def register_usage(self, fu_device_id, bo_device_id=None):
"""
Register usage of connector's forward_matrix.
:param fu_device_id: context in which `forward_matrix` will be used
:param bo_device_id: context in which `backward_matrix`
of the connector will be calculated
"""
if not self.bpropagable and bo_device_id:
raise ValueError(
"Nobody is going to use computation from backward step. "
"You mustn't register for backward propagate!")
if fu_device_id != self._fo_device_id and fu_device_id not in self._f_matrices:
self._f_matrices[fu_device_id] = Matrix.empty_like(
self, fu_device_id)
self.context[fu_device_id] = Context(fu_device_id)
if bo_device_id is None:
return self._f_matrices[fu_device_id]
for device_id in [self._bu_device_id, bo_device_id]:
if device_id not in self._b_matrices:
self._b_matrices[device_id] = Matrix.empty_like(
self, device_id)
if device_id not in self.context:
self.context[device_id] = Context(device_id)
if self._bu_device_id != bo_device_id and self._bu_device_id not in self._b_matrices_pool:
self._b_matrices_pool[self._bu_device_id] = Matrix.empty_like(
self, self._bu_device_id)
return self._f_matrices[fu_device_id], self._b_matrices[bo_device_id]
def test_bprop(self):
"""
compare `bprop` results for cpu and gpu backends
"""
r = []
for i in xrange(self.N):
batch_size, dim = self.rng.random_integers(2000, size=2)
y_hat = self.rng.randn(batch_size, dim).astype(dtype=np.float32)
y = self.rng.randn(batch_size, dim).astype(dtype=np.float32)
quagga.processor_type = 'gpu'
context = Context()
y_hat_gpu = Connector(Matrix.from_npa(y_hat), context, context)
y_gpu = Connector(Matrix.from_npa(y))
sse_block = SseBlock(y_hat_gpu, y_gpu)
sse_block.fprop()
sse_block.bprop()
dL_dy_hat_gpu = y_hat_gpu.backward_matrix.to_host()
quagga.processor_type = 'cpu'
context = Context()
y_hat_cpu = Connector(Matrix.from_npa(y_hat), context, context)
y_cpu = Connector(Matrix.from_npa(y))
sse_block = SseBlock(y_hat_cpu, y_cpu)
sse_block.fprop()
sse_block.bprop()
dL_dy_hat_cpu = y_hat_cpu.backward_matrix.to_host()
r.append(np.allclose(dL_dy_hat_gpu, dL_dy_hat_cpu))
self.assertEqual(sum(r), self.N)
def __init__(self, x, nonlinearity, device_id=None):
"""
"""
self.f_context = Context(device_id)
device_id = self.f_context.device_id
self.learning = x.bpropagable
if self.learning:
self.b_context = Context(device_id)
self.x, self.dL_dx = x.register_usage(device_id, device_id)
self._df_dpref = Matrix.empty_like(self.x, device_id)
else:
self.x = x.register_usage(device_id)
output = Matrix.empty_like(x, device_id)
self.output = Connector(output, device_id if self.learning else None)
if nonlinearity == 'sigmoid':
self.f = self.x.sigmoid
elif nonlinearity == 'tanh':
self.f = self.x.tanh
elif nonlinearity == 'relu':
self.f = self.x.relu
elif nonlinearity == 'softmax':
raise ValueError('For softmax nonlinearity use SoftmaxBlock!')
else:
raise ValueError('TODO!')
