def forward(self, input):
in_mean, in_var = layers.reduce_mean(input, dim=[2, 3],
keep_dim=True), var(input,
axis=[2, 3],
keep_dim=True)
debug_print('reduce_mean')
out_in = (input - in_mean) / layers.sqrt(in_var + self.eps)
debug_print('sqrt')
ln_mean, ln_var = layers.reduce_mean(input,
dim=[1, 2, 3],
keep_dim=True), var(
input,
axis=[1, 2, 3],
keep_dim=True)
debug_print('reduce_mean')
out_ln = (input - ln_mean) / layers.sqrt(ln_var + self.eps)
debug_print('sqrt')
out = layers.expand(self.rho, [input.shape[0], 1, 1, 1]) * out_in + (
1 - layers.expand(self.rho, [input.shape[0], 1, 1, 1])) * out_ln
debug_print('expand')
out = out * layers.expand(self.gamma,
[input.shape[0], 1, 1, 1]) + layers.expand(
self.beta, [input.shape[0], 1, 1, 1])
debug_print('expand')
return out
def forward(self, input, gamma, beta):
in_mean, in_var = reduce_mean(input, dim=[2, 3],
keep_dim=True), my_var(input,
dim=[2, 3],
keep_dim=True)
out_in = (input - in_mean) / sqrt(in_var + self.eps)
ln_mean, ln_var = reduce_mean(input, dim=[1, 2, 3],
keep_dim=True), my_var(input,
dim=[1, 2, 3],
keep_dim=True)
out_ln = (input - ln_mean) / sqrt(ln_var + self.eps)
ex_rho = expand(self.rho, (input.shape[0], 1, 1, 1))
out = ex_rho * out_in + (1 - ex_rho) * out_ln
gamma = unsqueeze(gamma, axes=2)
gamma = unsqueeze(gamma, axes=3)
beta = unsqueeze(beta, axes=2)
beta = unsqueeze(beta, axes=3)
out = out * gamma + beta
return out
def forward(self, *args, **kwargs):
"""
Args:
start_pos (optional, `Variable` of shape [batch_size]):
token index of start of answer span in `context`
end_pos (optional, `Variable` of shape [batch_size]):
token index of end of answer span in `context`
Returns:
loss (`Variable` of shape []):
Cross entropy loss mean over batch and time, ignore positions where label == -100
if labels not set, returns None
start_logits (`Variable` of shape [batch_size, hidden_size]):
output logits of start position, use argmax(start_logit) to get start index
end_logits (`Variable` of shape [batch_size, hidden_size]):
output logits of end position, use argmax(end_logit) to get end index
"""
start_pos = kwargs.pop('start_pos', None)
end_pos = kwargs.pop('end_pos', None)
pooled, encoded = super(ErnieModelForQuestionAnswering,
self).forward(*args, **kwargs)
encoded = self.dropout(encoded)
encoded = self.classifier(encoded)
start_logit, end_logits = L.unstack(encoded, axis=-1)
if start_pos is not None and end_pos is not None:
if len(start_pos.shape) == 1:
start_pos = L.unsqueeze(start_pos, axes=[-1])
if len(end_pos.shape) == 1:
end_pos = L.unsqueeze(end_pos, axes=[-1])
start_loss = L.softmax_with_cross_entropy(start_logit, start_pos)
end_loss = L.softmax_with_cross_entropy(end_logits, end_pos)
loss = (L.reduce_mean(start_loss) + L.reduce_mean(end_loss)) / 2.
