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[Clean-up] Clang-format some existing files #3179

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600e507
Updated docker to use clang-21
veblush Sep 19, 2025
d332bb8
clang-format some existing files
veblush Aug 19, 2025
eec0aa6
Fix
veblush Sep 20, 2025
ad15031
Undo tflite files
veblush Sep 20, 2025
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10 changes: 5 additions & 5 deletions ci/Dockerfile.micro
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Original file line number Diff line number Diff line change
Expand Up @@ -37,12 +37,12 @@ RUN apt-get install -y zip xxd sudo
RUN apt install -y lsb-release wget software-properties-common gnupg
RUN wget https://apt.llvm.org/llvm.sh
RUN chmod +x llvm.sh
RUN ./llvm.sh 16
RUN ln -s /usr/bin/clang-16 /usr/bin/clang
RUN ln -s /usr/bin/clang++-16 /usr/bin/clang++
RUN ./llvm.sh 21
RUN ln -s /usr/bin/clang-21 /usr/bin/clang
RUN ln -s /usr/bin/clang++-21 /usr/bin/clang++

RUN apt-get install clang-format-16
RUN ln -s /usr/bin/clang-format-16 /usr/bin/clang-format
RUN apt-get install clang-format-21
RUN ln -s /usr/bin/clang-format-21 /usr/bin/clang-format

# Needed when using the Dockerfile locally.
RUN git config --global --add safe.directory /opt/tflm
Expand Down
34 changes: 20 additions & 14 deletions tensorflow/lite/experimental/microfrontend/lib/filterbank_test.cc
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Expand Up @@ -76,8 +76,9 @@ TF_LITE_MICRO_TEST(FilterbankTest_CheckChannelFrequencyStarts) {
kSampleRate, kSpectrumSize));

const int16_t expected[] = {0, 4, 8};
TF_LITE_MICRO_EXPECT_EQ(state.num_channels + 1,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
TF_LITE_MICRO_EXPECT_EQ(
state.num_channels + 1,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
int i;
for (i = 0; i <= state.num_channels; ++i) {
TF_LITE_MICRO_EXPECT_EQ(state.channel_frequency_starts[i], expected[i]);
Expand All @@ -93,8 +94,9 @@ TF_LITE_MICRO_TEST(FilterbankTest_CheckChannelWeightStarts) {
kSampleRate, kSpectrumSize));

const int16_t expected[] = {0, 8, 16};
TF_LITE_MICRO_EXPECT_EQ(state.num_channels + 1,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
TF_LITE_MICRO_EXPECT_EQ(
state.num_channels + 1,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
int i;
for (i = 0; i <= state.num_channels; ++i) {
TF_LITE_MICRO_EXPECT_EQ(state.channel_weight_starts[i], expected[i]);
Expand All @@ -110,8 +112,9 @@ TF_LITE_MICRO_TEST(FilterbankTest_CheckChannelWidths) {
kSampleRate, kSpectrumSize));

const int16_t expected[] = {8, 8, 8};
TF_LITE_MICRO_EXPECT_EQ(state.num_channels + 1,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
TF_LITE_MICRO_EXPECT_EQ(
state.num_channels + 1,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
int i;
for (i = 0; i <= state.num_channels; ++i) {
TF_LITE_MICRO_EXPECT_EQ(state.channel_widths[i], expected[i]);
Expand All @@ -129,9 +132,10 @@ TF_LITE_MICRO_TEST(FilterbankTest_CheckWeights) {
const int16_t expected[] = {0, 3277, 2217, 1200, 222, 0, 0, 0,
0, 3376, 2468, 1591, 744, 0, 0, 0,
0, 4020, 3226, 2456, 1708, 983, 277, 0};
TF_LITE_MICRO_EXPECT_EQ(state.channel_weight_starts[state.num_channels] +
state.channel_widths[state.num_channels],
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
TF_LITE_MICRO_EXPECT_EQ(
state.channel_weight_starts[state.num_channels] +
state.channel_widths[state.num_channels],
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
for (size_t i = 0; i < sizeof(expected) / sizeof(expected[0]); ++i) {
TF_LITE_MICRO_EXPECT_EQ(state.weights[i], expected[i]);
}
Expand All @@ -148,9 +152,10 @@ TF_LITE_MICRO_TEST(FilterbankTest_CheckUnweights) {
const int16_t expected[] = {0, 819, 1879, 2896, 3874, 0, 0, 0,
0, 720, 1628, 2505, 3352, 0, 0, 0,
0, 76, 870, 1640, 2388, 3113, 3819, 0};
TF_LITE_MICRO_EXPECT_EQ(state.channel_weight_starts[state.num_channels] +
state.channel_widths[state.num_channels],
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
TF_LITE_MICRO_EXPECT_EQ(
state.channel_weight_starts[state.num_channels] +
state.channel_widths[state.num_channels],
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
for (size_t i = 0; i < sizeof(expected) / sizeof(expected[0]); ++i) {
TF_LITE_MICRO_EXPECT_EQ(state.unweights[i], expected[i]);
}
Expand Down Expand Up @@ -204,8 +209,9 @@ TF_LITE_MICRO_TEST(FilterbankTest_CheckSqrt) {
uint32_t* scaled_filterbank = FilterbankSqrt(&state, kScaleShift);

