bitsandbytes/v0.50.0/en/reference/optim/adam.md

Adam

Adam (Adaptive moment estimation) is an adaptive learning rate optimizer, combining ideas from SGD with momentum and RMSprop to automatically scale the learning rate:

• a weighted average of the past gradients to provide direction (first-moment)
• a weighted average of the squared past gradients to adapt the learning rate to each parameter (second-moment)

bitsandbytes also supports paged optimizers which take advantage of CUDAs unified memory to transfer memory from the GPU to the CPU when GPU memory is exhausted.

Adam[[api-class]][[bitsandbytes.optim.Adam]]

• params (torch.tensor) -- The input parameters to optimize.
• lr (float, defaults to 1e-3) -- The learning rate.
• betas (tuple(float, float), defaults to (0.9, 0.999)) -- The beta values are the decay rates of the first and second-order moment of the optimizer.
• eps (float, defaults to 1e-8) -- The epsilon value prevents division by zero in the optimizer.
• weight_decay (float, defaults to 0.0) -- The weight decay value for the optimizer.
• amsgrad (bool, defaults to False) -- Whether to use the AMSGrad variant of Adam that uses the maximum of past squared gradients instead.
• 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.
• is_paged (bool, defaults to False) -- Whether the optimizer is a paged optimizer or not.

Base Adam optimizer.

Adam8bit[[bitsandbytes.optim.Adam8bit]]

• params (torch.tensor) -- The input parameters to optimize.
• lr (float, defaults to 1e-3) -- The learning rate.
• betas (tuple(float, float), defaults to (0.9, 0.999)) -- The beta values are the decay rates of the first and second-order moment of the optimizer.
• eps (float, defaults to 1e-8) -- The epsilon value prevents division by zero in the optimizer.
• weight_decay (float, defaults to 0.0) -- The weight decay value for the optimizer.
• amsgrad (bool, defaults to False) -- Whether to use the AMSGrad variant of Adam that uses the maximum of past squared gradients instead. Note: This parameter is not supported in Adam8bit and must be False.
• optim_bits (int, defaults to 32) -- The number of bits of the optimizer state. Note: This parameter is not used in Adam8bit as it always uses 8-bit optimization.
• 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.
• is_paged (bool, defaults to False) -- Whether the optimizer is a paged optimizer or not.

8-bit Adam optimizer.

Adam32bit[[bitsandbytes.optim.Adam32bit]]

• params (torch.tensor) -- The input parameters to optimize.
• lr (float, defaults to 1e-3) -- The learning rate.
• betas (tuple(float, float), defaults to (0.9, 0.999)) -- The beta values are the decay rates of the first and second-order moment of the optimizer.
• eps (float, defaults to 1e-8) -- The epsilon value prevents division by zero in the optimizer.
• weight_decay (float, defaults to 0.0) -- The weight decay value for the optimizer.
• amsgrad (bool, defaults to False) -- Whether to use the AMSGrad variant of Adam that uses the maximum of past squared gradients instead.
• 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.
• is_paged (bool, defaults to False) -- Whether the optimizer is a paged optimizer or not.

32-bit Adam optimizer.

PagedAdam[[bitsandbytes.optim.PagedAdam]]

• params (torch.tensor) -- The input parameters to optimize.
• lr (float, defaults to 1e-3) -- The learning rate.
• betas (tuple(float, float), defaults to (0.9, 0.999)) -- The beta values are the decay rates of the first and second-order moment of the optimizer.
• eps (float, defaults to 1e-8) -- The epsilon value prevents division by zero in the optimizer.
• weight_decay (float, defaults to 0.0) -- The weight decay value for the optimizer.
• amsgrad (bool, defaults to False) -- Whether to use the AMSGrad variant of Adam that uses the maximum of past squared gradients instead.
• 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.
• is_paged (bool, defaults to False) -- Whether the optimizer is a paged optimizer or not.

Paged Adam optimizer.

PagedAdam8bit[[bitsandbytes.optim.PagedAdam8bit]]

• params (torch.tensor) -- The input parameters to optimize.
• lr (float, defaults to 1e-3) -- The learning rate.
• betas (tuple(float, float), defaults to (0.9, 0.999)) -- The beta values are the decay rates of the first and second-order moment of the optimizer.
• eps (float, defaults to 1e-8) -- The epsilon value prevents division by zero in the optimizer.
• weight_decay (float, defaults to 0.0) -- The weight decay value for the optimizer.
• amsgrad (bool, defaults to False) -- Whether to use the AMSGrad variant of Adam that uses the maximum of past squared gradients instead. Note: This parameter is not supported in PagedAdam8bit and must be False.
• optim_bits (int, defaults to 32) -- The number of bits of the optimizer state. Note: This parameter is not used in PagedAdam8bit as it always uses 8-bit optimization.
• 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.
• is_paged (bool, defaults to False) -- Whether the optimizer is a paged optimizer or not.

8-bit paged Adam optimizer.

PagedAdam32bit[[bitsandbytes.optim.PagedAdam32bit]]

• params (torch.tensor) -- The input parameters to optimize.
• lr (float, defaults to 1e-3) -- The learning rate.
• betas (tuple(float, float), defaults to (0.9, 0.999)) -- The beta values are the decay rates of the first and second-order moment of the optimizer.
• eps (float, defaults to 1e-8) -- The epsilon value prevents division by zero in the optimizer.
• weight_decay (float, defaults to 0.0) -- The weight decay value for the optimizer.
• amsgrad (bool, defaults to False) -- Whether to use the AMSGrad variant of Adam that uses the maximum of past squared gradients instead.
• 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.
• is_paged (bool, defaults to False) -- Whether the optimizer is a paged optimizer or not.

Paged 32-bit Adam optimizer.