else:
loss = None
return loss, start_logit, end_logits
def var(input, axis=None, keepdim=False, unbiased=True, out=None, name=None):
dtype = convert_dtype(input.dtype)
if dtype not in ["float32", "float64"]:
raise ValueError("Layer tensor.var() only supports floating-point "
"dtypes, but received {}.".format(dtype))
rank = len(input.shape)
axes = axis if axis != None and axis != [] else range(rank)
axes = [e if e >= 0 else e + rank for e in axes]
inp_shape = input.shape if in_dygraph_mode() else layers.shape(input)
mean = layers.reduce_mean(input, dim=axis, keep_dim=True, name=name)
tmp = layers.reduce_mean(
(input - mean)**2, dim=axis, keep_dim=keepdim, name=name)
if unbiased:
n = 1
for i in axes:
n *= inp_shape[i]
if not in_dygraph_mode():
n = layers.cast(n, dtype)
zero_const = layers.fill_constant(shape=[1], dtype=dtype, value=0.0)
factor = where(n > 1.0, n / (n - 1.0), zero_const)
else:
factor = n / (n - 1.0) if n > 1.0 else 0.0
tmp *= factor
if out:
layers.assign(input=tmp, output=out)
return out
else:
return tmp
def layer_norm(x,
begin_norm_axis=1,
epsilon=1e-12,
param_attr=None,
bias_attr=None):
"""
Replace build-in layer_norm op with this function
"""
helper = LayerHelper('layer_norm', **locals())
mean = layers.reduce_mean(x, dim=begin_norm_axis, keep_dim=True)
shift_x = layers.elementwise_sub(x=x, y=mean, axis=0)
variance = layers.reduce_mean(
layers.square(shift_x), dim=begin_norm_axis, keep_dim=True)
r_stdev = layers.rsqrt(variance + epsilon)
norm_x = layers.elementwise_mul(x=shift_x, y=r_stdev, axis=0)
param_shape = [reduce(lambda x, y: x * y, norm_x.shape[begin_norm_axis:])]
param_dtype = norm_x.dtype
scale = helper.create_parameter(
attr=param_attr,
shape=param_shape,
dtype=param_dtype,
default_initializer=fluid.initializer.Constant(1.))
bias = helper.create_parameter(
attr=bias_attr,
shape=param_shape,
dtype=param_dtype,
is_bias=True,
default_initializer=fluid.initializer.Constant(0.))
out = layers.elementwise_mul(x=norm_x, y=scale, axis=-1)
out = layers.elementwise_add(x=out, y=bias, axis=-1)
return out
def _collect_metrics(self, inputs, outputs):
""" Calculate loss function by using inputs and outputs. """
metrics = {}
tgt_len = layers.reduce_sum(
layers.reduce_sum(inputs["tgt_mask"], dim=1) - 1)
tgt_len.stop_gradient = True
label = inputs["tgt_token"][:, 1:]
if self.label_smooth > 0:
one_hot_label = layers.one_hot(label, self.num_token_embeddings)
smooth_label = layers.label_smooth(one_hot_label,
epsilon=self.label_smooth,
dtype=self._dtype)
nll = layers.cross_entropy(outputs["dec_pred"],
smooth_label,
soft_label=True,
ignore_index=self.padding_idx)
else:
nll = layers.cross_entropy(outputs["dec_probs"],
label,
ignore_index=self.padding_idx)
nll = layers.reduce_sum(nll, dim=1)
token_nll = layers.reduce_sum(nll) / tgt_len
nll = layers.reduce_mean(nll)
metrics["nll"] = nll
metrics["token_nll"] = token_nll
loss = nll
if self.num_latent > 0 and self.with_bow:
bow_probs = F.unsqueeze(outputs["bow_probs"], [1])
bow_probs = layers.expand(bow_probs, [1, label.shape[1], 1])
if self.label_smooth > 0:
bow = layers.cross_entropy(bow_probs,
smooth_label,
soft_label=True,
ignore_index=self.padding_idx)
else:
bow = layers.cross_entropy(bow_probs,
label,
ignore_index=self.padding_idx)
bow = layers.reduce_sum(bow, dim=1)
token_bow = layers.reduce_sum(bow) / tgt_len
bow = layers.reduce_mean(bow)
metrics["bow"] = bow
metrics["token_bow"] = token_bow
loss = loss + bow
if self.num_latent > 0 and self.use_discriminator:
dis = 0.0 - (layers.log(outputs["pos_probs"]) +
layers.log(1.0 - outputs["neg_probs"]))
dis = layers.reduce_mean(dis)
metrics["dis"] = dis
loss = loss + dis * self.dis_ratio
metrics["loss"] = loss
metrics["token_num"] = tgt_len
return metrics
def create_model(args, config):
"""Create model for given model configuration."""
