HIP (ROCm) semantics¶
ROCm™ is AMD’s open source software platform for GPU-accelerated high performance computing and machine learning. HIP is ROCm’s C++ dialect designed to ease conversion of CUDA applications to portable C++ code. HIP is used when converting existing CUDA applications like PyTorch to portable C++ and for new projects that require portability between AMD and NVIDIA.
HIP Interfaces Reuse the CUDA Interfaces¶
PyTorch for HIP intentionally reuses the existing
This helps to accelerate the porting of existing PyTorch code and models because
very few code changes are necessary, if any.
The example from CUDA semantics will work exactly the same for HIP:
cuda = torch.device('cuda') # Default HIP device cuda0 = torch.device('cuda:0') # 'rocm' or 'hip' are not valid, use 'cuda' cuda2 = torch.device('cuda:2') # GPU 2 (these are 0-indexed) x = torch.tensor([1., 2.], device=cuda0) # x.device is device(type='cuda', index=0) y = torch.tensor([1., 2.]).cuda() # y.device is device(type='cuda', index=0) with torch.cuda.device(1): # allocates a tensor on GPU 1 a = torch.tensor([1., 2.], device=cuda) # transfers a tensor from CPU to GPU 1 b = torch.tensor([1., 2.]).cuda() # a.device and b.device are device(type='cuda', index=1) # You can also use ``Tensor.to`` to transfer a tensor: b2 = torch.tensor([1., 2.]).to(device=cuda) # b.device and b2.device are device(type='cuda', index=1) c = a + b # c.device is device(type='cuda', index=1) z = x + y # z.device is device(type='cuda', index=0) # even within a context, you can specify the device # (or give a GPU index to the .cuda call) d = torch.randn(2, device=cuda2) e = torch.randn(2).to(cuda2) f = torch.randn(2).cuda(cuda2) # d.device, e.device, and f.device are all device(type='cuda', index=2)
Checking for HIP¶
Whether you are using PyTorch for CUDA or HIP, the result of calling
is_available() will be the same. If you are using a PyTorch
that has been built with GPU support, it will return True. If you must check
which version of PyTorch you are using, refer to this example below:
if torch.cuda.is_available() and torch.version.hip: # do something specific for HIP elif torch.cuda.is_available() and torch.version.cuda: # do something specific for CUDA
TensorFloat-32(TF32) on ROCm¶
TF32 is not supported on ROCm.
PyTorch uses a caching memory allocator to speed up memory allocations. This
allows fast memory deallocation without device synchronizations. However, the
unused memory managed by the allocator will still show as if used in
rocm-smi. You can use
max_memory_allocated() to monitor memory occupied by
tensors, and use
max_memory_reserved() to monitor the total amount of memory
managed by the caching allocator. Calling
releases all unused cached memory from PyTorch so that those can be used
by other GPU applications. However, the occupied GPU memory by tensors will not
be freed so it can not increase the amount of GPU memory available for PyTorch.
For more advanced users, we offer more comprehensive memory benchmarking via
memory_stats(). We also offer the capability to capture a
complete snapshot of the memory allocator state via
memory_snapshot(), which can help you understand the
underlying allocation patterns produced by your code.
To debug memory errors, set
PYTORCH_NO_CUDA_MEMORY_CACHING=1 in your environment to disable caching.
hipFFT/rocFFT plan cache¶
Setting the size of the cache for hipFFT/rocFFT plans is not supported.