# SGD

Stochastic gradient descent (SGD) is a basic gradient descent optimizer to minimize loss given a set of model parameters and updates the parameters in the opposite direction of the gradient. The update is performed on a randomly sampled mini-batch of data from the dataset.

bitsandbytes also supports momentum and Nesterov momentum to accelerate SGD by adding a weighted average of past gradients to the current gradient.

## SGD[[api-class]][[bitsandbytes.optim.SGD]]

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<docstring><name>class bitsandbytes.optim.SGD</name><anchor>bitsandbytes.optim.SGD</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/sgd.py#L8</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": ""}, {"name": "momentum", "val": " = 0"}, {"name": "dampening", "val": " = 0"}, {"name": "weight_decay", "val": " = 0"}, {"name": "nesterov", "val": " = False"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}]</parameters></docstring>



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<docstring><name>__init__</name><anchor>bitsandbytes.optim.SGD.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/sgd.py#L9</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": ""}, {"name": "momentum", "val": " = 0"}, {"name": "dampening", "val": " = 0"}, {"name": "weight_decay", "val": " = 0"}, {"name": "nesterov", "val": " = False"}, {"name": "optim_bits", "val": " = 32"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}]</parameters><paramsdesc>- **params** (`torch.tensor`) --
  The input parameters to optimize.
- **lr** (`float`) --
  The learning rate.
- **momentum** (`float`, defaults to 0) --
  The momentum value speeds up the optimizer by taking bigger steps.
- **dampening** (`float`, defaults to 0) --
  The dampening value reduces the momentum of the optimizer.
- **weight_decay** (`float`, defaults to 0.0) --
  The weight decay value for the optimizer.
- **nesterov** (`bool`, defaults to `False`) --
  Whether to use Nesterov momentum.
- **optim_bits** (`int`, defaults to 32) --
  The number of bits of the optimizer state.
- **args** (`object`, defaults to `None`) --
  An object with additional arguments.
- **min_8bit_size** (`int`, defaults to 4096) --
  The minimum number of elements of the parameter tensors for 8-bit optimization.
- **percentile_clipping** (`int`, defaults to 100) --
  Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
- **block_wise** (`bool`, defaults to `True`) --
  Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base SGD optimizer.




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## SGD8bit[[bitsandbytes.optim.SGD8bit]]

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<docstring><name>class bitsandbytes.optim.SGD8bit</name><anchor>bitsandbytes.optim.SGD8bit</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/sgd.py#L67</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": ""}, {"name": "momentum", "val": " = 0"}, {"name": "dampening", "val": " = 0"}, {"name": "weight_decay", "val": " = 0"}, {"name": "nesterov", "val": " = False"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}]</parameters></docstring>



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<docstring><name>__init__</name><anchor>bitsandbytes.optim.SGD8bit.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/sgd.py#L68</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": ""}, {"name": "momentum", "val": " = 0"}, {"name": "dampening", "val": " = 0"}, {"name": "weight_decay", "val": " = 0"}, {"name": "nesterov", "val": " = False"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}]</parameters><paramsdesc>- **params** (`torch.tensor`) --
  The input parameters to optimize.
- **lr** (`float`) --
  The learning rate.
- **momentum** (`float`, defaults to 0) --
  The momentum value speeds up the optimizer by taking bigger steps.
- **dampening** (`float`, defaults to 0) --
  The dampening value reduces the momentum of the optimizer.
- **weight_decay** (`float`, defaults to 0.0) --
  The weight decay value for the optimizer.
- **nesterov** (`bool`, defaults to `False`) --
  Whether to use Nesterov momentum.
- **args** (`object`, defaults to `None`) --
  An object with additional arguments.
- **min_8bit_size** (`int`, defaults to 4096) --
  The minimum number of elements of the parameter tensors for 8-bit optimization.
- **percentile_clipping** (`int`, defaults to 100) --
  Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
- **block_wise** (`bool`, defaults to `True`) --
  Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.</paramsdesc><paramgroups>0</paramgroups></docstring>

8-bit SGD optimizer.




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## SGD32bit[[bitsandbytes.optim.SGD32bit]]

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<docstring><name>class bitsandbytes.optim.SGD32bit</name><anchor>bitsandbytes.optim.SGD32bit</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/sgd.py#L123</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": ""}, {"name": "momentum", "val": " = 0"}, {"name": "dampening", "val": " = 0"}, {"name": "weight_decay", "val": " = 0"}, {"name": "nesterov", "val": " = False"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}]</parameters></docstring>



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<docstring><name>__init__</name><anchor>bitsandbytes.optim.SGD32bit.__init__</anchor><source>https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/sgd.py#L124</source><parameters>[{"name": "params", "val": ""}, {"name": "lr", "val": ""}, {"name": "momentum", "val": " = 0"}, {"name": "dampening", "val": " = 0"}, {"name": "weight_decay", "val": " = 0"}, {"name": "nesterov", "val": " = False"}, {"name": "args", "val": " = None"}, {"name": "min_8bit_size", "val": " = 4096"}, {"name": "percentile_clipping", "val": " = 100"}, {"name": "block_wise", "val": " = True"}]</parameters><paramsdesc>- **params** (`torch.tensor`) --
  The input parameters to optimize.
- **lr** (`float`) --
  The learning rate.
- **momentum** (`float`, defaults to 0) --
  The momentum value speeds up the optimizer by taking bigger steps.
- **dampening** (`float`, defaults to 0) --
  The dampening value reduces the momentum of the optimizer.
- **weight_decay** (`float`, defaults to 0.0) --
  The weight decay value for the optimizer.
- **nesterov** (`bool`, defaults to `False`) --
  Whether to use Nesterov momentum.
- **args** (`object`, defaults to `None`) --
  An object with additional arguments.
- **min_8bit_size** (`int`, defaults to 4096) --
  The minimum number of elements of the parameter tensors for 8-bit optimization.
- **percentile_clipping** (`int`, defaults to 100) --
  Adapts clipping threshold automatically by tracking the last 100 gradient norms and clipping the gradient at a certain percentile to improve stability.
- **block_wise** (`bool`, defaults to `True`) --
  Whether to independently quantize each block of tensors to reduce outlier effects and improve stability.</paramsdesc><paramgroups>0</paramgroups></docstring>

32-bit SGD optimizer.




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