def build_trainer_with_model_parallel(self, src, src_mask, trg, trg_mask, ite, ps_device, devices,
l1_reg_weight=1e-6, l2_reg_weight=1e-6):
assert K._BACKEND == 'tensorflow'
src_mask_3d = K.expand_dims(src_mask)
trg_mask_3d = K.expand_dims(trg_mask)
# compute loss and grads
loss = self.calc_loss_with_model_parallel(src,
src_mask_3d,
trg,
trg_mask_3d,
ps_device=ps_device,
devices=devices,
l1_reg_weight=l1_reg_weight,
l2_reg_weight=l2_reg_weight)
grads = tf.gradients(loss, self.params, colocate_gradients_with_ops=True)
grads = grad_clip(grads, self.clip_c)
updates = adadelta(self.params, grads)
inps = [src, src_mask, trg, trg_mask]
self.train_fn = K.function(inps,
[loss],
updates=updates)
def build_trainer(self, src, src_mask, trg, trg_mask, ite,
l1_reg_weight=1e-6,
l2_reg_weight=1e-6,
softmax_output_num_sampled=100000):
src_mask_3d = K.expand_dims(src_mask)
trg_mask_3d = K.expand_dims(trg_mask)
annotations = self.encoder.apply(src, src_mask_3d)
# init_context = annotations[0, :, -self.n_hids_src:]
# modification #1
# mean pooling
init_context = K.sum(annotations * src_mask_3d, axis=0) / K.sum(src_mask_3d, axis=0)
trg_emb = self.table_trg.apply(trg)
# shift_right assumes a 3D tensor, and time steps is dimension one
trg_emb_shifted = K.permute_dimensions(K.shift_right(K.permute_dimensions(trg_emb, [1, 0, 2])),
[1, 0, 2])
hiddens, readout, _ = self.decoder.run_pipeline(state_below=trg_emb_shifted,
mask_below=trg_mask_3d,
init_context=init_context,
c=annotations,
c_mask=src_mask_3d)
# apply dropout
if self.dropout > 0.:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(readout, self.dropout)
self.cost = calc_loss_from_readout(readout=readout,
targets=trg,
targets_mask=trg_mask_3d,
logisticRegressionLayer=self.logistic_layer,
softmax_output_num_sampled=softmax_output_num_sampled)
# for reconstruction
self.L1 = sum([K.sum(K.abs(param)) for param in self.params])
self.L2 = sum([K.sum(K.square(param)) for param in self.params])
params_regular = self.L1 * l1_reg_weight + self.L2 * l2_reg_weight
# train cost
train_cost = self.cost + params_regular
# gradients
grads = K.gradients(train_cost, self.params)
# apply gradient clipping here
grads = grad_clip(grads, self.clip_c)
# updates
updates = adadelta(self.params, grads)
# train function
inps = [src, src_mask, trg, trg_mask]
self.train_fn = K.function(inps, [train_cost], updates=updates, name='train_func')
def build_trainer_with_data_parallel(self,
src,
src_mask,
trg,
trg_mask,
ite,
devices,
l1_reg_weight=1e-6,
l2_reg_weight=1e-6,
softmax_output_num_sampled=100000):
assert K._BACKEND == 'tensorflow'
src_mask_3d = [K.expand_dims(mask) for mask in src_mask]
trg_mask_3d = [K.expand_dims(mask) for mask in trg_mask]
num_devices = len(devices)
loss_list = []
grads_list = []
# TODO: group the devices by hosts, first calculate the averaged gradients for each host
for i, device in enumerate(devices):
with tf.device(device):
loss = self.calc_loss(
src[i],
src_mask_3d[i],
trg[i],
trg_mask_3d[i],
l1_reg_weight=l1_reg_weight,
l2_reg_weight=l2_reg_weight,
softmax_output_num_sampled=softmax_output_num_sampled)
loss_list.append(loss)
grads = K.gradients(loss, self.params)
grads_list.append(grads)
avg_loss = sum(loss_list) / num_devices
# use customized version of gradient to enable colocate_gradients with_ops
# to ensure the gradient are computed by the same device that do the forward computation
grads = avg_grads(grads_list)
grads = grad_clip(grads, self.clip_c)
updates = adadelta(self.params, grads)
inps = src + src_mask + trg + trg_mask
self.train_fn = K.function(inps, [avg_loss] + loss_list,
updates=updates)
def build_trainer(self, src, src_mask, src_hist, src_hist_mask, trg,
trg_mask, ite):
# added by Longyue
# checked by Zhaopeng: sentence dim = n_steps, hist_len, batch_size (4, 3, 25)
# hist = (bath_size, sent_num, sent_len) --.T-->
# hist = (sent_len, sent_num, bath_size) --lookup table-->
# (sent_len, sent_num, bath_size, word_emb) --reshape-->
# (sent_len, sent_num*bath_size, word_emb) --word-level rnn-->
# (sent_len, sent_num*bath_size, hidden_size) --reshape-->
# (sent_len, sent_num, bath_size, hidden_size) --[-1]-->
# (sent_num, bath_size, hidden_size) --sent-level rnn-->
# (sent_num, bath_size, hidden_size) --[-1]-->
# (bath_size, hidden_size) = cross-sent context vector
annotations_1 = self.encoder_hist_1.apply_1(src_hist, src_hist_mask)
