introduce additional_arguments in config.yaml

additional_arguments allows ilab to implicitly support more complex flags from the libraries it calls without adding them directly to the config

Signed-off-by: Charlie Doern <[email protected]>

.spellcheck-en-custom.txt

@@ -22,6 +22,7 @@ Conda
 config
 Containerfile
 cpp
+ctx
 cuBLAS
 CUDA
 customizations
@@ -165,3 +166,4 @@ XT
 XTX
 Xu
 YAML
+yaml

docs/cli/ilab-config-structure.md

@@ -0,0 +1,121 @@
+# Modifying the ilab config structure to be more streamlined
+
+Currently, the `config.yaml` is large with major expansions in training, evaluation, and serving. Expansions help the user understand what the various configuration options are for each of these commands. However, there is a fine line between a verbose config and a cluttered one.
+
+This document describes a new structure for various parts of the config.yaml, and begins to outline how there are different levels of expertise in `ilab` which should dictate which options are available by default.
+
+## `additional_arguments` as a field in training, serving and evaluation
+
+Taking a look at the current training config it looks like: 
+
+```yaml
+train:
+  torch_args:
+    nnodes: 1
+    node_rank: 0
+    nproc_per_node: 1
+    rdzv_endpoint: 127.0.0.1:12222
+    rdzv_id: 123
+  train_args:
+    chat_tmpl_path: /home/ec2-user/instructlab/venv/lib64/python3.11/site-packages/instructlab/training/chat_templates/ibm_generic_tmpl.py
+    ckpt_output_dir: checkpoints
+    data_output_dir: train-output
+    data_path: ./taxonomy_data
+    deepspeed_options:
+      cpu_offload_optimizer: true
+      cpu_offload_optimizer_pin_memory: false
+      cpu_offload_optimizer_ratio: 1
+      save_samples: null
+    effective_batch_size: 100
+    is_padding_free: false
+    learning_rate: 2e-6
+    lora:
+      alpha: 32
+      dropout: 0.1
+      quantize_data_type: nf4
+      rank: 2
+      target_modules:
+      - q_proj
+      - k_proj
+      - v_proj
+      - o_proj
+    max_batch_len: 1000
+    max_seq_len: 96
+    mock_data: false
+    mock_data_len: 0
+    model_path: instructlab/granite-7b-lab
+    num_epochs: 1
+    random_seed: 42
+    save_samples: 100
+    warmup_steps: 10
+```
+While useful and clear to the user, this config is hard to maintain, and most users will not care about a large portion of the options.
+
+Keeping some of the key options like `num_epochs`, `deepspeed`, `lora`, and key directories a possible training config could look like:
+
+```yaml
+train:
+  train_args:
+    ckpt_output_dir: checkpoints
+    data_output_dir: train-output
+    data_path: ./taxonomy_data
+    deepspeed_options:
+      cpu_offload_optimizer: true
+      cpu_offload_optimizer_pin_memory: false
+      cpu_offload_optimizer_ratio: 1
+      save_samples: null
+    learning_rate: 2e-6
+    lora:
+      alpha: 32
+      dropout: 0.1
+      quantize_data_type: nf4
+      rank: 2
+      target_modules:
+      - q_proj
+      - k_proj
+      - v_proj
+      - o_proj
+    max_batch_len: 1000
+    model_path: instructlab/granite-7b-lab
+    num_epochs: 1
+    save_samples: 100
+    warmup_steps: 10
+    additional_arguments: ["--is-padding-free=False"...]
+```
+
+`additional_arguments` holds the rest of the training arguments. `ilab` would validate these against an internally maintained list of supported options before passing to the respective library.
+
+The same structure can be applied easily to the serve config.Currently this config looks like: 
+
+```yaml
+serve:
+  backend: ''
+  host_port: 127.0.0.1:8000
+  llama_cpp:
+    gpu_layers: -1
+    llm_family: ''
+    max_ctx_size: 4096
+  model_path: models/merlinite-7b-lab-Q4_K_M.gguf
+  vllm:
+    vllm_args: []
+```
+
+This has the opposite problem as training. The key here is that neither of these options are necessarily wrong, but to have both the verbose structure in the training config juxtaposed against the practically hidden structure of `vllm_args` is not ideal design practice. If we could merge the two approaches to use a common design language that exposes enough key arguments which are commonly edited while also not making the config confusing, this is what we should aim for.
+
+Being very general, this would look something like:
+
+```yaml
+serve:
+  backend: 'vllm'
+  host_port: 127.0.0.1:8000
+  max_ctx_size: 5120
+  gpus: 2
+  llm_family: ''
+  model_path: models/merlinite-7b-lab-Q4_K_M.gguf
+  served_model_name: "merlinite"
+  additional_arguments: ["--block-size=16", "--dtype=fp8"...]
+```
+
+Backends like vllm have a large amount of command line options. Adding each and every one of these to our config.yaml is out of the question. However, supporting a large amount implicitly via additional_arguments is a good compromise. Additionally, the above structure lets us choose which ones we think deserve a spot in the config and which are more uncommon or preserved for power users.
+
+This structure also allows us to flatten the config, something that is beneficial for flag mapping and config parsing. Options like max_ctx_size can apply to both vllm and llama-cpp. Options that are only applicable to a singular backend can be validated internally. Nested configurations within our config.yaml create a barrier both for the users and for flexible parsing of the config within `ilab`. additional_arguments will hopefully allow us to move generally away from nested configurations in both training and serving.