def __init__(self):
"""init function"""
super(Rerank_Downstream, self).__init__()
self.dense_0 = nn.Dense(in_channels=4096,
out_channels=8192,
has_bias=True)
self.relu_1 = nn.ReLU()
self.reducemean_2 = P.ReduceMean(keep_dims=True)
self.sub_3 = P.Sub()
self.sub_4 = P.Sub()
self.pow_5 = P.Pow()
self.pow_5_input_weight = 2.0
self.reducemean_6 = P.ReduceMean(keep_dims=True)
self.add_7 = P.Add()
self.add_7_bias = 9.999999960041972e-13
self.sqrt_8 = P.Sqrt()
self.div_9 = P.Div()
self.mul_10 = P.Mul()
self.mul_10_w = Parameter(Tensor(
np.random.uniform(0, 1, (8192, )).astype(np.float32)),
name=None)
self.add_11 = P.Add()
self.add_11_bias = Parameter(Tensor(
np.random.uniform(0, 1, (8192, )).astype(np.float32)),
name=None)
self.dense_12 = nn.Dense(in_channels=8192,
out_channels=2,
has_bias=True)
def __init__(self, batch_size, query_linear_bias, key_linear_bias,
value_linear_bias):
"""init function"""
super(MultiHeadAttn, self).__init__()
self.batch_size = batch_size
self.matmul = nn.MatMul()
self.add = P.Add()
self.reshape = P.Reshape()
self.transpose = P.Transpose()
self.div = P.Div()
self.softmax = nn.Softmax(axis=3)
self.query_linear_weight = Parameter(Tensor(
np.random.uniform(0, 1, (4096, 4096)).astype(np.float32)),
name=None)
self.query_linear_bias = query_linear_bias
self.key_linear_weight = Parameter(Tensor(
np.random.uniform(0, 1, (4096, 4096)).astype(np.float32)),
name=None)
self.key_linear_bias = key_linear_bias
self.value_linear_weight = Parameter(Tensor(
np.random.uniform(0, 1, (4096, 4096)).astype(np.float32)),
name=None)
self.value_linear_bias = value_linear_bias
self.reshape_shape = tuple([batch_size, 512, 64, 64])
self.w = Parameter(Tensor(
np.random.uniform(0, 1, (64, 64, 4096)).astype(np.float32)),
name=None)
self.b = Parameter(Tensor(
np.random.uniform(0, 1, (4096, )).astype(np.float32)),
name=None)
def construct(self, data, label):
"""
construct a compute flow.
"""
weights = self.weights
record_datas = self._split(data)
record_labels = self._split(label)
loss = self.network(record_datas[0], record_labels[0])
sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
record_grad = self.grad(self.network, weights)(record_datas[0], record_labels[0], sens)
record_grad = self._clip_by_global_norm(record_grad, GRADIENT_CLIP_TYPE, self._l2_norm)
grads = record_grad
total_loss = loss
for i in range(1, self._micro_batches):
loss = self.network(record_datas[i], record_labels[i])
sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
record_grad = self.grad(self.network, weights)(record_datas[i], record_labels[i], sens)
record_grad = self._clip_by_global_norm(record_grad, GRADIENT_CLIP_TYPE, self._l2_norm)
grads = self._tuple_add(grads, record_grad)
total_loss = P.TensorAdd()(total_loss, loss)
loss = P.Div()(total_loss, self._micro_float)
if self._mech is not None:
grad_noise = self._hyper_map(self._mech, grads)
grads = self._tuple_add(grads, grad_noise)
grads = self._hyper_map(F.partial(_grad_scale, self._micro_float), grads)
if self.reducer_flag:
# apply grad reducer on grads
grads = self.grad_reducer(grads)
return F.depend(loss, self.optimizer(grads))
def __init__(self,
