anwang2009 commented on a change in pull request #10450:
URL: https://github.com/apache/tvm/pull/10450#discussion_r818410945
##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -3935,6 +3935,58 @@ def try_resolve_to_const(x, dtype_override=None):
return y
+class MatMulInteger(OnnxOpConverter):
+ """Operator converter for MatMulInteger."""
+
+ @classmethod
+ def _impl_v10(cls, inputs, attr, params):
+ a = inputs[0]
+ b = inputs[1]
+
+ a_dtype = infer_type(a).checked_type.dtype
+ b_dtype = infer_type(b).checked_type.dtype
+
+ assert a_dtype in ("int8", "uint8"), "MatMulInteger: invalid dtype for
first input"
+ assert b_dtype in ("int8", "uint8"), "MatMulInteger: invalid dtype for
second input"
+
+ assert a_dtype == b_dtype, "MatMulInteger: input dtypes must match"
+
+ a_scale = _op.const(1.0, dtype="float32")
+ b_scale = _op.const(1.0, dtype="float32")
+ out_scale = _op.const(1.0, dtype="float32")
+
+ a_zero_point = _op.const(0.0, dtype=a_dtype)
Review comment:
yeah zp isn't necessarily fixed to 0, but the docs say 0 should be the
default. I do this a few lines down:
```
if len(inputs) == 4:
a_zero_point = inputs[2]
b_zero_point = inputs[3]
```
From the example it looks like the scale factors are 1 by default.
##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -3935,6 +3935,58 @@ def try_resolve_to_const(x, dtype_override=None):
return y
+class MatMulInteger(OnnxOpConverter):
+ """Operator converter for MatMulInteger."""
+
+ @classmethod
+ def _impl_v10(cls, inputs, attr, params):
+ a = inputs[0]
Review comment:
modified `QLinearMatMul` converter to skip requantize in the
MatMulInteger case
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