ifdef out the test cases for Conv2D that are currently failing for hifimini.
With this change it should become clearer what test cases currently pass for the optimized xtensa kernels. Manually confirmed that the following command passes: ``` make -f tensorflow/lite/micro/tools/make/Makefile -j8 TARGET=xtensa OPTIMIZED_KERNEL_DIR=xtensa TARGET_ARCH=hifimini XTENSA_CORE=mini1m1m_RG test_kernel_conv_test ``` Addresses http://b/170321206
This commit is contained in:
Advait Jain
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2335ed169a
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a82c2c89a8
@ -92,6 +92,7 @@ TfLiteStatus ValidateConvGoldens(TfLiteTensor* tensors, int tensors_size,
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return kTfLiteOk;
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}
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#if !defined(XTENSA) // Needed to avoid build errors from unused functions.
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void TestConvFloat(const int* input_dims_data, const float* input_data,
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const int* filter_dims_data, const float* filter_data,
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const int* bias_dims_data, const float* bias_data,
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@ -226,6 +227,7 @@ void TestConvQuantizedPerChannel(
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output_data, output_dims_count, conv_params,
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1.0 /* tolerance */));
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}
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#endif // !defined(XTENSA)
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} // namespace
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} // namespace testing
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@ -233,6 +235,9 @@ void TestConvQuantizedPerChannel(
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TF_LITE_MICRO_TESTS_BEGIN
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#if !defined(XTENSA) // TODO(b/170321206): xtensa kernels are less general than
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// reference kernels and we ifdef out test cases that are
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// currently known to fail.
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TF_LITE_MICRO_TEST(SimpleTestFloat) {
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float output_data[tflite::testing::kOutputElements];
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@ -573,90 +578,6 @@ TF_LITE_MICRO_TEST(Kernel1x1QuantizedPerChannelRelu6) {
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&conv_params);
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}
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TF_LITE_MICRO_TEST(FilterDimsNotMatchingAffineQuantization) {
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const int output_dims_count = 12;
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int8_t output_data[output_dims_count];
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const float input_scale = 0.5f;
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const float output_scale = 1.0f;
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int8_t input_quantized[tflite::testing::kInputElements];
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int8_t filter_quantized[tflite::testing::kFilterElements];
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int32_t bias_quantized[tflite::testing::kBiasElements];
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int8_t golden_quantized[tflite::testing::kOutputElements];
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int zero_points[tflite::testing::kBiasElements + 1];
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float scales[tflite::testing::kBiasElements + 1];
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TfLiteIntArray* input_dims =
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tflite::testing::IntArrayFromInts(tflite::testing::kInputShape);
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TfLiteIntArray* filter_dims =
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tflite::testing::IntArrayFromInts(tflite::testing::kFilterShape);
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TfLiteIntArray* bias_dims =
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tflite::testing::IntArrayFromInts(tflite::testing::kBiasShape);
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TfLiteIntArray* output_dims =
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tflite::testing::IntArrayFromInts(tflite::testing::kOutputShape);
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int filter_zero_points[5];
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float filter_scales[5];
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TfLiteAffineQuantization filter_quant;
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TfLiteAffineQuantization bias_quant;
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TfLiteTensor input_tensor = tflite::testing::CreateQuantizedTensor(
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tflite::testing::kInputData, input_quantized, input_dims, input_scale, 0);
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TfLiteTensor filter_tensor =
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tflite::testing::CreateSymmetricPerChannelQuantizedTensor(
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tflite::testing::kFilterData, filter_quantized, filter_dims,
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filter_scales, filter_zero_points, &filter_quant,
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0 /* quantized dimension */);
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TfLiteTensor bias_tensor =
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tflite::testing::CreatePerChannelQuantizedBiasTensor(
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tflite::testing::kBiasData, bias_quantized, bias_dims, input_scale,
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&filter_scales[1], scales, zero_points, &bias_quant, 0);
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TfLiteTensor output_tensor = tflite::testing::CreateQuantizedTensor(
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output_data, output_dims, output_scale, 0 /* quantized dimension */);
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float input_scales[] = {1, input_scale};
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int input_zero_points[] = {1, 128};
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TfLiteAffineQuantization input_quant = {
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tflite::testing::FloatArrayFromFloats(input_scales),
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tflite::testing::IntArrayFromInts(input_zero_points), 0};
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input_tensor.quantization = {kTfLiteAffineQuantization, &input_quant};
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constexpr int inputs_size = 3;
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constexpr int outputs_size = 1;
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constexpr int tensors_size = inputs_size + outputs_size;
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TfLiteTensor tensors[tensors_size] = {
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input_tensor,
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filter_tensor,
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bias_tensor,
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output_tensor,
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};
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tflite::Quantize(tflite::testing::kGoldenData, golden_quantized,
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output_dims_count, output_scale, 0);
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// Set filter quant to mismatched dimension.
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TfLiteAffineQuantization* quant = reinterpret_cast<TfLiteAffineQuantization*>(
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filter_tensor.quantization.params);
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// Choose arbitrary incorrect scale and zero point sizes which are neither 1
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// (for broadcast case) nor the quantized dimension size.
