def backprop_pytt_pooler_output(d_outputs, sgd=None):
total_grads = []
for doc, dY in zip(docs, d_outputs):
if doc._.pytt_d_pooler_output.size == 0:
xp = get_array_module(doc._.pytt_pooler_output)
grads = xp.zeros(doc._.pytt_pooler_output.shape, dtype="f")
doc._.pytt_d_pooler_output = grads
doc._.pytt_d_pooler_output += dY
xp = get_array_module(dY)
total_grads.append(float(xp.abs(dY).sum()))
return None
def from_truncated(cls, square: Array,
lengths: List[int]) -> "RaggedArray":
if len(lengths) != square.shape[0]:
raise ValueError(
"Truncated array must have shape[0] == len(lengths)")
width = square.shape[1]
max_len = max(lengths, default=0)
extra_dims = square.shape[2:]
if width == max_len:
return RaggedArray(square, lengths)
elif width > max_len:
raise ValueError(
"Expected width < max_len. Got {width} > {max_len}")
xp = get_array_module(square)
expanded = xp.zeros((sum(lengths), ) + extra_dims, dtype=square.dtype)
# TODO: I know there's a way to do this without the loop :(. Escapes
# me currently.
start = 0
for i, length in enumerate(lengths):
# We could have a row that's actually shorter than the width,
# if the array was padded. Make sure we don't get junk values.
row_width = min(width, length)
expanded[start:start + row_width] = square[i, :row_width]
start += length
return cls(expanded, lengths)
def get_pytt_class_tokens(docs, drop=0.0):
"""Output a List[array], where the array is the class vector
for each sentence in the document. To backprop, we increment the values
in the doc._.pytt_d_last_hidden_state array.
"""
xp = get_array_module(docs[0]._.pytt_last_hidden_state)
outputs = []
for doc in docs:
wp_tensor = doc._.pytt_last_hidden_state
class_vectors = []
for sent in doc.sents:
if sent._.pytt_start is not None:
class_vectors.append(wp_tensor[sent._.pytt_start])
else:
class_vectors.append(
xp.zeros((wp_tensor.shape[-1], ), dtype="f"))
Y = xp.vstack(class_vectors)
outputs.append(Y)
def backprop_pytt_class_tokens(d_outputs, sgd=None):
for doc, dY in zip(docs, d_outputs):
if doc._.pytt_d_last_hidden_state.size == 0:
xp = get_array_module(doc._.pytt_last_hidden_state)
grads = xp.zeros(doc._.pytt_last_hidden_state.shape, dtype="f")
doc._.pytt_d_last_hidden_state = grads
for i, sent in enumerate(doc.sents):
if sent._.pytt_start is not None:
doc._.pytt_d_last_hidden_state[sent._.pytt_start] += dY[i]
return None
return outputs, backprop_pytt_class_tokens
def get_class_tokens(docs, drop=0.0):
"""Output a List[array], where the array is the class vector
for each sentence in the document. To backprop, we increment the values
in the Doc's d_last_hidden_state array.
"""
xp = get_array_module(docs[0]._.get(ATTRS.last_hidden_state))
outputs = []
doc_class_tokens = []
for doc in docs:
class_tokens = []
for i, wp in enumerate(doc._.get(ATTRS.word_pieces_)):
if is_class_token(wp):
class_tokens.append(i)
doc_class_tokens.append(xp.array(class_tokens, dtype="i"))
wp_tensor = doc._.get(ATTRS.last_hidden_state)
outputs.append(wp_tensor[doc_class_tokens[-1]])
def backprop_class_tokens(d_outputs, sgd=None):
for doc, class_tokens, dY in zip(docs, doc_class_tokens, d_outputs):
if doc._.get(ATTRS.d_last_hidden_state).size == 0:
xp = get_array_module(doc._.get(ATTRS.last_hidden_state))
grads = xp.zeros(doc._.get(ATTRS.last_hidden_state).shape,
dtype="f")
doc._.set(ATTRS.d_last_hidden_state, grads)
doc._.get(ATTRS.d_last_hidden_state)[class_tokens] += dY
return None
return outputs, backprop_class_tokens
def cosine(vec1, vec2):
xp = get_array_module(vec1)
norm1 = xp.linalg.norm(vec1)
norm2 = xp.linalg.norm(vec2)
if norm1 == 0.0 or norm2 == 0.0:
return 0
else:
return vec1.dot(vec2) / (norm1 * norm2)
def cosine(vec1, vec2):
xp = get_array_module(vec1)
norm1 = xp.linalg.norm(vec1)
norm2 = xp.linalg.norm(vec2)
