def __init__(self):
super(LogSigmoid, self).__init__()
self.mul = P.Mul()
self.exp = P.Exp()
self.add = P.TensorAdd()
self.rec = P.Reciprocal()
self.log = P.Log()
def __init__(self, network):
super(NetWithLossClass, self).__init__(auto_prefix=False)
self.loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
self.network = network
self.reducesum = P.ReduceSum(keep_dims=False)
self.mul = P.Mul()
self.squeeze = P.Squeeze(axis=1)
self.zeroslike = P.ZerosLike()
self.concat = P.Concat(axis=1)
self.reciprocal = P.Reciprocal()
input_mask,
token_type_id,
next_sentence_labels,
masked_lm_positions,
masked_lm_ids,
masked_lm_weights,
self.cast(F.tuple_to_array((self.sens,)),
mstype.float32))
grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
grads = self.grad_reducer(grads)
succ = self.optimizer(grads)
return F.depend(loss, succ)
grad_scale = C.MultitypeFuncGraph("grad_scale")
reciprocal = P.Reciprocal()
@grad_scale.register("Tensor", "Tensor")
def tensor_grad_scale(scale, grad):
return grad * reciprocal(scale)
_grad_overflow = C.MultitypeFuncGraph("_grad_overflow")
grad_overflow = P.FloatStatus()
@_grad_overflow.register("Tensor")
def _tensor_grad_overflow(grad):
return grad_overflow(grad)
'desc_bprop': [[2, 512, 56, 56]],
'skip': ['backward']}),
('ACos', {
'block': P.ACos(),
'desc_inputs': [[2, 3]],
'desc_bprop': [[2, 3]]}),
('Acosh', {
'block': P.Acosh(),
'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16))],
'skip': ['backward']}),
('Sin', {
'block': P.Sin(),
'desc_inputs': [[2, 3]],
'desc_bprop': [[2, 3]]}),
('Reciprocal', {
'block': P.Reciprocal(),
'desc_inputs': [[2, 3, 3, 5]],
'desc_bprop': [[2, 3, 3, 5]]}),
('Minimum_0', {
'block': P.Minimum(),
'desc_inputs': [[2, 3, 3, 5], [3, 3, 5]],
'desc_bprop': [[2, 3, 3, 5]]}),
('Maximum', {
'block': P.Maximum(),
'desc_inputs': [[2, 3, 3, 5], [2, 3, 3, 5]],
'desc_bprop': [[2, 3, 3, 5]]}),
('Maximum_0', {
'block': P.Maximum(),
'desc_inputs': [[3, 5], [2, 3, 3, 5]],
'desc_bprop': [[2, 3, 3, 5]]}),
('MaximumGrad', {
def __init__(self):
super(Net, self).__init__()
self.reciprocal = P.Reciprocal()
def __init__(self, strategy1, strategy2):
super().__init__()
self.matmul = P.MatMul().set_strategy(strategy1)
self.reciprocal = P.Reciprocal().set_strategy(strategy2)
self.matmul2 = P.MatMul().set_strategy(strategy1)
('Rsqrt1', {
'block': (P.Rsqrt(),
{'exception': TypeError, 'error_keywords': ['Rsqrt']}),
'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
'skip': ['backward']}),
# input is Tensor(bool)
('Sqrt1', {
'block': (P.Sqrt(),
{'exception': TypeError, 'error_keywords': ['Sqrt']}),
'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_))],
'skip': ['backward']}),
# input is not Tensor
('Reciprocal1', {
'block': (P.Reciprocal(),
{'exception': TypeError, 'error_keywords': ['Reciprocal']}),
'desc_inputs': [5.0],
'skip': ['backward']}),
# input x is Tensor(bool)
('Pow1', {
'block': (P.Pow(),
{'exception': TypeError, 'error_keywords': ['Pow']}),
'desc_inputs': [Tensor(np.ones([2, 3]).astype(np.bool_)), 2.0],
'skip': ['backward']}),
# input is not Tensor
('Exp1', {
'block': (P.Exp(),
{'exception': TypeError, 'error_keywords': ['Exp']}),
def tensor_grad_scale(scale, grad):
return grad * P.Reciprocal()(scale)
def __init__(self, loop_count=1):
super().__init__()
self.loop_count = loop_count
self.op_seq = (P.Sqrt(), P.Reciprocal(), P.Square())