annotations_1 = annotations_1[-1] # get last hidden states
annotations_2 = self.encoder_hist_2.apply_2(annotations_1)
annotations_3 = annotations_2[-1] # get last hidden states
#modified by Longyue
annotations = self.encoder.apply(src, src_mask, annotations_3)
# init_context = annotations[0, :, -self.n_hids_src:]
# modification #1
# mean pooling
init_context = (annotations *
src_mask[:, :, None]).sum(0) / src_mask.sum(0)[:, None]
#added by Longyue
init_context = concatenate([init_context, annotations_3],
axis=annotations_3.ndim - 1)
trg_emb = self.table_trg.apply(trg)
trg_emb_shifted = T.zeros_like(trg_emb)
trg_emb_shifted = T.set_subtensor(trg_emb_shifted[1:], trg_emb[:-1])
# modified by Longyue
hiddens, readout, alignment = self.decoder.run_pipeline(
state_below=trg_emb_shifted,
mask_below=trg_mask,
init_context=init_context,
c=annotations,
c_mask=src_mask,
hist=annotations_3)
# apply dropout
if self.dropout < 1.0:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(self.trng, readout, 1, self.dropout)
p_y_given_x = self.logistic_layer.get_probs(readout)
self.cost = self.logistic_layer.cost(p_y_given_x, trg,
trg_mask) / trg.shape[1]
# self.cost = theano.printing.Print('likilihood cost:')(self.cost)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
if self.with_reconstruction:
# now hiddens is the annotations
inverse_init_context = (hiddens * trg_mask[:, :, None]
).sum(0) / trg_mask.sum(0)[:, None]
src_emb = self.table_src.apply(src)
src_emb_shifted = T.zeros_like(src_emb)
src_emb_shifted = T.set_subtensor(src_emb_shifted[1:],
src_emb[:-1])
inverse_hiddens, inverse_readout, inverse_alignment = self.inverse_decoder.run_pipeline(
state_below=src_emb_shifted,
mask_below=src_mask,
init_context=inverse_init_context,
c=hiddens,
c_mask=trg_mask)
# apply dropout
if self.dropout < 1.0:
# logger.info('Apply dropout with p = {}'.format(self.dropout))
inverse_readout = Dropout(self.srng, inverse_readout, 1,
self.dropout)
p_x_given_y = self.inverse_logistic_layer.get_probs(
inverse_readout)
self.reconstruction_cost = self.inverse_logistic_layer.cost(
p_x_given_y, src, src_mask) / src.shape[1]
# self.reconstruction_cost = theano.printing.Print('reconstructed cost:')(self.reconstruction_cost)
self.cost += self.reconstruction_cost * self.reconstruction_weight
self.L1 = sum(T.sum(abs(param)) for param in self.params)
self.L2 = sum(T.sum(param**2) for param in self.params)
params_regular = self.L1 * 1e-6 + self.L2 * 1e-6
# params_regular = theano.printing.Print('params_regular:')(params_regular)
# train cost
train_cost = self.cost + params_regular
# gradients
grads = T.grad(train_cost, self.params)
# apply gradient clipping here
grads = grad_clip(grads, self.clip_c)
# updates
updates = adadelta(self.params, grads)
# train function
# modified by Longyue
inps = [src, src_mask, src_hist, src_hist_mask, trg, trg_mask]
self.train_fn = theano.function(inps, [train_cost],
updates=updates,
name='train_function')
def build_trainer(self, src, src_mask, trg, trg_mask, ite):
annotations = self.encoder.apply(src, src_mask)
# init_context = annotations[0, :, -self.n_hids_src:]
# modification #1
# mean pooling
init_context = (annotations *
src_mask[:, :, None]).sum(0) / src_mask.sum(0)[:, None]
trg_emb = self.table_trg.apply(trg)
trg_emb_shifted = T.zeros_like(trg_emb)
trg_emb_shifted = T.set_subtensor(trg_emb_shifted[1:], trg_emb[:-1])
hiddens, readout, alignment = self.decoder.run_pipeline(
state_below=trg_emb_shifted,
mask_below=trg_mask,
init_context=init_context,
c=annotations,
c_mask=src_mask)
# apply dropout
if self.dropout < 1.0:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(self.trng, readout, 1, self.dropout)
p_y_given_x = self.logistic_layer.get_probs(readout)
self.cost = self.logistic_layer.cost(p_y_given_x, trg,
trg_mask) / trg.shape[1]
# self.cost = theano.printing.Print('likilihood cost:')(self.cost)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
if self.with_reconstruction:
# now hiddens is the annotations
inverse_init_context = (hiddens * trg_mask[:, :, None]
).sum(0) / trg_mask.sum(0)[:, None]
src_emb = self.table_src.apply(src)
src_emb_shifted = T.zeros_like(src_emb)
src_emb_shifted = T.set_subtensor(src_emb_shifted[1:],
src_emb[:-1])
inverse_hiddens, inverse_readout, inverse_alignment = self.inverse_decoder.run_pipeline(
