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a
This commit is contained in:
Sai Soundararaj
parent
b1b93088c5
commit
a9d8fdef90
4 changed files with 319 additions and 63 deletions
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@ -229,8 +229,8 @@ Before finalizing documentation, verify:
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|||
[x] 8. `/Users/saip/Documents/GitHub/llama-stack/llama_stack/apis/vector_dbs/vector_dbs.py` - Vector database management
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[x] 9. `/Users/saip/Documents/GitHub/llama-stack/llama_stack/apis/files/files.py` - File management
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[x] 10. `/Users/saip/Documents/GitHub/llama-stack/llama_stack/apis/datasets/datasets.py` - Dataset management
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11. `/Users/saip/Documents/GitHub/llama-stack/llama_stack/apis/datasetio/datasetio.py` - Dataset I/O operations
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12. `/Users/saip/Documents/GitHub/llama-stack/llama_stack/apis/post_training/post_training.py` - Training and fine-tuning
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[x] 11. `/Users/saip/Documents/GitHub/llama-stack/llama_stack/apis/datasetio/datasetio.py` - Dataset I/O operations
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[x] 12. `/Users/saip/Documents/GitHub/llama-stack/llama_stack/apis/post_training/post_training.py` - Training and fine-tuning
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13. `/Users/saip/Documents/GitHub/llama-stack/llama_stack/apis/eval/eval.py` - Evaluation framework
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14. `/Users/saip/Documents/GitHub/llama-stack/llama_stack/apis/scoring/scoring.py` - Scoring system
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15. `/Users/saip/Documents/GitHub/llama-stack/llama_stack/apis/scoring_functions/scoring_functions.py` - Scoring function definitions
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167
docs/_static/llama-stack-spec.html
vendored
167
docs/_static/llama-stack-spec.html
vendored
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@ -11252,13 +11252,15 @@
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"type": "object",
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"properties": {
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"job_uuid": {
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"type": "string"
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"type": "string",
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||||
"description": "Unique identifier for the training job"
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},
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||||
"checkpoints": {
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"type": "array",
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||||
"items": {
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||||
"$ref": "#/components/schemas/Checkpoint"
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}
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},
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"description": "List of model checkpoints created during training"
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}
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},
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"additionalProperties": false,
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@ -11273,7 +11275,8 @@
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"type": "object",
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"properties": {
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"job_uuid": {
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"type": "string"
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"type": "string",
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"description": "Unique identifier for the training job"
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},
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"status": {
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"type": "string",
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@ -11284,19 +11287,22 @@
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"scheduled",
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"cancelled"
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],
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"title": "JobStatus"
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"description": "Current status of the training job"
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},
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"scheduled_at": {
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"type": "string",
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"format": "date-time"
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"format": "date-time",
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"description": "(Optional) Timestamp when the job was scheduled"
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},
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"started_at": {
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"type": "string",
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"format": "date-time"
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"format": "date-time",
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"description": "(Optional) Timestamp when the job execution began"
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},
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"completed_at": {
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"type": "string",
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"format": "date-time"
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"format": "date-time",
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"description": "(Optional) Timestamp when the job finished, if completed"
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},
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"resources_allocated": {
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"type": "object",
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@ -11321,13 +11327,15 @@
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"type": "object"
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}
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]
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}
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},
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"description": "(Optional) Information about computational resources allocated to the job"
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},
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"checkpoints": {
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"type": "array",
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"items": {
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"$ref": "#/components/schemas/Checkpoint"
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}
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},
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"description": "List of model checkpoints created during training"
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}
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},
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"additionalProperties": false,
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@ -14644,16 +14652,20 @@
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"type": "object",
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"properties": {
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"reward_scale": {
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"type": "number"
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"type": "number",
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"description": "Scaling factor for the reward signal"
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},
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"reward_clip": {
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"type": "number"
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"type": "number",
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"description": "Maximum absolute value for reward clipping"
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},
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"epsilon": {
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"type": "number"
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"type": "number",
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"description": "Small value added for numerical stability"
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},
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"gamma": {
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"type": "number"
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"type": "number",
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"description": "Discount factor for future rewards"
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}
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},
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"additionalProperties": false,
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@ -14663,33 +14675,41 @@
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"epsilon",
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"gamma"
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],
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"title": "DPOAlignmentConfig"
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"title": "DPOAlignmentConfig",
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"description": "Configuration for Direct Preference Optimization (DPO) alignment."
