CANN: Refactor ND to NZ workspace to be per-device in Ascend backend

[noemotiovon]

• Replaced the previous single global ND→NZ workspace with a per-device cache using unordered_map keyed by device ID.
• Functions release_nz_workspace, relloc_nz_workspace, and get_nz_workspace now manage workspace independently for each device, preventing memory conflicts in multi-device / pipeline parallel scenarios.
• This change fixes potential precision issues caused by workspace overwrites when multiple devices perform ND→NZ conversions concurrently.

Co-authored-by: hipudding [email protected]

[noemotiovon]

Model test on 2 devices with nd2nz:

# script
GGML_CANN_WEIGHT_NZ=1 ./bin/llama-cli -m /home/lichenguang25/.ollama/models/blobs/sha256-6f96e01a3f550ca08aea1e5725bb8d5a7eccc6f281c30417e9d380b8c46467bd 
-p "Building a website can be done in 10 steps:" -ngl 32

# log
build: 6362 (a83dc461) with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for aarch64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device CANN0 (Ascend910B4) - 29848 MiB free
llama_model_load_from_file_impl: using device CANN1 (Ascend910B4) - 29843 MiB free
......
user
Building a website can be done in 10 steps:
assistant
Here’s a list of 10 steps to build a website, categorized by functionality and complexity:

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### Best Practices
- **SEO**: Use optimized content, meta tags, and mobile-friendly design.
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By following these steps, you can build a robust and functional website that meets your needs and expectations.

> 
llama_perf_sampler_print:    sampling time =     166.28 ms /   627 runs   (    0.27 ms per token,  3770.66 tokens per second)
llama_perf_context_print:        load time =    1726.04 ms
llama_perf_context_print: prompt eval time =      36.21 ms /    20 tokens (    1.81 ms per token,   552.35 tokens per second)
llama_perf_context_print:        eval time =    3310.65 ms /   606 runs   (    5.46 ms per token,   183.05 tokens per second)
llama_perf_context_print:       total time =    4368.10 ms /   626 tokens
llama_perf_context_print:    graphs reused =        603