def _update_run_op_for_map(beta1, beta2, eps, lr, weight_decay_tensor, param,
m, v, gradient, decay_flag):
op_mul = P.Mul()
op_square = P.Square()
op_sqrt = P.Sqrt()
op_cast = P.Cast()
op_reshape = P.Reshape()
op_shape = P.Shape()
param_fp32 = op_cast(param, mstype.float32)
m_fp32 = op_cast(m, mstype.float32)
v_fp32 = op_cast(v, mstype.float32)
gradient_fp32 = op_cast(gradient, mstype.float32)
next_m = op_mul(beta1, m_fp32) + op_mul(
op_cast(F.tuple_to_array(
(1.0, )), mstype.float32) - beta1, gradient_fp32)
next_v = op_mul(beta2, v_fp32) + op_mul(
op_cast(F.tuple_to_array(
(1.0, )), mstype.float32) - beta2, op_square(gradient_fp32))
update = next_m / (op_sqrt(next_v) + eps)
if decay_flag:
update = update + op_mul(weight_decay_tensor, param_fp32)
update_with_lr = op_mul(lr, update)
next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
next_v = F.depend(next_v, F.assign(param, next_param))
next_v = F.depend(next_v, F.assign(m, next_m))
next_v = F.depend(next_v, F.assign(v, next_v))
return next_v
def broadcast_params(self, optim_result):
"""
Apply Broadcast operations in the sequential order of parameter groups.
Returns:
bool, the status flag.
"""
param_group = []
key_group = []
for _ in range(self.dev_num):
param_group.append(F.make_tuple())
key_group.append(F.make_tuple())
for i in range(self.param_length):
param_group[self.param_rank[i]] = param_group[
self.param_rank[i]] + (self.parameters[i], )
key = P.MakeRefKey(self.param_names[i])()
key_group[
self.param_rank[i]] = key_group[self.param_rank[i]] + (key, )
new_param_group = []
for root in range(self.dev_num):
ops = P.Broadcast(root)
next_params = ops(param_group[root])
new_param_group.append(next_params)
for i in range(F.tuple_len(next_params)):
F.assign(key_group[root][i], next_params[i])
status = F.control_depend(optim_result, new_param_group[0][0])
for i in range(self.dev_num - 1):
status = F.depend(
F.control_depend(new_param_group[i],
new_param_group[i + 1][0]), status)
return status
def _update_run_op(beta1, beta2, eps, lr, weight_decay, param, m, v, gradient,
decay_flag, optim_filter):
"""
Update parameters.
Args:
beta1 (Tensor): The exponential decay rate for the 1st moment estimations. Should be in range (0.0, 1.0).
beta2 (Tensor): The exponential decay rate for the 2nd moment estimations. Should be in range (0.0, 1.0).
eps (Tensor): Term added to the denominator to improve numerical stability. Should be greater than 0.
lr (Tensor): Learning rate.
weight_decay (Number): Weight decay. Should be equal to or greater than 0.
param (Tensor): Parameters.
m (Tensor): m value of parameters.
v (Tensor): v value of parameters.
gradient (Tensor): Gradient of parameters.
decay_flag (bool): Applies weight decay or not.
optim_filter (bool): Applies parameter update or not.
Returns:
Tensor, the new value of v after updating.
