layerzero_status.md

LayerZero Comprehensive Status Report

Analysis Date: 2026-02-04 Total Source Files: ~130 Python files Total Lines of Code: ~40,000+ (production) + ~40,000+ (tests) Project Status: 60-70% Complete - Core infrastructure solid, integration incomplete

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Table of Contents

1. Executive Summary
2. What is LayerZero
3. Architecture Overview
4. Component Analysis
5. Implementation Status
6. Critical Issues & Bugs
7. Missing Functionality
8. Test Coverage
9. Performance Characteristics
10. How to Use for Inference Engine
11. Recommendations

---

1. Executive Summary

What Works Well

• Selection Engine Pipeline - Fully implemented with filter → score → select → cache flow
• MVCC Sharded Cache - Production-ready with 256 shards, O(1) invalidation, thundering herd prevention
• Policy Engine - Complete YAML-based rule system with locks, allows, denies, boosts
• Registry Systems - Thread-safe kernel and backend registries with health tracking
• KV Cache - Paged attention support with block allocation
• Telemetry - Metrics, circuit breakers, health monitoring, Prometheus/OpenTelemetry export
• Test Suite - 2,427 tests across unit, integration, property-based, fuzz, and stress testing

What Needs Work

• Backend Dispatch - Selection engine works but actual kernel dispatch NOT implemented (all falls back to torch SDPA)
• Integration Layer - Modules exist in isolation, no orchestration layer connects them
• Speculative Decoding - Algorithms correct but not wired to inference pipeline
• Quantization - Enums defined but validation/execution missing
• Distributed - Simulated tests only, no real torch.distributed testing

Overall Assessment

LayerZero is architecturally sound but functionally incomplete. The core selection infrastructure is production-ready, but the critical "last mile" - actually dispatching to optimized kernels - is missing. With 2-4 weeks of focused work on integration, this could be a production-ready kernel orchestration system.

---

2. What is LayerZero

Purpose

LayerZero is a kernel selection and orchestration framework for PyTorch inference that solves the problem of kernel fragmentation across ML inference libraries.

The Problem It Solves

Modern ML inference relies on optimized kernels from multiple specialized libraries:

• FlashAttention (v2/v3) - IO-aware attention
• FlashInfer - Flexible paged KV-cache attention
• xFormers - Memory-efficient attention variants
• Liger Kernel - Fused Triton kernels for norms/MLPs
• oneDNN/ZenDNN - CPU acceleration
• Triton - Custom GPU kernels

Every serving framework independently implements kernel selection logic, causing bugs and inconsistencies.

LayerZero's Solution

# Old way (duplicated everywhere)
if has_flash_attn and sm >= 80:
    output = flash_attn_func(q, k, v, causal=True)
elif has_xformers:
    output = xformers.ops.memory_efficient_attention(q, k, v)
else:
    output = F.scaled_dot_product_attention(q, k, v)

# New way (unified)
import layerzero as lz
output = lz.attention(q, k, v, causal=True)
# LayerZero handles selection, adaptation, and fallback automatically

---

3. Architecture Overview

Layered Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    USER CODE / FRAMEWORKS                        │
│            (vLLM, SGLang, HuggingFace, Custom)                   │
└─────────────────────────────────────────────────────────────────┘
                             │ lz.attention(q, k, v)
                             ▼
┌─────────────────────────────────────────────────────────────────┐
│                        LAYERZERO API                             │
│  lz.attention() │ lz.rms_norm() │ lz.layer_norm() │ lz.configure()
└─────────────────────────────────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────┐
│                      SELECTION ENGINE                            │
│  Policy Check → Filter (HW/Dtype) → Score (Priority) → Cache    │
└─────────────────────────────────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────┐
│              BACKEND LOADER / PLUGIN MANAGER                     │
│  (Dynamic import, entry_points, capabilities descriptors)       │
└─────────────────────────────────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────┐
│                    KERNEL REGISTRY                               │
│  flash_attn │ flashinfer │ xformers │ liger │ torch_sdpa        │
└─────────────────────────────────────────────────────────────────┘

