anijain2305 commented on a change in pull request #6675:
URL: https://github.com/apache/incubator-tvm/pull/6675#discussion_r503645960
##########
File path: src/relay/qnn/op/requantize.cc
##########
@@ -155,17 +155,20 @@ Expr RequantizeLower(const Expr& input_tensor, const
Expr& input_scale,
if (!IsEqualScalar(input_scale, output_scale)) {
int32_t fixed_point_multiplier, shift;
std::tie(fixed_point_multiplier, shift) =
GetFixedPointMultiplierShift(double_multiplier);
-
const bool is_upward_rounding = (param->rounding == "UPWARD");
- // When using upward rounding (i.e., x.5 rounded to x+1), leverage
- // the FixedPointMultiply operator
- scaled_int32_t =
- (is_upward_rounding
- ? FixedPointMultiply(scaled_int32_t, fixed_point_multiplier,
shift)
- : FixedPointMultiplyToNearest(scaled_int32_t,
double_multiplier, input_shape));
+ if (is_upward_rounding && fixed_point_multiplier == (1 << 30)) {
Review comment:
So, we use 'frexp' to represent a floating point numbers. It gives a
float significant which is between [0.5, 1). For power of 2, it is always 0.5.
We convert the float significand into a fixed point 32-bit integer, where
decimal point is between the first and second bit. 0.5 in this representation =
1 << 30
##########
File path: src/relay/qnn/op/requantize.cc
##########
@@ -155,17 +155,20 @@ Expr RequantizeLower(const Expr& input_tensor, const
Expr& input_scale,
if (!IsEqualScalar(input_scale, output_scale)) {
int32_t fixed_point_multiplier, shift;
std::tie(fixed_point_multiplier, shift) =
GetFixedPointMultiplierShift(double_multiplier);
-
const bool is_upward_rounding = (param->rounding == "UPWARD");
- // When using upward rounding (i.e., x.5 rounded to x+1), leverage
- // the FixedPointMultiply operator
- scaled_int32_t =
- (is_upward_rounding
- ? FixedPointMultiply(scaled_int32_t, fixed_point_multiplier,
shift)
- : FixedPointMultiplyToNearest(scaled_int32_t,
double_multiplier, input_shape));
+ if (is_upward_rounding && fixed_point_multiplier == (1 << 30)) {
Review comment:
So, we use `frexp` to represent a floating point numbers. It gives a
float significant which is between [0.5, 1). For power of 2, it is always 0.5.
We convert the float significand into a fixed point 32-bit integer, where
decimal point is between the first and second bit. 0.5 in this representation =
1 << 30
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