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# Owner(s): ["module: onnx"]
"""Unit tests for the _building module."""
from __future__ import annotations
import numpy as np
import onnxscript
from onnxscript import ir
import torch
from torch.onnx._internal.exporter import _building, _tensors
from torch.testing._internal import common_utils
class TestOpRecorder(common_utils.TestCase):
def setUp(self):
self.opset_version = 17
self.opset = onnxscript.values.Opset("", self.opset_version)
self.recorder = _building.OpRecorder(opset=self.opset, constant_farm={})
self.model = ir.Model(
graph=ir.Graph(
[],
[],
nodes=[],
opset_imports={
"": self.opset_version,
},
name="main_graph",
),
ir_version=9,
producer_name="pytorch",
producer_version=torch.__version__,
)
def test_skippable_castlike_is_ommited(self):
input_x = _tensors.SymbolicTensor(opset=self.opset, name="input_x")
input_x.dtype = ir.DataType.FLOAT
input_y = _tensors.SymbolicTensor(opset=self.opset, name="input_y")
input_y.dtype = ir.DataType.FLOAT
with onnxscript.evaluator.default_as(tracer := self.recorder):
cast = self.opset.CastLike(input_y, input_x)
_ = self.opset.Add(input_x, cast)
self.assertEqual(len(tracer.nodes), 1)
self.assertEqual(tracer.nodes[0].op_type, "Add")
def test_castlike_is_replaced_with_cast_when_it_is_traced(self):
input_x = _tensors.SymbolicTensor(opset=self.opset, name="input_x")
input_x.dtype = ir.DataType.FLOAT
input_y = _tensors.SymbolicTensor(opset=self.opset, name="input_y")
input_y.dtype = ir.DataType.INT64
with onnxscript.evaluator.default_as(tracer := self.recorder):
cast = self.opset.CastLike(input_y, input_x)
_ = self.opset.Add(input_x, cast)
self.assertEqual(len(tracer.nodes), 2)
self.assertEqual(tracer.nodes[0].op_type, "Cast")
self.assertEqual(tracer.nodes[1].op_type, "Add")
def test_python_constant_added_as_constant_nodes(self):
input_x = _tensors.SymbolicTensor(
opset=self.opset, name="input_x", shape=ir.Shape([2, 3, 4])
)
new_shape = [3, 2, 4]
with onnxscript.evaluator.default_as(tracer := self.recorder):
_ = self.opset.Reshape(input_x, new_shape)
self.assertEqual(len(tracer.nodes), 2)
self.assertEqual(tracer.nodes[0].op_type, "Constant")
self.assertEqual(
tracer.nodes[0].attributes["value"].value.numpy(), np.array(new_shape)
)
self.assertEqual(tracer.nodes[1].op_type, "Reshape")
def test_process_python_sequence_with_allowed_sequence_type(self):
input_x = _tensors.SymbolicTensor(
opset=self.opset, name="input_x", shape=ir.Shape([2, 3])
)
input_y = _tensors.SymbolicTensor(
opset=self.opset, name="input_y", shape=ir.Shape([2, 4])
)
input_z = _tensors.SymbolicTensor(
opset=self.opset, name="input_z", shape=ir.Shape([1, 3])
)
with onnxscript.evaluator.default_as(tracer := self.recorder):
_ = self.opset.SequenceAt([input_x, input_y, input_z], 1)
self.assertEqual(len(tracer.nodes), 3)
self.assertEqual(tracer.nodes[1].op_type, "SequenceConstruct")
def test_process_python_sequence_with_variadic_input(self):
input_x = _tensors.SymbolicTensor(
opset=self.opset, name="input_x", shape=ir.Shape([2, 3])
)
input_y = _tensors.SymbolicTensor(
opset=self.opset, name="input_y", shape=ir.Shape([2, 4])
)
input_z = _tensors.SymbolicTensor(
opset=self.opset, name="input_z", shape=ir.Shape([1, 3])
)
with onnxscript.evaluator.default_as(tracer := self.recorder):
_ = self.opset.Max(input_x, input_y, 0, input_z)
self.assertEqual(len(tracer.nodes), 2)
self.assertEqual(tracer.nodes[0].op_type, "Constant")
def test_process_python_sequence_creates_extra_concat(self):
# Elements in the list must be 0D tensors
input_x = _tensors.SymbolicTensor(
opset=self.opset, name="input_x", shape=ir.Shape([])
)
input_y = _tensors.SymbolicTensor(
opset=self.opset, name="input_y", shape=ir.Shape([])
)
input_z = _tensors.SymbolicTensor(
opset=self.opset, name="input_z", shape=ir.Shape([4, 3])
)
with onnxscript.evaluator.default_as(tracer := self.recorder):
_ = self.opset.Add([input_x, input_y], input_z)
self.assertEqual(len(tracer.nodes), 6)
self.assertEqual(tracer.nodes[-2].op_type, "Concat")
self.assertEqual(tracer.nodes[-2].attributes["axis"].value, 0)
def test_process_python_sequence_mix_symbolic_constant_creates_extra_concat(self):
# Elements in the list must be 0D tensors
input_x = _tensors.SymbolicTensor(
opset=self.opset, name="input_x", shape=ir.Shape([])
)
input_z = _tensors.SymbolicTensor(
opset=self.opset, name="input_z", shape=ir.Shape([4, 3])
)
with onnxscript.evaluator.default_as(tracer := self.recorder):
_ = self.opset.Add([input_x, 42], input_z)
self.assertEqual(len(tracer.nodes), 5)
self.assertEqual(tracer.nodes[-2].op_type, "Concat")
self.assertEqual(tracer.nodes[-2].attributes["axis"].value, 0)
def test_process_python_sequence_mix_constant_symbolic_creates_extra_concat(self):
# Elements in the list must be 0D tensors
input_x = _tensors.SymbolicTensor(
opset=self.opset, name="input_x", shape=ir.Shape([])
)
input_z = _tensors.SymbolicTensor(
opset=self.opset, name="input_z", shape=ir.Shape([4, 3])
)
with onnxscript.evaluator.default_as(tracer := self.recorder):
# Constant first
_ = self.opset.Add([42, input_x], input_z)
self.assertEqual(len(tracer.nodes), 5)
self.assertEqual(tracer.nodes[-2].op_type, "Concat")
self.assertEqual(tracer.nodes[-2].attributes["axis"].value, 0)
if __name__ == "__main__":
common_utils.run_tests()
|