self.training_mode = True
def __init__(self, data, char_to_idx, batch_size, x_device_id,
y_device_id):
self.data = HomogeneousDataIterator(data, char_to_idx, batch_size,
True, True)
self.data_iterator = iter(self.data)
self.x_context = Context(x_device_id)
self.y_context = Context(y_device_id)
max_len = 0
for sub_line in data:
cur_len = len(sub_line)
if cur_len > max_len:
max_len = cur_len
print max_len
self.x = Connector(
Matrix.empty(batch_size, max_len - 1, 'int', x_device_id))
self._y = Matrix.empty(batch_size, max_len - 1, 'int', y_device_id)
self.y = List([Connector(self._y[:, i]) for i in xrange(max_len - 1)],
self.x.ncols)
self.lengths = Matrix.empty(self.x.nrows, 1, 'int', x_device_id)
self._mask = Matrix.empty(self.x.nrows, self.x.ncols, 'float',
x_device_id)
self.mask = List(
[Connector(self._mask[:, i]) for i in xrange(max_len)],
self.x.ncols)
self.blocking_contexts = None
def __init__(self, W, b, x, device_id=None):
self.f_context = Context(device_id)
device_id = self.f_context.device_id
if W.bpropagable:
self.W, self.dL_dW = W.register_usage(device_id, device_id)
else:
self.W = W.register_usage(device_id)
if b:
if b.bpropagable:
self.b, self.dL_db = b.register_usage(device_id, device_id)
self.ones = Matrix.empty(x.nrows, 1, self.b.dtype, device_id)
self.ones.sync_fill(1.0)
else:
self.b = b.register_usage(device_id)
if x.bpropagable:
self.x, self.dL_dx = x.register_usage(device_id, device_id)
else:
self.x = x.register_usage(device_id)
output = Matrix.empty(x.nrows, self.W.ncols, device_id=device_id)
self.learning = hasattr(self, 'dL_dW') or hasattr(self, 'dL_db') or \
hasattr(self, 'dL_dx')
if self.learning:
self.b_context = Context(device_id)
self.output = Connector(output, device_id)
else:
self.output = Connector(output)
def test_bprop(self):
"""
compare `bprop` results for cpu and gpu backends
"""
r = []
for i in xrange(self.N):
max_input_sequence_len = self.rng.random_integers(500)
sequence_len = max_input_sequence_len if i == 0 else self.rng.random_integers(
max_input_sequence_len)
batch_size = self.rng.random_integers(512)
dim = self.rng.random_integers(1500)
x = [
self.rng.rand(batch_size, dim).astype(dtype=np.float32)
for _ in xrange(max_input_sequence_len)
]
state = self.rng.get_state()
quagga.processor_type = 'gpu'
context = Context()
x_gpu = List(
[Connector(Matrix.from_npa(e), context, context) for e in x])
smean_pooling_block_gpu = SequentialMeanPoolingBlock(x_gpu)
x_gpu.set_length(sequence_len)
_, dL_doutput = smean_pooling_block_gpu.output.register_usage(
context, context)
smean_pooling_block_gpu.fprop()
random_matrix = self.rng.rand(dL_doutput.nrows, dL_doutput.ncols)
Matrix.from_npa(random_matrix,
'float').copy_to(context, dL_doutput)
smean_pooling_block_gpu.bprop()
dL_dmatrices_gpu = [e.backward_matrix.to_host() for e in x_gpu]
self.rng.set_state(state)
quagga.processor_type = 'cpu'
context = Context()
x_cpu = List(
[Connector(Matrix.from_npa(e), context, context) for e in x])
smean_pooling_block_cpu = SequentialMeanPoolingBlock(x_cpu)
x_cpu.set_length(sequence_len)
_, dL_doutput = smean_pooling_block_cpu.output.register_usage(
context, context)
smean_pooling_block_cpu.fprop()
random_matrix = self.rng.rand(dL_doutput.nrows, dL_doutput.ncols)
Matrix.from_npa(random_matrix,
'float').copy_to(context, dL_doutput)
smean_pooling_block_cpu.bprop()
dL_dmatrices_cpu = [e.backward_matrix.to_host() for e in x_cpu]
for dL_dmatrix_gpu, dL_dmatrix_cpu in izip(dL_dmatrices_gpu,
dL_dmatrices_cpu):
