oneflow.optim.SGD

class oneflow.optim.SGD(params: Union[Iterator[oneflow.nn.Parameter], List[Dict]], lr: float = 0.001, momentum: float = 0.0, dampening: float = 0.0, weight_decay: float = 0.0, nesterov: bool = False, maximize: bool = False, contiguous_params: bool = False, fused: bool = False)

Implements SGD algorithm.

This algorithm takes a random sample’s gradient as an approximate estimate of the overall gradient in small batch gradient descent.

When the momentum = 0, the equation of parameters updating is:

\[param_{new} = param_{old} - learning\_rate * grad\]

With momentum, the equation of parameters updating is:

\[ \begin{align}\begin{aligned}& V_t = \beta * V_{t-1} - learning\_rate * (g_t + param_{old} * weight\_decay)\\& param_{new} = param_{old} + V_t\end{aligned}\end{align} \]

Parameters

• params (iterable) – iterable of parameters to optimize or dicts defining parameter groups

• lr (float, optional) – learning rate (default: 1e-3)

• momentum (float, optional) – Momentum factor (default: 0.0)

• weight_decay (float, optional) – weight decay (L2 penalty) (default: 0.0)

• contiguous_params (bool, optional) – whether to use contiguous ParamGroup which puts all parameters of the same type, device and group into the same tensor and update them together. (default: False)

• fused (bool, optional) – whether to divide all the parameters into several groups, then update each group of parameters with the fused kernel. (default: False)

For example:

Example 1:

# Assume net is a custom model.
sgd = flow.optim.SGD(net.parameters(), lr=1e-3)

for epoch in range(epochs):
    # Read data, Compute the loss and so on.
    # ...
    loss.backward()
    sgd.step()
    sgd.zero_grad()

Example 2:

# Assume net is a custom model.
sgd = flow.optim.SGD(
    [
        {
            "params": net.parameters(),
            "lr": learning_rate,
            "clip_grad_max_norm": 0.5,
            "clip_grad_norm_type": 2.0,
        }
    ],
)

for epoch in range(epochs):
    # Read data, Compute the loss and so on.
    # ...
    loss.backward()
    sgd.clip_grad()
    sgd.step()
    sgd.zero_grad()

If you want to use clip_grad, you can refer this example.

For more details of clip_grad_max_norm and clip_grad_norm_type, you can refer to oneflow.nn.utils.clip_grad_norm_().

__init__(params: Union[Iterator[oneflow.nn.Parameter], List[Dict]], lr: float = 0.001, momentum: float = 0.0, dampening: float = 0.0, weight_decay: float = 0.0, nesterov: bool = False, maximize: bool = False, contiguous_params: bool = False, fused: bool = False)

Initialize self. See help(type(self)) for accurate signature.

Methods

__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init__(params[, lr, momentum, dampening, …])	Initialize self.
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(**kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_check_variables_in_graph(vars_conf)
_check_variables_optimizer_bound(vars_conf)
_fused_update(param_group)
_generate_conf_for_graph(train_conf, vars_conf)
_generate_grad_clip_conf_for_optim_conf(…)
_generate_indexed_slices_optimizer_conf(…)
_generate_lr_scale_for_optim_conf(…)
_parse_input_parameters(parameters)	Supports such parameters:
_single_tensor_update(param_group)
add_param_group(param_group)	Add a param group to the Optimizer s param_groups.
clip_grad([error_if_nonfinite])	Clips gradient norm of an iterable of parameters.
load_state_dict(state_dict)	Load the state of the optimizer which is created by state_dict function.
state_dict()	Returns the state of the optimizer as a dict.
step([closure])	Performs a single optimization step. :param closure: A closure that reevaluates the model and returns the loss. :type closure: callable, optional.
zero_grad([set_to_none])	Sets the gradients of all optimized oneflow.Tensor s to zero.

Attributes

support_sparse

Whether SGD Optimizer support sparse update.