def test_stop_gradient():
x = C.sequence.input_variable(shape=(2, ),
sequence_axis=C.Axis("B"),
needs_gradient=True)
y = C.sequence.input_variable(shape=(2, ),
sequence_axis=C.Axis("B"),
needs_gradient=True)
z = C.element_times(x, y)
w = z + C.stop_gradient(z)
a = np.reshape(np.float32([0.25, 0.5, 0.1, 1]), (1, 2, 2))
b = np.reshape(np.float32([-1.25, 1.5, 0.1, -1]), (1, 2, 2))
bwd, fwd = w.forward({x: a, y: b}, [w.output], set([w.output]))
value = list(fwd.values())[0]
expected = np.multiply(a, b) * 2
assert np.allclose(value, expected)
grad = w.backward(bwd, {w.output: np.ones_like(value)}, set([x, y]))
assert np.allclose(grad[x], b)
assert np.allclose(grad[y], a)
#test stop_gradient with function as input whose arguments should have no gradients (zeros reading)
w = C.stop_gradient(z)
bwd, fwd = w.forward({x: a, y: b}, [w.output], set([w.output]))
value = list(fwd.values())[0]
expected = np.multiply(a, b)
assert np.allclose(value, expected)
grad = w.backward(bwd, {w.output: np.ones_like(value)}, set([x, y]))
#there should be no gradients backward to x and y
assert np.allclose(grad[x], np.zeros_like(b))
assert np.allclose(grad[y], np.zeros_like(a))
def test_cos_distane_backward():
x = C.sequence.input_variable(shape=(2, ),
sequence_axis=C.Axis("B"),
needs_gradient=True)
y = C.sequence.input_variable(shape=(2, ),
sequence_axis=C.Axis("B"),
needs_gradient=True)
z = C.cosine_distance(x, y)
a = np.reshape(np.float32([0.25, 0.5, 0.1, 1]), (1, 2, 2))
b = np.reshape(np.float32([-0.5, 1.5, -0.3, -1]), (1, 2, 2))
bwd, fwd = z.forward({x: a, y: b}, [z.output], set([z.output]))
value = list(fwd.values())[0]
expected = [[0.707107, -0.981665]]
assert np.allclose(value, expected)
grad = z.backward(bwd, {z.output: np.ones_like(value)}, set([x, y]))
x_driv_expected = np.ndarray(
(1, 2, 2),
dtype=np.float32,
buffer=np.float32([-1.131371, 0.565686, -0.188727, 0.018873]))
y_driv_expected = np.ndarray(
(1, 2, 2),
dtype=np.float32,
buffer=np.float32([0.424264, 0.141421, -0.174876, 0.052463]))
assert (np.all(np.absolute(grad[x] - x_driv_expected) < 1e-6))
assert (np.all(np.absolute(grad[y] - y_driv_expected) < 1e-6))
def test_cosine_distance_with_negative_samples_with_reduced_sequence():
a = C.sequence.input_variable((3, ), sequence_axis=C.Axis("a"))
b = C.sequence.input_variable((3, ), sequence_axis=C.Axis("b"))
cd = C.cosine_distance_with_negative_samples(C.sequence.first(a),
C.sequence.first(b), 1, 2)
data = np.random.random((4, 3)).astype(np.float32)
cd.eval({a: data, b: data})
def multi_headed_self_attention_layer(in_dims: int,
hidden_dims: int,
num_of_head: int,
name='multi_headed_self_attention',
as_block: bool = False,
k_ph: bool = False,
v_ph: bool = False,
mask_opt: bool = False) -> C.Function:
X = C.placeholder(
in_dims, (C.Axis.default_batch_axis(), C.Axis.default_dynamic_axis()),
name=name + '_ph')
outputs = []
if k_ph is False and v_ph is False:
for i in range(num_of_head):
layer = self_attention_layer(in_dims,
hidden_dims,
name=name + str(i),
as_block=not as_block,
mask_opt=mask_opt)
outputs.append(layer(X))
elif k_ph is True and v_ph is True:
k_ = C.placeholder(in_dims,
(C.Axis.default_batch_axis(), C.Axis('kv_seq')),
name=name + '_k_ph') # -3: sequence axis
v_ = C.placeholder(in_dims,
(C.Axis.default_batch_axis(), C.Axis('kv_seq')),
name=name + '_v_ph')
for i in range(num_of_head):
layer = self_attention_layer(in_dims,
in_dims,
name=name + str(i),
