def construct(self, gradients):
params = self.params
moments = self.moments
if self.weight_decay > 0:
gradients = self.hyper_map(F.partial(apply_decay, self.weight_decay), self.decay_tf, params, gradients)
if self.reciprocal_scale != 1.0:
gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
if self.dynamic_lr:
lr = self.gather(self.learning_rate, self.global_step, self.axis)
F.control_depend(lr, self.assignadd(self.global_step, self.one))
else:
lr = self.learning_rate
success = self.hyper_map(F.partial(momentum_opt, self.opt, lr, self.momentum), gradients, params, moments)
return success
def construct(self, grads):
params = self.parameters
moments = self.moments
linear = self.linear
lr = self.learning_rate
if self.weight_decay > 0.0:
grads = self.hyper_map(F.partial(_apply_decay, self.weight_decay),
self.decay_tf, params, grads)
grads = self.scale_grad(grads)
success = self.map_(
F.partial(_ftrl_opt, self.opt, self.sparse_opt, lr, self.l1,
self.l2, self.lr_power), linear, grads, params, moments)
return success
def construct(self,
input_ids,
input_mask,
token_type_id,
next_sentence_labels,
masked_lm_positions,
masked_lm_ids,
masked_lm_weights,
sens=None):
"""Defines the computation performed."""
weights = self.weights
loss = self.network(input_ids, input_mask, token_type_id,
next_sentence_labels, masked_lm_positions,
masked_lm_ids, masked_lm_weights)
if sens is None:
scaling_sens = self.loss_scale
else:
scaling_sens = sens
# alloc status and clear should be right before gradoperation
init = self.alloc_status()
self.clear_before_grad(init)
grads = self.grad(self.network,
weights)(input_ids, input_mask, token_type_id,
next_sentence_labels, masked_lm_positions,
masked_lm_ids, masked_lm_weights,
self.cast(scaling_sens, mstype.float32))
# apply grad reducer on grads
grads = self.grad_reducer(grads)
grads = self.hyper_map(
F.partial(grad_scale, scaling_sens * self.degree), grads)
grads = self.hyper_map(
F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE),
grads)
self.get_status(init)
flag_sum = self.reduce_sum(init, (0, ))
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 overflow:
succ = False
else:
succ = self.optimizer(grads)
ret = (loss, cond, scaling_sens)
return F.depend(ret, succ)
def construct(self, grads):
params = self.parameters
accum = self.accum
grads = self.decay_weight(grads)
grads = self.scale_grad(grads)
grads = self.gradients_centralization(grads)
lr = self.get_lr()
if self.is_group_lr:
success = self.map_(F.partial(_ada_grad_opt, self.opt), lr, params, accum,
grads)
else:
success = self.map_(F.partial(_ada_grad_opt, self.opt, lr), params, accum,
grads)
return success
def construct(self,
input_ids,
input_mask,
token_type_id,
label_ids,
sens=None):
weights = self.weights
init = self.alloc_status()
loss = self.network(input_ids,
input_mask,
token_type_id,
label_ids)
if sens is None:
scaling_sens = self.loss_scale
else:
scaling_sens = sens
grads = self.grad(self.network, weights)(input_ids,
input_mask,
token_type_id,
label_ids,
self.cast(scaling_sens,
mstype.float32))
clear_before_grad = self.clear_before_grad(init)
F.control_depend(loss, init)
self.depend_parameter_use(clear_before_grad, scaling_sens)
grads = self.hyper_map(F.partial(grad_scale, scaling_sens), grads)
grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
if self.reducer_flag:
grads = self.grad_reducer(grads)
flag = self.get_status(init)
flag_sum = self.reduce_sum(init, (0,))
if self.is_distributed:
flag_reduce = self.allreduce(flag_sum)
cond = self.less_equal(self.base, flag_reduce)
else:
cond = self.less_equal(self.base, flag_sum)
F.control_depend(grads, flag)
F.control_depend(flag, flag_sum)
overflow = cond
if sens is None:
overflow = self.loss_scaling_manager(self.loss_scale, cond)
if overflow:
succ = False
else:
succ = self.optimizer(grads)
ret = (loss, cond)
return F.depend(ret, succ)
def construct(self, gradients):
params = self.params
moments = self.moments
