sunggg commented on code in PR #15842:
URL: https://github.com/apache/tvm/pull/15842#discussion_r1358446279
##########
tests/python/relax/test_transform_legalize_ops.py:
##########
@@ -282,5 +284,77 @@ def main(A: R.Tensor([16, 32]), B: R.Tensor([32, 8])) ->
R.Tensor([16, 8]):
assert err_message.startswith("To legalize R.matmul")
+emit_legalization_through_builder = tvm.testing.parameter(
+ by_dict={
+ "return_relax_expr": False,
+ "return_relax_var": True,
+ }
+)
+
+
[email protected]
+def custom_op(emit_legalization_through_builder):
+ op_name = "custom_op.matmul_bias_add"
+
+ def infer_struct_info(call: relax.Call, context):
+ activations, weight, bias = call.args
+
+ matmul_call = relax.op.matmul(activations, weight)
+ matmul_sinfo =
tvm.ir.Op.get("relax.matmul").get_attr("FInferStructInfo")(
+ matmul_call, context
+ )
+
+ matmul_var = relax.Var("dummy_var", matmul_sinfo)
+ add_call = matmul_var + bias
+ add_sinfo =
tvm.ir.Op.get("relax.add").get_attr("FInferStructInfo")(add_call, context)
+
+ return add_sinfo
+
+ def legalize(bb: relax.BlockBuilder, call: relax.Call):
+ activations, weight, bias = call.args
+ legalized = relax.op.matmul(activations, weight) + bias
+ if emit_legalization_through_builder:
+ legalized = bb.emit(legalized)
+ return legalized
+
+ op_attrs = {
+ "FInferStructInfo": infer_struct_info,
+ "FLegalize": legalize,
+ "FPurity": True,
+ }
+
+ for key, value in op_attrs.items():
+ tvm.ir.register_op_attr(op_name, key, value)
+
+ op = tvm.ir.Op.get(op_name)
+ yield op
+
+ for key in op_attrs:
+ op.reset_attr(key)
+
+
+def test_recursive_legalization(custom_op):
+ """Legalization of an operator may produce new operators requiring
legalization"""
+
+ @I.ir_module
+ class Before:
+ @R.function
+ def main(
+ A: R.Tensor([16, 32, 64], "float32"),
+ Weight: R.Tensor([64, 128], "float32"),
+ Bias: R.Tensor([16, 32, 128], "float32"),
+ ):
+ return relax.Call(custom_op, [A, Weight, Bias])
+
+ AfterFirstIter = LegalizeOps()(Before)
Review Comment:
Does user need to perform `LegalizeOps` passes depending on their custom
ops? For example, user needs to call twice.
##########
tests/python/relax/test_transform_legalize_ops.py:
##########
@@ -282,5 +284,77 @@ def main(A: R.Tensor([16, 32]), B: R.Tensor([32, 8])) ->
R.Tensor([16, 8]):
assert err_message.startswith("To legalize R.matmul")
+emit_legalization_through_builder = tvm.testing.parameter(
+ by_dict={
+ "return_relax_expr": False,
+ "return_relax_var": True,
+ }
+)
+
+
[email protected]
+def custom_op(emit_legalization_through_builder):
+ op_name = "custom_op.matmul_bias_add"
+
+ def infer_struct_info(call: relax.Call, context):
+ activations, weight, bias = call.args
+
+ matmul_call = relax.op.matmul(activations, weight)
+ matmul_sinfo =
tvm.ir.Op.get("relax.matmul").get_attr("FInferStructInfo")(
+ matmul_call, context
+ )
+
+ matmul_var = relax.Var("dummy_var", matmul_sinfo)
+ add_call = matmul_var + bias
+ add_sinfo =
tvm.ir.Op.get("relax.add").get_attr("FInferStructInfo")(add_call, context)
+
+ return add_sinfo
+
+ def legalize(bb: relax.BlockBuilder, call: relax.Call):
Review Comment:
We have a similar pass although this does not support any recursion:
https://github.com/apache/tvm/blob/unity/python/tvm/relax/transform/transform.py#L994
Is there any use-case for recursion? Or is it more like a future-proof?
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