if not np.allclose(dL_dmatrix_gpu, dL_dmatrix_cpu):
r.append(False)
break
else:
r.append(True)
self.assertEqual(sum(r), self.N)
def __init__(self, dropout_prob, x, seed=42, device_id=None):
self.dropout_prob = dropout_prob
self.f_context = Context(device_id)
device_id = self.f_context.device_id
self.generator = Matrix.get_random_generator(seed)
if x.bpropagable:
self.b_context = Context(device_id)
self.x, self.dL_dx = x.register_usage(device_id, device_id)
else:
self.x = x.register_usage(device_id)
self.output = Matrix.empty_like(self.x)
self.output = Connector(self.output, device_id if x.bpropagable else None)
self.training_mode = True
def test_theano_grad(self):
quagga.processor_type = 'gpu'
r = []
for i in xrange(self.N):
batch_size, dim = self.rng.random_integers(2000, size=2)
y_hat = self.rng.randn(batch_size, dim).astype(dtype=np.float32)
y = self.rng.randn(batch_size, dim).astype(dtype=np.float32)
# Theano model
th_y_hat, th_y = T.fmatrix(), T.fmatrix()
loss = T.mean(T.sum((th_y_hat - th_y) ** 2, axis=1))
get_theano_grads = theano.function([th_y_hat, th_y], T.grad(loss, wrt=th_y_hat))
th_dL_dy_hat = get_theano_grads(y_hat, y)
# quagga model
context = Context()
y_hat_gpu = Connector(Matrix.from_npa(y_hat), context, context)
y_gpu = Connector(Matrix.from_npa(y))
sigmoid_ce_block = SseBlock(y_hat_gpu, y_gpu)
sigmoid_ce_block.fprop()
sigmoid_ce_block.bprop()
q_dL_dy_hat = y_hat_gpu.backward_matrix.to_host()
r.append(np.allclose(th_dL_dy_hat, q_dL_dy_hat))
self.assertEqual(sum(r), self.N)
def __init__(self, x, scale_factor=1.0):
self.context = Context(x.device_id)
device_id = self.context.device_id
self.output = x
if x.bpropagable:
_, self.dL_dx = x.register_usage(device_id, device_id)
self.scale_factor = ct.c_float(-scale_factor)
def test_bprop_vector(self):
r = []
for _ in xrange(self.N):
embd_dim = self.rng.random_integers(10000)
batch_size, output_dim = self.rng.random_integers(2000, size=2)
W = self.get_orthogonal_matrix(embd_dim, output_dim)
row_idxs = self.rng.randint(embd_dim, size=(batch_size, 1)).astype(np.int32)
true_labels = self.rng.randint(output_dim, size=(batch_size, 1)).astype(np.int32)
device_id = 0
output = {}
for processor_type in ['gpu', 'cpu']:
quagga.processor_type = processor_type
qrow_idxs = Connector(Matrix.from_npa(row_idxs))
qtrue_labels = Connector(Matrix.from_npa(true_labels))
qW = Connector(Matrix.from_npa(W), device_id)
row_slicing_block = RowSlicingBlock(qW, qrow_idxs)
sce_block = SoftmaxCeBlock(row_slicing_block.output, qtrue_labels)
qW.fprop()
qrow_idxs.fprop()
row_slicing_block.fprop()
sce_block.fprop()
sce_block.bprop()
row_slicing_block.bprop()
qW.add(Context(), qW.backward_matrix)
output[processor_type] = qW.to_host()
r.append(np.allclose(output['gpu'], output['cpu']))
self.assertEqual(sum(r), len(r))
def __init__(self,
kkk,
parameters,
learning_rate_policy,
beta1=0.9,
beta2=0.999,
epsilon=1e-20):
self.kkk = kkk
self.parameters = parameters
self.m = []
self.v = []
self.contexts = []
for p in self.parameters:
m = Matrix.empty_like(p)
m.sync_fill(0.0)
self.m.append(m)
v = Matrix.empty_like(p)
v.sync_fill(0.0)
self.v.append(v)
self.contexts.append(Context(p.device_id))
self.learning_rate_policy = learning_rate_policy
self.beta1 = beta1
self.beta2 = beta2
self.epsilon = epsilon
self.blocking_contexts = []
self.iteration = 0
def bprop(self):
if not self.bpropagable:
raise ValueError(
'Nobody was going to use computation from backward '
'step. You should not backward propagate!')
