117 lines
3.8 KiB
C++
117 lines
3.8 KiB
C++
//===------- OnnxMlirRuntime.h - ONNX-MLIR Runtime API Declarations -------===//
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//
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// Copyright 2019-2020 The IBM Research Authors.
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//
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// =============================================================================
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//
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// This file contains declaration of external OMTensor data structures and
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// helper functions.
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//
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//===----------------------------------------------------------------------===//
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#ifndef ONNX_MLIR_ONNXMLIRRUNTIME_H
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#define ONNX_MLIR_ONNXMLIRRUNTIME_H
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#ifdef __cplusplus
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#include <cstdint>
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#else
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#include <stdbool.h>
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#include <stdint.h>
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#endif
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#include <onnx-mlir/Runtime/OMTensor.h>
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#include <onnx-mlir/Runtime/OMTensorList.h>
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/*! \mainpage ONNX-MLIR Runtime API documentation
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*
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* \section intro_sec Introduction
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*
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* ONNX-MLIR project comes with an executable `onnx-mlir` capable
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* of compiling onnx models to a shared library. In this documentation, we
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* demonstrate how to interact programmatically with the compiled
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* shared library using ONNX-MLIR's Runtime API.
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*
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* \section c-runtime-api C Runtime API
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*
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* \subsection data-structures Data Structures
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*
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* `OMTensor` is the data structure used to describe the runtime information
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* (rank, shape, data type, etc) associated with a tensor input or output.
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*
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* `OMTensorList` is the data structure used to hold a list of pointers to
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* OMTensor so that they can be passed into and out of the compiled model as
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* inputs and outputs.
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*
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* \subsection model-entry-point-signature Model Entry Point Signature
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*
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* All compiled model will have the same exact C function signature equivalent
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* to:
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*
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* ```c
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* OMTensorList* run_main_graph(OMTensorList*);
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* ```
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*
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* Intuitively, the model takes a list of tensors as input and returns a list of
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* ensors as output.
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*
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* \subsection invoke-models-using-c-runtime-api Invoke Models Using C Runtime
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* API
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*
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* We demonstrate using the API functions to run a simple ONNX model consisting
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* of an add operation. To create such an onnx model, use this
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* <a href="gen_add_onnx.py" target="_blank"><b>python script</b></a>
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*
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* To compile the above model, run `onnx-mlir add.onnx` and a binary library
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* "add.so" should appear. We can use the following C code to call into the
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* compiled function computing the sum of two inputs:
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*
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* ```c
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* #include <OnnxMlirRuntime.h>
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* #include <stdio.h>
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*
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* OMTensorList *run_main_graph(OMTensorList *);
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*
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* int main() {
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* // Shared shape & rank.
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* int64_t shape[] = {2, 2};
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* int64_t rank = 2;
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* // Construct x1 omt filled with 1.
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* float x1Data[] = {1., 1., 1., 1., 1., 1.};
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* int64_t *x1Shape = {2, 2};
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* OMTensor *x1 = omTensorCreate(x1Data, shape, rank, ONNX_TYPE_FLOAT);
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* // Construct x2 omt filled with 2.
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* float x2Data[] = {2., 2., 2., 2., 2., 2.};
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* int64_t *x2Shape = {2, 2};
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* OMTensor *x2 = omTensorCreate(x2Data, shape, rank, ONNX_TYPE_FLOAT);
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* // Construct a list of omts as input.
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* OMTensor *list[2] = {x1, x2};
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* OMTensorList *input = omTensorListCreate(list, 2);
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* // Call the compiled onnx model function.
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* OMTensorList *outputList = run_main_graph(input);
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* // Get the first omt as output.
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* OMTensor *y = omTensorListGetOmtByIndex(outputList, 0);
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* float *outputPtr = (float *)omTensorGetDataPtr(y);
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* // Print its content, should be all 3.
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* for (int i = 0; i < 6; i++)
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* printf("%f ", outputPtr[i]);
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* return 0;
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* }
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* ```
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*
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* Compile with `gcc main.c add.so -o add`, you should see an executable `add`
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* appearing. Run it, and the output should be:
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*
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* ```
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* 3.000000 3.000000 3.000000 3.000000 3.000000 3.000000
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* ```
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* Exactly as it should be.
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*
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* \subsection reference Reference
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*
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* For full reference to available C Runtime API, refer to
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*`include/onnx-mlir/Runtime/OMTensor.h` and
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* `include/onnx-mlir/Runtime/OMTensorList.h`.
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*
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*/
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#endif // ONNX_MLIR_ONNXMLIRRUNTIME_H
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