moe-scattermoe-granite-ep4-padding-free-foak-sample-configuration.yaml

# FMS Acceleration Plugin Configuration. 
#
# Each stanza incorporates various configurations for 
# different fine-tuning / training tasks.
plugins:
  # Configurations to accelerate data packing/padding in training
  training:

    # attention module configurations
    # e.g. padding-free modifications to attention layer
    attention:

      # this controls the confgurations for padding free computation of flash attention
      padding_free:
        method: huggingface
    fused_ops_and_kernels:

      # if under training stanza, then putting
      # base_layer and fused_lora will be a misnomer
      # - this should be in peft.quantized
      # However, if it is specified, it will still 
      # be read. This is useful in use cases where
      # the yaml is system generated and not shown
      # to a user.

      # activate various unsloth optimizations
      # there are two versions of the plugin
      # - the FastKernel version supports individual kernels
      # - the FastQuantized version is all-or-nothing

      # fast loss triton kernels
      fast_loss: true

      # fast rms norm triton kernels
      fast_rms_layernorm: true

      # fast RoPE embedding triton kernels
      fast_rope_embeddings: true
    moe:

      # expert-parallel for MoE
      scattermoe:

        # The level of expert parallel sharding. 
        # - 1 means no sharding
        # - if > 1, please ensure that this divides the world_size. This is because
        #   the devices will be replicated for every ep_degree devices, and 
        #   the experts will be sharded within each group.
        # - if > 1, also ensure that it divides the number of experts, as each device
        #   will then have num_of_experts / ep_degree experts.
        ep_degree: 4