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# Copyright 2024 Advanced Micro Devices | ||
# | ||
# Licensed under the Apache License v2.0 with LLVM Exceptions. | ||
# See https://llvm.org/LICENSE.txt for license information. | ||
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception | ||
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# run.py creates runmodel.py by concatenating this file model.py | ||
# and tools/stubs/onnxmodel.py | ||
# Description: testing Expand | ||
# See https://onnx.ai/onnx/intro/python.html for intro on creating | ||
# onnx model using python onnx API | ||
import numpy, torch, sys | ||
import onnxruntime | ||
from onnx import numpy_helper, TensorProto, save_model | ||
from onnx.helper import ( | ||
make_model, | ||
make_node, | ||
make_graph, | ||
make_tensor_value_info, | ||
make_tensor, | ||
) | ||
from onnx.checker import check_model | ||
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# import from e2eshark/tools to allow running in current dir, for run through | ||
# run.pl, commutils is symbolically linked to allow any rundir to work | ||
sys.path.insert(0, "../../../tools/stubs") | ||
from commonutils import E2ESHARK_CHECK_DEF | ||
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# Create an instance of it for this test | ||
E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF) | ||
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# Create an input (ValueInfoProto) | ||
X = make_tensor_value_info("X", TensorProto.FLOAT, [4, 5]) | ||
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shape_tensor = make_tensor( | ||
name="shape", | ||
data_type=TensorProto.INT64, | ||
dims=(3,), | ||
vals=[3, 4, 5], | ||
) | ||
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shape_node = make_node( | ||
"Constant", | ||
inputs=[], | ||
outputs=["shape"], | ||
value=shape_tensor, | ||
) | ||
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# Create an output | ||
Z = make_tensor_value_info("Z", TensorProto.FLOAT, [3, 4, 5]) | ||
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# Create a node (NodeProto) | ||
expand_node = make_node( | ||
"Expand", | ||
["X", "shape"], | ||
["Z"], | ||
"expand_node", # node name # inputs # outputs | ||
) | ||
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# Create the graph (GraphProto) | ||
graph = make_graph( | ||
[shape_node, expand_node], | ||
"expand_graph", | ||
[X], | ||
[Z], | ||
) | ||
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# Create the model (ModelProto) | ||
onnx_model = make_model(graph) | ||
onnx_model.opset_import[0].version = 19 | ||
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# Save the model | ||
with open("model.onnx", "wb") as f: | ||
f.write(onnx_model.SerializeToString()) | ||
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session = onnxruntime.InferenceSession("model.onnx", None) | ||
model_input_X = numpy.random.randn(4, 5).astype(numpy.float32) | ||
inputs = session.get_inputs() | ||
outputs = session.get_outputs() | ||
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model_output = session.run( | ||
[outputs[0].name], | ||
{inputs[0].name: model_input_X}, | ||
) | ||
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print("Input shape:", model_input_X.shape) | ||
print("Output shape:", numpy.array(model_output[0]).shape) | ||
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# Moving to torch to handle bfloat16 as numpy does not support bfloat16 | ||
E2ESHARK_CHECK["input"] = [torch.from_numpy(model_input_X)] | ||
E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output] | ||
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print("Input:", E2ESHARK_CHECK["input"]) | ||
print("Output:", E2ESHARK_CHECK["output"]) |