Data Flow

Input: Attention tensors (q, k, v)
    ↓
SelectionContext.from_tensors(...)
    ↓
SelectionEngine.select(context)
    ├─ Check policy locks
    ├─ Query KernelRegistry by operation
    ├─ Apply policy allow/deny rules
    ├─ Filter by KernelSpec.check(ctx)
    ├─ Score with priority + policy boosts
    ├─ Select highest-scoring kernel
    └─ Cache result (MVCC)
    ↓
ExecutionPlan (kernel_id, transforms)
    ↓
Dispatch to kernel implementation  ← NOT IMPLEMENTED
    ↓
Output tensors

Core Components

Component	Status	Quality
SelectionEngine	✅ Complete	Excellent
MVCC Cache	✅ Complete	Excellent
KernelRegistry	✅ Complete	Good
BackendRegistry	✅ Complete	Good
Policy Engine	✅ Complete	Good
KV Cache	✅ Complete	Good
Telemetry	✅ Complete	Good
Graph Validator	✅ Complete	Good
Multi-Op Planner	✅ Complete	Good
PerfDB	✅ Complete	Good
Distributed	⚠️ Partial	Fair
Backend Dispatch	❌ Missing	N/A

---

4. Component Analysis

4.1 Selection Engine (src/layerzero/selection/)

Status: ✅ COMPLETE - 97% test coverage

Files:

• engine.py - Main SelectionEngine class (359 lines)
• mvcc_cache.py - Sharded MVCC cache (470 lines)
• cache.py - Simple LRU cache (203 lines)
• filter.py - Constraint-based filtering (68 lines)
• scorer.py - Priority/performance scoring (81 lines)
• memory_aware.py - Memory headroom checks (411 lines)

How It Works:

class SelectionEngine:
    def select(self, ctx: SelectionContext) -> ExecutionPlan:
        # 1. Check policy locks (force kernel if lock applies)
        if locked_kernel := self._rule_engine.get_locked_kernel(ctx):
            return ExecutionPlan(kernel_id=locked_kernel, ...)

        # 2. Check cache
        cache_key = ctx.cache_key()
        if cached := self._cache.get(cache_key, self._policy.hash):
            return cached

        # 3. Get candidates from registry
        candidates = self._registry.get_by_operation(ctx.operation)

        # 4. Apply allow/deny rules
        candidates = self._rule_engine.filter_kernels(candidates, ctx)

        # 5. Filter by compatibility
        valid, filtered_out = self._filter_phase.filter(candidates, ctx)

        # 6. Score candidates
        scores = self._scoring_phase.score(valid, ctx)

        # 7. Select highest score
        selected = max(scores, key=scores.get)

        # 8. Cache and return
        plan = ExecutionPlan(kernel_id=selected, ...)
        self._cache.put(cache_key, self._policy.hash, plan)
        return plan

Strengths:

• Thread-safe with RLock
• O(1) cache lookup after warmup
• Policy-driven with YAML configuration
• Comprehensive filtering (platform, SM, dtype, shape, features)

Issues Found:

1. _get_candidate_kernels() in planner is a stub - returns empty list
2. Memory-aware filtering implemented but not integrated into pipeline
3. No batch optimization - select_batch() just loops

4.2 MVCC Sharded Cache (selection/mvcc_cache.py)

Status: ✅ COMPLETE - 93% test coverage

Architecture:

MVCCShardedCache (256 shards by default)
├── CacheShard 0 (version=0, entries={}, lru_order=OrderedDict)
├── CacheShard 1
├── ...
└── CacheShard 255

Key Features:

• 256 shards for minimal lock contention
• MVCC versioning - O(1) policy invalidation via version bump
• Deduplication - Prevents thundering herd via threading.Event
• LRU eviction - Per-shard with configurable max entries
• TTL expiration - Default 1 hour

Performance:

• Target: 10K+ QPS
• Latency: <50µs cache lookup
• Memory: Bounded to 256 × 100 = 25.6K entries max

Issues Found:

