Add CUDA memory management APIs#1524
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Add the following torch.cuda APIs: - empty_cache() - Release unoccupied cached memory (dotnet#1521) - memory_allocated() - Current GPU memory occupied by tensors - max_memory_allocated() - Peak GPU memory occupied by tensors - reset_peak_memory_stats() - Reset peak memory tracking - memory_reserved() - Current GPU memory managed by caching allocator - max_memory_reserved() - Peak GPU memory managed by caching allocator - mem_get_info() - Free and total memory on device - set_device() - Set current CUDA device - current_device() - Get current CUDA device index These APIs are commonly used in PyTorch workflows for memory management and debugging, and are needed by TorchSharpExamples users. Native implementations use c10::cuda::CUDACachingAllocator with #if defined(USE_CUDA) guards for CPU-only build compatibility. Includes unit tests for all new APIs.
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Fixes
Add the following torch.cuda APIs:
These APIs are commonly used in PyTorch workflows for memory management and debugging, and are needed by TorchSharpExamples users.
Native implementations use c10::cuda::CUDACachingAllocator with #if defined(USE_CUDA) guards for CPU-only build compatibility.
Includes unit tests for all new APIs.