as_block=not as_block,
k_ph=k_ph,
v_ph=v_ph)
outputs.append(layer(X, k_, v_))
else:
raise Exception(f'k_ph:{k_ph}, v_ph:{v_ph}')
concat = C.splice(*outputs, name='concat')
result = C.layers.Dense(in_dims, name='W_o')(concat)
# init = C.initializer.normal(1)
# W_O = C.parameter((in_dims, hidden_dims*num_of_head), init=init, name=name+'_Wo')
# result = C.times_transpose(concat, W_O, name='result')
if as_block is True:
if k_ph is False and v_ph is False:
result = C.as_block(result, [(X, X)], 'multi_headed_self_attetion',
'multi_headed_self_attetion_')
elif k_ph is True and v_ph is True:
result = C.as_block(result, [(X, X), (k_, k_), (v_, v_)],
'multi_headed_self_attetion',
'multi_headed_self_attetion_')
else:
raise Exception(f'k_ph:{k_ph} v_ph:{v_ph}')
return result
def test_axis_str():
i = C.sequence.input_variable((1, 3))
assert str(C.Axis.all_axes()) == "Axis('AllAxes')"
assert str(C.Axis.all_static_axes()) == "Axis('AllStaticAxes')"
assert str(C.Axis.unknown_dynamic_axes()) == "(Axis('UnknownAxes'),)"
assert str(C.Axis(1)) == "Axis('staticAxisIdx=1')"
assert str(C.Axis(-1)) == "Axis('staticAxisIdx=-1')"
assert str(i.dynamic_axes) == "(Axis('defaultBatchAxis'), Axis('defaultDynamicAxis'))"
def test_clone_with_different_dynamic_axes():
q_axis = C.Axis('q')
a_axis = C.Axis('a')
question_input = C.sequence.input(shape=10, is_sparse=True, sequence_axis=q_axis)
answer_input = C.sequence.input(shape=10, is_sparse=True, sequence_axis=a_axis)
rnn = C.layers.Recurrence(C.layers.LSTM(5))(question_input)
rnn_cloned = rnn.clone(C.CloneMethod.share, {question_input:answer_input})
def test_rank0_output():
x = C.sequence.input_variable(shape=(768,), sequence_axis=C.Axis("B"), needs_gradient=True)
y = C.sequence.input_variable(shape=(768,), sequence_axis=C.Axis("B"), needs_gradient=True)
z = C.cosine_distance(x, y)
batch_num = 2
batch_size = 30
a = np.float32(np.random.rand(batch_num*batch_size,1500,768))
b = np.float32(np.random.rand(batch_num*batch_size,1500,768))
for i in range(batch_num):
bwd, fwd = z.forward({x:a[i*batch_size:(i+1)*batch_size], y:b[i*batch_size:(i+1)*batch_size]}, [z.output], set([z.output]))
grad = z.backward(bwd, {z.output:np.ones_like(fwd[z.output])}, set([x, y]))
def test_GRU(tmpdir):
def MakeGRUNameFromConfig(backward, initial_state, activition):
model_name = 'GRU.' + activition.__name__
if (initial_state != 0):
model_name += '.initial'
if (backward):
model_name += '.backward'
else:
model_name += '.forward'
return model_name
direction_options = [False, True]
activation_options = [C.tanh]
initial_state_options = [0]
input_dim = 2
cell_dim = 3
batch_size = 1
sequence_len = 5
for config in list(product(direction_options, initial_state_options, activation_options)):
model_filename = MakeGRUNameFromConfig(*config)
print(model_filename)
backward, initial_state, activation = config
x = C.input_variable(input_dim, dynamic_axes=[C.Axis.default_batch_axis(), C.Axis('sequenceAxis')])
GRUModel = C.layers.Recurrence(C.layers.GRU(cell_dim,
activation = activation),
initial_state = initial_state,
go_backwards=backward)(x)
#CLG.plot(GRUModel, filename=cntk_pdf_filename)
#plot_block_internals(GRUModel, 'GRU', model_filename)
data = np.random.uniform(low=0.0, high=1.0, size=(batch_size, sequence_len, input_dim)).astype('f')
verify_one_input(GRUModel, data, tmpdir, model_filename)
def test_LSTM(tmpdir):
pytest.skip('Need to support new ONNX spec.')