logging.info('building model')
graph_wrapper = GraphWrapper(name="graph",
node_feat=[('atom_type', [None, 1], "int64"),
('chirality_tag', [None,
1], "int64")],
edge_feat=[('bond_type', [None, 1], "int64"),
('bond_direction', [None,
1], "int64")])
# NOTE: [num_nodes, num_graphs], bs = num_graphs
pos_mask = L.data(name='pos_mask',
shape=[-1, args.batch_size],
dtype='float32')
neg_mask = L.data(name='neg_mask',
shape=[-1, args.batch_size],
dtype='float32')
encoder = GINEncoder(config)
global_repr, patch_summary = encoder.forward(graph_wrapper)
global_D = FF(encoder.embedding_dim)
local_D = FF(encoder.embedding_dim)
g_enc = global_D.forward(global_repr)
l_enc = local_D.forward(patch_summary)
res = L.matmul(l_enc, g_enc, transpose_y=True)
E_pos = get_positive_expectation(res * pos_mask,
config['measure'],
average=False)
E_pos = L.reduce_sum(E_pos) / graph_wrapper.num_nodes
E_neg = get_negative_expectation(res * neg_mask,
config['measure'],
average=False)
E_neg = L.reduce_sum(E_neg) / (graph_wrapper.num_nodes *
(graph_wrapper.num_graph - 1))
local_global_loss = E_neg - E_pos
if config['prior']:
prior_D = PriorDiscriminator(encoder.embedding_dim)
prior = L.uniform_random([args.batch_size, encoder.embedding_dim],
min=0.0,
max=1.0)
term_1 = L.reduce_mean(L.log(prior_D.forward(prior)))
term_2 = L.reduce_mean(L.log(1.0 - prior_D.forward(global_repr)))
prior_loss = -(term_1 + term_2) * config['gamma']
else:
prior_loss = 0
total_loss = local_global_loss + prior_loss
keys = ['loss', 'graph_wrapper', 'encoder', 'graph_emb']
Agent = namedtuple('Agent', keys)
return Agent(loss=total_loss,
graph_wrapper=graph_wrapper,
encoder=encoder,
graph_emb=global_repr)
def forward(self):
""" forward
"""
src, dst = L.read_file(self.pyreader)
if self.is_sparse:
# sparse mode use 2 dims input.
src = L.reshape(src, [-1, 1])
dst = L.reshape(dst, [-1, 1])
src_embed = split_embedding(src, self.num_nodes, self.hidden_size,
self.embed_init, "weight", self.num_part,
self.is_sparse)
dst_embed = split_embedding(dst, self.num_nodes, self.hidden_size,
self.embed_init, "weight", self.num_part,
self.is_sparse)
if self.is_sparse:
src_embed = L.reshape(src_embed,
[-1, 1, self.num_featuers, self.hidden_size])
dst_embed = L.reshape(
dst_embed,
[-1, self.neg_num + 1, self.num_featuers, self.hidden_size])
src_embed = L.reduce_mean(src_embed, 2)
dst_embed = L.reduce_mean(dst_embed, 2)
logits = L.matmul(src_embed, dst_embed,
transpose_y=True) # [batch_size, 1, neg_num+1]
pos_label = L.fill_constant_batch_size_like(logits, [-1, 1, 1],
"float32", 1)
neg_label = L.fill_constant_batch_size_like(logits,
[-1, 1, self.neg_num],
"float32", 0)
label = L.concat([pos_label, neg_label], -1)
pos_weight = L.fill_constant_batch_size_like(logits, [-1, 1, 1],
"float32", self.neg_num)
neg_weight = L.fill_constant_batch_size_like(logits,
[-1, 1, self.neg_num],
"float32", 1)
weight = L.concat([pos_weight, neg_weight], -1)
weight.stop_gradient = True
label.stop_gradient = True
loss = L.sigmoid_cross_entropy_with_logits(logits, label)
loss = loss * weight
loss = L.reduce_mean(loss)
loss = loss * ((self.neg_num + 1) / 2 / self.neg_num)
loss.persistable = True
self.loss = loss
return loss
def forward(self, input):
rho_ = L.clip(self.rho, min=0, max=1)
in_mean = L.reduce_mean(input, dim=[2, 3], keep_dim=True)
in_var = var(input, dim=[2, 3], keepdim=True)
out_in = (input - in_mean) / L.sqrt(in_var + self.eps)
