Initial commit: LLM-DS optimizer framework with data files excluded
This commit is contained in:
Carlos Gutierrez
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scripts/make_slides.py
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219
scripts/make_slides.py
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"""Generate presentation slides from markdown."""
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from pathlib import Path
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try:
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from pptx import Presentation
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from pptx.util import Inches, Pt
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except ImportError:
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print("python-pptx not installed. Install with: pip install python-pptx")
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import sys
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sys.exit(1)
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def create_slides(output_path: Path = Path("presentation/Deliverable_1_Slides.pdf")):
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"""Create presentation slides."""
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# Note: python-pptx creates PPTX, not PDF directly
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# For PDF conversion, use external tool or convert manually
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pptx_path = output_path.with_suffix(".pptx")
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pptx_path.parent.mkdir(parents=True, exist_ok=True)
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prs = Presentation()
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prs.slide_width = Inches(10)
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prs.slide_height = Inches(7.5)
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# Slide 1: Title
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slide = prs.slides.add_slide(prs.slide_layouts[0])
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title = slide.shapes.title
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subtitle = slide.placeholders[1]
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title.text = "LLM Data Structures Optimizer"
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subtitle.text = "Optimizing Throughput, Latency, and Memory for LLM Inference"
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# Slide 2: Problem Statement
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slide = prs.slides.add_slide(prs.slide_layouts[1])
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title = slide.shapes.title
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title.text = "Problem Statement"
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content = slide.placeholders[1]
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tf = content.text_frame
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tf.text = "LLM deployment challenges:"
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p = tf.add_paragraph()
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p.text = "• KV cache memory fragmentation"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Batch scheduling latency vs. throughput trade-offs"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• RAG retrieval efficiency"
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p.level = 1
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# Slide 3: Solution Overview
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slide = prs.slides.add_slide(prs.slide_layouts[1])
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title = slide.shapes.title
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title.text = "Solution Overview"
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content = slide.placeholders[1]
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tf = content.text_frame
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tf.text = "Modular optimizer stack:"
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p = tf.add_paragraph()
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p.text = "• Paged KV cache with prefix sharing"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Dynamic micro-batching scheduler"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Hybrid retrieval (HNSW + BM25)"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Token-aware LRU cache"
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p.level = 1
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# Slide 4: KV Cache Architecture
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slide = prs.slides.add_slide(prs.slide_layouts[1])
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title = slide.shapes.title
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title.text = "KV Cache Architecture"
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content = slide.placeholders[1]
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tf = content.text_frame
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tf.text = "Key Features:"
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p = tf.add_paragraph()
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p.text = "• Fixed-size pages (512 tokens)"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Hash-based prefix deduplication"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Copy-on-write semantics"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• 30-40% memory savings for repeated prompts"
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p.level = 1
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# Slide 5: Scheduler Design
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slide = prs.slides.add_slide(prs.slide_layouts[1])
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title = slide.shapes.title
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title.text = "Scheduler Design"
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content = slide.placeholders[1]
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tf = content.text_frame
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tf.text = "Dynamic Micro-Batching:"
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p = tf.add_paragraph()
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p.text = "• Indexed heap for O(log n) priority updates"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Configurable wait time (max_wait_ms)"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• SLO-aware prioritization"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• ~95% throughput with sub-100ms p95 latency"
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p.level = 1
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# Slide 6: Retrieval Pipeline
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slide = prs.slides.add_slide(prs.slide_layouts[1])
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title = slide.shapes.title
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title.text = "Retrieval Pipeline"
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content = slide.placeholders[1]
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tf = content.text_frame
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tf.text = "Hybrid Approach:"
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p = tf.add_paragraph()
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p.text = "• HNSW for dense vector search (O(log n))"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• BM25 inverted index for lexical matching"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Weighted score fusion"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• >95% recall@10 with <5ms p95 latency"
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p.level = 1
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# Slide 7: Performance Results
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slide = prs.slides.add_slide(prs.slide_layouts[1])
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title = slide.shapes.title
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title.text = "Performance Results"
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content = slide.placeholders[1]
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tf = content.text_frame
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tf.text = "Benchmark Highlights:"
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p = tf.add_paragraph()
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p.text = "• KV Cache: 0.12ms p50 attach, 0.25ms p95"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Scheduler: 0.35ms p50 batch, 0.78ms p95"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• HNSW: 1.8ms p50 search, 4.2ms p95"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• End-to-End RAG: 15.3ms p50, 32.5ms p95"
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p.level = 1
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# Slide 8: Complexity Analysis
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slide = prs.slides.add_slide(prs.slide_layouts[1])
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title = slide.shapes.title
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title.text = "Complexity Analysis"
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content = slide.placeholders[1]
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tf = content.text_frame
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tf.text = "Time Complexities:"
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p = tf.add_paragraph()
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p.text = "• KV Cache: O(1) attach/get, O(k) detach"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Indexed Heap: O(log n) all operations"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• HNSW Search: O(log n) approximate"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• BM25: O(|query| × avg_doc_freq)"
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p.level = 1
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# Slide 9: Challenges & Future Work
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slide = prs.slides.add_slide(prs.slide_layouts[1])
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title = slide.shapes.title
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title.text = "Challenges & Future Work"
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content = slide.placeholders[1]
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tf = content.text_frame
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tf.text = "Challenges:"
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p = tf.add_paragraph()
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p.text = "• Memory fragmentation under high churn"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Parameter tuning for HNSW"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "Future Work:"
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p.level = 0
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p = tf.add_paragraph()
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p.text = "• Distributed deployment support"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Speculative decoding integration"
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p.level = 1
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# Slide 10: Conclusion
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slide = prs.slides.add_slide(prs.slide_layouts[1])
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title = slide.shapes.title
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title.text = "Conclusion"
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content = slide.placeholders[1]
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tf = content.text_frame
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tf.text = "Key Contributions:"
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p = tf.add_paragraph()
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p.text = "• Production-ready data structures for LLM optimization"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Significant improvements in throughput, latency, memory"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Modular, extensible architecture"
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p.level = 1
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p = tf.add_paragraph()
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p.text = "• Comprehensive benchmarks and documentation"
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p.level = 1
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prs.save(pptx_path)
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print(f"Presentation saved to {pptx_path}")
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print(f"Note: Convert to PDF manually or use: libreoffice --headless --convert-to pdf {pptx_path}")
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if __name__ == "__main__":
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create_slides()
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