def CreateLSTMModel(activation, peepholes, self_stabilization, cell_dim,
initial_state):
return C.layers.Sequential([
C.layers.Recurrence(C.layers.LSTM(
cell_dim,
use_peepholes=peepholes,
activation=activation,
enable_self_stabilization=self_stabilization),
initial_state=initial_state)
])
def MakeLSTMNameFromConfig(use_peepholes, enable_self_stabilization,
initial_state, activition):
model_name = 'LSTM.' + activition.__name__
if (use_peepholes):
model_name += '.peephole'
if (enable_self_stabilization):
model_name += '.stabilize'
if (initial_state != 0):
model_name += '.initial'
return model_name
# lstm attributes
use_peepholes_options = [False]
enable_self_stabilization_options = [False]
activation_options = [C.tanh]
#Recurrence attributes
initial_state_options = [0, 0.23]
input_dim = 2
cell_dim = 3
batch_size = 1
sequence_len = 5
for config in list(
product(use_peepholes_options, enable_self_stabilization_options,
initial_state_options, activation_options)):
model_filename = MakeLSTMNameFromConfig(*config)
use_peepholes, enable_self_stabilization, initial_state, activation = config
x = C.input_variable(
input_dim,
dynamic_axes=[C.Axis.default_batch_axis(),
C.Axis('sequenceAxis')])
LSTMmodel = CreateLSTMModel(
peepholes=use_peepholes,
activation=activation,
initial_state=initial_state,
cell_dim=cell_dim,
self_stabilization=enable_self_stabilization)(x)
data = np.random.uniform(low=0.0,
high=1.0,
size=(batch_size, sequence_len,
input_dim)).astype('f')
verify_one_input(LSTMmodel, data, tmpdir, model_filename)
def test_sequence_max():
np.random.seed(0)
a = np.float32(np.random.rand(20, 100, 8))
src = C.sequence.input_variable(shape=(8), sequence_axis=C.Axis("Seq"))
out = C.sequence.reduce_max(src)
val = out.eval({src: a})
expected = np.max(a, 1)
assert np.allclose(val, expected)
def test_stop_gradient():
x = C.sequence.input_variable(shape=(2, ),
sequence_axis=C.Axis("B"),
needs_gradient=True)
y = C.sequence.input_variable(shape=(2, ),
sequence_axis=C.Axis("B"),
needs_gradient=True)
z = C.element_times(x, y)
w = z + C.stop_gradient(z)
a = np.reshape(np.float32([0.25, 0.5, 0.1, 1]), (1, 2, 2))
b = np.reshape(np.float32([-1.25, 1.5, 0.1, -1]), (1, 2, 2))
bwd, fwd = w.forward({x: a, y: b}, [w.output], set([w.output]))
value = list(fwd.values())[0]
expected = np.multiply(a, b) * 2
assert np.allclose(value, expected)
grad = w.backward(bwd, {w.output: np.ones_like(value)}, set([x, y]))
assert np.allclose(grad[x], b)
assert np.allclose(grad[y], a)
def test_sequence_softmax_with_large_numbers():
np.random.seed(0)
a = [500000 * np.ones(i, dtype=np.float32) for i in (7, 7, 7)]
src = C.sequence.input_variable(shape=(1), sequence_axis=C.Axis("Seq"))
out = C.sequence.softmax(src)
val = out.eval({src: a})
expected = [np_softmax(a_i, 0) for a_i in a]
for val_i, expected_i in zip(val, expected):
assert np.allclose(val_i, expected_i)
def test_cos_distane_backward2():
x = C.sequence.input_variable(shape=(100,), sequence_axis=C.Axis("B"), needs_gradient=True)
y = C.sequence.input_variable(shape=(100,), sequence_axis=C.Axis("B"), needs_gradient=True)
z = C.cosine_distance(x, y);
np.random.seed(0)
a = np.float32(np.random.rand(10,50,100))
b = np.float32(np.random.rand(10,50,100))
bwd, fwd = z.forward({x:a, y:b}, [z.output], set([z.output]))
value = list(fwd.values())[0]
expected_cos = numpy_cos(a,b)
expected = expected_cos.forward()
assert np.allclose(value, expected)
grad = z.backward(bwd, {z.output:np.ones_like(value)}, set([x, y]))
bwd = expected_cos.backward()
x_driv_expected = bwd['a']
y_driv_expected = bwd['b']
assert (np.all(np.absolute(grad[x]-x_driv_expected) < 1e-6))
assert (np.all(np.absolute(grad[y]-y_driv_expected) < 1e-6))
def test_sequence_max_with_variable_lengths():
np.random.seed(0)
a = [-np.ones(i, dtype=np.float32) for i in (7, 11, 13)]
src = C.sequence.input_variable(shape=(1), sequence_axis=C.Axis("Seq"))
out = C.sequence.reduce_max(src)
val = out.eval({src: a})
expected = [np.max(a_i) for a_i in a]
for val_i, expected_i in zip(val, expected):
assert np.allclose(val_i, expected_i)
def decoder(in_dims: int,
sa_dims: int,
