
# dot = tf.einsum("ijkl,ikl->ijl", A, X)
# note: this is just a duplicate of what einsum does
# internally; we do it manually so that we can move the
# perm/perm_inv constants into the pre-build step
A = tf.transpose(a=A, perm=self.perm)
X = tf.transpose(a=X, perm=self.perm)
dot = tf.matmul(A, X)
dot = tf.transpose(a=dot, perm=self.perm_inv)
dot.set_shape(
self.A_data.shape[:2] + (1, signals.minibatch_size))
elif not self.A_data.minibatched and self.X_data.minibatched:
dot = tf.matmul(A, X)
else:
# note: these cases never come up (so far) in nengo, since X
# is always minibatched. but preserving them here for
# posterity, in case they are ever used
# A minibatched, X not minibatched
# dot = tf.einsum("ijkl,ik->ijl", A, X)
# A not minibatched, X not minibatched
# dot = tf.einsum("ijk,ik->ij", A, X)
raise NotImplementedError
else:
dot = sparse_matmul(self.sparse_indices, A, self.A_shape, X)
dot.set_shape(self.Y_data.shape + (signals.minibatch_size,))
signals.scatter(self.Y_data, dot, mode=self.mode)
[docs] @staticmethod
def mergeable(x, y):
# if the matrix (A) is minibatched, then the first dimensions need
# to match up (to allow us to transpose the dimensions)
if x.A.minibatched:
for s0, s1 in zip(x.all_signals, y.all_signals):
shape0 = s0.shape[0] if s0.shape != () else 1
shape1 = s1.shape[0] if s1.shape != () else 1
if shape0 != shape1:
return False
return True
[docs]@Builder.register(SimPyFunc)
class SimPyFuncBuilder(OpBuilder):
"""
Build a group of `~nengo.builder.operator.SimPyFunc` operators.
"""
def __init__(self, ops, signals, config):
super(SimPyFuncBuilder, self).__init__(ops, signals, config)
logger.debug("t %s", [op.t for op in ops])
logger.debug("x %s", [op.x for op in ops])
logger.debug("fn %s", [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): # pragma: no cover (runs in TF)
outputs = []
offset = 0
for op in 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))
with tf.device("/cpu:0"):
node_outputs = tf_compat.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
[docs] @staticmethod
def mergeable(x, y):
# for these we need to make a special check that the functions
# all do/do not get time as input, otherwise we could end
# up confusing a node that only gets a scalar float input with
# a node that only gets time as input
return x.t == y.t
[docs]@Builder.register(SparseDotInc)
class SparseDotIncBuilder(OpBuilder):
"""
Build a group of `~nengo.builder.operator.SparseDotInc` operators.
"""
def __init__(self, ops, signals, config):
super().__init__(ops, signals, config)
self.Y_data = signals.combine([op.Y for op in ops])
# group all the A's and X's
self.A_data = signals.combine([op.A for op in ops])
self.X_data = signals.combine([op.X for op in ops])
# the only way A would be minibatched is if it is targeted by an
# online learning rule, which isn't supported for sparse transforms
assert not self.A_data.minibatched
assert self.X_data.minibatched and self.Y_data.minibatched
# arrange the sparse matrices into a (sparse) block diagonal matrix
# by adding an offset to each sparse matrix's indices
sparse_indices = []
corner = np.zeros(2, dtype=np.int64)
for op in ops:
if isinstance(op.A.initial_value, SparseMatrix):
idxs = np.array(op.A.initial_value.indices)
else:
initial_value = op.A.initial_value.tocoo()
idxs = np.stack((initial_value.row, initial_value.col), axis=1)
block_shape = (op.A.shape[0], op.A.shape[1])
idxs += corner
corner += block_shape
sparse_indices += [idxs]
sparse_indices = np.concatenate(sparse_indices, axis=0)
self.sparse_indices = signals.constant(sparse_indices, dtype=(
tf.int32 if np.all(sparse_indices < np.iinfo(np.int32).max)
else tf.int64))
self.A_shape = tf.constant(corner, dtype=tf.int64)
[docs] def build_step(self, signals):
A = signals.gather(self.A_data)
X = signals.gather(self.X_data)
dot = sparse_matmul(self.sparse_indices, A, self.A_shape, X)
dot.set_shape(self.Y_data.shape + (signals.minibatch_size,))
signals.scatter(self.Y_data, dot, mode="inc")
[docs] @staticmethod
def mergeable(x, y):
return True
