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[Fix][Relax] Preserve PyTorch grid_sample grid layout #20986

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[Fix][Relax] Preserve PyTorch grid_sample grid layout #20986

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3 changes: 2 additions & 1 deletion python/tvm/relax/frontend/torch/exported_program_translator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1314,7 +1314,8 @@ def _grid_sampler_2d(self, node: fx.Node) -> relax.Var:
"""Convert torch.nn.functional.grid_sample to relax.op.image.grid_sample."""
args = self.retrieve_args(node)
data = args[0]
grid = args[1]
# PyTorch uses [N, H_out, W_out, 2]; Relax NCHW uses [N, 2, H_out, W_out].
grid = self.block_builder.emit(relax.op.permute_dims(args[1], axes=[0, 3, 1, 2]))
interp_mode = args[2] if len(args) > 2 else 0
pad_mode = args[3] if len(args) > 3 else 0
align_corners = args[4] if len(args) > 4 else False
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30 changes: 24 additions & 6 deletions tests/python/relax/test_frontend_from_exported_program.py
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Expand Up @@ -8727,28 +8727,46 @@ class expected:
@R.function
def main(
input_1: R.Tensor((1, 3, 4, 4), dtype="float32"),
grid: R.Tensor((1, 2, 2, 2), dtype="float32"),
) -> R.Tuple(R.Tensor((1, 3, 2, 2), dtype="float32")):
grid: R.Tensor((1, 3, 5, 2), dtype="float32"),
) -> R.Tuple(R.Tensor((1, 3, 3, 5), dtype="float32")):
with R.dataflow():
lv: R.Tensor((1, 3, 2, 2), dtype="float32") = R.image.grid_sample(
grid_nchw: R.Tensor((1, 2, 3, 5), dtype="float32") = R.permute_dims(
grid, axes=[0, 3, 1, 2]
)
lv: R.Tensor((1, 3, 3, 5), dtype="float32") = R.image.grid_sample(
input_1,
grid,
grid_nchw,
method="bilinear",
layout="NCHW",
padding_mode="zeros",
align_corners=True,
)
gv: R.Tuple(R.Tensor((1, 3, 2, 2), dtype="float32")) = (lv,)
gv: R.Tuple(R.Tensor((1, 3, 3, 5), dtype="float32")) = (lv,)
R.output(gv)
return gv

example_args = (
torch.randn(1, 3, 4, 4, dtype=torch.float32),
torch.randn(1, 2, 2, 2, dtype=torch.float32),
torch.randn(1, 3, 5, 2, dtype=torch.float32),
)
verify_model(GridSample(), example_args, {}, expected)


@pytest.mark.parametrize("height,width", [(2, 2), (3, 5)])
def test_grid_sample_numerically(height, width):
class GridSample(Module):
def forward(self, data, grid):
return torch.nn.functional.grid_sample(data, grid, align_corners=False)

# In the 2x2 case, the wrong layout still produces the expected shape.
data = torch.arange(16, dtype=torch.float32).reshape(1, 1, 4, 4)
y, x = torch.meshgrid(
torch.linspace(-0.5, 0.5, height), torch.linspace(-0.5, 0.5, width), indexing="ij"
)
grid = torch.stack((x, y), dim=-1).unsqueeze(0)
verify_model_numerically(GridSample(), (data, grid), rtol=1e-6, atol=1e-6)


def test_torchvision_roi_align():
torchvision = pytest.importorskip("torchvision")

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