gradients = self.scale_grad(gradients)
new_grads = ()
if self.skfac:
for i in range(54):
g = gradients[i * 3]
g_shape = self.shape(g)
g = self.reshape(g, (g_shape[0], -1))
matrix_A = self.matrix_A[i]
matrix_G = self.matrix_G[i]
g = self.matmul(self.matmul(matrix_G, g), matrix_A)
fake_A = self.assign(self.matrix_A[i], matrix_A)
fake_G = self.assign(self.matrix_G[i], matrix_G)
g = F.depend(g, fake_A)
g = F.depend(g, fake_G)
if i == 53:
new_grads = new_grads + (g, )
else:
g = self.reshape(g, g_shape)
new_grads = new_grads + (g, gradients[i * 3 + 1],
gradients[i * 3 + 2])
else:
for i in range(54):
g = gradients[i * 3]
g_shape = self.shape(g)
g = self.reshape(g, (g_shape[0], -1))
matrix_A = self.matrix_A[i]
matrix_G = self.matrix_G[i]
matrix_A = F.depend(matrix_A, g)
matrix_G = F.depend(matrix_G, g)
g = self.matmul(self.matmul(matrix_G, g), matrix_A)
if i == 53:
new_grads = new_grads + (g, )
else:
g = self.reshape(g, g_shape)
new_grads = new_grads + (g, gradients[i * 3 + 1],
gradients[i * 3 + 2])
gradients = new_grads
if self.weight_decay > 0:
gradients = self.hyper_map(
F.partial(apply_decay, self.weight_decay), self.decay_flags,
params, gradients)
lr = self.get_lr()
success = self.hyper_map(
F.partial(_momentum_opt, self.opt, self.momentum, lr), gradients,
params, moments)
return success
def construct(self, gradients):
params = self.parameters
if self.dynamic_lr:
lr = self.gather(self.learning_rate, self.global_step, self.axis)
F.control_depend(lr, self.assignadd(self.global_step, 1))
else:
lr = self.learning_rate
if self.reciprocal_scale != 1.0:
gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
grad_t = self.hyper_map(F.partial(lars_opt, self.lars, self.weight_decay, lr),
gradients, params, self.decay_flag, self.lars_flag)
success = self.opt(grad_t)
return success
def construct(self, grads):
params = self.parameters
accum = self.accum
grads = self.decay_weight(grads)
grads = self.scale_grad(grads)
lr = self.get_lr()
if self.is_group_lr:
success = self.map_(
F.partial(_proximal_ada_grad_opt, self.opt, self.sparse_opt,
self.l1, self.l2), lr, grads, params, accum)
else:
success = self.map_(
F.partial(_proximal_ada_grad_opt, self.opt, self.sparse_opt,
self.l1, self.l2, lr), grads, params, accum)
return success
def construct(self, grads):
params = self.parameters
moments = self.moments
linear = self.linear
if self.weight_decay > 0.0:
grads = self.hyper_map(F.partial(apply_decay, self.weight_decay),
self.decay_tf, params, grads)
if self.reciprocal_scale != 1.0:
grads = self.hyper_map(
F.partial(grad_scale, self.reciprocal_scale), grads)
lr = self.learning_rate
success = self.hyper_map(
F.partial(ftrl_opt, self.opt, lr, self.l1, self.l2, self.lr_power),
linear, grads, params, moments)
return success
def construct(self, data, label):
weights = self.weights
loss = self.network(data, label)
sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
grads = self.grad(self.network, weights)(data, label, sens)
norm = self.hyper_map(F.partial(compute_norm), grads)
norm = self.concat(norm)
norm = self.norm(norm)
cond = self.greater(norm, self.cast(self.ten, self.dtype(norm)))
clip_val = self.select(cond, norm, self.cast(self.ten, self.dtype(norm)))
grads = self.hyper_map(F.partial(grad_div, clip_val), grads)
if self.reducer_flag:
# apply grad reducer on grads
grads = self.grad_reducer(grads)
return F.depend(loss, self.optimizer(grads))
def construct(self, gradients):
params = self.params
moments = self.moments
gradients = self.decay_weight(gradients)
gradients = self.scale_grad(gradients)
lr = self.get_lr()
if self.is_group_lr:
success = self.hyper_map(
F.partial(_momentum_opt, self.opt, self.momentum), lr,
gradients, params, moments, self.ps_parameters)
else:
success = self.hyper_map(
F.partial(_momentum_opt, self.opt, self.momentum, lr),
gradients, params, moments, self.ps_parameters)
return success
def construct(self, grads):
"""construct of DistributedGradReducerThor"""
