[GitHub] [tvm] anwang2009 commented on a change in pull request #8907: Add support for QLinearConcat contrib op

[GitBox <gi...@apache.org>]

anwang2009 commented on a change in pull request #8907:
URL: https://github.com/apache/tvm/pull/8907#discussion_r701329324



##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -3306,6 +3290,32 @@ def get_scalar(x, dtype="float32"):
         return _qnn.op.quantize(out, y_scale, y_zero_point, out_dtype=dtype)
 
 
+class QLinearConcat(OnnxOpConverter):
+    """Operator converter for QLinearConcat from Microsoft onnxruntime contrib opset."""
+
+    @classmethod
+    def _impl_v1(cls, inputs, attr, params):
+        # which axis to concat on
+        axis = attr["axis"]
+
+        y_scale = fold_constant(get_scalar(inputs[0], params))
+        y_zero_point = get_scalar(inputs[1], params, "int32")
+
+        # input tensors, scales, zero_points
+        assert (
+            len(inputs) % 3 == 2
+        ), "Additional input count must be a multiple of 3 -- tensor/scale/zero_point tuples"
+        tensors = []
+        scales = []
+        zero_points = []
+        for i in range(2, len(inputs), 3):

Review comment:
       yeah, the onnxruntime implementation expects a sequence split across inputs as well, not tuples




-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

To unsubscribe, e-mail: commits-unsubscribe@tvm.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org