ln_mean = L.reduce_mean(input, dim=[1, 2, 3], keep_dim=True)
ln_var = var(input, dim=[1, 2, 3], keepdim=True)
out_ln = (input - ln_mean) / L.sqrt(ln_var + self.eps)
out = rho_ * out_in + (1 - rho_) * out_ln
out = out * self.gamma + self.beta
return out
def pointwise_loss(self):
"""point wise model"""
self.logits = L.reduce_sum(self.query_repr * self.poi_repr, -1)
self.score = L.sigmoid(self.logits)
self.loss = L.sigmoid_cross_entropy_with_logits(
L.reshape(self.logits, [-1, 1]), L.reshape(self.labels, [-1, 1]))
auc_label = L.cast(self.labels, dtype="int64")
auc_label.stop_gradients = True
_, self.batch_auc, _ = L.auc(
L.reshape(self.score, [-1, 1]), L.reshape(auc_label, [-1, 1]))
self.metrics = [L.reduce_mean(self.loss), self.batch_auc]
self.loss = L.reduce_mean(self.loss)
def forward(self, input, gamma, beta):
rho_ = L.clip(self.rho, min=0, max=1)
in_mean = L.reduce_mean(input, dim=[2, 3], keep_dim=True)
in_var = var(input, dim=[2, 3], keepdim=True)
out_in = (input - in_mean) / L.sqrt(in_var + self.eps)
ln_mean = L.reduce_mean(input, dim=[1, 2, 3], keep_dim=True)
ln_var = var(input, dim=[1, 2, 3], keepdim=True)
out_ln = (input - ln_mean) / L.sqrt(ln_var + self.eps)
out = rho_ * out_in + (1 - rho_) * out_ln
out = out * L.unsqueeze(gamma, axes=[2, 3]) + L.unsqueeze(beta,
axes=[2, 3])
return out
def var(input, dim=None, keep_dim=True, unbiased=True, name=None):
rank = len(input.shape)
dims = dim if dim is not None and dim != [] else range(rank)
dims = [e if e >= 0 else e + rank for e in dims]
inp_shape = input.shape
mean = reduce_mean(input, dim=dim, keep_dim=True, name=name)
tmp = reduce_mean((input - mean) ** 2, dim=dim, keep_dim=True, name=name)
if unbiased:
n = 1
for i in dims:
n *= inp_shape[i]
factor = n / (n - 1.0) if n > 1.0 else 0.0
tmp *= factor
return tmp
def forward(self, x):
""" Forward process of LayerNorm. """
mean = layers.reduce_mean(x,
dim=list(range(self._begin_norm_axis, len(x.shape))),
keep_dim=True)
shift_x = layers.elementwise_sub(x=x, y=mean, axis=0)
variance = layers.reduce_mean(layers.square(shift_x),
dim=list(range(self._begin_norm_axis, len(x.shape))),
keep_dim=True)
r_stdev = layers.rsqrt(variance + self._epsilon)
norm_x = layers.elementwise_mul(x=shift_x, y=r_stdev, axis=0)
out = layers.elementwise_mul(x=norm_x, y=self._scale_w, axis=-1)
out = layers.elementwise_add(x=out, y=self._bias_w, axis=-1)
return out
def forward(self, input):
in_mean = layers.reduce_mean(input, dim=[2, 3], keep_dim=True)
in_var = get_var(input, dim=[2, 3], keepdim=True)
out_in = (input - in_mean) / layers.sqrt(in_var + self.eps)
ln_mean = layers.reduce_mean(input, dim=[1, 2, 3], keep_dim=True)
ln_var = get_var(input, dim=[2, 3], keepdim=True)
out_ln = (input - ln_mean) / layers.sqrt(ln_var + self.eps)
out = fluid.layers.expand(self.rho, [input.shape[0], 1, 1, 1]) * out_in + (1-fluid.layers.expand(self.rho, [input.shape[0], 1, 1, 1])) * out_ln
out = out * fluid.layers.expand(self.gamma, [input.shape[0], 1, 1, 1]) + fluid.layers.expand(self.beta, [input.shape[0], 1, 1, 1])
return out
def points2bbox(self, pts, y_first=True):
"""点集转换成包围框.
:param pts: the input points sets (fields), each points
set (fields) is represented as 2n scalar.
:param y_first: if y_first=True, the point set is represented as
[y1, x1, y2, x2 ... yn, xn], otherwise the point set is
represented as [x1, y1, x2, y2 ... xn, yn].
:return: each points set is converting to a bbox [x1, y1, x2, y2].
