[Relax] Additional unit tests for RemoveUnusedParameters

tests/python/relax/test_transform_remove_unused_parameters.py

@@ -24,7 +24,14 @@ class BaseCompare(tvm.testing.CompareBeforeAfter):
     transform = tvm.relax.transform.RemoveUnusedParameters()
 
 
-class TestSimple(BaseCompare):
+class TestRemoveUnusedRelaxParameter(BaseCompare):
+    """A relax parameter may be removed
+
+    This is only allowed for internal function calls, where all
+    callsites can be updated.  For externally-exposed functions, the
+    signature may not be modified.
+    """
+
     @I.ir_module
     class Before:
         @R.function
@@ -46,7 +53,15 @@ def func(A: R.Tensor) -> R.Tensor:
             return A
 
 
-class TestSymbolicVariables(BaseCompare):
+class TestReplaceSymbolicVariables(BaseCompare):
+    """If a parameter is only required for its symbolic variables, provide them directly
+
+    The relax parameter `A` isn't used by the subroutine.  However,
+    its shape defines the symbolic variables `m` and `n`.  When
+    removing the `R.Tensor` argument, we may need to provide
+    additional parameters to define the symbolic variables.
+    """
+
     @I.ir_module
     class Before:
         @R.function
@@ -78,7 +93,12 @@ def func(
 
 
 class TestNoExtraSymbolicVariables(BaseCompare):
-    """Don't add symbolic variables if they can be inferred."""
+    """Don't add symbolic variables if they can be inferred.
+
+    Even though some cases require adding new parameters to provide
+    symbolic variables, not every symbolic variable requires a
+    distinct parameter.
+    """
 
     @I.ir_module
     class Before:
@@ -97,5 +117,88 @@ def func(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
     Expected = Before
 
 
+class TestRemoveExtraPrimVariables(BaseCompare):
+    """Remove parameters that only serve to define existing symbolic variables
+
+    If a `R.Prim` parameter provies a definition of a symbolic
+    variable, but that symbolic variable can be determined from a
+    different parameter, then the `R.Prim` parameter can be removed.
+    """
+
+    @I.ir_module
+    class Before:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            return Before.func(A, R.prim_value(m), R.prim_value(n))
+
+        @R.function(private=True)
+        def func(
+            A: R.Tensor(["m", "n"], "float32"), _m: R.Prim(value="m"), _n: R.Prim(value="n")
+        ) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            zeros = R.zeros(R.shape([m, n]), dtype="float32")
+            out = R.add(A, zeros)
+            return out
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            return Expected.func(A)
+
+        @R.function(private=True)
+        def func(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            zeros = R.zeros(R.shape([m, n]), dtype="float32")
+            out = R.add(A, zeros)
+            return out
+
+
+class TestRemoveExtraShapeVariables(BaseCompare):
+    """Remove parameters that only serve to define existing symbolic variables
+
+    If a `R.Shape` parameter provides a definition of a symbolic
+    variable, but that symbolic variable can be determined from a
+    different parameter, then the `R.Shape` parameter can be removed.
+    """
+
+    @I.ir_module
+    class Before:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            return Before.func(A, R.shape([m, n]))
+
+        @R.function(private=True)
+        def func(
+            A: R.Tensor(["m", "n"], "float32"),
+            _: R.Shape(["m", "n"]),
+        ) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            zeros = R.zeros(R.shape([m, n]), dtype="float32")
+            out = R.add(A, zeros)
+            return out
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def main(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            return Expected.func(A)
+
+        @R.function(private=True)
+        def func(A: R.Tensor(["m", "n"], "float32")) -> R.Tensor(["m", "n"], "float32"):
+            m = T.int64()
+            n = T.int64()
+            zeros = R.zeros(R.shape([m, n]), dtype="float32")
+            out = R.add(A, zeros)
+            return out
+
+
 if __name__ == "__main__":
     tvm.testing.main()