Lunderberg commented on code in PR #16049:
URL: https://github.com/apache/tvm/pull/16049#discussion_r1382198806
##########
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:
Can we add a unit test for the case where the weights require symbolic
variables that are used, but not definable, from the shapes of the weights?
This would require the `DefinableTIRVarsInStructInfo` usage described earlier.
The following input would be able to trigger this case.
```python
def test_symbolic_var_defined_in_params_but_used_in_weights():
"""A symbolic variable's occurrence in the weights may not define it
In order to be a source of definition, a symbolic variable in the
parameters must occur as a distinct parameter, as a tensor shape
`R.Tensor(["var"])`, an explicit `R.Shape(["var"])`, or as a
`R.Prim(value="var")`. A variable that is part of a larger
expression, such as `R.Tensor(["m * n"])`, are variable usages,
not variable definitions.
"""
@tvm.script.ir_module
class Before:
@R.function
def main(
x: R.Tensor(["m", "n"], "float32"),
weight: R.Tensor(["m * n"], "float32"),
) -> R.Tensor(["m", "n"], "float32"):
m = T.int64()
n = T.int64()
R.func_attr({"num_input": 1})
with R.dataflow():
weight = R.add(weight, R.const(1, "float32"))
weight = R.reshape(weight, [m, n])
output = R.multiply(x, weight)
R.output(output)
return output
@tvm.script.ir_module
class Expected:
pass # TODO
After = relax.transform.LiftTransformParams()(Before)
tvm.ir.assert_structural_equal(Expected, After)
```
##########
src/relax/transform/lift_transform_params.cc:
##########
@@ -237,6 +238,13 @@ class LiftTransformParamsPlanner : public ExprVisitor {
builder_.UpdateBasedOnRuntimeInput(function->params[i]);
} else {
builder_.AddInput(function->params[i]);
+ if (function->params[i]->struct_info_.defined()) {
+ Array<tir::Var> symbolic_vars =
+
TIRVarsInStructInfo(Downcast<StructInfo>(function->params[i]->struct_info_.value()));
Review Comment:
Nit: This should be
[`DefinableTIRVarsInStructInfo`](https://github.com/apache/tvm/blob/unity/include/tvm/relax/analysis.h#L271-L283),
as not all symbolic variables that appear in the struct info can be used to
define a symbolic variable. For example, in `def func(A: R.Tensor(["m", "n"]),
B: R.Tensor("m * n"))`, the struct info of `A` can be used to define `m` and
`n`, but the struct info of `B` cannot.
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