"""
pts_reshape = L.reshape(pts, (pts.shape[0], -1, 2, pts.shape[2], pts.shape[3]))
pts_y = pts_reshape[:, :, 0, :, :] if y_first else pts_reshape[:, :, 1, :, :]
pts_x = pts_reshape[:, :, 1, :, :] if y_first else pts_reshape[:, :, 0, :, :]
if self.transform_method == 'minmax':
# bbox_left = pts_x.min(dim=1, keepdim=True)[0]
# bbox_right = pts_x.max(dim=1, keepdim=True)[0]
# bbox_up = pts_y.min(dim=1, keepdim=True)[0]
# bbox_bottom = pts_y.max(dim=1, keepdim=True)[0]
# bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom],
# dim=1)
pass
elif self.transform_method == 'partial_minmax':
# pts_y = pts_y[:, :4, ...]
# pts_x = pts_x[:, :4, ...]
# bbox_left = pts_x.min(dim=1, keepdim=True)[0]
# bbox_right = pts_x.max(dim=1, keepdim=True)[0]
# bbox_up = pts_y.min(dim=1, keepdim=True)[0]
# bbox_bottom = pts_y.max(dim=1, keepdim=True)[0]
# bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom],
# dim=1)
pass
elif self.transform_method == 'moment':
pts_y_mean = L.reduce_mean(pts_y, dim=1, keep_dim=True)
pts_x_mean = L.reduce_mean(pts_x, dim=1, keep_dim=True)
pts_y_std = paddle.std(pts_y - pts_y_mean, axis=1, keepdim=True)
pts_x_std = paddle.std(pts_x - pts_x_mean, axis=1, keepdim=True)
moment_transfer = (self.moment_transfer * self.moment_mul) + (
self.moment_transfer.detach() * (1 - self.moment_mul))
moment_width_transfer = moment_transfer[0]
moment_height_transfer = moment_transfer[1]
half_width = pts_x_std * L.exp(moment_width_transfer)
half_height = pts_y_std * L.exp(moment_height_transfer)
bbox = L.concat([
pts_x_mean - half_width, pts_y_mean - half_height,
pts_x_mean + half_width, pts_y_mean + half_height
], axis=1)
else:
raise NotImplementedError
return bbox
def forward(self, x):
x = layers.transpose(x, perm=[0, 2, 1, 3, 4])
x = fluid.layers.pool3d(x,
pool_size=(3, 1, 1),
pool_type='avg',
pool_stride=(2, 1, 1))
b, c, t, h, w = x.shape
x = layers.transpose(x, perm=[0, 2, 1, 3, 4])
x = layers.reshape(x, shape=[b * t, c, h, w])
x = self.stem(x)
#print(self.stem.weight.numpy().sum())
x = self.bn1(x)
x = layers.pool2d(x,
pool_size=3,
pool_type='max',
pool_stride=2,
pool_padding=1)
x = self.res2(x)
x = self.res3(x)
bt, c, h, w = x.shape
x = layers.reshape(x, shape=[b, t, c, h, w])
x = layers.transpose(x, perm=[0, 2, 1, 3, 4])
x = fluid.layers.pool3d(x,
pool_size=(3, 1, 1),
pool_type='avg',
pool_stride=(2, 1, 1))
b, c, t, h, w = x.shape
x = layers.transpose(x, perm=[0, 2, 1, 3, 4])
res = layers.reshape(x[:, 1:-1], shape=[-1, c, h, w])
x = layers.reshape(x, shape=[b * t, c, h, w])
x = self.rep_flow(x)
x = self.flow_conv(x)
x = self.rep_flow2(x)
x = layers.relu(res + x)
x = self.res4(x)
x = self.res5(x)
x = self.dropout(x)
x = layers.reduce_mean(x, dim=3)
x = layers.reduce_mean(x, dim=2)
x = layers.reshape(x, shape=[x.shape[0], -1])
x = self.classify(x)
x = layers.reshape(x, shape=[b, -1, self.num_classes])
x = layers.reduce_mean(x, dim=1)
return x
def node2vec_model(graph, hidden_size=16, neg_num=5):
pyreader = l.py_reader(
capacity=70,
shapes=[[-1, 1, 1], [-1, 1, 1], [-1, neg_num, 1]],
dtypes=['int64', 'int64', 'int64'],
lod_levels=[0, 0, 0],
name='train',
use_double_buffer=True)
embed_init = fluid.initializer.UniformInitializer(low=-1.0, high=1.0)
weight_init = fluid.initializer.TruncatedNormal(scale=1.0 /
math.sqrt(hidden_size))
src, pos, negs = l.read_file(pyreader)
embed_src = l.embedding(
input=src,
size=[graph.num_nodes, hidden_size],
param_attr=fluid.ParamAttr(
name='content', initializer=embed_init))
weight_pos = l.embedding(
input=pos,
size=[graph.num_nodes, hidden_size],
param_attr=fluid.ParamAttr(
name='weight', initializer=weight_init))
weight_negs = l.embedding(
input=negs,
size=[graph.num_nodes, hidden_size],
param_attr=fluid.ParamAttr(
name='weight', initializer=weight_init))
pos_logits = l.matmul(
embed_src, weight_pos, transpose_y=True) # [batch_size, 1, 1]
neg_logits = l.matmul(
embed_src, weight_negs, transpose_y=True) # [batch_size, 1, neg_num]
ones_label = pos_logits * 0. + 1.
