shoubhik commented on a change in pull request #5153: Adding support for QNN
subtract op
URL: https://github.com/apache/incubator-tvm/pull/5153#discussion_r398857587
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File path: src/relay/qnn/op/add.cc
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@@ -97,39 +66,29 @@ Expr QnnAddCanonicalize(const Attrs& attrs, const
Array<Expr>& new_args,
// Q_c = Q_a' + Q_b' - zp_c
// The add op is done in int32 precision.
- // Requantize LHS if necessary.
- auto requantized_lhs = lhs;
- if (!IsEqualScalar(lhs_scale, output_scale) ||
- !IsEqualScalar(lhs_zero_point, output_zero_point)) {
- requantized_lhs = Requantize(lhs, input_shape, lhs_scale, lhs_zero_point,
output_scale,
- output_zero_point, DataType::Int(32));
- } else {
- requantized_lhs = Cast(requantized_lhs, DataType::Int(32));
- }
- // Requantize RHS if necessary.
- auto requantized_rhs = rhs;
- if (!IsEqualScalar(rhs_scale, output_scale) ||
- !IsEqualScalar(rhs_zero_point, output_zero_point)) {
- requantized_rhs = Requantize(rhs, input_shape, rhs_scale, rhs_zero_point,
output_scale,
- output_zero_point, DataType::Int(32));
- } else {
- requantized_rhs = Cast(requantized_rhs, DataType::Int(32));
- }
+ // Requantize LHS if necessary. Computes Q_a'
+ auto requantized_lhs = requantizeIfNeeded(args.lhs, args.lhs_scale,
+ args.lhs_zero_point,
+ args.output_scale,
args.output_zero_point,
+ inputShapeAndDtype.input_shape);
+ // Requantize RHS if necessary. Computes Q_b'
+ auto requantized_rhs = requantizeIfNeeded(args.rhs, args.rhs_scale,
+ args.rhs_zero_point,
+ args.output_scale,
args.output_zero_point,
+ inputShapeAndDtype.input_shape);
+ // Computes Q_a' + Q_b'
auto output = Add(requantized_lhs, requantized_rhs);
- // Subtract zero point.
+ // Subtract zero point. Computes (Q_a' + Q_b') - zp_c
auto zero_scalar = MakeConstantScalar(DataType::Int(32), 0);
- if (!IsEqualScalar(output_zero_point, zero_scalar)) {
- output = Subtract(output, output_zero_point);
+ if (!IsEqualScalar(args.output_zero_point, zero_scalar)) {
+ output = Subtract(output, args.output_zero_point);
}
// Go back to lower precision.
- auto q_min = GetQmin(input_dtype);
- auto q_max = GetQmax(input_dtype);
- output = Clip(output, q_min, q_max);
- return Cast(output, input_dtype);
+ return lowerPrecision(output, inputShapeAndDtype.input_dtype);
Review comment:
Well `ShrinkBackToOutDtype` implies that the the output will be converted to
output dtype but we always convet to input dtype (what goes in comes out).
`ConvertDtype(fromExpression, toTargetDtype)` is the convention I am going
with. What do you think?
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