Topmusicanswers
- What is quantization in PyTorch?
- How does quantization aware training work?
- What is dynamic quantization?
- What is static quantization?
What is quantization in PyTorch?
Quantization refers to techniques for performing computations and storing tensors at lower bitwidths than floating point precision. ... PyTorch supports INT8 quantization compared to typical FP32 models allowing for a 4x reduction in the model size and a 4x reduction in memory bandwidth requirements.
How does quantization aware training work?
So basically, quant-aware training simulates low precision behavior in the forward pass, while the backward pass remains the same. This induces some quantization error which is accumulated in the total loss of the model and hence the optimizer tries to reduce it by adjusting the parameters accordingly.
What is dynamic quantization?
What is dynamic quantization? Quantizing a network means converting it to use a reduced precision integer representation for the weights and/or activations. ... This higher precision value is scaled back to INT8 if the next layer is quantized or converted to FP32 for output.
What is static quantization?
Static quantization quantizes the weights and activations of the model. It allows the user to fuse activations into preceding layers where possible. ... Therefore, static quantization is theoretically faster than dynamic quantization while the model size and memory bandwidth consumptions remain to be the same.