head_dims: int,
hidden_dims: int,
kv_memory,
name: str = 'decoder',
as_block: bool = False) -> C.Function:
X = C.placeholder(
in_dims, (C.Axis.default_batch_axis(), C.Axis.default_dynamic_axis()),
name=name + '_ph')
k_memory = C.placeholder(in_dims,
(C.Axis.default_batch_axis(), C.Axis('kv_seq')),
name=name + '_k_memory')
v_memory = C.placeholder(in_dims,
(C.Axis.default_batch_axis(), C.Axis('kv_seq')),
name=name + '_v_memory')
# placeholder 는 clone이 안되서 k, v를 kv로 하나의 placeholder로서 묶으면 안됨
mhsa_layer = multi_headed_self_attention_layer(in_dims,
sa_dims,
head_dims,
mask_opt=True)
eda_layer = multi_headed_self_attention_layer(in_dims,
sa_dims,
head_dims,
k_ph=True,
v_ph=True)
ff_layer = feed_forward_layer(in_dims, hidden_dims)
sa = layer_normalization(X + mhsa_layer(X)) # w/o mask
eda = layer_normalization(sa + eda_layer(sa, k_memory, v_memory))
ff = layer_normalization(eda + ff_layer(eda))
result = ff
if as_block is True:
return C.as_block(result, [(X, X), (k_memory, k_memory),
(v_memory, v_memory)], name)
else:
return result
def test_cosine_distance():
a = np.reshape(np.arange(25.0, dtype=np.float32), (5, 5))
b = np.reshape(np.arange(0, 5, dtype=np.float32), (1, 5))
src = C.sequence.input_variable(shape=(5), sequence_axis=C.Axis("Seq"))
tgt = C.input_variable(shape=(5))
tgt_br = C.sequence.broadcast_as(tgt, src)
cos_seq = C.cosine_distance(src, tgt_br)
assert len(cos_seq.dynamic_axes) == 2
assert cos_seq.dynamic_axes[1].name == "Seq"
val = cos_seq.eval({src: [a], tgt: [b]})
expected = [[1., 0.914659, 0.878459, 0.86155, 0.851852]]
assert np.allclose(val, expected)
def __init__(self, config_file):
data_config = importlib.import_module(config_file).data_config
model_config = importlib.import_module(config_file).model_config
self.word_count_threshold = data_config['word_count_threshold']
self.char_count_threshold = data_config['char_count_threshold']
self.word_size = data_config['word_size']
self.abs_path = os.path.dirname(os.path.abspath(__file__))
pickle_file = os.path.join(self.abs_path, data_config['pickle_file'])
with open(pickle_file, 'rb') as vf:
known, self.vocab, self.chars = pickle.load(vf)
self.wg_dim = known
self.wn_dim = len(self.vocab) - known
self.c_dim = len(self.chars)
self.a_dim = 1
self.hidden_dim = model_config['hidden_dim']
self.w2v_hidden_dim = model_config['w2v_hidden_dim']
self.convs = model_config['char_convs']
self.dropout = model_config['dropout']
self.char_emb_dim = model_config['char_emb_dim']
self.highway_layers = model_config['highway_layers']
self.two_step = model_config['two_step']
self.use_cudnn = model_config['use_cudnn']
self.use_sparse = True
# Source and target inputs to the model
inputAxis = C.Axis('inputAxis')
outputAxis = C.Axis('outputAxis')
InputSequence = C.layers.SequenceOver[inputAxis]
OutputSequence = C.layers.SequenceOver[outputAxis]
print('dropout', self.dropout)
print('use_cudnn', self.use_cudnn)
print('use_sparse', self.use_sparse)
def test_LSTM(tmpdir):
for config in list(
product(use_peepholes_options, enable_self_stabilization_options,
initial_state_options, activation_options)):
model_filename = MakeLSTMNameFromConfig(*config)
use_peepholes, enable_self_stabilization, initial_state, activation = config
x = C.input_variable(
input_dim,
dynamic_axes=[Axis.default_batch_axis(),
C.Axis('sequenceAxis')])
LSTMmodel = CreateLSTMModel(
peepholes=use_peepholes,
activation=activation,
initial_state=initial_state,
cell_dim=cell_dim,
self_stabilization=enable_self_stabilization)(x)
data = np.random.uniform(low=0.0,
high=1.0,
size=(batch_size, sequence_len,
input_dim)).astype('f')
verify_one_input(LSTMmodel, data, tmpdir, model_filename)
def test_sequence_unpack_with_broadcast_as(device_id, precision):
x = C.sequence.input_variable(5)
a = C.sequence.input_variable(4, sequence_axis=C.Axis('a'))
y, mask = C.sequence.unpack(x, 0).outputs
bvm = C.sequence.broadcast_as(0 * C.reduce_sum(y) + mask, a)
x1 = [
np.arange(7 * 5).reshape(7, 5).astype('f'),
np.arange(3 * 5).reshape(3, 5).astype('f')
]
a1 = [
np.arange(3 * 4).reshape(3, 4).astype('f'),
np.arange(6 * 4).reshape(6, 4).astype('f')
