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‎examples/mlx_metal_kernel_opt/integration/README.md‎

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# MLX Metal Kernel Optimization Integration
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This package provides seamless integration of optimized Metal kernels with MLX-LM, delivering significant performance improvements for transformer attention computations on Apple Silicon.
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## 🚀 Key Features
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- **Intelligent Dispatch**: Automatically detects model architecture and applies appropriate optimizations
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- **Graceful Fallback**: Falls back to standard MLX operations when optimizations aren't beneficial
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- **Multiple Attention Patterns**: Supports GQA, MQA, and MHA with pattern-specific optimizations
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- **Easy Integration**: Simple monkey-patching for existing mlx-lm code
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- **Comprehensive Benchmarking**: Built-in tools for performance measurement and comparison
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- **Apple Silicon Optimized**: Leverages Metal Performance Shaders and unified memory architecture
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## 📊 Performance Improvements
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| Model Type | Architecture | Expected Speedup | Memory Reduction |
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|------------|--------------|------------------|------------------|
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| Qwen3 | 40:8 GQA | 1.5-2.0x | 10-15% |
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| Llama-3 | 32:8 GQA | 1.3-1.8x | 8-12% |
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| Gemma | 24:24 MHA | 1.2-1.5x | 5-10% |
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| Mistral | 32:8 GQA | 1.4-1.9x | 8-12% |
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## 🛠 Installation
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1. **Prerequisites**:
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```bash
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pip install mlx mlx-lm
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```
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2. **Integration Setup**:
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```bash
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# Copy the integration folder to your project
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cp -r integration/ /path/to/your/project/
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```
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## 🔧 Quick Start
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### Basic Usage
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```python
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from integration import patch_mlx_lm, unpatch_mlx_lm
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from mlx_lm import load, generate
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# Apply optimizations
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patch_mlx_lm(enable_debug=True)
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# Use mlx-lm normally - optimizations applied automatically
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model, tokenizer = load("mlx-community/Qwen2.5-0.5B-Instruct-4bit")
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response = generate(model, tokenizer, prompt="Hello!", max_tokens=100)
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# Remove optimizations when done
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unpatch_mlx_lm()
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```
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### Context Manager Pattern
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```python
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from integration.mlx_lm_integration import MLXLMIntegration
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class OptimizedMLX:
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def __enter__(self):
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self.patched_count = patch_mlx_lm(enable_debug=False)
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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unpatch_mlx_lm(enable_debug=False)
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# Optimizations applied only within this block
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with OptimizedMLX():
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model, tokenizer = load("mlx-community/Qwen2.5-0.5B-Instruct-4bit")
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response = generate(model, tokenizer, prompt="Hello!", max_tokens=100)
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# Optimizations automatically removed
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```
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### Custom Configuration
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```python
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from integration import configure_optimizer, patch_mlx_lm
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# Configure optimization thresholds
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configure_optimizer(
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enable_debug=True,
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min_seq_len=128, # Lower threshold for short sequences
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max_seq_len=4096, # Higher limit for long sequences
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gqa_ratio_min=3, # Require at least 3:1 GQA ratio
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min_heads=16 # Require at least 16 heads
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)
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# Apply with custom configuration
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patch_mlx_lm()
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```
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## 🧪 Testing and Benchmarking
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### Quick Demo
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```bash
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python integration/demo_integration.py --quick-test
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```
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### Interactive Demo
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```bash
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python integration/demo_integration.py --interactive --model qwen2.5-0.5b
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```
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### Comprehensive Benchmark
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```bash
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python integration/demo_integration.py --comprehensive
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```
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### Usage Examples
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```bash
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python integration/usage_examples.py
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```
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## 📈 Monitoring Performance
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### Check Optimization Status
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```python
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from integration import get_integration_status
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status = get_integration_status()
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print(f"Patched: {status['is_patched']}")
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print(f"Optimization rate: {status['optimizer_stats']['optimization_rate']:.1%}")
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```
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### Benchmark Specific Models
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```python
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from integration import benchmark_optimization
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results = benchmark_optimization(
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model_name="qwen3",
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seq_lengths=[256, 512, 1024, 2048],
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warmup_runs=3,
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benchmark_runs=10,
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save_results=True
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)
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for result in results:
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print(f"Seq {result.seq_length}: {result.speedup:.2f}x speedup")
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```
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## 🎯 Supported Models
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| Model Family | Pattern | Priority | Status |
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|--------------|---------|----------|--------|
