sunggg commented on code in PR #15842:
URL: https://github.com/apache/tvm/pull/15842#discussion_r1364127634
##########
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:
Interesting! I did not know there is a training-specific version of batch
norm. SGTM. Let's discuss about it in the follow-up PR.
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