1. Potential infinite recursion in get_or_compute() if compute fails repeatedly
2. No timeout on Event.wait() - could hang if computing thread crashes
3. MD5 used for sharding (not cryptographically secure but fine for distribution)

4.3 Policy Engine (src/layerzero/policy/)

Status: ✅ COMPLETE

Files:

• policy.py - Policy container (120 lines)
• rule.py - Rules and conditions (226 lines)
• engine.py - Rule evaluation (177 lines)
• loader.py - YAML/env loading (302 lines)

Policy Structure:

version: "1.0"
locks:
  - operation: "attention.causal"
    kernel: "flash_attn.v3.causal"
    when:
      head_dim: ">=64"
allow:
  - backend: "flash_attn"
  - backend: "xformers"
deny:
  - kernel: "torch.sdpa"
boosts:
  - kernel: "flash_attn.*"
    priority_add: 20

Condition Operators:

• Comparison: ==, !=, >, >=, <, <=
• Set: in, not_in
• Pattern: match (glob via fnmatch)

Issues Found:

1. Loader.merge() has key typo (deny vs denies)
2. No type validation on condition values
3. No logging/audit trail for rule matches

4.4 Registry Systems (src/layerzero/registry/)

Status: ✅ COMPLETE

KernelRegistry:

• Thread-safe (RLock)
• Index by: operation, source, platform
• Methods: register(), register_many(), get(), get_by_operation(), filter()
• Atomic batch registration with rollback on error

BackendRegistry:

• Circuit breaker pattern for health tracking
• States: HEALTHY → DEGRADED → UNHEALTHY → COOLDOWN → HEALTHY
• Health tracking: failure_count, cooldown_until, total_requests

Issues Found:

1. get_health() mutates state during read - should be separated
2. get_available_backends() has nested lock acquisition
3. filter() doesn't use indexes - O(n) scan

4.5 KV Cache (src/layerzero/kv_cache/)

Status: ✅ COMPLETE with gaps

Strategies:

• CONTIGUOUS - Pre-allocated contiguous memory
• PAGED - Block-based allocation (vLLM-style)
• CHUNKED - Fixed-size chunks (not implemented)
• VIRTUAL - OS-level paging (not implemented)

PagedKVCache Features:

• Block allocation/deallocation
• Sequence tracking with auto-extending
• FlashInfer-compatible block tables
• Fragmentation ratio calculation

Issues Found:

1. BNHD layout conversion NOT implemented - raises NotImplementedError
2. get_seq_dim(BNHD) returns -1 (invalid)
3. defragment() only sorts free list, doesn't move data
4. No seq_len validation in allocate()
5. Double-free not detected

4.6 Telemetry & Health (src/layerzero/telemetry/, health/)

Status: ✅ COMPLETE

Metrics Tracked:

• Total selections
• Per-kernel usage counts
• Cache hit/miss rates
• Selection latency (p50, p95, p99)
• Backend health states

Exporters:

• Prometheus text format
• OpenTelemetry OTLP JSON

Circuit Breaker:

• CLOSED → OPEN after N failures
• OPEN → HALF_OPEN after cooldown
• HALF_OPEN → CLOSED on success

Issues Found:

1. Memory unbounded growth in metrics (latencies list never pruned)
2. Percentile calculation inaccurate for small samples
3. explain.py uses hardcoded kernel registry - not integrated with real system

4.7 Isolation System (src/layerzero/isolation/)

Status: ⚠️ PARTIAL - Design complete, implementation has issues

Purpose: Process isolation for ABI-incompatible backends

Components:

• Subprocess spawning with JSON IPC
• Worker process with signal handling
• ABI conflict detection

Issues Found:

1. CRITICAL: Threading deadlock risk - unprotected stdin/stdout access
2. CRITICAL: ABI isolation strategy inverted - isolates majority instead of minority
3. ThreadPoolExecutor never shut down (resource leak)
4. No timeout on operations - could hang
5. SharedMemoryChannel defined but never used

4.8 Speculative Decoding (src/layerzero/speculative/)

Status: ⚠️ PARTIAL - Algorithms correct, integration missing

Implemented:

