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Lite: Kernel Util bug fix #26834

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merged 1 commit into from Apr 30, 2019
Merged

Lite: Kernel Util bug fix #26834

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9 changes: 6 additions & 3 deletions tensorflow/lite/kernels/kernel_util.cc
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Expand Up @@ -110,11 +110,14 @@ TfLiteStatus GetQuantizedConvolutionMultipler(TfLiteContext* context,
TfLiteTensor* output,
double* multiplier) {
const double input_product_scale = input->params.scale * filter->params.scale;
const double bias_scale = bias->params.scale;
// TODO(ahentz): The following conditions must be guaranteed by the training
// pipeline.
TF_LITE_ENSURE(context, std::abs(input_product_scale - bias_scale) <=
1e-6 * std::min(input_product_scale, bias_scale));
if (bias) {
const double bias_scale = bias->params.scale;
TF_LITE_ENSURE(context,
std::abs(input_product_scale - bias_scale) <=
1e-6 * std::min(input_product_scale, bias_scale));
}
return GetQuantizedConvolutionMultipler(context, input, filter, output,
multiplier);
}
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179 changes: 179 additions & 0 deletions tensorflow/lite/kernels/kernel_util_test.cc
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Expand Up @@ -360,6 +360,185 @@ TEST_F(KernelUtilTest, CheckAndPopulateZeroValue) {
TfLiteTensorFree(&output);
}

TEST_F(KernelUtilTest, CheckAndPopulateUint8) {
// Create input.
TfLiteTensor input;
input.type = kTfLiteUInt8;
input.allocation_type = kTfLiteArenaRw;
input.dims = TfLiteIntArrayCreate(1);
input.dims->data[0] = 2;
TfLiteQuantizationParams input_quant = {1, 5};
input.params = input_quant;
input.quantization.type = kTfLiteAffineQuantization;
auto* input_params = reinterpret_cast<TfLiteAffineQuantization*>(
malloc(sizeof(TfLiteAffineQuantization)));
input_params->scale = TfLiteFloatArrayCreate(1);
input_params->scale->data[0] = 1;
input_params->zero_point = TfLiteIntArrayCreate(1);
input_params->zero_point->data[0] = 5;
input.quantization.params = reinterpret_cast<void*>(input_params);

// Create filter.
TfLiteTensor filter;
filter.type = kTfLiteUInt8;
filter.allocation_type = kTfLiteArenaRw;
filter.dims = TfLiteIntArrayCreate(4);
filter.dims->data[0] = 3;
filter.dims->data[1] = 4;
filter.dims->data[2] = 5;
filter.dims->data[3] = 6;
TfLiteQuantizationParams filter_quant = {4.6566129e-10, 0};
filter.params = filter_quant;
filter.quantization.type = kTfLiteAffineQuantization;
auto* filter_params = reinterpret_cast<TfLiteAffineQuantization*>(
malloc(sizeof(TfLiteAffineQuantization)));
filter_params->scale = TfLiteFloatArrayCreate(1);
int32_t two_pow_neg_31 = 0x30000000; // 2^-31 so shift = -30.
filter_params->scale->data[0] = *reinterpret_cast<float*>(&two_pow_neg_31);
filter_params->zero_point = TfLiteIntArrayCreate(1);
filter_params->zero_point->data[0] = 0;
filter_params->quantized_dimension = 0;
filter.quantization.params = reinterpret_cast<void*>(filter_params);

// Create bias.
TfLiteTensor bias;
bias.type = kTfLiteInt32;
bias.allocation_type = kTfLiteArenaRw;
bias.dims = TfLiteIntArrayCreate(4);
TfLiteQuantizationParams bias_quant = {4.6566129e-10, 9};
bias.params = bias_quant;
bias.quantization.type = kTfLiteAffineQuantization;
auto* bias_params = reinterpret_cast<TfLiteAffineQuantization*>(
malloc(sizeof(TfLiteAffineQuantization)));
bias_params->scale = TfLiteFloatArrayCreate(1);
bias_params->scale->data[0] = 4.6566129e-10; // 2^-31
bias_params->zero_point = TfLiteIntArrayCreate(1);
bias_params->zero_point->data[0] = 11;
bias.quantization.params = reinterpret_cast<void*>(bias_params);

// Create output.
TfLiteTensor output;
output.type = kTfLiteUInt8;
output.allocation_type = kTfLiteArenaRw;
output.dims = nullptr;
TfLiteQuantizationParams output_quant = {1, -128};
output.params = output_quant;
output.quantization.type = kTfLiteAffineQuantization;
auto* output_params = reinterpret_cast<TfLiteAffineQuantization*>(
malloc(sizeof(TfLiteAffineQuantization)));
output_params->scale = TfLiteFloatArrayCreate(1);
output_params->scale->data[0] = 1;
output_params->zero_point = TfLiteIntArrayCreate(1);
output_params->zero_point->data[0] = -128;
output.quantization.params = reinterpret_cast<void*>(output_params);

