Torch-MLU - Cambricon's open-source PyTorch backend plugin, supporting one-click migration of large models.

What is Torch-MLU?

Torch-MLU is an open-source PyTorch device backend extension plugin from Cambricon, enabling developers to use Cambricon's MLU series smart accelerator cards as an acceleration backend for PyTorch. The plugin provides native support for PyTorch, allowing developers to seamlessly migrate GPU-based deep learning models to Cambricon MLU hardware, improving model training and inference efficiency. The open-source nature of Torch-MLU further promotes the collaborative development of the AI ecosystem, providing a more flexible and efficient development environment for developers worldwide.

Main functions of Torch-MLU

• Native PyTorch supportIt allows developers to train and infer deep learning models using Cambricon MLU hardware without modifying the PyTorch core code.
• Device backend expansionAs a device backend extension for PyTorch, Torch-MLU supports performing PyTorch operations on the MLU device, enabling PyTorch to utilize the computing power of the MLU.
• Model transferSupports migrating GPU-based deep learning models to MLU devices, simplifying the migration process from GPU to MLU.
• Performance optimizationImprove the efficiency of model execution on the MLU through operations and algorithms specifically optimized for MLU hardware.

The technical principle of Torch-MLU

• PyTorch backend extension mechanismTorch-MLU is a backend extension mechanism based on PyTorch. By defining and implementing a series of hardware-related operations (Ops), it enables PyTorch to perform computations on Cambricon's MLU hardware. It allows developers to write models using PyTorch's high-level APIs and leverage the computational capabilities of the MLU at the underlying level.
• Device-specific operator implementationTorch-MLU enables the execution of deep learning models on the MLU, providing hardware-optimized operator implementations for MLU, including convolution, matrix multiplication, and activation functions.
• Computational graph optimizationTorch-MLU optimizes the computation graph, such as through operator fusion and redundant computation elimination, thereby improving the model's execution efficiency on the MLU.
• Automatic Mixing Precision (AMP)While maintaining model accuracy, Torch-MLU improves training speed and reduces memory usage, supporting automatic mixed-precision training. It dynamically adjusts the data precision during model training by combining single-precision and half-precision floating-point numbers.

Torch-MLU project address

• GitHubstorehouse:https://github.com/Cambricon/torch_mlu
• GitEEstorehouse:https://gitee.com/cambricon/torch_mlu

Application scenarios of Torch-MLU

• Deep learning research and developmentResearchers and developers used Torch-MLU to train and infer deep learning models on Cambricon MLU hardware, including in fields such as computer vision, natural language processing, and speech recognition.
• Large model trainingFor large neural network models with a large amount of computing resources, Torch-MLU provides efficient hardware acceleration, making the training process faster and reducing the development cycle.
• Intelligent video analyticsTorch-MLU accelerates the processing and analysis of video data in applications such as video surveillance, content moderation, and facial recognition.
• Speech recognition and synthesisTorch-MLU is used to improve the performance of speech recognition and speech synthesis models and speed up speech processing tasks.
• Recommendation systemIn recommendation systems for e-commerce, social media, and other fields, Torch-MLU helps to quickly train and deploy recommendation algorithms.