def define_recurrent_layers(out_axes=None,
celltype='RNN',
recurrent_units=[32],
init=GlorotInit(),
return_sequence=True):
layers = []
for e, i in enumerate(recurrent_units):
layer_return_sequence = e < len(recurrent_units) - 1 or return_sequence
if celltype == 'RNN':
layers.append(
Recurrent(nout=i,
init=init,
backward=False,
activation=Tanh(),
return_sequence=layer_return_sequence))
elif celltype == 'LSTM':
layers.append(
LSTM(nout=i,
init=init,
backward=False,
activation=Tanh(),
gate_activation=Logistic(),
return_sequence=layer_return_sequence))
if out_axes is not None:
affine_layer = Affine(weight_init=init,
bias_init=init,
activation=Identity(),
axes=out_axes)
layers.append(affine_layer)
return layers
ax.Y.length = time_steps
# create iterator and placeholders for training data
train_set = TSPSequentialArrayIterator(data_arrays=tsp_data['train'],
nfeatures=num_features,
batch_size=args.batch_size,
time_steps=time_steps,
total_iterations=args.num_iterations)
inputs = train_set.make_placeholders()
# weight initializationn
init = UniformInit(low=-0.08, high=0.08)
# build computational graph
enc = LSTM(args.hs, init, activation=Tanh(), reset_cells=True,
gate_activation=Logistic(), return_sequence=True)
dec = LSTM(args.hs, init, activation=Tanh(), reset_cells=True,
gate_activation=Logistic(), return_sequence=True)
if args.emb is True:
# encoder input embedding
hidden_feature_axis = ng.make_axis(length=args.hs, name='hidden_feature_axis')
feature_axis = ng.make_axis(length=num_features, name='feature_axis')
W_emb = ng.variable(axes=[hidden_feature_axis, feature_axis], initial_value=init)
emb_enc_inputs = ng.dot(W_emb, inputs['inp_txt'])
# decoder input embedding
emb_dec_input = []
ax.N.length = args.batch_size
for i in range(ax.N.length):
inputs = train_set.make_placeholders()
ax.Y.length = len(tree_bank_data.vocab)
def expand_onehot(x):
return ng.one_hot(x, axis=ax.Y)
# weight initialization
init = UniformInit(low=-0.08, high=0.08)
if args.layer_type == "lstm":
rlayer1 = LSTM(hidden_size,
init,
activation=Tanh(),
gate_activation=Logistic(),
return_sequence=True)
rlayer2 = LSTM(hidden_size,
init,
activation=Tanh(),
gate_activation=Logistic(),
return_sequence=True)
# model initialization
seq1 = Sequential([
Preprocess(functor=expand_onehot), rlayer1, rlayer2,
Affine(init, activation=Softmax(), bias_init=init, axes=(ax.Y, ))
])
optimizer = RMSProp(gradient_clip_value=gradient_clip_value)
filter_init,
strides=1,
padding=1,
activation=lrelu,
batch_norm=True),
Convolution((1, 1, 16),
filter_init,
strides=1,
padding=0,
activation=lrelu,
batch_norm=True),
Convolution((7, 7, 1),
filter_init,
strides=1,
padding=0,
activation=Logistic(),
batch_norm=False)
]
discriminator = Sequential(conv_layers, name="Discriminator")
# noise placeholder
N = ng.make_axis(name='N', length=args.batch_size)
noise_ax_names = 'CDHW'
noise_axes = ng.make_axes([
ng.make_axis(name=nm, length=l) for nm, l in zip(noise_ax_names, noise_dim)
])
z_ax = noise_axes + N
z = ng.placeholder(axes=z_ax)
# image placeholder
C = ng.make_axis(name='C', length=1)
np.random.seed(args.rng_seed)
# Create the dataloader
train_data, valid_data = MNIST(args.data_dir).load_data()
train_set = ArrayIterator(train_data, args.batch_size, total_iterations=args.num_iterations)
valid_set = ArrayIterator(valid_data, args.batch_size)
inputs = train_set.make_placeholders()
ax.Y.length = 10
######################
# Model specification
seq1 = Sequential([Preprocess(functor=lambda x: x / 255.),
Affine(nout=100, weight_init=GaussianInit(), activation=Rectlin()),