# In some circumstances, the data precision of grads could be mixed with float16 and float32. Thus, the
# result of AllReduce is unreliable. To solve the problem, grads should be cast to float32 before AllReduce,
# and cast back after the operation.
datatypes = self.hyper_map(F.partial(_get_datatype), grads)
grads = self.hyper_map(F.partial(_cast_datatype, mstype.float32), grads)
if self.mean:
new_grad = self.hyper_map(F.partial(reduce_opt, self.mul, self.degree), grads)
else:
new_grad = self.hyper_map(F.partial(reduce_opt), self.allreduce_filter, grads)
new_grad = self.hyper_map(F.partial(_cast_datatype), datatypes, new_grad)
return new_grad
def construct(self, gradients):
params = self.parameters
accum = self.accum
stat = self.stat
gradients = self.scale_grad(gradients)
lr = self.get_lr()
if self.is_group_lr:
success = self.hyper_map(
F.partial(_sgd_opt, self.opt, self.momentum), lr, gradients,
params, accum, stat)
else:
success = self.hyper_map(
F.partial(_sgd_opt, self.opt, self.momentum, lr), gradients,
params, accum, stat)
return success
def construct(self, x):
square_sum = self.hyper_map(get_square_sum, x)
global_norm = F.sqrt(F.addn(square_sum))
cond = self.greater_equal(global_norm, self.clip_norm)
global_norm = F.select(cond, global_norm, self.clip_norm)
clip_x = self.hyper_map(F.partial(apply_global_norm, self.clip_norm, global_norm), x)
return clip_x
def construct(self, input_ids, input_mask, token_type_id, label_ids):
"""Defines the computation performed."""
weights = self.weights
for i in range(self.length):
F.assign(self.saved_params[i], weights[i])
for i in range(self.quant_embedding_list_length):
quant_embedding = self.quantize_embedding(
weights[self.quant_embedding_list[i]])
F.assign(weights[self.quant_embedding_list[i]], quant_embedding)
for i in range(self.quant_weight_list_length):
quant_weight = self.quantize_weight(
weights[self.quant_weight_list[i]])
F.assign(weights[self.quant_weight_list[i]], quant_weight)
grads = self.grad(self.network,
weights)(input_ids, input_mask, token_type_id,
label_ids,
self.cast(F.tuple_to_array((self.sens, )),
mstype.float32))
# apply grad reducer on grads
grads = self.grad_reducer(grads)
grads = self.hyper_map(
F.partial(clip_grad, self.clip_type, self.clip_value), grads)
for i in range(self.length):
param = F.depend(self.saved_params[i], grads)
F.assign(weights[i], param)
succ = self.optimizer(grads)
return succ
def construct(self, gradients):
# TODO: perform all_reduce
# gradients = self._map(self._all_reduce, gradients)
self.acc_step = self.acc_step + 1
q = self.mod_op(self.acc_step, self.apply_period)
# log_gradient = True
log_gradient = False
if log_gradient:
gradients = self.hyper_map(log_tensor, gradients)
accu_grads = self.hyper_map(add_grads, self.accu_grads, gradients)
accu_succ = self.hyper_map(update_accu_grads, self.accu_grads,
accu_grads)
if q == 0:
mean_grads = self.hyper_map(
F.partial(grad_scale, self.apply_period), accu_grads)
apply_succ = super(CumulativeSGDOptimizer,
self).construct(mean_grads)
reset_succ = self.hyper_map(reset_accu_grads, self.accu_grads)
succ = F.depend(reset_succ, apply_succ)
else:
succ = True
succ = F.depend(succ, accu_succ)
return F.depend(gradients, succ)
def construct(self, gradients):
lr = self.get_lr()
