jinhongyii commented on code in PR #16049:
URL: https://github.com/apache/tvm/pull/16049#discussion_r1385544554
##########
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:
The unittest should be introduced in another PR because it involves another
problem. Below is the program after applying the pass and you can see that
main_transform_params is not well defined because m and n are not defined.
```@I.ir_module
class Module:
@R.function
def main_transform_params(params: R.Tuple(R.Tensor(("m * n",),
dtype="float32"))) -> R.Tuple(R.Tensor(dtype="float32", ndim=1)):
m = T.int64()
n = T.int64()
with R.dataflow():
lv: R.Tensor((m * n,), dtype="float32") = params[0]
gv: R.Tuple(R.Tensor((m * n,), dtype="float32")) = (lv,)
R.output(gv)
return gv
@R.function
def main(x: R.Tensor(("m", "n"), dtype="float32"), transformed_param_0:
R.Tensor(dtype="float32", ndim=1)) -> R.Tensor(("m", "n"), dtype="float32"):
m = T.int64()
n = T.int64()
R.func_attr({"num_input": 1})
with R.dataflow():
lv: R.Tensor(dtype="float32", ndim=1) = transformed_param_0
weight: R.Tensor(dtype="float32", ndim=1) = R.add(lv, R.const(1,
"float32"))
weight_1: R.Tensor((m, n), dtype="float32") = R.reshape(weight,
R.shape([m, n]))
output: R.Tensor((m, n), dtype="float32") = R.multiply(x,
weight_1)
R.output(output)
return output
```
An ideal output should be
```@I.ir_module
class Module:
@R.function
def main_transform_params(params: R.Tuple(R.Tensor(("k",),
dtype="float32"))) -> R.Tuple(R.Tensor(dtype="float32", ndim=1)):
k = T.int64()
with R.dataflow():
lv: R.Tensor((k,), dtype="float32") = params[0]
gv: R.Tuple(R.Tensor((k,), dtype="float32")) = (lv,)
R.output(gv)
return gv
@R.function
def main(x: R.Tensor(("m", "n"), dtype="float32"), transformed_param_0:
R.Tensor(dtype="float32", ndim=1)) -> R.Tensor(("m", "n"), dtype="float32"):
m = T.int64()
n = T.int64()
R.func_attr({"num_input": 1})
with R.dataflow():
lv: R.Tensor(dtype="float32", ndim=1) = transformed_param_0
weight: R.Tensor(dtype="float32", ndim=1) = R.add(lv, R.const(1,
"float32"))
weight_1: R.Tensor((m, n), dtype="float32") = R.reshape(weight,
R.shape([m, n]))
output: R.Tensor((m, n), dtype="float32") = R.multiply(x,
weight_1)
R.output(output)
return output
```
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