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)
class SequentialMeanPoolingBlock(object):
# TODO(sergii): change sequentially_tile to add_sequentially_tile, because can erase gradients
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 fprop(self):
self.output.assign_sequential_mean_pooling(self.context,
self.matrices[:self.length])
self.output.fprop()
def bprop(self):
dL_doutput = self.output.backward_matrix
dL_doutput.scale(self.context, ct.c_float(1.0 / self.length))
Matrix.sequentially_tile(self.context, dL_doutput,
self.dL_dmatrices[:self.length])
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 __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)
class ColSlicingBlock(object):
"""
Parameters
----------
W : Matrix (GpuMatrix or CpuMatrix)
col_indexes
"""
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 fprop(self):
self.W.slice_columns(self.context, self.col_indexes, self.output)
self.output.fprop()
def bprop(self):
if hasattr(self, 'dL_dW'):
self.dL_dW.add_columns_slice(self.col_indexes, self.output.bprop())
class ArgmaxBlock(object):
"""
Determines argmax values along the specified ``axis`` in the input matrix.
The block returns a vector (matrix with one of its dimensions equals 1) of
argmax values.
Parameters
----------
x : Matrix (GpuMatrix or CpuMatrix)
Block's input
axis : int
Axis along which argmax is determined
device_id : int
Defines the device's id on which the computation will take place
Returns
-------
vector
A vector containing argmax values (e.g. argmax for each row if axis == 1).
"""
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 fprop(self):
self.x.argmax(self.context, self.output, self.axis)
self.output.fprop()
class LastSelectorBlock(object):
"""
TODO(igor).
Parameters
----------
x : Matrix (GpuMatrix or CpuMatrix)
"""
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 fprop(self):
self.output.assign(self.context, self.x[self.last_idx])
self.output.fprop()
def bprop(self):
self.dL_dx[self.last_idx].add(self.context, self.output.backward_matrix)
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, 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
class RepeatBlock(object):
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 fprop(self):
self.output.assign_repeat(self.context, self.x, self.repeats, self.axis)
self.output.fprop()
def bprop(self):
if hasattr(self, 'dL_dx'):
self.dL_dx.add_repeat_derivative(self.context, self.output.backward_matrix, self.repeats, self.axis)
class LastSelectorBlock(object):
"""
TODO(igor).
Parameters
----------
x : Matrix (GpuMatrix or CpuMatrix)
"""
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 fprop(self):
self.output.assign(self.context, self.x[self.last_idx])
self.output.fprop()
def bprop(self):
self.dL_dx[self.last_idx].add(self.context,
self.output.backward_matrix)
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 test_fprop(self):
"""
compare `fprop` 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'
x_gpu = List([Connector(Matrix.from_npa(e)) for e in x])
smean_pooling_block_gpu = SequentialMeanPoolingBlock(x_gpu)
x_gpu.set_length(sequence_len)
smean_pooling_block_gpu.fprop()
output_gpu = smean_pooling_block_gpu.output.to_host()
self.rng.set_state(state)
quagga.processor_type = 'cpu'
x_cpu = List([Connector(Matrix.from_npa(e)) for e in x])
smean_pooling_block_cpu = SequentialMeanPoolingBlock(x_cpu)
x_cpu.set_length(sequence_len)
smean_pooling_block_cpu.fprop()
output_cpu = smean_pooling_block_cpu.output.to_host()
r.append(np.allclose(output_gpu, output_cpu))
self.assertEqual(sum(r), self.N)
class NonlinearityBlock(object):
"""
Applies nonlinear functions (``sigmoid``, ``tahn``, ``relu``) on input.
Parameters
----------
x : Matrix (GpuMatrix or CpuMatrix)
nonlinearity : string
device_id : int
"""
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
@property
def df_dpref(self):
if self.training_mode and self.learning:
return self._df_dpref
def fprop(self):
self.f(self.f_context, self.output, self.df_dpref)
self.output.fprop()
def bprop(self):
if hasattr(self, "dL_dx"):
# dL/dpref = dL/df .* df/dpref
dL_df = self.output.backward_matrix
self.dL_dx.add_hprod(self.b_context, dL_df, self.df_dpref)
def set_training_mode(self):
self.training_mode = True
def set_testing_mode(self):
self.training_mode = False
class NonlinearityBlock(object):
"""
Applies nonlinear functions (``sigmoid``, ``tahn``, ``relu``) on input.