norm_bound=1.5,
initial_noise_multiplier=5.0,
alpha=6e-4,
decay_policy='Step'):
super(AdaGaussianRandom, self).__init__()
initial_noise_multiplier = check_value_positive(
'initial_noise_multiplier', initial_noise_multiplier)
initial_noise_multiplier = Tensor(
np.array(initial_noise_multiplier, np.float32))
self._initial_noise_multiplier = Parameter(
initial_noise_multiplier, name='initial_noise_multiplier')
self._noise_multiplier = Parameter(initial_noise_multiplier,
name='noise_multiplier')
norm_bound = check_value_positive('norm_bound', norm_bound)
self._norm_bound = Tensor(np.array(norm_bound, np.float32))
alpha = check_param_type('alpha', alpha, float)
self._alpha = Tensor(np.array(alpha, np.float32))
self._decay_policy = check_param_type('decay_policy', decay_policy,
str)
self._mean = 0.0
self._sub = P.Sub()
self._mul = P.Mul()
self._add = P.TensorAdd()
self._div = P.Div()
self._stddev = self._update_stddev()
self._dtype = mstype.float32
def __init__(self, dim, n_heads):
super().__init__()
# h
self.n_heads = n_heads
# v = V / h
self.size_per_head = dim // n_heads
scores_mul = 1.0 / np.sqrt(float(self.size_per_head))
self.scores_mul = ms.Tensor(scores_mul, ms.float32)
self.exones = P.Ones()((1, 1, n_heads, 1, 1), ms.int32)
# shape = (h, v)
self.reshape_tail = (self.n_heads, self.size_per_head)
self.output = Dense(dim, dim, has_bias=False)
self.mul = P.Mul()
self.div = P.Div()
self.softmax = P.Softmax()
self.bmm = P.BatchMatMul()
self.bmmt = P.BatchMatMul(transpose_b=True)
self.squeeze = P.Squeeze(-2)
self.reducesum = P.ReduceSum(keep_dims=True)
self.transpose = P.Transpose()
self.trans_shape = (0, 1, 3, 2, 4)
def __init__(self, sparse=False, stra_list=None):
super(SoftmaxCrossEntropyExpand, self).__init__()
if stra_list is None:
stra_list = []
if len(stra_list) < 11:
stra_list = [None] * 11
self.exp = P.Exp()
self.reduce_sum = P.ReduceSum(keep_dims=True).set_strategy(
strategy=stra_list[1])
self.onehot = P.OneHot().set_strategy(strategy=stra_list[2])
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.div = P.Div().set_strategy(strategy=stra_list[3])
self.log = P.Log().set_strategy(strategy=stra_list[4])
self.sum_cross_entropy = P.ReduceSum(keep_dims=False).set_strategy(
strategy=stra_list[5])
self.mul = P.Mul().set_strategy(strategy=stra_list[6])
self.mul2 = P.Mul().set_strategy(strategy=stra_list[7])
self.cast = P.Cast()
self.reduce_mean = P.ReduceMean(keep_dims=False).set_strategy(
strategy=stra_list[8])
self.sparse = sparse
self.reduce_max = P.ReduceMax(keep_dims=True).set_strategy(
strategy=stra_list[9])
self.sub = P.Sub().set_strategy(strategy=stra_list[10])
def __init__(self, rgb_range, rgb_mean, rgb_std=(1.0, 1.0, 1.0), sign=-1):
"""Construct the class MeanShift.
:param rgb_range: range of tensor, usually 1.0 or 255.0
:param rgb_mean: mean of rgb value
:param rgb_std: std of rgb value
:param sign: -1 for subtract, 1 for add
"""
super(MeanShift, self).__init__()
self.conv2d = nn.Conv2d(3,
3,
kernel_size=1,
stride=1,
padding=0,
has_bias=True,
group=1,
dilation=1,
pad_mode="pad")
self.conv2d.update_parameters_name("conv2d_" + uuid.uuid1().hex[:8] +
".")