state_below=src_emb_shifted,
mask_below=src_mask,
init_context=inverse_init_context,
c=hiddens,
c_mask=trg_mask)
# apply dropout
if self.dropout < 1.0:
# logger.info('Apply dropout with p = {}'.format(self.dropout))
inverse_readout = Dropout(self.srng, inverse_readout, 1,
self.dropout)
p_x_given_y = self.inverse_logistic_layer.get_probs(
inverse_readout)
self.reconstruction_cost = self.inverse_logistic_layer.cost(
p_x_given_y, src, src_mask) / src.shape[1]
# self.reconstruction_cost = theano.printing.Print('reconstructed cost:')(self.reconstruction_cost)
self.cost += self.reconstruction_cost * self.reconstruction_weight
self.L1 = sum(T.sum(abs(param)) for param in self.params)
self.L2 = sum(T.sum(param**2) for param in self.params)
params_regular = self.L1 * 1e-6 + self.L2 * 1e-6
# params_regular = theano.printing.Print('params_regular:')(params_regular)
# train cost
train_cost = self.cost + params_regular
# gradients
grads = T.grad(train_cost, self.params)
# apply gradient clipping here
grads = grad_clip(grads, self.clip_c)
# updates
updates = adadelta(self.params, grads)
# train function
inps = [src, src_mask, trg, trg_mask]
self.train_fn = theano.function(inps, [train_cost],
updates=updates,
name='train_function')
def build_trainer(self, src, src_mask, trg, trg_mask):
annotations = self.encoder.apply(src, src_mask)
# init_context = annotations[0, :, -self.n_hids_src:]
# modification #1
# mean pooling
init_context = (annotations *
src_mask[:, :, None]).sum(0) / src_mask.sum(0)[:, None]
trg_emb = self.table_trg.apply(trg)
trg_emb_shifted = T.zeros_like(trg_emb)
trg_emb_shifted = T.set_subtensor(trg_emb_shifted[1:], trg_emb[:-1])
results = self.decoder.run_pipeline(state_below=trg_emb_shifted,
mask_below=trg_mask,
init_context=init_context,
c=annotations,
c_mask=src_mask)
hiddens, ctxs, readout, alignment = results[:4]
# apply dropout
if self.dropout < 1.0:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(self.trng, readout, 1, self.dropout)
p_y_given_x = self.logistic_layer.get_probs(readout)
self.cost = self.logistic_layer.cost(p_y_given_x, trg,
trg_mask) / trg.shape[1]
# self.cost = theano.printing.Print('likilihood cost:')(self.cost)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
if self.with_reconstruction:
# now hiddens is the annotations
inverse_init_context = (hiddens * trg_mask[:, :, None]
).sum(0) / trg_mask.sum(0)[:, None]
src_emb = self.table_src.apply(src)
src_emb_shifted = T.zeros_like(src_emb)
src_emb_shifted = T.set_subtensor(src_emb_shifted[1:],
src_emb[:-1])
inverse_results = self.inverse_decoder.run_pipeline(
state_below=src_emb_shifted,
mask_below=src_mask,
init_context=inverse_init_context,
c=hiddens,
c_mask=trg_mask)
inverse_hiddens, inverse_ctxs, inverse_readout, inverse_alignment = inverse_results[:
4]
# apply dropout
if self.dropout < 1.0:
# logger.info('Apply dropout with p = {}'.format(self.dropout))
inverse_readout = Dropout(self.srng, inverse_readout, 1,
self.dropout)
p_x_given_y = self.inverse_logistic_layer.get_probs(
inverse_readout)
self.reconstruction_cost = self.inverse_logistic_layer.cost(
p_x_given_y, src, src_mask) / src.shape[1]
# self.reconstruction_cost = theano.printing.Print('reconstructed cost:')(self.reconstruction_cost)
self.cost += self.reconstruction_cost * self.reconstruction_weight
self.L1 = sum(T.sum(abs(param)) for param in self.params)
self.L2 = sum(T.sum(param**2) for param in self.params)
params_regular = self.L1 * 1e-6 + self.L2 * 1e-6
# params_regular = theano.printing.Print('params_regular:')(params_regular)
# train cost
train_cost = self.cost + params_regular
# gradients
grads = T.grad(train_cost, self.params)
# apply gradient clipping here
grads = grad_clip(grads, self.clip_c)
# train function
inps = [src, src_mask, trg, trg_mask]
outs = [train_cost]
if self.with_layernorm:
inps = [src, src_mask, trg, trg_mask]
lr = T.scalar(name='lr')
print 'Building optimizers...',
self.train_fn, self.update_fn = adam(lr, self.params, grads, inps,
outs)
else:
# updates
updates = adadelta(self.params, grads)
# mode=theano.Mode(linker='vm') for ifelse
# Unless linker='vm' or linker='cvm' are used, ifelse will compute both variables and take the same computation time as switch.
self.train_fn = theano.function(inps,
outs,
updates=updates,
name='train_function',
mode=theano.Mode(linker='vm'))