if not self._b_matrices and not self._b_sparse_matrix:
# When no one registered for providing derivatives zero dense
# matrix will be returned
bwd = Matrix.empty_like(self, self._bu_device_id)
if self._bu_device_id not in self.context:
self.context[self._bu_device_id] = Context(self._bu_device_id)
bwd.fill(self.context[self._bu_device_id], 0.0)
self._b_matrices[self._bu_device_id] = bwd
return bwd
if not self._b_matrices and self._b_sparse_matrix:
return self._b_sparse_matrix
for bo_device_id, bwd_matrix in self._b_matrices.iteritems():
if self._bu_device_id != bo_device_id:
self._b_matrices_pool[self._bu_device_id].assign(
self.context[self._bu_device_id], bwd_matrix)
self._b_matrices[self._bu_device_id].add(
self.context[self._bu_device_id],
self._b_matrices_pool[self._bu_device_id])
if self._b_sparse_matrix:
self._b_matrices[self._bu_device_id].add(
self.context[self._bu_device_id], self._b_sparse_matrix)
return self._b_matrices[self._bu_device_id]
def __init__(self, matrix, axis=1, device_id=None):
self.context = Context(device_id)
self._ctype = matrix.c_dtype
self._zero = self._ctype(0.0)
if axis == 0:
self._ones = Matrix.empty(1, matrix.nrows, matrix.dtype, device_id)
self.output = Matrix.empty(1, matrix.ncols, matrix.dtype,
device_id)
self.alpha = self._ctype(1.0 / matrix.nrows)
elif axis == 1:
self._ones = Matrix.empty(matrix.ncols, 1, matrix.dtype, device_id)
self.output = Matrix.empty(matrix.nrows, 1, matrix.dtype,
device_id)
self.alpha = None
else:
raise ValueError('Invalid axis!')
self._ones.sync_fill(1.0)
self.axis = axis
if matrix.bpropagable:
self.matrix, self.dL_dmatrix = matrix.register_usage(
self.context, self.context)
self.output = Connector(self.output, self.context, self.context)
else:
self.matrix = matrix.register_usage(self.context)
self.output = Connector(self.output, self.context)
def __init__(self, train_data, valid_data, batch_size, word_dropout_prob, device_id):
self.train_data = HomogeneousDataIterator(train_data, batch_size, randomize=True, infinite=True)
self.valid_data = HomogeneousDataIterator(valid_data, batch_size)
self.train_data_iterator = iter(self.train_data)
self.valid_data_iterator = iter(self.valid_data)
self.word_keep_prob = 1.0 - word_dropout_prob
self.rnd = RandomState(47571)
self.unk_idx = word_to_idx['<UNK>']
self.context = Context(device_id)
c = Counter([len(line) for line in chain(train_data, valid_data)])
print c.most_common()
max_len = max([len(line) for line in chain(train_data, valid_data)])
self.enc_x = Connector(Matrix.empty(batch_size, max_len, 'int', device_id))
self.enc_lengths = Matrix.empty(self.enc_x.nrows, 1, 'int', device_id)
self._enc_mask = Matrix.empty(self.enc_x.nrows, self.enc_x.ncols, 'float', device_id)
self.enc_mask = List([Connector(self._enc_mask[:, i]) for i in xrange(max_len)], self.enc_x.ncols)
self.dec_x = Connector(Matrix.empty(batch_size, max_len + 1, 'int', device_id))
self._dec_y = Matrix.empty(batch_size, max_len + 1, 'int', device_id)
self.dec_y = List([Connector(self._dec_y[:, i]) for i in xrange(max_len + 1)], self._dec_y.ncols)
self.dec_lengths = Matrix.empty(self.dec_x.nrows, 1, 'int', device_id)
self._dec_mask = Matrix.empty(self.dec_x.nrows, self.dec_x.ncols, 'float', device_id)
self.dec_mask = List([Connector(self._dec_mask[:, i]) for i in xrange(max_len + 1)], self.dec_x.ncols)
self.blocking_contexts = None
self.training_mode = True
def __init__(self, x, regularization_value):
self.context = Context(x.device_id)
device_id = self.context.device_id
if x.bpropagable:
self.x, self.dL_dx = x.register_usage(device_id, device_id)
else:
self.x = x.register_usage(device_id)
self.reg_value = ct.c_float(2 * regularization_value)
def test_bprop(self):
"""
compare `bprop` results for cpu and gpu backends
"""
r = []
for i in xrange(self.N):
batch_size, x_dim = self.rng.random_integers(3000, size=2)
