def __init__(self, layers, dataset=None, weights_only=False, name="model", optimizer=None):
super(Model, self).__init__(name)
self.optimizer = optimizer
self.params = None # should be able to remove
self.states = None # should be able to remove
self.epoch_index = 0
self.finished = False
self.initialized = False
self.cost = None
self.nbatches = 0
self.ndata = 0
if dataset is not None:
logger.warning('dataset is a deprecated argument and will be ignored')
if type(layers) in (ModelDescription, dict):
# load up the model from a serialized file (dataset could be None here)
self.deserialize(layers, load_states=(not weights_only))
elif type(layers) is str:
self.load_params(layers, load_states=(not weights_only))
else:
# Wrap the list of layers in a Sequential container if a raw list of layers
if type(layers) in (Sequential, Tree, SingleOutputTree):
self.layers = layers
else:
self.layers = Sequential(layers)
self.layers.propagate_parallelism("Data")
def __init__(self):
self.in_shape = [1024, (2538, 38)]
init = Constant(0)
image_path = Sequential(
[Affine(20, init, bias=init),
Affine(10, init, bias=init)])
sent_path = Sequential([Affine(30, init, bias=init), Affine(10, init)])
layers = [
MergeMultistream(layers=[image_path, sent_path],
merge="recurrent"),
Dropout(keep=0.5),
LSTM(4,
init,
activation=Logistic(),
gate_activation=Tanh(),
reset_cells=True),
Affine(20, init, bias=init, activation=Softmax())
]
self.layers = layers
self.cost = GeneralizedCostMask(CrossEntropyMulti())
self.model = Model(layers=layers)
self.model.initialize(self.in_shape, cost=self.cost)
def inception_bare(ref_module, kvals, name="i"):
(p1, p2, p3) = kvals
branch1 = [Conv(fshape(1, p1[0]), **common)] if p1[0] else []
branch2 = [
Conv(fshape(1, p2[0]), **common),
Conv(fshape(3, p2[1]), **commonp1)
]
branch3 = [Pooling(op=p3[0], **pool3s1p1)
] + ([Conv(fshape(1, p3[1]), **common)] if p3[1] else [])
branch1 = Sequential(branch1)
branch2 = Sequential(branch2)
branch3 = Sequential(branch3)
(branch1_ref, branch2_ref, branch3_ref) = ref_module[0].layers
if p1[0]:
for ll, lr in zip(branch1.layers, branch1_ref.layers):
if ll.has_params:
ll.set_params({'params': {'W': lr.W.get()}})
for ll, lr in zip(branch2.layers, branch2_ref.layers):
if ll.has_params:
ll.set_params({'params': {'W': lr.W.get()}})
if p3[1]:
for ll, lr in zip(branch3.layers, branch3_ref.layers):
if ll.has_params:
ll.set_params({'params': {'W': lr.W.get()}})
return (branch1.layers, branch2.layers, branch3.layers)
def inception(kvals, name="i"):
(p1, p2, p3) = kvals
branch1 = [Sequential([Conv(fshape(1, p1[0]), **common)])] if p1[0] else []
branch2 = [Sequential([Conv(fshape(1, p2[0]), **common),
Conv(fshape(3, p2[1]), **commonp1)])]
branch3 = [Sequential([Pooling(op=p3[0], **pool3s1p1)] + (
[Conv(fshape(1, p3[1]), **common)] if p3[1] else []))]
partitions = branch1 + branch2 + branch3
return [MergeBroadcast(layers=partitions, merge="depth")]
def __init__(self, layers, dataset=None, weights_only=False, name="model", optimizer=None):
super(Model, self).__init__(name)
self.optimizer = optimizer
self.params = None # should be able to remove
self.states = None # should be able to remove
self.epoch_index = 0
self.finished = False
self.initialized = False
self.cost = None
self.nbatches = 0
self.ndata = 0
if dataset is not None:
logger.warning('dataset is a deprecated argument and will be ignored')
if type(layers) in (ModelDescription, dict):
# load up the model from a serialized file (dataset could be None here)
self.deserialize(layers, load_states=(not weights_only))
elif type(layers) is str:
self.load_params(layers, load_states=(not weights_only))
else:
# Wrap the list of layers in a Sequential container if a raw list of layers
if type(layers) in (Sequential, Tree, SingleOutputTree):
self.layers = layers
else:
self.layers = Sequential(layers)
self.layers.propagate_parallelism("Data")
def test_concat_l1_l1(backend_default, allrand_args):
# test two linear layers that are merged with concat
dtypeu = np.float32
w_rng, rngmax = allrand_args
# Diff size inputs and outputs
nins = [128, 1024]
nouts = [64, 2048]
batch_size = 16
NervanaObject.be.bsz = batch_size
be = NervanaObject.be
init_unif = Uniform(low=w_rng[0], high=w_rng[1])
layers = [Sequential(Affine(nout=nout, init=init_unif)) for nout in nouts]
inputs = [be.array(dtypeu(np.random.random((nin, batch_size)))) for nin in nins]
merge = MergeMultistream(layers, merge="stack")
assert(len(inputs) == len(layers))
merge.configure(inputs)
merge.allocate()
merge.set_deltas(None)
out = merge.fprop(inputs).get()
sublayers = [s.layers[0] for s in layers]
weights = [layer.W.get() for layer in sublayers]
out_exp = np.concatenate([np.dot(w, inp.get()) for (w, inp) in zip(weights, inputs)])
assert np.allclose(out, out_exp, atol=1e-3)
err_lst = [dtypeu(np.random.random((nout, batch_size))) for nout in nouts]
err_concat = np.concatenate(err_lst)
merge.bprop(be.array(err_concat))
dW_exp_lst = [np.dot(err, inp.get().T) for (err, inp) in zip(err_lst, inputs)]
for layer, dW_exp in zip(sublayers, dW_exp_lst):
assert np.allclose(layer.dW.get(), dW_exp)
return
def __init__(self, layers, name="model", optimizer=None):
super(Model, self).__init__(name)
self.optimizer = optimizer
self.params = None # should be able to remove
self.states = None # should be able to remove
self.epoch_index = 0
self.finished = False
self.initialized = False
self.cost = None
# Wrap the list of layers in a Sequential container if a raw list of layers
self.layers = layers if type(layers) in (Sequential, Tree) else Sequential(layers)
self.layers_to_optimize = self.layers.layers_to_optimize
def test_concat_sequence_l1_l1(backend_default, allrand_args, deltas_buffer):
# test two linear layers that are merged with concat
dtypeu = np.float32
w_rng, rngmax = allrand_args
# Diff size input steps
nin = 128
steps = [32, 64]
nout = 256
batch_size = 16
NervanaObject.be.bsz = batch_size
be = NervanaObject.be
init_unif = Uniform(low=w_rng[0], high=w_rng[1])
layers = [Sequential(Affine(nout=nout, init=init_unif)) for _ in (0, 1)]
inputs = [
be.array(dtypeu(np.random.random((nin, batch_size * step))))
for step in steps
]
merge = MergeMultistream(layers, merge="recurrent")
assert (len(inputs) == len(layers))
merge.configure(inputs)
merge.allocate()
merge.allocate_deltas(deltas_buffer)
deltas_buffer.allocate_buffers()
merge.set_deltas(deltas_buffer)
out = merge.fprop(inputs).get()
sublayers = [s.layers[0] for s in layers]
weights = [layer.W.get() for layer in sublayers]
out_exp = np.concatenate(
[np.dot(w, inp.get()) for (w, inp) in zip(weights, inputs)], axis=1)
assert allclose_with_out(out, out_exp, atol=1e-3)
err_lst = [
dtypeu(np.random.random((nout, batch_size * step))) for step in steps
]
err_concat = be.array(np.concatenate(err_lst, axis=1))
merge.bprop(err_concat)
dW_exp_lst = [
np.dot(err,
inp.get().T) for (err, inp) in zip(err_lst, inputs)
]
for layer, dW_exp in zip(sublayers, dW_exp_lst):
assert allclose_with_out(layer.dW.get(), dW_exp)
return
def __init__(self, layers, dataset=None, weights_only=False, name="model", optimizer=None):
super(Model, self).__init__(name)
self.optimizer = optimizer
self.params = None # should be able to remove
self.states = None # should be able to remove
self.epoch_index = 0
self.finished = False
self.initialized = False
self.cost = None
self.weights_only = weights_only
self.nbatches = 0
self.ndata = 0
if type(layers) is ModelDescription or type(layers) is dict:
# load up the model from a serialized file (dataset could be None here)
load_states = not self.weights_only
self.deserialize(layers, dataset, load_states)
else:
# Wrap the list of layers in a Sequential container if a raw list of layers
if type(layers) in (Sequential, Tree, SingleOutputTree):
self.layers = layers
else:
self.layers = Sequential(layers)
self.layers_to_optimize = self.layers.layers_to_optimize
def __init__(self, layers, dataset=None, inference=False, name="model", optimizer=None):
super(Model, self).__init__(name)
self.optimizer = optimizer
self.params = None # should be able to remove
self.states = None # should be able to remove
self.epoch_index = 0
self.finished = False
self.initialized = False
self.cost = None
self.inference = inference
self.nbatches = 0
self.ndata = 0
if type(layers) is ModelDescription or type(layers) is dict:
# load up the model from a serialized file
assert dataset is not None, 'Need data set to initialize model from serialized file'
self.deserialize(layers, dataset, weights_only=False)
else:
# Wrap the list of layers in a Sequential container if a raw list of layers
if type(layers) in (Sequential, Tree, SingleOutputTree):
self.layers = layers
else:
self.layers = Sequential(layers)
self.layers_to_optimize = self.layers.layers_to_optimize
def test_branch_model():
NervanaObject.be = gen_backend("gpu", batch_size=64)
be = NervanaObject.be
main1 = main_branch()
i1 = inception([(32,), (32, 32), ('max', 16)])
top = top_branch()
neon_layer = Sequential(main1 + i1 + top)
inshape = (3, 224, 224)
insize = np.prod(inshape)
inpa = np.random.random((insize, batch_size))
neon_layer.configure(inshape)
inp = neon_layer.be.array(inpa)
neon_layer.allocate()
print neon_layer.nested_str()
neon_layer.layers[0].prev_layer = True
neon_layer.allocate_deltas()
neon_layer.layers[0].set_deltas([be.iobuf(inshape)])
neon_out = neon_layer.fprop(inp).get()
# Now make the reference pathways:
main_trunk2 = Sequential(main_branch())
main_trunk2.configure(inshape)
main2 = main_trunk2.layers
main2[0].prev_layer = True
main2[0].set_deltas([be.iobuf(inshape)])
(b1, b2, b3) = inception_bare(i1, [(32,), (32, 32), ('max', 16)])
for bb in (b1, b2, b3):
oshape = inshape
for ll in main2 + bb:
oshape = ll.configure(oshape)
main1_trunk = neon_layer.layers[:8]
for ll, lo in zip(main2, main1_trunk):
if ll.has_params:
ll.set_params(lo.W.get())
ll.allocate()
ll.set_deltas([be.iobuf(ll.in_shape)])
for bb in (b1, b2, b3):
for ll in bb:
ll.allocate()
ll.set_deltas([be.iobuf(ll.in_shape)])
# Create the combined output buffer
merge_output = be.empty_like(neon_layer.layers[8].outputs)
x = inp
for ll in main2:
x = ll.fprop(x)
start = 0
for bb in (b1, b2, b3):
xb = x
for ll in bb:
xb = ll.fprop(xb)
end = start + xb.shape[0]
merge_output[start:end] = xb
start = end
x = merge_output
top_trunk = Sequential(top).layers
for ll in top_trunk:
x = ll.fprop(x)
neon_out_ref = x.get()
difference = neon_out_ref - neon_out
assert np.max(np.abs(difference)) < 1e-7
print np.max(np.abs(difference))
print "Beginning Back prop"
erra = np.random.random(neon_out.shape)
err = be.array(erra)
for ll in reversed(neon_layer.layers[8:]):
err = ll.bprop(err)
neon_deltas = err.get()
for bb, errb in zip((b1, b2, b3), neon_layer.layers[8].error_views):
for ll in reversed(bb):
errb = ll.bprop(errb)
# Now sum up the deltas at the root of the branch layer and compare
ref_deltas = be.zeros_like(b1[0].deltas)
ref_deltas[:] = b1[0].deltas + b2[0].deltas + b3[0].deltas
neon_ref_deltas = ref_deltas.get()
difference = neon_deltas - neon_ref_deltas
print np.max(np.abs(difference))
assert np.max(np.abs(difference)) < 1e-8
def insert_branch_layer(network, b):
return Sequential(layers=(network, b))
class Model(NervanaObject):
"""
Basic model class which stores a list of layers describing the model. Can train the layer
weights on a dataset, evaluate on a test set and serialize the mode.
