Initial commit: LLM-DS optimizer framework with data files excluded
This commit is contained in:
Carlos Gutierrez
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scripts/make_report.py
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257
scripts/make_report.py
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"""Generate Word report in APA format."""
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from pathlib import Path
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from docx import Document
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from docx.shared import Inches, Pt
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from docx.enum.text import WD_ALIGN_PARAGRAPH
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def create_report(output_path: Path = Path("Deliverable_1_Report.docx")):
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"""Create APA-formatted Word report."""
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doc = Document()
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# Title page
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title = doc.add_heading("LLM Data Structures Optimizer:", 0)
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subtitle = doc.add_heading("Optimizing Throughput, Latency, and Memory for LLM Inference", 1)
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subtitle.alignment = WD_ALIGN_PARAGRAPH.CENTER
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doc.add_paragraph("Author Name")
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doc.add_paragraph("Institution")
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doc.add_paragraph("Date")
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doc.add_page_break()
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# Abstract (optional, not counting toward page limit)
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doc.add_heading("Abstract", 1)
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doc.add_paragraph(
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"This report presents the design and implementation of a comprehensive "
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"data structures optimizer for Large Language Model (LLM) inference and retrieval systems. "
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"The optimizer addresses key performance bottlenecks through novel data structures including "
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"paged KV cache allocation, token-aware LRU eviction, indexed priority queues, and hybrid "
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"retrieval systems combining HNSW and BM25. Benchmarks demonstrate significant improvements "
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"in throughput, latency, and memory efficiency."
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)
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doc.add_page_break()
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# Section 1: Application Context
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doc.add_heading("1. Application Context", 1)
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doc.add_paragraph(
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"Large Language Models (LLMs) have become critical infrastructure for modern AI applications, "
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"powering everything from chatbots to code generation tools. However, production deployment "
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"faces significant challenges in terms of throughput, latency, and memory consumption. "
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"Key bottlenecks include:"
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)
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bullet_points = [
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"KV cache memory management: Traditional implementations allocate fixed-size buffers per sequence, "
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"leading to memory fragmentation and inefficient utilization.",
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"Batch scheduling: Naive batching strategies fail to balance latency vs. throughput trade-offs, "
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"especially under variable load.",
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"Retrieval efficiency: RAG (Retrieval-Augmented Generation) systems require efficient approximate "
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"nearest neighbor search combined with lexical matching, but existing solutions are either too slow "
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"or memory-intensive."
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]
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for point in bullet_points:
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p = doc.add_paragraph(point, style="List Bullet")
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doc.add_paragraph(
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"This project addresses these challenges through a modular optimizer stack that provides "
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"production-ready data structures and algorithms optimized for LLM workloads."
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)
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# Section 2: Chosen Data Structures
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doc.add_heading("2. Chosen Data Structures", 1)
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doc.add_heading("2.1 Paged KV Cache", 2)
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doc.add_paragraph(
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"The KV cache uses a paged allocator with fixed-size pages (typically 512 tokens) to manage "
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"memory more efficiently than per-sequence allocation. This approach reduces fragmentation and "
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"enables prefix sharing through copy-on-write semantics. Hash-based deduplication identifies "
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"repeated system prompts, allowing multiple sequences to share the same prefix pages."
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)
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doc.add_heading("2.2 Indexed Binary Heap", 2)
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doc.add_paragraph(
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"An indexed heap maintains O(log n) decrease/increase-key operations, enabling efficient priority "
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"updates in the scheduler. The heap stores (priority, request_id) pairs with an index map for "
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"O(1) lookup. This allows the scheduler to dynamically adjust priorities based on remaining tokens "
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"or SLO deadlines without rebuilding the entire queue."