std = Tensor(rgb_std, mindspore.float32)
self.conv2d.weight = Tensor(
np.eye(3).reshape(3, 3, 1, 1).astype(np.float32))
self.reshape = P.Reshape()
self.div = P.Div()
self.conv2d.weight = self.div(self.conv2d.weight,
self.reshape(std, (3, 1, 1, 1)))
self.conv2d.bias = sign * rgb_range * Tensor(rgb_mean,
mindspore.float32)
self.conv2d.bias = self.div(self.conv2d.bias, std)
self.requires_grad = False
def __init__(self):
super(MultiHeadAttn, self).__init__()
self.matmul_0 = nn.MatMul()
self.matmul_0.to_float(mstype.float16)
self.matmul_0_w = Parameter(Tensor(
np.random.uniform(0, 1, (768, 768)).astype(np.float32)),
name=None)
self.matmul_1 = nn.MatMul()
self.matmul_1.to_float(mstype.float16)
self.matmul_1_w = Parameter(Tensor(
np.random.uniform(0, 1, (768, 768)).astype(np.float32)),
name=None)
self.matmul_2 = nn.MatMul()
self.matmul_2.to_float(mstype.float16)
self.matmul_2_w = Parameter(Tensor(
np.random.uniform(0, 1, (768, 768)).astype(np.float32)),
name=None)
self.add_3 = P.Add()
self.add_3_bias = Parameter(Tensor(
np.random.uniform(0, 1, (768, )).astype(np.float32)),
name=None)
self.add_4 = P.Add()
self.add_4_bias = Parameter(Tensor(
np.random.uniform(0, 1, (768, )).astype(np.float32)),
name=None)
self.add_5 = P.Add()
self.add_5_bias = Parameter(Tensor(
np.random.uniform(0, 1, (768, )).astype(np.float32)),
name=None)
self.reshape_6 = P.Reshape()
self.reshape_6_shape = tuple([BATCH_SIZE, 448, 12, 64])
self.reshape_7 = P.Reshape()
self.reshape_7_shape = tuple([BATCH_SIZE, 448, 12, 64])
self.reshape_8 = P.Reshape()
self.reshape_8_shape = tuple([BATCH_SIZE, 448, 12, 64])
self.transpose_9 = P.Transpose()
self.transpose_10 = P.Transpose()
self.transpose_11 = P.Transpose()
self.matmul_12 = nn.MatMul()
self.matmul_12.to_float(mstype.float16)
self.div_13 = P.Div()
self.div_13_w = 8.0
self.add_14 = P.Add()
self.softmax_15 = nn.Softmax(axis=3)
self.matmul_16 = nn.MatMul()
self.matmul_16.to_float(mstype.float16)
self.transpose_17 = P.Transpose()
self.reshape_18 = P.Reshape()
self.reshape_18_shape = tuple([BATCH_SIZE, 448, 768])
self.matmul_19 = nn.MatMul()
self.matmul_19.to_float(mstype.float16)
self.matmul_19_w = Parameter(Tensor(
np.random.uniform(0, 1, (768, 768)).astype(np.float32)),
name=None)
self.add_20 = P.Add()
self.add_20_bias = Parameter(Tensor(
np.random.uniform(0, 1, (768, )).astype(np.float32)),
name=None)
def __init__(self):
super(GeLU, self).__init__()
self.div = P.Div()
self.div_w = 1.4142135381698608
self.erf = P.Erf()
self.add = P.Add()
self.add_bias = 1.0
self.mul = P.Mul()
self.mul_w = 0.5
def __init__(self):
super().__init__()
self.mul = P.Mul()
self.add = P.Add()
self.sub = P.Sub()
self.div = P.Div()
self.assign = P.Assign()
self.param_a = Parameter(Tensor(5, mstype.int32), name='a')
self.param_b = Parameter(Tensor(2, mstype.int32), name='b')
self.param_c = Parameter(Tensor(20, mstype.int32), name='c')
def __init__(self):
super(GeLU, self).__init__()
self.div_0 = P.Div()
self.div_0_w = 1.4142135381698608
self.erf_1 = P.Erf()
self.add_2 = P.Add()
self.add_2_bias = 1.0
self.mul_3 = P.Mul()
self.mul_4 = P.Mul()
self.mul_4_w = 0.5
def __init__(self, layer_norm_weight, layer_norm_bias):
"""init function"""
super(LayerNorm, self).__init__()
self.reducemean = P.ReduceMean(keep_dims=True)
self.sub = P.Sub()
self.pow = P.Pow()
self.add = P.Add()
self.sqrt = P.Sqrt()
self.div = P.Div()
self.mul = P.Mul()
self.layer_norm_weight = layer_norm_weight
self.layer_norm_bias = layer_norm_bias
def __init__(self):
super().__init__()
self.relu = nn.ReLU()
self.mul = P.Mul()
self.add = P.Add()
self.sub = P.Sub()
self.div = P.Div()
self.assign = P.Assign()
param_a = np.full((1, ), 5, dtype=np.float32)