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},
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"DataConfig": {
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"type": "object",
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"properties": {
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"dataset_id": {
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"type": "string"
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"type": "string",
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"description": "Unique identifier for the training dataset"
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},
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"batch_size": {
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"type": "integer"
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"type": "integer",
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"description": "Number of samples per training batch"
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},
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"shuffle": {
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"type": "boolean"
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"type": "boolean",
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"description": "Whether to shuffle the dataset during training"
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},
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"data_format": {
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"$ref": "#/components/schemas/DatasetFormat"
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"$ref": "#/components/schemas/DatasetFormat",
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"description": "Format of the dataset (instruct or dialog)"
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},
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"validation_dataset_id": {
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"type": "string"
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"type": "string",
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"description": "(Optional) Unique identifier for the validation dataset"
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},
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"packed": {
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"type": "boolean",
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"default": false
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"default": false,
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"description": "(Optional) Whether to pack multiple samples into a single sequence for efficiency"
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},
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"train_on_input": {
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"type": "boolean",
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"default": false
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"default": false,
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"description": "(Optional) Whether to compute loss on input tokens as well as output tokens"
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}
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},
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"additionalProperties": false,
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@ -14699,7 +14719,8 @@
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"shuffle",
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"data_format"
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],
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"title": "DataConfig"
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"title": "DataConfig",
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"description": "Configuration for training data and data loading."
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},
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"DatasetFormat": {
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"type": "string",
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@ -14707,45 +14728,55 @@
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"instruct",
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"dialog"
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],
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"title": "DatasetFormat"
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"title": "DatasetFormat",
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"description": "Format of the training dataset."
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},
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"EfficiencyConfig": {
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"type": "object",
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"properties": {
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"enable_activation_checkpointing": {
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"type": "boolean",
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"default": false
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"default": false,
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"description": "(Optional) Whether to use activation checkpointing to reduce memory usage"
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},
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"enable_activation_offloading": {
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"type": "boolean",
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"default": false
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"default": false,
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"description": "(Optional) Whether to offload activations to CPU to save GPU memory"
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},
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"memory_efficient_fsdp_wrap": {
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"type": "boolean",
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"default": false
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"default": false,
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"description": "(Optional) Whether to use memory-efficient FSDP wrapping"
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},
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"fsdp_cpu_offload": {
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"type": "boolean",
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"default": false
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"default": false,
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"description": "(Optional) Whether to offload FSDP parameters to CPU"
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}
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},
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"additionalProperties": false,
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"title": "EfficiencyConfig"
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"title": "EfficiencyConfig",
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"description": "Configuration for memory and compute efficiency optimizations."
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},
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"OptimizerConfig": {
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"type": "object",
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"properties": {
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"optimizer_type": {
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"$ref": "#/components/schemas/OptimizerType"
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"$ref": "#/components/schemas/OptimizerType",
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"description": "Type of optimizer to use (adam, adamw, or sgd)"
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},
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"lr": {
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"type": "number"
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"type": "number",
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"description": "Learning rate for the optimizer"
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},
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"weight_decay": {
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"type": "number"
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"type": "number",
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"description": "Weight decay coefficient for regularization"
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},
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"num_warmup_steps": {
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"type": "integer"
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"type": "integer",
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"description": "Number of steps for learning rate warmup"
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}
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},
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"additionalProperties": false,
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@ -14755,7 +14786,8 @@
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"weight_decay",
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"num_warmup_steps"
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],
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"title": "OptimizerConfig"
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"title": "OptimizerConfig",
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"description": "Configuration parameters for the optimization algorithm."
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},
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"OptimizerType": {
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"type": "string",
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@ -14764,38 +14796,47 @@
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"adamw",
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"sgd"
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],
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"title": "OptimizerType"
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"title": "OptimizerType",
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"description": "Available optimizer algorithms for training."