Parameters
----------
x : Matrix (GpuMatrix or CpuMatrix)
nonlinearity : string
device_id : int
"""
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
@property
def df_dpref(self):
if self.training_mode and self.learning:
return self._df_dpref
def fprop(self):
self.f(self.f_context, self.output, self.df_dpref)
self.output.fprop()
def bprop(self):
if hasattr(self, 'dL_dx'):
# dL/dpref = dL/df .* df/dpref
dL_df = self.output.backward_matrix
self.dL_dx.add_hprod(self.b_context, dL_df, self.df_dpref)
def set_training_mode(self):
self.training_mode = True
def set_testing_mode(self):
self.training_mode = False
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)
class SoftmaxCeBlock(object):
"""
Softmax nonlinearity with mean cross entropy loss
"""
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 fprop(self):
self.x.softmax(self.context, self.probs)
self.probs.fprop()
def bprop(self):
if not hasattr(self, 'dL_dx'):
return
# error = (probs - true_labels) / M
if self.true_labels.dtype == 'int':
self.dL_dx.add_softmax_ce_derivative(self.context, self.probs,
self.true_labels)
else:
self.dL_dx.add_scaled_subtraction(self.context,
1. / self.probs.nrows,
self.probs, self.true_labels)
if hasattr(self, 'mask'):
self.dL_dx.hprod(self.context, self.mask)
def calculate_loss(self, context):
true_labels_np = self.true_labels.to_host(context)
probs_np = self.probs.to_host(context)
if hasattr(self, 'mask'):
mask = self.mask.to_host(context)
context.add_callback(self._calculate_ce_loss, true_labels_np,
probs_np, mask)
else:
context.add_callback(self._calculate_ce_loss, true_labels_np,
probs_np)
def _calculate_ce_loss(self, true_labels_np, probs_np, mask=None):
if self.true_labels.dtype == 'int':
idxs = range(probs_np.shape[0]), true_labels_np.flatten()
logs = np.log(probs_np[idxs] + 1e-20)
else:
logs = np.log(np.sum(true_labels_np * probs_np, axis=1) + 1e-20)
if mask is not None:
logs *= mask[:, 0]
self.loss = -np.sum(logs) / np.sum(mask)
else:
self.loss = -np.mean(logs)
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))
class PtbMiniBatchesGenerator(object):
def __init__(self, ptb_train, ptb_valid, batch_size, sentence_max_len, device_id):
self.blocking_contexts = None
self.context = Context(device_id)
device_id = self.context.device_id
self.train_offsets = HomogeneousDataGenerator(ptb_train, batch_size, sentence_max_len, randomize=True, infinite=True)
self.valid_offsets = HomogeneousDataGenerator(ptb_valid, batch_size, sentence_max_len)
train_sentences = np.array([self.train_offsets.flatten_sentences])
valid_sentences = np.array([self.valid_offsets.flatten_sentences])
self.train_sents = Matrix.from_npa(train_sentences, 'int', device_id)
self.valid_sents = Matrix.from_npa(valid_sentences, 'int', device_id)
self._sent_lengths = np.empty((batch_size, 1), dtype=np.int32, order='F')[...]
self.sent_lengths = Matrix.from_npa(self._sent_lengths, device_id=device_id)
sentence_batch = Matrix.empty(batch_size, sentence_max_len, 'int', device_id)
self.sentence_batch = Connector(sentence_batch, self.context)
self.sentence_batch.sync_fill(0)
self._mask = Matrix.empty(sentence_batch.nrows, self.sentence_batch.ncols, 'float', device_id)
self.mask = List([Connector(self._mask[:, i]) for i in xrange(sentence_max_len)], self.sentence_batch.ncols)
self.train_offsets_iterator = iter(self.train_offsets)
self.valid_offsets_iterator = iter(self.valid_offsets)
self.training_mode = True
def set_training_mode(self):
self.training_mode = True
def set_testing_mode(self):
self.training_mode = False
def fprop(self):
if self.training_mode:
offsets = next(self.train_offsets_iterator)
sents = self.train_sents
else:
try:
offsets = next(self.valid_offsets_iterator)
sents = self.valid_sents
except StopIteration as e:
self.valid_offsets_iterator = iter(self.valid_offsets)
raise e
self.context.wait(*self.blocking_contexts)
self._sent_lengths = self._sent_lengths.base[:len(offsets)]
self.sentence_batch.nrows = len(offsets)
for k, offset in enumerate(offsets):
self.sentence_batch[k].assign(self.context, sents[:, offset[0]:offset[1]])
self._sent_lengths[k] = offset[1] - offset[0]
max_sent_len = int(np.max(self._sent_lengths))
self.sentence_batch.last_modification_context = self.context
self.sentence_batch.ncols = max_sent_len
self.sent_lengths.assign_npa(self.context, self._sent_lengths)
self._mask.mask_column_numbers_row_wise(self.context, self.sent_lengths)
for e in self.mask:
e.last_modification_context = self.context
self.sentence_batch.fprop()
self.mask.fprop()
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 test_fprop_matrix(self):
"""
compare `fprop` results for cpu and gpu backends
"""
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(max_input_sequence_len)
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, max_input_sequence_len)).astype(np.int32)
output = {}
for processor_type in ['gpu', 'cpu']:
