def __init__(self, compute_type=mstype.float32):
super(Mod, self).__init__()
self.compute_type = compute_type
self.floor_div = P.FloorDiv()
self.sub = P.Sub()
self.multiply = P.Mul()
def __init__(self, mul_size, test_size, strategy=None, strategy2=None):
super().__init__()
mul_np = np.full(mul_size, 0.5, dtype=np.float32)
floordiv_np = np.full(test_size, 0.1, dtype=np.float32)
self.mul_weight = Parameter(Tensor(mul_np), name="mul_weight")
self.floordiv_weight = Parameter(Tensor(floordiv_np),
name="floordiv_weight")
self.mul = TwoInputBprop()
self.floor_div = P.FloorDiv()
if strategy is not None:
self.mul.op.shard(strategy2)
self.floor_div.shard(strategy)
def __init__(self,
batch_size,
seq_length,
vocab_size,
decoder,
beam_width=4,
length_penalty_weight=1.0,
max_decode_length=128,
sos_id=1,
eos_id=2,
compute_type=mstype.float32):
super(BeamSearchDecoder, self).__init__(auto_prefix=False)
self.seq_length = seq_length
self.batch_size = batch_size
self.vocab_size = vocab_size
self.beam_width = beam_width
self.length_penalty_weight = length_penalty_weight
self.max_decode_length = max_decode_length
self.decoder = decoder
self.add = P.TensorAdd()
self.expand = P.ExpandDims()
self.reshape = P.Reshape()
self.shape_flat = (-1, )
self.shape = P.Shape()
self.zero_tensor = Tensor(np.zeros([batch_size, beam_width]),
mstype.float32)
self.ninf_tensor = Tensor(np.full([batch_size, beam_width], -INF),
mstype.float32)
self.select = P.Select()
self.flat_shape = (batch_size, beam_width * vocab_size)
self.topk = P.TopK(sorted=True)
self.floor_div = P.FloorDiv()
self.vocab_size_tensor = Tensor(self.vocab_size, mstype.int32)
self.real_div = P.RealDiv()
self.mod = Mod()
self.equal = P.Equal()
self.eos_ids = Tensor(np.full([batch_size, beam_width], eos_id),
mstype.int32)
beam_ids = np.tile(
np.arange(beam_width).reshape((1, beam_width)), [batch_size, 1])
self.beam_ids = Tensor(beam_ids, mstype.int32)
batch_ids = np.arange(batch_size * beam_width).reshape(
(batch_size, beam_width)) // beam_width
self.batch_ids = Tensor(batch_ids, mstype.int32)
self.concat = P.Concat(axis=-1)
self.gather_nd = P.GatherNd()
self.greater_equal = P.GreaterEqual()
self.sub = P.Sub()
self.cast = P.Cast()
self.zeroslike = P.ZerosLike()
# init inputs and states
self.start_ids = Tensor(np.full([batch_size * beam_width, 1], sos_id),
mstype.int32)
self.init_seq = Tensor(np.full([batch_size, beam_width, 1], sos_id),
mstype.int32)
init_scores = np.tile(np.array([[0.] + [-INF] * (beam_width - 1)]),
[batch_size, 1])
self.init_scores = Tensor(init_scores, mstype.float32)
self.init_finished = Tensor(
np.zeros([batch_size, beam_width], dtype=np.bool))
self.init_length = Tensor(
np.zeros([batch_size, beam_width], dtype=np.int32))
self.length_penalty = LengthPenalty(weight=length_penalty_weight)
self.one = Tensor(1, mstype.int32)
def __init__(self, strategy1, strategy2):
super().__init__()
self.matmul = P.MatMul().set_strategy(strategy1)
self.floordiv = P.FloorDiv().set_strategy(strategy2)
'skip': ['backward']}),
('RealDiv_0', {
'block': P.RealDiv(),
'desc_const': [Tensor(2048.0), Tensor(0.0)],
'desc_inputs': [],
'skip': ['backward']}),
('RealDiv', {
'block': P.RealDiv(),
'desc_inputs': [[4], Tensor(np.ones(4).astype(np.float32))],
'desc_bprop': [[4]]}),
('RealDiv_1', {
'block': P.RealDiv(),
'desc_inputs': [[512, 1024], [512, 1024]],
'desc_bprop': [[512, 1024]]}),
('FloorDiv', {
'block': P.FloorDiv(),
'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16)),
