Principle:FMInference FlexLLMGen Runtime Configuration Parsing
| Field | Value |
|---|---|
| Sources | Upstream: DeepSpeed, Paper: FlexGen |
| Domains | Configuration_Management, Runtime_Infrastructure |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
A configuration management pattern that parses declarative JSON specifications into validated, structured runtime objects, decoupling training infrastructure settings from application code.
Description
Runtime configuration parsing is the practice of externalizing training infrastructure decisions into a declarative configuration file (typically JSON) that is parsed, validated, and resolved into a structured object at initialization time. This enables users to change complex distributed training settings without modifying code.
The parsing process involves several stages:
- Loading -- The JSON file is loaded and parsed, with duplicate key detection to catch configuration errors early.
- Extraction with defaults -- Each configuration section is extracted using typed accessor functions that provide sensible defaults. Missing optional fields gracefully fall back to defaults rather than failing.
- Cross-field validation -- Interdependent fields are validated together. For example, train_batch_size, train_micro_batch_size_per_gpu, and gradient_accumulation_steps are resolved together: given any two, the third is computed.
- Sub-configuration delegation -- Complex feature areas (ZeRO optimization, sparse attention, compression) have their own sub-configuration parsers that produce structured objects.
- Immutable result -- The final configuration object provides read-only access to all resolved settings, preventing runtime mutation.
The key design decisions are:
- Convention over configuration -- Defaults are provided for virtually every setting, so a minimal config file works out of the box.
- Fail-fast validation -- Invalid or contradictory settings raise descriptive errors at initialization time rather than causing subtle failures during training.
- Hierarchical structure -- Related settings are grouped into nested dictionaries (e.g., fp16.enabled, zero_optimization.stage), making the configuration self-documenting.
Usage
This pattern is applicable whenever a training system has many interacting configuration parameters. The declarative approach enables experiment management, reproducibility (configuration files can be version-controlled), and clear separation between infrastructure and model code.
Theoretical Basis
Declarative configuration follows the principle of separation of concerns: the application code defines what capabilities are available, while the configuration specifies which capabilities are activated and how they are parameterized. The resolution of interdependent parameters (e.g., computing gradient accumulation steps from batch size and micro-batch size) is a form of constraint satisfaction that reduces the user's configuration burden.