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quant->scale->size = 2;
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TF_LITE_MICRO_EXPECT_EQ(
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kTfLiteError,
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tflite::testing::ValidateConvGoldens(
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tensors, tensors_size, golden_quantized, output_data,
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output_dims_count, &tflite::testing::common_conv_params));
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// Set scale back to correct dimension, and make zero point array too short.
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quant->scale->size = tflite::testing::kFilterShape[0];
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quant->zero_point->size = 2;
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TF_LITE_MICRO_EXPECT_EQ(
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kTfLiteError,
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tflite::testing::ValidateConvGoldens(
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tensors, tensors_size, golden_quantized, output_data,
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output_dims_count, &tflite::testing::common_conv_params));
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}
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TF_LITE_MICRO_TEST(BroadcastPerLayerQuantizationToPerChannelShouldMatchGolden) {
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const int output_dims_count = 12;
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int8_t output_data[output_dims_count];
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@ -743,6 +664,92 @@ TF_LITE_MICRO_TEST(BroadcastPerLayerQuantizationToPerChannelShouldMatchGolden) {
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output_dims_count, &tflite::testing::common_conv_params));
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}
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#endif // !defined(XTENSA)
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TF_LITE_MICRO_TEST(FilterDimsNotMatchingAffineQuantization) {
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const int output_dims_count = 12;
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int8_t output_data[output_dims_count];
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const float input_scale = 0.5f;
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const float output_scale = 1.0f;
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int8_t input_quantized[tflite::testing::kInputElements];
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int8_t filter_quantized[tflite::testing::kFilterElements];
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int32_t bias_quantized[tflite::testing::kBiasElements];
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int8_t golden_quantized[tflite::testing::kOutputElements];
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int zero_points[tflite::testing::kBiasElements + 1];
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float scales[tflite::testing::kBiasElements + 1];
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TfLiteIntArray* input_dims =
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tflite::testing::IntArrayFromInts(tflite::testing::kInputShape);
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TfLiteIntArray* filter_dims =
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tflite::testing::IntArrayFromInts(tflite::testing::kFilterShape);
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TfLiteIntArray* bias_dims =
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tflite::testing::IntArrayFromInts(tflite::testing::kBiasShape);
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TfLiteIntArray* output_dims =
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tflite::testing::IntArrayFromInts(tflite::testing::kOutputShape);
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int filter_zero_points[5];
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float filter_scales[5];
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TfLiteAffineQuantization filter_quant;
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TfLiteAffineQuantization bias_quant;
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TfLiteTensor input_tensor = tflite::testing::CreateQuantizedTensor(
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tflite::testing::kInputData, input_quantized, input_dims, input_scale, 0);
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TfLiteTensor filter_tensor =
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tflite::testing::CreateSymmetricPerChannelQuantizedTensor(
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tflite::testing::kFilterData, filter_quantized, filter_dims,
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filter_scales, filter_zero_points, &filter_quant,
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0 /* quantized dimension */);
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TfLiteTensor bias_tensor =
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tflite::testing::CreatePerChannelQuantizedBiasTensor(
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tflite::testing::kBiasData, bias_quantized, bias_dims, input_scale,
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&filter_scales[1], scales, zero_points, &bias_quant, 0);
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TfLiteTensor output_tensor = tflite::testing::CreateQuantizedTensor(
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output_data, output_dims, output_scale, 0 /* quantized dimension */);
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float input_scales[] = {1, input_scale};
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int input_zero_points[] = {1, 128};
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TfLiteAffineQuantization input_quant = {
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tflite::testing::FloatArrayFromFloats(input_scales),
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tflite::testing::IntArrayFromInts(input_zero_points), 0};
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input_tensor.quantization = {kTfLiteAffineQuantization, &input_quant};
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constexpr int inputs_size = 3;
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constexpr int outputs_size = 1;
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constexpr int tensors_size = inputs_size + outputs_size;
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TfLiteTensor tensors[tensors_size] = {
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input_tensor,
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filter_tensor,
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bias_tensor,
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output_tensor,
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};
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tflite::Quantize(tflite::testing::kGoldenData, golden_quantized,
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output_dims_count, output_scale, 0);
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// Set filter quant to mismatched dimension.
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TfLiteAffineQuantization* quant = reinterpret_cast<TfLiteAffineQuantization*>(
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filter_tensor.quantization.params);
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// Choose arbitrary incorrect scale and zero point sizes which are neither 1
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// (for broadcast case) nor the quantized dimension size.
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quant->scale->size = 2;
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TF_LITE_MICRO_EXPECT_EQ(
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kTfLiteError,
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tflite::testing::ValidateConvGoldens(
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tensors, tensors_size, golden_quantized, output_data,
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output_dims_count, &tflite::testing::common_conv_params));
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// Set scale back to correct dimension, and make zero point array too short.
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quant->scale->size = tflite::testing::kFilterShape[0];
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quant->zero_point->size = 2;
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TF_LITE_MICRO_EXPECT_EQ(
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kTfLiteError,
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tflite::testing::ValidateConvGoldens(
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tensors, tensors_size, golden_quantized, output_data,
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output_dims_count, &tflite::testing::common_conv_params));
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}
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TF_LITE_MICRO_TEST(Int8Input32x1Filter32x32ShouldMatchGolden) {
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constexpr int kSampleSize = 32;
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constexpr int kNumFilters = 32;
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