tmoreau89 commented on a change in pull request #8952:
URL: https://github.com/apache/tvm/pull/8952#discussion_r703749864
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File path: python/tvm/relay/frontend/onnx.py
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@@ -3333,6 +3333,44 @@ def get_scalar(x, dtype="float32"):
return _qnn.op.quantize(out, c_scale, c_zero_point, out_dtype=dtype)
+class QLinearMatMul(OnnxOpConverter):
+ """Operator converter for QLinearMatMul from Microsoft onnxruntime contrib
opset."""
+
+ @classmethod
+ def _impl_v10(cls, inputs, attr, params):
+ def get_scalar(x, dtype="float32"):
+ if isinstance(x, _expr.Var) and x.name_hint in params:
+ return _op.const(params[x.name_hint].numpy(), dtype)
+ rank = len(infer_shape(x))
+ assert rank <= 1, "QLinearMatMul scale and zero_point input must
be scalars"
+ if rank == 1:
+ x = _op.squeeze(x, [0])
+ return _op.cast(x, dtype)
+
+ a = inputs[0]
+ a_scale = get_scalar(inputs[1])
+ a_zero_point = get_scalar(inputs[2], "int32")
+
+ b = inputs[3]
+ b_scale = get_scalar(inputs[4])
+ b_zero_point = get_scalar(inputs[5], "int32")
+
+ y_scale = fold_constant(get_scalar(inputs[6]))
+ y_zero_point = get_scalar(inputs[7], "int32")
+
+ dtype = infer_type(a).checked_type.dtype
+
+ a_rank = len(infer_shape(a))
+ b_rank = len(infer_shape(b))
+
+ assert ((a_rank == 2) and (b_rank == 2)), "QLinearMatMul importer
currently requires both 'a' and 'b' tensors to be 2D, but rank(a)={},
rank(b)={}".format(a_rank, b_rank)
Review comment:
I believe that should be easily resolved with `make format`:
https://github.com/apache/tvm/blob/cf19c889214ca9a1b8c420baff35aa10986b3d9c/Makefile#L94-L97
Then for sanity check, `make pylint`
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