Memory management using PYTORCH_CUDA_ALLOC_CONF

In the field of deep learning, where models are becoming increasingly intricate and datasets are expanding, effective memory management is crucial for achieving optimal performance. The substantial memory requirements of deep learning models often surpass the capabilities of available hardware. To tackle these challenges when using PyTorch and CUDA, a powerful tool called PYTORCH_CUDA_ALLOC_CONF becomes essential.

PyTorch, a widely adopted deep learning framework, in conjunction with CUDA, a parallel computing platform, empowers developers to leverage GPU capabilities for accelerated training and inference. However, ensuring efficient GPU memory management is vital to prevent out-of-memory errors, make the most of hardware resources, and attain faster computation times.

PYTORCH_CUDA_ALLOC_CONF is an environment variable that can configure PyTorch’s memory management behavior for CUDA tensors. It takes a comma-separated list of options, each in the format <option>:<value>.

Options list

The available options are:

backend: There are two primary options for the backend:
1. native: This is PyTorch’s native memory allocator, which is the default choice. It provides memory allocation and management functionality implemented within PyTorch itself.
2. cudaMallocAsync: This option utilizes CUDA’s built-in asynchronous memory allocator cudaMallocAsync. It relies on CUDA’s functionality for efficient memory management.
max_split_size_mb: This option prevents the allocator from splitting blocks larger than this size (in MB). This can help prevent fragmentation and might allow some borderline workloadsBorderline workloads refer to computing tasks or workloads that operate very close to the limits of available system resources, such as CPU processing power, memory, or storage capacity. to complete without running out of memory. The default value is unlimited, i.e., all blocks can be split.
roundup_power2_divisions: This option helps round the requested allocation size to the nearest power-of-2 division. It can improve memory block utilization, especially for large allocations. For example, the block sizes could be $512$ , $1024$ , $2048$ , and so on. Let’s say you have an allocation size of 1200, and you specify a roundup_power2_divisions rule with $4$ divisions. This allocation size falls between $1024$ and $2048$ . With $4$ divisions, you might get rounded sizes like $1024$ , $1280$ , $1536$ , and $1792$ . In this case, your allocation size of $1200$ would be rounded up to $1280$ , which is the nearest power-of-2 division.
roundup_bypass_threshold_mb: This option allows for skipping the usual rounding of allocation sizes when the requested memory size exceeds a specified threshold (in MB). This is beneficial for large, long-lasting allocations as it decreases memory overhead, meaning that the system doesn’t allocate more memory than you actually need, which can be important for efficient resource utilization, especially when dealing with substantial memory requirements.
garbage_collection_threshold: This option helps actively reclaim unused GPU memory to avoid expensive sync-and-reclaim-allReleasing cached blocks operations. The allocator starts reclaiming GPU memory blocks if the GPU memory usage exceeds the specified threshold (e.g., $80$ % of total memory allocated to the GPU application). The algorithm prefers to free old and unused blocks first to minimize disruption to actively reused blocks. The threshold value should be between $0.0$ and $1.0$ .

Note: The above options are meaningful when using the native backend for allocation, while these are ignored when using the cudaMallocAsync backend.

Example

Let’s take a code example to configure the PYTORCH_CUDA_ALLOC_CONF environment variable:

backend : native
max_split_size_mb : $1024$ MB
roundup_power2_divisions : $8$
roundup_bypass_threshold_mb : $256$
garbage_collection_threshold : $0.8$

import os
import torch
def main():
    # checking if your device has GPU or not
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    # Setting up PyTorch CUDA memory allocation configuration
    os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "backend:native,\
                                            max_split_size_mb:1024,\
                                            roundup_power2_divisions:8,\
                                            roundup_bypass_threshold_mb:256,\
                                            garbage_collection_threshold:0.8"
    # Create a large CUDA tensor.
    tensor = torch.randn(1000000, device=device)
    
    # Print the tensor and its length
    print(tensor)
    print("tensor length: ", len(tensor))
if __name__ == '__main__':
    main()

Explanation

Lines 1–2: Import os and torch modules.
Line 6: Set the variable device to CUDA if the GPU is available; otherwise, set it to CPU.
Lines 9–13: Set the environment variable PYTORCH_CUDA_ALLOC_CONF to the specified value.
Line 16: Create a large CUDA tensor with $1000000$ elements.
Line 19: Print the tensor to the console.
Line 20: Print the tensor length to the console.

Conclusion

These environment variables provide a way to customize memory allocation and management for PyTorch GPU operations, which can be especially useful when dealing with specific memory constraints or performance optimization requirements. Be sure to adjust these settings carefully to achieve the desired balance between memory efficiency and performance for your specific workload.

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