self.param_a = Parameter(Tensor(param_a), name='a')
param_b = np.full((1, ), 2, dtype=np.float32)
self.param_b = Parameter(Tensor(param_b), name='b')
def __init__(self, decay_policy, decay_rate, cur_noise_multiplier, init_noise_multiplier):
super(_MechanismsParamsUpdater, self).__init__()
self._decay_policy = decay_policy
self._decay_rate = decay_rate
self._cur_noise_multiplier = cur_noise_multiplier
self._init_noise_multiplier = init_noise_multiplier
self._div = P.Div()
self._add = P.TensorAdd()
self._assign = P.Assign()
self._sub = P.Sub()
self._one = Tensor(1, mstype.float32)
self._mul = P.Mul()
self._exp = P.Exp()
def __init__(self,
batch_size,
labels,
rnn_hidden_size,
nb_layers,
audio_conf,
rnn_type='LSTM',
bidirectional=True,
device_target='GPU'):
super(DeepSpeechModel, self).__init__()
self.batch_size = batch_size
self.hidden_size = rnn_hidden_size
self.hidden_layers = nb_layers
self.rnn_type = rnn_type
self.audio_conf = audio_conf
self.labels = labels
self.bidirectional = bidirectional
self.reshape_op = P.Reshape()
self.shape_op = P.Shape()
self.transpose_op = P.Transpose()
self.add = P.Add()
self.div = P.Div()
sample_rate = self.audio_conf.sample_rate
window_size = self.audio_conf.window_size
num_classes = len(self.labels)
self.conv = MaskConv()
# This is to calculate
self.pre, self.stride = self.get_conv_num()
# Based on above convolutions and spectrogram size using conv formula (W - F + 2P)/ S+1
rnn_input_size = int(math.floor((sample_rate * window_size) / 2) + 1)
rnn_input_size = int(math.floor(rnn_input_size + 2 * 20 - 41) / 2 + 1)
rnn_input_size = int(math.floor(rnn_input_size + 2 * 10 - 21) / 2 + 1)
rnn_input_size *= 32
self.RNN = BatchRNN(batch_size=self.batch_size,
input_size=rnn_input_size,
num_layers=nb_layers,
hidden_size=rnn_hidden_size,
bidirectional=bidirectional,
batch_norm=False,
rnn_type=self.rnn_type,
device_target=device_target)
fully_connected = nn.Dense(rnn_hidden_size,
num_classes,
has_bias=False)
self.fc = SequenceWise(fully_connected)
def __init__(self, mul_7_w_shape, add_8_bias_shape):
"""init function"""
super(LayerNorm, self).__init__()
self.reducemean_0 = P.ReduceMean(keep_dims=True)
self.sub_1 = P.Sub()
self.pow_2 = P.Pow()
self.pow_2_input_weight = 2.0
self.reducemean_3 = P.ReduceMean(keep_dims=True)
self.add_4 = P.Add()
self.add_4_bias = 9.999999960041972e-13
self.sqrt_5 = P.Sqrt()
self.div_6 = P.Div()
self.mul_7 = P.Mul()
self.mul_7_w = Parameter(Tensor(np.random.uniform(0, 1, mul_7_w_shape).astype(np.float32)), name=None)
self.add_8 = P.Add()
self.add_8_bias = Parameter(Tensor(np.random.uniform(0, 1, add_8_bias_shape).astype(np.float32)), name=None)
def __init__(self, passthrough_w_0, passthrough_w_1):
"""init function"""
super(LayerNorm, self).__init__()
self.reducemean_0 = P.ReduceMean(keep_dims=True)
self.sub_1 = P.Sub()
self.pow_2 = P.Pow()
self.pow_2_input_weight = 2.0
self.reducemean_3 = P.ReduceMean(keep_dims=True)
self.add_4 = P.Add()
self.add_4_bias = 9.999999960041972e-13
self.sqrt_5 = P.Sqrt()
self.div_6 = P.Div()
self.mul_7 = P.Mul()
self.mul_7_w = passthrough_w_0
self.add_8 = P.Add()
self.add_8_bias = passthrough_w_1
def __init__(self, batch_size, passthrough_w_0, passthrough_w_1,
passthrough_w_2):
"""init function"""
super(MultiHeadAttn, self).__init__()
self.batch_size = batch_size
self.matmul_0 = nn.MatMul()
self.matmul_0_w = Parameter(Tensor(
np.random.uniform(0, 1, (4096, 4096)).astype(np.float32)),
name=None)
self.matmul_1 = nn.MatMul()
self.matmul_1_w = Parameter(Tensor(
np.random.uniform(0, 1, (4096, 4096)).astype(np.float32)),
name=None)
self.matmul_2 = nn.MatMul()
self.matmul_2_w = Parameter(Tensor(
np.random.uniform(0, 1, (4096, 4096)).astype(np.float32)),
name=None)
