Add XCCL Backend Support for Intel GPU in TorchComms

[zhangxiaoli73]

Motivation:

As design illustrated in RFC #51, we would like to add XCCL (Intel GPU Collective Communications Library) backend support to TorchComms.

In this PR, we will enable allreduce in the XCCL backend as an entry point. Full collectives support will come later.

PR explanation:

Core Files

File	Purpose
TorchCommXCCL.cpp/.hpp	Main collectives implementation
TorchWorkXCCL.cpp/.hpp	Asynchronous work tracking
TorchWorkXCCLQueue.cpp	Work queue management
TorchCommXCCLBootstrap.cpp/.hpp	Communicator initialization
TorchCommXCCLUtils.cpp/.hpp	Utility functions (type conversion)
TorchCommXCCLPy.cpp	Python bindings

API Abstraction Layers

File	Purpose
XcclApi.cpp/.hpp	oneCCL API wrapper
device/XpuApi.cpp/.hpp	SYCL/XPU runtime abstraction

Build System

File	Purpose
xccl/CMakeLists.txt	CMake configuration for XCCL backend
setup.py	Python package build (USE_XCCL flag)

Examples:

import torch
import torchcomms
from torchcomms import new_comm

# Initialize communicator
device = torch.device('xpu')
comm = new_comm("xccl", device, name="main_comm")
rank = torchcomms.get_rank()
current_device = torch.device(f"xpu:{rank}")

# Perform collective operation
tensor = torch.randn(1024, device=current_device)
work = comm.all_reduce(tensor, torchcomms.ReduceOp.SUM, async_op=True)
work.wait()

# Cleanup
comm.finalize()

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[zhangxiaoli73]

Hi, @d4l3k This is our initial PR to integrate XCCL backend into TorchComms. Could you please help review? cc. @pkourdis @siju-samuel @newtdms

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