Affine(axes=ax.Y, weight_init=GaussianInit(), activation=Logistic())])
optimizer = GradientDescentMomentum(0.1, 0.9)
output_prob = seq1.train_outputs(inputs['image'])
errors = ng.not_equal(ng.argmax(output_prob, out_axes=[ax.N]), inputs['label'])
loss = ng.cross_entropy_binary(output_prob, ng.one_hot(inputs['label'], axis=ax.Y))
mean_cost = ng.mean(loss, out_axes=())
updates = optimizer(loss)
train_outputs = dict(batch_cost=mean_cost, updates=updates)
loss_outputs = dict(cross_ent_loss=loss, misclass_pct=errors)
# Now bind the computations we are interested in
transformer = ngt.make_transformer()
train_computation = make_bound_computation(transformer, train_outputs, inputs)
def check_lstm(seq_len, input_size, hidden_size,
batch_size, init_func, return_seq=True, backward=False,
reset_cells=False, num_iter=2):
Cin = ng.make_axis(input_size)
REC = ng.make_axis(seq_len, name='R')
N = ng.make_axis(batch_size, name='N')
with ExecutorFactory() as ex:
np.random.seed(0)
inp_ng = ng.placeholder([Cin, REC, N])
lstm_ng = LSTM(hidden_size, init_func, activation=Tanh(), gate_activation=Logistic(),
reset_cells=reset_cells, return_sequence=return_seq,
backward=backward)
out_ng = lstm_ng.train_outputs(inp_ng)
fprop_neon_fun = ex.executor(out_ng, inp_ng)
fprop_neon_list = []
input_value_list = []
for i in range(num_iter):
# fprop on random inputs
input_value = rng.uniform(-1, 1, inp_ng.axes)
fprop_neon = fprop_neon_fun(input_value).copy()
if return_seq is True:
fprop_neon = fprop_neon[:, :, 0]
input_value_list.append(input_value)
fprop_neon_list.append(fprop_neon)
if reset_cells is False:
# look at the last hidden states
assert ng.testing.allclose(fprop_neon[:, -1].reshape(-1, 1),
lstm_ng.h_init.value.get(None),
rtol=rtol, atol=atol)
# after the rnn graph has been executed, can get the W values. Get copies so
# shared values don't confuse derivatives
# concatenate weights to i, f, o, g together (in this order)
gates = ['i', 'f', 'o', 'g']
Wxh_neon = [lstm_ng.W_input[k].value.get(None).copy().T for k in gates]
Whh_neon = [lstm_ng.W_recur[k].value.get(None).copy().T for k in gates]
bh_neon = [lstm_ng.b[k].value.get(None).copy() for k in gates]
# reference numpy LSTM
lstm_ref = RefLSTM()
WLSTM = lstm_ref.init(input_size, hidden_size)
# make ref weights and biases with neon model
WLSTM[0, :] = np.concatenate(bh_neon)
WLSTM[1:input_size + 1, :] = np.concatenate(Wxh_neon, 1)
WLSTM[input_size + 1:] = np.concatenate(Whh_neon, 1)
# transpose input X and do fprop
fprop_ref_list = []
c0 = h0 = None
for i in range(num_iter):
input_value = input_value_list[i]
inp_ref = input_value.copy().transpose([1, 2, 0])
(Hout_ref, cprev, hprev, batch_cache) = lstm_ref.forward(inp_ref,
WLSTM,
c0, h0)
if reset_cells is False:
c0 = cprev
h0 = hprev
# the output needs transpose as well
Hout_ref = Hout_ref.reshape(seq_len * batch_size, hidden_size).T
fprop_ref_list.append(Hout_ref)
for i in range(num_iter):
assert ng.testing.allclose(fprop_neon_list[i],
fprop_ref_list[i], rtol=rtol, atol=atol)
def check_stacked_lstm(seq_len, input_size, hidden_size,
batch_size, init_func, return_seq=True, backward=False,
reset_cells=False, num_iter=2):
Cin = ng.make_axis(input_size)
REC = ng.make_axis(seq_len, name='R')
N = ng.make_axis(batch_size, name='N')
with ExecutorFactory() as ex:
np.random.seed(0)
inp_ng = ng.placeholder([Cin, REC, N])
lstm_ng_1 = LSTM(hidden_size, init_func, activation=Tanh(), gate_activation=Logistic(),
reset_cells=reset_cells, return_sequence=return_seq,
backward=backward)
lstm_ng_2 = LSTM(hidden_size, init_func, activation=Tanh(), gate_activation=Logistic(),
reset_cells=reset_cells, return_sequence=return_seq,
