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[Bugfix][Relax] Fix IOHW batch norm folding #20535

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[Bugfix][Relax] Fix IOHW batch norm folding #20535

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2 changes: 1 addition & 1 deletion python/tvm/relax/transform/fold_batch_norm_to_conv2d_for_inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,7 +89,7 @@ def rewriter(expr, matches):
if conv_attrs["kernel_layout"] == "OIHW":
wt = relax.op.reshape(wt, shape=(bn_weight.ty.shape[0], 1, 1, 1))
elif conv_attrs["kernel_layout"] == "IOHW":
wt = wt.reshape(1, bn_weight.ty.shape[0], 1, 1)
wt = relax.op.reshape(wt, shape=(1, bn_weight.ty.shape[0], 1, 1))
else:
return expr
wt_conv = relax.op.multiply(conv_weight, wt)
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35 changes: 35 additions & 0 deletions tests/python/relax/test_transform_fold_batch_norm_to_conv2d.py
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Expand Up @@ -141,5 +141,40 @@ def test_fold_batchnorm_info_conv2d_transform():
VerifyFolding().visit(mod)


def test_fold_batchnorm_iohw_kernel_layout():
@I.ir_module
class Module:
@R.function
def main(
x: R.Tensor((1, 1, 2, 2), "float32"),
weight: R.Tensor((1, 2, 1, 1), "float32"),
gamma: R.Tensor((2,), "float32"),
beta: R.Tensor((2,), "float32"),
mean: R.Tensor((2,), "float32"),
variance: R.Tensor((2,), "float32"),
):
conv = R.nn.conv2d(
x,
weight,
data_layout="NCHW",
kernel_layout="IOHW",
out_layout="NCHW",
out_dtype="float32",
)
return R.nn.batch_norm(conv, gamma, beta, mean, variance, axis=1, training=False)[0]

params = {
"weight": tvm.runtime.tensor(np.array([[[[1.5]], [[-2.0]]]], "float32")),
"gamma": tvm.runtime.tensor(np.array([2.0, 3.0], "float32")),
"beta": tvm.runtime.tensor(np.array([5.0, 7.0], "float32")),
"mean": tvm.runtime.tensor(np.array([1.0, -1.0], "float32")),
"variance": tvm.runtime.tensor(np.array([4.0, 9.0], "float32")),
}
source = relax.transform.BindParams("main", params)(Module)
folded = relax.transform.FoldBatchnormToConv2D()(source)
assert relax.analysis.check_well_formed(folded, check_ty=True)
VerifyFolding().visit(folded)


if __name__ == "__main__":
tvm.testing.main()
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