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},
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"TrainingConfig": {
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"type": "object",
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"properties": {
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"n_epochs": {
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"type": "integer"
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"type": "integer",
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"description": "Number of training epochs to run"
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},
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"max_steps_per_epoch": {
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"type": "integer",
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"default": 1
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"default": 1,
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"description": "Maximum number of steps to run per epoch"
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},
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"gradient_accumulation_steps": {
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"type": "integer",
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"default": 1
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"default": 1,
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"description": "Number of steps to accumulate gradients before updating"
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},
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"max_validation_steps": {
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"type": "integer",
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"default": 1
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"default": 1,
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"description": "(Optional) Maximum number of validation steps per epoch"
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},
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"data_config": {
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"$ref": "#/components/schemas/DataConfig"
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"$ref": "#/components/schemas/DataConfig",
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"description": "(Optional) Configuration for data loading and formatting"
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},
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"optimizer_config": {
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"$ref": "#/components/schemas/OptimizerConfig"
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"$ref": "#/components/schemas/OptimizerConfig",
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"description": "(Optional) Configuration for the optimization algorithm"
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},
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"efficiency_config": {
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"$ref": "#/components/schemas/EfficiencyConfig"
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"$ref": "#/components/schemas/EfficiencyConfig",
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"description": "(Optional) Configuration for memory and compute optimizations"
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},
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"dtype": {
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"type": "string",
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"default": "bf16"
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"default": "bf16",
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"description": "(Optional) Data type for model parameters (bf16, fp16, fp32)"
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}
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},
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"additionalProperties": false,
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@ -14804,7 +14845,8 @@
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"max_steps_per_epoch",
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"gradient_accumulation_steps"
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],
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"title": "TrainingConfig"
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"title": "TrainingConfig",
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"description": "Comprehensive configuration for the training process."
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},
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"PreferenceOptimizeRequest": {
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"type": "object",
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@ -16101,33 +16143,41 @@
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"type": {
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"type": "string",
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"const": "LoRA",
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"default": "LoRA"
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"default": "LoRA",
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"description": "Algorithm type identifier, always \"LoRA\""
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},
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"lora_attn_modules": {
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"type": "array",
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"items": {
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"type": "string"
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}
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},
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"description": "List of attention module names to apply LoRA to"
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},
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"apply_lora_to_mlp": {
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"type": "boolean"
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"type": "boolean",
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"description": "Whether to apply LoRA to MLP layers"
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},
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"apply_lora_to_output": {
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"type": "boolean"
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"type": "boolean",
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"description": "Whether to apply LoRA to output projection layers"
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},
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"rank": {
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"type": "integer"
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"type": "integer",
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"description": "Rank of the LoRA adaptation (lower rank = fewer parameters)"
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},
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"alpha": {
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"type": "integer"
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"type": "integer",
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"description": "LoRA scaling parameter that controls adaptation strength"
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},
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"use_dora": {
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"type": "boolean",
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"default": false
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"default": false,
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"description": "(Optional) Whether to use DoRA (Weight-Decomposed Low-Rank Adaptation)"
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},
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"quantize_base": {
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"type": "boolean",
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"default": false
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"default": false,
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"description": "(Optional) Whether to quantize the base model weights"
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}
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},
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"additionalProperties": false,
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@ -16139,7 +16189,8 @@
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"rank",
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"alpha"
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],
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"title": "LoraFinetuningConfig"
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"title": "LoraFinetuningConfig",
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"description": "Configuration for Low-Rank Adaptation (LoRA) fine-tuning."
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},
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"QATFinetuningConfig": {
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"type": "object",
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@ -16147,13 +16198,16 @@
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"type": {
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"type": "string",
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"const": "QAT",
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"default": "QAT"
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"default": "QAT",
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"description": "Algorithm type identifier, always \"QAT\""
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},
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"quantizer_name": {
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"type": "string"
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"type": "string",
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"description": "Name of the quantization algorithm to use"
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},
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"group_size": {
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"type": "integer"
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"type": "integer",
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"description": "Size of groups for grouped quantization"
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}
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},
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"additionalProperties": false,
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@ -16162,7 +16216,8 @@
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"quantizer_name",
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"group_size"
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],
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"title": "QATFinetuningConfig"
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"title": "QATFinetuningConfig",
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"description": "Configuration for Quantization-Aware Training (QAT) fine-tuning."
