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_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_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)
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.fprop()
row_slicing_block.fprop()
q_output = row_slicing_block.output.to_host()
trow_idxs = T.ivector()
row_slicing_layer = RowSlicingLayer(W)
t_output = row_slicing_layer.get_output_expr(trow_idxs)
t_output = theano.function([trow_idxs], t_output)(row_idxs[:, 0])
r.append(np.allclose(q_output, t_output))
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_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_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
quagga.processor_type = 'gpu'
qrow_idxs = Connector(Matrix.from_npa(row_idxs))
qW = Connector(Matrix.from_npa(W), device_id)
qtrue_labels = Connector(Matrix.from_npa(true_labels))
row_slicing_block = RowSlicingBlock(qW, qrow_idxs)
sce_block = SoftmaxCeBlock(row_slicing_block.output, qtrue_labels)
qtrue_labels.fprop()
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)
th_row_idxs = T.ivector()
th_true_labels = T.ivector()
row_slicing_layer = RowSlicingLayer(W)
toutput = row_slicing_layer.get_output_expr(th_row_idxs)
loss = SoftmaxLayer.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[:, 0], true_labels[:, 0])
r.append(np.allclose(qW.to_host(), row_slicing_layer.W.get_value()))
self.assertEqual(sum(r), len(r))
def test_bprop_matrix(self):
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)
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
output = {}
for processor_type in ['gpu', 'cpu']:
quagga.processor_type = processor_type
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)
output[processor_type] = qW.to_host()
r.append(np.allclose(output['gpu'], output['cpu']))
self.assertEqual(sum(r), len(r))
0
},
sce_dot_block_W={
'init': Orthogonal(1024, len(vocab)),
'device_id': 0
},
sce_dot_block_b={
'init': Constant(1, len(vocab)),
'device_id': 0
})
data_block = PtbMiniBatchesGenerator(ptb_train,
ptb_valid,
batch_size=64,
sentence_max_len=100,
device_id=0)
seq_embd_block = RowSlicingBlock(p['embd_W'], data_block.sentence_batch)
# remove last in the list
output = List(seq_embd_block.output[:-1], seq_embd_block.output.length - 1)
c_fwd_repeat_block = RepeatBlock(p['lstm_fwd_c0'],
data_block.sentence_batch.nrows,
axis=0,
device_id=0)
h_fwd_repeat_block = RepeatBlock(p['lstm_fwd_h0'],
data_block.sentence_batch.nrows,
axis=0,
device_id=0)
fwd_lstm_block = SequencerBlock(
block_class=LstmBlock,
params=[p['lstm_fwd_W'], p['lstm_fwd_R'], 0.5],
sequences=[output, data_block.mask],
output_names=['h'],
'device_id': 1
},
ff_lstm_R={
'init': lambda: ff_lstm_R,
'device_id': 1
},
sce_dot_block_W={
'init': lambda: dot_block_W,
'device_id': 1
},
sce_dot_block_b={
'init': lambda: dot_block_b,
'device_id': 1
})
data_block = DataBlock(char_to_idx, device_id=1)
embd_block = RowSlicingBlock(W=p['embd_W'], row_indexes=data_block.char_idx)
f_lstm_rnn_block = LstmBlock(p['f_lstm_W'],
p['f_lstm_R'],
None,
embd_block.output,
None,
p['f_lstm_c0'],
p['f_lstm_h0'],
device_id=1)
s_lstm_rnn_block = LstmBlock(p['s_lstm_W'],
p['s_lstm_R'],
None,
f_lstm_rnn_block.h,
None,
p['s_lstm_c0'],
p['s_lstm_h0'],
enc_lstm_W={'init': H5pyInitializer(model_file_name, 'enc_lstm_W'),
'device_id': 0},
enc_lstm_R={'init': H5pyInitializer(model_file_name, 'enc_lstm_R'),
'device_id': 0},
dec_lstm_c0={'init': H5pyInitializer(model_file_name, 'dec_lstm_c0'),
'device_id': 0},
dec_lstm_W={'init': H5pyInitializer(model_file_name, 'dec_lstm_W'),
'device_id': 0},
dec_lstm_R={'init': H5pyInitializer(model_file_name, 'dec_lstm_R'),
'device_id': 0},
sce_dot_block_W={'init': H5pyInitializer(model_file_name, 'sce_dot_block_W'),
'device_id': 0},
sce_dot_block_b={'init': H5pyInitializer(model_file_name, 'sce_dot_block_b'),
'device_id': 0})
data_block = DataBlock(train_data, valid_data, 64, word_dropout_prob=0.99, device_id=0)
enc_embd_block = RowSlicingBlock(p['embd_W'], data_block.enc_x)
enc_c_repeat_block = RepeatBlock(p['enc_lstm_c0'], data_block.enc_x.nrows, axis=0, device_id=0)
enc_h_repeat_block = RepeatBlock(p['enc_lstm_h0'], data_block.enc_x.nrows, axis=0, device_id=0)
enc_lstm_block = SequencerBlock(block_class=LstmBlock,
params=[p['enc_lstm_W'], p['enc_lstm_R'], 0.25],
sequences=[enc_embd_block.output, data_block.enc_mask],
output_names=['h'],
prev_names=['c', 'h'],
paddings=[enc_c_repeat_block.output, enc_h_repeat_block.output],
reverse=False,
device_id=0)
dec_embd_block = RowSlicingBlock(p['embd_W'], data_block.dec_x)
dec_c_repeat_block = RepeatBlock(p['dec_lstm_c0'], data_block.enc_x.nrows, axis=0, device_id=0)
last_selector_block = LastSelectorBlock(enc_lstm_block.h)
l2_reg_block = L2RegularizationBlock(last_selector_block.output, 0.001)
dec_lstm_block = SequencerBlock(block_class=LstmBlock,
'trainable': False
},
enc_lstm_W={
'init': H5pyInitializer(model_file_name,
'enc_lstm_W'),
'device_id': 1,
'trainable': False
},
enc_lstm_R={
'init': H5pyInitializer(model_file_name,
'enc_lstm_R'),
'device_id': 1,
'trainable': False
})
data_block = DataBlock(word_to_idx, device_id=1)
enc_embd_block = RowSlicingBlock(p['embd_W'], data_block.word_idx)
enc_lstm_block = LstmBlock(p['enc_lstm_W'],
p['enc_lstm_R'],
None,
enc_embd_block.output,
None,
p['enc_lstm_c0'],
p['enc_lstm_h0'],
device_id=1)
encoder_model = Model([p, data_block, enc_embd_block, enc_lstm_block])
def encoder_step(word, begin=False):
data_block.word = word
if begin:
enc_lstm_block.prev_c.assign_npa(enc_lstm_block.f_context, enc_lstm_c0)
