Leaderboard Deploy agents

Avoid duplicate function calls, repeated tensor operations, and redundant computations that can significantly impact performance. Implement caching mechanisms for expensive operations and pre-allocate tensors when possible.

Key optimization strategies:

1. Cache expensive function results: Store results of costly operations like get_normalized_target_modules() or is_npu() to avoid repeated execution
2. Pre-allocate tensors: Use pre-allocated buffers instead of creating new tensors with torch.arange() or torch.zeros() in hot paths
3. Avoid duplicate initialization: Prevent initializing the same components multiple times (e.g., ragged wrappers) within a single forward pass
4. Use lookup tables: Pre-compute valid cases in lookup tables rather than dynamic computation when the search space is bounded

Example of caching expensive operations:

# Before: Redundant calls
for lora_id, config in self.configs.items():
    user_normalized_modules = get_normalized_target_modules(config.target_modules)

# After: Cache and reuse
normalized_cache = {}
for lora_id, config in self.configs.items():
    if config.target_modules not in normalized_cache:
        normalized_cache[config.target_modules] = get_normalized_target_modules(config.target_modules)
    user_normalized_modules = normalized_cache[config.target_modules]

Example of pre-allocation:

# Before: Creating tensors in hot path
q_indptr = torch.arange(0, bs + 1, dtype=torch.int32, device=device)

# After: Pre-allocate during initialization
self.q_indptr_decode = torch.arange(0, max_bs + 1, dtype=torch.int32, device=device)
# Use pre-allocated buffer: q_indptr = self.q_indptr_decode[:bs + 1]

Performance impact can be substantial - caching reduced execution time from 133μs to 5μs in one measured case. Always profile critical paths and eliminate redundant work through strategic caching and pre-allocation.