quagga.processor_type = processor_type
qrow_idxs = Connector(Matrix.from_npa(row_idxs))
qW = Connector(Matrix.from_npa(W))
row_slicing_block = RowSlicingBlock(qW, qrow_idxs)
qW.fprop()
qrow_idxs.ncols = sequence_len
qrow_idxs.fprop()
row_slicing_block.fprop()
output[processor_type] = row_slicing_block.output.to_host()
for output_gpu, output_cpu in izip(output['gpu'], output['cpu']):
r.append(np.allclose(output_gpu, output_cpu))
self.assertEqual(sum(r), len(r))
def test_theano_fprop_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(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)
quagga.processor_type = 'gpu'
qrow_idxs = Connector(Matrix.from_npa(row_idxs))
qW = Connector(Matrix.from_npa(W))
row_slicing_block = RowSlicingBlock(qW, qrow_idxs)
qW.fprop()
qrow_idxs.ncols = sequence_len
qrow_idxs.fprop()
row_slicing_block.fprop()
q_output = row_slicing_block.output.to_host()
th_row_idxs = T.imatrix()
row_slicing_layer = RowSlicingLayer(W)
toutput = row_slicing_layer.get_output_expr(th_row_idxs)
th_output = theano.function([th_row_idxs], toutput)(row_idxs)
for i in xrange(sequence_len):
r.append(np.allclose(q_output[i], th_output[i]))
self.assertEqual(sum(r), len(r))
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 test_bprop(self):
r = []
for i in xrange(self.N):
matrices = []
ncols = self.rng.random_integers(1, 3000)
nrows = [0]
row_slices = []
device_ids = []
for _ in xrange(self.rng.random_integers(1, 10)):
_nrows = self.rng.random_integers(1, 2000)
nrows.append(nrows[-1] + _nrows)
if self.rng.choice([True, False]):
device_ids.append(0)
row_slices.append((nrows[-2], nrows[-1]))
else:
device_ids.append(None)
matrices.append(
self.rng.rand(_nrows, ncols).astype(np.float32))
true_labels = self.rng.randint(ncols, size=(nrows[-1],
1)).astype(np.int32)
if not row_slices:
r.append(True)
continue
output = {}
for processor_type in ['gpu', 'cpu']:
quagga.processor_type = processor_type
qmatrices = [
Connector(Matrix.from_npa(m), d_id)
for m, d_id in izip(matrices, device_ids)
]
qtrue_labels = Connector(Matrix.from_npa(true_labels))
vstack_block = VerticalStackBlock(*qmatrices)
sce_block = SoftmaxCeBlock(vstack_block.output, qtrue_labels)
for m in qmatrices:
m.fprop()
qtrue_labels.fprop()
vstack_block.fprop()
sce_block.fprop()
sce_block.bprop()
vstack_block.bprop()
output[processor_type] = [
m.backward_matrix.to_host() for m in qmatrices
if m.bpropagable
]
for dL_dm_gpu, dL_dm_cpu in izip(output['gpu'], output['cpu']):
if not np.allclose(dL_dm_gpu, dL_dm_cpu):
r.append(False)
break
else:
r.append(True)
self.assertEqual(sum(r), self.N)
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, R, b, grad_clipping, mask, prev_c, prev_h, device_id=None):
self.f_context = Context(device_id)
device_id = self.f_context.device_id
if R.bpropagable:
self.R, self.dL_dR = R.register_usage(device_id, device_id)
self.R_b_context = Context(device_id)
else:
self.R = R.register_usage(device_id)
if b.bpropagable:
self.b, self.dL_db = b.register_usage(device_id, device_id)
self.b_b_context = Context(device_id)
else:
self.b = b.register_usage(device_id)
self.grad_clipping = grad_clipping
if mask:
self.mask = mask.register_usage(device_id)
if prev_c.bpropagable:
self.prev_c, self.dL_dprev_c = prev_c.register_usage(device_id, device_id)
else:
self.prev_c = prev_c.register_usage(device_id)
if prev_h.bpropagable:
self.prev_h, self.dL_dprev_h = prev_h.register_usage(device_id, device_id)
else:
self.prev_h = prev_h.register_usage(device_id)
self.learning = R.bpropagable or prev_c.bpropagable or prev_h.bpropagable
if self.learning:
self.b_context = Context(device_id)
dim = self.R.nrows
batch_size = self.prev_c.nrows
self.zifo = Matrix.empty(batch_size, 4 * dim, device_id=device_id)
self.z = self.zifo[:, 0*dim:1*dim]
self.i = self.zifo[:, 1*dim:2*dim]
self.f = self.zifo[:, 2*dim:3*dim]
self.o = self.zifo[:, 3*dim:4*dim]
self.c = Matrix.empty_like(self.prev_c, device_id)
self.c = Connector(self.c, device_id if self.learning else None)
self.tanh_c = Matrix.empty_like(self.c, device_id)
self.h = Matrix.empty_like(self.c, device_id)
self.h = Connector(self.h, device_id if self.learning else None)
if self.learning:
self._dzifo_dpre_zifo = Matrix.empty_like(self.zifo)
self.dz_dpre_z = self._dzifo_dpre_zifo[:, 0*dim:1*dim]
self.di_dpre_i = self._dzifo_dpre_zifo[:, 1*dim:2*dim]
self.df_dpre_f = self._dzifo_dpre_zifo[:, 2*dim:3*dim]
self.do_dpre_o = self._dzifo_dpre_zifo[:, 3*dim:4*dim]
self.dL_dpre_zifo = self._dzifo_dpre_zifo
self.dL_dpre_z = self.dz_dpre_z
self.dL_dpre_i = self.di_dpre_i
self.dL_dpre_f = self.df_dpre_f
self.dL_dpre_o = self.do_dpre_o
self._dtanh_c_dc = Matrix.empty_like(self.c)
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
class DropoutBlock(object):
"""
Sets elements of input matrix ``x`` to zero with probability
``dropout_prob`` in training mode. Scales ``x`` by factor of
``1-dropout_prob`` during in testing mode.