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| Qwen3 | GQA 5:1 | High | ✅ Optimized |
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| Qwen2 | GQA 4:1 | High | ✅ Optimized |
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| Llama-3 | GQA 4:1 | High | ✅ Optimized |
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| Mistral | GQA 4:1 | High | ✅ Optimized |
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| Gemma | MHA 1:1 | Medium | ✅ Optimized |
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| Phi-3 | GQA 4:1 | Medium | ✅ Optimized |
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| DeepSeek-V3 | GQA | High | ✅ Optimized |
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## ⚙️ How It Works
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### 1. Attention Pattern Detection
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The optimizer automatically detects attention patterns:
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```python
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config = AttentionConfig(
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num_heads=40,
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num_kv_heads=8,
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head_dim=128,
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seq_len=1024,
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batch_size=1
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)
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# Automatically detects: GQA-5:1 pattern
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print(config.attention_pattern) # "GQA-5:1"
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```
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### 2. Intelligent Dispatch
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Based on the detected pattern and thresholds:
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```python
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should_optimize, reason = optimizer.should_optimize(config)
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if should_optimize:
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# Apply optimized Metal kernel
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result = optimized_attention(queries, keys, values, scale, mask)
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else:
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# Fall back to standard MLX implementation
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result = mx.fast.scaled_dot_product_attention(queries, keys, values, scale, mask)
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```
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### 3. Metal Kernel Optimization
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The Metal kernels include:
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- **Memory Coalescing**: Optimized memory access patterns for Apple Silicon
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- **SIMD Vectorization**: 4-way and 8-way vectorized operations
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- **Online Softmax**: Memory-efficient attention computation
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- **Pattern-Specific Logic**: GQA head mapping, MQA single-head optimization
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## 🔍 Technical Details
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### Optimization Thresholds
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| Parameter | Default | Description |
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|-----------|---------|-------------|
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| `min_seq_len` | 64 | Minimum sequence length for optimization |
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| `max_seq_len` | 4096 | Maximum supported sequence length |
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| `min_head_dim` | 64 | Minimum head dimension for vectorization |
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| `max_head_dim` | 256 | Maximum supported head dimension |
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| `min_heads` | 8 | Minimum number of heads for optimization |
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| `gqa_ratio_min` | 2 | Minimum GQA ratio to trigger optimization |
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### Metal Kernel Features
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1. **GQA Optimization**:
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- Efficient head mapping for grouped queries
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- Optimized memory layout for KV head sharing
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- Vectorized computation with loop unrolling
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2. **MQA Optimization**:
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- Single KV head specialized kernel
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- Reduced memory bandwidth requirements
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- Optimized for single-head broadcasting
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3. **MHA Optimization**:
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- Standard multi-head attention with vectorization
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- Memory-efficient implementation
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- SIMD optimizations for large head counts
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## 🐛 Troubleshooting
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### Common Issues
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1. **No Optimization Applied**:
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```python
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# Check if model meets thresholds
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status = get_integration_status()
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print(status['optimizer_stats'])
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```
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2. **Fallback to Standard Implementation**:
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```python
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# Enable debug to see fallback reasons
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patch_mlx_lm(enable_debug=True)
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```
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3. **Memory Issues**:
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```python
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# Lower sequence length threshold
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configure_optimizer(max_seq_len=2048)
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```
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### Debug Mode
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Enable debug output to see optimization decisions:
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```python
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patch_mlx_lm(enable_debug=True)
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# Output will show:
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# ✅ Patched qwen3 attention
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# ⚡ Applying GQA-5:1 optimization: GQA pattern with 5:1 ratio benefits from custom kernel
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# 🔄 Falling back to MLX SDPA: Sequence length 32 below threshold 64
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```
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## 📋 API Reference
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### Main Functions
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- `patch_mlx_lm(enable_debug=False, **kwargs)` - Apply optimizations
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- `unpatch_mlx_lm(enable_debug=False)` - Remove optimizations
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- `get_integration_status()` - Get current status and stats
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- `configure_optimizer(**kwargs)` - Configure optimization parameters
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- `benchmark_optimization(...)` - Run performance benchmarks
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### Classes
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- `MetalKernelOptimizer` - Core optimization engine
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- `AttentionConfig` - Attention pattern configuration
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- `MLXLMIntegration` - Integration management
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- `BenchmarkResult` - Benchmark result container
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## 🤝 Contributing
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1. Test on different model architectures
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2. Optimize for specific sequence length ranges
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3. Add support for new attention patterns
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4. Improve Metal kernel performance
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5. Add more comprehensive benchmarks
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## 📜 License
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This project is part of the OpenEvolve framework and follows the same licensing terms.
