emx-onnx-cgen 1.1.1

emmtrix ONNX-to-C Code Generator

emmtrix ONNX-to-C Code Generator (emx-onnx-cgen) compiles ONNX models to portable, deterministic C code for deeply embedded systems. The generated code is designed to run without dynamic memory allocation, operating-system services, or external runtimes, making it suitable for safety-critical and resource-constrained targets.

Key characteristics:

• No dynamic memory allocation (malloc, free, heap usage)
• Static, compile-time known memory layout for parameters, activations, and temporaries
• Deterministic control flow (explicit loops, no hidden dispatch or callbacks)
• No OS dependencies, using only standard C headers (for example, stdint.h and stddef.h)
• Single-threaded execution model
• Bitwise-stable code generation for reproducible builds
• Readable, auditable C code suitable for certification and code reviews
• Generated C output format spec: docs/output-format.md
• Designed for bare-metal and RTOS-based systems

For PyTorch models, see the related project emx-pytorch-cgen.

Goals

• Correctness-first compilation with outputs comparable to ONNX Runtime.
• Deterministic and reproducible C code generation.
• Clean, pass-based compiler architecture (import → normalize → optimize → lower → emit).
• Minimal C runtime with explicit, predictable data movement.

Non-goals

• Aggressive performance optimizations in generated C.
• Implicit runtime dependencies or dynamic loading.
• Training/backpropagation support.

Features

• CLI for ONNX-to-C compilation and verification.
• Deterministic codegen with explicit tensor shapes and loop nests.
• Minimal C runtime templates in src/emx_onnx_cgen/templates/.
• ONNX Runtime comparison for end-to-end validation.
• Official ONNX operator coverage tracking.
• Support for a wide range of ONNX operators (see SUPPORT_OPS.md).
• Supported data types:
  • bfloat16, float16, float, double
  • int8, uint8, int16, uint16, int32, uint32, int64, uint64
  • bool
  • string (fixed-size '\0'-terminated C strings; see docs/output-format.md)
  • sequence(<tensor type>) (fixed-capacity tensor sequences with presence/length metadata; see docs/output-format.md)
  • optional(<tensor type>) (optional tensors represented via an extra _Bool <name>_present flag; see docs/output-format.md)
  • Not supported: float8*, float4e2m1, int2/int4, uint2/uint4, complex64/complex128, and ONNX map/sparse_tensor/opaque value types.
• Optional support for dynamic dimensions using C99 variable-length arrays (VLAs), when the target compiler supports them.

Usage Scenarios

1. Fully Embedded, Standalone C Firmware

The generated C code can be embedded directly into a bare-metal C firmware or application where all model weights and parameters are compiled into the C source.

Typical characteristics:

• No file system or OS required.
• All weights stored as static const arrays in flash/ROM.
• Deterministic memory usage with no runtime allocation.
• Suitable for:
  • Microcontrollers
  • Safety-critical firmware
  • Systems with strict certification requirements

This scenario is enabled via --large-weight-threshold 0, forcing all weights to be embedded directly into the generated C code.

2. Embedded or Host C/C++ Application with External Weights

The generated C code can be embedded into C or C++ applications where large model weights are stored externally and loaded from a binary file at runtime.

Typical characteristics:

• Code and control logic compiled into the application.
• Large constant tensors packed into a separate .bin file.
• Explicit, generated loader functions handle weight initialization.
• Suitable for:
  • Embedded Linux or RTOS systems
  • Applications with limited flash but available external storage
  • Larger models where code size must be minimized

This scenario is enabled automatically once the cumulative weight size exceeds --large-weight-threshold (default: 102400 bytes).

3. Target-Optimized Code Generation via emmtrix Source-to-Source Tooling

In both of the above scenarios, the generated C code can serve as input to emmtrix source-to-source compilation and optimization tools, enabling target-specific optimizations while preserving functional correctness.

Examples of applied transformations include:

• Kernel fusion and loop restructuring
• Memory layout optimization and buffer reuse
• Reduction of internal temporary memory
• Utilization of SIMD / vector instruction sets
• Offloading of large weights to external memory
• Dynamic loading of weights or activations via DMA

This workflow allows a clear separation between:

• Correctness-first, deterministic ONNX lowering, and
• Target-specific performance and memory optimization,

while keeping the generated C code readable, auditable, and traceable.

The generated C code is intentionally structured to make such transformations explicit and analyzable, rather than relying on opaque backend-specific code generation.

Installation

Install the package directly from PyPI (recommended):

pip install emx-onnx-cgen

Minimum Python version: 3.10.

Development

For local setup, testing, and contributor workflows, see docs/development.md.

Quickstart

Compile an ONNX model into a C source file:

emx-onnx-cgen compile path/to/model.onnx build/model.c

Verify an ONNX model end-to-end against ONNX Runtime (default):

emx-onnx-cgen verify path/to/model.onnx

If a model cannot be code-generated without representative concrete input shapes, pass them explicitly to both commands:

emx-onnx-cgen compile path/to/model.onnx build/model.c \
  --shape-inference-shapes "x=1x3x224x224;size=[1,3,224,224]"

emx-onnx-cgen verify path/to/model.onnx \
  --shape-inference-shapes "x=1x3x224x224;size=[1,3,224,224]"

--test-data-dir is verification input/output data only. It does not change the generated C code.