Additional functionality can be added to fit through callback functions.
Arguments:
layers: layer container, or a list of layers (that will be containerized),
or a serialized model description
dataset (iterator): Data set (ignored, will be removed)
weights_only (bool): set to True if you do not want to recreate layers
and states during deserialization from a serialized model
description. Defaults to False.
name (str): Model name. Defaults to "model"
optimizer (Optimizer): Optimizer object which defines the learning rule
for updating model parameters (ie DescentMomentum, AdaDelta)
"""
def __init__(self, layers, dataset=None, weights_only=False, name="model", optimizer=None):
super(Model, self).__init__(name)
self.optimizer = optimizer
self.params = None # should be able to remove
self.states = None # should be able to remove
self.epoch_index = 0
self.finished = False
self.initialized = False
self.cost = None
self.nbatches = 0
self.ndata = 0
if dataset is not None:
logger.warning('dataset is a deprecated argument and will be ignored')
if type(layers) in (ModelDescription, dict):
# load up the model from a serialized file (dataset could be None here)
self.deserialize(layers, load_states=(not weights_only))
elif type(layers) is str:
self.load_params(layers, load_states=(not weights_only))
else:
# Wrap the list of layers in a Sequential container if a raw list of layers
if type(layers) in (Sequential, Tree, SingleOutputTree):
self.layers = layers
else:
self.layers = Sequential(layers)
self.layers.propagate_parallelism("Data")
@property
def layers_to_optimize(self):
return self.layers.layers_to_optimize
def set_shortcut(self):
# infer whether bprop shortcut can be used on final activation
# self.cost should be set to run this otherwise do nothing
lastlayer = self.layers[-1]
try:
if self.cost.costfunc.__class__ is CrossEntropyBinary:
if (lastlayer.__class__ is Activation and
lastlayer.transform.__class__ is Logistic):
lastlayer.transform.set_shortcut(True)
except:
# if any attributes are not set or any other exception
# is thrown leave transform.shortcut as is (do nothing)
pass
def initialize(self, dataset, cost=None):
if self.initialized:
return
# Propagate shapes through the layers to configure
prev_input = dataset
prev_input = self.layers.configure(prev_input)
if cost is not None:
cost.initialize(prev_input)
self.cost = cost
# Now allocate space
self.layers.allocate()
self.layers.allocate_deltas()
self.initialized = True
def __str__(self):
"""
String representation of model's layers
"""
config_string = "Network Layers:\n" + self.layers.nested_str()
return config_string
def fit(self, dataset, cost, optimizer, num_epochs, callbacks):
"""
Trains the model parameters on a dataset by minimizing the cost function through
gradient descent and updates the layer weights according to a learning rule
defined in optimizer.
Arguments:
dataset (iterator): An iterable of minibatches where each
element is a (x, y) tuple where x is the input data and y are the labels.
x is of dimension (feature_size, batch_size)
y is of dimension (label_size, batch_size)
Length of the iterator is num_batches which is num_data / batch_size
cost (Cost): Defines the function which the model is minimizing based
on the output of the last layer and the input labels
optimizer (Optimizer): Defines the learning rule for updating the model parameters
num_epochs: Number of times to iterate over the dataset.
callbacks (Callbacks): Defines callbacks to run at the end of each mini-batch / epoch.
"""
self.nbatches = dataset.nbatches
self.ndata = dataset.ndata
# self.set_shortcut() # infer if bprop shortcut can be used
self.total_cost = self.be.empty((1, 1), dtype=np.float32)
self.optimizer = optimizer
self.initialize(dataset, cost)
callbacks.on_train_begin(num_epochs)
while self.epoch_index < num_epochs and not self.finished:
self.nbatches = dataset.nbatches
callbacks.on_epoch_begin(self.epoch_index)
self._epoch_fit(dataset, callbacks)
callbacks.on_epoch_end(self.epoch_index)
self.epoch_index += 1
callbacks.on_train_end()
def _epoch_fit(self, dataset, callbacks):
"""
Helper function for fit which performs training on a dataset for one epoch.
Arguments:
dataset (iterable): Dataset iterator to perform fit on
"""
epoch = self.epoch_index
self.total_cost[:] = 0
# iterate through minibatches of the dataset
for mb_idx, (x, t) in enumerate(dataset):
callbacks.on_minibatch_begin(epoch, mb_idx)
self.be.begin(Block.minibatch, mb_idx)
x = self.fprop(x)
self.total_cost[:] = self.total_cost + self.cost.get_cost(x, t)
# deltas back propagate through layers
# for every layer in reverse except the 0th one
delta = self.cost.get_errors(x, t)
self.bprop(delta)
self.optimizer.optimize(self.layers_to_optimize, epoch=epoch)
self.be.end(Block.minibatch, mb_idx)
callbacks.on_minibatch_end(epoch, mb_idx)
# now we divide total cost by the number of batches,
# so it was never total cost, but sum of averages
# across all the minibatches we trained on
self.total_cost[:] = self.total_cost / dataset.nbatches
def fprop(self, x, inference=False):
"""
Forward propagates a minibatch x through the model.
Arguments:
x (Tensor): Input minibatch data
inference (bool): Flag for performing training or inference
Only affects batch norm and dropout layers.
Returns:
Tensor: the output of the final layer in the model
"""
return self.layers.fprop(x, inference)
def bprop(self, delta):
"""
Back propagates the error of a minibatch through the model.
Arguments:
delta (Tensor): Derivative of cost with respect to the last layer's output
"""
return self.layers.bprop(delta)
def eval(self, dataset, metric):
"""
Evaluates a model on a dataset according to an input metric.
Arguments:
datasets (iterable): dataset to evaluate on.
metric (Cost): what function to evaluate dataset on.
"""
self.initialize(dataset)
running_error = np.zeros((len(metric.metric_names)), dtype=np.float32)
nprocessed = 0
dataset.reset()
for x, t in dataset:
x = self.fprop(x, inference=True)
# This logic is for handling partial batch sizes at the end of the dataset
nsteps = x.shape[1] / self.be.bsz if not isinstance(x, list) else \
x[0].shape[1] / self.be.bsz
bsz = min(dataset.ndata - nprocessed, self.be.bsz)
running_error += metric(x, t, calcrange=slice(0, nsteps * bsz)) * nsteps * bsz
nprocessed += bsz * nsteps
running_error /= nprocessed
return running_error
def get_outputs(self, dataset):
"""
Get the activation outputs of the final model layer for the dataset
Arguments:
dataset (iterable): Dataset iterator to perform fit on
Returns:
Host numpy array: the output of the final layer for the entire Dataset
"""
self.initialize(dataset)
dataset.reset() # Move "pointer" back to beginning of dataset
n = dataset.nbatches
x = self.layers.layers[-1].outputs
assert not isinstance(x, list), "Can not get_outputs with Branch terminal"
Ypred = None
for idx, (x, t) in enumerate(dataset):
x = self.fprop(x, inference=True)
if Ypred is None:
(dim0, dim1) = x.shape
Ypred = np.empty((n * dim1, dim0), dtype=x.dtype)
nsteps = dim1 / self.be.bsz
cur_batch = slice(idx * dim1, (idx + 1) * dim1)
Ypred[cur_batch] = x.get().T
# Handle the recurrent case.
if nsteps != 1:
b, s = (self.be.bsz, nsteps)
Ypred = Ypred.reshape((n, s, b, -1)).transpose(0, 2, 1, 3).copy().reshape(n*b, s, -1)
return Ypred[:dataset.ndata]
def get_description(self, get_weights=False, keep_states=False):
"""
Gets a description of the model required to reconstruct the model with
no weights like from a yaml file.
Returns:
dict: Description of each component of the model.
"""
pdict = dict()
pdict['neon_version'] = __neon_version__
compat_mode = self.be.compat_mode if self.be.compat_mode is not None else 'neon'
pdict['backend'] = {'type': self.be.__class__.__name__,
'compat_mode': compat_mode,
'rng_seed': self.be.rng_seed,
'rng_state': self.be.rng_get_state()}
if self.cost:
pdict['cost'] = self.cost.get_description()
if self.optimizer:
pdict['optimizer'] = self.optimizer.get_description()
pdict['model'] = self.layers.get_description(get_weights=get_weights,
keep_states=keep_states)
return pdict
def save_params(self, param_path, keep_states=True):
"""
Serializes and saves model parameters to the path specified.
Arguments:
param_path (str): File to write serialized parameter dict to.
keep_states (bool): Whether to save optimizer states too.
Defaults to True.
"""
self.serialize(keep_states=keep_states, fn=param_path)
def load_params(self, param_path, load_states=True):
"""
Loads the model parameters (per layer weights, epochs run, optimizer
states) saved in param_path from serialize().
Arguments:
param_path (str): File containing serialized python dict with layer
weights and states.
load_states (bool): if False, then only the weights will be loaded
into a model in which the layers have already been
created, otherwise will (re)create the layers from
the serialized parameters and set the learning
states as well
"""
self.deserialize(load_obj(param_path), load_states=load_states)
logger.info('Model weights loaded from %s', param_path)
def load_weights(self, weight_path):
"""
.. deprecated:: 1.1.4
Use :func:`load_params` instead
"""
logger.warning('Calling deprecated load_weights function. Use '
'load_params instead')
self.load_params(weight_path)
def deserialize(self, model_dict, data=None, load_states=True):
"""
Loads per layer (weights, states) and other model parameters from the
dictionary passed.
Arguments:
model_dict (dict): dictionary describing the model including layers,
cost, optimizers, backend settings, etc.
generated by the serialize function
data (iterator): Data set (ignored, will be removed)
load_states (bool): if False, then only the weights will be loaded
into a model in which the layers have already been
created, otherwise will (re)create the layers from
the serialized parameters and set the learning
states as well
"""
if data is not None:
logger.warning('data is a deprecated argument and will be ignored')
if 'epoch_index' in model_dict:
self.epoch_index = model_dict['epoch_index']
if 'model' not in model_dict:
logger.error('Using old model serialization format. '
'Serialized the model into new format')
param_layers = [l for l in self.layers_to_optimize]
param_dict_list = model_dict['layer_params_states']
for l, ps in zip(param_layers, param_dict_list):
l.set_params(ps)
if 'states' in ps and load_states:
l.set_states(ps)
return
if 'backend' in model_dict:
if 'compat_mode' in model_dict['backend']:
self.be.compat_mode = model_dict['backend']['compat_mode']
else:
model_dict['backend'] = {}
typ = model_dict['model']['type']
main_container = load_class(typ)
if not hasattr(self, 'layers'):
self.layers = main_container.gen_class(model_dict['model']['config'])
self.layers.load_weights(model_dict['model'], load_states)
if load_states and 'rng_state' in model_dict['backend']:
try:
self.be.rng_set_state(model_dict['backend']['rng_state'])
except ValueError as e:
# could come about when switching backend types (ex GPU to CPU)
logger.warning("Problems restoring existing RNG state: %s", str(e))
# serialize tells how to write out the parameters we've learned so
# far and associate them with layers. it can ignore layers with no
# learned parameters. the model stores states to pass to the
# optimizers. if we're saving the model out for inference, we
# don't need to remember states.
def serialize(self, fn=None, keep_states=True):
"""
Creates a dictionary storing the layer parameters and epochs complete.