• Rejection sampling (mathematically verified)
• Greedy verification
• Kernel pair selection (draft/target)
• Tree attention config (partial)

Issues Found:

1. CRITICAL: No integration with inference pipeline
2. Kernel IDs not validated against registry
3. Model validation too weak (only vocab_size)
4. Speedup estimation formula oversimplified

4.9 MLP, Sampling, Position Encoding (src/layerzero/mlp/, sampling/, posenc/)

Status: ✅ COMPLETE

MLP:

• SwiGLU, GeGLU, ReGLU activations
• Fused MLP operation
• Memory-efficient chunked MLP

Sampling:

• Top-k sampling
• Top-p (nucleus) sampling
• Combined top-k + top-p
• Temperature scaling

Position Encoding:

• ALiBi with caching
• Causal bias generation
• Slope computation for non-power-of-2 heads

Issues Found:

1. ALiBi cache not thread-safe (global dict)
2. CUDA generator parameter silently ignored
3. RoPE mentioned but not implemented

4.10 Graphs & Planner (src/layerzero/graphs/, planner/)

Status: ✅ COMPLETE

Graph Safety:

• CUDA graph safety whitelist (17 safe, 19 unsafe operations)
• Warmup protocol (cuBLAS, cuDNN initialization)
• Memory tracking during capture
• Dummy capture validation

Multi-Op Planner:

• Joint optimization (exponential - O(K^N))
• Greedy optimization (linear - O(K*N))
• Transform cost calculation (layout, dtype)
• Plan caching with LRU/FIFO

Issues Found:

1. CRITICAL: _get_candidate_kernels() is a stub - returns empty list
2. Joint planner scales exponentially - only viable for <8 ops
3. Dtype comparison bug in greedy planner

4.11 PerfDB (src/layerzero/perfdb/)

Status: ✅ COMPLETE

Features:

• SQLite with WAL mode
• Thread-local connections
• Bucketing for seq_len and batch_size
• Confidence scoring based on sample count and variance
• Invalidation by driver/CUDA version

Schema:

CREATE TABLE perf_records (
    kernel_id, operation, device_id, sm_version, dtype,
    head_dim, seq_bucket, batch_bucket,
    median_us, p95_us, samples, variance_us, warmup_ms,
    kernel_version, cuda_version, driver_version, ...
    UNIQUE(kernel_id, operation, device_id, sm_version, dtype,
           head_dim, seq_bucket, batch_bucket)
);

Issues Found:

1. No index on valid flag - queries scan full table
2. SM version stored as string, parsed on every query
3. No query cache

4.12 Distributed (src/layerzero/distributed/)

Status: ⚠️ PARTIAL

Implemented:

• Version consistency checking
• Selection hash synchronization
• TP invariance filtering

Issues Found:

1. All tests use SimulatedProcessGroup - no real torch.distributed
2. No timeout on broadcast operations
3. No error recovery mechanism

4.13 API (src/layerzero/api/)

Status: ⚠️ PARTIAL - Framework exists, dispatch not implemented

Implemented APIs:

• attention(), paged_attention(), rope(), rms_norm(), layer_norm()
• sample_topk(), sample_topp(), tokenize(), detokenize()
• configure(), lock(), unlock(), get_config()
• select(), explain(), which(), list_kernels(), validate()
• doctor(), readiness_check(), compile(), tune()

CRITICAL ISSUE: All operations fall back to torch SDPA regardless of selection!

# In operations.py
def _dispatch_attention(q, k, v, ...):
    # TODO: Actually use selected kernel
    return F.scaled_dot_product_attention(q, k, v, ...)  # Always SDPA!

