[ckpt] feat: add kimi ckpt engine backend

[kip-cxj]

What does this PR do?

Based on ckpt engine abstraction add checkpoint-engine abstraction, in this PR, we add kimi_ckpt_engine backend to support both GPU and huawei Ascend NPU.

Since establishing communication domains across trainer and rollout workers is required, this PR also depends on the newly added communication domain support in kimi_ckpt_engine.

TODO:

• Add detailed performance testing results in checkpoint engine README.

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Test

We have verified the functionality on both GPU and NPU. Performance benchmarks on a 32 NPU environment show promising results; however, due to a lack of available GPU resources, performance data for GPU is still pending.

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[gemini-code-assist]

Code Review

This pull request introduces a new checkpoint engine backend, kimi_ckpt_engine, designed to support both GPU and Huawei Ascend NPU environments. The implementation is comprehensive, including the core engine logic, integration with the existing checkpointing framework, and a new test suite. My review focuses on ensuring correctness and maintainability. I've identified a critical thread-safety issue in the weight sending logic that could lead to data corruption. Additionally, I've suggested a minor but important rename in the test suite to improve clarity.

[gemini-code-assist]

The use of ThreadPoolExecutor to concurrently update the named_tensors dictionary is not thread-safe. Dictionaries in Python are not safe for concurrent writes from multiple threads. Even if the keys are unique, internal operations like resizing can lead to race conditions, data corruption, or intermittent crashes.

To ensure thread safety, it's better to have each thread return its result independently and then collect and update the dictionary in the main thread.

Suggested change

	def offload_cpu(named_tensors: dict[str, torch.Tensor], name: str, tensor: torch.Tensor):
	named_tensors[name] = tensor.to("cpu", non_blocking=True)
	start_time = time.time()
	named_tensors = {}
	for named_tensors_gpu in ckpt_get_named_tensor_buckets(
	weights, self.bucket_size, self.train_world_size, self.rank, self.rollout_dtype
	):
	with concurrent.futures.ThreadPoolExecutor(max_workers=32) as executor:
	futures = [
	executor.submit(
	offload_cpu,
	named_tensors,
	name,
	tensor,
	)
	for name, tensor in named_tensors_gpu.items()
	]
	for future in concurrent.futures.as_completed(futures):
	future.result()
	def offload_cpu(name: str, tensor: torch.Tensor) -> tuple[str, torch.Tensor]:
	return name, tensor.to("cpu", non_blocking=True)
	start_time = time.time()
	named_tensors = {}
	for named_tensors_gpu in ckpt_get_named_tensor_buckets(
	weights, self.bucket_size, self.train_world_size, self.rank, self.rollout_dtype
	):
	with concurrent.futures.ThreadPoolExecutor(max_workers=32) as executor:
	futures = [
	executor.submit(
	offload_cpu,
	name,
	tensor,
	)
	for name, tensor in named_tensors_gpu.items()
	]
	for future in concurrent.futures.as_completed(futures):
	name, tensor_cpu = future.result()
	named_tensors[name] = tensor_cpu