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},
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"SupervisedFineTuneRequest": {
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"type": "object",
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100
docs/_static/llama-stack-spec.yaml
vendored
100
docs/_static/llama-stack-spec.yaml
vendored
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@ -8064,10 +8064,13 @@ components:
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properties:
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job_uuid:
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type: string
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description: Unique identifier for the training job
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checkpoints:
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type: array
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items:
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$ref: '#/components/schemas/Checkpoint'
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description: >-
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List of model checkpoints created during training
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additionalProperties: false
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required:
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- job_uuid
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@ -8079,6 +8082,7 @@ components:
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properties:
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job_uuid:
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type: string
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description: Unique identifier for the training job
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status:
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type: string
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enum:
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@ -8087,16 +8091,22 @@ components:
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- failed
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- scheduled
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- cancelled
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title: JobStatus
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description: Current status of the training job
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scheduled_at:
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type: string
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format: date-time
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description: >-
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(Optional) Timestamp when the job was scheduled
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started_at:
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type: string
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format: date-time
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description: >-
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(Optional) Timestamp when the job execution began
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completed_at:
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type: string
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format: date-time
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description: >-
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(Optional) Timestamp when the job finished, if completed
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resources_allocated:
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type: object
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additionalProperties:
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@ -8107,10 +8117,15 @@ components:
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- type: string
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- type: array
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- type: object
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description: >-
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||||
(Optional) Information about computational resources allocated to the
|
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job
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||||
checkpoints:
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||||
type: array
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||||
items:
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||||
$ref: '#/components/schemas/Checkpoint'
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||||
description: >-
|
||||
List of model checkpoints created during training
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||||
additionalProperties: false
|
||||
required:
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||||
- job_uuid
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||||
|
|
@ -10491,12 +10506,18 @@ components:
|
|||
properties:
|
||||
reward_scale:
|
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type: number
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description: Scaling factor for the reward signal
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||||
reward_clip:
|
||||
type: number
|
||||
description: >-
|
||||
Maximum absolute value for reward clipping
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epsilon:
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type: number
|
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description: >-
|
||||
Small value added for numerical stability
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||||
gamma:
|
||||
type: number
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||||
description: Discount factor for future rewards
|
||||
additionalProperties: false
|
||||
required:
|
||||
- reward_scale
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||||
|
|
@ -10504,25 +10525,41 @@ components:
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- epsilon
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- gamma
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||||
title: DPOAlignmentConfig
|
||||
description: >-
|
||||
Configuration for Direct Preference Optimization (DPO) alignment.
|
||||
DataConfig:
|
||||
type: object
|
||||
properties:
|
||||
dataset_id:
|
||||
type: string
|
||||
description: >-
|
||||
Unique identifier for the training dataset
|
||||
batch_size:
|
||||
type: integer
|
||||
description: Number of samples per training batch
|
||||
shuffle:
|
||||
type: boolean
|
||||
description: >-
|
||||
Whether to shuffle the dataset during training
|
||||
data_format:
|
||||
$ref: '#/components/schemas/DatasetFormat'
|
||||
description: >-
|
||||
Format of the dataset (instruct or dialog)
|
||||
validation_dataset_id:
|
||||
type: string
|
||||
description: >-
|
||||
(Optional) Unique identifier for the validation dataset
|
||||
packed:
|
||||
type: boolean
|
||||
default: false
|
||||
description: >-
|
||||
(Optional) Whether to pack multiple samples into a single sequence for
|
||||
efficiency
|
||||
train_on_input:
|
||||
type: boolean
|
||||
default: false
|
||||
description: >-
|
||||
(Optional) Whether to compute loss on input tokens as well as output tokens
|
||||
additionalProperties: false
|
||||
required:
|
||||
- dataset_id
|
||||
|
|
@ -10530,40 +10567,59 @@ components:
|
|||
- shuffle
|
||||
- data_format
|
||||
title: DataConfig
|
||||
description: >-
|
||||
Configuration for training data and data loading.
|
||||
DatasetFormat:
|
||||
type: string
|
||||
enum:
|
||||
- instruct
|
||||
- dialog
|
||||
title: DatasetFormat
|
||||
description: Format of the training dataset.
