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c3d0544 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | # SPDX-FileCopyrightText: Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES.
# SPDX-FileCopyrightText: All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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__")
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
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 (PhysicsNeMo 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
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