MLAS failing with "Could not find an implementation for QLinearMatMul"

[saurabhtangri]

Describe the issue

model execution failing with error

NotImplemented: [ONNXRuntimeError] : 9 : NOT_IMPLEMENTED : Could not find an implementation for QLinearMatMul(21) node with name ''

To reproduce

import numpy as np import onnx from onnx import helper, TensorProto, numpy_helper import onnxruntime as ort

Define the input dimensions and types

input_dim = [2, 2] input_type = TensorProto.INT8

Create the inputs

input_A = helper.make_tensor_value_info('input_A', input_type, input_dim) input_B = helper.make_tensor_value_info('input_B', input_type, input_dim) input_A_scale = helper.make_tensor_value_info('input_A_scale', TensorProto.FLOAT, []) input_A_zero_point = helper.make_tensor_value_info('input_A_zero_point', input_type, []) input_B_scale = helper.make_tensor_value_info('input_B_scale', TensorProto.FLOAT, []) input_B_zero_point = helper.make_tensor_value_info('input_B_zero_point', input_type, []) output_scale = helper.make_tensor_value_info('output_scale', TensorProto.FLOAT, []) output_zero_point = helper.make_tensor_value_info('output_zero_point', input_type, [])

Create the output

output = helper.make_tensor_value_info('output', input_type, input_dim)

Create the QLinearMatMul node

qlinearmatmul_node = helper.make_node( 'QLinearMatMul', inputs=[ 'input_A', 'input_A_scale', 'input_A_zero_point', 'input_B', 'input_B_scale', 'input_B_zero_point', 'output_scale', 'output_zero_point' ], outputs=['output'] )

Create the graph

graph_def = helper.make_graph( [qlinearmatmul_node], 'qlinearmatmul-graph', [input_A, input_A_scale, input_A_zero_point, input_B, input_B_scale, input_B_zero_point, output_scale, output_zero_point], [output] )

Create the model

model_def = helper.make_model(graph_def, producer_name='qlinearmatmul-model') model_def.ir_version = 5 onnx.checker.check_model(model_def) onnx.save(model_def, 'qlinearmatmul_model.onnx')

Test the model using ONNX Runtime

Prepare the input data

input_A_data = np.random.randint(-128, 127, size=(16, 16)).astype(np.int8) input_B_data = np.random.randint(-128, 127, size=(16, 16)).astype(np.int8) input_A_scale_data = np.array(0.1, dtype=np.float32) input_A_zero_point_data = np.array(0, dtype=np.int8) input_B_scale_data = np.array(0.1, dtype=np.float32) input_B_zero_point_data = np.array(0, dtype=np.int8) output_scale_data = np.array(0.2, dtype=np.float32) output_zero_point_data = np.array(0, dtype=np.int8)

Prepare the inputs for ONNX Runtime

inputs = { 'input_A': input_A_data, 'input_A_scale': input_A_scale_data, 'input_A_zero_point': input_A_zero_point_data, 'input_B': input_B_data, 'input_B_scale': input_B_scale_data, 'input_B_zero_point': input_B_zero_point_data, 'output_scale': output_scale_data, 'output_zero_point': output_zero_point_data, }

Enable verbose logging for ONNX Runtime session

sess_options = ort.SessionOptions()

sess_options.log_severity_level = 0 # 0 is the most verbose logging level

Run the model on the input data

ort_session = ort.InferenceSession('qlinearmatmul_model.onnx', sess_options)

ort_outputs = ort_session.run(None, inputs)

Print the output

print("Output:", ort_outputs[0])

Urgency

not urgent and not sure if the script is doing something wrong.

Platform

Windows

OS Version

Windows 11+WSL

ONNX Runtime Installation

Released Package

ONNX Runtime Version or Commit ID

18.1

ONNX Runtime API

Python

Architecture

X64

Execution Provider

Default CPU

Execution Provider Library Version

No response

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[Jubengo]

I also encounter this error. Is QLinearMatMul not handled at all by ORT or does it work only on a specific type of input/output ?