"""
if optim_filter:
op_mul = P.Mul()
op_square = P.Square()
op_sqrt = P.Sqrt()
op_cast = P.Cast()
op_reshape = P.Reshape()
op_shape = P.Shape()
param_fp32 = op_cast(param, mstype.float32)
m_fp32 = op_cast(m, mstype.float32)
v_fp32 = op_cast(v, mstype.float32)
gradient_fp32 = op_cast(gradient, mstype.float32)
next_m = op_mul(beta1, m_fp32) + op_mul(
op_cast(F.tuple_to_array(
(1.0, )), mstype.float32) - beta1, gradient_fp32)
next_v = op_mul(beta2, v_fp32) + op_mul(
op_cast(F.tuple_to_array(
(1.0, )), mstype.float32) - beta2, op_square(gradient_fp32))
update = next_m / (eps + op_sqrt(next_v))
if decay_flag:
update = op_mul(weight_decay, param_fp32) + update
update_with_lr = op_mul(lr, update)
next_param = param_fp32 - op_reshape(update_with_lr,
op_shape(param_fp32))
next_param = F.depend(
next_param, F.assign(param, op_cast(next_param, F.dtype(param))))
next_param = F.depend(next_param,
F.assign(m, op_cast(next_m, F.dtype(m))))
next_param = F.depend(next_param,
F.assign(v, op_cast(next_v, F.dtype(v))))
return op_cast(next_param, F.dtype(param))
return gradient
def construct(self, x, y):
out = self.zero
for i in range(self.max_cycles):
if out <= 20:
self.weight = out
F.assign(self.weight, i)
out = x * y + out
return out, self.weight
def step_end(self, run_context):
cb_params = run_context.original_args()
arr_lr = cb_params.optimizer.learning_rate.asnumpy()
lr = float(np.array2string(arr_lr))
new_lr = self.learning_rate_function(lr, cb_params.cur_step_num)
if not math.isclose(lr, new_lr, rel_tol=1e-10):
F.assign(cb_params.optimizer.learning_rate, Tensor(new_lr, mstype.float32))
print(f'At step {cb_params.cur_step_num}, learning_rate change to {new_lr}')
def construct(self, x, y):
out = self.zero
i = self.i
if x > y:
while i < self.max_cycles:
self.weight = i
F.assign(self.weight, i)
out = x * y + out
i = i + 1
return out, self.weight
def construct(self, x, y):
i = self.i
out = self.zero
while i < self.max_cycles:
if out <= 20:
out = x * y + out
# use F.Assign will throw NameSpace error.
F.assign(self.weight, i)
self.weight = i
i = i + 1
return out, self.weight
def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov, target, beta1_power,
beta2_power, beta1, beta2, eps, lr, gradient, param, m, v, ps_parameter, cache_enable):
"""Apply sparse adam optimizer to the weight parameter when the gradient is sparse."""
success = True
indices = gradient.indices
values = gradient.values
if ps_parameter and not cache_enable:
op_shape = P.Shape()
shapes = (op_shape(param), op_shape(m), op_shape(v),
op_shape(beta1_power), op_shape(beta2_power), op_shape(lr), op_shape(beta1),
op_shape(beta2), op_shape(eps), op_shape(values), op_shape(indices))
success = F.depend(success, pull(push((beta1_power, beta2_power, lr, beta1, beta2,
eps, values, indices), shapes), param))
return success
if not target:
success = F.depend(success, sparse_opt(param, m, v, beta1_power, beta2_power, lr, beta1, beta2,
eps, values, indices))
else:
op_mul = P.Mul()
op_square = P.Square()
op_sqrt = P.Sqrt()
scatter_add = P.ScatterAdd(use_locking)
success = F.depend(success, F.assign(m, op_mul(beta1, m)))
success = F.depend(success, F.assign(v, op_mul(beta2, v)))
grad_indices = gradient.indices
grad_value = gradient.values
next_m = scatter_add(m,
grad_indices,
op_mul(F.tuple_to_array((1.0,)) - beta1, grad_value))
next_v = scatter_add(v,
grad_indices,
op_mul(F.tuple_to_array((1.0,)) - beta2, op_square(grad_value)))
if use_nesterov:
m_temp = next_m * _scaler_ten
F.assign(m, op_mul(beta1, next_m))
div_value = scatter_add(m,
op_mul(grad_indices, _scaler_one),
op_mul(F.tuple_to_array((1.0,)) - beta1, grad_value))
param_update = div_value / (op_sqrt(next_v) + eps)
F.assign(m, m_temp / _scaler_ten)
else:
param_update = next_m / (op_sqrt(next_v) + eps)
lr_t = lr * op_sqrt(1 - beta2_power) / (1 - beta1_power)
next_param = param - lr_t * param_update
success = F.depend(success, F.assign(param, next_param))
success = F.depend(success, F.assign(m, next_m))
success = F.depend(success, F.assign(v, next_v))
return success
def construct(self, x, y):
i = self.i
out = self.zero
while i < self.max_cycles:
F.assign(self.weight, i)
self.weight = i
out = x * y + out
i = i + 1
if out >= 20:
F.assign(self.weight, out)
self.weight = out
out = out - 20
return out, self.weight
def construct(self,
input_ids,
input_mask,
token_type_id,
label_ids,
sens=None):
"""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)
if sens is None:
scaling_sens = self.loss_scale
else:
scaling_sens = sens
# alloc status and clear should be right before grad operation
init = self.alloc_status()
self.clear_before_grad(init)
grads = self.grad(self.network,
weights)(input_ids, input_mask, token_type_id,
label_ids,
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, 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)
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)
return succ
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 _update_run_op(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v,
gradient, decay_flag):
"""
Update parameters.