4.14 Integrations (src/layerzero/integrations/)

Status: ⚠️ PARTIAL - Patching framework exists, but wrapper doesn't use LayerZero

Implemented:

• HuggingFace Transformers integration
• Diffusers integration
• Model patching mechanism
• Tokenization pipeline

CRITICAL ISSUE: LayerZeroAttentionWrapper.forward() never calls LayerZero!

def forward(self, *args, **kwargs):
    # Always calls original, never LayerZero!
    return self._original(*args, **kwargs)

---

5. Implementation Status

Completed Tasks (2/48)

• ✅ Task 8: Selection Engine Pipeline
• ✅ Task 9: MVCC Sharded Cache

Pending Core Tasks

Task	Status	Priority
Task 1: Core Enums	Code exists	HIGH
Task 2: Data Models	Code exists	HIGH
Task 6: Registries	Code exists	HIGH
Task 7: Policy Engine	Code exists	HIGH
Task 10: PerfDB	Code exists	MEDIUM

Pending Backend Tasks

Task	Status	Priority
Task 11: Torch SDPA	Adapter exists	HIGH
Task 12: FlashAttention	Adapter exists	CRITICAL
Task 13: FlashInfer	Adapter exists	HIGH
Task 14: xFormers	Adapter exists	HIGH
Task 15: Liger	Adapter exists	HIGH
Task 16: Triton	Partial	HIGH
Task 17: CPU Backends	Partial	MEDIUM

Missing Critical Pieces

1. Backend Dispatch - Selection works, dispatch doesn't
2. Integration Layer - No orchestration connecting modules
3. Real Distributed Tests - Only simulated
4. Production Validation - No end-to-end production tests

---

6. Critical Issues & Bugs

Priority 1: CRITICAL (Blocking Production)

#	Issue	Location	Impact
1	Backend dispatch not implemented	api/operations.py	All kernels fall back to SDPA
2	LayerZeroAttentionWrapper doesn't use LayerZero	integrations/model_patching.py	Patching has no effect
3	_get_candidate_kernels() is stub	planner/multi_op.py	Planning always uses dummy kernels
4	Subprocess threading deadlock	isolation/subprocess_backend.py	Potential hang
5	ABI isolation strategy inverted	isolation/abi_detector.py	Isolates wrong backends

Priority 2: HIGH (Quality Issues)

#	Issue	Location	Impact
6	BNHD layout conversion missing	kv_cache/layouts.py	Paged attention broken
7	get_health() mutates state	registry/backend_registry.py	Race conditions
8	Infinite recursion in cache	selection/mvcc_cache.py	Potential crash
9	No timeout on Event.wait()	selection/mvcc_cache.py	Potential hang
10	Dtype comparison bug	planner/multi_op.py	Wrong transform costs

Priority 3: MEDIUM (Should Fix)

#	Issue	Location	Impact
11	ALiBi cache not thread-safe	posenc/alibi.py	Race conditions
12	Memory unbounded in metrics	telemetry/metrics.py	Memory leak
13	No index on valid flag	perfdb/database.py	Slow queries
14	Speculative decoding not integrated	speculative/	Feature non-functional
15	Exponential joint planner	planner/multi_op.py	Timeout for >8 ops

---

7. Missing Functionality

Must Have (For Production)

1. Backend Dispatch Implementation

   • Map kernel_id to actual kernel function
   • Handle layout/dtype conversions
   • Execute with proper error handling

2. Integration Orchestrator

   • Connect selection → dispatch → execution
   • Handle KV cache management
   • Coordinate speculative decoding

3. Real Distributed Testing

   • Test with actual torch.distributed
   • Multi-GPU validation
   • Rank consistency verification

Should Have

1. RoPE position encoding
2. CHUNKED and VIRTUAL KV cache strategies
3. Quantization validation (INT4, INT8, FP8)
4. Defragmentation with data movement
5. Large model testing (13B+)

Nice to Have

1. AutoTune mechanism
2. ONNX/TorchScript export
3. HPU/XPU support
4. Performance regression detection
5. Distributed profiling

---

8. Test Coverage

Test Suite Statistics

• Total Tests: 2,427
• Test Files: 130
• Lines of Test Code: 40,011
• Benchmark Files: 9 (2,594 lines)

Coverage by Component

Component	Tests	Coverage
Selection Engine	101	97%
MVCC Cache	34	93%
Policy Engine	40+	90%+
Registries	25+	85%+
KV Cache	30+	85%+
Backends	600+	80%+
Telemetry	30+	80%+
API	None found	0%
Integrations	20+	Limited

