Lunderberg commented on code in PR #16049:
URL: https://github.com/apache/tvm/pull/16049#discussion_r1385589114
##########
tests/python/relax/test_transform_lift_transform_params.py:
##########
@@ -642,5 +642,95 @@ def slice(
tvm.ir.assert_structural_equal(Expected, after)
+def test_symbolic_var_in_param_shape():
+ @tvm.script.ir_module
+ class Before:
+ @R.function
+ def main(
+ x: R.Tensor((1, 16, 224, "n"), "float32"),
+ w1: R.Tensor((16, "m", 3, 3), "float32"),
+ w2: R.Tensor((16, "m", 3, 3), "float32"),
+ ) -> R.Tensor((1, 16, 224, 224), "float32"):
+ m = T.int64()
+ n = T.int64()
+ R.func_attr({"num_input": 1})
+ with R.dataflow():
+ zeros = R.zeros((n, n), "float32")
+ w1 = R.add(w1, R.const(1, "float32"))
+ conv1 = R.nn.conv2d(x, w1, padding=(1, 1), data_layout="NCHW",
kernel_layout="OIHW")
+ conv2 = R.nn.conv2d(
+ conv1, w2, padding=(1, 1), data_layout="NCHW",
kernel_layout="OIHW"
+ )
+ R.output(conv2)
+ return conv2
+
+ @I.ir_module
+ class Expected:
+ @R.function
+ def main_transform_params(
+ params: R.Tuple(
+ R.Tensor((16, "m", 3, 3), dtype="float32"),
+ R.Tensor((16, "m", 3, 3), dtype="float32"),
+ )
+ ) -> R.Tuple(
+ R.Tensor((16, "m", 3, 3), dtype="float32"), R.Tensor((16, "m", 3,
3), dtype="float32")
+ ):
+ m = T.int64()
+ with R.dataflow():
+ lv: R.Tensor((16, m, 3, 3), dtype="float32") = params[1]
+ lv1: R.Tensor((16, m, 3, 3), dtype="float32") = params[0]
+ lv2: R.Tensor((16, m, 3, 3), dtype="float32") = R.add(lv1,
R.const(1, "float32"))
+ gv: R.Tuple(
+ R.Tensor((16, m, 3, 3), dtype="float32"),
+ R.Tensor((16, m, 3, 3), dtype="float32"),
+ ) = (lv, lv2)
+ R.output(gv)
+ return gv
+
+ @R.function
+ def main(
+ x: R.Tensor((1, 16, 224, "n"), dtype="float32"),
+ transformed_param_0: R.Tensor((16, "m", 3, 3), dtype="float32"),
+ transformed_param_1: R.Tensor((16, "m", 3, 3), dtype="float32"),
+ ) -> R.Tensor((1, 16, 224, 224), dtype="float32"):
+ n = T.int64()
+ m = T.int64()
+ R.func_attr({"num_input": 1})
+ with R.dataflow():
+ zeros: R.Tensor((n, n), dtype="float32") = R.zeros(R.shape([n,
n]), dtype="float32")
+ lv: R.Tensor((16, m, 3, 3), dtype="float32") =
transformed_param_1
+ conv1: R.Tensor((1, 16, 224, n), dtype="float32") =
R.nn.conv2d(
+ x,
+ lv,
+ strides=[1, 1],
+ padding=[1, 1, 1, 1],
+ dilation=[1, 1],
+ groups=1,
+ data_layout="NCHW",
+ kernel_layout="OIHW",
+ out_layout="NCHW",
+ out_dtype="void",
+ )
+ lv1: R.Tensor((16, m, 3, 3), dtype="float32") =
transformed_param_0
+ conv2: R.Tensor((1, 16, 224, n), dtype="float32") =
R.nn.conv2d(
+ conv1,
+ lv1,
+ strides=[1, 1],
+ padding=[1, 1, 1, 1],
+ dilation=[1, 1],
+ groups=1,
+ data_layout="NCHW",
+ kernel_layout="OIHW",
+ out_layout="NCHW",
+ out_dtype="void",
+ )
+ R.output(conv2)
+ return conv2
+
+ mod = Before
+ after = relax.transform.LiftTransformParams()(mod)
+ tvm.ir.assert_structural_equal(after, Expected)
+
Review Comment:
Hmm, you're right. I had thought that the proposed unit test would be
normalized to `R.Tensor(ndim=1)`, and that the lifting of the parameter would
result in an undefined usage of the symbolic variable. Instead, this test
cases causes undefined variables in the output of `LiftTransformParams` prior
to this PR as well.
Can we include the unit test, but mark it with `@pytest.mark.xfail`? That
way, it wouldn't cause the CI to fail, but the known failure mode would be
documented.
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