Args:
beta1 (Tensor): The exponential decay rate for the 1st moment estimates. Should be in range (0.0, 1.0).
beta2 (Tensor): The exponential decay rate for the 2nd moment estimates. Should be in range (0.0, 1.0).
eps (Tensor): Term added to the denominator to improve numerical stability. Should be greater than 0.
lr (Tensor): Learning rate.
weight_decay_tensor (Tensor): Weight decay. Should be equal to or greater than 0.
param (Tensor): Parameters.
m (Tensor): m value of parameters.
v (Tensor): v value of parameters.
gradient (Tensor): Gradient of parameters.
Returns:
Tensor, the new value of v after updating.
"""
op_mul = P.Mul()
op_square = P.Square()
op_sqrt = P.Sqrt()
op_cast = P.Cast()
op_reshape = P.Reshape()
op_shape = P.Shape()
param = op_cast(param, mstype.float32)
m = op_cast(m, mstype.float32)
v = op_cast(v, mstype.float32)
gradient = op_cast(gradient, mstype.float32)
next_m = op_mul(beta1, m) + op_mul(
op_cast(F.tuple_to_array((1.0, )), mstype.float32) - beta1, gradient)
next_v = op_mul(beta2, v) + op_mul(
op_cast(F.tuple_to_array(
(1.0, )), mstype.float32) - beta2, op_square(gradient))
update = next_m / (op_sqrt(next_v) + eps)
if decay_flag:
update = update + op_mul(weight_decay_tensor, param)
update_with_lr = op_mul(lr, update)
next_param = param - op_reshape(update_with_lr, op_shape(param))
next_v = F.depend(next_v, F.assign(param, next_param))
next_v = F.depend(next_v, F.assign(m, next_m))
next_v = F.depend(next_v, F.assign(v, next_v))
return next_v
def update_opt_step(learning_rate, batch_size, parameter, gradient):
"""
Update opt step.
Args:
learning_rate (Tensor): Learning rate.
batch_size (Tensor): Batch Size.
parameter (Tensor): Parameter.
gradient (Tensor): Gradients.
Returns:
"""
next_param = parameter - learning_rate * gradient / batch_size
F.assign(parameter, next_param)
return next_param
def tensor_run_opt(opt, iters, learning_rate, momentum, gradient, variable,
moment):
""" tensor_run_opt """
success = True
new_weight = opt(variable, moment, learning_rate, gradient, momentum)
success = F.depend(success, F.assign(variable, new_weight))
return success
def tensor_grad_scale(scale, grad, accu_grad):
#mul = P.Mul()
new_grad = accu_grad * reciprocal(scale)
zeros = F.tensor_mul(accu_grad, 0.0)
clear = F.assign(accu_grad, zeros)
F.control_depend(new_grad, clear)
F.control_depend(grad, new_grad)
return new_grad
def construct(self, beta1, beta2, one_sub_beta_1, one_sub_beta_2, gradient, eps, weight_decay_tensor, lr):
F.assign(self.param, self.x)
param_fp32 = self.op_cast(self.param, mstype.float32)
m_fp32 = self.op_cast(self.m, mstype.float32)
v_fp32 = self.op_cast(self.v, mstype.float32)
gradient_fp32 = self.op_cast(gradient, mstype.float32)
next_m = self.op_mul(beta1, m_fp32) + \
self.op_mul(self.op_cast(one_sub_beta_1,
mstype.float32), gradient_fp32)
next_v = self.op_mul(beta2, v_fp32) + self.op_mul(self.op_cast(one_sub_beta_2,
mstype.float32), self.op_square(gradient_fp32))
update = next_m / (eps + self.op_sqrt(next_v))
if self.decay_flag:
update = self.op_mul(weight_decay_tensor, param_fp32) + update
update_with_lr = self.op_mul(lr, update)
next_param = param_fp32 - \
self.op_reshape(update_with_lr, self.op_shape(param_fp32))
depend_v = F.depend(next_param, F.assign(self.param, next_param))
depend_v = F.depend(depend_v, F.assign(self.m, next_m))
depend_v = F.depend(depend_v, F.assign(self.v, next_v))
F.assign(self.x, self.m)
return depend_v
def construct(self, input_ids, input_mask, token_type_id, label_ids):
"""Defines the computation performed."""