if norm1 == 0.0 or norm2 == 0.0:
return 0
else:
return vec1.dot(vec2) / (norm1 * norm2)
def backprop_pooler_output(d_outputs, sgd=None):
for doc, dY in zip(docs, d_outputs):
if doc._.get(ATTRS.d_pooler_output).size == 0:
xp = get_array_module(doc._.get(ATTRS.pooler_output))
grads = xp.zeros(doc._.get(ATTRS.pooler_output).shape, dtype="f")
doc._.set(ATTRS.d_pooler_output, grads)
doc._.set(ATTRS.d_pooler_output, doc._.get(ATTRS.d_pooler_output) + dY)
return None
def backprop_class_tokens(d_outputs, sgd=None):
for doc, class_tokens, dY in zip(docs, doc_class_tokens, d_outputs):
if doc._.get(ATTRS.d_last_hidden_state).size == 0:
xp = get_array_module(doc._.get(ATTRS.last_hidden_state))
grads = xp.zeros(doc._.get(ATTRS.last_hidden_state).shape, dtype="f")
doc._.set(ATTRS.d_last_hidden_state, grads)
doc._.get(ATTRS.d_last_hidden_state)[class_tokens] += dY
return None
def backprop_pytt_pooler_output(d_outputs, sgd=None):
for doc, dY in zip(docs, d_outputs):
if doc._.pytt_d_pooler_output.size == 0:
xp = get_array_module(doc._.pytt_pooler_output)
grads = xp.zeros(doc._.pytt_pooler_output.shape, dtype="f")
doc._.pytt_d_pooler_output = grads
doc._.pytt_d_pooler_output += dY
return None
def backprop_pytt_class_tokens(d_outputs, sgd=None):
for doc, class_tokens, dY in zip(docs, doc_class_tokens, d_outputs):
if doc._.pytt_d_last_hidden_state.size == 0:
xp = get_array_module(doc._.pytt_last_hidden_state)
grads = xp.zeros(doc._.pytt_last_hidden_state.shape, dtype="f")
doc._.pytt_d_last_hidden_state = grads
doc._.pytt_d_last_hidden_state[class_tokens] += dY
return None
def backprop_pytt_last_hidden(d_outputs, sgd=None):
for doc, d_lh in zip(docs, d_outputs):
xp = get_array_module(d_lh)
shape = d_lh.shape
dtype = d_lh.dtype
if doc._.pytt_d_last_hidden_state.size == 0:
doc._.pytt_d_last_hidden_state = xp.zeros(shape, dtype=dtype)
doc._.pytt_d_last_hidden_state += d_lh
return None
def cosine_similarity(vec1, vec2) -> float:
"""Compute the cosine similarity of two vectors."""
if vec1.all() == 0 or vec2.all() == 0:
return 0.0
xp = get_array_module(vec1)
norm1 = xp.linalg.norm(vec1)
norm2 = xp.linalg.norm(vec2)
if norm1 == norm2:
return 1.0
return xp.dot(vec1, vec2) / (norm1 * norm2)
def from_padded(cls, padded: Array, lengths: List[int]) -> "RaggedArray":
if max(lengths, default=0) > padded.shape[1]:
return cls.from_truncated(padded, lengths)
mask = lengths2mask(lengths)
assert sum(mask) == sum(lengths)
all_rows = padded.reshape((-1,) + padded.shape[2:])
xp = get_array_module(all_rows)
data = xp.ascontiguousarray(all_rows[mask])
assert data.shape[0] == sum(lengths)
return cls(data, lengths)
def tanh(X, drop=0.0):
xp = get_array_module(X)
Y = xp.tanh(X)
def backprop_tanh(dY, sgd=None):
one = Y.dtype.type(1)
dX = dY * (one - Y * Y)
return dX
return Y, backprop_tanh
def backprop_pytt_class_tokens(d_outputs, sgd=None):
for doc, dY in zip(docs, d_outputs):
if doc._.pytt_d_last_hidden_state.size == 0:
xp = get_array_module(doc._.pytt_last_hidden_state)
grads = xp.zeros(doc._.pytt_last_hidden_state.shape, dtype="f")
doc._.pytt_d_last_hidden_state = grads
for i, sent in enumerate(doc.sents):
if sent._.pytt_start is not None:
doc._.pytt_d_last_hidden_state[sent._.pytt_start] += dY[i]
return None
def backprop_last_hidden(d_outputs, sgd=None):
for doc, d_lh in zip(docs, d_outputs):
xp = get_array_module(d_lh)
shape = d_lh.shape
dtype = d_lh.dtype
if doc._.get(ATTRS.d_last_hidden_state).size == 0:
doc._.set(ATTRS.d_last_hidden_state,
xp.zeros(shape, dtype=dtype))
doc._.set(ATTRS.d_last_hidden_state,
doc._.get(ATTRS.d_last_hidden_state) + d_lh)
return None
def backprop_tensors(d_tensors, sgd=None):
for doc, d_t in zip(docs, d_tensor):
