Scenario requirements
The 30×30 maze task requires a single inference to be completed within 200ms, which challenges the loop structure of the HRM.
optimization strategy
- Restructuring::
- Limit the number of planning steps for high-level modules (max_plan_steps=5)
- Use -enable-flash-attn to accelerate attention calculation
- Engineering Optimization::
- Lookup Table with pre-generated maze features
- Converting Low-Level Modules to TorchScript Boosts Execution Efficiency
- Capturing Computational Streams with CUDA Graph
- Hardware fit::
- Enable Tensor Core computation (set torch.backends.cuda.matmul.allow_tf32=True)
- Allocate fixed memory (pin_memory=True) to reduce transfer latency
real effect
Optimization comparison on RTX 4070:
- Raw delay: 320ms
- Optimized: 182ms (to meet real-time requirements)
Key Optimization Contributions:
1. FlashAttention: 40% Acceleration
2. TorchScript: 25% acceleration
3. CUDA Graph: 15% acceleration
This answer comes from the articleHRM: Hierarchical Reasoning Model for Complex ReasoningThe
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