|
||||
EfficiencyConfig:
|
||||
type: object
|
||||
properties:
|
||||
enable_activation_checkpointing:
|
||||
type: boolean
|
||||
default: false
|
||||
description: >-
|
||||
(Optional) Whether to use activation checkpointing to reduce memory usage
|
||||
enable_activation_offloading:
|
||||
type: boolean
|
||||
default: false
|
||||
description: >-
|
||||
(Optional) Whether to offload activations to CPU to save GPU memory
|
||||
memory_efficient_fsdp_wrap:
|
||||
type: boolean
|
||||
default: false
|
||||
description: >-
|
||||
(Optional) Whether to use memory-efficient FSDP wrapping
|
||||
fsdp_cpu_offload:
|
||||
type: boolean
|
||||
default: false
|
||||
description: >-
|
||||
(Optional) Whether to offload FSDP parameters to CPU
|
||||
additionalProperties: false
|
||||
title: EfficiencyConfig
|
||||
description: >-
|
||||
Configuration for memory and compute efficiency optimizations.
|
||||
OptimizerConfig:
|
||||
type: object
|
||||
properties:
|
||||
optimizer_type:
|
||||
$ref: '#/components/schemas/OptimizerType'
|
||||
description: >-
|
||||
Type of optimizer to use (adam, adamw, or sgd)
|
||||
lr:
|
||||
type: number
|
||||
description: Learning rate for the optimizer
|
||||
weight_decay:
|
||||
type: number
|
||||
description: >-
|
||||
Weight decay coefficient for regularization
|
||||
num_warmup_steps:
|
||||
type: integer
|
||||
description: Number of steps for learning rate warmup
|
||||
additionalProperties: false
|
||||
required:
|
||||
- optimizer_type
|
||||
|
|
@ -10571,6 +10627,8 @@ components:
|
|||
- weight_decay
|
||||
- num_warmup_steps
|
||||
title: OptimizerConfig
|
||||
description: >-
|
||||
Configuration parameters for the optimization algorithm.
|
||||
OptimizerType:
|
||||
type: string
|
||||
enum:
|
||||
|
|
@ -10578,35 +10636,53 @@ components:
|
|||
- adamw
|
||||
- sgd
|
||||
title: OptimizerType
|
||||
description: >-
|
||||
Available optimizer algorithms for training.
|
||||
TrainingConfig:
|
||||
type: object
|
||||
properties:
|
||||
n_epochs:
|
||||
type: integer
|
||||
description: Number of training epochs to run
|
||||
max_steps_per_epoch:
|
||||
type: integer
|
||||
default: 1
|
||||
description: Maximum number of steps to run per epoch
|
||||
gradient_accumulation_steps:
|
||||
type: integer
|
||||
default: 1
|
||||
description: >-
|
||||
Number of steps to accumulate gradients before updating
|
||||
max_validation_steps:
|
||||
type: integer
|
||||
default: 1
|
||||
description: >-
|
||||
(Optional) Maximum number of validation steps per epoch
|
||||
data_config:
|
||||
$ref: '#/components/schemas/DataConfig'
|
||||
description: >-
|
||||
(Optional) Configuration for data loading and formatting
|
||||
optimizer_config:
|
||||
$ref: '#/components/schemas/OptimizerConfig'
|
||||
description: >-
|
||||
(Optional) Configuration for the optimization algorithm
|
||||
efficiency_config:
|
||||
$ref: '#/components/schemas/EfficiencyConfig'
|
||||
description: >-
|
||||
(Optional) Configuration for memory and compute optimizations
|
||||
dtype:
|
||||
type: string
|
||||
default: bf16
|
||||
description: >-
|
||||
(Optional) Data type for model parameters (bf16, fp16, fp32)
|
||||
additionalProperties: false
|
||||
required:
|
||||
- n_epochs
|
||||
- max_steps_per_epoch
|
||||
- gradient_accumulation_steps
|
||||
title: TrainingConfig
|
||||
description: >-
|
||||
Comprehensive configuration for the training process.