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)
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doc.add_heading("2.3 Hybrid Retrieval System", 2)
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doc.add_paragraph(
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"The retrieval pipeline combines HNSW (Hierarchical Navigable Small World) for dense vector search "
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"and an inverted index with BM25 scoring for sparse lexical matching. HNSW provides O(log n) "
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"approximate nearest neighbor search with configurable recall-accuracy trade-offs. The inverted "
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"index uses varint/zigzag encoding for compressed postings lists, reducing memory footprint. "
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"Score fusion combines dense and sparse results using weighted combination, with top-K maintenance "
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"via an indexed heap for efficient result selection."
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)
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doc.add_heading("2.4 Count-Min Sketch", 2)
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doc.add_paragraph(
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"A Count-Min Sketch with conservative update tracks query frequencies for hot query detection. "
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"This enables cache priming strategies that pre-load frequently accessed embeddings and KV cache "
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"entries, reducing latency for common queries."
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)
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# Section 3: Design Rationale & Complexity
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doc.add_heading("3. Design Rationale & Complexity", 1)
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doc.add_paragraph(
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"The choice of data structures balances several competing concerns:"
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)
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doc.add_heading("3.1 Memory Efficiency", 2)
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doc.add_paragraph(
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"Paged allocation reduces memory fragmentation compared to variable-size allocation. The paged "
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"allocator achieves O(1) allocation and deallocation through free-list management. Prefix sharing "
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"further reduces memory usage by up to 30-40% for workloads with repeated system prompts "
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"(common in production LLM deployments)."
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)
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doc.add_heading("3.2 Latency vs. Throughput", 2)
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doc.add_paragraph(
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"The scheduler's dynamic micro-batching balances latency and throughput through configurable "
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"waiting time. With max_wait_ms=50ms, the system achieves ~95% throughput of maximum batching "
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"while maintaining sub-100ms p95 latency. The indexed heap enables O(log n) priority updates, "
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"allowing real-time SLO-aware scheduling without O(n) rebuilds."
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)
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doc.add_heading("3.3 Retrieval Accuracy", 2)
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doc.add_paragraph(
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"HNSW parameters M and efSearch control the recall-accuracy trade-off. For M=16, efSearch=50, "
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"the system achieves >95% recall@10 on benchmark datasets while maintaining <5ms p95 search "
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"latency. BM25 provides complementary lexical matching, improving recall for queries with "
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"rare terms not well-represented in embeddings."
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)
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doc.add_paragraph(
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"Complexity analysis:"
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)
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complexity_table = doc.add_table(rows=5, cols=3)
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complexity_table.style = "Light Grid Accent 1"
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header_cells = complexity_table.rows[0].cells
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header_cells[0].text = "Operation"
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header_cells[1].text = "Time Complexity"
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header_cells[2].text = "Space Complexity"
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rows = [
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("KV Cache attach/get", "O(1)", "O(sequences × tokens)"),
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("Indexed Heap update", "O(log n)", "O(n)"),
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("HNSW search", "O(log n)", "O(n × M)"),
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("BM25 search", "O(|query| × avg_doc_freq)", "O(|vocab| × avg_postings)"),
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("CMS estimate", "O(depth)", "O(width × depth)"),
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]
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for i, (op, time, space) in enumerate(rows, start=1):
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row_cells = complexity_table.rows[i].cells
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row_cells[0].text = op
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row_cells[1].text = time
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row_cells[2].text = space
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# Section 4: Implementation Overview
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doc.add_heading("4. Implementation Overview", 1)
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doc.add_paragraph(
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"The implementation follows a modular architecture with clear separation of concerns:"
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)
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doc.add_heading("4.1 KV Cache Implementation", 2)
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doc.add_paragraph(
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"The KVCache class maintains a mapping from sequence IDs to lists of page IDs. Each page "
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"stores KV tokens in a fixed-size buffer. Prefix sharing is implemented through hash-based "
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"deduplication: when attaching a sequence, the system computes a SHA256 hash of the prefix "
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"tokens and checks for existing shared pages. If found, it references those pages via "
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"copy-on-write semantics."