x = self.rng.rand(batch_size, x_dim).astype(np.float32)
device_id = 0
for nonlinearity in ['sigmoid', 'tanh', 'relu']:
state = self.rng.get_state()
quagga.processor_type = 'gpu'
x_gpu = Connector(Matrix.from_npa(x), device_id)
nonlinearity_block = NonlinearityBlock(x_gpu, nonlinearity)
x_gpu.fprop()
nonlinearity_block.fprop()
_, dL_doutput = nonlinearity_block.output.register_usage(
device_id, device_id)
random_matrix = self.rng.rand(dL_doutput.nrows,
dL_doutput.ncols)
dL_doutput.assign(Context(),
Matrix.from_npa(random_matrix, 'float'))
nonlinearity_block.bprop()
dL_dx_gpu = x_gpu.backward_matrix.to_host()
self.rng.set_state(state)
quagga.processor_type = 'cpu'
x_cpu = Connector(Matrix.from_npa(x), device_id)
nonlinearity_block = NonlinearityBlock(x_cpu, nonlinearity)
x_cpu.fprop()
nonlinearity_block.fprop()
_, dL_doutput = nonlinearity_block.output.register_usage(
device_id, device_id)
random_matrix = self.rng.rand(dL_doutput.nrows,
dL_doutput.ncols)
dL_doutput.assign(Context(),
Matrix.from_npa(random_matrix, 'float'))
nonlinearity_block.bprop()
dL_dx_cpu = x_cpu.backward_matrix.to_host()
r.append(np.allclose(dL_dx_gpu, dL_dx_cpu))
self.assertEqual(sum(r), len(r))
def __init__(self, probs, true_labels, schedule, seed, device_id=None):
self.schedule = schedule
self.rnd = np.random.RandomState(seed)
self.context = Context(device_id)
device_id = self.context.device_id
self.probs = probs.register_usage(device_id)
self.true_labels = true_labels.register_usage(device_id)
self.output = Connector(Matrix.empty_like(self.true_labels))
def __init__(self, x, axis, device_id=None):
if axis != 1:
raise NotImplementedError
self.axis = axis
self.context = Context(device_id)
device_id = self.context.device_id
self.x = x.register_usage(device_id)
self.output = Connector(Matrix.empty(x.nrows, 1, x.dtype, device_id))
def __init__(self, y_hat, y, device_id=None):
if y_hat.nrows != y.nrows or y_hat.ncols != y.ncols:
raise ValueError('TODO!')
self.context = Context(device_id)
if y_hat.bpropagable:
self.y_hat, self.dL_dy_hat = y_hat.register_usage(self.context, self.context)
else:
self.y_hat = y_hat.register_usage(self.context)
self.y = y.register_usage(self.context)
def __init__(self, W, col_indexes):
device_id = W.device_id
self.context = Context(device_id)
learning = W.bpropagable
if learning:
self.W, self.dL_dW = W.register_usage_with_sparse_backward_matrix()
else:
self.W = W.register_usage(device_id)
self.col_indexes = col_indexes.register_usage(device_id)
output = Matrix.empty(W.nrows, col_indexes.ncols, device_id=device_id)
self.output = Connector(output, device_id if learning else None)
def __init__(self, parameters, learning_rate_policy, momentum_policy):
self.parameters = parameters
self.velocity = []
for p in self.parameters:
v = Matrix.empty_like(p)
v.sync_fill(0.0)
self.velocity.append(v)
self.learning_rate_policy = learning_rate_policy
self.momentum_policy = momentum_policy
self.contexts = [Context(p.device_id) for p in parameters]
self.blocking_contexts = []
def __init__(self, x):
device_id = x[0].device_id
learning = x[0].bpropagable
self.context = Context(device_id)
self.output = Matrix.empty_like(x[0])
self.output = Connector(self.output, device_id if learning else None)
if learning:
self.x, self.dL_dx = izip(*x.register_usage(device_id, device_id))
else:
self.x = x.register_usage(device_id)
self.last_idx = x.length - 1
def __init__(self, x_sequence, y_sequence, device_id=None):
"""
TODO
"""
# TODO add during hsplit otherwise wrong accumulation of gradients
if all(e.bpropagable for e in chain(x_sequence, y_sequence)):
learning = True
elif all(not e.bpropagable for e in chain(x_sequence, y_sequence)):
learning = False
else:
raise ValueError('All elements should be bpropagable or '
'non-bpropagable. Mixed state is not allowed!')