|
||||
PreferenceOptimizeRequest:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -11535,24 +11611,38 @@ components:
|
|||
type: string
|
||||
const: LoRA
|
||||
default: LoRA
|
||||
description: Algorithm type identifier, always "LoRA"
|
||||
lora_attn_modules:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
description: >-
|
||||
List of attention module names to apply LoRA to
|
||||
apply_lora_to_mlp:
|
||||
type: boolean
|
||||
description: Whether to apply LoRA to MLP layers
|
||||
apply_lora_to_output:
|
||||
type: boolean
|
||||
description: >-
|
||||
Whether to apply LoRA to output projection layers
|
||||
rank:
|
||||
type: integer
|
||||
description: >-
|
||||
Rank of the LoRA adaptation (lower rank = fewer parameters)
|
||||
alpha:
|
||||
type: integer
|
||||
description: >-
|
||||
LoRA scaling parameter that controls adaptation strength
|
||||
use_dora:
|
||||
type: boolean
|
||||
default: false
|
||||
description: >-
|
||||
(Optional) Whether to use DoRA (Weight-Decomposed Low-Rank Adaptation)
|
||||
quantize_base:
|
||||
type: boolean
|
||||
default: false
|
||||
description: >-
|
||||
(Optional) Whether to quantize the base model weights
|
||||
additionalProperties: false
|
||||
required:
|
||||
- type
|
||||
|
|
@ -11562,6 +11652,8 @@ components:
|
|||
- rank
|
||||
- alpha
|
||||
title: LoraFinetuningConfig
|
||||
description: >-
|
||||
Configuration for Low-Rank Adaptation (LoRA) fine-tuning.
|
||||
QATFinetuningConfig:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -11569,16 +11661,22 @@ components:
|
|||
type: string
|
||||
const: QAT
|
||||
default: QAT
|
||||
description: Algorithm type identifier, always "QAT"
|
||||
quantizer_name:
|
||||
type: string
|
||||
description: >-
|
||||
Name of the quantization algorithm to use
|
||||
group_size:
|
||||
type: integer
|
||||
description: Size of groups for grouped quantization
|
||||
additionalProperties: false
|
||||
required:
|
||||
- type
|
||||
- quantizer_name
|
||||
- group_size
|
||||
title: QATFinetuningConfig
|
||||
description: >-
|
||||
Configuration for Quantization-Aware Training (QAT) fine-tuning.
|
||||
SupervisedFineTuneRequest:
|
||||
type: object
|
||||
properties:
|
||||
|
|
|
|||
|
|
@ -18,6 +18,12 @@ from llama_stack.schema_utils import json_schema_type, register_schema, webmetho
|
|||
|
||||
@json_schema_type
|
||||
class OptimizerType(Enum):
|
||||
"""Available optimizer algorithms for training.
|
||||
|
||||
:cvar adam: Adaptive Moment Estimation optimizer
|
||||
:cvar adamw: AdamW optimizer with weight decay
|
||||
:cvar sgd: Stochastic Gradient Descent optimizer
|
||||
"""
|
||||
adam = "adam"
|
||||
adamw = "adamw"
|
||||
sgd = "sgd"
|
||||
|
|
@ -25,12 +31,27 @@ class OptimizerType(Enum):
|
|||
|
||||
@json_schema_type
|
||||
class DatasetFormat(Enum):
|
||||
"""Format of the training dataset.
|
||||
|
||||
:cvar instruct: Instruction-following format with prompt and completion
|
||||
:cvar dialog: Multi-turn conversation format with messages
|
||||
"""
|
||||
instruct = "instruct"
|
||||
dialog = "dialog"
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class DataConfig(BaseModel):
|
||||
"""Configuration for training data and data loading.
|
||||
|
||||
:param dataset_id: Unique identifier for the training dataset
|
||||
:param batch_size: Number of samples per training batch
|
||||
:param shuffle: Whether to shuffle the dataset during training
|
||||
:param data_format: Format of the dataset (instruct or dialog)
|
||||
:param validation_dataset_id: (Optional) Unique identifier for the validation dataset
|
||||
:param packed: (Optional) Whether to pack multiple samples into a single sequence for efficiency
|
||||
:param train_on_input: (Optional) Whether to compute loss on input tokens as well as output tokens
|
||||
"""
|
||||
dataset_id: str
|
||||
batch_size: int
|
||||
shuffle: bool
|
||||
|
|
@ -42,6 +63,13 @@ class DataConfig(BaseModel):
|
|||
|
||||
@json_schema_type
|
||||
class OptimizerConfig(BaseModel):
|
||||
"""Configuration parameters for the optimization algorithm.