Arguments:
fn (str): file to save pkl formatted model dictionary
keep_states (bool): Whether to save optimizer states.
Returns:
dict: Model data including layer parameters and epochs complete.
"""
# get the model dict with the weights
pdict = self.get_description(get_weights=True, keep_states=keep_states)
pdict['epoch_index'] = self.epoch_index + 1
if self.initialized:
pdict['train_input_shape'] = self.layers.in_shape
if fn is not None:
save_obj(pdict, fn)
return
return pdict
def set_batch_size(self, N):
"""
Set the actual minibatch size, so eventhough the buffers are allocated considering
excessive padding, the processing for some layers may be shortened.
Currently most of the neon layers don't use that to control the processing. The
interface is here only for when someone wants to set that information and experiment.
"""
return self.layers.set_batch_size(N)
def set_seq_len(self, S):
"""
Set the actual minibatch sequence length, so eventhough the buffers are allocated
considering excessive padding, the processing for some layers may be shortened.
Currently most of the neon layers don't use that to control the processing. The
interface is here only for when someone wants to set that information and experiment.
"""
return self.layers.set_seq_len(S)
def benchmark(self, dataset, inference=False, cost=None, optimizer=None,
niterations=20, nskip=2):
"""
Measure runtime for computing fprop and bprop seperately, as well as
full minibatch run times. For inference case, only the fprop
Arguments:
dataset (iterable): Dataset iterator to perform fit on
cost (Cost): Defines the function which the model is minimizing based
on the output of the last layer and the input labels
niterations (optional, int): Number of minibatches to average over
nskip (optional, int): number of iterations at the beginning to skip
when calculating the runtime statistics
Returns:
dictionary with fprop, bprop run times
"""
# initialize model
if inference is False:
assert cost is not None and optimizer is not None, "Need cost and optimizer to \
benchmark bprop and update"
self.cost = cost
self.initialize(dataset, cost)
self.optimizer = optimizer
self.total_cost = self.be.empty((1, 1))
self.total_cost[:] = 0
# iterate through minibatches of the dataset
times = OrderedDict()
time_keys = ['fprop'] if inference else ['fprop', 'bprop', 'iteration']
for ky in time_keys:
times[ky] = np.full(niterations + nskip, -1.0)
count = 0
fprop_start = self.be.init_mark()
fprop_end = self.be.init_mark()
bprop_end = self.be.init_mark()
while count < niterations + nskip:
dataset.reset()
for mb_idx, (x, t) in enumerate(dataset):
self.be.record_mark(fprop_start) # mark start of fprop
x = self.fprop(x)
if inference is False:
self.total_cost[:] = self.total_cost + self.cost.get_cost(x, t)
self.be.record_mark(fprop_end) # mark end of fprop and start of bprop
if inference is False:
delta = self.cost.get_errors(x, t)
self.bprop(delta)
self.optimizer.optimize(self.layers_to_optimize, epoch=0)
self.be.record_mark(bprop_end) # mark end of bprop
self.be.synchronize_mark(bprop_end)
else:
self.be.synchronize_mark(fprop_end)
times['fprop'][count] = self.be.get_time(fprop_start, fprop_end)
if inference is False:
times['bprop'][count] = self.be.get_time(fprop_end, bprop_end)
times['iteration'][count] = times['fprop'][count] + times['bprop'][count]
count += 1
if count >= niterations + nskip:
break
# print results
header = ('Func', 'Mean', 'Median', 'Min', 'Max', 'Units')
stats = tuple(stat.lower() for stat in header[1:-1])
fmt_titles = '| {:^11} '*len(header) + '|'
fmt_nums = '| {func:<11} ' + '| {%s:<10.5g} '*len(stats) % (stats) + '| {units:^11} |'
head_str = fmt_titles.format(*header)
sep = '-'*len(head_str)
head_str = sep + '\n' + head_str + '\n' + sep
print(head_str)
out_stats = {}
for step in times:
timesu = np.array(times[step][nskip:]) # in ms
out_stats[step] = {}
for stat in stats:
out_stats[step][stat] = getattr(np, stat)(timesu)
print(fmt_nums.format(units='msec', func=step, **out_stats[step]))
print(sep)
return out_stats
def test_model_serialize(backend_default, data):
(X_train, y_train), (X_test, y_test), nclass = load_mnist(path=data)
train_set = ArrayIterator([X_train, X_train],
y_train,
nclass=nclass,
lshape=(1, 28, 28))
init_norm = Gaussian(loc=0.0, scale=0.01)
# initialize model
path1 = Sequential([
Conv((5, 5, 16),
init=init_norm,
bias=Constant(0),
activation=Rectlin()),
Pooling(2),
Affine(nout=20, init=init_norm, bias=init_norm, activation=Rectlin())
])
path2 = Sequential([
Affine(nout=100,
init=init_norm,
bias=Constant(0),
activation=Rectlin()),
Dropout(keep=0.5),
Affine(nout=20, init=init_norm, bias=init_norm, activation=Rectlin())
])
layers = [
MergeMultistream(layers=[path1, path2], merge="stack"),
Affine(nout=20, init=init_norm, batch_norm=True, activation=Rectlin()),
Affine(nout=10, init=init_norm, activation=Logistic(shortcut=True))
]
tmp_save = 'test_model_serialize_tmp_save.pickle'
mlp = Model(layers=layers)
mlp.optimizer = GradientDescentMomentum(learning_rate=0.1,
momentum_coef=0.9)
mlp.cost = GeneralizedCost(costfunc=CrossEntropyBinary())
mlp.initialize(train_set, cost=mlp.cost)
n_test = 3
num_epochs = 3
# Train model for num_epochs and n_test batches
for epoch in range(num_epochs):
for i, (x, t) in enumerate(train_set):
x = mlp.fprop(x)
delta = mlp.cost.get_errors(x, t)
mlp.bprop(delta)
mlp.optimizer.optimize(mlp.layers_to_optimize, epoch=epoch)
if i > n_test:
break
# Get expected outputs of n_test batches and states of all layers
outputs_exp = []
pdicts_exp = [l.get_params_serialize() for l in mlp.layers_to_optimize]
for i, (x, t) in enumerate(train_set):
outputs_exp.append(mlp.fprop(x, inference=True))
if i > n_test:
break
# Serialize model
mlp.save_params(tmp_save, keep_states=True)
# Load model
mlp = Model(tmp_save)
mlp.initialize(train_set)
outputs = []
pdicts = [l.get_params_serialize() for l in mlp.layers_to_optimize]
for i, (x, t) in enumerate(train_set):
outputs.append(mlp.fprop(x, inference=True))
if i > n_test:
break
# Check outputs, states, and params are the same
for output, output_exp in zip(outputs, outputs_exp):
assert np.allclose(output.get(), output_exp.get())
for pd, pd_exp in zip(pdicts, pdicts_exp):
for s, s_e in zip(pd['states'], pd_exp['states']):
if isinstance(s, list): # this is the batch norm case
for _s, _s_e in zip(s, s_e):
assert np.allclose(_s, _s_e)
else:
assert np.allclose(s, s_e)
for p, p_e in zip(pd['params'], pd_exp['params']):
assert type(p) == type(p_e)
if isinstance(p, list): # this is the batch norm case
for _p, _p_e in zip(p, p_e):
assert np.allclose(_p, _p_e)
elif isinstance(p, np.ndarray):
assert np.allclose(p, p_e)
else:
assert p == p_e
os.remove(tmp_save)
def mergesum_test_config(modfunc, use_stride=1):
NervanaObject.be = gen_backend("gpu", batch_size=64)
be = NervanaObject.be
l1 = Conv(**conv_params(3, 16))
neon_layer = modfunc(16, use_stride)
inshape = (16, 32, 32)
insize = np.prod(inshape)
inpa = np.random.random((insize, batch_size))
neon_seq = Sequential([l1] + neon_layer)
neon_seq.configure(inshape)
inp = be.array(inpa)
neon_seq.allocate()
# print neon_layer.nested_str()
# neon_layer.layers[0].prev_layer = True
neon_seq.allocate_deltas()
neon_out = neon_seq.fprop(inp).get()
# Now make the reference pathways:
p1, p2 = module_factory_copy(neon_layer, modfunc, 16, use_stride)
l11 = Conv(**conv_params(3, 16))
l12 = Conv(**conv_params(3, 16))
for ll in (l11, l12):
for lcopy, lref in zip(ll, l1):
if lcopy.has_params:
lcopy.set_params(lref.get_params_serialize())
path1 = Sequential([l11] + p1)
path2 = Sequential([l12] + p2)
for ll in (path1, path2):
ll.configure(inshape)
ll.allocate()
ll.allocate_deltas()
o1 = path1.fprop(inp).get()
o2 = path2.fprop(inp).get()
# Now relu it
neon_out_ref = np.maximum(o1+o2, 0)
difference = neon_out_ref - neon_out
print np.max(np.abs(difference))
# need to have bsum false for this test to be valid
# assert np.max(np.abs(difference)) < 1e-7
print "Fprop matching"
print "Beginning Back prop"
erra = np.random.random(neon_out.shape)
err = be.array(erra)
ebr = neon_seq.layers[4].bprop(err)
print "Orig Error", ebr.get()[0, :20]
ebr = neon_seq.layers[3].bprop(ebr)
trunk_neon = ebr.get()
err = be.array(erra)
err[:] = be.greater(be.array(neon_out_ref), 0) * err
eb1 = err
for l in reversed(path1.layers[3:]):
eb1 = l.bprop(eb1)
t1 = eb1.get()
err = be.array(erra)
err[:] = be.greater(be.array(neon_out_ref), 0) * err
eb2 = err
for l in reversed(path2.layers[3:]):
eb2 = l.bprop(eb2)
t2 = eb2.get()
print np.max(np.abs(trunk_neon - (t1 + t2)))
# parse the command line arguments
parser = NeonArgparser(__doc__)
args = parser.parse_args()
# hyperparameters
num_epochs = args.epochs
(X_train, y_train), (X_test, y_test), nclass = load_mnist(path=args.data_dir)
train_set = ArrayIterator([X_train, X_train], y_train, nclass=nclass, lshape=(1, 28, 28))
valid_set = ArrayIterator([X_test, X_test], y_test, nclass=nclass, lshape=(1, 28, 28))
# weight initialization
init_norm = Gaussian(loc=0.0, scale=0.01)
# initialize model
path1 = Sequential(layers=[Affine(nout=100, init=init_norm, activation=Rectlin()),
Affine(nout=100, init=init_norm, activation=Rectlin())])
path2 = Sequential(layers=[Affine(nout=100, init=init_norm, activation=Rectlin()),
Affine(nout=100, init=init_norm, activation=Rectlin())])
layers = [MergeMultistream(layers=[path1, path2], merge="stack"),
Affine(nout=10, init=init_norm, activation=Logistic(shortcut=True))]
model = Model(layers=layers)
cost = GeneralizedCost(costfunc=CrossEntropyBinary())
# fit and validate
optimizer = GradientDescentMomentum(learning_rate=0.1, momentum_coef=0.9)
