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Introduces the primary Python benchmark runner, measuring model metadata, data throughput, forward latency, training-step latency, and autoregressive generation. Includes utility functions for dynamic module loading, timing, and percentile calculation.
Eamon2009
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May 17, 2026
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Summary
Introduces the primary Python benchmark runner, measuring model metadata, data throughput, forward latency, training-step latency, and autoregressive generation. Includes utility functions for dynamic module loading, timing, and percentile calculation.
Model BenchmarkingLatency Profiling:
Tracks forward pass, training step, and autoregressive generation latencies.Throughput Tracking: Measures tokenizer processing speeds and data throughput.Resource Monitoring: Captures model metadata and system memory footprints during runs.
Math UtilitiesDynamic Loading:
Implements safe runtime module loading via importlib to dynamically interact with engine/inference.py.Statistical Metrics: Adds custom mathematical utility functions, including a precise percentile calculator ($P_{50}$ , $P_{90}$ , $P_{99}$ ) for latency distribution reporting.Standardized Exports: Lays the groundwork for structured JSON and CSV output formatting.