12 Questions
Which of the following is an example of a pointwise operation in PyTorch?
torch.cos()
What is the main optimization that inductor provides in PyTorch?
Fusion
What is the purpose of CUDA graphs in PyTorch?
To eliminate overhead from launching individual kernels
Which backend can you use to run the resnet50 model in PyTorch?
cudagraphs
What type of models does the community frequently use with Dynamo and inductor?
pretrained models from transformers or TIMM
What happens if you remove the to(device="cuda:0") from the model and encoded_input in the HuggingFace hub example?
Triton will generate C++ kernels optimized for running on CPU
Match the following operations with their descriptions:
torch.cos() and torch.sin() = Examples of pointwise ops in PyTorch torch.relu() = A famous pointwise op you might want to use in PyTorch Fusion = An optimization that turns 2 reads and 2 writes into 1 read and 1 write CUDA graphs = Helps eliminate the overhead from launching individual kernels from a Python program
Match the following terms with their definitions:
Pointwise ops = Operations that operate element by element on a vector Memory bandwidth = How quickly you can send data to a GPU Compute = How quickly your GPU can crunch floating point operations Triton kernels = Generated by inductor and can be inspected by running TORCH_COMPILE_DEBUG=1
Match the following features with their functions:
Inductor = Provides optimization by fusing operations and reducing memory bandwidth TorchDynamo = Supports many different backends CUDA graphs = Reduces overhead from launching individual kernels from a Python program Triton = It's in Python so you can easily understand it even if you have not written many CUDA kernels
Match the following tools with their functionalities:
resnet50 = A real model from the PyTorch hub torch._dynamo.list_backends() = Used to see all the available backends in PyTorch HuggingFace hub = Used to download a pretrained model for optimization Triton = Generates C++ kernels optimized for CPU when device is removed from the model and encoded_input
Match the following terms with their descriptions:
Dynamo and inductor = Designed to work out of the box with any model C++ kernels for BERT = More complex than the trigonometry example and can be optimized for running on your CPU TIMM example = A type of model used in the PyTorch community to(device="cuda:0") = Device specification in the model and encoded_input
Match the following models with the tools they are associated with:
resnet50 = PyTorch hub Pretrained models from transformers or TIMM = Dynamo and inductor BERT = Triton or C++ kernels Model without device specification = Optimization for running on CPU
Test your knowledge on using torch.cos() and torch.sin() in PyTorch with this quiz. Learn about pointwise operations and how they can be applied element by element on a vector.
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