Parameters
----------
dropout_prob : float
x : :class:`~quagga.matrix.CpuMatrix` or :class:`~quagga.matrix.GpuMatrix`
seed : int
device_id : int
Defines the device's id on which the computation will take place
Notes
-----
The dropout block is a regularizer that randomly sets input values to zero
in training mode. This procedure is supposed to improve generalization.
During testing, the dropout block scales input values.
"""
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 fprop(self):
if self.training_mode:
self.x.dropout(self.f_context, self.generator, self.dropout_prob,
self.output)
else:
self.x.scale(self.f_context, 1.0 - self.dropout_prob, self.output)
self.output.fprop()
def bprop(self):
if hasattr(self, 'dL_dx') and self.training_mode:
dL_doutput = self.output.backward_matrix
self.dL_dx.add_mask_zeros(self.b_context, dL_doutput, self.output)
def set_training_mode(self):
self.training_mode = True
def set_testing_mode(self):
self.training_mode = False
def __init__(self, **kwargs):
self.parameters = {}
self.trainable_parameters = {}
for name, definition in kwargs.iteritems():
device_id = definition['device_id']
matrix = Matrix.from_npa(definition['init'](), device_id=device_id)
if 'trainable' not in definition or definition['trainable']:
param = Connector(matrix, device_id)
self.trainable_parameters[name] = param
else:
param = Connector(matrix)
self.parameters[name] = param
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
class SoftmaxCeBlock(object):
"""
Softmax nonlinearity with mean cross entropy loss
"""
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 fprop(self):
self.x.softmax(self.context, self.probs)
self.probs.fprop()
def bprop(self):
if not hasattr(self, 'dL_dx'):
return
# error = (probs - true_labels) / M
if self.true_labels.dtype == 'int':
self.dL_dx.add_softmax_ce_derivative(self.context, self.probs, self.true_labels)
else:
self.dL_dx.add_scaled_subtraction(self.context, 1. / self.probs.nrows, self.probs, self.true_labels)
if hasattr(self, 'mask'):
self.dL_dx.hprod(self.context, self.mask)
def calculate_loss(self, context):
true_labels_np = self.true_labels.to_host(context)
probs_np = self.probs.to_host(context)
if hasattr(self, 'mask'):
mask = self.mask.to_host(context)
context.add_callback(self._calculate_ce_loss, true_labels_np, probs_np, mask)
else:
context.add_callback(self._calculate_ce_loss, true_labels_np, probs_np)
def _calculate_ce_loss(self, true_labels_np, probs_np, mask=None):
if self.true_labels.dtype == 'int':
idxs = range(probs_np.shape[0]), true_labels_np.flatten()
logs = np.log(probs_np[idxs] + 1e-20)
else:
logs = np.log(np.sum(true_labels_np * probs_np, axis=1) + 1e-20)
if mask is not None:
logs *= mask[:, 0]
self.loss = - np.sum(logs) / np.sum(mask)
else:
self.loss = - np.mean(logs)
class DataBlock(object):
def __init__(self, word_to_idx, device_id):
self.context = Context(device_id)
device_id = self.context.device_id
self.word_idx = Connector(Matrix.empty(1, 1, 'int', device_id))
self.word_to_idx = word_to_idx
self.word = None
def fprop(self):
word_npa = np.zeros((1, 1), np.int32, 'F')
word_npa[0][0] = self.word_to_idx[self.word] if self.word in self.word_to_idx else self.word_to_idx['<UNK>']
self.word_idx.assign_npa(self.context, word_npa)
self.word_idx.fprop()
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
class DataBlock(object):
def __init__(self, char_to_idx, device_id):
self.context = Context(device_id)
device_id = self.context.device_id
self.char_idx = Connector(Matrix.empty(1, 1, 'int', device_id))
self.char_to_idx = char_to_idx
self.char = None
def fprop(self):
char_npa = np.zeros((1, 1), np.int32, 'F')
char_npa[0][0] = self.char_to_idx[self.char] if self.char in self.char_to_idx else self.char_to_idx['<unk>']
self.char_idx.assign_npa(self.context, char_npa)
self.char_idx.fprop()
class GaussianNoiseBlock(object):
"""
Adds Gaussian noise to the block's input. Adding Gaussian noise can be
viewed as a regularization.