updated_velocity = self.map(
F.partial(adam_opt_for_map, self.beta1, self.beta2, self.eps, lr,
self.weight_decay_tensor), self.params, self.moments1,
self.moments2, gradients, self.decay_flag)
return updated_velocity
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 construct(self, gradients):
params = self.parameters
if self.reciprocal_scale != 1.0:
gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients)
if self.dynamic_lr:
lr = self.gather(self.learning_rate, self.global_step, self.axis)
F.control_depend(lr, self.assignadd(self.global_step, self.one))
else:
lr = self.learning_rate
if self.centered:
success = self.hyper_map(F.partial(centered_rmsprop_opt, self.opt, lr, self.decay, self.epsilon,
self.momentum), params, self.mg, self.ms, self.moment, gradients)
else:
success = self.hyper_map(F.partial(rmsprop_opt, self.opt, lr, self.decay, self.epsilon,
self.momentum), params, self.ms, self.moment, gradients)
return success
def construct(self, gradients):
params = self.params
accum = self.accum
stat = self.stat
if self.reciprocal_scale != 1.0:
gradients = self.hyper_map(
F.partial(grad_scale, self.reciprocal_scale), gradients)
if self.dynamic_lr:
lr = self.gather(self.learning_rate, self.global_step, self.axis)
F.control_depend(lr, self.assignadd(self.global_step, 1))
else:
lr = self.learning_rate
success = self.hyper_map(
F.partial(sgd_opt, self.opt, lr, self.momentum), gradients, params,
accum, stat)
return success
def construct(self, gradients):
params = self.parameters
gradients = self.decay_weight(gradients)
gradients = self.scale_grad(gradients)
lr = self.get_lr()
if self.centered:
success = self.hyper_map(
F.partial(centered_rmsprop_opt, self.opt, lr, self.decay,
self.epsilon, self.momentum), params, self.mg,
self.ms, self.moment, gradients)
else:
success = self.hyper_map(
F.partial(rmsprop_opt, self.opt, lr, self.decay, self.epsilon,
self.momentum), params, self.ms, self.moment,
gradients)
return success
def construct(self, img, label_indices, text, sequence_length, lab_len):
"""
Cell's forward
Args:
img: input
label_indices: get from the data generator
text: label got from the data generator
sequence_length: get from the data generator
lab_len: get from the data generator
Returns:
loss: loss value
"""
weights = self.weights
loss = self.network(img, label_indices, text, sequence_length)
scaling_sens = self.scale_sense
grads = self.grad(self.network, weights)(img, label_indices, text, sequence_length,
self.cast(scaling_sens, mstype.float32))
grads = self.hyper_map(F.partial(grad_scale, scaling_sens), grads)
grads = self.clip_gradients(grads, GRADIENT_CLIP_MIN, GRADIENT_CLIP_MAX)
if self.reducer_flag:
# apply grad reducer on grads
grads = self.grad_reducer(grads)
success = self.optimizer(grads)
ret = (loss, scaling_sens)
return F.depend(ret, success)
def construct(self, *inputs):
weights = self.weights
loss = self.network(*inputs)
sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
grads = self.grad(self.network, weights)(*inputs, sens)
return F.depend(
loss, self.hyper_map(F.partial(_sum_op), self.grad_sum, grads))
def construct(self,
input_ids,
input_mask,
token_type_id,
next_sentence_labels,
masked_lm_positions,
masked_lm_ids,
masked_lm_weights):
"""Defines the computation performed."""