]
expected = [
np.ones((3, 7), dtype=np.float32),
np.ones((6, 7), dtype=np.float32)
]
expected[1][:, 3:] = 0
actual = bvm.eval({x: x1, a: a1})
for actual_i, expected_i in zip(actual, expected):
assert np.allclose(actual_i, expected_i)
def test_lstm_over_lstm_thought_vectors_2(device_id):
dev = cntk_device(device_id)
input_vocab_size = 3
emb_dim = 2
hidden_dim = 2
num_labels = 2
utterances_input = C.sequence.input_variable((input_vocab_size),
is_sparse=True,
name='utterances')
conversation_lengths_input = C.input_variable(
(), name='conversation_sequence_lengths')
label_input = C.sequence.input_variable(
num_labels,
is_sparse=True,
sequence_axis=C.Axis('label_sequence'),
name='labels')
with C.default_options(initial_state=0.1):
model = C.layers.Embedding(emb_dim, name='embed')(utterances_input)
model = C.layers.Recurrence(C.layers.LSTM(hidden_dim),
go_backwards=False)(model)
model = C.sequence.last(model)
model = C.user_function(
UtteranceBatchReshape(model, conversation_lengths_input))
model = C.to_sequence_like(model, label_input)
model = C.layers.Recurrence(C.layers.LSTM(hidden_dim),
go_backwards=False)(model)
model = C.layers.Dense(num_labels, name='classify')(model)
z = model
ce = C.cross_entropy_with_softmax(z, label_input)
sentinel_utt_data = C.NDArrayView.from_csr(_to_csr([[0, 0, 1]]),
device=C.cpu())
c1_utt1_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 1], [0, 1, 0],
[1, 0, 0]]),
device=C.cpu())
c1_utt2_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 0], [0, 1, 1]]),
device=C.cpu())
c1_utt3_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 1], [0, 1, 0]]),
device=C.cpu())
c2_utt1_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 1]]), device=C.cpu())
c3_utt1_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 0], [0, 1, 1],
[1, 0, 0]]),
device=C.cpu())
c3_utt2_data = C.NDArrayView.from_csr(_to_csr([[0, 1, 0]]), device=C.cpu())
all_utt_data = C.Value.create(C.sequence.input_variable(
(input_vocab_size), is_sparse=True), [
c1_utt1_data, c1_utt2_data, c1_utt3_data, c2_utt1_data,
sentinel_utt_data, sentinel_utt_data, c3_utt1_data, c3_utt2_data,
sentinel_utt_data
],
device=C.cpu()).data
conversation_lengths_data = np.asarray([3, 1, 2], dtype=np.float32)
seq1_label_data = [[0, 1], [0, 1], [1, 0]]
seq2_label_data = [[1, 0]]
seq3_label_data = [[1, 0], [0, 1]]
label_data = [
_to_csr(seq1_label_data),
_to_csr(seq2_label_data),
_to_csr(seq3_label_data)
]
param_grads, loss_result = ce.grad(
{
utterances_input: all_utt_data,
label_input: label_data,
conversation_lengths_input: conversation_lengths_data
},
wrt=ce.parameters,
outputs=[ce],
as_numpy=False)
loss_result = loss_result.as_sequences()
absolute_tolerance = 0.01
assert np.allclose(loss_result[0], [[0.678914], [0.668076], [0.728129]],
atol=absolute_tolerance)
assert np.allclose(loss_result[1], [[0.679029]], atol=absolute_tolerance)
assert np.allclose(loss_result[2], [[0.705393], [0.674243]],
atol=absolute_tolerance)
def test_RNN(tmpdir, dtype):
with C.default_options(dtype=dtype):
def CreatRNN(cell_dim,
activation,
initial_state,
direction,
num_layers,
init=C.default_override_or(C.glorot_uniform()),
init_bias=C.default_override_or(0)):
if direction == 'bidirectional':
return C.layers.Sequential([
C.layers.For(
range(num_layers), lambda i: [
(C.layers.Recurrence(C.layers.RNNStep(
cell_dim,
activation=activation,
init=init,
init_bias=init_bias),
initial_state=initial_state,
return_full_state=False,
go_backwards=False),
C.layers.Recurrence(C.layers.RNNStep(
cell_dim,
activation=activation,
init=init,
init_bias=init_bias),
initial_state=initial_state,
return_full_state=False,
go_backwards=True)), C.splice
])
])
else:
go_backward = False if direction == 'forward' else True
return C.layers.Sequential([
C.layers.For(
range(num_layers), lambda i: [
C.layers.Recurrence(C.layers.RNNStep(
cell_dim,
activation=activation,
init=init,
init_bias=init_bias),
initial_state=initial_state,
return_full_state=False,
go_backwards=go_backward)
])
])
def MakeRNNNameFromConfig(direction, num_layers, initial_state,
activition):
model_name = 'RNN.' + direction + '.'
