Buckets:
| # AdamW | |
| [AdamW](https://hf.co/papers/1711.05101) is a variant of the `Adam` optimizer that separates weight decay from the gradient update based on the observation that the weight decay formulation is different when applied to `SGD` and `Adam`. | |
| 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. | |
| ## AdamW[[api-class]][[bitsandbytes.optim.AdamW]] | |
| - **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 1e-2) -- | |
| The weight decay value for the optimizer. | |
| - **amsgrad** (`bool`, defaults to `False`) -- | |
| Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) 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 AdamW optimizer. | |
| ## AdamW8bit[[bitsandbytes.optim.AdamW8bit]] | |
| - **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 1e-2) -- | |
| The weight decay value for the optimizer. | |
| - **amsgrad** (`bool`, defaults to `False`) -- | |
| Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. | |
| Note: This parameter is not supported in AdamW8bit 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 AdamW8bit 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 AdamW optimizer. | |
| ## AdamW32bit[[bitsandbytes.optim.AdamW32bit]] | |
| - **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 1e-2) -- | |
| The weight decay value for the optimizer. | |
| - **amsgrad** (`bool`, defaults to `False`) -- | |
| Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) 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 AdamW optimizer. | |
| ## PagedAdamW[[bitsandbytes.optim.PagedAdamW]] | |
| - **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 1e-2) -- | |
| The weight decay value for the optimizer. | |
| - **amsgrad** (`bool`, defaults to `False`) -- | |
| Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) 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. | |
| Paged AdamW optimizer. | |
| ## PagedAdamW8bit[[bitsandbytes.optim.PagedAdamW8bit]] | |
| - **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 1e-2) -- | |
| The weight decay value for the optimizer. | |
| - **amsgrad** (`bool`, defaults to `False`) -- | |
| Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) variant of Adam that uses the maximum of past squared gradients instead. | |
| Note: This parameter is not supported in PagedAdamW8bit 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 PagedAdamW8bit 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. | |
| Paged 8-bit AdamW optimizer. | |
| ## PagedAdamW32bit[[bitsandbytes.optim.PagedAdamW32bit]] | |
| - **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 1e-2) -- | |
| The weight decay value for the optimizer. | |
| - **amsgrad** (`bool`, defaults to `False`) -- | |
| Whether to use the [AMSGrad](https://hf.co/papers/1904.09237) 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. | |
| Paged 32-bit AdamW optimizer. | |
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