Parameters
----------
mean : float
Expected value of Gaussian noise
std : float
Standard deviation of added Gaussian noise
x : matrix
Block's input
seed : int
Seed for :func:`quagga.cuda.curand.create_generator`
device_id: int
Defines the device's id on which the computation will take place
"""
def __init__(self, mean, std, x, seed=42, device_id=None):
self.mean = mean
self.std = std
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 fprop(self):
if self.training_mode:
self.x.add_gaussian_noise(self.f_context, self.generator,
self.mean, self.std, self.output)
else:
self.output.assign(self.f_context, self.x)
self.output.fprop()
def bprop(self):
self.dL_dx.add(self.b_context, self.output.backward_matrix)
def set_training_mode(self):
self.training_mode = True
def set_testing_mode(self):
self.training_mode = False
def test_fprop(self):
"""
compare `fprop` 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_x, dim_y = self.rng.random_integers(1500, size=2)
x = [
self.rng.rand(batch_size, dim_x).astype(dtype=np.float32)
for _ in xrange(max_input_sequence_len)
]
y = [
self.rng.rand(batch_size, dim_y).astype(dtype=np.float32)
for _ in xrange(max_input_sequence_len)
]
state = self.rng.get_state()
quagga.processor_type = 'gpu'
x_gpu = List([Connector(Matrix.from_npa(e)) for e in x])
y_gpu = List([Connector(Matrix.from_npa(e)) for e in y])
seq_hstack_block_gpu = SequentialHorizontalStackBlock(x_gpu, y_gpu)
x_gpu.length = sequence_len
y_gpu.length = sequence_len
if sequence_len == 0:
pass
seq_hstack_block_gpu.fprop()
output_sequence_gpu = seq_hstack_block_gpu.output.to_host()
self.rng.set_state(state)
quagga.processor_type = 'cpu'
x_cpu = List([Connector(Matrix.from_npa(e)) for e in x])
y_cpu = List([Connector(Matrix.from_npa(e)) for e in y])
seq_hstack_block_cpu = SequentialHorizontalStackBlock(x_cpu, y_cpu)
x_cpu.length = sequence_len
y_cpu.length = sequence_len
seq_hstack_block_cpu.fprop()
output_sequence_cpu = seq_hstack_block_cpu.output.to_host()
for out_gpu, out_cpu in izip(output_sequence_gpu,
output_sequence_cpu):
if not np.allclose(out_gpu, out_cpu):
r.append(False)
break
else:
r.append(True)
self.assertEqual(sum(r), self.N)
class DataBlock(object):
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 fprop(self):
self.x_context.wait(*self.blocking_contexts)
self.y_context.wait(*self.blocking_contexts)
data = next(self.data_iterator)
lengths_npa = np.array([[len(e) - 1] for e in data],
np.int32,
order='F')
x_npa = np.zeros((len(data), int(np.max(lengths_npa))), np.int32, 'F')
for k, e in enumerate(data):
x_npa[k, :len(e) - 1] = e[:-1]
self.x.assign_npa(self.x_context, x_npa)
y_npa = np.zeros((len(data), int(np.max(lengths_npa))), np.int32, 'F')
for k, e in enumerate(data):
y_npa[k, :len(e) - 1] = e[1:]
self._y.assign_npa(self.y_context, y_npa)
for e in self.y:
e.last_modification_context = self.y_context
self.lengths.assign_npa(self.x_context, lengths_npa)
self._mask.mask_column_numbers_row_wise(self.x_context, self.lengths)
for e in self.mask:
e.last_modification_context = self.x_context
self.x.fprop()
self.y.fprop()
self.mask.fprop()
class DropoutBlock(object):
"""
Sets elements of input matrix ``x`` to zero with probability
``dropout_prob`` in training mode. Scales ``x`` by factor of
``1-dropout_prob`` during in testing mode.