if num_layers == 1:
model_name += 'one_layer.'
else:
assert (num_layers == 2), "needs 1 or 2 layers!"
model_name += 'two_layer.'
if (initial_state != 0):
model_name += 'initial.'
model_name += activition.__name__
return model_name
direction_options = ['forward', 'reverse', 'bidirectional']
num_layers_options = [1, 2]
initial_state_options = [0]
activation_options = [C.tanh, C.relu, C.sigmoid]
input_dim = 2
hidden_dim = 3
batch_size = 1
sequence_len = 5
for config in list(
product(direction_options, num_layers_options,
initial_state_options, activation_options)):
model_filename = MakeRNNNameFromConfig(*config)
print(model_filename)
direction, num_layers, initial_state, activation = config
x = C.input_variable(input_dim,
dynamic_axes=[
C.Axis.default_batch_axis(),
C.Axis('sequenceAxis')
])
RNNModel = CreatRNN(hidden_dim, activation, initial_state,
direction, num_layers)(x)
data = np.random.uniform(low=0.0,
high=1.0,
size=(batch_size, sequence_len,
input_dim)).astype(dtype)
verify_one_input(RNNModel, data, tmpdir, model_filename)
def self_attention_layer(in_dims: int,
out_dims: int,
name='self_attention',
as_block: bool = False,
k_ph: bool = False,
v_ph: bool = False,
mask_opt: bool = False) -> C.Function:
sq_sa_dims = C.Constant(C.sqrt(out_dims).eval(), name='sq_dims')
X = C.placeholder(
in_dims, (C.Axis.default_batch_axis(), C.Axis.default_dynamic_axis()),
name=name + '_ph')
if k_ph is False and v_ph is False:
q = C.layers.Dense(out_dims, name=name + '_q')(
X
) # W_Q = C.parameter((in_dims, out_dims), init=init, name=name+'_q')
k = C.layers.Dense(out_dims, name=name + '_k')(
X
) # W_K = C.parameter((in_dims, out_dims), init=init, name=name+'_k')
v = C.layers.Dense(out_dims, name=name + '_v')(
X
) # W_V = C.parameter((in_dims, out_dims), init=init, name=name+'_v')
elif k_ph is True and v_ph is True:
q = C.layers.Dense(out_dims, name=name + '_q')(X)
k = C.placeholder(out_dims,
(C.Axis.default_batch_axis(), C.Axis('kv_seq')),
name=name + '_k_ph')
v = C.placeholder(out_dims,
(C.Axis.default_batch_axis(), C.Axis('kv_seq')),
name=name + '_v_ph')
else:
raise Exception(f'k_ph:{k_ph}, v_ph:{v_ph}')
q_ = C.sequence.unpack(q, 0, True, name=name + '_unpack_q')
k_ = C.sequence.unpack(k, 0, True, name=name + '_unpack_k')
v_ = C.sequence.unpack(v, 0, True, name=name + '_unpack_v')
scores = C.times_transpose(q_, k_, name=name + '_score_matrix')
scaled = scores / sq_sa_dims # div_k
if mask_opt:
mask = triangular_matrix_seq(2)(X)
inf_mask = -np.inf * (mask - 0.5)
inf_mask = C.as_block(inf_mask, [(X, X)], 'mask', 'mask')
scaled = C.element_min(scaled, inf_mask)
softmax = C.softmax(scaled, name=name + '_softmax')
attention = C.times(softmax, v_, name=name + '_attention')
result = C.to_sequence_like(attention, X)
if as_block:
if k_ph is False and v_ph is False:
return C.as_block(result, [(X, X)], 'self_attention',
'self_attention_')
elif k_ph is True and v_ph is True:
return C.as_block(result, [(X, X), (k, k), (v, v)],
'self_attention', 'self_attention_')
else:
raise Exception(f'k_ph:{k_ph} v_ph:{v_ph}')
else:
return result
evaluate_decoder(test_reader, model, i2w)
# ============= configure =====================
input_vocab_dim = 69
label_vocab_dim = 69
hidden_dim = 512
num_layers = 2
attention_dim = 128
use_attention = True
use_embedding = True
embedding_dim = 200
length_increase = 1.5
InputSequence = C.layers.SequenceOver[C.Axis('inputAxis')]
LabelSequence = C.layers.SequenceOver[C.Axis('labelAxis')]
vocab, i2w, _ = get_vocab(dataPath['vocab_file'])
train_reader = create_reader(dataPath['training'], True)
valid_reader = create_reader(dataPath['validation'], True)
sentence_start = C.Constant(
np.array([w == '<s>' for w in vocab], dtype=np.float))
