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ONNX Export Min and Max ops with dim #19689

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16 changes: 16 additions & 0 deletions test/onnx/test_pytorch_onnx_caffe2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1249,6 +1249,22 @@ def forward(self, input):
x = torch.randn(1, 2, 3, 4, requires_grad=True)
self.run_model_test(FlattenModel(), train=False, input=x, batch_size=BATCH_SIZE)

def test_max(self):
class MaxModel(torch.nn.Module):
def forward(self, input):
return torch.max(input, dim=1)

x = torch.randn(4, 4, requires_grad=True)
self.run_model_test(MaxModel(), train=False, input=x, batch_size=BATCH_SIZE)

def test_min(self):
class MinModel(torch.nn.Module):
def forward(self, input):
return torch.min(input, dim=1)

x = torch.randn(4, 4, requires_grad=True)
self.run_model_test(MinModel(), train=False, input=x, batch_size=BATCH_SIZE)

def test_argmax(self):
class ArgmaxModel(torch.nn.Module):
def forward(self, input):
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26 changes: 12 additions & 14 deletions torch/onnx/symbolic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1117,37 +1117,35 @@ def clamp_max(g, self, max):
# torch.max (same for torch.min) actually has two interfaces smashed together:
# torch.max(x, dim, keepdim) and torch.max(x, y)
def max(g, self, dim_or_y=None, keepdim=None):
# torch.max(input)
if dim_or_y is None and keepdim is None:
return g.op("ReduceMax", self, keepdims_i=0)
# torch.max(input, other)
if keepdim is None:
return g.op("Max", self, dim_or_y)
# torch.max(input, dim, keepdim)
else:
dim = _get_const(dim_or_y, 'i', 'dim')
keepdim = _get_const(keepdim, 'i', 'keepdim')
# TODO: export it as ReduceMax
return g.op("ATen",
self,
operator_s="max",
dim_i=dim,
keepdim_i=keepdim,
outputs=2)
max = g.op("ReduceMax", self, axes_i=[dim], keepdims_i=keepdim)
indices = g.op('ArgMax', self, axis_i=dim, keepdims_i=keepdim)
return max, indices


def min(g, self, dim_or_y=None, keepdim=None):
# torch.min(input)
if dim_or_y is None and keepdim is None:
return g.op("ReduceMin", self, keepdims_i=0)
# torch.min(input, other)
if keepdim is None:
return g.op("Min", self, dim_or_y)
# torch.min(input, dim, keepdim)
else:
dim = _get_const(dim_or_y, 'i', 'dim')
keepdim = _get_const(keepdim, 'i', 'keepdim')
# TODO: export it as ReduceMax
return g.op("ATen",
self,
operator_s="min",
dim_i=dim,
keepdim_i=keepdim,
outputs=2)
min = g.op("ReduceMin", self, axes_i=[dim], keepdims_i=keepdim)
indices = g.op('ArgMin', self, axis_i=dim, keepdims_i=keepdim)
return min, indices


def exp(g, self):
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