self.add_3 = P.Add()
self.add_3_bias = passthrough_w_0
self.add_4 = P.Add()
self.add_4_bias = passthrough_w_1
self.add_5 = P.Add()
self.add_5_bias = passthrough_w_2
self.reshape_6 = P.Reshape()
self.reshape_6_shape = tuple([batch_size, 512, 64, 64])
self.reshape_7 = P.Reshape()
self.reshape_7_shape = tuple([batch_size, 512, 64, 64])
self.reshape_8 = P.Reshape()
self.reshape_8_shape = tuple([batch_size, 512, 64, 64])
self.transpose_9 = P.Transpose()
self.transpose_10 = P.Transpose()
self.transpose_11 = P.Transpose()
self.matmul_12 = nn.MatMul()
self.div_13 = P.Div()
self.div_13_w = 8.0
self.add_14 = P.Add()
self.softmax_15 = nn.Softmax(axis=3)
self.matmul_16 = nn.MatMul()
self.transpose_17 = P.Transpose()
self.matmul_18 = P.MatMul()
self.matmul_18_weight = Parameter(Tensor(
np.random.uniform(0, 1, (64, 64, 4096)).astype(np.float32)),
name=None)
self.add_19 = P.Add()
self.add_19_bias = Parameter(Tensor(
np.random.uniform(0, 1, (4096, )).astype(np.float32)),
name=None)
def __init__(self, sparse=False):
super(SoftmaxCrossEntropyExpand, self).__init__()
self.exp = P.Exp()
self.reduce_sum = P.ReduceSum(keep_dims=True)
self.onehot = P.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.div = P.Div()
self.log = P.Log()
self.sum_cross_entropy = P.ReduceSum(keep_dims=False)
self.mul = P.Mul()
self.mul2 = P.Mul()
self.cast = P.Cast()
self.reduce_mean = P.ReduceMean(keep_dims=False)
self.sparse = sparse
self.reduce_max = P.ReduceMax(keep_dims=True)
self.sub = P.Sub()
def __init__(self):
super(LayerNorm, self).__init__()
self.reducemean = P.ReduceMean(keep_dims=True)
self.sub = P.Sub()
self.cast = P.Cast()
self.cast_to = mstype.float32
self.pow = P.Pow()
self.pow_weight = 2.0
self.add = P.Add()
self.add_bias_0 = 9.999999960041972e-13
self.sqrt = P.Sqrt()
self.div = P.Div()
self.mul = P.Mul()
self.mul_weight = Parameter(Tensor(
np.random.uniform(0, 1, (768, )).astype(np.float32)),
name=None)
self.add_bias_1 = Parameter(Tensor(
np.random.uniform(0, 1, (768, )).astype(np.float32)),
name=None)
def __init__(self,
num_bits=2,
compute_type=mstype.float32,
clip_value=1.0,
per_channel=False):
self.num_bits = num_bits
self.compute_type = compute_type
self.clip_value = clip_value
self.per_channel = per_channel
self.clamp = C.clip_by_value
self.abs = P.Abs()
self.sum = P.ReduceSum()
self.nelement = F.size
self.div = P.Div()
self.cast = P.Cast()
self.max = P.ReduceMax()
self.min = P.ReduceMin()
self.floor = P.Floor()
def __init__(self,
num_bits=8,
compute_type=mstype.float32,
clip_value=1.0,
per_channel=False):
super(QuantizeWeightCell, self).__init__()
self.num_bits = num_bits
self.compute_type = compute_type
self.clip_value = clip_value
self.per_channel = per_channel
self.clamp = C.clip_by_value
self.abs = P.Abs()
self.sum = P.ReduceSum()
self.nelement = F.size
self.div = P.Div()
self.cast = P.Cast()
self.max = P.ReduceMax()
self.min = P.ReduceMin()
self.round = P.Round()
def __init__(self):
super(LayerNorm, self).__init__()
self.reducemean_0 = P.ReduceMean(keep_dims=True)
self.sub_1 = P.Sub()
self.cast_2 = P.Cast()
self.cast_2_to = mstype.float32
self.pow_3 = P.Pow()
self.pow_3_input_weight = 2.0
self.reducemean_4 = P.ReduceMean(keep_dims=True)
self.add_5 = P.Add()
self.add_5_bias = 9.999999960041972e-13
self.sqrt_6 = P.Sqrt()
self.div_7 = P.Div()
self.mul_8 = P.Mul()
self.mul_8_w = Parameter(Tensor(
np.random.uniform(0, 1, (768, )).astype(np.float32)),
name=None)
self.add_9 = P.Add()
self.add_9_bias = Parameter(Tensor(
np.random.uniform(0, 1, (768, )).astype(np.float32)),
name=None)
def __init__(self,
bert_layer_norm_weight_shape,
bert_layer_norm_bias_shape,
eps=1e-12):
"""init function"""
super(BertLayerNorm, self).__init__()