// Create call parameters.
TfLiteContext context;
int32_t multiplier;
int shift;
int32_t output_activation_min;
int32_t output_activation_max;
std::vector<int32_t> per_channel_multiplier(1);
std::vector<int> per_channel_shift(1);

// Call and verify results for per channel case.
EXPECT_EQ(
kTfLiteOk,
PopulateConvolutionQuantizationParams(
&context, &input, &filter, &bias, &output, kTfLiteActRelu,
&multiplier, &shift, &output_activation_min, &output_activation_max,
per_channel_multiplier.data(), per_channel_shift.data()));
EXPECT_THAT(per_channel_multiplier, ::testing::ElementsAre(1073741824));
EXPECT_THAT(per_channel_shift, ::testing::ElementsAre(-30));

// Release.
TfLiteTensorFree(&input);
TfLiteTensorFree(&filter);
TfLiteTensorFree(&bias);
TfLiteTensorFree(&output);
}

TEST_F(KernelUtilTest, CheckAndPopulateWithoutBias) {
// Create input.
TfLiteTensor input;
input.type = kTfLiteUInt8;
input.allocation_type = kTfLiteArenaRw;
input.dims = TfLiteIntArrayCreate(1);
input.dims->data[0] = 2;
TfLiteQuantizationParams input_quant = {1, 5};
input.params = input_quant;
input.quantization.type = kTfLiteAffineQuantization;
auto* input_params = reinterpret_cast<TfLiteAffineQuantization*>(
malloc(sizeof(TfLiteAffineQuantization)));
input_params->scale = TfLiteFloatArrayCreate(1);
input_params->scale->data[0] = 1;
input_params->zero_point = TfLiteIntArrayCreate(1);
input_params->zero_point->data[0] = 5;
input.quantization.params = reinterpret_cast<void*>(input_params);

// Create filter.
TfLiteTensor filter;
filter.type = kTfLiteUInt8;
filter.allocation_type = kTfLiteArenaRw;
filter.dims = TfLiteIntArrayCreate(4);
filter.dims->data[0] = 3;
filter.dims->data[1] = 4;
filter.dims->data[2] = 5;
filter.dims->data[3] = 6;
TfLiteQuantizationParams filter_quant = {4.6566129e-10, 0};
filter.params = filter_quant;
filter.quantization.type = kTfLiteAffineQuantization;
auto* filter_params = reinterpret_cast<TfLiteAffineQuantization*>(
malloc(sizeof(TfLiteAffineQuantization)));
filter_params->scale = TfLiteFloatArrayCreate(1);
int32_t two_pow_neg_31 = 0x30000000; // 2^-31 so shift = -30.
filter_params->scale->data[0] = *reinterpret_cast<float*>(&two_pow_neg_31);
filter_params->zero_point = TfLiteIntArrayCreate(1);
filter_params->zero_point->data[0] = 0;
filter_params->quantized_dimension = 0;
filter.quantization.params = reinterpret_cast<void*>(filter_params);

// Create output.
TfLiteTensor output;
output.type = kTfLiteUInt8;
output.allocation_type = kTfLiteArenaRw;
output.dims = nullptr;
TfLiteQuantizationParams output_quant = {1, -128};
output.params = output_quant;
output.quantization.type = kTfLiteAffineQuantization;
auto* output_params = reinterpret_cast<TfLiteAffineQuantization*>(
malloc(sizeof(TfLiteAffineQuantization)));
output_params->scale = TfLiteFloatArrayCreate(1);
output_params->scale->data[0] = 1;
output_params->zero_point = TfLiteIntArrayCreate(1);
output_params->zero_point->data[0] = -128;
output.quantization.params = reinterpret_cast<void*>(output_params);

// Create call parameters.
TfLiteContext context;
int32_t multiplier;
int shift;
int32_t output_activation_min;
int32_t output_activation_max;
std::vector<int32_t> per_channel_multiplier(1);
std::vector<int> per_channel_shift(1);

// Call and verify results for per channel case.
EXPECT_EQ(
kTfLiteOk,
PopulateConvolutionQuantizationParams(
&context, &input, &filter, nullptr, &output, kTfLiteActRelu,
&multiplier, &shift, &output_activation_min, &output_activation_max,
per_channel_multiplier.data(), per_channel_shift.data()));
EXPECT_THAT(per_channel_multiplier, ::testing::ElementsAre(1073741824));
EXPECT_THAT(per_channel_shift, ::testing::ElementsAre(-30));

// Release.
TfLiteTensorFree(&input);
TfLiteTensorFree(&filter);
TfLiteTensorFree(&output);
}

} // namespace
} // namespace tflite

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