weights = self.weights
saved = ()
for i in range(self.length):
saved = saved + (F.assign(self.saved_params[i], weights[i]), )
assign_embedding = ()
for i in range(self.quant_embedding_list_length):
quant_embedding = self.quantize_embedding(
weights[self.quant_embedding_list[i]])
assign_embedding = assign_embedding + (F.assign(
weights[self.quant_embedding_list[i]], quant_embedding), )
F.control_depend(saved, assign_embedding[i])
assign_weight = ()
for i in range(self.quant_weight_list_length):
quant_weight = self.quantize_weight(
weights[self.quant_weight_list[i]])
assign_weight = assign_weight + (F.assign(
weights[self.quant_weight_list[i]], quant_weight), )
F.control_depend(saved, assign_weight[i])
for i in range(self.quant_embedding_list_length):
F.control_depend(assign_embedding[i], input_ids)
for i in range(self.quant_weight_list_length):
F.control_depend(assign_weight[i], input_ids)
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))
F.control_depend(input_ids, grads)
# apply grad reducer on grads
grads = self.grad_reducer(grads)
grads = self.hyper_map(
F.partial(clip_grad, gradient_cfg.clip_type,
gradient_cfg.clip_value), grads)
restore = ()
for i in range(self.length):
restore = restore + (F.assign(weights[i], self.saved_params[i]), )
F.control_depend(grads, restore[i])
succ = self.optimizer(grads)
for i in range(self.length):
F.control_depend(restore[i], succ)
return succ
def construct(self, beta1, beta2, gradient, eps, weight_decay_tensor, lr):
param_fp32 = self.op_cast(self.param, mstype.float32)
m_fp32 = self.op_cast(self.m, mstype.float32)
v_fp32 = self.op_cast(self.v, mstype.float32)
gradient_fp32 = self.op_cast(gradient, mstype.float32)
next_m = self.op_mul(beta1, m_fp32) + \
self.op_mul(self.op_cast(F.tuple_to_array((1.0,)), mstype.float32) - beta1, gradient_fp32)
next_v = self.op_mul(beta2, v_fp32) + self.op_mul(self.op_cast(F.tuple_to_array((1.0,)), mstype.float32) - \
beta2, self.op_square(gradient_fp32))
update = next_m / (eps + self.op_sqrt(next_v))
if self.decay_flag:
update = self.op_mul(weight_decay_tensor, param_fp32) + update
update_with_lr = self.op_mul(lr, update)
next_param = param_fp32 - self.op_reshape(update_with_lr,
self.op_shape(param_fp32))
next_v = F.depend(next_v, F.assign(self.param, next_param))
next_v = F.depend(next_v, F.assign(self.m, next_m))
next_v = F.depend(next_v, F.assign(self.v, next_v))
return next_v
def _reset_accu_grads(accu_grad):
succ = True
return F.depend(succ, F.assign(accu_grad, zeroslike(accu_grad)))
def _update_accu_grads(accu_grad, grad):
succ = True
return F.depend(succ, F.assign(accu_grad, cast(grad, mstype.float32)))
def construct(self, x, y):
add_res = self.add(x, y)
F.depend(add_res, F.assign(self.param, add_res))
return add_res
def _update_run_op(beta1, beta2, eps, global_step, lr, weight_decay, param, m,
v, gradient, decay_flag, optim_filter):
"""
Update parameters.
