FP16 | Conversion to/from half-precision floating point formats | Development Tools library

For half-precision, you can just use sycl::half as the template parameter for either of these.

Finally, I found that I didn't need to change FP32 to FP16 in my inference engine code.

And my MYRIAD device could work normally, appreciate!

You need to save the saved_model.pb file inside the saved_model folder, because the --saved_model_dir argument must provide a path to the SavedModel directory.

For instance, your current location is C:\Users\Hsien\Desktop\NCS2\OCT, move the model to C:\Users\Hsien\Desktop\NCS2\saved_model.

it jus out of memory cases. The parameter and dataset weren't loaded on my gpu memory. so i changed my model mt5-base to mt5-small, delete save point, reduce dataset

You can use cv2.imread("image.png") instead.

I recommend that you refer to the official OpenVINO documentation: Integrate Inference Engine with Your Python Application

Please bear in mind that you'll need to exactly know the model's input shape, its layout, and the input data precision (FP32/FP16/etc) to get the correct output

bfloat16 is generally easier to use, because it works as a drop-in replacement for float32. If your code doesn't create nan/inf numbers or turn a non-0 into a 0 with float32, then it shouldn't do it with bfloat16 either, roughly speaking. So, if your hardware supports it, I'd pick that.

Check out AMP if you choose float16.

In your case, output represents the logits. One way of getting a probability out of them is to use the Softmax function. As it seems that output contains the outputs from a batch, not a single sample, you can do something like this:

1. The Tensorcore designers in this case chose to provide a FP32 accumulate option so that the results of many multiply-accumulate steps could be represented both with greater precision (more mantissa bits) as well as greater range (more exponent bits). This was considered valuable for the overall computational problems they wanted to support, including HPC and AI calculations. The product of two FP16 numbers might be not representable in FP16, whereas many more or most products of two FP16 numbers will be representable in FP32.

2. The scale factors alpha and beta are provided so that the provided GEMM operation could easily correspond to the well-known BLAS GEMM operation, which is widely used in numerical computation. This allows developers to more easily use the Tensorcore capability to provide a commonly used calculation paradigm in existing numerical computation codes. It is the same reason that the CUBLAS GEMM implementation provides these adjustable parameters.

Mixed precision does not mean that your model becomes half original size. The parameters remain in float32 dtype by default and they are cast to float16 automatically during certain operations of the neural network training. This is applicable to input data as well.

The torch.cuda.amp provides the functionality to perform this automatic conversion from float32 to float16 during certain operations of training like Convolutions. Your model size will remain the same. Reducing model size is called quantization and it is different than mixed-precision training.

You can read to more about mixed-precision training at NVIDIA's blog and Pytorch's blog.