def __infer__(self, x, name):
src_type = x['dtype']
validator.check_subclass("input", src_type, [mstype.tensor],
name["value"])
out = {'shape': None, 'dtype': None, 'value': None}
return out
def __init__(self,
vocab_size,
embedding_size,
use_one_hot=False,
embedding_table='normal',
dtype=mstype.float32):
super(Embedding, self).__init__()
validator.check_subclass("dtype", dtype, mstype.number_type,
self.cls_name)
validator.check_value_type('use_one_hot', use_one_hot, [bool],
self.cls_name)
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.use_one_hot = use_one_hot
self.embedding_table = Parameter(initializer(
embedding_table, [vocab_size, embedding_size]),
name='embedding_table')
self.dtype = dtype
self.expand = P.ExpandDims()
self.reshape_flat = P.Reshape()
self.shp_flat = (-1, )
self.gather = P.GatherV2()
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0, self.dtype)
self.off_value = Tensor(0.0, self.dtype)
self.array_mul = P.MatMul()
self.reshape = P.Reshape()
self.get_shp = P.Shape()
def __init__(self, vocab_size, embedding_size, use_one_hot=False, embedding_table='normal',
dtype=mstype.float32, padding_idx=None):
super(Embedding, self).__init__()
self.vocab_size = validator.check_value_type('vocab_size', vocab_size, [int], self.cls_name)
self.embedding_size = validator.check_value_type('embedding_size', embedding_size, [int], self.cls_name)
validator.check_value_type('use_one_hot', use_one_hot, [bool], self.cls_name)
validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name)
self.use_one_hot = use_one_hot
self.dtype = dtype
self.init_tensor = initializer(embedding_table, [vocab_size, embedding_size])
self.padding_idx = padding_idx
if padding_idx is not None:
self.padding_idx = validator.check_int_range(padding_idx, 0, vocab_size, Rel.INC_BOTH,
"padding_idx", self.cls_name)
if isinstance(self.init_tensor, Tensor) and self.init_tensor.init is not None:
self.init_tensor = self.init_tensor.init_data()
self.init_tensor = self.init_tensor.asnumpy()
self.init_tensor[self.padding_idx] = 0
self.init_tensor = Tensor(self.init_tensor)
self.embedding_table = Parameter(self.init_tensor, name='embedding_table')
self.expand = P.ExpandDims()
self.reshape_flat = P.Reshape()
self.shp_flat = (-1,)
self.gather = P.GatherV2()
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0, self.dtype)
self.off_value = Tensor(0.0, self.dtype)
self.array_mul = P.MatMul()
self.reshape = P.Reshape()
self.get_shp = P.Shape()
def __init__(self,
vocab_size,
embedding_size,
use_one_hot=False,
embedding_table='normal',
dtype=mstype.float32,
padding_idx=None):
super(Embedding_Thor, self).__init__()
self.vocab_size = Validator.check_value_type('vocab_size', vocab_size,
[int], self.cls_name)
self.embedding_size = Validator.check_value_type(
'embedding_size', embedding_size, [int], self.cls_name)
Validator.check_value_type('use_one_hot', use_one_hot, [bool],
self.cls_name)
Validator.check_subclass("dtype", dtype, mstype.number_type,
self.cls_name)
self.use_one_hot = use_one_hot
self.dtype = dtype
self.init_tensor = initializer(embedding_table,
[vocab_size, embedding_size])
self.padding_idx = padding_idx
if padding_idx is not None:
self.padding_idx = Validator.check_int_range(
padding_idx, 0, vocab_size, Rel.INC_BOTH, "padding_idx",
self.cls_name)
self.init_tensor = self.init_tensor.to_tensor().asnumpy()
self.init_tensor[self.padding_idx] = 0
self.embedding_table = Parameter(self.init_tensor,
name='embedding_table')
self.expand = P.ExpandDims()
self.reshape_flat = P.Reshape()
self.shp_flat = (-1, )
self.gather = P.GatherV2()
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0, self.dtype)
self.off_value = Tensor(0.0, self.dtype)
self.array_mul = P.MatMul()
self.reshape = P.Reshape()
self.get_shp = P.Shape()
self.thor = True
self.matrix_A = Parameter(Tensor(
np.zeros([vocab_size]).astype(np.float32)),
name='matrix_A',
requires_grad=False)
self.matrix_G = Parameter(Tensor(
np.zeros([embedding_size, embedding_size]).astype(np.float32)),
name="matrix_G",
requires_grad=False)
self.reduce_sum = P.ReduceSum(keep_dims=False)
self.getG = P.InsertGradientOf(self.save_gradient)
self.cast = P.Cast()
if context.get_context("device_target") == "Ascend":
self.cube_matmul = P.CusMatMulCube(transpose_a=True)
else:
self.cube_matmul = P.MatMul(transpose_a=True)
self.mul = P.Mul()
def __init__(self, keep_prob=0.5, dtype=mstype.float32):
super(Dropout, self).__init__()
if keep_prob <= 0 or keep_prob > 1:
raise ValueError("dropout probability should be a number in range (0, 1], but got {}".format(keep_prob))
Validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name)
Validator.check_value_type('keep_prob', keep_prob, [float], self.cls_name)
self.keep_prob = keep_prob
seed0, seed1 = _get_graph_seed(0, "dropout")
self.seed0 = seed0
self.seed1 = seed1
self.dropout = P.Dropout(keep_prob, seed0, seed1)
def __infer__(self, params, indices, axis):
validator.check_subclass("params", params['dtype'], mstype.tensor, self.name)
validator.check_tensor_type_same({"indices": indices['dtype']}, mstype.int_type, self.name)
validator.check_subclass("axis", axis['dtype'], mstype.int_, self.name)
axis_v = axis['value']
params_shp = params['shape']
rank = len(params_shp)
validator.check_int_range("axis", axis_v, -rank, rank, Rel.INC_LEFT, self.name)
if axis_v < 0:
axis_v += rank
out_shape = params_shp[:axis_v] + indices['shape'] + params_shp[axis_v + 1:]
out = {'shape': out_shape,
'dtype': params['dtype'],
'value': None}
return out
def __init__(self, keep_prob=0.5, dtype=mstype.float32):
super(Dropout, self).__init__()
if keep_prob <= 0 or keep_prob > 1:
raise ValueError("dropout probability should be a number in range (0, 1], but got {}".format(keep_prob))
Validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name)
Validator.check_value_type('keep_prob', keep_prob, [float], self.cls_name)
self.keep_prob = keep_prob
seed0, seed1 = _get_graph_seed(0, "dropout")
self.seed0 = seed0
self.seed1 = seed1
self.dtype = dtype
self.get_shape = P.Shape()
self.dropout_gen_mask = P.DropoutGenMask(Seed0=self.seed0, Seed1=self.seed1)
self.dropout_do_mask = P.DropoutDoMask()
self.cast = P.Cast()
self.is_gpu = context.get_context('device_target') in ["GPU"]
self.dropout = P.Dropout(keep_prob)