# Count how often each word-piece token is represented. This allows
# a weighted sum, so that we can make sure doc.tensor.sum()
# equals wp_tensor.sum(). Do this with sensitivity to boundary tokens
wp_rows, align_sizes = _get_boundary_sensitive_alignment(doc)
d_lh = _get_or_set_d_last_hidden_state(doc)
for i, word_piece_slice in enumerate(wp_rows):
for j in word_piece_slice:
d_lh[j] += d_tensor[i]
xp = get_array_module(d_lh)
d_lh /= xp.array(align_sizes, dtype="f").reshape(-1, 1)
return None
def get_cossim_loss(yh, y):
# Add a small constant to avoid 0 vectors
yh = yh + 1e-8
y = y + 1e-8
# https://math.stackexchange.com/questions/1923613/partial-derivative-of-cosine-similarity
xp = get_array_module(yh)
norm_yh = xp.linalg.norm(yh, axis=1, keepdims=True)
norm_y = xp.linalg.norm(y, axis=1, keepdims=True)
mul_norms = norm_yh * norm_y
cosine = (yh * y).sum(axis=1, keepdims=True) / mul_norms
d_yh = (y / mul_norms) - (cosine * (yh / norm_yh ** 2))
loss = xp.abs(cosine - 1).sum()
return loss, -d_yh
def get_cossim_loss(yh, y):
# Add a small constant to avoid 0 vectors
yh = yh + 1e-8
y = y + 1e-8
# https://math.stackexchange.com/questions/1923613/partial-derivative-of-cosine-similarity
xp = get_array_module(yh)
norm_yh = xp.linalg.norm(yh, axis=1, keepdims=True)
norm_y = xp.linalg.norm(y, axis=1, keepdims=True)
mul_norms = norm_yh * norm_y
cosine = (yh * y).sum(axis=1, keepdims=True) / mul_norms
d_yh = (y / mul_norms) - (cosine * (yh / norm_yh**2))
loss = xp.abs(cosine - 1).sum()
return loss, -d_yh
def logistic(X, drop=0.0):
xp = get_array_module(X)
if not isinstance(X, xp.ndarray):
X = xp.asarray(X)
# Clip to range (-10, 10)
X = xp.minimum(X, 10.0, X)
X = xp.maximum(X, -10.0, X)
Y = 1.0 / (1.0 + xp.exp(-X))
def logistic_bwd(dY, sgd=None):
dX = dY * (Y * (1 - Y))
return dX
return Y, logistic_bwd
def logistic(X, drop=0.0):
xp = get_array_module(X)
if not isinstance(X, xp.ndarray):
X = xp.asarray(X)
# Clip to range (-10, 10)
X = xp.minimum(X, 10.0, X)
X = xp.maximum(X, -10.0, X)
Y = 1.0 / (1.0 + xp.exp(-X))
def logistic_bwd(dY, sgd=None):
dX = dY * (Y * (1 - Y))
return dX
return Y, logistic_bwd
def s2v_doc_similarity(self, obj1, other):
"""Make a semantic similarity estimate. The default estimate is cosine
similarity using an average of word vectors.
other (object): The object to compare with. By default, accepts `Doc`,
`Span`, `Token` and `Lexeme` objects.
RETURNS (float): A scalar similarity score. Higher is more similar.
DOCS: https://spacy.io/api/doc#similarity
"""
vector1 = self.get_s2v_doc_vector(obj1)
vector2 = self.get_s2v_doc_vector(other)
if len(vector1) == 0 or len(vector2) == 0:
return -1.0
xp = get_array_module(vector1)
return xp.dot(vector1, vector2) / (self.vector_norm(vector1) *
self.vector_norm(vector2))
def tanh(X, drop=0.):
xp = get_array_module(X)
if not isinstance(X, xp.ndarray):
X = xp.asarray(X)
# Clip to range (-10, 10)
X = xp.minimum(X, 10., X)
X = xp.maximum(X, -10., X)
e = xp.exp(2*X)
Y = (e - 1.) / (e + 1.)
def tanh_bwd(dY, sgd=None):
dX = dY * (1 - Y * Y)
return dX
return Y, tanh_bwd
def get_cossim_loss(yh, y, ignore_zeros=False):
xp = get_array_module(yh)
# Find the zero vectors
if ignore_zeros:
zero_indices = xp.abs(y).sum(axis=1) == 0
# Add a small constant to avoid 0 vectors
yh = yh + 1e-8
y = y + 1e-8
# https://math.stackexchange.com/questions/1923613/partial-derivative-of-cosine-similarity
norm_yh = xp.linalg.norm(yh, axis=1, keepdims=True)
norm_y = xp.linalg.norm(y, axis=1, keepdims=True)
mul_norms = norm_yh * norm_y
cosine = (yh * y).sum(axis=1, keepdims=True) / mul_norms
d_yh = (y / mul_norms) - (cosine * (yh / norm_yh**2))
losses = xp.abs(cosine - 1)
if ignore_zeros:
# If the target was a zero vector, don't count it in the loss.
d_yh[zero_indices] = 0
losses[zero_indices] = 0
loss = losses.sum()
return loss, -d_yh
def xp(self):
return get_array_module(self.data)