self.reducemean = P.ReduceMean(keep_dims=True)
self.sub = P.Sub()
self.pow = P.Pow()
self.add = P.Add()
self.sqrt = P.Sqrt()
self.div = P.Div()
self.mul = P.Mul()
self.variance_epsilon = eps
self.bert_layer_norm_weight = Parameter(Tensor(
np.random.uniform(0, 1, bert_layer_norm_weight_shape).astype(
np.float32)),
name=None)
self.bert_layer_norm_bias = Parameter(Tensor(
np.random.uniform(0, 1,
bert_layer_norm_bias_shape).astype(np.float32)),
name=None)
def __init__(self, seq_len):
super(MultiHeadAttn, self).__init__()
self.matmul = nn.MatMul()
self.matmul.to_float(mstype.float16)
self.query = Parameter(Tensor(
np.random.uniform(0, 1, (768, 768)).astype(np.float32)),
name=None)
self.key = Parameter(Tensor(
np.random.uniform(0, 1, (768, 768)).astype(np.float32)),
name=None)
self.value = Parameter(Tensor(
np.random.uniform(0, 1, (768, 768)).astype(np.float32)),
name=None)
self.add = P.Add()
self.query_bias = Parameter(Tensor(
np.random.uniform(0, 1, (768, )).astype(np.float32)),
name=None)
self.key_bias = Parameter(Tensor(
np.random.uniform(0, 1, (768, )).astype(np.float32)),
name=None)
self.value_bias = Parameter(Tensor(
np.random.uniform(0, 1, (768, )).astype(np.float32)),
name=None)
self.reshape = P.Reshape()
self.to_shape_0 = tuple([BATCH_SIZE, seq_len, 12, 64])
self.transpose = P.Transpose()
self.div = P.Div()
self.div_w = 8.0
self.softmax = nn.Softmax(axis=3)
self.to_shape_1 = tuple([BATCH_SIZE, seq_len, 768])
self.context_weight = Parameter(Tensor(
np.random.uniform(0, 1, (768, 768)).astype(np.float32)),
name=None)
self.context_bias = Parameter(Tensor(
np.random.uniform(0, 1, (768, )).astype(np.float32)),
name=None)
def __init__(self,
temperature=0.07,
contrast_mode='all',
base_temperature=0.07):
super(SupConLoss, self).__init__()
self.temperature = temperature
self.contrast_mode = contrast_mode
self.base_temperature = base_temperature
self.normalize = P.L2Normalize(axis=2)
self.eye = P.Eye()
self.unbind = P.Unstack(axis=1)
self.cat = P.Concat(axis=0)
self.matmul = P.MatMul()
self.div = P.Div()
self.transpose = P.Transpose()
self.maxes = P.ArgMaxWithValue(axis=1, keep_dims=True)
self.tile = P.Tile()
self.scatter = P.ScatterNd()
self.oneslike = P.OnesLike()
self.exp = P.Exp()
self.sum = P.ReduceSum(keep_dims=True)
self.log = P.Log()
self.reshape = P.Reshape()
self.mean = P.ReduceMean()
def __init__(self,
norm_bound=1.0,
initial_noise_multiplier=1.5,
noise_decay_rate=6e-4,
decay_policy='Time',
seed=0):
super(AdaGaussianRandom, self).__init__()
norm_bound = check_value_positive('norm_bound', norm_bound)
initial_noise_multiplier = check_value_positive(
'initial_noise_multiplier', initial_noise_multiplier)
self._norm_bound = Tensor(norm_bound, mstype.float32)
initial_noise_multiplier = Tensor(initial_noise_multiplier,
mstype.float32)
self._initial_noise_multiplier = Parameter(
initial_noise_multiplier, name='initial_noise_multiplier')
self._noise_multiplier = Parameter(initial_noise_multiplier,
name='noise_multiplier')
self._mean = Tensor(0, mstype.float32)
noise_decay_rate = check_param_type('noise_decay_rate',
noise_decay_rate, float)
check_param_in_range('noise_decay_rate', noise_decay_rate, 0.0, 1.0)
self._noise_decay_rate = Tensor(noise_decay_rate, mstype.float32)
if decay_policy not in ['Time', 'Step']:
raise NameError(
"The decay_policy must be in ['Time', 'Step'], but "
"get {}".format(decay_policy))
self._decay_policy = decay_policy
self._sub = P.Sub()
self._mul = P.Mul()
self._add = P.TensorAdd()
self._div = P.Div()
self._dtype = mstype.float32
self._normal = P.Normal(seed=seed)
self._assign = P.Assign()
self._one = Tensor(1, self._dtype)
def construct(self, data, label, sens=None):
"""
construct a compute flow.
