def __init__(self):
super(NetEqualCount, self).__init__()
self.equalcount = P.EqualCount()
x = Tensor(np.array([1, 20, 5]).astype(np.int32))
y = Tensor(np.array([2, 20, 5]).astype(np.int32))
self.x = Parameter(initializer(x, x.shape), name='x')
self.y = Parameter(initializer(y, y.shape), name='y')
def extract_logits_for_lambada(logits=None, label_ids=None, input_mask=None):
# equalcount = P.EqualCount()
all_one = Tensor(np.ones(gpt2_net_cfg.seq_length), mindspore.int32)
no_mask_length = []
for i in range(input_mask.shape[0]):
input_mask_row = input_mask[i, ::]
valid_length = int(P.EqualCount()(input_mask_row,
all_one).asnumpy()[0])
# print("valid_length type is {}, length is {}".format(type(valid_length), valid_length)) # count the no padding token number
no_mask_length.append(valid_length)
logit = logits[i:i + 1:1, valid_length - 3:valid_length - 2:1, ::]
label = label_ids[i:i + 1:1, valid_length - 2:valid_length - 1:1]
# print("extract_logits logit shape: {}".format(logit.shape))
if i == 0:
output_logits = logit
final_label_ids = label
else:
output_logits = P.Concat()((output_logits, logit))
final_label_ids = P.Concat()((final_label_ids, label))
print("output_logits shape : {}".format(output_logits.shape))
print("output logits:\n{}".format(output_logits[:3, :, 7:15]))
# print("final_label_ids shape : {}".format(final_label_ids.shape))
# print("output final_label_ids:\n{}".format(final_label_ids))
return output_logits, final_label_ids, no_mask_length
'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# type of x and y not match
('Equal1', {
'block': (P.Equal(), {'exception': TypeError, 'error_keywords': ['Equal']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# shape of x and y not match
('Equal2', {
'block': (P.Equal(), {'exception': ValueError, 'error_keywords': ['Equal']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
'skip': ['backward']}),
# input is not tensor
('EqualCount0', {
'block': (P.EqualCount(), {'exception': TypeError, 'error_keywords': ['EqualCount']}),
'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# type of x and y not match
('EqualCount1', {
'block': (P.EqualCount(), {'exception': TypeError, 'error_keywords': ['EqualCount']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# shape of x and y not match
# input is not tensor
('NotEqual0', {
'block': (P.NotEqual(), {'exception': TypeError, 'error_keywords': ['NotEqual']}),
'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# type of x and y not match
def __init__(self):
super(NetEqualCount, self).__init__()
self.equalcount = P.EqualCount()
# shape of x and y not match
('Equal2', {
'block': (P.Equal(), {
'exception': ValueError,
'error_keywords': ['Equal']
}),
'desc_inputs': [
Tensor(np.ones([3, 4]).astype(np.float32)),
Tensor(np.ones([3, 2]).astype(np.float32))
],
'skip': ['backward']
}),
# input is not tensor
('EqualCount0', {
'block': (P.EqualCount(), {
'exception': TypeError,
'error_keywords': ['EqualCount']
}),
'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']
}),
# type of x and y not match
('EqualCount1', {
'block': (P.EqualCount(), {
'exception': TypeError,
'error_keywords': ['EqualCount']
}),
'desc_inputs': [
Tensor(np.ones([3, 4]).astype(np.int32)),
Tensor(np.ones([3, 4]).astype(np.float32))