Parameters
----------
dropout_prob : float
x : Matrix (GpuMatrix or CpuMatrix)
seed : int
device_id : int
Defines the device's id on which the computation will take place
Notes
-----
The dropout block is a regularizer that randomly sets input values to zero
in training mode. This procedure is supposed to improve generalization.
During testing, the dropout block scales input values.
"""
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 fprop(self):
if self.training_mode:
self.x.dropout(self.f_context, self.generator, self.dropout_prob, self.output)
else:
self.x.scale(self.f_context, 1.0 - self.dropout_prob, self.output)
self.output.fprop()
def bprop(self):
if hasattr(self, 'dL_dx') and self.training_mode:
dL_doutput = self.output.backward_matrix
self.dL_dx.add_mask_zeros(self.b_context, dL_doutput, self.output)
def set_training_mode(self):
self.training_mode = True
def set_testing_mode(self):
self.training_mode = False
class AttentionBlock(object):
"""
Location based attention block
out = sum_{i=1}^{T}a_i * h_i
a_i = softmax(h_i * u)
"""
def __init__(self, matrices, u, mask=None, 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 or u.bpropagable
self.output = Connector(self.output, device_id if learning else None)
if matrices[0].bpropagable:
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
if u.bpropagable:
self.u, self.dL_du = u.register_usage(device_id, device_id)
else:
self.u = u.register_usage(device_id)
if mask:
self.mask = mask.register_usage(device_id)
self.a = Matrix.empty(matrices[0].nrows, matrices.length,
'float', device_id)
self.dL_dpre_a = Matrix.empty_like(self.a)
self.a_cols = [self.a[:, i] for i in xrange(len(self.matrices))]
def fprop(self):
for i in xrange(self.length):
self.a_cols[i].assign_dot(self.context, self.matrices[i], self.u)
if hasattr(self, 'mask'):
self.a.fill(self.context, -3.402823466e+38, self.mask, 0.0)
self.a.softmax(self.context, self.a)
self.output.assign_sequential_weighted_sum(self.context, self.a,
self.matrices[:self.length])
self.output.fprop()
def bprop(self):
dL_doutput = self.output.backward_matrix
self.dL_dpre_a.assign_dL_dpre_a(self.context, dL_doutput, self.a,
self.matrices[:self.length])
if hasattr(self, 'dL_dmatrices'):
Matrix.add_attention_tile(self.context, dL_doutput, self.a,
self.dL_dpre_a, self.u,
self.dL_dmatrices[:self.length])
if hasattr(self, 'dL_du'):
self.dL_du.add_attention_derivative(self.context, self.dL_dpre_a,
self.matrices[:self.length])
class GaussianNoiseBlock(object):
"""
Adds Gaussian noise to the block's input. Adding Gaussian noise can be
viewed as a regularization.
Parameters
----------
mean : float
Expected value of Gaussian noise
std : float
Standard deviation of added Gaussian noise
x : matrix
Block's input
seed : int
Seed for :func:`~quagga.cuda.curand.create_generator`
device_id: int
Defines the device's id on which the computation will take place
"""
def __init__(self, mean, std, x, seed=42, device_id=None):
self.mean = mean
self.std = std
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 fprop(self):
if self.training_mode:
self.x.add_gaussian_noise(self.f_context, self.generator, self.mean, self.std, self.output)
else:
self.output.assign(self.f_context, self.x)
self.output.fprop()
def bprop(self):
self.dL_dx.add(self.b_context, self.output.backward_matrix)
def set_training_mode(self):
self.training_mode = True
def set_testing_mode(self):
self.training_mode = False
class SigmoidCeBlock(object):
"""
Sigmoid nonlinearity with mean cross entropy loss
"""
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 fprop(self):
self.x.sigmoid(self.context, self.probs)
self.probs.fprop()
def bprop(self):
# error = (probs - true_labels) / M
self.dL_dx.add_scaled_subtraction(self.context,
1. / float(self.probs.nrows),
self.probs, self.true_labels)
if hasattr(self, 'mask'):
self.dL_dx.hprod(self.context, self.mask)
def calculate_loss(self, context):
true_labels_np = self.true_labels.to_host(context)
probs_np = self.probs.to_host(context)
if hasattr(self, 'mask'):
mask = self.mask.to_host(context)
context.add_callback(self._calculate_ce_loss,
true_labels_np, probs_np, mask)
else:
context.add_callback(self._calculate_ce_loss,
true_labels_np, probs_np)