"""
init = False
if not self.gpu_target:
# init overflow buffer
init = self.alloc_status()
# clear overflow buffer
self.clear_status(init)
if sens is None:
scaling_sens = self.loss_scale
else:
scaling_sens = sens
# DP clip
weights = self.weights
record_datas = self._split(data)
record_labels = self._split(label)
# first index
loss = self.network(record_datas[0], record_labels[0])
scaling_sens_filled = C.ones_like(loss) * F.cast(
scaling_sens, F.dtype(loss))
record_grad = self.grad(self.network,
weights)(record_datas[0], record_labels[0],
scaling_sens_filled)
beta = self._zero
square_sum = self._zero
for grad in record_grad:
square_sum = self._add(square_sum,
self._reduce_sum(self._square_all(grad)))
norm_grad = self._sqrt(square_sum)
beta = self._add(
beta,
self._cast(self._less(norm_grad, self._norm_bound),
mstype.float32))
record_grad = self._clip_by_global_norm(record_grad,
GRADIENT_CLIP_TYPE,
self._norm_bound)
grads = record_grad
total_loss = loss
for i in range(1, self._micro_batches):
loss = self.network(record_datas[i], record_labels[i])
scaling_sens_filled = C.ones_like(loss) * F.cast(
scaling_sens, F.dtype(loss))
record_grad = self.grad(self.network,
weights)(record_datas[i], record_labels[i],
scaling_sens_filled)
square_sum = self._zero
for grad in record_grad:
square_sum = self._add(
square_sum, self._reduce_sum(self._square_all(grad)))
norm_grad = self._sqrt(square_sum)
beta = self._add(
beta,
self._cast(self._less(norm_grad, self._norm_bound),
mstype.float32))
record_grad = self._clip_by_global_norm(record_grad,
GRADIENT_CLIP_TYPE,
self._norm_bound)
grads = self._tuple_add(grads, record_grad)
total_loss = P.TensorAdd()(total_loss, loss)
loss = P.Div()(total_loss, self._micro_float)
beta = self._div(beta, self._micro_batches)
if self._noise_mech is not None:
grad_noise_tuple = ()
for grad_item in grads:
grad_noise = self._mech(grad_item)
grad_noise_tuple = grad_noise_tuple + (grad_noise, )
grads = self._tuple_add(grads, grad_noise_tuple)
grads = self._hyper_map(F.partial(_grad_scale, self._micro_float),
grads)
# update mech parameters
if self._noise_mech_param_updater is not None:
multiplier = self._noise_mech_param_updater()
loss = F.depend(loss, multiplier)
grads = self.hyper_map(F.partial(_grad_scale, scaling_sens), grads)
# apply grad reducer on grads
grads = self.grad_reducer(grads)
# get the overflow buffer
if not self.gpu_target:
self.get_status(init)
# sum overflow buffer elements, 0:not overflow , >0:overflow
flag_sum = self.reduce_sum(init, (0, ))
else:
flag_sum = self.hyper_map(F.partial(_grad_overflow), grads)
flag_sum = self.addn(flag_sum)
# convert flag_sum to scalar
flag_sum = self.reshape(flag_sum, (()))
if self.is_distributed:
# sum overflow flag over devices
flag_reduce = self.allreduce(flag_sum)
cond = self.less_equal(self.base, flag_reduce)
else:
cond = self.less_equal(self.base, flag_sum)
overflow = cond
if sens is None:
overflow = self.loss_scaling_manager(self.loss_scale, cond)
# if there is no overflow, do optimize
if overflow:
opt = False
else:
opt = self.optimizer(grads)
ret = (loss, cond, scaling_sens)
if self._clip_mech is not None:
next_norm_bound = self._clip_mech(beta, self._norm_bound)
P.assign(self._norm_bound, next_norm_bound)
return F.depend(ret, opt)
def __init__(self,
network,
optimizer,
norm_bound=1.0,
sens=1.0,
micro_batches=None,
noise_mech=None,
clip_mech=None):
super(_TrainOneStepCell, self).__init__(auto_prefix=False)
self.network = network
self.network.set_grad()
self.network.add_flags(defer_inline=True)
self.weights = optimizer.parameters
self.optimizer = optimizer
self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
self.sens = sens
self.reducer_flag = False
self.grad_reducer = None
parallel_mode = _get_parallel_mode()
if parallel_mode in (ParallelMode.DATA_PARALLEL,
ParallelMode.HYBRID_PARALLEL):
self.reducer_flag = True
if self.reducer_flag:
mean = _get_mirror_mean()