x_ncols = x_sequence[0].ncols
y_ncols = y_sequence[0].ncols
dtype = x_sequence[0].dtype
for x, y in izip(x_sequence, y_sequence):
if x.ncols != x_ncols or y.ncols != y_ncols:
raise ValueError(
"All matrices in the sequence should have the same number of columns!"
)
if x.nrows != y.nrows:
raise ValueError(
"Can't stack matrices in sequence with different number of rows!"
)
if x.dtype != dtype or y.dtype != dtype:
raise ValueError("Can't stack matrices with different dtypes!")
self.context = Context(device_id)
device_id = self.context.device_id
if learning:
self.x_sequence, self.dL_dx_sequences = izip(
*x_sequence.register_usage(device_id, device_id))
self.y_sequence, self.dL_dy_sequences = izip(
*y_sequence.register_usage(device_id, device_id))
self.dL_dx_sequences = List(self.dL_dx_sequences,
x_sequence.length)
self.dL_dy_sequences = List(self.dL_dy_sequences,
y_sequence.length)
else:
self.x_sequence = x_sequence.register_usage(device_id)
self.y_sequence = y_sequence.register_usage(device_id)
self.x_sequence = List(self.x_sequence, x_sequence.length)
self.y_sequence = List(self.y_sequence, y_sequence.length)
output = []
for _ in xrange(x_sequence.length):
matrix = Matrix.empty(x_sequence[0].nrows, x_ncols + y_ncols,
dtype, device_id)
output.append(Connector(matrix, device_id))
self.output = List(output, x_sequence.length)
if learning:
self.dL_dx_sequences = List(self.dL_dx_sequences,
x_sequence.length)
self.dL_dy_sequences = List(self.dL_dy_sequences,
x_sequence.length)
def __init__(self, x, true_labels, mask=None, device_id=None):
self.context = Context(device_id)
device_id = self.context.device_id
if x.bpropagable:
self.x, self.dL_dx = x.register_usage(device_id, device_id)
else:
self.x = x.register_usage(device_id)
self.true_labels = true_labels.register_usage(device_id)
if mask:
self.mask = mask.register_usage(device_id)
self.probs = Connector(Matrix.empty_like(self.x))
self.loss = None
def __init__(self, matrices, device_id=None):
self.context = Context(device_id)
device_id = self.context.device_id
self.output = Matrix.empty_like(matrices[0], device_id)
learning = matrices[0].bpropagable
self.output = Connector(self.output, device_id if learning else None)
if learning:
self.matrices, self.dL_dmatrices = izip(
*matrices.register_usage(device_id, device_id))
else:
self.matrices = matrices.register_usage(device_id)
self.length = matrices.length
def __init__(self, f_matrix, bu_device_id=None):
self._fo_device_id = f_matrix.device_id
self._f_matrices = {self._fo_device_id: f_matrix}
self.context = {self._fo_device_id: Context(self._fo_device_id)}
if bu_device_id is not None:
self._bu_device_id = bu_device_id
self._b_matrices = dict()
self._b_matrices_pool = dict()
self._b_sparse_matrix = None
# We need do this trick because instead we will add attribute
# to the Connector instance by setting it
# instead of setting attribute in f_matrix
self.__f_matrix_setable_attributes = f_matrix.get_setable_attributes()
for attr_name in self.__f_matrix_setable_attributes:
getattr(self, attr_name)
def __init__(self,
parameters,
learning_rate_policy,
ema_decay=0.9,
epsilon=1e-6):
self.parameters = parameters
self.grad_sqr = []
for p in self.parameters:
grad_sqr = Matrix.empty_like(p)
grad_sqr.sync_fill(0.0)
self.grad_sqr.append(grad_sqr)
self.learning_rate_policy = learning_rate_policy