def _calculate_ce_loss(self, true_labels_np, probs_np, mask=None):
logs = true_labels_np * np.log(probs_np + 1e-20) + \
(1.0 - true_labels_np) * np.log(1. - probs_np + 1e-20)
if mask is not None:
logs *= mask
self.loss = - np.sum(logs) / (np.sum(mask) * logs.shape[1])
else:
self.loss = - np.mean(logs)
def test_bprop(self):
r = []
for i in xrange(self.N):
matrices = []
nrows = self.rng.random_integers(1, 3000)
ncols = [0]
col_slices = []
device_ids = []
for _ in xrange(self.rng.random_integers(1, 10)):
_ncols = self.rng.random_integers(1, 2000)
ncols.append(ncols[-1] + _ncols)
if self.rng.choice([True, False]):
device_ids.append(0)
col_slices.append((ncols[-2], ncols[-1]))
else:
device_ids.append(None)
matrices.append(self.rng.rand(nrows, _ncols).astype(np.float32))
true_labels = self.rng.randint(ncols[-1], size=(nrows, 1)).astype(np.int32)
if not col_slices:
r.append(True)
continue
output = {}
for processor_type in ['gpu', 'cpu']:
quagga.processor_type = processor_type
qmatrices = [Connector(Matrix.from_npa(m), d_id) for m, d_id in izip(matrices, device_ids)]
qtrue_labels = Connector(Matrix.from_npa(true_labels))
hstack_block = HorizontalStackBlock(*qmatrices)
sce_block = SoftmaxCeBlock(hstack_block.output, qtrue_labels)
for m in qmatrices:
m.fprop()
qtrue_labels.fprop()
hstack_block.fprop()
sce_block.fprop()
sce_block.bprop()
hstack_block.bprop()
output[processor_type] = [m.backward_matrix.to_host()
for m in qmatrices if m.bpropagable]
for dL_dm_gpu, dL_dm_cpu in izip(output['gpu'], output['cpu']):
if not np.allclose(dL_dm_gpu, dL_dm_cpu):
r.append(False)
break
else:
r.append(True)
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
class HorizontalStackBlock(object):
"""
Concatenates input matrices horizontally.
Parameters
----------
matrices : Matrix (GpuMatrix or CpuMatrix)
Input matrices that need to be concatenated.
device_id: int
Defines the device's id on which the computation will take place
"""
def __init__(self, *matrices, **kwargs):
# TODO(sergii): change hsplit to aditive_hsplit for propper gradients accumulation
self.context = Context(kwargs.get('device_id'))
device_id = self.context.device_id
self.matrices = []
self.dL_dmatrices = []
self.bpropagable = []
for matrix in matrices:
self.bpropagable.append(matrix.bpropagable)
if matrix.bpropagable:
matrix, dL_dmatrix = matrix.register_usage(device_id, device_id)
self.dL_dmatrices.append(dL_dmatrix)
else:
matrix = matrix.register_usage(device_id)
self.matrices.append(matrix)
ncols = [matrix.ncols for matrix in matrices]
ncols = sum([e for e in ncols[1:]], ncols[0])
dtype = matrices[0].dtype
bu_device_id = device_id if self.dL_dmatrices else None
output = Matrix.empty(matrices[0].nrows, ncols, dtype, device_id)
self.output = Connector(output, bu_device_id)
def fprop(self):
self.output.assign_hstack(self.context, self.matrices)
self.output.fprop()
def bprop(self):
if self.dL_dmatrices:
col_slices = []
ncols = [0]
for matrix, bpropagable in izip(self.matrices, self.bpropagable):
ncols.append(ncols[-1] + int(matrix.ncols))
if bpropagable:
col_slices.append((ncols[-2], ncols[-1]))
self.output.backward_matrix.hsplit(self.context, self.dL_dmatrices, col_slices)
class ScheduledSamplingBlock(object):
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 fprop(self):
if self.rnd.binomial(1, self.schedule.value):
self.output.assign(self.context, self.true_labels)
else:
self.probs.argmax(self.context, self.output, axis=1)
self.output.fprop()
def __init__(self, x, device_id=None):
self.context = Context(device_id)
device_id = self.context.device_id
self.learning = x.bpropagable
if self.learning:
self.x, self.dL_dx = x.register_usage(device_id, device_id)
else:
self.x = x.register_usage(device_id)
self.x = x.register_usage(device_id)
self.output = Connector(Matrix.empty_like(self.x), device_id if self.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 test_theano_grad(self):
quagga.processor_type = 'gpu'
r = []
for i in xrange(self.N):
for sparse in [True, False]:
batch_size, dim = self.rng.random_integers(2000, size=2)
if sparse:
true_labels = np.zeros((batch_size, dim), np.float32)
for k, j in enumerate(self.rng.randint(dim, size=batch_size)):
true_labels[k, j] = 1.0
else:
