onnxruntime

07b5377f - Add INT16 and UINT16 compatibility for relu_quantizelinear (#20187)

Add INT16 and UINT16 compatibility for relu_quantizelinear (#20187) ### Description <!-- Describe your changes. --> There is a problem in relu_quantizelinear transformer that causes wrong results. The purpose of this PR is to solve this problem. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> This does not take into account the situation where Q's zeropoint is tensor(int16), tensor(uint16), so when this happens, an error will occur. How to verify： ```python import onnx import onnxruntime as ort import numpy as np model_name = 'relu_quantize_testcase.onnx' model = onnx.load(model_name) ort_input0 = np.random.rand((1, 64, 64, 128),np.float32) # infer with GraphOptimizationLevel=0 so = ort.SessionOptions() so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL ort_session = ort.InferenceSession( model_name, providers=["CPUExecutionProvider"], sess_options=so ) outputs = [x.name for x in ort_session.get_outputs()] ort_outs_mod = ort_session.run(outputs, { 'generator/conv2d_input/conv2d/Conv2D:0': ort_input0} ) del ort_session # infer with GraphOptimizationLevel=default model_orig = onnx.load(model_name) ort_session_orig = ort.InferenceSession(model_orig.SerializeToString()) outputs_orig = [x.name for x in ort_session_orig.get_outputs()] ort_outs_orig = ort_session_orig.run(outputs_orig, { 'generator/conv2d_input/conv2d/Conv2D:0': ort_input0} ) # diff print(np.linalg.norm(ort_outs_mod[0].astype(np.float32) - ort_outs_orig[0].astype(np.float32))) del ort_session_orig ``` [relu_quantize_testcase.zip](https://github.com/microsoft/onnxruntime/files/14848160/relu_quantize_testcase.zip) --------- Co-authored-by: genmingz <genming.zhong@amd.com>