[GitHub] [tvm] Lunderberg commented on a diff in pull request #15509: [Unity] Implement relax.Function.bind_symbolic_vars

["Lunderberg (via GitHub)" <gi...@apache.org>]

Lunderberg commented on code in PR #15509:
URL: https://github.com/apache/tvm/pull/15509#discussion_r1290609732


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tests/python/relax/test_bind_symbolic_vars.py:
##########
@@ -0,0 +1,205 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import pytest
+
+import tvm
+import tvm.testing
+from tvm.script import relax as R, tir as T
+
+replace_by_tir_var = tvm.testing.parameter(
+    by_dict={"replace-by-string": False, "replace-by-tir-var": True}
+)
+
+
+def test_bind_static_value(replace_by_tir_var):
+    """Symbolic vars may be replaced
+
+    The replaced variables may be given either as strings, or as TIR variables
+    """
+
+    @R.function(private=True)
+    def before(A: R.Tensor(("M", "K")), B: R.Tensor(("K", "N"))) -> R.Tensor(("M", "N")):
+        return R.matmul(A, B)
+
+    @R.function(private=True)
+    def expected(A: R.Tensor((128, 64)), B: R.Tensor((64, 32))) -> R.Tensor((128, 32)):
+        return R.matmul(A, B)
+
+    if replace_by_tir_var:
+        M, K = before.params[0].struct_info.shape
+        _, N = before.params[1].struct_info.shape
+        symbolic_var_map = {M: 128, K: 64, N: 32}
+    else:
+        symbolic_var_map = {"M": 128, "K": 64, "N": 32}
+
+    after = before.bind_symbolic_vars(symbolic_var_map)
+    tvm.ir.assert_structural_equal(expected, after)
+
+
+def test_error_with_duplicate_var_names():
+    """Duplicate variable names may not be replaced by string

Review Comment:
   On the C++ side, both TIR variables and Relax variables have reference identity, and duplicate names can exist within the same function.  (Technically, the variables only have a `name_hint`, not a name, which I believe is to avoid the assumption of unique names.  (Though, double parenthetically, some of the python APIs expose `name_hint` as `name`.))  TVMScript has a tighter restriction, where variables tracked by the parser may not repeat within the same scope.  In this test, I define the variables with the same TIR variable name outside of TVMScript, to produce the ambiguous test case anyways.
   
   As a bit of fun, the function can be inspected, and even though TVMScript modifies the variable names to avoid printing duplicate variables, the underlying objects still have duplicate names.
   
   ```python
   M = tir.Var("M", "int64")
   N1 = tir.Var("N", "int64")
   N2 = tir.Var("N", "int64")
   
   dtype = "float32"
   
   
   @R.function
   def func(
       A: R.Tensor([M, N2], dtype), B: R.Tensor([N1, N2], dtype)
   ) -> R.Tensor([M, N1], dtype):
       A: R.Tensor([M, N2], dtype) = A
       B: R.Tensor([N1, N2], dtype) = B
       out = R.linear(A, B)
       return out
   
   
   # Prints  B: R.Tensor(("N", "N_1"), dtype="float32")
   func.show()
   # But the true name can still be extracted
   assert func.params[1].struct_info.shape[0].name == "N"
   assert func.params[1].struct_info.shape[1].name == "N"
   # And the function is well-formed, despite the duplicate variable
   # names.
   assert tvm.relax.analysis.well_formed(tvm.IRModule.from_expr(func))
   ```



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