# configure callbacks
callbacks = Callbacks(model, eval_set=valid_set, **args.callback_args)
# setup backend
be = gen_backend(**extract_valid_args(args, gen_backend))
# download dataset
data_path = load_flickr8k(path=args.data_dir) # Other setnames are flickr30k and coco
# load data
train_set = ImageCaption(path=data_path, max_images=-1)
# weight initialization
init = Uniform(low=-0.08, high=0.08)
init2 = Constant(val=train_set.be.array(train_set.bias_init))
# model initialization
image_path = Sequential([Affine(hidden_size, init, bias=Constant(val=0.0))])
sent_path = Sequential([Affine(hidden_size, init, linear_name='sent')])
layers = [
MergeMultistream(layers=[image_path, sent_path], merge="recurrent"),
Dropout(keep=0.5),
LSTM(hidden_size, init, activation=Logistic(), gate_activation=Tanh(), reset_cells=True),
Affine(train_set.vocab_size, init, bias=init2, activation=Softmax())
]
cost = GeneralizedCostMask(costfunc=CrossEntropyMulti(usebits=True))
# configure callbacks
checkpoint_model_path = "~/image_caption2.pickle"
if args.callback_args['save_path'] is None:
args.callback_args['save_path'] = checkpoint_model_path
def test_branch_model_fork():
from neon.layers import BranchNode, Tree
NervanaObject.be = gen_backend("gpu", batch_size=64)
be = NervanaObject.be
bnode = BranchNode()
i1 = inception([(32, ), (32, 32), ('max', 16)])
top1 = top_branch()
top2 = top_branch()
p1 = Sequential(main_branch() + [bnode, i1] + top1)
p2 = [bnode] + top2
alpha2 = 0.3
neon_layer = Tree([p1, p2], alphas=[1.0, alpha2])
inshape = (3, 224, 224)
insize = np.prod(inshape)
inpa = np.random.random((insize, batch_size))
neon_layer.configure(inshape)
inp = neon_layer.be.array(inpa)
neon_layer.allocate()
print neon_layer.nested_str()
neon_layer.layers[0].layers[0].prev_layer = True
neon_layer.allocate_deltas()
neon_layer.layers[0].layers[0].set_deltas([be.iobuf(inshape)])
neon_out_dev = neon_layer.fprop(inp)
neon_out = [d.get() for d in neon_out_dev]
# Now make the reference pathways:
main_trunk2 = Sequential(main_branch())
main_trunk2.configure(inshape)
main2 = main_trunk2.layers
main2[0].prev_layer = True
main2[0].set_deltas([be.iobuf(inshape)])
branch2 = Sequential(top_branch())
lbranch2 = branch2.layers
(b1, b2, b3) = inception_bare(i1, [(32, ), (32, 32), ('max', 16)])
for bb in (b1, b2, b3, lbranch2):
oshape = inshape
for ll in main2 + bb:
oshape = ll.configure(oshape)
main1_trunk = neon_layer.layers[0].layers[:8]
for ll, lo in zip(main2, main1_trunk):
if ll.has_params:
ll.set_params({'params': {'W': lo.W.get()}})
ll.allocate()
ll.set_deltas([be.iobuf(ll.in_shape)])
for ll, lo in zip(lbranch2, neon_layer.layers[1].layers[1:]):
if ll.has_params:
ll.set_params({'params': {'W': lo.W.get()}})
for bb in (b1, b2, b3, lbranch2):
for ll in bb:
ll.allocate()
ll.set_deltas([be.iobuf(ll.in_shape)])
# Create the combined output buffer
merge_output = be.empty_like(neon_layer.layers[0].layers[9].outputs)
x = inp
for ll in main2:
x = ll.fprop(x)
main2_out = x
start = 0
for bb in (b1, b2, b3):
xb = main2_out
for ll in bb:
xb = ll.fprop(xb)
end = start + xb.shape[0]
merge_output[start:end] = xb
start = end
x = merge_output
top_trunk = Sequential(top1).layers
for ll in top_trunk:
x = ll.fprop(x)
neon_out_ref = x.get()
difference = neon_out_ref - neon_out[0]
assert np.max(np.abs(difference)) < 1e-7
print np.max(np.abs(difference))
# Now do second branch
neon_out_ref2 = branch2.fprop(main2_out).get()
difference = neon_out_ref2 - neon_out[1]
assert np.max(np.abs(difference)) < 1e-7
print np.max(np.abs(difference))
print "Beginning Back prop"
erra = [np.random.random(d.shape) for d in neon_out]
err = [be.array(d) for d in erra]
neon_layer.layers[0].layers[0].deltas = be.iobuf(inshape)
neon_layer.bprop(err)
bottom_neon_deltas = neon_layer.layers[0].layers[1].deltas.get()
middle_neon_deltas = neon_layer.layers[1].layers[1].deltas.get()
err0 = err[0]
for ll in reversed(top_trunk):
err0 = ll.bprop(err0)
err1 = err[1]
for ll in reversed(lbranch2):
err1 = ll.bprop(err1)
for bb, errb in zip((b1, b2, b3),
neon_layer.layers[0].layers[-5].error_views):
for ll in reversed(bb):
errb = ll.bprop(errb)
# Now sum up the deltas at the root of the branch layer and compare
ref_deltas = be.zeros_like(b1[0].deltas)
ref_deltas[:] = b1[0].deltas + b2[0].deltas + b3[
0].deltas + alpha2 * lbranch2[0].deltas
neon_ref_deltas = ref_deltas.get()
difference = middle_neon_deltas - neon_ref_deltas
print np.max(np.abs(difference))
assert np.max(np.abs(difference)) < 1e-8
x = ref_deltas
main2[0].deltas = be.iobuf(inshape)
for ll in reversed(main2):
x = ll.bprop(x)
bottom_neon_ref_deltas = main2[1].deltas.get()
difference = bottom_neon_deltas - bottom_neon_ref_deltas
print np.max(np.abs(difference))
assert np.max(np.abs(difference)) < 1e-8
def test_branch_model_cpu(backend_cpu64):
np.random.seed(0)
be = NervanaObject.be
be.bsz = 32
main1 = main_branch()
i1 = inception([(32,), (32, 32), ('max', 16)])
top = top_branch()
neon_layer = Sequential(main1 + i1 + top)
inshape = (4, 224, 224)
insize = np.prod(inshape)
inpa = np.random.random((insize, batch_size))
neon_layer.configure(inshape)
inp = neon_layer.be.array(inpa)
neon_layer.allocate()
neon_logger.display(neon_layer.nested_str())
neon_layer.layers[0].prev_layer = True
neon_layer.allocate_deltas()
neon_out = neon_layer.fprop(inp).get()
# Now make the reference pathways:
main_trunk2 = Sequential(main_branch())
main_trunk2.configure(inshape)
main2 = main_trunk2.layers
main2[0].prev_layer = True
main2[0].deltas = be.iobuf(inshape)
(b1, b2, b3) = inception_bare(i1, [(32,), (32, 32), ('max', 16)])
for bb in (b1, b2, b3):
oshape = inshape
for ll in main2 + bb:
oshape = ll.configure(oshape)
main1_trunk = neon_layer.layers[:8]
for ll, lo in zip(main2, main1_trunk):
if ll.has_params:
ll.set_params({'params': {'W': lo.W.get()}})
ll.allocate()
temp_buff = DeltasTree()
ll.allocate_deltas(temp_buff)
temp_buff.allocate_buffers()
ll.set_deltas(temp_buff)
for bb in (b1, b2, b3):
for ll in bb:
ll.allocate()
temp_buff = DeltasTree()
ll.allocate_deltas(temp_buff)
temp_buff.allocate_buffers()
ll.set_deltas(temp_buff)
# Create the combined output buffer
merge_output = be.empty_like(neon_layer.layers[8].outputs)
x = inp
for ll in main2:
x = ll.fprop(x)
start = 0
for bb in (b1, b2, b3):
xb = x
for ll in bb:
xb = ll.fprop(xb)
end = start + xb.shape[0]
merge_output[start:end] = xb
start = end
x = merge_output
top_trunk = Sequential(top).layers
for ll in top_trunk:
x = ll.fprop(x)
neon_out_ref = x.get()
assert allclose_with_out(neon_out, neon_out_ref, rtol=0)
neon_logger.display("Beginning Back prop")
erra = np.random.random(neon_out.shape)
err = be.array(erra)
for ll in reversed(neon_layer.layers[8:]):
err = ll.bprop(err)
neon_deltas = err.get()
for bb, errb in zip((b1, b2, b3), neon_layer.layers[8].error_views):
for ll in reversed(bb):
errb = ll.bprop(errb)
# Now sum up the deltas at the root of the branch layer and compare
ref_deltas = be.zeros_like(b1[0].deltas)
ref_deltas[:] = b3[0].deltas + b2[0].deltas + b1[0].deltas
neon_ref_deltas = ref_deltas.get()
assert allclose_with_out(neon_deltas, neon_ref_deltas, rtol=0)
def test_branch_model():
NervanaObject.be = gen_backend("gpu", batch_size=64)
be = NervanaObject.be
main1 = main_branch()
i1 = inception([(32, ), (32, 32), ('max', 16)])
top = top_branch()
neon_layer = Sequential(main1 + i1 + top)
inshape = (3, 224, 224)
insize = np.prod(inshape)
inpa = np.random.random((insize, batch_size))
neon_layer.configure(inshape)
inp = neon_layer.be.array(inpa)
neon_layer.allocate()
print neon_layer.nested_str()
neon_layer.layers[0].prev_layer = True
neon_layer.allocate_deltas()
neon_layer.layers[0].set_deltas([be.iobuf(inshape)])
neon_out = neon_layer.fprop(inp).get()
# Now make the reference pathways:
main_trunk2 = Sequential(main_branch())
main_trunk2.configure(inshape)
main2 = main_trunk2.layers
main2[0].prev_layer = True
main2[0].set_deltas([be.iobuf(inshape)])
(b1, b2, b3) = inception_bare(i1, [(32, ), (32, 32), ('max', 16)])
for bb in (b1, b2, b3):
oshape = inshape
for ll in main2 + bb:
oshape = ll.configure(oshape)
main1_trunk = neon_layer.layers[:8]
for ll, lo in zip(main2, main1_trunk):
if ll.has_params:
ll.set_params({'params': {'W': lo.W.get()}})
ll.allocate()
ll.set_deltas([be.iobuf(ll.in_shape)])
for bb in (b1, b2, b3):
for ll in bb:
ll.allocate()
ll.set_deltas([be.iobuf(ll.in_shape)])
# Create the combined output buffer
merge_output = be.empty_like(neon_layer.layers[8].outputs)
x = inp
for ll in main2:
x = ll.fprop(x)
start = 0
for bb in (b1, b2, b3):
xb = x
for ll in bb:
xb = ll.fprop(xb)
end = start + xb.shape[0]
merge_output[start:end] = xb
start = end
x = merge_output
top_trunk = Sequential(top).layers
for ll in top_trunk:
x = ll.fprop(x)
neon_out_ref = x.get()
difference = neon_out_ref - neon_out
assert np.max(np.abs(difference)) < 1e-7
print np.max(np.abs(difference))
print "Beginning Back prop"
erra = np.random.random(neon_out.shape)
err = be.array(erra)
for ll in reversed(neon_layer.layers[8:]):
err = ll.bprop(err)
neon_deltas = err.get()
for bb, errb in zip((b1, b2, b3), neon_layer.layers[8].error_views):
for ll in reversed(bb):
errb = ll.bprop(errb)
# Now sum up the deltas at the root of the branch layer and compare
ref_deltas = be.zeros_like(b1[0].deltas)
ref_deltas[:] = b1[0].deltas + b2[0].deltas + b3[0].deltas
neon_ref_deltas = ref_deltas.get()
difference = neon_deltas - neon_ref_deltas
print np.max(np.abs(difference))
assert np.max(np.abs(difference)) < 1e-8
def test_branch_model(backend_gpu):
np.random.seed(0)
be = NervanaObject.be
be.bsz = 64
main1 = main_branch()