|
||||
|
||||
:param optimizer_type: Type of optimizer to use (adam, adamw, or sgd)
|
||||
:param lr: Learning rate for the optimizer
|
||||
:param weight_decay: Weight decay coefficient for regularization
|
||||
:param num_warmup_steps: Number of steps for learning rate warmup
|
||||
"""
|
||||
optimizer_type: OptimizerType
|
||||
lr: float
|
||||
weight_decay: float
|
||||
|
|
@ -50,6 +78,13 @@ class OptimizerConfig(BaseModel):
|
|||
|
||||
@json_schema_type
|
||||
class EfficiencyConfig(BaseModel):
|
||||
"""Configuration for memory and compute efficiency optimizations.
|
||||
|
||||
:param enable_activation_checkpointing: (Optional) Whether to use activation checkpointing to reduce memory usage
|
||||
:param enable_activation_offloading: (Optional) Whether to offload activations to CPU to save GPU memory
|
||||
:param memory_efficient_fsdp_wrap: (Optional) Whether to use memory-efficient FSDP wrapping
|
||||
:param fsdp_cpu_offload: (Optional) Whether to offload FSDP parameters to CPU
|
||||
"""
|
||||
enable_activation_checkpointing: bool | None = False
|
||||
enable_activation_offloading: bool | None = False
|
||||
memory_efficient_fsdp_wrap: bool | None = False
|
||||
|
|
@ -58,6 +93,17 @@ class EfficiencyConfig(BaseModel):
|
|||
|
||||
@json_schema_type
|
||||
class TrainingConfig(BaseModel):
|
||||
"""Comprehensive configuration for the training process.
|
||||
|
||||
:param n_epochs: Number of training epochs to run
|
||||
:param max_steps_per_epoch: Maximum number of steps to run per epoch
|
||||
:param gradient_accumulation_steps: Number of steps to accumulate gradients before updating
|
||||
:param max_validation_steps: (Optional) Maximum number of validation steps per epoch
|
||||
:param data_config: (Optional) Configuration for data loading and formatting
|
||||
:param optimizer_config: (Optional) Configuration for the optimization algorithm
|
||||
:param efficiency_config: (Optional) Configuration for memory and compute optimizations
|
||||
:param dtype: (Optional) Data type for model parameters (bf16, fp16, fp32)
|
||||
"""
|
||||
n_epochs: int
|
||||
max_steps_per_epoch: int = 1
|
||||
gradient_accumulation_steps: int = 1
|
||||
|
|
@ -70,6 +116,17 @@ class TrainingConfig(BaseModel):
|
|||
|
||||
@json_schema_type
|
||||
class LoraFinetuningConfig(BaseModel):
|
||||
"""Configuration for Low-Rank Adaptation (LoRA) fine-tuning.
|
||||
|
||||
:param type: Algorithm type identifier, always "LoRA"
|
||||
:param lora_attn_modules: List of attention module names to apply LoRA to
|
||||
:param apply_lora_to_mlp: Whether to apply LoRA to MLP layers
|
||||
:param apply_lora_to_output: Whether to apply LoRA to output projection layers
|
||||
:param rank: Rank of the LoRA adaptation (lower rank = fewer parameters)
|
||||
:param alpha: LoRA scaling parameter that controls adaptation strength
|
||||
:param use_dora: (Optional) Whether to use DoRA (Weight-Decomposed Low-Rank Adaptation)
|
||||
:param quantize_base: (Optional) Whether to quantize the base model weights
|
||||
"""
|
||||
type: Literal["LoRA"] = "LoRA"
|
||||
lora_attn_modules: list[str]
|
||||
apply_lora_to_mlp: bool
|
||||
|
|
@ -82,6 +139,12 @@ class LoraFinetuningConfig(BaseModel):
|
|||
|
||||
@json_schema_type
|
||||
class QATFinetuningConfig(BaseModel):
|
||||
"""Configuration for Quantization-Aware Training (QAT) fine-tuning.