Tensor(np.random.rand(4).astype(np.float16))],
'skip': ['backward']}),
('FloorMod', {
'block': P.FloorMod(),
'desc_inputs': [Tensor(np.random.rand(4).astype(np.float16)),
Tensor(np.random.rand(4).astype(np.float16))],
'skip': ['backward']}),
('identity', {
'block': ops.functional.identity,
'desc_inputs': [[2, 2]],
'skip': ['backward']}),
('MatMul_1', {
'block': P.MatMul(transpose_a=False, transpose_b=False),
'desc_inputs': [[1024, 160], [160, 1024]],
def __init__(self,
batch_size,
seq_length,
vocab_size,
decoder,
beam_width=4,
decoder_layers_nums=4,
length_penalty_weight=0.6,
cov_penalty_factor=0.1,
hidden_size=1024,
max_decode_length=64,
sos_id=2,
eos_id=3,
compute_type=mstype.float32):
super(BeamSearchDecoder, self).__init__()
self.encoder_length = seq_length
self.hidden_size = hidden_size
self.batch_size = batch_size
self.vocab_size = vocab_size
self.beam_width = beam_width
self.decoder_layers_nums = decoder_layers_nums
self.length_penalty_weight = length_penalty_weight
self.cov_penalty_factor = cov_penalty_factor
self.max_decode_length = max_decode_length
self.decoder = decoder
self.add = P.TensorAdd()
self.expand = P.ExpandDims()
self.reshape = P.Reshape()
self.shape_flat = (-1,)
self.shape = P.Shape()
self.zero_tensor = Tensor(np.zeros([batch_size, beam_width]), mstype.float32)
self.ninf_tensor = Tensor(np.full([batch_size, beam_width], -INF), mstype.float32)
self.select = P.Select()
self.flat_shape = (batch_size, beam_width * vocab_size)
self.topk = P.TopK(sorted=True)
self.floor_div = P.FloorDiv()
self.vocab_size_tensor = Tensor(self.vocab_size, mstype.int32)
self.real_div = P.RealDiv()
self.mod = Mod()
self.equal = P.Equal()
self.eos_ids = Tensor(np.full([batch_size, beam_width], eos_id), mstype.int32)
beam_ids = np.tile(np.arange(beam_width).reshape((1, beam_width)), [batch_size, 1])
self.beam_ids = Tensor(beam_ids, mstype.int32)
batch_ids = np.arange(batch_size * beam_width).reshape((batch_size, beam_width)) // beam_width
self.batch_ids = Tensor(batch_ids, mstype.int32)
self.concat = P.Concat(axis=-1)
self.gather_nd = P.GatherNd()
self.start = Tensor(0, dtype=mstype.int32)
self.start_ids = Tensor(np.full([batch_size * beam_width, 1], sos_id), mstype.int32)
self.init_seq = Tensor(np.full([batch_size, beam_width, self.max_decode_length], sos_id), mstype.int32)
init_scores = np.tile(np.array([[0.] + [-INF] * (beam_width - 1)]), [batch_size, 1])
self.init_scores = Tensor(init_scores, mstype.float32)
self.init_finished = Tensor(np.zeros([batch_size, beam_width], dtype=np.bool))
self.init_length = Tensor(np.zeros([batch_size, beam_width], dtype=np.int32))
self.length_penalty = LengthPenalty(weight=length_penalty_weight)
self.one = Tensor(1, mstype.int32)
self.prob_concat = P.Concat(axis=1)
self.cast = P.Cast()
self.decoder_hidden_state = Tensor(np.zeros([self.decoder_layers_nums, 2,
self.batch_size * self.beam_width,
hidden_size]), mstype.float32)
self.zeros_scores = Tensor(np.zeros([batch_size, beam_width], dtype=np.float))
self.active_index = Tensor(np.ones([batch_size, beam_width], dtype=np.int32))
self.init_zeros = Tensor(np.zeros([batch_size, beam_width], dtype=np.int32))
self.init_ones = Tensor(np.ones([batch_size, beam_width], dtype=np.float32))
self.accu_attn_scores = Tensor(np.zeros([batch_size, beam_width, self.encoder_length], dtype=np.float32))
self.zeros = Tensor([0], mstype.int32)
self.eos_tensor = Tensor(np.full([batch_size, beam_width, beam_width], eos_id), mstype.int32)
