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# Owner(s): ["module: onnx"]
import onnxruntime
import torch
from pytorch_test_common import skipIfNoCuda
from torch.onnx import verification
from torch.testing._internal import common_utils
def _jit_graph_to_onnx_model(graph, operator_export_type, opset_version):
r"""
This function exports torch::jit::Graph object
to serialized ONNX ModelProto.
This function is for testing purpose.
It only keeps the essential parts for IR graph conversions.
It also does not interact with actual PyTorch modules nor
PyTorch tensor inputs.
"""
torch.onnx.symbolic_helper._set_opset_version(opset_version)
graph = torch.onnx.utils._optimize_graph(
graph, operator_export_type, params_dict={}
)
proto, _, _, _ = graph._export_onnx(
{},
opset_version,
{},
False,
operator_export_type,
False,
False,
{},
True,
"",
{},
)
return proto
class _TestJITIRToONNX:
"""Abstract base class for test cases.
Intentionally not a sub-class of unittest.TestCase so that unittest / pytest
don't run it directly. unitest.TestCase is mixed in as another base class when
creating concrete sub-types. See MakeTestCase().
"""
opset_version = -1 # Sub-classes must override
ort_providers = ["CPUExecutionProvider"]
check_shape = True
check_dtype = True
ignore_none = True # True for tracing, and Flase for scripting
def run_test(self, graph_ir, example_inputs):
graph = torch._C.parse_ir(graph_ir)
jit_outs = torch._C._jit_interpret_graph(graph, example_inputs)
onnx_proto = _jit_graph_to_onnx_model(
graph, torch.onnx.OperatorExportTypes.ONNX, self.opset_version
)
ort_sess = onnxruntime.InferenceSession(
onnx_proto, providers=self.ort_providers
)
ort_outs = verification._run_ort(ort_sess, example_inputs)
verification._compare_ort_pytorch_outputs(
ort_outs,
jit_outs,
rtol=1e-3,
atol=1e-7,
check_shape=self.check_shape,
check_dtype=self.check_dtype,
ignore_none=self.ignore_none,
acceptable_error_percentage=None,
)
def test_example_ir(self):
graph_ir = """
graph(%1 : Float(2, 3),
%2 : Float(2, 3)):
%3 : int = prim::Constant[value=1]()
%4 : Float(2, 3) = aten::add(%1, %2, %3)
return (%4)
"""
a = torch.randn(2, 3)
b = torch.randn(2, 3)
self.run_test(graph_ir, (a, b))
def test_add_sub_with_graph_inputs(self):
for op in ["add", "sub", "rsub"]:
graph_ir = f"""
graph(%1 : Float(2, 3),
%2 : Float(2, 3),
%3 : int):
%4 : Float(2, 3) = aten::{op}(%1, %2, %3)
return (%4)
"""
a = torch.randn(2, 3)
b = torch.randn(2, 3)
self.run_test(graph_ir, (a, b, 2))
def test_native_layer_norm(self):
graph_ir = """
graph(%x : Float(2, 3, 2),
%w : Float(3, 2),
%b : Float(3, 2)):
%5 : int = prim::Constant[value=3]()
%6 : int = prim::Constant[value=2]()
%7 : int[] = prim::ListConstruct(%5, %6)
%10 : float = prim::Constant[value=1.0000000000000001e-05]()
%11 : Float(2, 3, 2), %12 : Float(2, 1, 1), %13 : Float(2, 1, 1) = aten::native_layer_norm(%x, %7, %w, %b, %10)
return (%11, %12, %13)
"""
x = torch.randn(2, 3, 2)
w = torch.randn(3, 2)
b = torch.randn(3, 2)
self.run_test(graph_ir, (x, w, b))
def test_convolution(self):
graph_ir = """
graph(%1 : Tensor,
%2 : Tensor):
%3 : NoneType = prim::Constant()
%4 : int[] = prim::Constant[value=[1, 1]]()
%5 : int[] = prim::Constant[value=[0, 0]]()
%6 : bool = prim::Constant[value=0]()
%7 : int = prim::Constant[value=1]()
%8 : Tensor = aten::convolution(%1, %2, %3, %4, %5, %4, %6, %5, %7)
return (%8)
"""
x = torch.randn(8, 1, 5, 5)
w = torch.randn(4, 1, 3, 3)
self.run_test(graph_ir, (x, w))
def test_log_softmax(self):
graph_ir = """
graph(%x: Tensor):
%half_to_float: bool = prim::Constant[value=0]()
%dim: int = prim::Constant[value=1]()
%y = aten::_log_softmax(%x, %dim, %half_to_float)
return (%y)
"""
x = torch.randn(5, 2)
self.run_test(graph_ir, (x,))
@skipIfNoCuda
def test_log_softmax_half_to_float(self):
graph_ir = """
graph(%x: Tensor):
%half_to_float: bool = prim::Constant[value=1]()
%dim: int = prim::Constant[value=1]()
%y = aten::_log_softmax(%x, %dim, %half_to_float)
return (%y)
"""
x = torch.randn(5, 2).half().to("cuda")
self.run_test(graph_ir, (x,))
def test_native_dropout(self):
graph_ir = """
graph(%1 : Float(2, 3)):
%2 : float = prim::Constant[value=0.0]()
%training : bool = prim::Constant[value=1]()
%3 : Tensor, %4 : Tensor = aten::native_dropout(%1, %2, %training)
return (%3, %4)
"""
a = torch.randn(2, 3)
self.run_test(graph_ir, (a,))
def MakeTestCase(opset_version: int) -> type:
name = f"TestJITIRToONNX_opset{opset_version}"
return type(
str(name),
(common_utils.TestCase,),
dict(_TestJITIRToONNX.__dict__, opset_version=opset_version),
)
TestJITIRToONNX_opset14 = MakeTestCase(14)
if __name__ == "__main__":
common_utils.run_tests()
|
