onnxruntime

c8399a81 - Quantization tool: support float 8 with MatMul, support float 16 weights (#18043)

Quantization tool: support float 8 with MatMul, support float 16 weights (#18043) ### Description Whenever a node QuantizeLinear or DequantizeLinear, the type of the weights before being quantize must be known to create the scale with the expected type. Another option would be to add many operator CastLike but that would push the burden to onnxruntime optimizer. The PR tries to avoid changing the signature. To do so, it modified the scale computation to use a numpy array to store the result and not a python float. The numpy array must be of the same type than the weights to quantize. The PR adds many `assert` to check the type of the scale is not a python type or a float64. This was added to make sure all the code follows the same logic. These lines were kept for the first review. DequantizeLinear, QuantizeLinear cannot be tested with onnx==1.15. PR https://github.com/onnx/onnx/pull/5709 is missing to fix shape inference. PR https://github.com/onnx/onnx/pull/5473) is missing to support QLinearMatMul with float 16. That explains why some tests are disabled with float 16. ### Motivation and Context The current quantization tool assumes every weight is float 32. For large models such as LLAMA, it is usually float 16. The quantization needs to quantize such weights.