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# Owner(s): ["module: onnx"]
import unittest
import torch
from onnx_test_common import run_model_test
from torch.onnx import OperatorExportTypes
from torch.onnx._globals import GLOBALS
from torch.onnx.utils import _model_to_graph
class TestAutogradFuns(unittest.TestCase):
opset_version = GLOBALS.export_onnx_opset_version
keep_initializers_as_inputs = False
onnx_shape_inference = True
def test_single_output(self):
class SingleOut(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
result = i.exp()
result = result.log()
ctx.save_for_backward(result)
return result
@staticmethod
def backward(ctx, grad_output):
(result,) = ctx.saved_tensors
return grad_output * result
class Caller(torch.nn.Module):
def forward(self, input):
result = input + 5
return SingleOut.apply(result) + 3
model = Caller()
input = torch.ones(1)
run_model_test(self, model, input_args=(input,))
def test_multi_output(self):
class MultiOut(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
result_exp = i.exp()
result_log = result_exp.log()
ctx.save_for_backward(result_exp, result_log)
return result_exp, result_log
@staticmethod
def backward(ctx, grad_output):
(result,) = ctx.saved_tensors
return grad_output * result
class Caller(torch.nn.Module):
def forward(self, input):
return MultiOut.apply(input)
model = Caller()
input = torch.ones(1, 5)
run_model_test(self, model, input_args=(input,))
def test_partial_output(self):
class PartialOut(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
values, indices = torch.topk(input, 3)
return values
class Caller(torch.nn.Module):
def forward(self, input):
return PartialOut.apply(input)
model = Caller()
input = torch.ones(1, 5)
run_model_test(self, model, input_args=(input,))
def test_nested_autograd(self):
class Child(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
result = i.log()
result_log = result.log()
ctx.save_for_backward(result_log)
return result_log
@staticmethod
def backward(ctx, grad_output):
(result,) = ctx.saved_tensors
return grad_output * result
class Parent(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
result_exp = i.exp()
result_log = Child.apply(result_exp)
ctx.save_for_backward(result_exp, result_log)
return result_exp, result_log
@staticmethod
def backward(ctx, grad_output):
(result,) = ctx.saved_tensors
return grad_output * result
class Caller(torch.nn.Module):
def forward(self, input):
return Parent.apply(input)
model = Caller()
input = torch.ones(1, 5)
run_model_test(self, model, input_args=(input,))
# Run export in ONNX_FALLTHROUGH mode as torch.erf() is not supported
def test_aten_unsupported(self):
class Erf(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
erf_out = torch.special.erf(x)
ctx.save_for_backward(erf_out)
return erf_out
@staticmethod
def backward(ctx, grad_output):
result = ctx.saved_tensors
return torch.special.erfinv(result), None
class Caller(torch.nn.Module):
def forward(self, input):
return Erf.apply(input)
model = Caller()
input = torch.ones(1, 5)
# Test ONNX_FALLTHROUGH_MODE
graph, _, _ = _model_to_graph(
model,
(input,),
operator_export_type=OperatorExportTypes.ONNX_FALLTHROUGH,
)
iter = graph.nodes()
self.assertEqual(next(iter).kind(), "prim::PythonOp")
# Test ATEN_FALLBACK_MODE
graph, _, _ = _model_to_graph(
model,
(input,),
operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK,
)
iter = graph.nodes()
self.assertEqual(next(iter).kind(), "prim::PythonOp")
def test_inline_and_symbolic(self):
class Exp(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
ctx.save_for_backward(input)
return i.exp()
@staticmethod
def symbolic(g, input):
return g.op("Exp", input)
class LogLog(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
ctx.save_for_backward(input)
return i.log().log()
class Caller(torch.nn.Module):
def forward(self, input):
exp_result = Exp.apply(input)
return LogLog.apply(exp_result)
model = Caller()
input = torch.ones(1)
run_model_test(self, model, input_args=(input,))
def test_inline_with_scoped_tracing(self):
class Exp(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
ctx.save_for_backward(input)
return i.exp()
@staticmethod
def symbolic(g, input):
return g.op("Exp", input)
class LogLog(torch.autograd.Function):
@staticmethod
def forward(ctx, i):
ctx.save_for_backward(input)
return i.log().log()
class Caller(torch.nn.Module):
def forward(self, input):
exp_result = Exp.apply(input)
return LogLog.apply(exp_result)
model = Caller()
input = torch.ones(1)
torch.jit._trace._trace_module_map = {
_m: torch.typename(type(_m)) for _m in model.modules()
}
run_model_test(self, model, input_args=(input,))
torch.jit._trace._trace_module_map = None
if __name__ == "__main__":
unittest.main()
|