i1 = inception([(32,), (32, 32), ('max', 16)])
top = top_branch()
neon_layer = Sequential(main1 + i1 + top)
inshape = (4, 224, 224)
insize = np.prod(inshape)
inpa = np.random.random((insize, batch_size))
neon_layer.configure(inshape)
inp = neon_layer.be.array(inpa)
neon_layer.allocate()
neon_logger.display(neon_layer.nested_str())
neon_layer.layers[0].prev_layer = True
neon_layer.allocate_deltas()
neon_out = neon_layer.fprop(inp).get()
# Now make the reference pathways:
main_trunk2 = Sequential(main_branch())
main_trunk2.configure(inshape)
main2 = main_trunk2.layers
main2[0].prev_layer = True
main2[0].deltas = be.iobuf(inshape)
(b1, b2, b3) = inception_bare(i1, [(32,), (32, 32), ('max', 16)])
for bb in (b1, b2, b3):
oshape = inshape
for ll in main2 + bb:
oshape = ll.configure(oshape)
main1_trunk = neon_layer.layers[:6]
for ll, lo in zip(main2, main1_trunk):
if ll.has_params:
ll.set_params({'params': {'W': lo.W.get(), 'weight_bias': lo.weight_bias.get()}})
ll.allocate()
temp_buff = DeltasTree()
ll.allocate_deltas(temp_buff)
temp_buff.allocate_buffers()
ll.set_deltas(temp_buff)
for bb in (b1, b2, b3):
for ll in bb:
ll.allocate()
temp_buff = DeltasTree()
ll.allocate_deltas(temp_buff)
temp_buff.allocate_buffers()
ll.set_deltas(temp_buff)
# Create the combined output buffer
merge_output = be.empty_like(neon_layer.layers[6].outputs)
x = inp
for ll in main2:
x = ll.fprop(x)
start = 0
for bb in (b1, b2, b3):
xb = x
for ll in bb:
xb = ll.fprop(xb)
end = start + xb.shape[0]
merge_output[start:end] = xb
start = end
x = merge_output
top_trunk = Sequential(top).layers
for ll in top_trunk:
x = ll.fprop(x)
neon_out_ref = x.get()
assert allclose_with_out(neon_out, neon_out_ref, rtol=0)
neon_logger.display("Beginning Back prop")
erra = np.random.random(neon_out.shape)
err = be.array(erra)
for ll in reversed(neon_layer.layers[6:]):
err = ll.bprop(err)
neon_deltas = err.get()
for bb, errb in zip((b1, b2, b3), neon_layer.layers[6].error_views):
for ll in reversed(bb):
errb = ll.bprop(errb)
# Now sum up the deltas at the root of the branch layer and compare
ref_deltas = be.zeros_like(b1[0].deltas)
ref_deltas[:] = b3[0].deltas + b2[0].deltas + b1[0].deltas
neon_ref_deltas = ref_deltas.get()
assert allclose_with_out(neon_deltas, neon_ref_deltas, rtol=0)
def test_branch_model_fork_cpu(backend_cpu64):
from neon.layers import BranchNode, Tree
np.random.seed(0)
be = NervanaObject.be
be.bsz = 32
bnode = BranchNode()
i1 = inception([(32,), (32, 32), ('max', 16)])
top1 = top_branch()
top2 = top_branch()
p1 = Sequential(main_branch() + [bnode, i1] + top1)
p2 = [bnode] + top2
alpha2 = 0.3
neon_layer = Tree([p1, p2], alphas=[1.0, alpha2])
inshape = (4, 224, 224)
insize = np.prod(inshape)
inpa = np.random.random((insize, batch_size))
neon_layer.configure(inshape)
inp = neon_layer.be.array(inpa)
neon_layer.allocate()
neon_layer.layers[0].layers[0].prev_layer = True
neon_layer.allocate_deltas()
neon_out_dev = neon_layer.fprop(inp)
neon_out = [d.get() for d in neon_out_dev]
# Now make the reference pathways:
main_trunk2 = Sequential(main_branch())
main_trunk2.configure(inshape)
main2 = main_trunk2.layers
main2[0].prev_layer = True
main2[0].deltas = be.iobuf(inshape)
branch2 = Sequential(top_branch())
lbranch2 = branch2.layers
(b1, b2, b3) = inception_bare(i1, [(32,), (32, 32), ('max', 16)])
for bb in (b1, b2, b3, lbranch2):
oshape = inshape
for ll in main2 + bb:
oshape = ll.configure(oshape)
main1_trunk = neon_layer.layers[0].layers[:8]
for ll, lo in zip(main2, main1_trunk):
if ll.has_params:
ll.set_params({'params': {'W': lo.W.get()}})
ll.allocate()
temp_deltas = DeltasTree()
temp_deltas.proc_layer(ll)
temp_deltas.allocate_buffers()
ll.set_deltas(temp_deltas)
for ll, lo in zip(lbranch2, neon_layer.layers[1].layers[1:]):
if ll.has_params:
ll.set_params({'params': {'W': lo.W.get()}})
for bb in (b1, b2, b3, lbranch2):
for ll in bb:
ll.allocate()
temp_deltas = DeltasTree()
temp_deltas.proc_layer(ll)
temp_deltas.allocate_buffers()
ll.set_deltas(temp_deltas)
# Create the combined output buffer
merge_output = be.empty_like(neon_layer.layers[0].layers[9].outputs)
x = inp
for ll in main2:
x = ll.fprop(x)
main2_out = x
start = 0
for bb in (b1, b2, b3):
xb = main2_out
for ll in bb:
xb = ll.fprop(xb)
end = start + xb.shape[0]
merge_output[start:end] = xb
start = end
x = merge_output
top_trunk = Sequential(top1).layers
for ll in top_trunk:
x = ll.fprop(x)
neon_out_ref = x.get()
assert allclose_with_out(neon_out_ref, neon_out[0], rtol=0)
# Now do second branch
neon_out_ref2 = branch2.fprop(main2_out).get()
assert allclose_with_out(neon_out_ref2, neon_out[1])
neon_logger.display("Beginning Back prop")
erra = [np.random.random(d.shape) for d in neon_out]
err = [be.array(d) for d in erra]
neon_layer.layers[0].layers[0].deltas = be.iobuf(inshape)
neon_layer.bprop(err)
bottom_neon_deltas = neon_layer.layers[0].layers[1].deltas.get()
middle_neon_deltas = neon_layer.layers[1].layers[1].deltas.get()
err0 = err[0]
for ll in reversed(top_trunk):
err0 = ll.bprop(err0)
err1 = err[1]
for ll in reversed(lbranch2):
err1 = ll.bprop(err1)
for bb, errb in zip((b1, b2, b3), neon_layer.layers[0].layers[-5].error_views):
for ll in reversed(bb):
errb = ll.bprop(errb)
# Now sum up the deltas at the root of the branch layer and compare
ref_deltas = be.zeros_like(b1[0].deltas)
ref_deltas[:] = alpha2 * lbranch2[0].deltas
ref_deltas[:] = ref_deltas + b3[0].deltas + b2[0].deltas + b1[0].deltas
neon_ref_deltas = ref_deltas.get()
assert allclose_with_out(middle_neon_deltas, neon_ref_deltas, rtol=0)
x = ref_deltas
main2[0].deltas = be.iobuf(inshape)
for ll in reversed(main2):
x = ll.bprop(x)
bottom_neon_ref_deltas = main2[1].deltas.get()
assert allclose_with_out(bottom_neon_deltas, bottom_neon_ref_deltas, rtol=0)
def mergesum_test_config(be, modfunc, use_stride=1):
l1 = Conv(**conv_params(3, 16))
neon_layer = modfunc(16, use_stride)
inshape = (16, 32, 32)
insize = np.prod(inshape)
inpa = np.random.random((insize, batch_size))
neon_seq = Sequential([l1] + neon_layer)
neon_seq.configure(inshape)
inp = be.array(inpa)
neon_seq.allocate()
# print neon_layer.nested_str()
# neon_layer.layers[0].prev_layer = True
neon_seq.allocate_deltas()
neon_out = neon_seq.fprop(inp).get()
# Now make the reference pathways:
p1, p2 = module_factory_copy(neon_layer, modfunc, 16, use_stride)
l11 = Conv(**conv_params(3, 16))
l12 = Conv(**conv_params(3, 16))
for ll in (l11, l12):
for lcopy, lref in zip(ll, l1):
if lcopy.has_params:
lcopy.set_params(lref.get_params_serialize())
path1 = Sequential([l11] + p1)
path2 = Sequential([l12] + p2)
for ll in (path1, path2):
ll.configure(inshape)
ll.allocate()
ll.allocate_deltas()
o1 = path1.fprop(inp)
o2 = path2.fprop(inp)
neon_out_ref = be.empty_like(o1)
neon_out_ref[:] = be.maximum(o1 + o2, 0)
# need to have bsum false for this test to be valid
assert allclose_with_out(neon_out_ref.get(), neon_out, rtol=0)
print "Fprop matching"
print "Beginning Back prop"
erra = np.random.random(neon_out.shape)
err = be.array(erra)
ebr = neon_seq.layers[-1].bprop(err)
ebr = neon_seq.layers[-2].bprop(ebr)
trunk_neon = ebr.get()
err = be.array(erra)
err[:] = be.greater(neon_out_ref, 0) * err
pstart = len(l1)
eb1 = err
for l in reversed(path1.layers[pstart:]):
eb1 = l.bprop(eb1)
eb2 = err
for l in reversed(path2.layers[pstart:]):
eb2 = l.bprop(eb2)
err_ref = be.empty_like(eb1)
err_ref[:] = eb1 + eb2
assert allclose_with_out(err_ref.get(), trunk_neon, rtol=0)
def create_model(dis_model='dc',
gen_model='dc',
cost_type='wasserstein',
noise_type='normal',
im_size=64,
n_chan=3,
n_noise=100,
n_gen_ftr=64,
n_dis_ftr=64,
depth=4,
n_extra_layers=0,
batch_norm=True,
gen_squash=None,
dis_squash=None,
dis_iters=5,
wgan_param_clamp=None,
wgan_train_sched=False):
"""
Create a GAN model and associated GAN cost function for image generation
Arguments:
dis_model (str): Discriminator type, can be 'mlp' for a simple MLP or
'dc' for a DC-GAN style model. (defaults to 'dc')
gen_model (str): Generator type, can be 'mlp' for a simple MLP or
'dc' for a DC-GAN style model. (defaults to 'dc')
cost_type (str): Cost type, can be 'original', 'modified' following
Goodfellow2014 or 'wasserstein' following Arjovsky2017
(defaults to 'wasserstein')
noise_type (str): Noise distribution, can be 'uniform or' 'normal'
(defaults to 'normal')
im_size (int): Image size (defaults to 64)
n_chan (int): Number of image channels (defaults to 3)
n_noise (int): Number of noise dimensions (defaults to 100)
n_gen_ftr (int): Number of generator feature maps (defaults to 64)
n_dis_ftr (int): Number of discriminator feature maps (defaults to 64)
depth (int): Depth of layers in case of MLP (defaults to 4)
n_extra_layers (int): Number of extra conv layers in case of DC (defaults to 0)
batch_norm (bool): Enable batch normalization (defaults to True)
gen_squash (str or None): Squashing function at the end of generator (defaults to None)
dis_squash (str or None): Squashing function at the end of discriminator (defaults to None)
dis_iters (int): Number of critics for discriminator (defaults to 5)
wgan_param_clamp (float or None): In case of WGAN weight clamp value, None for others
wgan_train_sched (bool): Enable training schedule of number of critics (defaults to False)
"""
assert dis_model in ['mlp', 'dc'], \
"Unsupported model type for discriminator net, supported: 'mlp' and 'dc'"
assert gen_model in ['mlp', 'dc'], \
"Unsupported model type for generator net, supported: 'mlp' and 'dc'"
assert cost_type in ['original', 'modified', 'wasserstein'], \
"Unsupported GAN cost function type, supported: 'original', 'modified' and 'wasserstein'"