const uint32_t expected[] = {247311, 508620};
TF_LITE_MICRO_EXPECT_EQ(state.num_channels,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
TF_LITE_MICRO_EXPECT_EQ(
state.num_channels,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
int i;
for (i = 0; i < state.num_channels; ++i) {
TF_LITE_MICRO_EXPECT_EQ(scaled_filterbank[i], expected[i]);
Expand Down
10 changes: 6 additions & 4 deletions tensorflow/lite/experimental/microfrontend/lib/noise_reduction_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -48,8 +48,9 @@ TF_LITE_MICRO_TEST(NoiseReductionTest_TestNoiseReductionEstimate) {
NoiseReductionApply(&state, signal);

const uint32_t expected[] = {6321887, 31248341};
TF_LITE_MICRO_EXPECT_EQ(state.num_channels,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
TF_LITE_MICRO_EXPECT_EQ(
state.num_channels,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
int i;
for (i = 0; i < state.num_channels; ++i) {
TF_LITE_MICRO_EXPECT_EQ(state.estimate[i], expected[i]);
Expand All @@ -68,8 +69,9 @@ TF_LITE_MICRO_TEST(NoiseReductionTest_TestNoiseReduction) {
NoiseReductionApply(&state, signal);

const uint32_t expected[] = {241137, 478104};
TF_LITE_MICRO_EXPECT_EQ(state.num_channels,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
TF_LITE_MICRO_EXPECT_EQ(
state.num_channels,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
int i;
for (i = 0; i < state.num_channels; ++i) {
TF_LITE_MICRO_EXPECT_EQ(signal[i], expected[i]);
Expand Down
5 changes: 3 additions & 2 deletions tensorflow/lite/experimental/microfrontend/lib/pcan_gain_control_test.cc
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Expand Up @@ -52,8 +52,9 @@ TF_LITE_MICRO_TEST(PcanGainControlTest_TestPcanGainControl) {
PcanGainControlApply(&state, signal);

const uint32_t expected[] = {3578, 1533};
TF_LITE_MICRO_EXPECT_EQ(state.num_channels,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
TF_LITE_MICRO_EXPECT_EQ(
state.num_channels,
static_cast<int>(sizeof(expected) / sizeof(expected[0])));
int i;
for (i = 0; i < state.num_channels; ++i) {
TF_LITE_MICRO_EXPECT_EQ(signal[i], expected[i]);
Expand Down
4 changes: 2 additions & 2 deletions tensorflow/lite/micro/cortex_m_corstone_300/system_setup.cc
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Expand Up @@ -104,8 +104,8 @@ uint32_t GetCurrentTimeTicks() {

#ifdef ETHOS_U
#if defined(ETHOSU_FAST_MEMORY_SIZE) && ETHOSU_FAST_MEMORY_SIZE > 0
__attribute__((aligned(16), section(".bss.ethosu_scratch")))
uint8_t ethosu0_scratch[ETHOSU_FAST_MEMORY_SIZE];
__attribute__((aligned(16), section(".bss.ethosu_scratch"))) uint8_t
ethosu0_scratch[ETHOSU_FAST_MEMORY_SIZE];
#else
#define ethosu0_scratch 0
#define ETHOSU_FAST_MEMORY_SIZE 0
Expand Down
38 changes: 20 additions & 18 deletions tensorflow/lite/micro/kernels/arc_mli/mli_function_specializations.h
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Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,8 @@ typedef mli_status (*conv_func_ptr)(const mli_tensor* /*in*/,
mli_tensor* /*out*/);

#ifdef MLI_2_0
conv_func_ptr __attribute__((weak))
mli_krn_conv2d_hwcn(const mli_tensor* weights) {
conv_func_ptr
__attribute__((weak)) mli_krn_conv2d_hwcn(const mli_tensor* weights) {
int filter_w = weights->shape[KRNL_W_DIM_HWCN];
int filter_h = weights->shape[KRNL_H_DIM_HWCN];

