mirror of
https://github.com/saymrwulf/onnxruntime.git
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a54716e5a9
* Fix bug in Transpose CUDA kernel (#7329) * Fix permission error for ORTModule lock file (#7814) * fix topo sort in quant tool (#7833) * fix topo sort in quant tool * add unit test and make the topo sort stable * Relax tol for Conv1D fp16 test (#7844) * Relax tol for Conv1D fp16 test Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> * Resolve issue with wrapped ORTModule load_state_dict (#7847) * Encapsulate children modules inside a ModuleAccessor object to prevent erroneuos iteration over children while loading the state dictionary * Add named_models, models, apply methods, change ModuleAccessor to ModuleMetadata and modify unit tests * Change ModuleMetadata module getter logic, raise NotImplementedError for add_modules * Add comment explaining why overriding _load_from_state_dict method is needed * fixed bugs in packed mode and enable pack mode tests in ci (#7848) * fixed bugs in packed mode and enable pack mode tests in ci * removed unnecessary space * pr comments * pr comments * disable an average pool test * try disabling another avg pool * disable more avg pool tests * disable maxpool tests * add environment variable to control default training package's local version (#7849) * [js] update documents (#7852) * [js] update documents * escape double quotes * update operators.md * resolve comments * Support bool type for Pad CPU (#7856) * Initial commit * update * nit * Include ORT C/C++ API headers in the ORT Mobile AAR package (#7858) * Add header files of ort c/c++ api to aar package * Move header file selection to cmake based on EP choice * fix duplicated node name (#7865) * Clean up CPU kernel definition for opset 13 Pad (#7867) Co-authored-by: Hariharan Seshadri <shariharan91@gmail.com> Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com> Co-authored-by: Yufeng Li <liyufeng1987@gmail.com> Co-authored-by: Sherlock <baihan.huang@gmail.com> Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> Co-authored-by: baijumeswani <bmeswani@microsoft.com> Co-authored-by: Tixxx <tix@microsoft.com> Co-authored-by: liqunfu <liqfu@microsoft.com> Co-authored-by: Yulong Wang <yulongw@microsoft.com> Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com> Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
107 lines
4.7 KiB
Python
107 lines
4.7 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License. See License.txt in the project root for
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# license information.
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# --------------------------------------------------------------------------
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import unittest
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import onnx
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import onnxruntime
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import numpy as np
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from onnx import helper, TensorProto, numpy_helper
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from onnxruntime.quantization.onnx_model import ONNXModel
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from op_test_utils import TestDataFeeds, check_model_correctness, check_op_type_count, check_op_type_order
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def generate_input_initializer(tensor_shape, tensor_dtype, input_name):
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'''
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Helper function to generate initializers for test inputs
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'''
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tensor = np.random.normal(0, 0.3, tensor_shape).astype(tensor_dtype)
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init = numpy_helper.from_array(tensor, input_name)
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return init
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class TestONNXModel(unittest.TestCase):
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def construct_model(self, model_path):
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# (input)
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# |
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# GRU
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# / \
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# Conv(1) \
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# | \
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# Relu Conv(2)
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# | |
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# \ /
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# Add
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# |
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# (output)
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initializers = []
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input = helper.make_tensor_value_info('input', TensorProto.FLOAT, [4, 8, 12])
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [4, 2, 8, 8])
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# make GRU
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initializers.append(generate_input_initializer([2, 24, 12], np.float32, 'W_GRU'))
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initializers.append(generate_input_initializer([2, 24, 8], np.float32, 'R_GRU'))
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initializers.append(generate_input_initializer([2, 8, 8], np.float32, 'H_GRU'))
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gru_node = onnx.helper.make_node(
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'GRU',
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['input', 'W_GRU', 'R_GRU', '', '', 'H_GRU'],
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['GRU_O'],
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hidden_size = 8,
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direction = 'bidirectional')
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initializers.append(generate_input_initializer([2, 2, 1, 1], np.float32, 'W1'))
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initializers.append(generate_input_initializer([2, 2, 1, 1], np.float32, 'W2'))
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initializers.append(generate_input_initializer([2], np.float32, 'B1'))
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initializers.append(generate_input_initializer([2], np.float32, 'B2'))
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conv_node_1 = onnx.helper.make_node('Conv', ['GRU_O', 'W1', 'B1'], ['Conv1_O'], name='Conv1')
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conv_node_2 = onnx.helper.make_node('Conv', ['GRU_O', 'W2', 'B2'], ['Conv2_O'], name='Conv2')
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relu_node = onnx.helper.make_node('Relu', ['Conv1_O'], ['Relu_O'], name='Relu')
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add_node = onnx.helper.make_node('Add', ['Relu_O', 'Conv2_O'], ['output'], name='Add')
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graph = helper.make_graph([conv_node_1, relu_node, conv_node_2, gru_node, add_node],
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'onnx_model_test', [input], [output], initializer=initializers)
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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onnx.save(model, model_path)
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def construct_model_Constant(self, model_path):
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# (input) Constant
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# \ /
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# \ /
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# \ /
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# \ /
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# Add
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# |
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# (output)
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initializers = []
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input = helper.make_tensor_value_info('input', TensorProto.FLOAT, [4, 8, 12])
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [4, 8, 12])
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# make nodes
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constant_node = onnx.helper.make_node('Constant', [], ['const_output'], value_float=42.0)
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add_node = onnx.helper.make_node('Add', ['input', 'const_output'], ['output'], name='Add')
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graph = helper.make_graph([add_node, constant_node],
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'onnx_model_test', [input], [output], initializer=initializers)
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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onnx.save(model, model_path)
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def test_topo_sort(self):
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test_model_path = 'onnx_model_topo_sort.onnx'
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self.construct_model(test_model_path)
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onnx_model = ONNXModel(onnx.load(test_model_path))
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check_op_type_order(self, onnx_model.model, ['Conv', 'Relu', 'Conv', 'GRU', 'Add'])
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onnx_model.topological_sort()
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check_op_type_order(self, onnx_model.model, ['GRU', 'Conv', 'Conv', 'Relu', 'Add'])
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def test_topo_sort_constant(self):
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test_model_path = 'onnx_model_topo_sort_constant.onnx'
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self.construct_model_Constant(test_model_path)
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onnx_model = ONNXModel(onnx.load(test_model_path))
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check_op_type_order(self, onnx_model.model, ['Add', 'Constant'])
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onnx_model.topological_sort()
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check_op_type_order(self, onnx_model.model, ['Constant', 'Add'])
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if __name__ == '__main__':
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unittest.main()
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