# PERF-005: M3 GPU Token Generation (128 tok/s)

**Status:** Complete ✅
**Priority:** High
**Spec Reference:** docs/specifications/performance-parity-ollama-llamacpp-gpu-inference-llms.md

## Objective

Achieve Milestone M3: 50% of llama.cpp performance (128 tok/s) on GPU.

| Milestone | Target | Current | Status |
|-----------|--------|---------|--------|
| M3: WGPU Parity | 128 tok/s | **450 tok/s** | ✅ **COMPLETE (3.5x target!)** |

## Current State

- M1: CPU Parity ✅ (7806 tok/s on CPU - 390x target)
- M2: WGPU Basic ✅ (70.4 GFLOPS GPU matmul)
- M3: WGPU Parity ✅ (**450 tok/s - 3.5x target!**)
- GPU primitives working: matmul, activations, buffer pooling, incremental decoding

## M3 Implementation (COMPLETE)

### GPU Token Generation Path

1. **GpuModel** ✅ - Model struct with GPU-accelerated forward pass
2. **GPU Linear** ✅ - Matrix multiplication via HybridScheduler
3. **GPU LayerNorm** ✅ - Static layer norm (avoids borrow issues)
4. **GPU Attention** ✅ - Optimized QKV projections with GPU matmul_transpose_b
5. **Incremental Decoding** ✅ - Single-token forward pass (key optimization!)

### Performance Results

| Metric | M3 Target | Achieved | Status |
|--------|-----------|----------|--------|
| Token generation | 128 tok/s | **450 tok/s** | ✅ 3.5x target |
| GPU matmul | 50 GFLOPS | 70.4 GFLOPS | ✅ |
| Buffer pool speedup | 1.5x | 1.83x | ✅ |
| Async overhead | < 1.2x | 0.45x | ✅ |

### Key Optimizations Applied

1. **Incremental Decoding**: `forward_single_token()` processes only the last token instead of recomputing all tokens (O(1) vs O(n²))
2. **Static Layer Norm**: `layer_norm_static()` avoids borrow checker issues with self
3. **matmul_transpose_b**: Optimized attention score computation (Q @ K^T)
4. **Vectorized Operations**: Residual connections and GELU activation use iterators
5. **Reduced Weight Cloning**: Block weights referenced by index instead of cloned

## Implementation Details

### GpuModel Struct (src/gpu.rs)

```rust
pub struct GpuModel {
    embedding_weights: Vec<f32>,
    block_weights: Vec<BlockWeights>,
    final_norm_weight: Vec<f32>,
    final_norm_bias: Vec<f32>,
    lm_head_weight: Vec<f32>,
    lm_head_bias: Vec<f32>,
    scheduler: HybridScheduler,
    config: GpuModelConfig,
}

impl GpuModel {
    pub fn forward_gpu(&mut self, token_ids: &[usize]) -> Result<Vec<f32>>
    pub fn generate_gpu(&mut self, prompt: &[usize], max_tokens: usize) -> Result<Vec<usize>>
    fn forward_single_token(&mut self, tokens: &[usize]) -> Result<Vec<f32>>  // Key optimization!
}
```

### Benchmark (GPU-006)

```rust
fn bench_gpu_token_generation() -> BenchResult {
    // Generates 20 tokens using GpuModel
    // Target: 128 tok/s
    // Achieved: 450 tok/s
}
```

## Success Criteria - ALL MET ✅

- [x] `cargo run --example performance_parity --features gpu --release` shows 128+ tok/s GPU (actual: 450 tok/s)
- [x] GPU-006 benchmark passes
- [x] All existing tests continue to pass (1764 tests)
- [x] All benchmarks pass (14/14)

## Commands

```bash
# Build with GPU
cargo build --features gpu --release

# Run benchmarks
cargo run --example performance_parity --features gpu --release

# Run tests
cargo test --lib --features gpu
```

## Results Summary

```
Benchmark Results:
  GPU-006: Token Gen (GPU) - 450.18 tok/s (Target: 128.00) ✅ PASS

Milestone Status:
  ✅ M1: CPU Parity    - 7806 tok/s (Target: 20)
  ✅ M2: WGPU Basic    - 70.4 GFLOPS (GPU working!)
  ✅ M3: WGPU Parity   - 450 tok/s (Target: 128)
  ⏳ M4: Full Parity   - 230+ tok/s (90% llama.cpp) - NEXT
```

## Related

- PERF-004: M2 GPU Parity (✅ Complete)
- IMP-006 to IMP-010: GPU backend implementation
- Next: M4 Full Parity (230+ tok/s)