weights = self.weights
loss = self.network(input_ids,
input_mask,
token_type_id,
next_sentence_labels,
masked_lm_positions,
masked_lm_ids,
masked_lm_weights)
grads = self.grad(self.network, weights)(input_ids,
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)
def construct(self, gradients):
updated_velocity = self.hyper_map(
F.partial(adam_opt, self.beta1, self.beta2, self.eps, self.lr,
self.weight_decay_tensor), self.params, self.moments1,
self.moments2, gradients)
return updated_velocity
def construct(self, grads):
global_norm = self.global_norm(grads)
cond = P.GreaterEqual()(global_norm, self.clip_norm)
global_norm = F.select(cond, global_norm, self.clip_norm)
grads = self.hyper_map(
F.partial(apply_global_norm, self.clip_norm, global_norm), grads)
return grads
def construct(self, x, sens=None):
"""Defines the computation performed."""
weights = self.weights
loss = self.network(x)
if sens is None:
scaling_sens = self.loss_scale
else:
scaling_sens = sens
# alloc status and clear should be right before gradoperation
init = self.alloc_status()
self.clear_before_grad(init)
grads = self.grad(self.network,
weights)(x, self.cast(scaling_sens, mstype.float32))
# apply grad reducer on grads
grads = self.grad_reducer(grads)
grads = self.hyper_map(
F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE),
grads)
self.get_status(init)
flag_sum = self.reduce_sum(init, (0, ))
cond = self.less_equal(self.base, flag_sum)
overflow = cond
if sens is None:
overflow = self.loss_scaling_manager(self.loss_scale, cond)
if overflow:
succ = False
else:
succ = self.optimizer(grads)
ret = (loss, cond, scaling_sens)
return F.depend(ret, succ)
def construct(self,
input_ids,
input_mask,
token_type_id,
next_sentence_labels,
masked_lm_positions,
masked_lm_ids,
masked_lm_weights,
sens=None):
"""Defines the computation performed."""
weights = self.weights
loss = self.network(input_ids,
input_mask,
token_type_id,
next_sentence_labels,
masked_lm_positions,
masked_lm_ids,
masked_lm_weights)
if sens is None:
scaling_sens = self.loss_scale
else:
scaling_sens = sens
status, scaling_sens = self.start_overflow_check(loss, scaling_sens)
grads = self.grad(self.network, weights)(input_ids,
input_mask,
token_type_id,
next_sentence_labels,
masked_lm_positions,
masked_lm_ids,
masked_lm_weights,
self.cast(scaling_sens,
mstype.float32))
# apply grad reducer on grads
grads = self.grad_reducer(grads)
grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads)
grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
cond = self.get_overflow_status(status, grads)
overflow = cond
if sens is None:
overflow = self.loss_scaling_manager(self.loss_scale, cond)
if overflow:
succ = False
else:
succ = self.optimizer(grads)
ret = (loss, cond, scaling_sens)
return F.depend(ret, succ)
def construct(self, grads):
success = True
weights = self.weights
moments = self.moments
success = self.hyper_map(
F.partial(run_opt, self.opt, self.iter, self.learning_rate,
self.momentum), grads, weights, moments)
return success
def construct(self, gradients):
gradients = self.decay_weight(gradients)
gradients = self.scale_grad(gradients)
lr = self.get_lr()
self.beta1_power = self.beta1_power * self.beta1
self.beta2_power = self.beta2_power * self.beta2
if self.is_group_lr:
success = self.map_(F.partial(_lazy_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
self.beta1_power, self.beta2_power, self.beta1, self.beta2, self.eps),
lr, gradients, self.parameters, self.moment1, self.moment2, self.ps_parameters)
else:
success = self.map_(F.partial(_lazy_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
self.beta1_power, self.beta2_power, self.beta1, self.beta2, self.eps, lr),
gradients, self.parameters, self.moment1, self.moment2, self.ps_parameters)
return success
