[CANN] Optimize RMS_NORM using cache

[noemotiovon]

Description：

This PR introduces a cache-based optimization for the RMS_NORM operator in the CANN backend. By reusing pre-allocated zero and one float32 tensors, it reduces redundant memory allocations and improves runtime performance for RMS normalization operations.

What does this PR do?

• Initializes a reusable cache for zero-filled and one-filled float32 tensors.

• Expands the cache dynamically if the requested tensor size exceeds the current cache capacity.

• Modifies RMS_NORM computations to utilize the cache, reducing memory operations.

• Improves performance for large-scale tensor normalization in CANN backend.

Why is this needed?

• Avoids frequent memory allocation and deallocation during RMS normalization.

• Reduces overhead and latency in tensor computations, especially for large models.

Performance Impact:

• Significant reduction in memory allocation overhead for RMS_NORM.

• Faster execution for large tensors due to cache reuse.

[noemotiovon]

Opt Test:

Backend 1/2: CANN0
  Device description: Ascend910B4
  Device memory: 30196 MB (29851 MB free)

new_pool_for_device: device 0 use vmm pool
  RMS_NORM(type=f32,ne=[64,5,4,3],v=0,eps=0.000000): OK
  RMS_NORM(type=f32,ne=[64,5,4,3],v=1,eps=0.000000): OK
  RMS_NORM(type=f32,ne=[64,5,4,3],v=0,eps=0.000001): OK
  RMS_NORM(type=f32,ne=[64,5,4,3],v=1,eps=0.000001): OK
  RMS_NORM(type=f32,ne=[64,5,4,3],v=0,eps=0.000100): OK
  RMS_NORM(type=f32,ne=[64,5,4,3],v=1,eps=0.000100): OK
  RMS_NORM(type=f32,ne=[64,5,4,3],v=0,eps=0.100000): OK
  RMS_NORM(type=f32,ne=[64,5,4,3],v=1,eps=0.100000): OK
  10844/10844 tests passed
  Backend CANN0: OK
Backend 2/2: CPU
  Skipping
2/2 backends passed
OK

Model Test:

Script

./bin/llama-cli -m /home/lichenguang25/.ollama/models/blobs/sha256-6f96e01a3f550ca08aea1e5725bb8d5a7eccc6f281c30417e9d380b8c46467bd -p "Building a website can be done in 10 steps:" -ngl 32 -fa

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assistant
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> 
llama_perf_sampler_print:    sampling time =     157.75 ms /   636 runs   (    0.25 ms per token,  4031.70 tokens per second)
llama_perf_context_print:        load time =    6836.93 ms
llama_perf_context_print: prompt eval time =      33.86 ms /    20 tokens (    1.69 ms per token,   590.75 tokens per second)
llama_perf_context_print:        eval time =    3965.67 ms /   615 runs   (    6.45 ms per token,   155.08 tokens per second)
llama_perf_context_print:       total time =   30899.38 ms /   635 tokens
llama_perf_context_print:    graphs reused =        612