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Support symbolic for conv_tbc (#58359) #58692

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Support symbolic for conv_tbc (#58359) #58692

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9d4e9a4
Support symbolic for conv_tbc (#58359)
jiafatom May 20, 2021
e2f0992
Update on "Support symbolic for conv_tbc (#58359)"
BowenBao May 20, 2021
a00da05
Update on "Support symbolic for conv_tbc (#58359)"
BowenBao May 26, 2021
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36 changes: 36 additions & 0 deletions test/onnx/test_pytorch_onnx_onnxruntime.py
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Original file line number Diff line number Diff line change
Expand Up @@ -419,6 +419,42 @@ def forward(self, x):
x = torch.randn(2, 3, 10, 50, 100, requires_grad=True)
self.run_test(model, (x,), rtol=1e-3, atol=1e-6)

def test_conv_tbc(self):
from torch.nn.modules.utils import _single

class ConvTBC(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, padding=0):
super(ConvTBC, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = _single(kernel_size)
self.padding = _single(padding)

self.weight = torch.nn.Parameter(
torch.Tensor(self.kernel_size[0], in_channels, out_channels)
)
self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
self.reset_parameters()

def reset_parameters(self):
torch.nn.init.xavier_normal_(self.weight)
torch.nn.init.zeros_(self.bias)

def conv_tbc(self, input):
return torch.conv_tbc(
input.contiguous(), self.weight, self.bias, self.padding[0]
)

def forward(self, input):
return self.conv_tbc(input)

in_channels = 3
out_channels = 5
kernel_size = 5
model = ConvTBC(in_channels, out_channels, kernel_size, padding=0)
x = torch.randn(10, 7, in_channels, requires_grad=True)
self.run_test(model, (x,), atol=1e-5)

def test_reshape_constant_fold(self):
class Reshape(torch.nn.Module):
def __init__(self, ):
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10 changes: 9 additions & 1 deletion torch/onnx/symbolic_opset9.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1686,7 +1686,15 @@ def conv_tbc(g, input, weight, bias, pad):
if sym_help._operator_export_type == torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK:
return g.op("ATen", input, weight, bias, operator_s="conv_tbc", pad_i=pad)
else:
return sym_help._onnx_unsupported('conv_tbc')
# input must have 3 dimensions, see:
# https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/ConvolutionTBC.cpp#L8-L10
# input = (time, batch, in_channels)
# weight = (kernel_width, in_channels, out_channels)
# bias = (out_channels,)
input = g.op("Transpose", input, perm_i=[1, 2, 0])
weight = g.op("Transpose", weight, perm_i=[2, 1, 0])
conv = conv1d(g, input, weight, bias, [1], [pad], [1], 1)
return g.op("Transpose", conv, perm_i=[2, 0, 1])


@parse_args('v', 'i', 'i')
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