sentence_end_idx = vocab.index('</s>') # first </s>
if __name__ == '__main__':
model = create_model()
a = model.find_by_name('encode_h')
#x = x.root_function
print(a)
def trainNetwork():
mapper, gens = loadData(dir + fileName,
'./data/Shakespeare',
batchSize,
timeSteps,
timeShift,
load=False,
lineShape=(0, 40000))
# Input with dynamic sequence axis
# consisting of a matrix of [steps-in-time X number-of-possible-characters]
inputSeqAxis = cntk.Axis('inputAxis')
input = cntk.sequence.input_variable((timeSteps, mapper.numClasses),
sequence_axis=inputSeqAxis,
name='input')
model = createNetwork(input, layers, mapper.numClasses)
label = cntk.sequence.input_variable(mapper.numClasses,
sequence_axis=inputSeqAxis,
name='label')
z = model(input)
loss = cntk.cross_entropy_with_softmax(z, label)
error = cntk.classification_error(z, label)
printer = cntk.logging.ProgressPrinter(tag='Training',
freq=100,
num_epochs=maxEpochs)
lr_per_sample = cntk.learning_parameter_schedule_per_sample(0.001)
momentum_schedule = cntk.momentum_schedule_per_sample(0.9990913221888589)
learner = cntk.momentum_sgd(z.parameters,
lr_per_sample,
momentum_schedule,
gradient_clipping_threshold_per_sample=5.0,
gradient_clipping_with_truncation=True)
#learner = cntk.momentum_sgd(z.parameters, lr, 0.9, minibatch_size=batchSize)
#learner = cntk.fsadagrad(model.parameters, lr=lr, minibatch_size=batchSize, momentum=0.9, unit_gain=True)
trainer = cntk.Trainer(z, (loss, error), learner, [printer])
numMinibatch = mapper.samples // batchSize
print("Input sequence length: {}; unique characters {};".format(
timeSteps, mapper.numClasses))
cntk.logging.log_number_of_parameters(z)
print("Datset size {}; {} Epochs; {} minibatches per epoch".format(
mapper.samples, maxEpochs, numMinibatch))
for epoch in range(maxEpochs):
mask = [True]
for mb in range(numMinibatch):
X, Y = next(gens['train'])
#X, Y = get_data(mb, batchSize, data, mapper)
arguments = ({input: X, label: Y}, mask)
mask = [False]
trainer.train_minibatch(arguments)
if mb % 100 == 0:
print(generateText(z, mapper, 200) + '\n')
trainer.summarize_training_progress()
print(generateText(z, mapper, 100))
def test_axis():
a = C.Axis(1)
assert isinstance(a.is_static_axis, bool)
assert a.is_static_axis == True
assert a.static_axis_index() == 1
return pos_encoding
#endregion
if __name__ == '__main__':
VOCAB_DIMS = 100 # size of vocabulary
TOKEN_DIMS = 4 # size of tokens (# of embedding)
SA_DIMS = 3 # size of self attention
HEAD_DIMS = 8 # size of multi-headed self attention
HIDDEN_DIMS = 24 # feed forward layer hidden
v = np.array([[1, 0, 0, 0], [1, 1, 1, 1], [0, 1, 0, 0]], np.float32) # seq
X = C.sequence.input_variable(TOKEN_DIMS,
name='encoder_input',
sequence_axis=C.Axis('encoder_seq'))
#region encoder model
encoder_model = encoder(TOKEN_DIMS,
SA_DIMS,
HEAD_DIMS,
HIDDEN_DIMS,
as_block=False)(X)
print(encoder_model.eval({encoder_model.arguments[0]: v}))
#endregion
#region encoder-decoder model
input_size = 6
y = np.array(range(TOKEN_DIMS * input_size),
np.float32).reshape(input_size, TOKEN_DIMS)
Y = C.sequence.input_variable(
def test_lstm_over_lstm_thought_vectors(device_id):
dev = cntk_device(device_id)
input_vocab_size = 3
emb_dim = 2
hidden_dim = 2
num_labels = 2
x_seq_input = C.sequence.input_variable(
(C.FreeDimension, input_vocab_size), is_sparse=True, name='features')
label_seq_input = C.sequence.input_variable(
num_labels,
is_sparse=True,
sequence_axis=C.Axis('label_sequence'),
name='labels')
with C.default_options(initial_state=0.1):
model = C.layers.Embedding(emb_dim, name='embed')(x_seq_input)
model = C.layers.Recurrence(C.layers.LSTM(hidden_dim),
go_backwards=False)(model)
model = C.sequence.last(model)
model = C.to_sequence_like(model, label_seq_input)
