rope-vs-absolute-pe-benchmark

Author: João Felipe De Souza

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Overview

Empirical comparison of Absolute Positional Embeddings vs Rotary Position Embeddings (RoPE) across four dimensions:

1. Attention sink strength
2. KV-cache eviction tolerance
3. Perplexity scaling with context
4. Decode throughput

Models compared:

• GPT-2-medium (354M, absolute learned PE)
• Qwen2-0.5B (494M, RoPE)

For architecture details, see DESIGN.md.

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Why This Matters

The choice between absolute PE and RoPE affects:

• how strongly the model relies on attention sinks
• how well the model tolerates KV-cache eviction
• how context scaling behaves

This project closes the loop between the attention-sink-profiler and the kv-cache-eviction-benchmark by comparing the positional encoding systems behind them.

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Key Findings

Finding 1 — Absolute PE produces stronger attention sinks

At seq_len=512:

PE type	Sink share (first 4)	Boost vs uniform
Absolute	0.432	55.3×
RoPE	0.315	40.3×

Absolute PE concentrates 37% more attention on the first tokens than RoPE.

Finding 2 — RoPE tolerates eviction slightly better

PPL ratio at budget=64 (vs full 512-token context):

PE type	Sliding window	Sink8 + window
Absolute	57.5×	50.5×
RoPE	42.1×	38.2×

Both degrade badly at extreme compression, but RoPE is ~25% more tolerant.

Finding 3 — Sink-preserving helps both, but neither wins cleanly

At budget=128:

PE type	Sliding	Sink8 + window
Absolute	6.1×	8.0×
RoPE	5.2×	6.4×

Interestingly, sink-preserving sometimes makes things slightly worse at moderate budgets because the discontinuity between sink tokens and recent tokens confuses the model.

Finding 4 — Perplexity scales similarly

seq_len	Absolute PPL	RoPE PPL
128	10.49	8.68
256	3.27	3.27
512	1.81	1.98
768	1.49	1.67

RoPE is better at short context; absolute PE is better at longer context. Both converge at moderate lengths.

Finding 5 — GPT-2-medium is faster despite being smaller

Model	PE	seq=512 throughput
GPT-2-medium	Absolute	49.7 tok/s
Qwen2-0.5B	RoPE	33.4 tok/s

GPT-2-medium has fewer parameters (354M vs 494M) and simpler architecture, so it decodes faster despite being an older design.

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Finding 6 (strongest result) — Masked-key ablation proves the difference definitively

Using the correct methodology (masking keys, not truncating input), the difference between absolute PE and RoPE becomes dramatic:

Masking first 8 tokens:

PE type	PPL ratio	Degradation
Absolute	1.6458	+64.6%
RoPE	1.0118	+1.2%

Masking recent 8 tokens:

PE type	PPL ratio
Absolute	1.7604 (+76%)
RoPE	1.0873 (+8.7%)

Masking middle 8 tokens:

PE type	PPL ratio
Absolute	1.0099 (~0%)
RoPE	0.9985 (~0%)

Absolute PE is 63× more sensitive to sink token removal than RoPE.

This means:

• for absolute PE models: sink-preserving KV-cache policies are essential
• for RoPE models: simple sliding window eviction works well
• StreamingLLM-style sink preservation is mainly needed for absolute PE architectures

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Main Conclusion

1. Absolute PE produces stronger attention sinks than RoPE
2. RoPE tolerates KV-cache eviction slightly better
3. Both PE types need sink-preserving policies for extreme compression
4. Perplexity scaling is similar — PE type alone does not determine context quality

The practical implication:

• for long-context serving with aggressive eviction, RoPE has a slight edge
• for short-context serving, the PE difference matters less than model quality

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Results Files

File	Description
results/attention_sink.csv	Per-layer sink metrics
results/perplexity.csv	PPL at different context lengths
results/eviction_tolerance.csv	Eviction policy comparison
results/decode_throughput.csv	Decode performance
results/metadata.json	Configuration

Plots

File	Description
plots/sink_comparison.png	Sink strength: absolute vs RoPE
plots/perplexity_comparison.png	PPL vs context length
plots/eviction_tolerance.png	Eviction tolerance comparison

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Repository Structure

rope-vs-absolute-pe-benchmark/
├── rope_benchmark.py
├── plot_rope.py
├── README.md
├── DESIGN.md
├── LICENSE
├── requirements.txt
├── results/
└── plots/

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How to Run

python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
python3 rope_benchmark.py
python3 plot_rope.py

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Limitations

• Only two models compared (not the same parameter count)
• Single GPU, FP16
• Eviction test uses simple input truncation, not masked-key ablation
• Different tokenizers between models affect token count

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References

• Su et al., RoFormer: Enhanced Transformer with Rotary Position Embedding (2021)
• Xiao et al., Efficient Streaming Language Models with Attention Sinks (2023)
• Vaswani et al., Attention Is All You Need (2017) — absolute PE