mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-30 20:18:08 +00:00
fix topo sort in quantization tool (#16003)
### Description
<!-- Describe your changes. -->
Should not set up dependent node list for empty('') input
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
This commit is contained in:
Yufeng Li
GitHub
parent
ea7b2deffd
commit
0fed00c04d
2 changed files with 132 additions and 78 deletions
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@ -414,6 +414,8 @@ class ONNXModel:
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continue
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for input_name in node.input:
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if not input_name:
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continue
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if input_name not in deps_to_nodes:
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deps_to_nodes[input_name] = [node_idx]
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else:
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@ -5,14 +5,15 @@
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# license information.
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# --------------------------------------------------------------------------
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import tempfile
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import unittest
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from pathlib import Path
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import numpy as np
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import onnx
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from onnx import TensorProto, helper, numpy_helper
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from op_test_utils import TestDataFeeds, check_model_correctness, check_op_type_count, check_op_type_order # noqa: F401
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from op_test_utils import check_op_type_order
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import onnxruntime # noqa: F401
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from onnxruntime.quantization.onnx_model import ONNXModel
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@ -25,97 +26,148 @@ def generate_input_initializer(tensor_shape, tensor_dtype, input_name):
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return init
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def construct_model_for_topo_sort(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 = 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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)
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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 = helper.make_node("Conv", ["GRU_O", "W1", "B1"], ["Conv1_O"], name="Conv1")
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conv_node_2 = helper.make_node("Conv", ["GRU_O", "W2", "B2"], ["Conv2_O"], name="Conv2")
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relu_node = helper.make_node("Relu", ["Conv1_O"], ["Relu_O"], name="Relu")
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add_node = helper.make_node("Add", ["Relu_O", "Conv2_O"], ["output"], name="Add")
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graph = helper.make_graph(
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[conv_node_1, relu_node, conv_node_2, gru_node, add_node],
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"onnx_model_test",
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[input],
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[output],
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initializer=initializers,
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)
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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_for_topo_sort_constant(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 = helper.make_node("Constant", [], ["const_output"], value_float=42.0)
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add_node = helper.make_node("Add", ["input", "const_output"], ["output"], name="Add")
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graph = helper.make_graph(
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[add_node, constant_node],
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"onnx_model_test",
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[input],
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[output],
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initializer=initializers,
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)
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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_for_topo_sort_empty_input_output(model_path):
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# (input1) (input2)
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# | |
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# Op1 Op1
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# \ /
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# \ /
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# \ /
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# \ /
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# Op2
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# |
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# Op3
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# |
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# (output)
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input1 = helper.make_tensor_value_info("input1", TensorProto.FLOAT, [4, 8, 12])
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input2 = helper.make_tensor_value_info("input2", 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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op1_node_1 = helper.make_node("Op1", ["input1"], ["", "", "Op1_1_output"], name="op1_1", domain="Test")
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op1_node_2 = helper.make_node("Op1", ["input2"], ["", "", "Op1_2_output"], name="op1_2", domain="Test")
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op2_node = helper.make_node("Op2", ["Op1_1_output", "Op1_2_output"], ["op2_output"], name="op2", domain="Test")
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op3_node = helper.make_node("Op3", ["", "op2_output"], ["output"], name="op3", domain="Test")
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graph = helper.make_graph(
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[op1_node_1, op1_node_2, op3_node, op2_node],
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"onnx_model_topo_test",
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[input1, input2],
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[output],
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)
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("Test", 1), helper.make_opsetid("", 13)])
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onnx.save(model, model_path)
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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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@classmethod
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def setUpClass(cls):
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cls._tmp_model_dir = tempfile.TemporaryDirectory(prefix="test_onnx_model.")
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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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)
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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(
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[conv_node_1, relu_node, conv_node_2, gru_node, add_node],
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"onnx_model_test",
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[input],
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[output],
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initializer=initializers,
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)
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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): # noqa: N802
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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(
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[add_node, constant_node],
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"onnx_model_test",
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[input],
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[output],
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initializer=initializers,
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)
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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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@classmethod
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def tearDownClass(cls):
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cls._tmp_model_dir.cleanup()
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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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test_model_path = str(Path(self._tmp_model_dir.name) / "onnx_model_topo_sort.onnx")
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construct_model_for_topo_sort(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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test_model_path = str(Path(self._tmp_model_dir.name) / "onnx_model_topo_sort_constant.onnx")
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construct_model_for_topo_sort_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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def test_topo_sort_empty_input_output(self):
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test_model_path = str(Path(self._tmp_model_dir.name) / "onnx_model_topo_empty_input_output.onnx")
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construct_model_for_topo_sort_empty_input_output(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, ["Op1", "Op1", "Op3", "Op2"])
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onnx_model.topological_sort()
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check_op_type_order(self, onnx_model.model, ["Op1", "Op1", "Op2", "Op3"])
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if __name__ == "__main__":
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unittest.main()
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