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Fix uint8 MUL operator with broadcast

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The MulSimpleBroadcast function must use MultiplyByQuantizedMultiplier instead of MultiplyByQuantizedMultiplierSmallerThanOneExp as the MUL operator doesn't guarantee that the quantized multiplier is smaller than 1.
This commit is contained in:
Thibaut Goetghebuer-Planchon 2020-08-28 09:51:01 +01:00
parent cd035167ca
commit c1348607d1
2 changed files with 22 additions and 15 deletions
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6
tensorflow/lite/kernels/internal/optimized/optimized_ops.h
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@ -2536,9 +2536,9 @@ inline void MulSimpleBroadcast(int size, const ArithmeticParams& params,
const int32 input2_val = params.input2_offset + input2_data[i];
const int32 unclamped_result =
params.output_offset +
MultiplyByQuantizedMultiplierSmallerThanOneExp(input1_val * input2_val,
params.output_multiplier,
params.output_shift);
MultiplyByQuantizedMultiplier(input1_val * input2_val,
params.output_multiplier,
params.output_shift);
const int32 clamped_output =
std::min(params.quantized_activation_max,
std::max(params.quantized_activation_min, unclamped_result));

31
tensorflow/lite/kernels/mul_test.cc
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@ -395,19 +395,26 @@ void WithBroadcast() {
float kQuantizedTolerance = GetTolerance(-3.0, 3.0);
std::vector<std::vector<int>> test_shapes = {
{6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
// Test with a smaller than 1 and greater than 1 quantization multiplier
std::vector<std::pair<float, float>> test_input_range = {{-3.0, 3.0},
{-6.0, 6.0}};
for (int i = 0; i < test_shapes.size(); ++i) {
QuantizedMulOpModel m({tensor_type, test_shapes[i], -3.0, 3.0},
{tensor_type, {}, -3.0, 3.0}, // always a scalar
{tensor_type, {}, -3.0, 3.0},
ActivationFunctionType_NONE);
m.QuantizeAndPopulate<integer_dtype>(m.input1(),
{-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
m.QuantizeAndPopulate<integer_dtype>(m.input2(), {0.1});
m.Invoke();
EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear(
{-0.2, 0.02, 0.07, 0.08, 0.11, 0.2}, kQuantizedTolerance)))
<< "With shape number " << i;
for (int j = 0; j < test_input_range.size(); ++j) {
const std::pair<float, float>& input_range = test_input_range[j];
QuantizedMulOpModel m(
{tensor_type, test_shapes[i], input_range.first, input_range.second},
{tensor_type, {}, input_range.first, input_range.second},
{tensor_type, {}, -0.2, 0.2}, ActivationFunctionType_NONE);
m.QuantizeAndPopulate<integer_dtype>(m.input1(),
{-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
m.QuantizeAndPopulate<integer_dtype>(m.input2(), {0.1});
m.Invoke();
EXPECT_THAT(
m.GetDequantizedOutput<integer_dtype>(),
ElementsAreArray(ArrayFloatNear({-0.2, 0.02, 0.07, 0.08, 0.11, 0.2},
kQuantizedTolerance)))
<< "With shape number " << i << " and range number " << j;
}
}
}