Test Types

• Unit Tests: 115 files (comprehensive)
• Integration Tests: 8 files (69 marked tests)
• Correctness Tests: 43 marked tests
• Property-Based: Hypothesis support (optional)
• Fuzz Tests: Fallback implementation
• Stress Tests: 34 marked tests
• Benchmarks: 9 experiment files

Missing Test Coverage

1. API operations (0% coverage)
2. Real distributed tests (simulated only)
3. Large model tests (13B+)
4. Long context tests (32K+)
5. OOM recovery tests
6. Multi-GPU tests

---

9. Performance Characteristics

Selection Engine

• Cache Hit Latency: <50µs
• Cache Miss Latency: <1ms
• Throughput Target: 10K+ QPS
• Memory per Shard: ~100 entries max

MLP Operations

• Large batch (32×2048×4096): <60s CPU, <10s GPU
• Memory bandwidth limited on GPU

Sampling

• Top-k: 100 samples @ 128K vocab → <5s CPU, <1s GPU
• Top-p: 6x slower than top-k (full sort required)

ALiBi

• seq_len=4096, 32 heads: <1s generation
• Memory: num_heads × seq_len² × 4 bytes

Benchmarks Available

1. full_system_benchmark.py - Comprehensive system test
2. exp_01_selection_overhead.py - <10µs/selection target
3. exp_02_import_isolation.py - Backend import timing
4. exp_03_jit_cold_start.py - JIT warmup costs
5. exp_04_correctness_variance.py - Precision analysis

---

10. How to Use for Inference Engine

Current State

LayerZero cannot currently be used for production inference because:

1. Backend dispatch is not implemented
2. All operations fall back to torch SDPA
3. Integration layer is missing

What Would Be Needed

Step 1: Implement Backend Dispatch (2-3 weeks)

# In api/operations.py
def _dispatch_attention(kernel_id, q, k, v, ...):
    if kernel_id.startswith("flash_attn"):
        return _call_flash_attention(q, k, v, ...)
    elif kernel_id.startswith("flashinfer"):
        return _call_flashinfer(q, k, v, ...)
    elif kernel_id.startswith("xformers"):
        return _call_xformers(q, k, v, ...)
    else:
        return F.scaled_dot_product_attention(q, k, v, ...)

Step 2: Fix Integration Wrapper (1 week)

# In integrations/model_patching.py
def forward(self, *args, **kwargs):
    # Actually call LayerZero
    return torch.ops.layerzero.attention(...)

Step 3: Wire Up Speculative Decoding (1-2 weeks)

# Create inference orchestrator
class InferenceEngine:
    def generate(self, input_ids, ...):
        # 1. Run draft model
        # 2. Run target verification
        # 3. Accept/reject tokens
        # 4. Manage KV cache

Step 4: Add Distributed Support (1 week)

• Replace SimulatedProcessGroup with real torch.distributed
• Add timeout handling
• Test multi-GPU scenarios

Integration with Bud Waav

For the Bud Waav inference engine:

1. Use LayerZero for Kernel Selection

   • Import and configure LayerZero
   • Let it handle backend selection

2. Integrate with Gateway

   • Selection happens at request routing
   • Cache warmup during initialization

3. KV Cache Management

   • Use LayerZero's PagedKVCache for vLLM-style serving
   • Integrate with request scheduling

4. Monitoring

   • Export LayerZero metrics to Prometheus
   • Track selection decisions and latencies

Example Integration Code

import layerzero as lz

# Configure at startup
lz.configure(
    default_backend="flash_attn",
    cache_size=10000,
    strict_mode=False,
)

# Lock specific kernels for production
lz.lock("attention.causal", "flash_attn.v3.causal")

# Check system health
report = lz.doctor()
if not report.healthy:
    logger.warning(f"LayerZero issues: {report.summary}")

# In inference loop
class WhisperInference:
    def forward(self, audio_features):
        # LayerZero automatically selects best kernel
        attention_output = lz.attention(
            query, key, value,
            is_causal=True,
        )
        return attention_output

---

11. Recommendations

Immediate Actions (Week 1)