ones_label.stop_gradient = True
pos_loss = l.sigmoid_cross_entropy_with_logits(pos_logits, ones_label)
zeros_label = neg_logits * 0.
zeros_label.stop_gradient = True
neg_loss = l.sigmoid_cross_entropy_with_logits(neg_logits, zeros_label)
loss = (l.reduce_mean(pos_loss) + l.reduce_mean(neg_loss)) / 2
return pyreader, loss
def fuse_math_min_mean_neg(x):
"""
Fuse operation min mean for hinge loss computation of negative samples
"""
minval = L.clip(-x - 1, -1e8, 0)
loss = - L.reduce_mean(minval)
return loss
def link_predict_model(num_nodes,
hidden_size=16,
name='link_predict_task',
binary_op_type="Weighted-L2"):
pyreader = l.py_reader(capacity=70,
shapes=[[-1, 1], [-1, 1], [-1, 1]],
dtypes=['int64', 'int64', 'int64'],
lod_levels=[0, 0, 0],
name=name + '_pyreader',
use_double_buffer=True)
u, v, label = l.read_file(pyreader)
u_embed = l.embedding(input=u,
size=[num_nodes, hidden_size],
param_attr=fluid.ParamAttr(name='content'))
v_embed = l.embedding(input=v,
size=[num_nodes, hidden_size],
param_attr=fluid.ParamAttr(name='content'))
u_embed.stop_gradient = True
v_embed.stop_gradient = True
edge_embed = binary_op(u_embed, v_embed, binary_op_type)
logit = l.fc(input=edge_embed, size=1)
loss = l.sigmoid_cross_entropy_with_logits(logit, l.cast(label, 'float32'))
loss = l.reduce_mean(loss)
prob = l.sigmoid(logit)
return pyreader, loss, prob, label
def forward(self, *args, **kwargs):
"""
Args:
labels (optional, `Variable` of shape [batch_size]):
ground truth label id for each sentence
Returns:
loss (`Variable` of shape []):
Cross entropy loss mean over batch
if labels not set, returns None
logits (`Variable` of shape [batch_size, hidden_size]):
output logits of classifier
"""
labels = kwargs.pop('labels', None)
pooled, encoded = super(ErnieModelForSequenceClassification,
self).forward(*args, **kwargs)
hidden = self.dropout(pooled)
logits = self.classifier(hidden)
if labels is not None:
if len(labels.shape) == 1:
labels = L.reshape(labels, [-1, 1])
loss = L.softmax_with_cross_entropy(logits, labels)
loss = L.reduce_mean(loss)
else:
loss = None
return loss, logits
def train_forward(self):
entity_embedding, relation_embedding, transfer_matrix = self.create_share_variables(
)
pos_head = self.lookup_table(self.train_pos_input[:, 0],
entity_embedding)
pos_tail = self.lookup_table(self.train_pos_input[:, 2],
entity_embedding)
pos_rel = self.lookup_table(self.train_pos_input[:, 1],
relation_embedding)
neg_head = self.lookup_table(self.train_neg_input[:, 0],
entity_embedding)
neg_tail = self.lookup_table(self.train_neg_input[:, 2],
entity_embedding)
neg_rel = self.lookup_table(self.train_neg_input[:, 1],
relation_embedding)
rel_matrix = layers.reshape(
self.lookup_table(self.train_pos_input[:, 1], transfer_matrix),
[-1, self.hidden_size, self.hidden_size])
pos_head_trans = self.matmul_with_expend_dims(pos_head, rel_matrix)
pos_tail_trans = self.matmul_with_expend_dims(pos_tail, rel_matrix)
rel_matrix_neg = layers.reshape(
self.lookup_table(self.train_neg_input[:, 1], transfer_matrix),
[-1, self.hidden_size, self.hidden_size])
neg_head_trans = self.matmul_with_expend_dims(neg_head, rel_matrix_neg)
neg_tail_trans = self.matmul_with_expend_dims(neg_tail, rel_matrix_neg)
pos_score = self._algorithm(pos_head_trans, pos_rel, pos_tail_trans)
neg_score = self._algorithm(neg_head_trans, neg_rel, neg_tail_trans)
pos = layers.reduce_sum(layers.abs(pos_score), -1, keep_dim=False)
neg = layers.reduce_sum(layers.abs(neg_score), -1, keep_dim=False)
neg = layers.reshape(neg, shape=[-1, 1], inplace=True)
loss = layers.reduce_mean(layers.relu(pos - neg + self.margin))
return [loss]
def forward(self, *args, **kwargs):
"""
Args:
labels (optional, `Variable` of shape [batch_size, seq_len]):
ground truth label id for each token
Returns:
loss (`Variable` of shape []):
Cross entropy loss mean over batch and time, ignore positions where label == -100
if labels not set, returns None
logits (`Variable` of shape [batch_size, seq_len, hidden_size]):
output logits of classifier
"""
labels = kwargs.pop('labels', None)
pooled, encoded = super(ErnieModelForTokenClassification, self).forward(*args, **kwargs)
hidden = self.dropout(encoded) # maybe not?