model = C.layers.Recurrence(C.layers.LSTM(hidden_dim),
go_backwards=False)(model)
model = C.layers.Dense(num_labels, name='classify')(model)
z = model
ce = C.cross_entropy_with_softmax(z, label_seq_input)
seq1_data = [[[0, 1, 1], [0, 1, 0], [1, 0, 0]],
[[1, 1, 0], [0, 0, 1], [1, 0, 1]],
[[1, 0, 0], [0, 0, 1], [1, 1, 0]]]
csr_seq1 = _to_csr(seq1_data)
ndarrayview1 = C.NDArrayView.from_csr(csr_seq1,
shape=(3, 3, 3),
device=C.cpu())
seq2_data = [[[0, 0, 1], [0, 1, 1], [1, 0, 1]],
[[0, 1, 0], [1, 0, 1], [0, 0, 0]]]
csr_seq2 = _to_csr(seq2_data)
ndarrayview2 = C.NDArrayView.from_csr(csr_seq2,
shape=(2, 3, 3),
device=C.cpu())
x_seq_data = C.Value.create(C.sequence.input_variable((3, 3),
is_sparse=True),
[ndarrayview1, ndarrayview2],
device=C.cpu()).data
seq1_label_data = [[0, 1], [0, 1], [1, 0]]
seq2_label_data = [[1, 0], [0, 1]]
label_seq_data = [_to_csr(seq1_label_data), _to_csr(seq2_label_data)]
param_grads, loss_result = ce.grad(
{
x_seq_input: x_seq_data,
label_seq_input: label_seq_data
},
wrt=ce.parameters,
outputs=[ce],
as_numpy=False)
loss_result = loss_result.as_sequences()
absolute_tolerance = 0.02
assert np.allclose(loss_result[0], [[0.67126], [0.676331], [0.765814]],
atol=absolute_tolerance)
assert np.allclose(loss_result[1], [[0.685199], [0.681736]],
atol=absolute_tolerance)
def test_lstm_over_lstm_thought_vectors(device_id):
dev = cntk_device(device_id)
input_vocab_size = 3
emb_dim = 2
hidden_dim = 2
num_labels = 2
x_seq_input = C.sequence.input_variable(
(C.FreeDimension, input_vocab_size), is_sparse=True, name='features')
label_seq_input = C.sequence.input_variable(
num_labels,
is_sparse=True,
sequence_axis=C.Axis('label_sequence'),
name='labels')
with C.default_options(initial_state=0.1):
model = C.layers.Embedding(emb_dim, name='embed')(x_seq_input)
model = C.layers.Recurrence(C.layers.LSTM(hidden_dim),
go_backwards=False)(model)
model = C.sequence.last(model)
model = C.to_sequence_like(model, label_seq_input)
model = C.layers.Recurrence(C.layers.LSTM(hidden_dim),
go_backwards=False)(model)
model = C.layers.Dense(num_labels, name='classify')(model)
z = model
ce = C.cross_entropy_with_softmax(z, label_seq_input)
seq1_data = [[[0, 1, 1], [0, 1, 0], [1, 0, 0]],
[[1, 1, 0], [0, 0, 1], [1, 0, 1]],
[[1, 0, 0], [0, 0, 1], [1, 1, 0]]]
csr_seq1 = _to_csr(seq1_data)
ndarrayview1 = C.NDArrayView.from_csr(csr_seq1,
shape=(3, 3, 3),
device=C.cpu())
seq2_data = [[[0, 0, 1], [0, 1, 1], [1, 0, 1]],
[[0, 1, 0], [1, 0, 1], [0, 0, 0]]]
csr_seq2 = _to_csr(seq2_data)
ndarrayview2 = C.NDArrayView.from_csr(csr_seq2,
shape=(2, 3, 3),
device=C.cpu())
x_seq_data = C.Value.create(C.sequence.input_variable((3, 3),
is_sparse=True),
[ndarrayview1, ndarrayview2],
device=C.cpu()).data
seq1_label_data = [[0, 1], [0, 1], [1, 0]]
seq2_label_data = [[1, 0], [0, 1]]
label_seq_data = [_to_csr(seq1_label_data), _to_csr(seq2_label_data)]
param_grads, loss_result = ce.grad(
{
x_seq_input: x_seq_data,
label_seq_input: label_seq_data
},
wrt=ce.parameters,
outputs=[ce],
as_numpy=False)
loss_result = loss_result.as_sequences()
# TODO: The tolerance here is inordinately high due to the non-determinism in initialization
# of parameters as the individual tests are not run in separate processes resulting in the
# addition or removal of tests to affect the random initialization of parameters in all other
# tests that do not explicitly specify the random seed. The tolerance should be lowered to
# 0.01 after this issue in the test infrastructure has been fixed.
absolute_tolerance = 0.02
assert np.allclose(loss_result[0], [[0.63504], [0.673343], [0.698446]],
atol=absolute_tolerance)
assert np.allclose(loss_result[1], [[0.772344], [0.64295]],
atol=absolute_tolerance)
