onnx2fx 0.0.0

Yet another ONNX to PyTorch FX converter

onnx2fx

Yet another ONNX to PyTorch FX converter.

⚠️ Note: This project is under active development. The public API may change at any time.

onnx2fx converts ONNX models into PyTorch FX GraphModules, enabling seamless integration with PyTorch's ecosystem for optimization, analysis, and deployment.

Features

• Simple API: Convert ONNX models with a single function call
• Extensive Operator Support: Wide ONNX operator coverage including standard and Microsoft domain operators
• Multi-Opset Version Support: Automatic selection of version-specific operator handlers based on model opset
• Custom Operator Registration: Easily extend support for unsupported or custom ONNX operators
• PyTorch FX Output: Get a torch.fx.GraphModule for easy inspection, optimization, and compilation
• Dynamic Shape Support: Handle models with dynamic input dimensions
• Quantization Support: Support for quantized operators (QLinear*, DequantizeLinear, etc.)
• Training Support: Convert models to trainable modules with make_trainable() utility

Tested Models

The following models have been tested and verified to work with onnx2fx:

• PaddleOCRv5: Text detection and recognition models (mobile and server variants)
  • PP-OCRv5_mobile_det, PP-OCRv5_mobile_rec
  • PP-OCRv5_server_det, PP-OCRv5_server_rec
• TorchVision Models: ResNet, VGG, MobileNet, etc. (via ONNX export)
• LFM2: Liquid Foundation Model (LFM2-350M-ENJP-MT)
• LFM2.5: Liquid Foundation Model 2.5
• TinyLlama: TinyLlama-1.1B-Chat

Installation

Requirements

• Python >= 3.11
• PyTorch >= 2.9.0
• ONNX >= 1.19.1

From Source

git clone https://github.com/mshr-h/onnx2fx.git
cd onnx2fx
uv sync

Development Installation

git clone https://github.com/mshr-h/onnx2fx.git
cd onnx2fx
uv sync --dev

Quick Start

Basic Conversion

import torch
import onnx
from onnx2fx import convert

# Load from file path
fx_module = convert("model.onnx")

# Or from onnx.ModelProto
onnx_model = onnx.load("model.onnx")
fx_module = convert(onnx_model)

# For models with external data, you can pass base_dir.
# memmap_external_data avoids loading external data into memory.
fx_module = convert("model.onnx", base_dir="/path/to/model_dir", memmap_external_data=True)

# Run inference
input_tensor = torch.randn(1, 3, 224, 224)
output = fx_module(input_tensor)

Inspecting the Converted Graph

from onnx2fx import convert

fx_module = convert("model.onnx")

# Print the FX graph
print(fx_module.graph)

# Get the graph code
print(fx_module.code)

Registering Custom Operators

For unsupported or custom ONNX operators, you can register your own handlers:

import torch
from onnx2fx import convert, register_op

# Using decorator
@register_op("MyCustomOp")
def my_custom_op(builder, node):
    x = builder.get_value(node.input[0])
    return builder.call_function(torch.sigmoid, args=(x,))

# Or register directly
def my_handler(builder, node):
    x = builder.get_value(node.input[0])
    return builder.call_function(torch.tanh, args=(x,))

register_op("TanhCustom", my_handler)

# For custom domains (e.g., Microsoft operators)
@register_op("BiasGelu", domain="com.microsoft")
def bias_gelu(builder, node):
    x = builder.get_value(node.input[0])
    bias = builder.get_value(node.input[1])
    return builder.call_function(
        lambda t, b: torch.nn.functional.gelu(t + b),
        args=(x, bias)
    )

Note: ai.onnx.ml is treated as a distinct domain. If you register or query operators in that domain, pass domain="ai.onnx.ml" explicitly.

Multi-Opset Version Support

The library automatically selects the appropriate operator handler based on the model's opset version. For operators with version-specific behavior (e.g., Softmax changed default axis in opset 13), the correct implementation is used automatically:

from onnx2fx import convert

# Models with different opset versions are handled automatically
fx_module_v11 = convert("model_opset11.onnx")  # Uses opset 11 semantics
fx_module_v17 = convert("model_opset17.onnx")  # Uses opset 17 semantics

Training Converted Models

By default, ONNX weights are loaded as non-trainable buffers. Use make_trainable() to enable training:

import torch
from onnx2fx import convert, make_trainable

# Convert and make trainable
fx_module = convert("model.onnx")
fx_module = make_trainable(fx_module)  # Convert buffers to trainable parameters

# Now you can train the model
optimizer = torch.optim.Adam(fx_module.parameters(), lr=1e-4)
criterion = torch.nn.CrossEntropyLoss()

for inputs, targets in dataloader:
    optimizer.zero_grad()
    outputs = fx_module(inputs)
    loss = criterion(outputs, targets)
    loss.backward()
    optimizer.step()

Querying Supported Operators

from onnx2fx import (
    get_supported_ops,
    get_all_supported_ops,
    get_registered_domains,
    is_supported,
)

# Check if an operator is supported
print(is_supported("Conv"))  # True
print(is_supported("BiasGelu", domain="com.microsoft"))  # True

# Get all operators for a domain
standard_ops = get_supported_ops()  # Default ONNX domain
microsoft_ops = get_supported_ops("com.microsoft")

# Get all operators across all domains
all_ops = get_all_supported_ops()

# Get registered domains
domains = get_registered_domains()  # ['', 'com.microsoft']

Analyzing Model Compatibility

Before converting, you can analyze a model to check operator support:

from onnx2fx import analyze_model

# Analyze an ONNX model
result = analyze_model("model.onnx")

# Check results
print(f"Supported operators: {result.supported_ops}")
print(f"Unsupported operators: {result.unsupported_ops}")
print(f"Is fully supported: {result.is_fully_supported()}")

# Get detailed summary
print(result.summary())

Exception Handling

Handle conversion errors gracefully:

from onnx2fx import (
    convert,
    Onnx2FxError,
    UnsupportedOpError,
    ConversionError,
)

try:
    fx_module = convert("model.onnx")
except UnsupportedOpError as e:
    print(f"Unsupported operator: {e}")
except ConversionError as e:
    print(f"Conversion failed: {e}")
except Onnx2FxError as e:
    print(f"onnx2fx error: {e}")

Supported Operators

Standard ONNX Domain

This is a short list of representative operators. For the full list, call get_supported_ops() or get_all_supported_ops().

• Core tensor & shape: Reshape, Transpose, Concat, Split, Slice, Gather, Pad, Resize, Shape, Cast
• Math & activations: Add, Mul, MatMul, Gemm, Relu, Gelu, SiLU, Softmax, LogSoftmax
• Normalization & pooling: BatchNormalization, LayerNormalization, InstanceNormalization, GroupNormalization, MaxPool, AveragePool, GlobalAveragePool
• Reductions & indexing: ReduceSum, ReduceMean, ArgMax, ArgMin, TopK
• Control flow & sequence: If, Loop, SequenceConstruct, SplitToSequence, ConcatFromSequence
• Quantization: QuantizeLinear, DequantizeLinear, QLinearConv, QLinearMatMul
• Other: Einsum, NonMaxSuppression, StringNormalizer

Attention & Normalization Extensions

• Attention (opset 24+)
• RotaryEmbedding (opset 23+)
• GroupQueryAttention
• EmbedLayerNormalization
• SkipLayerNormalization
• SimplifiedLayerNormalization
• SkipSimplifiedLayerNormalization

Microsoft Domain (com.microsoft)

Note: Some operators are available in both the standard and Microsoft domains (e.g., Attention, RotaryEmbedding, SimplifiedLayerNormalization, SkipSimplifiedLayerNormalization, GroupQueryAttention, SkipLayerNormalization, EmbedLayerNormalization).

• Attention
• RotaryEmbedding
• SimplifiedLayerNormalization, SkipSimplifiedLayerNormalization
• SkipLayerNormalization, EmbedLayerNormalization
• GroupQueryAttention

API Reference

convert(model)

Converts an ONNX model to a PyTorch FX GraphModule.

Parameters:

• model (Union[onnx.ModelProto, str]): Either an in-memory onnx.ModelProto or a file path to an ONNX model.

Returns:

• torch.fx.GraphModule: A PyTorch FX Graph module.

register_op(op_type, handler=None, domain="", since_version=1)

Register a custom ONNX operator handler.

Parameters:

• op_type (str): The ONNX operator type name.
• handler (OpHandler, optional): The handler function. If not provided, returns a decorator.
• domain (str, optional): The ONNX domain. Default is "" (standard ONNX domain).
• since_version (int, optional): The minimum opset version for this handler. Default is 1.

unregister_op(op_type, domain="", since_version=None)

Unregister an operator handler.

Parameters:

• op_type (str): The ONNX operator type name.
• domain (str, optional): The ONNX domain.
• since_version (int, optional): The specific opset handler to remove. If None, removes all versions.

Returns:

• bool: True if the operator was unregistered.

is_supported(op_type, domain="")

Check if an operator is supported.

get_supported_ops(domain="")

Get list of supported ONNX operators for a domain.

get_all_supported_ops()

Get all supported operators across all domains.

get_registered_domains()

Get list of registered domains.

analyze_model(model)

Analyze an ONNX model for operator support.

Parameters:

• model (Union[onnx.ModelProto, str]): Either an in-memory onnx.ModelProto or a file path.

Returns:

• AnalysisResult: Analysis results with supported/unsupported operators.

AnalysisResult

Dataclass containing model analysis results.

Attributes:

• total_nodes (int): Total number of nodes in the model graph.
• unique_ops (Set[Tuple[str, str]]): Set of unique (op_type, domain) tuples.
• supported_ops (List[Tuple[str, str]]): List of supported (op_type, domain) tuples.
• unsupported_ops (List[Tuple[str, str, int]]): List of unsupported (op_type, domain, opset_version) tuples.
• opset_versions (Dict[str, int]): Mapping of domain to opset version.
• op_counts (Dict[Tuple[str, str], int]): Count of each (op_type, domain) in the model.

Methods:

• is_fully_supported(): Returns True if all operators are supported.
• summary(): Returns a human-readable summary string.

Exceptions

• Onnx2FxError: Base exception for all onnx2fx errors.
• UnsupportedOpError: Raised when an operator is not supported.
• ConversionError: Raised when conversion fails.
• ValueNotFoundError: Raised when a value is not found in the environment.

Development

Running Tests

# Run all tests
pytest

# Run all tests in parallel for faster execution
pytest -n auto

# Run specific test file
pytest tests/test_activation.py

# Skip slow tests
pytest -m "not slow"

Code Formatting

# Format code with ruff
ruff format .

# Check linting
ruff check .

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Author

Masahiro Hiramori ([e-mail])