ifdef out known failing test cases for xtensa for depthwise_conv
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_depthwise_conv_test ``` http://b/170322965
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Advait Jain
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@ -24,8 +24,10 @@ namespace tflite {
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namespace testing {
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namespace {
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#if !defined(XTENSA) // Needed to avoid build errors from unused variables.
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constexpr int kMaxFilterChannels = 64;
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constexpr int kMaxBiasChannels = 64;
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#endif // !defined(XTENSA)
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// Index of the output tensor in context->tensors, specific to
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// DepthwiseConv.
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@ -78,6 +80,7 @@ TfLiteStatus ValidateDepthwiseConvGoldens(
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return kTfLiteOk;
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}
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#if !defined(XTENSA) // Needed to avoid build errors from unsused functions.
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void TestDepthwiseConvFloat(const int* input_dims_data, const float* input_data,
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const int* filter_dims_data,
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const float* filter_data, const int* bias_dims_data,
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@ -226,12 +229,17 @@ void TestDepthwiseConvQuantizedPerChannel(
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1.0, tensors_size, tensors));
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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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} // namespace tflite
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TF_LITE_MICRO_TESTS_BEGIN
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#if !defined(XTENSA) // TODO(b/170322965): 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(SimpleTest) {
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const int input_shape[] = {4, 1, 3, 2, 2};
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const float input_values[] = {1, 2, 7, 8, 3, 4, 9, 10, 5, 6, 11, 12};
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@ -665,94 +673,6 @@ TF_LITE_MICRO_TEST(TestQuantizedPerChannelCompareWithFloat) {
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golden, output_dims, &conv_params, output_float);
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}
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TF_LITE_MICRO_TEST(FilterDimsNotMatchingAffineQuantization) {
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const int input_shape[] = {4, 1, 2, 3, 2};
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const float input_data[] = {3, 2, 1, -1, -2, -3, 4, 3, 2, -2, -3, -4};
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const int filter_shape[] = {4, 1, 2, 2, 4};
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const float filter_data[] = {1, 2, 3, 4, 3, 4, 5, 6, 7, 8, 5, 6, 3, 4, 1, 2};
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const int bias_shape[] = {4, 1, 1, 1, 4};
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const float bias_data[] = {3, -2, 4, 6};
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const int output_shape[] = {4, 1, 1, 2, 4};
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const int input_size = 12;
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const int filter_size = 16;
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const int output_size = 8;
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const int bias_size = 4;
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int8_t input_quantized[input_size];
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int8_t filter_quantized[filter_size];
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int32_t bias_quantized[bias_size];
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int8_t golden_quantized[output_size];
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int zero_points[bias_size + 1];
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float scales[bias_size + 1];
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int8_t output_data[output_size];
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const float input_scale = 0.5;
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const float output_scale = 1.0;
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const int input_zero_point = 0;
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const int output_zero_point = 0;
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TfLiteIntArray* input_dims = tflite::testing::IntArrayFromInts(input_shape);
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TfLiteIntArray* filter_dims = tflite::testing::IntArrayFromInts(filter_shape);
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TfLiteIntArray* bias_dims = tflite::testing::IntArrayFromInts(bias_shape);
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TfLiteIntArray* output_dims = tflite::testing::IntArrayFromInts(output_shape);
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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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input_data, input_quantized, input_dims, input_scale, input_zero_point);
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TfLiteTensor filter_tensor =
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tflite::testing::CreateSymmetricPerChannelQuantizedTensor(
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filter_data, filter_quantized, filter_dims, filter_scales,
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filter_zero_points, &filter_quant, 0 /* quantized dimension */);
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TfLiteTensor bias_tensor =
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tflite::testing::CreatePerChannelQuantizedBiasTensor(
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bias_data, bias_quantized, bias_dims, input_scale, &filter_scales[1],
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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, output_zero_point);
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float input_scales[] = {1, input_scale};
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int input_zero_points[] = {1, input_zero_point};
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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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TfLiteDepthwiseConvParams conv_params;
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conv_params.activation = kTfLiteActNone;
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conv_params.dilation_width_factor = 1;
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conv_params.dilation_height_factor = 1;
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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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quant->scale->size = 2;
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteError,
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tflite::testing::ValidateDepthwiseConvGoldens(
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golden_quantized, output_size, &conv_params, 1e-5,
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tensors_size, tensors));
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// Set scale back to correct dimension, and make zero point array too short.