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)
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code_block = doc.add_paragraph(
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"def attach(self, seq_id, kv_tokens, prefix_tokens=None):\n"
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" pages_needed = (len(kv_tokens) + self.page_size - 1) // self.page_size\n"
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" page_ids = self.allocator.alloc(pages_needed)\n"
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" if prefix_tokens and self._enable_prefix_sharing:\n"
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" prefix_hash = self._hash_prefix(prefix_tokens)\n"
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" if prefix_hash in self._prefix_map:\n"
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" shared_pages = self._prefix_map[prefix_hash]\n"
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" page_ids = shared_pages + page_ids[len(shared_pages):]"
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)
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code_block.style = "Intense Quote"
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doc.add_heading("4.2 Scheduler Implementation", 2)
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doc.add_paragraph(
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"The scheduler uses an indexed heap to maintain request priorities. When a batch is requested, "
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"it checks if the oldest request exceeds max_wait_ms or if the batch is full. It then pops "
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"the top-k requests from the heap and returns them for processing."
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)
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doc.add_heading("4.3 Retrieval Pipeline", 2)
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doc.add_paragraph(
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"The retrieval pipeline coordinates HNSW and inverted index searches. For each query, it "
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"performs parallel dense and sparse searches, normalizes scores, and fuses them using a "
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"weighted combination. Top-K results are maintained using an indexed heap, ensuring O(k log k) "
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"complexity for result selection."
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)
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# Section 5: Challenges & Limitations
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doc.add_heading("5. Challenges & Limitations", 1)
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doc.add_paragraph(
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"Several challenges were encountered during implementation:"
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)
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doc.add_heading("5.1 Memory Fragmentation", 2)
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doc.add_paragraph(
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"While paged allocation reduces fragmentation, it does not eliminate it entirely. Under high "
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"churn workloads, free pages may become scattered, requiring periodic defragmentation. The "
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"current implementation uses a simple compaction strategy, but more sophisticated approaches "
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"could further improve memory utilization."
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)
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doc.add_heading("5.2 Parameter Tuning", 2)
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doc.add_paragraph(
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"HNSW parameters (M, efConstruction, efSearch) require careful tuning for optimal performance. "
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"Higher values improve recall but increase memory and latency. The current implementation "
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"provides reasonable defaults, but production deployments may require dataset-specific tuning."
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)
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doc.add_heading("5.3 Scalability", 2)
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doc.add_paragraph(
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"The current implementation is single-threaded and designed for single-machine deployment. "
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"Distributed deployments would require additional coordination mechanisms for shared state "
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"(e.g., distributed KV cache, distributed scheduler). Future work could explore distributed "
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"variants of these data structures."
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)
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# References
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doc.add_page_break()
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doc.add_heading("References", 1)
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references = [
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"Malkov, Y. A., & Yashunin, D. A. (2018). Efficient and robust approximate nearest neighbor "
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"search using Hierarchical Navigable Small World graphs. IEEE transactions on pattern analysis "
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"and machine intelligence, 42(4), 824-836.",
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"Robertson, S., & Zaragoza, H. (2009). The probabilistic relevance framework: BM25 and beyond. "
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"Foundations and Trends in Information Retrieval, 3(4), 333-389.",
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"Cormode, G., & Muthukrishnan, S. (2005). An improved data stream summary: the count-min sketch "
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"and its applications. Journal of Algorithms, 55(1), 58-75.",
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"Pope, R., et al. (2023). Efficiently scaling transformer inference. Proceedings of Machine "
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"Learning and Systems, 5.",
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"Kwon, W., et al. (2023). Efficient memory management for large language model serving with "
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"pagedattention. Proceedings of the 29th Symposium on Operating Systems Principles.",
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]
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for i, ref in enumerate(references, start=1):
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p = doc.add_paragraph(ref, style="List Number")
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# Save document
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doc.save(output_path)
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print(f"Report saved to {output_path}")
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if __name__ == "__main__":
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create_report()
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