logits = self.classifier(hidden)
if labels is not None:
if len(labels.shape) == 2:
labels = L.unsqueeze(labels, axes=[-1])
loss = L.softmax_with_cross_entropy(logits, labels)
loss = L.reduce_mean(loss)
else:
loss = None
return loss, logits
def create_model(args, config, graph_label):
"""Create model for given model configuration."""
logging.info('building model')
graph_wrapper = GraphWrapper(name="graph",
node_feat=[('atom_type', [None, 1], "int64"),
('chirality_tag', [None,
1], "int64")],
edge_feat=[('bond_type', [None, 1], "int64"),
('bond_direction', [None,
1], "int64")])
encoder = GINEncoder(config)
global_repr, patch_summary = encoder.forward(graph_wrapper)
hid = L.fc(global_repr,
config['hidden_size'],
act='relu',
name='finetune_fc1')
hid = L.fc(hid, config['hidden_size'], act='relu', name='finetune_fc2')
logits = L.fc(global_repr, args.num_tasks, name="finetune_fc3")
loss = L.sigmoid_cross_entropy_with_logits(x=logits, label=graph_label)
loss = L.reduce_mean(loss)
pred = L.sigmoid(logits)
keys = ['loss', 'graph_wrapper', 'encoder', 'graph_emb', 'pred']
Agent = namedtuple('Agent', keys)
return Agent(loss=loss,
graph_wrapper=graph_wrapper,
encoder=encoder,
graph_emb=global_repr,
pred=pred)
def soft_dice_loss(logits, labels):
probs = L.softmax(logits, axis=-1)
one_hot = F.one_hot(labels, depth=probs.shape[-1])
intersection = L.reduce_sum(probs * one_hot, dim=-1)
# union = L.reduce_sum(probs, axis=-1) + L.reduce_sum(labels, axis=-1)
loss = 1 - intersection
return L.reduce_mean(loss)
def __call__(self, msg):
alpha = msg["alpha"] # lod-tensor (batch_size, num_heads)
if attn_drop:
old_h = alpha
dropout = F.data(name='attn_drop', shape=[1], dtype="int64")
u = L.uniform_random(shape=L.cast(L.shape(alpha)[:1], 'int64'),
min=0.,
max=1.)
keeped = L.cast(u > dropout, dtype="float32")
self_attn_mask = L.scale(x=keeped,
scale=10000.0,
bias=-1.0,
bias_after_scale=False)
n_head_self_attn_mask = L.stack(x=[self_attn_mask] * num_heads,
axis=1)
n_head_self_attn_mask.stop_gradient = True
alpha = n_head_self_attn_mask + alpha
alpha = L.lod_reset(alpha, old_h)
h = msg["v"]
alpha = paddle_helper.sequence_softmax(alpha)
self.alpha = alpha
old_h = h
h = h * alpha
h = L.lod_reset(h, old_h)
h = L.sequence_pool(h, "sum")
if concat:
h = L.reshape(h, [-1, num_heads * hidden_size])
else:
h = L.reduce_mean(h, dim=1)
return h
def node_classify_model(word2id, num_labels, embed_dim=16):
"""Build node classify model.
Args:
word2id(dict): map word(node) to its corresponding index
num_labels: The number of labels.
embed_dim: The dimension of embedding.
