ashutosh-arm commented on code in PR #12042:
URL: https://github.com/apache/tvm/pull/12042#discussion_r1018896087
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src/relay/qnn/op/dense.cc:
##########
@@ -47,12 +47,14 @@ bool QnnDenseRel(const Array<Type>& types, int num_inputs,
const Attrs& attrs,
if (data == nullptr || weight == nullptr) return false;
const auto* param = attrs.as<DenseAttrs>();
ICHECK(param != nullptr) << "DenseAttrs cannot be nullptr.";
- ICHECK(data->dtype == DataType::Int(8) || data->dtype == DataType::UInt(8))
- << "Expected quantized dense type(int8, uint8) for input but was " <<
data->dtype;
+ ICHECK(data->dtype == DataType::Int(8) || data->dtype == DataType::UInt(8) ||
+ data->dtype == DataType::Int(16) || data->dtype == DataType::UInt(16))
+ << "Expected quantized dense type(int8, uint8, int16, uint16) for input
but was "
+ << data->dtype;
ICHECK(weight->dtype == DataType::Int(8) || weight->dtype ==
DataType::UInt(8))
<< "Expected quantized dense type(int8, uint8) for weight but was " <<
weight->dtype;
- ICHECK(param->out_dtype == DataType::Int(32))
- << "Expected quantized dense type(int32) for output but was " <<
param->out_dtype;
+ ICHECK(param->out_dtype == DataType::Int(32) || param->out_dtype ==
DataType::Int(64))
+ << "Expected quantized dense type(int32, int64) for output but was " <<
param->out_dtype;
Review Comment:
Do we need additional dtype checks for bias as well based on output dtype?
If yes, then probably we should check the QNN Conv2D type checker too.
##########
tests/python/contrib/test_ethosn/test_convert_equivalents.py:
##########
@@ -227,7 +227,7 @@ def expected():
@requires_ethosn
@pytest.mark.parametrize(
"dtype,shape,constant_shape",
- [("int16", (1, 16, 12, 4), None)],
+ [("float32", (1, 16, 12, 4), None)],
Review Comment:
Just curious: why the dtype change?
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