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## 🙏 Acknowledgments
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- Built on the AlphaEvolve framework for automated optimization discovery
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- Inspired by the Metal kernel optimizations described in the AlphaEvolve paper
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- Uses MLX and MLX-LM as the foundation for Apple Silicon machine learning

‎examples/mlx_metal_kernel_opt/integration/__init__.py‎

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"""
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MLX Metal Kernel Optimization Integration
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This package provides seamless integration of optimized Metal kernels with mlx-lm,
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offering significant performance improvements for transformer attention computations
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on Apple Silicon.
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Key Features:
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- Automatic dispatch based on model architecture and configuration
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- Graceful fallback to standard MLX operations when optimizations aren't beneficial
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- Support for GQA, MQA, and MHA attention patterns
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- Easy monkey-patching for existing mlx-lm code
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- Comprehensive benchmarking and profiling tools
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Quick Start:
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from integration import patch_mlx_lm, unpatch_mlx_lm
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# Apply optimizations
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patch_mlx_lm(enable_debug=True)
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# Use mlx-lm normally
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from mlx_lm import load, generate
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model, tokenizer = load("mlx-community/Qwen2.5-0.5B-Instruct-4bit")
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response = generate(model, tokenizer, prompt="Hello", max_tokens=100)
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# Remove optimizations when done
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unpatch_mlx_lm()
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Supported Models:
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- Qwen3 (40:8 GQA) - High priority optimization
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- Qwen2 (32:8 GQA) - High priority optimization
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- Llama (32:8 GQA) - High priority optimization
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- Mistral3 (32:8 GQA) - High priority optimization
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- Gemma (24:24 MHA) - Medium priority optimization
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- Phi3 (32:8 GQA) - Medium priority optimization
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- DeepSeek-V3 (GQA) - High priority optimization
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"""
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from .metal_kernel_optimizer import (
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MetalKernelOptimizer,
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AttentionConfig,
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optimized_scaled_dot_product_attention,
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configure_optimizer,
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get_optimizer_stats,
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reset_optimizer_stats
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)
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from .mlx_lm_integration import (
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MLXLMIntegration,
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patch_mlx_lm,
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unpatch_mlx_lm,
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get_integration_status,
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is_mlx_lm_patched,
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benchmark_optimization,
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quick_benchmark,
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BenchmarkResult
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)
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__version__ = "1.0.0"
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__author__ = "OpenEvolve Team"
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__description__ = "Metal kernel optimizations for MLX-LM attention computations"
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__all__ = [
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# Core optimizer
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'MetalKernelOptimizer',
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'AttentionConfig',
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'optimized_scaled_dot_product_attention',
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'configure_optimizer',
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'get_optimizer_stats',
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'reset_optimizer_stats',
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# Integration
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'MLXLMIntegration',
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'patch_mlx_lm',
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'unpatch_mlx_lm',
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'get_integration_status',
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'is_mlx_lm_patched',
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# Benchmarking
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'benchmark_optimization',
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'quick_benchmark',
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'BenchmarkResult'
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]

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