backward=backward)
out_ng_1 = lstm_ng_1.train_outputs(inp_ng)
out_ng_2 = lstm_ng_2.train_outputs(out_ng_1)
fprop_neon_fun_2 = ex.executor(out_ng_2, inp_ng)
# fprop on random inputs for multiple iterations
fprop_neon_2_list = []
input_value_list = []
for i in range(num_iter):
input_value = rng.uniform(-1, 1, inp_ng.axes)
fprop_neon_2 = fprop_neon_fun_2(input_value).copy()
# comparing outputs
if return_seq is True:
fprop_neon_2 = fprop_neon_2[:, :, 0]
input_value_list.append(input_value)
fprop_neon_2_list.append(fprop_neon_2)
if reset_cells is False:
# look at the last hidden states
assert ng.testing.allclose(fprop_neon_2[:, -1].reshape(-1, 1),
lstm_ng_2.h_init.value.get(None),
rtol=rtol, atol=atol)
# after the rnn graph has been executed, can get the W values. Get copies so
# shared values don't confuse derivatives
# concatenate weights to i, f, o, g together (in this order)
gates = ['i', 'f', 'o', 'g']
Wxh_neon_1 = \
np.concatenate([lstm_ng_1.W_input[k].value.get(None).copy().T for k in gates], 1)
Whh_neon_1 = \
np.concatenate([lstm_ng_1.W_recur[k].value.get(None).copy().T for k in gates], 1)
bh_neon_1 = \
np.concatenate([lstm_ng_1.b[k].value.get(None).copy() for k in gates])
Wxh_neon_2 = \
np.concatenate([lstm_ng_2.W_input[k].value.get(None).copy().T for k in gates], 1)
Whh_neon_2 = \
np.concatenate([lstm_ng_2.W_recur[k].value.get(None).copy().T for k in gates], 1)
bh_neon_2 = \
np.concatenate([lstm_ng_2.b[k].value.get(None).copy() for k in gates])
# reference numpy LSTM
lstm_ref_1 = RefLSTM()
lstm_ref_2 = RefLSTM()
WLSTM_1 = lstm_ref_1.init(input_size, hidden_size)
WLSTM_2 = lstm_ref_2.init(hidden_size, hidden_size)
# make ref weights and biases the same with neon model
WLSTM_1[0, :] = bh_neon_1
WLSTM_1[1:input_size + 1, :] = Wxh_neon_1
WLSTM_1[input_size + 1:] = Whh_neon_1
WLSTM_2[0, :] = bh_neon_2
WLSTM_2[1:hidden_size + 1, :] = Wxh_neon_2
WLSTM_2[hidden_size + 1:] = Whh_neon_2
# transpose input X and do fprop
fprop_ref_2_list = []
c0_1 = h0_1 = None
c0_2 = h0_2 = None
for i in range(num_iter):
input_value = input_value_list[i]
inp_ref = input_value.copy().transpose([1, 2, 0])
(Hout_ref_1, cprev_1, hprev_1, batch_cache) = lstm_ref_1.forward(inp_ref, WLSTM_1,
c0_1, h0_1)
(Hout_ref_2, cprev_2, hprev_2, batch_cache) = lstm_ref_2.forward(Hout_ref_1, WLSTM_2,
c0_2, h0_2)
if reset_cells is False:
c0_1 = cprev_1
h0_1 = hprev_1
c0_2 = cprev_2
h0_2 = hprev_2
# the output needs transpose as well
Hout_ref_2 = Hout_ref_2.reshape(seq_len * batch_size, hidden_size).T
fprop_ref_2_list.append(Hout_ref_2)
for i in range(num_iter):
assert ng.testing.allclose(fprop_neon_2_list[i],
fprop_ref_2_list[i], rtol=rtol, atol=atol)
feature_axis = ng.make_axis(length=n_features, name="F")
out_axis = ng.make_axis(length=n_features, name="Fo")
in_axes = ng.make_axes([batch_axis, time_axis, feature_axis])
out_axes = ng.make_axes([batch_axis, time_axis, out_axis])
# Build placeholders for the created axes
inputs = dict(X=ng.placeholder(in_axes),
y=ng.placeholder(out_axes),
iteration=ng.placeholder(axes=()))
# define model
if args.modeltype == "TCN":
affine_layer = Affine(axes=out_axis,
weight_init=GaussianInit(0, 0.01),
activation=Logistic())
model = Sequential(
[lambda op: ng.map_roles(op, {
'F': 'C',
'REC': 'W'
})] +
tcn(n_features, hidden_sizes, kernel_size=kernel_size,
dropout=dropout).layers +
[lambda op: ng.map_roles(op, {
'C': 'F',
'W': 'REC'
})] + [affine_layer])
elif args.modeltype == "LSTM":
model = Sequential(
recurrent_model.define_model(out_axis,
celltype=args.modeltype,
generator_layers = [
affine_layer(h_dim, Rectlin(), name='g0'),
