anwang2009 commented on a change in pull request #10718:
URL: https://github.com/apache/tvm/pull/10718#discussion_r833480692
##########
File path: tests/python/relay/test_pass_fake_quantization_to_integer.py
##########
@@ -600,13 +600,100 @@ def test_fake_quantize_binary(operator):
compare_fq_to_int(op, [x_np, y_np])
[email protected](
+ "operator",
+ [relay.op.add, relay.op.multiply, relay.op.subtract, relay.op.minimum,
relay.op.maximum],
+)
+def test_fake_quantize_binary_per_channel(operator):
+ def verify_binary_per_channel(lhs_scale, rhs_scale, lhs_zp, rhs_zp,
out_zp, lhs_axis, rhs_axis):
+ if operator == relay.op.multiply:
+ out_scale = relay.const(2.0)
+ rhs_axis = lhs_axis # TODO: Support different axes for
per-channel quantized multiply
+ else:
+ out_scale = relay.const(0.1)
+ x = relay.var("x", shape=[1, 3, 224, 224], dtype="int8")
+ x = relay.qnn.op.dequantize(x, relay.const(lhs_scale),
relay.const(lhs_zp), axis=lhs_axis)
+
+ y = relay.var("y", shape=[1, 3, 224, 224], dtype="int8")
+ y = relay.qnn.op.dequantize(y, relay.const(rhs_scale),
relay.const(rhs_zp), axis=rhs_axis)
+
+ op = operator(x, y)
+ if operator == relay.op.multiply:
+ out_scale = relay.const(2.0)
+ else:
+ out_scale = relay.const(0.1)
Review comment:
nit: this block isn't needed, you've set out_scale above
##########
File path: src/relay/qnn/op/op_common.h
##########
@@ -286,7 +346,10 @@ static inline bool QnnBroadcastRel(const Array<Type>&
types, int num_inputs, con
.add_argument("rhs_zero_point", "Tensor", "The zero_point of the rhs
tensor.") \
.add_argument("output_scale", "Tensor", "The scale of the output
tensor.") \
.add_argument("output_zero_point", "Tensor", "The zero_point of the
output tensor.") \
+ .add_argument("lhs_axis", "Tensor", "The channel quantization of the lhs
tensor.") \
+ .add_argument("rhs_axis", "Tensor", "The channel quantization of the rhs
tensor.") \
.add_type_rel("QnnBroadcast", QnnBroadcastRel)
\
+ .set_attr<TOpPattern>("TOpPattern", kOpaque)
\
Review comment:
Just confirming my assumptions, this is to prevent qnn ops from being
fused?
##########
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_data->shape.size() +
lhs_axis : 0) : lhs_axis;
+ rhs_axis = (rhs_axis < 0) ? ((rhs_rank > 0) ? rhs_data->shape.size() +
rhs_axis : 0) : rhs_axis;
Review comment:
nit
```suggestion
lhs_axis = (lhs_axis < 0) ? ((lhs_rank > 0) ? lhs_rank + lhs_axis : 0) :
lhs_axis;
rhs_axis = (rhs_axis < 0) ? ((rhs_rank > 0) ? rhs_rank + rhs_axis : 0) :
rhs_axis;
```
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