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quant->scale->size = filter_shape[0];
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quant->zero_point->size = 2;
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteError,
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tflite::testing::ValidateDepthwiseConvGoldens(
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golden_quantized, output_size, &conv_params, 1e-5,
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tensors_size, tensors));
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}
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TF_LITE_MICRO_TEST(PerChannelBroadcastQuantizationParams) {
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const float input_scale = 1.0f;
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const float filter_scale = 1.0f;
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@ -853,6 +773,96 @@ TF_LITE_MICRO_TEST(PerChannelBroadcastQuantizationParams) {
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tensors_size, tensors));
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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 input_shape[] = {4, 1, 2, 3, 2};
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const float input_data[] = {3, 2, 1, -1, -2, -3, 4, 3, 2, -2, -3, -4};
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const int filter_shape[] = {4, 1, 2, 2, 4};
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const float filter_data[] = {1, 2, 3, 4, 3, 4, 5, 6, 7, 8, 5, 6, 3, 4, 1, 2};
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const int bias_shape[] = {4, 1, 1, 1, 4};
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const float bias_data[] = {3, -2, 4, 6};
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const int output_shape[] = {4, 1, 1, 2, 4};
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const int input_size = 12;
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const int filter_size = 16;
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const int output_size = 8;
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const int bias_size = 4;
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int8_t input_quantized[input_size];
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int8_t filter_quantized[filter_size];
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int32_t bias_quantized[bias_size];
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int8_t golden_quantized[output_size];
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int zero_points[bias_size + 1];
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float scales[bias_size + 1];
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int8_t output_data[output_size];
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const float input_scale = 0.5;
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const float output_scale = 1.0;
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const int input_zero_point = 0;
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const int output_zero_point = 0;
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TfLiteIntArray* input_dims = tflite::testing::IntArrayFromInts(input_shape);
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TfLiteIntArray* filter_dims = tflite::testing::IntArrayFromInts(filter_shape);
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TfLiteIntArray* bias_dims = tflite::testing::IntArrayFromInts(bias_shape);
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TfLiteIntArray* output_dims = tflite::testing::IntArrayFromInts(output_shape);
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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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input_data, input_quantized, input_dims, input_scale, input_zero_point);
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TfLiteTensor filter_tensor =
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tflite::testing::CreateSymmetricPerChannelQuantizedTensor(
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filter_data, filter_quantized, filter_dims, filter_scales,
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filter_zero_points, &filter_quant, 0 /* quantized dimension */);
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TfLiteTensor bias_tensor =
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tflite::testing::CreatePerChannelQuantizedBiasTensor(
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bias_data, bias_quantized, bias_dims, input_scale, &filter_scales[1],
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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, output_zero_point);
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float input_scales[] = {1, input_scale};
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int input_zero_points[] = {1, input_zero_point};
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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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TfLiteDepthwiseConvParams conv_params;
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conv_params.activation = kTfLiteActNone;
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conv_params.dilation_width_factor = 1;
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conv_params.dilation_height_factor = 1;
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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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quant->scale->size = 2;
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteError,
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tflite::testing::ValidateDepthwiseConvGoldens(
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golden_quantized, output_size, &conv_params, 1e-5,
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tensors_size, tensors));
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// Set scale back to correct dimension, and make zero point array too short.
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quant->scale->size = filter_shape[0];
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quant->zero_point->size = 2;
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteError,
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tflite::testing::ValidateDepthwiseConvGoldens(
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golden_quantized, output_size, &conv_params, 1e-5,
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tensors_size, tensors));
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}
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TF_LITE_MICRO_TEST(Int8Input32x4Filter32x4ShouldMatchGolden) {
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const int input_elements = 32 * 4;
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const int filter_elements = 32 * 4;
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