affine_layer(1, Identity(), name='g1')
]
generator = Sequential(generator_layers)
# discriminator
discriminator_layers = [
affine_layer(2 * h_dim, Tanh(), name='d0'),
affine_layer(2 * h_dim, Tanh(), name='d1')
]
if minibatch_discrimination:
raise NotImplementedError
else:
discriminator_layers.append(affine_layer(2 * h_dim, Tanh(), name='d2'))
discriminator_layers.append(affine_layer(1, Logistic(), name='d3'))
discriminator = Sequential(discriminator_layers)
# TODO discriminator pre-training
# dataloader
np.random.seed(1)
toy_gan_data = ToyGAN(batch_size, num_iterations)
train_data = toy_gan_data.load_data()
train_set = ArrayIterator(train_data, batch_size, num_iterations)
# reset seed for weights
np.random.seed(2)
# build network graph
inputs = train_set.make_placeholders()
z_ax = noise_axes + N
z = ng.placeholder(axes=z_ax)
# image placeholder
C = ng.make_axis(name='C', length=1)
D = ng.make_axis(name='D', length=1)
H = ng.make_axis(name='H', length=28)
W = ng.make_axis(name='W', length=28)
image_axes = ng.make_axes([C, D, H, W, N])
image = ng.placeholder(axes=image_axes)
# DCGAN
if args.loss_type == "DCGAN":
generator = make_generator(bn=True)
discriminator = make_discriminator(bn=True, disc_activation=Logistic())
# build network graph
generated = generator(z)
D1 = discriminator(image)
D2 = discriminator(generated)
weight_clip_value = None # no weight clipping
gp_scale = None # no gradient penalty
loss_d = -ng.log(D1) - ng.log(1 - D2)
loss_g = -ng.log(D2)
# Wasserstein GAN
elif args.loss_type == "WGAN":
def train_mnist_mlp(transformer_name,
data_dir=None,
rng_seed=12,
batch_size=128,
train_iter=10,
eval_iter=10):
assert transformer_name in ['cpu', 'hetr']
assert isinstance(rng_seed, int)
# Apply this metadata to graph regardless of transformer,
# but it is ignored for non-HeTr case
hetr_device_ids = (0, 1)
# use consistent rng seed between runs
np.random.seed(rng_seed)
# Data
train_data, valid_data = MNIST(path=data_dir).load_data()
train_set = ArrayIterator(train_data,
batch_size,
total_iterations=train_iter)
valid_set = ArrayIterator(valid_data, batch_size)
inputs = train_set.make_placeholders()
ax.Y.length = 10
# Model
with ng.metadata(device_id=hetr_device_ids, parallel=ax.N):
seq1 = Sequential([
Preprocess(functor=lambda x: x / 255.),
Affine(nout=100, weight_init=GaussianInit(), activation=Rectlin()),
Affine(axes=ax.Y,
weight_init=GaussianInit(),
activation=Logistic())
])
train_prob = seq1(inputs['image'])
train_loss = ng.cross_entropy_binary(
train_prob, ng.one_hot(inputs['label'], axis=ax.Y))
optimizer = GradientDescentMomentum(0.1, 0.9)
batch_cost = ng.sequential(
[optimizer(train_loss),
ng.mean(train_loss, out_axes=())])
train_outputs = dict(batch_cost=batch_cost)
with Layer.inference_mode_on():
inference_prob = seq1(inputs['image'])
errors = ng.not_equal(ng.argmax(inference_prob, out_axes=[ax.N]),
inputs['label'])
eval_loss = ng.cross_entropy_binary(
inference_prob, ng.one_hot(inputs['label'], axis=ax.Y))
eval_outputs = dict(cross_ent_loss=eval_loss, misclass_pct=errors)
# Runtime
with closing(
ngt.make_transformer_factory(transformer_name)()) as transformer:
train_computation = make_bound_computation(transformer, train_outputs,
inputs)
loss_computation = make_bound_computation(transformer, eval_outputs,
inputs)
train_costs = list()
for step in range(train_iter):
out = train_computation(next(train_set))
train_costs.append(float(out['batch_cost']))
ce_loss = list()
for step in range(eval_iter):
out = loss_computation(next(valid_set))
ce_loss.append(np.mean(out['cross_ent_loss']))
return train_costs, ce_loss