degree = _get_device_num()
self.grad_reducer = DistributedGradReducer(optimizer.parameters,
mean, degree)
# dp params
if micro_batches is None:
msg = 'micro_batches must give in differential privacy, but got value: {}'.format(
micro_batches)
LOGGER.error(TAG, msg)
raise ValueError(msg)
self._micro_batches = micro_batches
self._norm_bound = norm_bound
self._split = P.Split(0, self._micro_batches)
self._clip_by_global_norm = _ClipGradients()
self._noise_mech = noise_mech
self._clip_mech = clip_mech
self._tuple_add = _TupleAdd()
self._add = P.TensorAdd()
self._norm = nn.Norm()
self._hyper_map = C.HyperMap()
self._zero = Tensor(0, mstype.float32)
self._assign = P.Assign()
self._div = P.Div()
self._sqrt = P.Sqrt()
self._reduce_sum = P.ReduceSum()
self._square_all = P.Square()
self._less = P.Less()
self._cast = P.Cast()
self._micro_float = Tensor(micro_batches, mstype.float32)
self._noise_mech_param_updater = None
if self._noise_mech is not None and self._noise_mech._decay_policy is not None:
self._noise_mech_param_updater = _MechanismsParamsUpdater(
decay_policy=self._noise_mech._decay_policy,
decay_rate=self._noise_mech._noise_decay_rate,
cur_noise_multiplier=self._noise_mech._noise_multiplier,
init_noise_multiplier=self._noise_mech.
_initial_noise_multiplier)
def __init__(self,
network,
optimizer,
scale_update_cell=None,
micro_batches=None,
norm_bound=1.0,
noise_mech=None,
clip_mech=None):
super(_TrainOneStepWithLossScaleCell, self).__init__(auto_prefix=False)
self.network = network
self.network.set_grad()
self.network.add_flags(defer_inline=True)
self.weights = ParameterTuple(network.trainable_params())
self.optimizer = optimizer
self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
self.hyper_map = C.HyperMap()
if context.get_context("device_target") == "GPU":
self.gpu_target = True
self.float_status = P.FloatStatus()
self.addn = P.AddN()
self.reshape = P.Reshape()
else:
self.gpu_target = False
self.alloc_status = NPUAllocFloatStatus()
self.get_status = NPUGetFloatStatus()
self.clear_status = NPUClearFloatStatus()
self.reduce_sum = ReduceSum(keep_dims=False)
self.base = Tensor(1, mstype.float32)
self.less_equal = LessEqual()
self.depend_parameter_use = ControlDepend(depend_mode=1)
self.allreduce = P.AllReduce()
self.parallel_mode = _get_parallel_mode()
self.grad_reducer = F.identity
self.reducer_flag = self.parallel_mode in [
ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL
]
if self.reducer_flag:
mean = _get_mirror_mean()
degree = _get_device_num()
self.grad_reducer = DistributedGradReducer(optimizer.parameters,
mean, degree)
self.is_distributed = self.parallel_mode != ParallelMode.STAND_ALONE
self.loss_scale = None
self.loss_scaling_manager = scale_update_cell
if scale_update_cell:
self.loss_scale = Parameter(Tensor(
scale_update_cell.get_loss_scale(), dtype=mstype.float32),
name="loss_scale")
self.add_flags(has_effect=True)
# dp params
self._micro_batches = micro_batches
self._norm_bound = norm_bound
self._split = P.Split(0, self._micro_batches)
self._clip_by_global_norm = _ClipGradients()
self._noise_mech = noise_mech
self._clip_mech = clip_mech
self._add = P.TensorAdd()
self._norm = nn.Norm()
self._tuple_add = _TupleAdd()
self._hyper_map = C.HyperMap()
self._micro_float = Tensor(micro_batches, mstype.float32)
self._zero = Tensor(0, mstype.float32)
self._assign = P.Assign()
self._div = P.Div()
self._sqrt = P.Sqrt()
self._reduce_sum = P.ReduceSum()
self._square_all = P.Square()
self._less = P.Less()
self._cast = P.Cast()
self._noise_mech_param_updater = None
if self._noise_mech is not None and self._noise_mech._decay_policy is not None:
self._noise_mech_param_updater = _MechanismsParamsUpdater(
decay_policy=self._noise_mech._decay_policy,
decay_rate=self._noise_mech._noise_decay_rate,
cur_noise_multiplier=self._noise_mech._noise_multiplier,
init_noise_multiplier=self._noise_mech.
_initial_noise_multiplier)
