Issue/414: Async model loader

[qinyiqun]

Summary

用于主存到显存之间的异步内存拷贝，可以提速现有的主存->显存速度，根据模型不同，差距几倍到几十倍不等

Motivation

Closes #

Type of Change

• feat — new feature / new model
• fix — bug fix
• perf — performance improvement (no behavioral change)
• refactor — code restructuring without behavior change
• test — adding or fixing tests only
• docs — documentation only
• build / ci — build system or CI configuration
• chore — tooling, formatting, or other non-code changes
• Breaking change

Test Results of Involved Models on Supported Platforms (Please attach screenshots)

Benchmark / Performance Impact

Notes for Reviewers

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Checklist

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Scope and Design

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General Code Hygiene (applies to all languages)

• The code is self-explanatory; comments were added only where the why is non-obvious (CONTRIBUTING.md §Code/General).
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C++ Specific (if C++ files changed)

• Code follows the Google C++ Style Guide strictly.
• Error and warning message wording follows the LLVM Coding Standards (CONTRIBUTING.md §C++).
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• Changed files are formatted by scripts/format.py.
• No changes/reference to csrc/models/llama_legacy/.

Python Specific (if Python files changed)

• Code is PEP 8 compliant.
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Testing

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• Passed single request test (examples/test_infer.py), or specify the reason for skipping.
• Passed offline performance test (examples/bench.py), or specify the reason for skipping.
• Passed sanity test (test/bench/test_benchmark.py), or specify the reason for skipping.
• Passed service test (python/infinilm/server/inference_server.py + scripts/test_perf.py), or specify the reason for skipping.

Build, CI, and Tooling

• The project builds cleanly from a fresh directory on at least one affected platform.

Documentation

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Security and Safety

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• No unsafe pointer arithmetic, uninitialized reads, or missing bounds checks were introduced.

[pengcheng888]

找不到key的话，要continue，这是正常么。 正常下local_params一定在local_state_dict中吧， 不在的话要抛出 异常么