deeplearning/modulus/modulus-core-v040/_modules/modulus/deploy/onnx/utils.html

Source code for modulus.deploy.onnx.utils

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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import io
import logging

import torch
import torch.nn as nn

try:
    import onnxruntime as ort
except ImportError:
    ort = None

from typing import Tuple, Union

Tensor = torch.Tensor
logger = logging.getLogger("__name__")


[docs]def check_ort_install(func): """Decorator to check if ONNX runtime is installed""" def _wrapper_ort_install(*args, **kwargs): if ort is None: raise ModuleNotFoundError( "ONNXRuntime is not installed. 'pip install \ onnxruntime onnxruntime_gpu'" ) func(*args, **kwargs) return func(*args, **kwargs) return _wrapper_ort_install
[docs]def export_to_onnx_stream( model: nn.Module, invars: Union[Tensor, Tuple[Tensor, ...]], verbose: bool = False, ) -> bytes: """Exports PyTorch model to byte stream instead of a file Parameters ---------- model : nn.Module PyTorch model to export invars : Union[Tensor, Tuple[Tensor,...]] Input tensor(s) verbose : bool, optional Print out a human-readable representation of the model, by default False Returns ------- bytes ONNX model byte stream Note ---- Exporting a ONNX model while training when using CUDA graphs will likely break things. Because model must be copied to the CPU and back for export. Note ---- ONNX exporting can take a longer time when using custom ONNX functions. """ # Move inputs to CPU for ONNX export if isinstance(invars, Tensor): invars = (invars.detach().cpu(),) else: invars = tuple([invar.detach().cpu() for invar in invars]) # Use model's device if provided (Modulus modules have this) if hasattr(model, "device"): model_device = model.device elif len(list(model.parameters())) > 0: model_device = next(model.parameters()).device else: model_device = "cpu" with io.BytesIO() as onnx_model: # Export to ONNX. torch.onnx.export( model.cpu(), invars, onnx_model, operator_export_type=torch.onnx.OperatorExportTypes.ONNX, opset_version=15, verbose=verbose, ) # Move model back to original device model.to(model_device) return onnx_model.getvalue()

@check_ort_install def get_ort_session( model: Union[bytes, str], device: torch.device = "cuda", ): """Create a ORT session for performing inference of an onnx model Parameters ---------- model : Union[bytes, str] ONNX model byte string or file name/path device : torch.device, optional Device to run ORT, by default "cuda" Returns ------- ort.InferenceSession ONNX runtime session """ providers = ["CPUExecutionProvider"] if "cuda" in str(device): providers = ["CUDAExecutionProvider"] + providers # Must run on GPU as Rfft is currently implemented only for GPU. ort_sess = ort.InferenceSession(model, providers=providers) return ort_sess @check_ort_install def run_onnx_inference( model: Union[bytes, str], invars: Union[Tensor, Tuple[Tensor, ...]], device: torch.device = "cuda", ) -> Tuple[Tensor]: """Runs ONNX model in ORT session Parameters ---------- model : Union[bytes, str] ONNX model byte string or file name/path invars : Union[Tensor, Tuple[Tensor,...]] Input tensors device : torch.device, optional Device to run ORT, by default "cuda" Returns ------- Tuple[Tensor] Tuple of output tensors on CPU """ ort_sess = get_ort_session(model, device) # fmt: off if isinstance(invars, Tensor): invars = (invars,) ort_inputs = {inp.name: v.detach().cpu().numpy() for inp, v in zip(ort_sess.get_inputs(), invars)} # fmt: on ort_outputs = ort_sess.run(None, ort_inputs) # Convert to tensors outputs = tuple([torch.Tensor(v) for v in ort_outputs]) return outputs

© Copyright 2023, NVIDIA Modulus Team. Last updated on Jan 25, 2024.