# types of final squashing functions
squash_func = dict(nosquash=Identity(), sym=Tanh(), asym=Logistic())
if cost_type == 'wasserstein':
if gen_model == 'mlp':
gen_squash = gen_squash or 'nosquash'
elif gen_model == 'dc':
gen_squash = gen_squash or 'sym'
dis_squash = dis_squash or 'nosquash'
else: # for all GAN costs other than Wasserstein
gen_squash = gen_squash or 'sym'
dis_squash = dis_squash or 'asym'
assert gen_squash in ['nosquash', 'sym', 'asym'], \
"Unsupported final squashing function for generator," \
" supported: 'nosquash', 'sym' and 'asym'"
assert dis_squash in ['nosquash', 'sym', 'asym'], \
"Unsupported final squashing function for discriminator," \
" supported: 'nosquash', 'sym' and 'asym'"
gfa = squash_func[gen_squash]
dfa = squash_func[dis_squash]
# create model layers
if gen_model == 'mlp':
gen = create_mlp_generator(im_size,
n_chan,
n_gen_ftr,
depth,
batch_norm=False,
finact=gfa)
noise_dim = (n_noise, )
elif gen_model == 'dc':
gen = create_dc_generator(im_size,
n_chan,
n_noise,
n_gen_ftr,
n_extra_layers,
batch_norm,
finact=gfa)
noise_dim = (n_noise, 1, 1)
if dis_model == 'mlp':
dis = create_mlp_discriminator(im_size,
n_dis_ftr,
depth,
batch_norm=False,
finact=dfa)
elif dis_model == 'dc':
dis = create_dc_discriminator(im_size,
n_chan,
n_dis_ftr,
n_extra_layers,
batch_norm,
finact=dfa)
layers = GenerativeAdversarial(generator=Sequential(gen, name="Generator"),
discriminator=Sequential(
dis, name="Discriminator"))
return GAN(layers=layers, noise_dim=noise_dim, noise_type=noise_type, k=dis_iters,
wgan_param_clamp=wgan_param_clamp, wgan_train_sched=wgan_train_sched), \
GeneralizedGANCost(costfunc=GANCost(func=cost_type))
def test_branch_model_fork():
from neon.layers import BranchNode, Tree
NervanaObject.be = gen_backend("gpu", batch_size=64)
be = NervanaObject.be
bnode = BranchNode()
i1 = inception([(32,), (32, 32), ('max', 16)])
top1 = top_branch()
top2 = top_branch()
p1 = Sequential(main_branch() + [bnode, i1] + top1)
p2 = [bnode] + top2
alpha2 = 0.3
neon_layer = Tree([p1, p2], alphas=[1.0, alpha2])
inshape = (3, 224, 224)
insize = np.prod(inshape)
inpa = np.random.random((insize, batch_size))
neon_layer.configure(inshape)
inp = neon_layer.be.array(inpa)
neon_layer.allocate()
print neon_layer.nested_str()
neon_layer.layers[0].layers[0].prev_layer = True
neon_layer.allocate_deltas()
neon_layer.layers[0].layers[0].set_deltas([be.iobuf(inshape)])
neon_out_dev = neon_layer.fprop(inp)
neon_out = [d.get() for d in neon_out_dev]
# Now make the reference pathways:
main_trunk2 = Sequential(main_branch())
main_trunk2.configure(inshape)
main2 = main_trunk2.layers
main2[0].prev_layer = True
main2[0].set_deltas([be.iobuf(inshape)])
branch2 = Sequential(top_branch())
lbranch2 = branch2.layers
(b1, b2, b3) = inception_bare(i1, [(32,), (32, 32), ('max', 16)])
for bb in (b1, b2, b3, lbranch2):
oshape = inshape
for ll in main2 + bb:
oshape = ll.configure(oshape)
main1_trunk = neon_layer.layers[0].layers[:8]
for ll, lo in zip(main2, main1_trunk):
if ll.has_params:
ll.set_params(lo.W.get())
ll.allocate()
ll.set_deltas([be.iobuf(ll.in_shape)])
for ll, lo in zip(lbranch2, neon_layer.layers[1].layers[1:]):
if ll.has_params:
ll.set_params(lo.W.get())
for bb in (b1, b2, b3, lbranch2):
for ll in bb:
ll.allocate()
ll.set_deltas([be.iobuf(ll.in_shape)])
# Create the combined output buffer
merge_output = be.empty_like(neon_layer.layers[0].layers[9].outputs)
x = inp
for ll in main2:
x = ll.fprop(x)
main2_out = x
start = 0
for bb in (b1, b2, b3):
xb = main2_out
for ll in bb:
xb = ll.fprop(xb)
end = start + xb.shape[0]
merge_output[start:end] = xb
start = end
x = merge_output
top_trunk = Sequential(top1).layers
for ll in top_trunk:
x = ll.fprop(x)
neon_out_ref = x.get()
difference = neon_out_ref - neon_out[0]
assert np.max(np.abs(difference)) < 1e-7
print np.max(np.abs(difference))
# Now do second branch
neon_out_ref2 = branch2.fprop(main2_out).get()
difference = neon_out_ref2 - neon_out[1]
assert np.max(np.abs(difference)) < 1e-7
print np.max(np.abs(difference))
print "Beginning Back prop"
erra = [np.random.random(d.shape) for d in neon_out]
err = [be.array(d) for d in erra]
neon_layer.layers[0].layers[0].deltas = be.iobuf(inshape)
neon_layer.bprop(err)
bottom_neon_deltas = neon_layer.layers[0].layers[1].deltas.get()
middle_neon_deltas = neon_layer.layers[1].layers[1].deltas.get()
err0 = err[0]
for ll in reversed(top_trunk):
err0 = ll.bprop(err0)
err1 = err[1]
for ll in reversed(lbranch2):
err1 = ll.bprop(err1)
for bb, errb in zip((b1, b2, b3), neon_layer.layers[0].layers[-5].error_views):
for ll in reversed(bb):
errb = ll.bprop(errb)
# Now sum up the deltas at the root of the branch layer and compare
ref_deltas = be.zeros_like(b1[0].deltas)
ref_deltas[:] = b1[0].deltas + b2[0].deltas + b3[0].deltas + alpha2 * lbranch2[0].deltas
neon_ref_deltas = ref_deltas.get()
difference = middle_neon_deltas - neon_ref_deltas
print np.max(np.abs(difference))
assert np.max(np.abs(difference)) < 1e-8
x = ref_deltas
main2[0].deltas = be.iobuf(inshape)
for ll in reversed(main2):
x = ll.bprop(x)
bottom_neon_ref_deltas = main2[1].deltas.get()
difference = bottom_neon_deltas - bottom_neon_ref_deltas
print np.max(np.abs(difference))
assert np.max(np.abs(difference)) < 1e-8
class Model(NervanaObject):
"""
Basic model class which stores a list of layers describing the model. Can train the layer
weights on a dataset, evaluate on a test set and serialize the mode.
Additional functionality can be added to fit through callback functions.
Arguments:
layers: layer container, or a list of layers (that will be containerized),
or a serialized model description
dataset (iterator): Data set (ignored, will be removed)
weights_only (bool): set to True if you do not want to recreate layers
and states during deserialization from a serialized model
description. Defaults to False.
name (str): Model name. Defaults to "model"
optimizer (Optimizer): Optimizer object which defines the learning rule
for updating model parameters (ie DescentMomentum, AdaDelta)
"""
def __init__(self, layers, dataset=None, weights_only=False, name="model", optimizer=None):
super(Model, self).__init__(name)
self.optimizer = optimizer
self.params = None # should be able to remove
self.states = None # should be able to remove
self.epoch_index = 0
self.finished = False
self.initialized = False
self.cost = None
self.nbatches = 0
self.ndata = 0
if dataset is not None:
logger.warning('dataset is a deprecated argument and will be ignored')
if type(layers) in (ModelDescription, dict):
# load up the model from a serialized file (dataset could be None here)
self.deserialize(layers, load_states=(not weights_only))
elif type(layers) is str:
self.load_params(layers, load_states=(not weights_only))
else:
# Wrap the list of layers in a Sequential container if a raw list of layers
if type(layers) in (Sequential, Tree, SingleOutputTree):
self.layers = layers
else:
self.layers = Sequential(layers)
self.layers.propagate_parallelism("Data")
@property
def layers_to_optimize(self):
return self.layers.layers_to_optimize
def set_shortcut(self):
# infer whether bprop shortcut can be used on final activation
# self.cost should be set to run this otherwise do nothing
lastlayer = self.layers[-1]
try:
if self.cost.costfunc.__class__ is CrossEntropyBinary:
if (lastlayer.__class__ is Activation and
lastlayer.transform.__class__ is Logistic):
lastlayer.transform.set_shortcut(True)
except:
# if any attributes are not set or any other exception
# is thrown leave transform.shortcut as is (do nothing)
pass
def initialize(self, dataset, cost=None):
if self.initialized:
return
# Propagate shapes through the layers to configure
prev_input = dataset
prev_input = self.layers.configure(prev_input)
if cost is not None:
cost.initialize(prev_input)
self.cost = cost
# Now allocate space
self.layers.allocate()
self.layers.allocate_deltas()
self.initialized = True
def __str__(self):
"""
String representation of model's layers
"""
config_string = "Network Layers:\n" + self.layers.nested_str()
return config_string
def fit(self, dataset, cost, optimizer, num_epochs, callbacks):
"""
Trains the model parameters on a dataset by minimizing the cost function through
gradient descent and updates the layer weights according to a learning rule
defined in optimizer.
Arguments:
dataset (iterator): An iterable of minibatches where each
element is a (x, y) tuple where x is the input data and y are the labels.
x is of dimension (feature_size, batch_size)
y is of dimension (label_size, batch_size)
Length of the iterator is num_batches which is num_data / batch_size
cost (Cost): Defines the function which the model is minimizing based
on the output of the last layer and the input labels
optimizer (Optimizer): Defines the learning rule for updating the model parameters
num_epochs: Number of times to iterate over the dataset.
callbacks (Callbacks): Defines callbacks to run at the end of each mini-batch / epoch.
"""
self.nbatches = dataset.nbatches
self.ndata = dataset.ndata
# self.set_shortcut() # infer if bprop shortcut can be used
self.total_cost = self.be.empty((1, 1), dtype=np.float32)
self.optimizer = optimizer
self.initialize(dataset, cost)
callbacks.on_train_begin(num_epochs)
while self.epoch_index < num_epochs and not self.finished:
self.nbatches = dataset.nbatches
callbacks.on_epoch_begin(self.epoch_index)
self._epoch_fit(dataset, callbacks)
callbacks.on_epoch_end(self.epoch_index)
self.epoch_index += 1
callbacks.on_train_end()
def _epoch_fit(self, dataset, callbacks):
"""
Helper function for fit which performs training on a dataset for one epoch.