Expand All @@ -41,8 +41,9 @@ mli_krn_conv2d_hwcn(const mli_tensor* weights) {
}
}
#else
conv_func_ptr __attribute__((weak))
mli_krn_conv2d_hwcn(const mli_tensor* weights, const mli_conv2d_cfg* cfg) {
conv_func_ptr
__attribute__((weak)) mli_krn_conv2d_hwcn(const mli_tensor* weights,
const mli_conv2d_cfg* cfg) {
return mli_krn_conv2d_nhwc_sa8_sa8_sa32;
}
#endif
Expand All @@ -55,8 +56,8 @@ typedef mli_status (*depthwise_func_ptr)(const mli_tensor* /*in*/,
mli_tensor* /*out*/);

#ifdef MLI_2_0
depthwise_func_ptr __attribute__((weak))
mli_krn_depthwise_conv2d(const mli_tensor* weights) {
depthwise_func_ptr
__attribute__((weak)) mli_krn_depthwise_conv2d(const mli_tensor* weights) {
int filter_w = weights->shape[KRNL_DW_W_DIM_HW1N];
int filter_h = weights->shape[KRNL_DW_H_DIM_HW1N];

Expand All @@ -69,15 +70,16 @@ mli_krn_depthwise_conv2d(const mli_tensor* weights) {
}
}
#else
depthwise_func_ptr __attribute__((weak))
mli_krn_depthwise_conv2d(const mli_tensor* weights, const mli_conv2d_cfg* cfg) {
depthwise_func_ptr
__attribute__((weak)) mli_krn_depthwise_conv2d(const mli_tensor* weights,
const mli_conv2d_cfg* cfg) {
return mli_krn_depthwise_conv2d_hwcn_sa8_sa8_sa32;
}
#endif

#ifdef MLI_2_0
depthwise_func_ptr __attribute__((weak))
mli_krn_group_conv2d(const mli_tensor* weights) {
depthwise_func_ptr
__attribute__((weak)) mli_krn_group_conv2d(const mli_tensor* weights) {
int filter_w = weights->shape[KRNL_DW_W_DIM_HW1N];
int filter_h = weights->shape[KRNL_DW_H_DIM_HW1N];

Expand All @@ -97,8 +99,8 @@ typedef mli_status (*pooling_func_ptr)(const mli_tensor* /*in*/,
mli_tensor* /*out*/);

#ifdef MLI_2_0
pooling_func_ptr __attribute__((weak))
mli_krn_avepool(const mli_pool_cfg* cfg) {
pooling_func_ptr
__attribute__((weak)) mli_krn_avepool(const mli_pool_cfg* cfg) {
int filter_w = cfg->kernel_width;
int filter_h = cfg->kernel_height;

Expand All @@ -111,15 +113,15 @@ mli_krn_avepool(const mli_pool_cfg* cfg) {
}
}
#else
pooling_func_ptr __attribute__((weak))
mli_krn_avepool(const mli_pool_cfg* cfg) {
pooling_func_ptr
__attribute__((weak)) mli_krn_avepool(const mli_pool_cfg* cfg) {
return mli_krn_avepool_hwc_sa8;
}
#endif

#ifdef MLI_2_0
pooling_func_ptr __attribute__((weak))
mli_krn_maxpool(const mli_pool_cfg* cfg) {
pooling_func_ptr
__attribute__((weak)) mli_krn_maxpool(const mli_pool_cfg* cfg) {
int filter_w = cfg->kernel_width;
int filter_h = cfg->kernel_height;

Expand All @@ -132,8 +134,8 @@ mli_krn_maxpool(const mli_pool_cfg* cfg) {
}
}
#else
pooling_func_ptr __attribute__((weak))
mli_krn_maxpool(const mli_pool_cfg* cfg) {
pooling_func_ptr
__attribute__((weak)) mli_krn_maxpool(const mli_pool_cfg* cfg) {
return mli_krn_maxpool_hwc_sa8;
}
#endif
Expand Down
2 changes: 1 addition & 1 deletion tensorflow/lite/micro/kernels/arc_mli/mli_interface.h
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Expand Up @@ -33,7 +33,7 @@ class MliTensorInterface {
public:
// Make sure that lifetime of MliTensorInterface instance isn't bigger than
// related mli_tensor.
MliTensorInterface(mli_tensor* tensor) : tensor_(tensor){};
MliTensorInterface(mli_tensor* tensor) : tensor_(tensor) {};
MliTensorInterface() = default;
~MliTensorInterface() = default;