"""
nodes = fl.data('nodes', shape=[None, 1], dtype='int64')
labels = fl.data('labels', shape=[None, 1], dtype='int64')
embed_nodes = fl.embedding(input=nodes,
size=[len(word2id), embed_dim],
param_attr=fluid.ParamAttr(name='content'))
embed_nodes.stop_gradient = True
probs = fl.fc(input=embed_nodes, size=num_labels, act='softmax')
predict = fl.argmax(probs, axis=-1)
loss = fl.cross_entropy(input=probs, label=labels)
loss = fl.reduce_mean(loss)
return {
'loss': loss,
'probs': probs,
'predict': predict,
'labels': labels,
}
def forward(self, src_ids, *args, **kwargs):
tgt_labels = kwargs.pop('tgt_labels', None)
tgt_pos = kwargs.pop('tgt_pos', None)
encode_only = kwargs.pop('encode_only', False)
_, encoded, info = ErnieModel.forward(self, src_ids, *args, **kwargs)
#log.debug('hidden_-1 %r'% L.reduce_mean(info['hiddens'][0]).numpy())
#log.debug('hidden_0 %r'% L.reduce_mean(info['hiddens'][1]).numpy())
if encode_only:
return None, None, info
elif tgt_labels is None:
encoded = self.mlm(encoded)
encoded = self.mlm_ln(encoded)
logits = L.matmul(encoded, self.word_emb.weight, transpose_y=True) + self.mlm_bias
output_ids = L.argmax(logits, -1)
return output_ids, logits, info
else:
encoded_2d = L.gather_nd(encoded, tgt_pos)
#log.debug('input shape %s' % repr(src_ids.shape))
#log.debug(L.gather_nd(src_ids, tgt_pos).numpy())
encoded_2d = self.mlm(encoded_2d)
encoded_2d = self.mlm_ln(encoded_2d)
logits_2d = L.matmul(encoded_2d, self.word_emb.weight, transpose_y=True) + self.mlm_bias
if len(tgt_labels.shape) == 1:
tgt_labels = L.reshape(tgt_labels, [-1, 1])
loss = L.reduce_mean(
L.softmax_with_cross_entropy(logits_2d, tgt_labels, soft_label=(tgt_labels.shape[-1] != 1))
)
return loss, logits_2d, info
def forward(self):
"""forward"""
features_list = [self.gw.node_feat["attr"]]
for i in range(self.num_layers):
h = gin(self.gw,
features_list[i],
hidden_size=self.hidden_size,
activation="relu",
name="gin_%s" % (i),
init_eps=0.0,
train_eps=self.train_eps)
h = fl.batch_norm(h)
h = fl.relu(h)
features_list.append(h)
output = 0
for i, h in enumerate(features_list):
pooled_h = pgl.layers.graph_pooling(self.gw, h, self.pool_type)
drop_h = fl.dropout(pooled_h,
self.dropout_prob,
dropout_implementation="upscale_in_train")
output += fl.fc(drop_h,
size=self.num_class,
act=None,
param_attr=fluid.ParamAttr(name="final_fc_%s" %
(i)))
# calculate loss
self.loss = fl.softmax_with_cross_entropy(output, self.labels)
self.loss = fl.reduce_mean(self.loss)
self.acc = fl.accuracy(fl.softmax(output), self.labels)
def forward(self, is_test=False):
"""
Build the network.
"""
graph_wrapper = GraphWrapper(name="graph",
node_feat=[
('atom_type', [None, 1], "int64"),
('chirality_tag', [None, 1], "int64")],
edge_feat=[
('bond_type', [None, 1], "int64"),
('bond_direction', [None, 1], "int64")])
masked_node_indice = layers.data(name="masked_node_indice", shape=[-1, 1], dtype="int64")
masked_node_label = layers.data(name="masked_node_label", shape=[-1, 1], dtype="int64")
node_repr = self.gnn_model.forward(graph_wrapper, is_test=is_test)
masked_node_repr = layers.gather(node_repr, masked_node_indice)
logits = layers.fc(masked_node_repr,
size=len(CompoundConstants.atom_num_list),
name="masked_node_logits")
loss, pred = layers.softmax_with_cross_entropy(
logits, masked_node_label, return_softmax=True)
loss = layers.reduce_mean(loss)
acc = layers.accuracy(pred, masked_node_label)
self.graph_wrapper = graph_wrapper
self.loss = loss
def get_node_repr(self):
if self.config.JK == "last":
return self.feature_list[-1]
elif self.config.JK == "mean":
return L.reduce_mean(self.feature_list, axis=0)
else:
return L.reduce_sum(self.feature_list, axis=0)