self.ema_decay = ema_decay
self.epsilon = epsilon
self.contexts = [Context(p.device_id) for p in parameters]
self.blocking_contexts = []
def __init__(self, x, repeats, axis=None, device_id=None):
self.context = Context(device_id)
device_id = self.context.device_id
self.repeats = repeats
self.axis = axis
learning = x.bpropagable
if learning:
self.x, self.dL_dx = x.register_usage(device_id, device_id)
else:
self.x = x.register_usage(device_id)
if axis == 0:
self.output = Matrix.empty(x.nrows * repeats, x.ncols, x.dtype, device_id)
elif axis == 1:
self.output = Matrix.empty(x.nrows, x.ncols * repeats, x.dtype, device_id)
else:
raise ValueError('TODO')
self.output = Connector(self.output, device_id if learning else None)
def test_theano_bprop_matrix(self):
r = []
for i in xrange(self.N):
max_input_sequence_len = self.rng.random_integers(300)
sequence_len = max_input_sequence_len if i == 0 else self.rng.random_integers(2, max_input_sequence_len)
embd_dim = self.rng.random_integers(10000)
batch_size = self.rng.random_integers(500)
output_dim = self.rng.random_integers(2000)
W = self.get_orthogonal_matrix(embd_dim, output_dim)
row_idxs = self.rng.randint(embd_dim, size=(batch_size, max_input_sequence_len)).astype(np.int32)
true_labels = [self.rng.randint(output_dim, size=(batch_size, 1)).astype(np.int32) for _ in xrange(max_input_sequence_len)]
device_id = 0
quagga.processor_type = 'gpu'
qrow_idxs = Connector(Matrix.from_npa(row_idxs))
qtrue_labels = List([Connector(Matrix.from_npa(e)) for e in true_labels], qrow_idxs.ncols)
qW = Connector(Matrix.from_npa(W), device_id)
row_slicing_block = RowSlicingBlock(qW, qrow_idxs)
seq_sce_block = SequencerBlock(block_class=SoftmaxCeBlock,
params=[],
sequences=[row_slicing_block.output, qtrue_labels])
qW.fprop()
qrow_idxs.ncols = sequence_len
qrow_idxs.fprop()
row_slicing_block.fprop()
seq_sce_block.fprop()
seq_sce_block.bprop()
row_slicing_block.bprop()
qW.add(Context(), qW.backward_matrix)
th_row_idxs = T.imatrix()
th_true_labels = T.imatrix()
row_slicing_layer = RowSlicingLayer(W)
toutput = row_slicing_layer.get_output_expr(th_row_idxs)
loss = SequentialSoftmaxLayer.get_loss(toutput, th_true_labels)
dL_dW = T.grad(loss, row_slicing_layer.W)
fun = theano.function([th_row_idxs, th_true_labels],
updates=[(row_slicing_layer.W, row_slicing_layer.W + dL_dW)])
fun(row_idxs, np.hstack(true_labels[:sequence_len]))
r.append(np.allclose(qW.to_host(), row_slicing_layer.W.get_value(), atol=1e-5))
self.assertEqual(sum(r), len(r))
def __init__(self, params, period, parameters_file_path, logger):
self.params = params
self.period = period
self.parameters_file_path = parameters_file_path
self.logger = logger
self.iteration = 0
# we can use our own contexts because during Connector fprop
# derivative matrices are filling with 0.0 in param's
# last_usage_context, to_host changes param's last_usage_context.
# We know that parameters change only during updates of optimizer.
# Add derivatives can't be calculated until there are jobs to be done
# in the obtaining context, which happens to be last_usage_context.
# That is why we are save here.
# Parameters can be changing during callbacks calls.
self.context = {}
for param in params.itervalues():
if param.device_id not in self.context:
self.context[param.device_id] = Context(param.device_id)
self.npa_params = {}
