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[Relay][Pytorch] Add support for aten::unflatten #16131

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Merged
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Nov 22, 2023
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[Relay][Pytorch] Add support for aten::unflatten #16131

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bca8a84
add support for `aten::unflatten`
mshr-h Nov 16, 2023
252a58b
Add check that dshape[dim] % multiplication of dimensions in unflatte…
mshr-h Nov 16, 2023
4a8656d
Update shape check
mshr-h Nov 17, 2023
d5798f2
handle `dim=-1`
mshr-h Nov 17, 2023
4d26be7
formatting
mshr-h Nov 20, 2023
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22 changes: 22 additions & 0 deletions python/tvm/relay/frontend/pytorch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1546,6 +1546,27 @@ def flatten(self, inputs, input_types):
out = _op.squeeze(out, axis=squeeze_axes)
return out

def unflatten(self, inputs, input_types):
data = inputs[0]
dim = int(inputs[1])
unflattened_size = tuple(inputs[2])
dshape = get_const_tuple(self.infer_shape_with_prelude(data))

dim = dim if dim >= 0 else len(dshape) + dim
assert len(dshape) > dim >= 0

assert unflattened_size.count(-1) <= 1

mult = np.multiply.reduce(unflattened_size)
if mult < 0:
assert dshape[dim] % mult == 0
else:
assert dshape[dim] == mult

new_shape = dshape[:dim] + unflattened_size + dshape[dim + 1 :]
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Add check that dshape[dim] == multiplication of dimensions in unflattened_size

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@mshr-h mshr-h Nov 15, 2023 •
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@vvchernov Thanks!
I don't think we have to check it because torch.jit.trace does it.
They provide something like the below RuntimeError when the shape is wrong.

RuntimeError: unflatten: Provided sizes [3, 5, 3, -1] don't multiply up to the size of dim 0 (60) in the input tensor

Should we add the check in TVM's PyTorch frontend?

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Hello @mshr-h! Ok torch.jit.trace do it, but in this case we do not need assert len(dshape) > dim.
It looks like TVM usually rechecks all corner cases.
About -1 in unflattened_size. In this case we can multiply together other dimension and check dshape[dim] % mult == 0

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@vvchernov Thanks. Added the assertion.

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Hello @mshr-h! There are two cases: unflattened_size has -1 and does not have. You check only the first one. Example: dshape[dim] = 8, unflattened_size = [2, 2, 1, 1] pass your assert, but it is failure case

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I've checked that _op.reshape does not check size correctness by it-self. Nevertheless I found that dim can be not only -1, but from list {0, -1, -2, -3, -4} See

tvm/python/tvm/relay/op/transform.py

Line 243 in 748882a

def reshape(data, newshape, allowzero=False):

I suggest to check -1 case only. It looks like torch does not have other options

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@vvchernov
Thanks! Updated the shape check.

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Ah, dim can be negative but I didn't check that. I'll add the check.

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Ah, dim can be negative but I didn't check that. I'll add the check.

Done.

out = _op.reshape(data, new_shape)
return out

def addmm(self, inputs, input_types):
input_mat = inputs[0]
mat1 = inputs[1]
Expand Down Expand Up @@ -3945,6 +3966,7 @@ def create_convert_map(self):
"aten::t": self.transpose,
"aten::numpy_T": self.numpy_T,
"aten::flatten": self.flatten,
"aten::unflatten": self.unflatten,
"aten::addmm": self.addmm,
"aten::size": self.size,
"aten::view": self.view,
Expand Down
36 changes: 35 additions & 1 deletion tests/python/frontend/pytorch/test_forward.py
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Expand Up @@ -1544,6 +1544,40 @@ def _test_flatten(start_dim, end_dim):
verify_model(_test_flatten(-3, -2), inp)


@tvm.testing.uses_gpu
def test_unflatten():
"""test_unflatten"""

def _test_unflatten(dim, unflattened_size):
return lambda inp: torch.unflatten(inp, dim, unflattened_size)

inp = torch.rand(60)

# [60] -> [3, 5, 2, 2]
verify_model(_test_unflatten(0, (3, 5, 2, 2)), inp)
verify_model(_test_unflatten(0, (-1, 5, 2, 2)), inp)
verify_model(_test_unflatten(0, (3, -1, 2, 2)), inp)
verify_model(_test_unflatten(0, (3, 5, -1, 2)), inp)
verify_model(_test_unflatten(0, (3, 5, 2, -1)), inp)
verify_model(_test_unflatten(-1, (3, 5, 2, 2)), inp)
verify_model(_test_unflatten(-1, (-1, 5, 2, 2)), inp)
verify_model(_test_unflatten(-1, (3, -1, 2, 2)), inp)
verify_model(_test_unflatten(-1, (3, 5, -1, 2)), inp)
verify_model(_test_unflatten(-1, (3, 5, 2, -1)), inp)

inp = torch.rand(3, 4, 1)

# [3, 4, 1] -> [3, 2, 2, 1]
verify_model(_test_unflatten(1, (2, 2)), inp)
verify_model(_test_unflatten(1, (-1, 2)), inp)

inp = torch.rand(5, 12, 3)

# [5, 12, 3] -> [5, 2, 2, 3, 1, 1, 3]
verify_model(_test_unflatten(1, (2, 2, 3, 1, 1)), inp)
verify_model(_test_unflatten(-2, (2, 2, 3, 1, 1)), inp)


@tvm.testing.uses_gpu
def test_forward_transpose():
"""test_forward_transpose"""
Expand Down Expand Up @@ -4744,7 +4778,7 @@ def test_fn(x, mask):
verify_model(test_fn, [inp.to(torch.float64), inp > 0.5])


@pytest.mark.skip(reason="unsupported op: 'aten::scaled_dot_product_attention', 'aten::unflatten'")
@pytest.mark.skip(reason="unsupported op: 'aten::scaled_dot_product_attention'")
def test_transformer():
"""test_transformer"""
model = torch.nn.Transformer(d_model=256, nhead=8, num_encoder_layers=6, num_decoder_layers=6)
Expand Down