Block interface: parameter and linear config, separate SSM config.

[jlamypoirier]

✨ Description

Not really complete by itself, but extracted as a separate PR to limit PR scope.

New(ish) concepts:

• Parameters are created through ParameterConfig.get_parameter, and most layers (more to come) are created through [LayerConfig].get_layer. This ensures correct, standardized creation, and leave more room for new additions (ex. dynamic types.)
• Parameter and linear (will) support fine-grained customization, but are typically set by the parent layer. Because of this, several parameters will be left "unset" by default (None or with special default marker), and get_parameter/get_layer take default values as arguments. This way we keep existing behaviour as default and make the new options truly optional and opt-in. (Otherwise, things like disabling biases, setting initialization scale or lr scale would have needed manual setting of every single parameter.)

Main changes:

• Add ParameterConfig as the new standard way to configure and instantiate (get_parameter) every parameter. Currently a placeholder config, but standard parameters (lr scale, initialization, maybe more) will be added in next PRs.
• Add OptionalParameterConfig for weights that may be enabled or disabled (ex. biases). It comes with an enabled option, with default provided by the parent layer.
• Add (mostly empty) configuration for linear layers. Distinguish pure linear (no bias) from affine linear (optional bias) . Linear layers are created through get_layer, which takes non-config arguments as well as defaults for bias.enabled (default_add_bias) and initialization (customizable initialization will come later).
• Add CausalConv1d layer (based on Mamba 2 and Discrete Mamba 2 implementations) and config. Config is similar to AffineLinearConfig, but also supports custom activation, with default set by the parent layer.
• Rework all layers and their configs to use the new configs.
• Make the SSM config dynamic, separating it into MambaConfig, Mamba2Config, DiscreteMamba2Config. Things are a bit awkward for now because of the couble configuration (hybrid_block_layout, ssm.type), but this will be addressed in upcoming PR.
• Remove auto_grad_accumulation arguments, as things work without it, and removing it allows mixing auto and non-auto accumulation (dt bias).

Config/breaking changes:

• SSM arguments removed for SSM types that don't use them. (Ex. setting Mamba 2 option d_xb in a Mamba 1 layer will cause a crash.)
• [temporary hack ] SSM configs need an explicitly set type.
• Remove ssm.add_bias_linear, AddLinearBiasChoices. add_linear_biases: bool is kept as the only global option for biases, at least for now. Other options may be achieved through individual layer configs.
• ssm.expansion_factor removed (redundant)
• ssm.conv_kernel_dimension -> ssm.convolution_layer.kernel_size
• ssm.activation_type -> ssm.convolution_layer.activation
• Renamed conv1d_weight -> convolution.weight
• Renamed conv1d_bias -> convolution.bias
• Option to enable or disable all supported linear and convolution biases (unchanged defaults)
• Mamba:
  • Added support for convolution bias
  • Renamed dt_proj_weight -> dt_proj.weight
  • Renamed dt_proj_bias -> dt_proj.bias
• Mamba 2
  • dt_proj_bias -> dt_proj.bias
  • Add support for custom convolution activation, backup torch implementation

TODO:

• Allow separate configuration for concatenated layers (ex. key_value, ssm in_proj)
• Deal with conversion.
• Review global bias options.