true_labels = self.rng.randint(dim, size=(batch_size, 1)).astype(np.int32)
x = self.rng.randn(batch_size, dim).astype(np.float32)
mask = (self.rng.rand(batch_size, 1) < 0.8).astype(np.float32)
device_id = 0
for with_mask in [False, True]:
# Theano model
th_x = T.fmatrix()
th_mask = T.fcol()
th_true_labels = T.fmatrix() if sparse else T.ivector()
if with_mask:
probs = T.nnet.softmax(th_mask * th_x)
else:
probs = T.nnet.softmax(th_x)
loss = T.mean(T.nnet.categorical_crossentropy(probs, th_true_labels))
if with_mask:
get_theano_grads = theano.function([th_x, th_true_labels, th_mask], T.grad(loss, wrt=th_x))
th_dL_dx = get_theano_grads(x, true_labels if sparse else true_labels[:, 0], mask)
else:
get_theano_grads = theano.function([th_x, th_true_labels], T.grad(loss, wrt=th_x))
th_dL_dx = get_theano_grads(x, true_labels if sparse else true_labels[:, 0])
# quagga model
x_gpu = Connector(Matrix.from_npa(x), device_id)
true_labels_gpu = Connector(Matrix.from_npa(true_labels))
mask_gpu = Connector(Matrix.from_npa(mask)) if with_mask else None
softmax_ce_block = SoftmaxCeBlock(x_gpu, true_labels_gpu, mask_gpu)
x_gpu.fprop()
true_labels_gpu.fprop()
if with_mask:
mask_gpu.fprop()
softmax_ce_block.fprop()
softmax_ce_block.bprop()
q_dL_dx = x_gpu.backward_matrix.to_host()
r.append(np.allclose(th_dL_dx, q_dL_dx))
self.assertEqual(sum(r), len(r))
class RowSlicingBlock(object):
def __init__(self, W, row_indexes, dense=True):
self.dense = dense
device_id = W.device_id
self.context = Context(device_id)
learning = W.bpropagable
if learning:
if dense:
self.W, self.dL_dW = W.register_usage(device_id, device_id)
else:
self.W, self.dL_dW = W.register_usage_with_sparse_backward_matrix()
else:
self.W = W.register_usage(device_id)
self.row_indexes = row_indexes.register_usage(device_id)
if row_indexes.ncols > 1:
self.output = []
for i in xrange(row_indexes.ncols):
output = Matrix.empty(row_indexes.nrows, W.ncols, device_id=device_id)
output = Connector(output, device_id if learning else None)
self.output.append(output)
self.output = List(self.output, row_indexes.ncols)
else:
output = Matrix.empty(row_indexes.nrows, W.ncols, device_id=device_id)
self.output = Connector(output, device_id if learning else None)
def fprop(self):
if isinstance(self.output, List):
self.W.slice_rows_batch(self.context, self.row_indexes, self.output)
else:
self.W.slice_rows(self.context, self.row_indexes, self.output)
self.output.fprop()
def bprop(self):
if hasattr(self, 'dL_dW'):
if isinstance(self.output, List):
update_method = self.dL_dW.add_rows_batch_slice
else:
update_method = self.dL_dW.add_rows_slice
if self.dense:
update_method(self.context, self.row_indexes, self.output.bprop())
else:
update_method(self.row_indexes, self.output.bprop())
class DataBlock(object):
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 fprop(self):
self.x_context.wait(*self.blocking_contexts)
self.y_context.wait(*self.blocking_contexts)
data = next(self.data_iterator)
lengths_npa = np.array([[len(e) - 1] for e in data], np.int32, order='F')
x_npa = np.zeros((len(data), int(np.max(lengths_npa))), np.int32, 'F')
for k, e in enumerate(data):
x_npa[k, :len(e) - 1] = e[:-1]
self.x.assign_npa(self.x_context, x_npa)
y_npa = np.zeros((len(data), int(np.max(lengths_npa))), np.int32, 'F')
for k, e in enumerate(data):
y_npa[k, :len(e) - 1] = e[1:]
self._y.assign_npa(self.y_context, y_npa)
for e in self.y:
e.last_modification_context = self.y_context
self.lengths.assign_npa(self.x_context, lengths_npa)
self._mask.mask_column_numbers_row_wise(self.x_context, self.lengths)
for e in self.mask:
e.last_modification_context = self.x_context
self.x.fprop()
self.y.fprop()
self.mask.fprop()
def test_fprop(self):
r = []
for i in xrange(self.N):
repeats = self.rng.random_integers(42)
axis = self.rng.randint(2)
input_dim, output_dim = self.rng.random_integers(2000, size=2)
x = self.get_normal_matrix(input_dim, output_dim)
output = {}
for processor_type in ['gpu', 'cpu']:
quagga.processor_type = processor_type
qx = Connector(Matrix.from_npa(x))
repeat_block = RepeatBlock(qx, repeats, axis)
qx.fprop()
repeat_block.fprop()
output[processor_type] = repeat_block.output.to_host()
r.append(np.allclose(output['gpu'], output['cpu']))
self.assertEqual(sum(r), len(r))