Use emx-onnx-cgen as an importable ONNX backend:

import onnx
from onnx.backend import prepare

import emx_onnx_cgen.onnx_backend as emx_backend

model = onnx.load("path/to/model.onnx")
rep = prepare(model, backend=emx_backend)
outputs = rep.run(inputs)

The backend module is emx_onnx_cgen.onnx_backend. It compiles the ONNX model to C on demand, builds a temporary executable, and runs that executable through the standard ONNX backend interface.

You can also call it directly without onnx.backend.prepare:

import onnx

from emx_onnx_cgen.onnx_backend import run_model

model = onnx.load("path/to/model.onnx")
outputs = run_model(model, inputs)

CLI Reference

emx-onnx-cgen provides two subcommands: compile and verify.

Common options

These options are accepted by both compile and verify:

• --model-base-dir: Base directory for resolving the model path (and related paths).
• --color: Colorize CLI output (auto, always, never; default: auto).
• --large-weight-threshold: Store weights in a binary file once the cumulative byte size exceeds this threshold (default: 102400; set to 0 to disable).
• --large-temp-threshold: Mark local arrays larger than this threshold as static (default: 1024). This applies to generated model temporaries and to generated testbench input/output buffers.
• --fp32-accumulation-strategy: Accumulation strategy for float32 inputs (simple uses float32, fp64 uses double; default: fp64).
• --fp16-accumulation-strategy: Accumulation strategy for float16 inputs (simple uses float16, fp32 uses float; default: fp32).
• --replicate-ort-bugs: Compatibility switch for verification/debugging. Enables emulation of known behavior differences of the ONNX Runtime version pinned in requirements-ci.txt.
• --shape-inference-shapes: Explicit shape/value specs used only to concretize dynamic shapes during code generation. Use name=2x3x4 for ordinary tensors, name=[1,3,224,224] for shape-carrying 1D tensors, and name=seq[4]:8x16 for sequences. This option is explicit and behaves the same for compile and verify.

compile

emx-onnx-cgen compile <model.onnx> <output.c> [options]

Options:

• --model-name: Override the generated model name (default: output file stem).
• --emit-testbench: Emit a JSON-producing main() testbench for validation.
• --testbench-file: Emit the testbench into a separate C file at the given path (implies --emit-testbench). If not set, the testbench is embedded in the main output C file (legacy behavior).
• --emit-data-file: Emit constant data arrays into a companion _data C file.
• --no-restrict-arrays: Disable restrict qualifiers on generated array parameters.

verify

emx-onnx-cgen verify <model.onnx> [options]

Options:

• --cc: Explicit C compiler command for building the testbench binary.
• --sanitize: Enable sanitizer instrumentation when compiling the verification binary (-fsanitize=address,undefined). If EMX_ENABLE_SANITIZE is set, it overrides this flag.
• --per-node-accuracy: Also compare intermediate tensor outputs and print max error per node.
• --max-ulp: Maximum allowed ULP distance for floating outputs (default: 100).
• --atol-eps: Absolute tolerance as a multiple of machine epsilon for floating outputs (default: 1.0).
• --runtime: Runtime backend for verification (onnxruntime or onnx-reference, default: onnxruntime).
• --replicate-ort-bugs: Verification-only compatibility mode to reproduce known behavior differences of the ONNX Runtime version pinned in requirements-ci.txt.
• --temp-dir-root: Root directory in which to create a temporary verification directory (default: system temp dir).
• --temp-dir: Exact directory to use for temporary verification files (default: create a temporary directory).
• --keep-temp-dir: Keep the temporary verification directory instead of deleting it.

How verification works:

1. Compile with a testbench: the compiler is invoked with --emit-testbench, generating a C program that runs the model and prints inputs/outputs as JSON.
2. Build and execute: the testbench is compiled with the selected C compiler (--cc, CC, or a detected cc/gcc/clang) and executed in a temporary directory.
3. Run runtime backend: the JSON inputs from the testbench are fed to the selected runtime (onnxruntime or onnx-reference) using the same model. The compiler no longer ships a Python runtime evaluator.
4. Compare outputs: floating outputs are compared by maximum ULP distance. Floating-point verification first ignores very small differences up to --atol-eps × machine epsilon of the evaluated floating-point type, treating such values as equal. For values with a larger absolute difference, the ULP distance is computed, and the maximum ULP distance is reported; non-floating outputs must match exactly. Missing outputs or mismatches are treated as failures.
5. ORT unsupported models: when using onnxruntime, if ORT reports NOT_IMPLEMENTED, verification is skipped with a warning (exit code 0).

Official ONNX test coverage

• ONNX_SUPPORT.md: overview of ONNX models and their current verification status.
• ONNX_ERRORS.md: summary of the most common verification outcomes and failure reasons.
• SUPPORT_OPS.md: list of ONNX operators and whether they are currently supported.

Related Projects

• emx-pytorch-cgen
  A PyTorch-to-C compiler following the same design principles as emx-onnx-cgen, but operating directly on PyTorch models instead of ONNX graphs.
  https://github.com/emmtrix/emx-pytorch-cgen
• onnx2c
  An ONNX-to-C code generator with a different design focus and code generation approach.
  https://github.com/kraiskil/onnx2c

Supporting Projects

• emx-regex-cgen
  A regex-to-C code generator used to implement the ONNX RegexFullMatch operator in emx-onnx-cgen.
  https://github.com/emmtrix/emx-regex-cgen

Maintained by

This project is maintained by emmtrix.