Arguments:
dataset (iterable): Dataset iterator to perform fit on
"""
epoch = self.epoch_index
self.total_cost[:] = 0
# iterate through minibatches of the dataset
for mb_idx, (x, t) in enumerate(dataset):
callbacks.on_minibatch_begin(epoch, mb_idx)
self.be.begin(Block.minibatch, mb_idx)
x = self.fprop(x)
self.total_cost[:] = self.total_cost + self.cost.get_cost(x, t)
# deltas back propagate through layers
# for every layer in reverse except the 0th one
delta = self.cost.get_errors(x, t)
self.bprop(delta)
self.optimizer.optimize(self.layers_to_optimize, epoch=epoch)
self.be.end(Block.minibatch, mb_idx)
callbacks.on_minibatch_end(epoch, mb_idx)
# now we divide total cost by the number of batches,
# so it was never total cost, but sum of averages
# across all the minibatches we trained on
self.total_cost[:] = self.total_cost / dataset.nbatches
def fprop(self, x, inference=False):
"""
Forward propagates a minibatch x through the model.
Arguments:
x (Tensor): Input minibatch data
inference (bool): Flag for performing training or inference
Only affects batch norm and dropout layers.
Returns:
Tensor: the output of the final layer in the model
"""
return self.layers.fprop(x, inference)
def bprop(self, delta):
"""
Back propagates the error of a minibatch through the model.
Arguments:
delta (Tensor): Derivative of cost with respect to the last layer's output
"""
return self.layers.bprop(delta)
def eval(self, dataset, metric):
"""
Evaluates a model on a dataset according to an input metric.
Arguments:
datasets (iterable): dataset to evaluate on.
metric (Cost): what function to evaluate dataset on.
"""
self.initialize(dataset)
running_error = np.zeros((len(metric.metric_names)), dtype=np.float32)
nprocessed = 0
dataset.reset()
for x, t in dataset:
x = self.fprop(x, inference=True)
# This logic is for handling partial batch sizes at the end of the dataset
nsteps = x.shape[1] / self.be.bsz if not isinstance(x, list) else \
x[0].shape[1] / self.be.bsz
bsz = min(dataset.ndata - nprocessed, self.be.bsz)
running_error += metric(x, t, calcrange=slice(0, nsteps * bsz)) * nsteps * bsz
nprocessed += bsz * nsteps
running_error /= nprocessed
return running_error
def get_outputs(self, dataset):
"""
Get the activation outputs of the final model layer for the dataset
Arguments:
dataset (iterable): Dataset iterator to perform fit on
Returns:
Host numpy array: the output of the final layer for the entire Dataset
"""
self.initialize(dataset)
dataset.reset() # Move "pointer" back to beginning of dataset
n = dataset.nbatches
x = self.layers.layers[-1].outputs
assert not isinstance(x, list), "Can not get_outputs with Branch terminal"
Ypred = None
for idx, (x, t) in enumerate(dataset):
x = self.fprop(x, inference=True)
if Ypred is None:
(dim0, dim1) = x.shape
Ypred = np.empty((n * dim1, dim0), dtype=x.dtype)
nsteps = dim1 / self.be.bsz
cur_batch = slice(idx * dim1, (idx + 1) * dim1)
Ypred[cur_batch] = x.get().T
# Handle the recurrent case.
if nsteps != 1:
b, s = (self.be.bsz, nsteps)
Ypred = Ypred.reshape((n, s, b, -1)).transpose(0, 2, 1, 3).copy().reshape(n*b, s, -1)
return Ypred[:dataset.ndata]
def get_description(self, get_weights=False, keep_states=False):
"""
Gets a description of the model required to reconstruct the model with
no weights like from a yaml file.
Returns:
dict: Description of each component of the model.
"""
pdict = dict()
pdict['neon_version'] = __neon_version__
compat_mode = self.be.compat_mode if self.be.compat_mode is not None else 'neon'
pdict['backend'] = {'type': self.be.__class__.__name__,
'compat_mode': compat_mode,
'rng_seed': self.be.rng_seed,
'rng_state': self.be.rng_get_state()}
if self.cost:
pdict['cost'] = self.cost.get_description()
if self.optimizer:
pdict['optimizer'] = self.optimizer.get_description()
pdict['model'] = self.layers.get_description(get_weights=get_weights,
keep_states=keep_states)
return pdict
def save_params(self, param_path, keep_states=True):
"""
Serializes and saves model parameters to the path specified.
Arguments:
param_path (str): File to write serialized parameter dict to.
keep_states (bool): Whether to save optimizer states too.
Defaults to True.
"""
self.serialize(keep_states=keep_states, fn=param_path)
def load_params(self, param_path, load_states=True):
"""
Loads the model parameters (per layer weights, epochs run, optimizer
states) saved in param_path from serialize().
Arguments:
param_path (str): File containing serialized python dict with layer
weights and states.
load_states (bool): if False, then only the weights will be loaded
into a model in which the layers have already been
created, otherwise will (re)create the layers from
the serialized parameters and set the learning
states as well
"""
self.deserialize(load_obj(param_path), load_states=load_states)
logger.info('Model weights loaded from %s', param_path)
def load_weights(self, weight_path):
"""
.. deprecated:: 1.1.4
Use :func:`load_params` instead
"""
logger.warning('Calling deprecated load_weights function. Use '
'load_params instead')
self.load_params(weight_path)
def deserialize(self, model_dict, data=None, load_states=True):
"""
Loads per layer (weights, states) and other model parameters from the
dictionary passed.
Arguments:
model_dict (dict): dictionary describing the model including layers,
cost, optimizers, backend settings, etc.
generated by the serialize function
data (iterator): Data set (ignored, will be removed)
load_states (bool): if False, then only the weights will be loaded
into a model in which the layers have already been
created, otherwise will (re)create the layers from
the serialized parameters and set the learning
states as well
"""
if data is not None:
logger.warning('data is a deprecated argument and will be ignored')
if 'epoch_index' in model_dict:
self.epoch_index = model_dict['epoch_index']
if 'model' not in model_dict:
logger.error('Using old model serialization format. '
'Serialized the model into new format')
param_layers = [l for l in self.layers_to_optimize]
param_dict_list = model_dict['layer_params_states']
for l, ps in zip(param_layers, param_dict_list):
l.set_params(ps)
if 'states' in ps and load_states:
l.set_states(ps)
return
if 'backend' in model_dict:
if 'compat_mode' in model_dict['backend']:
self.be.compat_mode = model_dict['backend']['compat_mode']
else:
model_dict['backend'] = {}
typ = model_dict['model']['type']
main_container = load_class(typ)
if not hasattr(self, 'layers'):
self.layers = main_container.gen_class(model_dict['model']['config'])
self.layers.load_weights(model_dict['model'], load_states)
if load_states and 'rng_state' in model_dict['backend']:
try:
self.be.rng_set_state(model_dict['backend']['rng_state'])
except ValueError as e:
# could come about when switching backend types (ex GPU to CPU)
logger.warning("Problems restoring existing RNG state: %s", str(e))
# serialize tells how to write out the parameters we've learned so
# far and associate them with layers. it can ignore layers with no
# learned parameters. the model stores states to pass to the
# optimizers. if we're saving the model out for inference, we
# don't need to remember states.
def serialize(self, fn=None, keep_states=True):
"""
Creates a dictionary storing the layer parameters and epochs complete.
Arguments:
fn (str): file to save pkl formatted model dictionary
keep_states (bool): Whether to save optimizer states.
Returns:
dict: Model data including layer parameters and epochs complete.
"""
# get the model dict with the weights
pdict = self.get_description(get_weights=True, keep_states=keep_states)
pdict['epoch_index'] = self.epoch_index + 1
if self.initialized:
pdict['train_input_shape'] = self.layers.in_shape
if fn is not None:
save_obj(pdict, fn)
return
return pdict
def set_batch_size(self, N):
"""
Set the actual minibatch size, so eventhough the buffers are allocated considering
excessive padding, the processing for some layers may be shortened.
Currently most of the neon layers don't use that to control the processing. The
interface is here only for when someone wants to set that information and experiment.
"""
return self.layers.set_batch_size(N)
def set_seq_len(self, S):
"""
Set the actual minibatch sequence length, so eventhough the buffers are allocated
considering excessive padding, the processing for some layers may be shortened.
Currently most of the neon layers don't use that to control the processing. The
interface is here only for when someone wants to set that information and experiment.