Expand Down
4 changes: 2 additions & 2 deletions tensorflow/lite/micro/kernels/circular_buffer.cc
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Expand Up @@ -91,8 +91,8 @@ TfLiteStatus CircularBufferEval(TfLiteContext* context, TfLiteNode* node) {
EvalInt8(tflite::micro::GetTensorData<int8_t>(input), num_slots, depth,
tflite::micro::GetTensorData<int8_t>(output));
} else {
MicroPrintf("Type %s (%d) not supported.",
TfLiteTypeGetName(input->type), input->type);
MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
input->type);
return kTfLiteError;
}

Expand Down
2 changes: 1 addition & 1 deletion tensorflow/lite/micro/kernels/reduce_common.cc
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Expand Up @@ -120,7 +120,7 @@ enum MinMaxEvalType { kEvalMin, kEvalMax };
template <typename T>
struct MinMaxReducerCompare {
MinMaxReducerCompare() = delete;
MinMaxReducerCompare(MinMaxEvalType evalType) : type_(evalType){};
MinMaxReducerCompare(MinMaxEvalType evalType) : type_(evalType) {};
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@ddavis-2015 ddavis-2015 Sep 20, 2025 •
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I believe the Google style rules say that a space is not allowed between the parenthesis and the brace, and my IDE with it's auto formatting agrees. But the Pigweed style checker says that this is wrong and spacing must be there. I am much interested in your final resolution of this. This difference has only appeared in the last year or so.

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I'm sticking with the current format because it follows the rule "There should be a space between the close parenthesis and the open curly brace." (link

Given that the Pigweed style is implemented by clang-format (following Google conventions), the practical decision is to treat the tool's output as the final authority, overriding minor written style guide disagreements.


constexpr T initialValue() const {
return (type_ == kEvalMin) ? std::numeric_limits<T>::max()
Expand Down
12 changes: 8 additions & 4 deletions tensorflow/lite/micro/kernels/resize_bilinear_test.cc
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Expand Up @@ -168,8 +168,10 @@ TF_LITE_MICRO_TEST(VerticalResizeInt8) {
TF_LITE_MICRO_TEST(TwoDimensionalResize) {
int input_dims[] = {4, 1, 2, 2, 1};
const float input_data[] = {
3, 6, //
9, 12, //
3,
6, //
9,
12, //
};
const int32_t expected_size_data[] = {3, 3};
const float expected_output_data[] = {
Expand All @@ -194,8 +196,10 @@ TF_LITE_MICRO_TEST(TwoDimensionalResize) {
TF_LITE_MICRO_TEST(TwoDimensionalResizeInt8) {
int input_dims[] = {4, 1, 2, 2, 1};
const int8_t input_data[] = {
3, 6, //
9, 12, //
3,
6, //
9,
12, //
};
const int32_t expected_size_data[] = {3, 3};
const int8_t expected_output_data[] = {
Expand Down
18 changes: 12 additions & 6 deletions tensorflow/lite/micro/kernels/resize_nearest_neighbor_test.cc
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Expand Up @@ -156,8 +156,10 @@ TF_LITE_MICRO_TEST(VerticalResizeInt16) {
TF_LITE_MICRO_TEST(TwoDimensionalResize) {
int input_dims[] = {4, 1, 2, 2, 1};
const float input_data[] = {
3, 6, //
9, 12, //
3,
6, //
9,
12, //
};
const int32_t expected_size_data[] = {3, 3};
const float expected_output_data[] = {
Expand All @@ -177,8 +179,10 @@ TF_LITE_MICRO_TEST(TwoDimensionalResize) {
TF_LITE_MICRO_TEST(TwoDimensionalResizeInt8) {
int input_dims[] = {4, 1, 2, 2, 1};
const int8_t input_data[] = {
3, -6, //
9, 12, //
3,
-6, //
9,
12, //
};
const int32_t expected_size_data[] = {3, 3};
const int8_t expected_output_data[] = {
Expand All @@ -197,8 +201,10 @@ TF_LITE_MICRO_TEST(TwoDimensionalResizeInt8) {
TF_LITE_MICRO_TEST(TwoDimensionalResizeInt16) {
int input_dims[] = {4, 1, 2, 2, 1};
const int16_t input_data[] = {
3, -6, //
9, 12, //
3,
-6, //
9,
12, //
};
const int32_t expected_size_data[] = {3, 3};
const int16_t expected_output_data[] = {
Expand Down
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