1. Implement Backend Dispatch

   • Create kernel dispatch map in api/operations.py
   • Add backend-specific call wrappers
   • Wire selection result to dispatch

2. Fix Integration Wrapper

   • Make LayerZeroAttentionWrapper actually call LayerZero
   • Test with HuggingFace models

3. Add API Tests

   • Currently 0% coverage
   • Add unit tests for all public APIs

Short-Term (Weeks 2-4)

4. Fix Critical Bugs

   • BNHD layout conversion
   • ABI isolation strategy
   • Subprocess threading issues

5. Wire Speculative Decoding

   • Create inference orchestrator
   • Connect to selection engine
   • Test with real models

6. Add Distributed Tests

   • Replace SimulatedProcessGroup
   • Test multi-rank consistency

Medium-Term (Months 1-2)

7. Complete Backend Adapters

   • Validate all adapters work end-to-end
   • Add missing constraint checks

8. Performance Optimization

   • Profile and optimize hot paths
   • Add PerfDB integration

9. Production Hardening

   • Add timeouts everywhere
   • Improve error messages
   • Add graceful degradation

Long-Term (Months 2-3)

10. Advanced Features

    • AutoTune mechanism
    • Quantization support
    • Multi-GPU optimization

11. Documentation

    • API documentation
    • Architecture guide
    • Integration tutorials

---

Appendix: File Structure

src/layerzero/
├── __init__.py
├── device.py              # GPU detection, SM versions
├── enums.py               # Core enumerations
├── api/                   # Public API
│   ├── operations.py      # lz.attention(), etc.
│   ├── config.py          # lz.configure(), locks
│   ├── inspection.py      # lz.explain(), lz.which()
│   └── system.py          # lz.doctor(), readiness
├── core/                  # Validation utilities
├── models/                # Data models
│   ├── device_spec.py
│   ├── kernel_spec.py
│   ├── selection_context.py
│   ├── execution_plan.py
│   ├── operation_spec.py
│   └── backend_spec.py
├── selection/             # Selection engine
│   ├── engine.py
│   ├── mvcc_cache.py
│   ├── filter.py
│   ├── scorer.py
│   └── memory_aware.py
├── registry/              # Kernel/backend registries
│   ├── kernel_registry.py
│   └── backend_registry.py
├── policy/                # Policy engine
│   ├── policy.py
│   ├── rule.py
│   ├── engine.py
│   └── loader.py
├── kv_cache/              # KV cache management
│   ├── strategy.py
│   ├── layouts.py
│   └── paged.py
├── telemetry/             # Metrics and monitoring
│   ├── metrics.py
│   ├── explain.py
│   ├── selection_report.py
│   └── exporters/
├── health/                # Health tracking
│   ├── circuit_breaker.py
│   └── backend_health.py
├── graphs/                # CUDA graph safety
│   ├── validator.py
│   ├── whitelist.py
│   ├── warmup.py
│   └── memory_tracker.py
├── planner/               # Multi-op planning
│   ├── multi_op.py
│   └── plan_cache.py
├── perfdb/                # Performance database
│   ├── database.py
│   ├── record.py
│   └── schema.py
├── distributed/           # Distributed support
│   ├── consistency.py
│   └── tp_invariance.py
├── isolation/             # Process isolation
│   ├── worker.py
│   ├── subprocess_backend.py
│   ├── ipc.py
│   └── abi_detector.py
├── speculative/           # Speculative decoding
│   ├── coordination.py
│   └── verification.py
├── mlp/                   # MLP operations
│   ├── fused.py
│   └── linear.py
├── sampling/              # Token sampling
│   ├── topk.py
│   ├── topp.py
│   ├── combined.py
│   └── temperature.py
├── posenc/                # Position encoding
│   └── alibi.py
└── integrations/          # Framework integrations
    ├── transformers.py
    ├── diffusers.py
    ├── model_patching.py
    └── tokenization_pipeline.py

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Report Generated: 2026-02-04 Analysis Depth: Line-by-line review of all source files Agents Used: 15 parallel exploration agents