|
||||
|
||||
:param type: Algorithm type identifier, always "QAT"
|
||||
:param quantizer_name: Name of the quantization algorithm to use
|
||||
:param group_size: Size of groups for grouped quantization
|
||||
"""
|
||||
type: Literal["QAT"] = "QAT"
|
||||
quantizer_name: str
|
||||
group_size: int
|
||||
|
|
@ -93,7 +156,11 @@ register_schema(AlgorithmConfig, name="AlgorithmConfig")
|
|||
|
||||
@json_schema_type
|
||||
class PostTrainingJobLogStream(BaseModel):
|
||||
"""Stream of logs from a finetuning job."""
|
||||
"""Stream of logs from a finetuning job.
|
||||
|
||||
:param job_uuid: Unique identifier for the training job
|
||||
:param log_lines: List of log message strings from the training process
|
||||
"""
|
||||
|
||||
job_uuid: str
|
||||
log_lines: list[str]
|
||||
|
|
@ -101,11 +168,22 @@ class PostTrainingJobLogStream(BaseModel):
|
|||
|
||||
@json_schema_type
|
||||
class RLHFAlgorithm(Enum):
|
||||
"""Available reinforcement learning from human feedback algorithms.
|
||||
|
||||
:cvar dpo: Direct Preference Optimization algorithm
|
||||
"""
|
||||
dpo = "dpo"
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class DPOAlignmentConfig(BaseModel):
|
||||
"""Configuration for Direct Preference Optimization (DPO) alignment.
|
||||
|
||||
:param reward_scale: Scaling factor for the reward signal
|
||||
:param reward_clip: Maximum absolute value for reward clipping
|
||||
:param epsilon: Small value added for numerical stability
|
||||
:param gamma: Discount factor for future rewards
|
||||
"""
|
||||
reward_scale: float
|
||||
reward_clip: float
|
||||
epsilon: float
|
||||
|
|
@ -114,7 +192,19 @@ class DPOAlignmentConfig(BaseModel):
|
|||
|
||||
@json_schema_type
|
||||
class PostTrainingRLHFRequest(BaseModel):
|
||||
"""Request to finetune a model."""
|
||||
"""Request to finetune a model using reinforcement learning from human feedback.
|
||||
|
||||
:param job_uuid: Unique identifier for the training job
|
||||
:param finetuned_model: URL or path to the base model to fine-tune
|
||||
:param dataset_id: Unique identifier for the training dataset
|
||||
:param validation_dataset_id: Unique identifier for the validation dataset
|
||||
:param algorithm: RLHF algorithm to use for training
|
||||
:param algorithm_config: Configuration parameters for the RLHF algorithm
|
||||
:param optimizer_config: Configuration parameters for the optimization algorithm
|
||||
:param training_config: Configuration parameters for the training process
|
||||
:param hyperparam_search_config: Configuration for hyperparameter search
|
||||
:param logger_config: Configuration for training logging
|
||||
"""
|
||||
|
||||
job_uuid: str
|
||||
|
||||
|
|
@ -140,7 +230,16 @@ class PostTrainingJob(BaseModel):
|
|||
|
||||
@json_schema_type
|
||||
class PostTrainingJobStatusResponse(BaseModel):
|
||||
"""Status of a finetuning job."""
|
||||
"""Status of a finetuning job.
|
||||
|
||||
:param job_uuid: Unique identifier for the training job
|
||||
:param status: Current status of the training job
|
||||
:param scheduled_at: (Optional) Timestamp when the job was scheduled
|
||||
:param started_at: (Optional) Timestamp when the job execution began
|
||||
:param completed_at: (Optional) Timestamp when the job finished, if completed
|
||||
:param resources_allocated: (Optional) Information about computational resources allocated to the job
|
||||
:param checkpoints: List of model checkpoints created during training
|
||||
"""
|
||||
|
||||
job_uuid: str
|
||||
status: JobStatus
|
||||
|
|
@ -160,7 +259,11 @@ class ListPostTrainingJobsResponse(BaseModel):
|
|||
|
||||
@json_schema_type
|
||||
class PostTrainingJobArtifactsResponse(BaseModel):
|
||||
"""Artifacts of a finetuning job."""
|
||||
"""Artifacts of a finetuning job.
|
||||
|
||||
:param job_uuid: Unique identifier for the training job
|
||||
:param checkpoints: List of model checkpoints created during training
|
||||
"""
|
||||
|
||||
job_uuid: str
|
||||
checkpoints: list[Checkpoint] = Field(default_factory=list)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue