TMVA_SOFIE_ONNX.py File Reference

Detailed Description

This macro provides a simple example for:

• creating a model with Pytorch and export to ONNX
• parsing the ONNX file with SOFIE and generate C++ code
• compiling the model using ROOT Cling
• run the code and optionally compare with ONNXRuntime

The PyTorch export and ROOT's SOFIE parser are both linked against protobuf, but usually against different versions, so loading them in the same process leads to a symbol clash. We therefore run the PyTorch -> ONNX export in a separate Python process and only import ROOT afterwards.

 
import os
import sys
import subprocess
 
import numpy as np
import ROOT
 
 
# The PyTorch export, as a small standalone script run in its own process.
# It takes the model name as its only argument and writes <modelName>.onnx.
EXPORT_SCRIPT = r"""
import sys
import inspect
import warnings
import contextlib
 
import torch
import torch.nn as nn
 
modelName = sys.argv[1]
 
 
@contextlib.contextmanager
def expect_warning(category, message):
    # Silence a known third-party warning and raise if it stops firing.
 
    # Notifies us to drop the workaround once the upstream library is fixed.
    with warnings.catch_warnings(record=True) as caught:
        warnings.simplefilter("always")
        yield
    seen = False
    for w in caught:
        if issubclass(w.category, category) and message in str(w.message):
            seen = True
        else:
            warnings.warn_explicit(w.message, w.category, w.filename, w.lineno)
    if not seen:
        raise RuntimeError(
            f"Expected {category.__name__} containing {message!r} was not "
            "emitted. This tutorial's workaround can probably be removed."
        )
 
 
def CreateAndTrainModel(modelName):
 
    model = nn.Sequential(nn.Linear(32, 16), nn.ReLU(), nn.Linear(16, 8), nn.ReLU(), nn.Linear(8, 2), nn.Softmax(dim=1))
 
    criterion = nn.MSELoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
 
    # train model with the random data
    for i in range(500):
        x = torch.randn(2, 32)
        y = torch.randn(2, 2)
        y_pred = model(x)
        loss = criterion(y_pred, y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
 
    # *******************************************************
    ##  EXPORT to ONNX
    #
    #  need to evaluate the model before exporting to ONNX
    #  and to provide a dummy input tensor to set the input model shape
    model.eval()
 
    modelFile = modelName + ".onnx"
    dummy_x = torch.randn(1, 32)
    model(dummy_x)
 
    # check for torch.onnx.export parameters
    def filtered_kwargs(func, **candidate_kwargs):
        sig = inspect.signature(func)
        return {k: v for k, v in candidate_kwargs.items() if k in sig.parameters}
 
    kwargs = filtered_kwargs(
        torch.onnx.export,
        input_names=["input"],
        output_names=["output"],
        external_data=False,  # may not exist
        dynamo=True,  # may not exist
    )
    print("calling torch.onnx.export with parameters", kwargs)
 
    try:
        # torch.onnx.export (dynamo path) pickles its export program through
        # copyreg, which still references the deprecated LeafSpec. The warning
        # is emitted from inside PyTorch and cannot be avoided from user code.
        with expect_warning(FutureWarning, "isinstance(treespec, LeafSpec)"):
            torch.onnx.export(model, dummy_x, modelFile, **kwargs)
        print("model exported to ONNX as", modelFile)
        return modelFile
    except TypeError:
        print("Cannot export model from pytorch to ONNX - with version ", torch.__version__)
        # leave no .onnx behind: which the parent process treats as a RuntimeError
        sys.exit()
 
CreateAndTrainModel(modelName)
"""
 
 
def ParseModel(modelFile, verbose=False):
 
    parser = ROOT.TMVA.Experimental.SOFIE.RModelParser_ONNX()
    model = parser.Parse(modelFile, verbose)
    #
    # print model weights
    if verbose:
        model.PrintInitializedTensors()
        data = model.GetTensorData["float"]("0weight")
        print("0weight", data)
        data = model.GetTensorData["float"]("2weight")
        print("2weight", data)
 
    # Generating inference code
    model.Generate()
    # generate header file (and .dat file) with modelName+.hxx
    model.OutputGenerated()
    if verbose:
        model.PrintGenerated()
 
    modelCode = modelFile.replace(".onnx", ".hxx")
    print("Generated model header file ", modelCode)
    return modelCode
 
 
###################################################################
## Step 1 : Create and train the model, export it to ONNX
##          (done in a separate process to avoid the protobuf clash)
###################################################################
 
# use an arbitrary modelName
modelName = "LinearModel"
modelFile = modelName + ".onnx"
 
subprocess.run([sys.executable, "-c", EXPORT_SCRIPT, modelName])
if not os.path.exists(modelFile):
    raise RuntimeError("ONNX model could not be exported")
 
 
###################################################################
## Step 2 : Parse model and generate inference code with SOFIE
###################################################################
 
modelCode = ParseModel(modelFile, False)
 
###################################################################
## Step 3 : Compile the generated C++ model code
###################################################################
 
ROOT.gInterpreter.Declare('#include "' + modelCode + '"')
 
###################################################################
## Step 4: Evaluate the model
###################################################################
 
# get first the SOFIE session namespace
sofie = getattr(ROOT, "TMVA_SOFIE_" + modelName)
session = sofie.Session()
 
x = np.random.normal(0, 1, (1, 32)).astype(np.float32)
print("\n************************************************************")
print("Running inference with SOFIE ")
print("\ninput to model is ", x)
y = session.infer(x)
# output shape is (1,2)
y_sofie = np.asarray(y.data())
print("-> output using SOFIE = ", y_sofie)
 
# check inference with onnx
try:
    import onnxruntime as ort
 
    # Load model
    print("Running inference with ONNXRuntime ")
    ort_session = ort.InferenceSession(modelFile)
 
    # Run inference
    outputs = ort_session.run(None, {"input": x})
    y_ort = outputs[0]
    print("-> output using ORT =", y_ort)
 
    testFailed = abs(y_sofie - y_ort) > 0.01
    if np.any(testFailed):
        raise RuntimeError("Result is different between SOFIE and ONNXRT")
    else:
        print("OK")
 
except ImportError:
    print("Missing ONNXRuntime: skipping comparison test")

calling torch.onnx.export with parameters {'input_names': ['input'], 'output_names': ['output'], 'external_data': False, 'dynamo': True}
[torch.onnx] Obtain model graph for `Sequential([...]` with `torch.export.export(..., strict=False)`...
[torch.onnx] Obtain model graph for `Sequential([...]` with `torch.export.export(..., strict=False)`... ✅
[torch.onnx] Run decompositions...
[torch.onnx] Run decompositions... ✅
[torch.onnx] Translate the graph into ONNX...
[torch.onnx] Translate the graph into ONNX... ✅
[torch.onnx] Optimize the ONNX graph...
[torch.onnx] Optimize the ONNX graph... ✅
model exported to ONNX as LinearModel.onnx
Generated model header file  LinearModel.hxx
 
************************************************************
Running inference with SOFIE 
 
input to model is  [[ 1.2114118   1.451967   -0.14347067  0.97046924  0.16031976 -0.32432693
  -0.15014982 -0.93951786 -1.4363766   1.892936    0.69244826 -1.468675
  -1.1324115  -0.5894106   0.5675628  -1.4235257  -0.2687674  -1.5356702
   0.01113858 -0.98209095  2.127685   -0.2864464   0.75798196  0.4604458
   0.02719817  1.2260139  -1.01243    -0.6166083   0.36727965 -1.4795494
   1.7457016  -1.143139  ]]
-> output using SOFIE =  [0.51601905 0.48398095]
Missing ONNXRuntime: skipping comparison test

Author

Lorenzo Moneta

Definition in file TMVA_SOFIE_ONNX.py.