File size: 3,714 Bytes
5fed0fc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
Decoding Attention Optimization Problem
========================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for Decoding Attention computation on GPU. This problem focuses on implementing efficient attention kernels for decoder-only transformer models using Triton's JIT compilation system.
The challenge involves optimizing:
- **Attention computation**: Efficient computation of scaled dot-product attention
- **Memory access patterns**: Efficient loading and storing of Q, K, V tensors
- **Numerical stability**: Handling softmax operations with proper numerical stability
- **Block tiling**: Optimal block sizes for GPU execution across different sequence lengths
- **Performance benchmarking**: Achieving speedup over baseline PyTorch implementations
Target
------
- **Primary**: Maximize geometric mean speedup over baseline (higher is better)
- **Secondary**: Ensure correctness across diverse sequence lengths and attention heads
- **Tertiary**: Minimize kernel launch overhead and memory usage
API Specification
-----------------
Implement a `Solution` class that returns a Triton kernel implementation:
```python
class Solution:
def solve(self, spec_path: str = None) -> dict:
"""
Returns a dict with either:
- {"code": "python_code_string"}
- {"program_path": "path/to/kernel.py"}
"""
# Your implementation
pass
```
Your kernel implementation must provide:
```python
import torch
import triton
import triton.language as tl
def decoding_attn(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor) -> torch.Tensor:
"""
Decoding attention computation.
Args:
Q: Input tensor of shape (Z, H, M, Dq) - query tensor (float16)
K: Input tensor of shape (Z, H, N, Dq) - key tensor (float16)
V: Input tensor of shape (Z, H, N, Dv) - value tensor (float16)
Returns:
Output tensor of shape (Z, H, M, Dv) - attention output (float16)
"""
pass
```
API Usage Notes
---------------
- The evaluator looks for a `decoding_attn` function in the module namespace
- Function must handle tensor strides and memory layouts correctly
- Must use Triton JIT compilation for kernel definition
- Should leverage Triton's autotuning features for optimization
- Kernel must handle variable sequence lengths efficiently
- Output must be float16 tensor of shape (Z, H, M, Dv)
Scoring (0-100)
---------------
Performance is measured against GPU baseline implementations:
```
geometric_mean_gpu_time = geometric_mean(gpu_baseline_times)
geometric_mean_answer_time = geometric_mean(answer_times)
# Linear interpolation: 0 points = 1x GPU baseline, 100 points = 3x GPU baseline
target_time_0 = geometric_mean_gpu_time # 0 points (1x GPU baseline)
target_time_100 = geometric_mean_gpu_time / 3.0 # 100 points (3x speedup over GPU)
score = 100 * (target_time_0 - geometric_mean_answer_time) / (target_time_0 - target_time_100)
```
- 0 points = 1x GPU baseline performance
- 100 points = 3x speedup over GPU baseline
- Score is linearly interpolated between these two points
Note: Correctness is verified against GPU baseline, and scoring spans from 1x GPU baseline (0 points) to 3x GPU baseline (100 points).
Evaluation Details
------------------
- Tested on multiple sequence lengths: N ∈ {1024, 2048, 4096, 8192} (default)
- Fixed dimensions: Z=1, H=8, M=1, Dq=64, Dv=64 (configurable via metadata)
- Can also test custom shapes specified in metadata
- Correctness verified with tolerance: rtol=1e-2, atol=5e-3
- Performance measured using median execution time
- Requires CUDA backend and GPU support
- All tests must pass for any score > 0
|