"""
return self.layers.set_seq_len(S)
def benchmark(self, dataset, inference=False, cost=None, optimizer=None,
niterations=20, nskip=2):
"""
Measure runtime for computing fprop and bprop seperately, as well as
full minibatch run times. For inference case, only the fprop
Arguments:
dataset (iterable): Dataset iterator to perform fit on
cost (Cost): Defines the function which the model is minimizing based
on the output of the last layer and the input labels
niterations (optional, int): Number of minibatches to average over
nskip (optional, int): number of iterations at the beginning to skip
when calculating the runtime statistics
Returns:
dictionary with fprop, bprop run times
"""
# initialize model
if inference is False:
assert cost is not None and optimizer is not None, "Need cost and optimizer to \
benchmark bprop and update"
self.cost = cost
self.initialize(dataset, cost)
self.optimizer = optimizer
self.total_cost = self.be.empty((1, 1))
self.total_cost[:] = 0
# iterate through minibatches of the dataset
times = OrderedDict()
time_keys = ['fprop'] if inference else ['fprop', 'bprop', 'iteration']
for ky in time_keys:
times[ky] = np.full(niterations + nskip, -1.0)
count = 0
fprop_start = self.be.init_mark()
fprop_end = self.be.init_mark()
bprop_end = self.be.init_mark()
while count < niterations + nskip:
dataset.reset()
for mb_idx, (x, t) in enumerate(dataset):
self.be.record_mark(fprop_start) # mark start of fprop
x = self.fprop(x)
if inference is False:
self.total_cost[:] = self.total_cost + self.cost.get_cost(x, t)
self.be.record_mark(fprop_end) # mark end of fprop and start of bprop
if inference is False:
delta = self.cost.get_errors(x, t)
self.bprop(delta)
self.optimizer.optimize(self.layers_to_optimize, epoch=0)
self.be.record_mark(bprop_end) # mark end of bprop
self.be.synchronize_mark(bprop_end)
else:
self.be.synchronize_mark(fprop_end)
times['fprop'][count] = self.be.get_time(fprop_start, fprop_end)
if inference is False:
times['bprop'][count] = self.be.get_time(fprop_end, bprop_end)
times['iteration'][count] = times['fprop'][count] + times['bprop'][count]
count += 1
if count >= niterations + nskip:
break
# print results
header = ('Func', 'Mean', 'Median', 'Min', 'Max', 'Units')
stats = tuple(stat.lower() for stat in header[1:-1])
fmt_titles = '| {:^11} '*len(header) + '|'
fmt_nums = '| {func:<11} ' + '| {%s:<10.5g} '*len(stats) % (stats) + '| {units:^11} |'
head_str = fmt_titles.format(*header)
sep = '-'*len(head_str)
head_str = sep + '\n' + head_str + '\n' + sep
print(head_str)
out_stats = {}
for step in times:
timesu = np.array(times[step][nskip:]) # in ms
out_stats[step] = {}
for stat in stats:
out_stats[step][stat] = getattr(np, stat)(timesu)
print(fmt_nums.format(units='msec', func=step, **out_stats[step]))
print(sep)
return out_stats
def mergesum_test_config(be, modfunc, use_stride=1):
l1 = Conv(**conv_params(3, 16))
neon_layer = modfunc(16, use_stride)
inshape = (16, 32, 32)
insize = np.prod(inshape)
inpa = np.random.random((insize, batch_size))
neon_seq = Sequential([l1] + neon_layer)
neon_seq.configure(inshape)
inp = be.array(inpa)
neon_seq.allocate()
# neon_layer.layers[0].prev_layer = True
neon_seq.allocate_deltas()
neon_out = neon_seq.fprop(inp).get()
# Now make the reference pathways:
p1, p2 = module_factory_copy(neon_layer, modfunc, 16, use_stride)
l11 = Conv(**conv_params(3, 16))
l12 = Conv(**conv_params(3, 16))
for ll in (l11, l12):
for lcopy, lref in zip(ll, l1):
if lcopy.has_params:
lcopy.set_params(lref.get_params_serialize())
path1 = Sequential([l11] + p1)
path2 = Sequential([l12] + p2)
for ll in (path1, path2):
ll.configure(inshape)
ll.allocate()
ll.allocate_deltas()
o1 = path1.fprop(inp)
o2 = path2.fprop(inp)
# convert mkl buffer to cpu for following cpu execution
be.convert_data(o1, False)
be.convert_data(o2, False)
neon_out_ref = be.empty_like(o1)
neon_out_ref[:] = be.maximum(o1 + o2, 0)
# need to have bsum false for this test to be valid
assert allclose_with_out(neon_out_ref.get(), neon_out, rtol=0)
erra = np.random.random(neon_out.shape)
err = be.array(erra)
ebr = neon_seq.layers[-1].bprop(err)
ebr = neon_seq.layers[-2].bprop(ebr)
trunk_neon = ebr.get()
err = be.array(erra)
err[:] = be.greater(neon_out_ref, 0) * err
pstart = len(l1)
eb1 = err
for l in reversed(path1.layers[pstart:]):
eb1 = l.bprop(eb1)
eb2 = err
for l in reversed(path2.layers[pstart:]):
eb2 = l.bprop(eb2)
be.convert_data(eb1, False)
be.convert_data(eb2, False)
err_ref = be.empty_like(eb1)
err_ref[:] = eb1 + eb2
assert allclose_with_out(err_ref.get(), trunk_neon, rtol=0)
def test_branch_model_fork(backend_gpu):
from neon.layers import BranchNode, Tree
np.random.seed(0)
be = NervanaObject.be
if be.gpu_memory_size < 6.1 * 1024 * 1024 * 1024:
pytest.skip(msg='Test requires more than 6.1GB')
be.bsz = 64
bnode = BranchNode()
i1 = inception([(32,), (32, 32), ('max', 16)])
top1 = top_branch()
top2 = top_branch()
p1 = Sequential(main_branch() + [bnode, i1] + top1)
p2 = [bnode] + top2
alpha2 = 0.3
neon_layer = Tree([p1, p2], alphas=[1.0, alpha2])
inshape = (4, 224, 224)
insize = np.prod(inshape)
inpa = np.random.random((insize, batch_size))
neon_layer.configure(inshape)
inp = neon_layer.be.array(inpa)
neon_layer.allocate()
neon_layer.layers[0].layers[0].prev_layer = True
neon_layer.allocate_deltas()
neon_out_dev = neon_layer.fprop(inp)
neon_out = [d.get() for d in neon_out_dev]
# Now make the reference pathways:
main_trunk2 = Sequential(main_branch())
main_trunk2.configure(inshape)
main2 = main_trunk2.layers
main2[0].prev_layer = True
main2[0].deltas = be.iobuf(inshape)
branch2 = Sequential(top_branch())
lbranch2 = branch2.layers
(b1, b2, b3) = inception_bare(i1, [(32,), (32, 32), ('max', 16)])
for bb in (b1, b2, b3, lbranch2):
oshape = inshape
for ll in main2 + bb:
oshape = ll.configure(oshape)
main1_trunk = neon_layer.layers[0].layers[:8]
for ll, lo in zip(main2, main1_trunk):
if ll.has_params:
ll.set_params({'params': {'W': lo.W.get()}})
ll.allocate()
temp_deltas = DeltasTree()
temp_deltas.proc_layer(ll)
temp_deltas.allocate_buffers()
ll.set_deltas(temp_deltas)
for ll, lo in zip(lbranch2, neon_layer.layers[1].layers[1:]):
if ll.has_params:
ll.set_params({'params': {'W': lo.W.get()}})
for bb in (b1, b2, b3, lbranch2):
for ll in bb:
ll.allocate()
temp_deltas = DeltasTree()
temp_deltas.proc_layer(ll)
temp_deltas.allocate_buffers()
ll.set_deltas(temp_deltas)
# Create the combined output buffer
merge_output = be.empty_like(neon_layer.layers[0].layers[9].outputs)
x = inp
for ll in main2:
x = ll.fprop(x)
main2_out = x
start = 0
for bb in (b1, b2, b3):
xb = main2_out
for ll in bb:
xb = ll.fprop(xb)
end = start + xb.shape[0]
merge_output[start:end] = xb
start = end
x = merge_output
top_trunk = Sequential(top1).layers
for ll in top_trunk:
x = ll.fprop(x)
neon_out_ref = x.get()
assert allclose_with_out(neon_out_ref, neon_out[0], rtol=0)
# Now do second branch
neon_out_ref2 = branch2.fprop(main2_out).get()
assert allclose_with_out(neon_out_ref2, neon_out[1])
neon_logger.display("Beginning Back prop")
erra = [np.random.random(d.shape) for d in neon_out]
err = [be.array(d) for d in erra]
neon_layer.layers[0].layers[0].deltas = be.iobuf(inshape)
neon_layer.bprop(err)
bottom_neon_deltas = neon_layer.layers[0].layers[1].deltas.get()
middle_neon_deltas = neon_layer.layers[1].layers[1].deltas.get()
err0 = err[0]
for ll in reversed(top_trunk):
err0 = ll.bprop(err0)
err1 = err[1]
for ll in reversed(lbranch2):
err1 = ll.bprop(err1)
for bb, errb in zip((b1, b2, b3), neon_layer.layers[0].layers[-5].error_views):
for ll in reversed(bb):
errb = ll.bprop(errb)
# Now sum up the deltas at the root of the branch layer and compare
ref_deltas = be.zeros_like(b1[0].deltas)
ref_deltas[:] = alpha2 * lbranch2[0].deltas
ref_deltas[:] = ref_deltas + b3[0].deltas + b2[0].deltas + b1[0].deltas
neon_ref_deltas = ref_deltas.get()
assert allclose_with_out(middle_neon_deltas, neon_ref_deltas, rtol=0)
x = ref_deltas
main2[0].deltas = be.iobuf(inshape)
for ll in reversed(main2):
x = ll.bprop(x)
bottom_neon_ref_deltas = main2[1].deltas.get()
assert allclose_with_out(bottom_neon_deltas, bottom_neon_ref_deltas, rtol=0)
conv4 = dict(init=init,
batch_norm=True,
activation=lrelu,
dilation=dict(dil_h=2, dil_w=2, dil_d=2))
conv5 = dict(init=init,
batch_norm=True,
activation=lrelu,
padding=dict(pad_h=2, pad_w=2, pad_d=0),
dilation=dict(dil_h=2, dil_w=2, dil_d=3))
conv6 = dict(init=init,
batch_norm=False,
activation=lrelu,
padding=dict(pad_h=1, pad_w=0, pad_d=3))
G_layers = [
Linear(64 * 7 * 7, init=init), # what's about the input volume
Reshape((7, 7, 8, 8)),
Conv((6, 6, 8, 64), **conv4),
Conv((6, 5, 8, 6), **conv5),
Conv((3, 3, 8, 6), **conv6),
Conv((2, 2, 2, 1), init=init, batch_norm=False, activation=relu)
]
# what's about Embedding
layers = GenerativeAdversarial(generator=Sequential(G_layers,
name="Generator"),
discriminator=Sequential(D_layers,
name="Discriminator"))
# setup cost function as CrossEntropy
cost = GeneralizedGANCost(costfunc=GANCost(func="modified"))
def test_branch_model(backend_gpu):
be = NervanaObject.be
trunk = [{
'layer': Conv,
'config': dict(fshape=(5, 5, 16), **common)
}, {
'layer': Pooling,
'config': dict(op='max', **pool2s1p1)
}]
branch1 = [{
'layer': Conv,
'config': dict(fshape=(5, 5, 32), **common)
}, {
'layer': Pooling,
'config': dict(op='max', **pool2s1p1)
}, {
'layer': Affine,
'config': dict(nout=200, **common)
}, {
'layer': Affine,
'config': dict(nout=10, init=init1, activation=relu)
}]
branch2 = [{
'layer': Conv,
'config': dict(fshape=(3, 3, 32), **common)
}, {
'layer': Pooling,
'config': dict(op='max', **pool2s1p1)
}, {
'layer': Affine,
'config': dict(nout=256, **common)
}, {
'layer': Affine,
'config': dict(nout=10, init=init1, activation=relu)
}]
alphas = [1, 1]
neon_layer, t, b1, b2 = make_tree(trunk, branch1, branch2, alphas)
inshape = (16, 32, 32)
insize = np.prod(inshape)
# Let's force bprop deltas computation for
inpa = np.random.random((insize, be.bsz))
inp = be.array(inpa)
neon_layer.configure(inshape)
neon_layer.allocate()
neon_layer.allocate_deltas()
neon_out = [i.get() for i in neon_layer.fprop(inp)]
ref_layers = [Sequential(t), Sequential(b1), Sequential(b2)]
ref_layers[0].configure(inshape)
ref_layers[1].configure(ref_layers[0].out_shape)
ref_layers[2].configure(ref_layers[0].out_shape)
[r.allocate() for r in ref_layers]
[r.allocate_deltas() for r in ref_layers]
# Now copy the weights
ref_all_layers = ref_layers[0].layers + ref_layers[1].layers + ref_layers[
2].layers
ref_weight_layers = [l for l in ref_all_layers if l.has_params]
neon_weight_layers = neon_layer.layers_to_optimize
for rl, nl in zip(ref_weight_layers, neon_weight_layers):
rl.set_params({'params': {'W': nl.W.get()}})
# Forward prop
inp_middle = ref_layers[0].fprop(inp)
ref_out = [r.fprop(inp_middle).get() for r in ref_layers[1:]]
for h, r in zip(neon_out, ref_out):
difference = np.max(np.abs(h - r))
assert (difference < 1e-9)
# Back prop
erra = [np.random.random(ll.shape) for ll in neon_out]
err = [be.array(e) for e in erra]
input_layer = neon_layer.layers[0].layers[
0] # reference the trunk, then the root
input_layer.prev_layer = True
input_layer.set_deltas([be.iobuf(inshape)])
neon_layer.bprop(err)
errp = input_layer.deltas.get()
for i, r in enumerate(ref_layers):
r.layers[0].prev_layer = True
_inshape = inshape if i == 0 else ref_layers[0].out_shape
r.layers[0].set_deltas([be.iobuf(_inshape)])
joined_err = be.iobuf(ref_layers[0].out_shape)
branch_errs = [
r.bprop(e, a)
for r, e, a in reversed(list(zip(ref_layers[1:], err, alphas)))
]
joined_err[:] = branch_errs[0] + branch_errs[1]
err_ref = ref_layers[0].bprop(joined_err).get()
difference = np.max(np.abs(err_ref - errp))
neon_logger.display("Max difference: {}".format(difference))
assert (difference < 1e-9)