Args:
beta1 (Tensor): The exponential decay rate for the 1st moment estimations. Should be in range (0.0, 1.0).
beta2 (Tensor): The exponential decay rate for the 2nd moment estimations. Should be in range (0.0, 1.0).
eps (Tensor): Term added to the denominator to improve numerical stability. Should be greater than 0.
lr (Tensor): Learning rate.
weight_decay (Number): Weight decay. Should be equal to or greater than 0.
global_step (Tensor): Global step.
param (Tensor): Parameters.
m (Tensor): m value of parameters.
v (Tensor): v value of parameters.
gradient (Tensor): Gradient of parameters.
decay_flag (bool): Specifies whether param update with weight decay.
optim_filter(bool): Applies parameter update or not.
Returns:
Tensor, the new value of v after updating.
"""
if optim_filter:
op_mul = P.Mul()
op_sqrt = P.Sqrt()
op_rsqrt = P.Rsqrt()
op_square = P.Square()
op_cast = P.Cast()
op_reshape = P.Reshape()
op_shape = P.Shape()
op_pow = P.Pow()
op_norm = layer.Norm()
op_select = P.Select()
op_greater = P.Greater()
op_fill = P.Fill()
op_dtype = P.DType()
param_fp32 = op_cast(param, mstype.float32)
m_fp32 = op_cast(m, mstype.float32)
v_fp32 = op_cast(v, mstype.float32)
gradient_fp32 = op_cast(gradient, mstype.float32)
next_m = op_mul(beta1, m_fp32) + op_mul(
op_cast(num_one, mstype.float32) - beta1, gradient_fp32)
next_v = op_mul(beta2, v_fp32) + op_mul(
op_cast(num_one, mstype.float32) - beta2, op_square(gradient_fp32))
next_mm = next_m / (op_cast(num_one, mstype.float32) - op_pow(
beta1, op_cast(global_step + num_one, mstype.float32)))
next_vv = next_v / (op_cast(num_one, mstype.float32) - op_pow(
beta2, op_cast(global_step + num_one, mstype.float32)))
w_norm = op_norm(param_fp32)
g_norm = op_norm(gradient_fp32)
g_norm_hat = op_norm(
op_mul(next_mm, op_rsqrt(next_vv + eps)) +
weight_decay * param_fp32)
zeros = F.zeros_like(w_norm)
ones = op_fill(op_dtype(w_norm), op_shape(w_norm), 1.0)
trust_ratio = op_select(
op_greater(w_norm, zeros),
op_select(op_greater(g_norm, zeros), w_norm / g_norm_hat, ones),
ones)
tens = op_fill(op_dtype(trust_ratio), op_shape(trust_ratio), 10.0)
trust_ratio = C.clip_by_value(trust_ratio, zeros, tens)
update = next_mm / (op_sqrt(next_vv) + eps)
if decay_flag:
update = update + op_mul(weight_decay, param_fp32)
update_with_lr = op_mul(op_mul(trust_ratio, lr), update)
next_param = param_fp32 - op_reshape(update_with_lr,
op_shape(param_fp32))
next_param = F.depend(
next_param, F.assign(param, op_cast(next_param, F.dtype(param))))
next_param = F.depend(next_param,
F.assign(m, op_cast(next_m, F.dtype(m))))
next_param = F.depend(next_param,
F.assign(v, op_cast(next_v, F.dtype(v))))
return op_cast(next_param, F.dtype(param))
return gradient
def _clear_grad_sum(grad_sum, zero):
"""Apply zero to clear grad_sum."""
success = True
success = F.depend(success, F.assign(grad_sum, zero))
return success
def construct(self, x, y):
F.assign(self.cov_step, y)
F.assign(x, y)
return x
