sfvaroglu commented on a change in pull request #10718:
URL: https://github.com/apache/tvm/pull/10718#discussion_r836722294
##########
File path: src/relay/qnn/op/op_common.h
##########
@@ -243,10 +246,62 @@ static inline bool QnnBroadcastRel(const Array<Type>&
types, int num_inputs, con
return false;
}
}
- ICHECK(IsScalarType(types[2], DataType::Float(32))); // lhs_scale
- ICHECK(IsScalarType(types[3], DataType::Int(32))); // lhs_zero_point
- ICHECK(IsScalarType(types[4], DataType::Float(32))); // rhs_scale
- ICHECK(IsScalarType(types[5], DataType::Int(32))); // rhs_zero_point
+
+ const auto* lhs_data = types[0].as<TensorTypeNode>();
+ const auto* rhs_data = types[1].as<TensorTypeNode>();
+
+ if (lhs_data == nullptr || rhs_data == nullptr) {
+ return false;
+ }
+
+ const BroadcastAttrs* broadcast_attrs = attrs.as<BroadcastAttrs>();
+ int lhs_axis = broadcast_attrs->lhs_axis;
+ int rhs_axis = broadcast_attrs->rhs_axis;
+
+ auto lhs_rank = static_cast<int>(lhs_data->shape.size());
+ auto rhs_rank = static_cast<int>(rhs_data->shape.size());
+
+ lhs_axis = (lhs_axis < 0) ? ((lhs_rank > 0) ? lhs_rank + lhs_axis : 0) :
lhs_axis;
Review comment:
I couldn't see a less confusing/ugly way to refactor this out
##########
File path: src/relay/qnn/op/mul.cc
##########
@@ -51,44 +53,108 @@ Expr QnnMulCanonicalize(const Attrs& attrs, const
Array<Expr>& new_args,
const auto int32_dtype = DataType::Int(32);
const auto float32_dtype = DataType::Float(32);
- /*
- A tensor multiplication c = a * b can be written in terms of respective
- quantized tensors, scales and zero points as
- S_c * (Q_c - zp_c) = S_a * (Q_a - zp_a) * S_b * (Q_b - zp_b).
-
- We can consider the product (Q_a - zp_a) * (Q_b - zp_b) as a different
- quantized tensor of c, Q', with corresponding scale S' = S_a * S_b and zp' =
- 0. The quantized multiplication then becomes
- Q_c = S'/S_c Q' + z_c,
- which is essentially a requantization of tensor Q' into tensor Q_c.
- */
-
- auto lhs_shifted = Cast(args.lhs, int32_dtype);
- auto rhs_shifted = Cast(args.rhs, int32_dtype);
-
- auto zero_scalar = MakeConstantScalar(int32_dtype, 0);
- if (!IsEqualScalar(args.lhs_zero_point, zero_scalar)) {
- lhs_shifted = Subtract(lhs_shifted, args.lhs_zero_point);
+ const auto* broadcast_attrs = attrs.as<BroadcastAttrs>();
+ ICHECK(broadcast_attrs != nullptr);
+
+ auto lhs_axis = broadcast_attrs->lhs_axis;
+ auto rhs_axis = broadcast_attrs->rhs_axis;
+
+ if (lhs_axis == -1 && rhs_axis == -1) {
Review comment:
Fixed
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