self.ones_3d = Tensor(np.full([batch_size, beam_width, self.encoder_length], 1), mstype.float32)
self.neg_inf_3d = Tensor(np.full([batch_size, beam_width, self.encoder_length], -INF), mstype.float32)
self.zeros_3d = Tensor(np.full([batch_size, beam_width, self.encoder_length], 0), mstype.float32)
self.zeros_2d = Tensor(np.full([batch_size * beam_width, self.encoder_length], 0), mstype.int32)
self.argmin = P.ArgMinWithValue(axis=1)
self.reducesum = P.ReduceSum()
self.div = P.Div()
self.shape_op = P.Shape()
self.mul = P.Mul()
self.log = P.Log()
self.less = P.Less()
self.tile = P.Tile()
self.noteq = P.Neg()
self.zeroslike = P.ZerosLike()
self.greater_equal = P.GreaterEqual()
self.sub = P.Sub()
def __init__(self):
super().__init__()
self.matmul = P.MatMul()
self.floordiv = P.FloorDiv()
'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input two tensors, but element types are not same
('Div1', {
'block': (P.Div(), {'exception': TypeError, 'error_keywords': ['Div']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input two tensors, their shapes do not match
('Div2', {
'block': (P.Div(), {'exception': ValueError, 'error_keywords': ['Div']}),
'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# one input is scalar, and another is Tensor(float32)
('FloorDiv0', {
'block': (P.FloorDiv(), {'exception': TypeError, 'error_keywords': ['FloorDiv']}),
'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input two tensors, but element types are not same
('FloorDiv1', {
'block': (P.FloorDiv(), {'exception': TypeError, 'error_keywords': ['FloorDiv']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input two tensors, their shapes do not match
('FloorDiv2', {
'block': (P.FloorDiv(), {'exception': ValueError, 'error_keywords': ['FloorDiv']}),
'desc_inputs': [Tensor(np.ones([3, 5]).astype(np.float32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# input x is Tensor(int32), not Tensor(float)
('Floor1', {
# input two tensors, their shapes do not match
('Div2', {
'block': (P.Div(), {
'exception': ValueError,
'error_keywords': ['Div']
}),
'desc_inputs': [
Tensor(np.ones([3, 5]).astype(np.float32)),
Tensor(np.ones([3, 4]).astype(np.float32))
],
'skip': ['backward']
}),
# input two tensors, their shapes do not match
('FloorDiv2', {
'block': (P.FloorDiv(), {
'exception': ValueError,
'error_keywords': ['FloorDiv']
}),
'desc_inputs': [
Tensor(np.ones([3, 5]).astype(np.float32)),
Tensor(np.ones([3, 4]).astype(np.float32))
],
'skip': ['backward']
}),
# input x is Tensor(int32), not Tensor(float)
('Floor1', {
'block': (P.Floor(), {
'exception': TypeError,
'error_keywords': ['Floor']
'desc_inputs': [],
'skip': ['backward']
}),
('RealDiv', {
'block': P.RealDiv(),
'desc_inputs': [[4], Tensor(np.ones(4).astype(np.float32))],
'desc_bprop': [[4]]
}),
('RealDiv_1', {
'block': P.RealDiv(),
'desc_inputs': [[512, 1024], [512, 1024]],
'desc_bprop': [[512, 1024]]
}),
('FloorDiv', {
'block':
P.FloorDiv(),
'desc_inputs': [
Tensor(np.random.rand(4).astype(np.float16)),
Tensor(np.random.rand(4).astype(np.float16))
],
'skip': ['backward']
}),
('FloorMod', {
'block':
P.FloorMod(),
'desc_inputs': [
Tensor(np.random.rand(4).astype(np.float16)),
Tensor(np.random.rand(4).astype(np.float16))
],
'skip': ['backward']
}),
def __init__(self):
super(NetFloorDiv, self).__init__()
self.floordiv = P.FloorDiv()
def __init__(self):
super(FloorDivNet, self).__init__()
self.floor_div = P.FloorDiv()