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Remove normalization_epsilon parameter of MeanStddevNormalization; change it to a compile-time constant.

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PiperOrigin-RevId: 279430358
Change-Id: I25aee6a065617e94b5b8d0a20f9cb2d3dce62314
This commit is contained in:
Robert David 2019-11-08 18:17:08 -08:00 committed by TensorFlower Gardener
parent 43d77b42e7
commit 7e48fd7fcf
9 changed files with 27 additions and 53 deletions
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6
tensorflow/lite/kernels/internal/optimized/neon_tensor_utils.h
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@ -254,10 +254,8 @@ void ReductionSumVector(const float* input_vector, float* output_vector,
}
void MeanStddevNormalization(const float* input_vector, float* output_vector,
int v_size, int n_batch,
float normalization_epsilon) {
PortableMeanStddevNormalization(input_vector, output_vector, v_size, n_batch,
normalization_epsilon);
int v_size, int n_batch) {
PortableMeanStddevNormalization(input_vector, output_vector, v_size, n_batch);
}
} // namespace tensor_utils

6
tensorflow/lite/kernels/internal/optimized/sse_tensor_utils.h
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@ -264,10 +264,8 @@ void ReductionSumVector(const float* input_vector, float* output_vector,
}
void MeanStddevNormalization(const float* input_vector, float* output_vector,
int v_size, int n_batch,
float normalization_epsilon) {
PortableMeanStddevNormalization(input_vector, output_vector, v_size, n_batch,
normalization_epsilon);
int v_size, int n_batch) {
PortableMeanStddevNormalization(input_vector, output_vector, v_size, n_batch);
}
} // namespace tensor_utils

5
tensorflow/lite/kernels/internal/reference/portable_tensor_utils.cc
View File

@ -627,7 +627,7 @@ void PortableReductionSumVector(const float* input_vector, float* output_vector,
void PortableMeanStddevNormalization(const float* input_vector,
float* output_vector, int v_size,
int n_batch, float normalization_epsilon) {
int n_batch) {
for (int batch = 0; batch < n_batch; ++batch) {
float sum = 0.0f;
float sum_sq = 0.0f;
@ -639,7 +639,8 @@ void PortableMeanStddevNormalization(const float* input_vector,
float stddev_inv = 0.0f;
const float variance = sum_sq / v_size - mean * mean;
if (variance == 0) {
stddev_inv = 1.0f / std::sqrt(normalization_epsilon);
constexpr float kNormalizationConstant = 1e-8;
stddev_inv = 1.0f / std::sqrt(kNormalizationConstant);
} else {
stddev_inv = 1.0f / std::sqrt(variance);
}

6
tensorflow/lite/kernels/internal/reference/portable_tensor_utils.h
View File

@ -258,10 +258,8 @@ void ReductionSumVector(const float* input_vector, float* output_vector,
}
void MeanStddevNormalization(const float* input_vector, float* output_vector,
int v_size, int n_batch,
float normalization_epsilon) {
PortableMeanStddevNormalization(input_vector, output_vector, v_size, n_batch,
normalization_epsilon);
int v_size, int n_batch) {
PortableMeanStddevNormalization(input_vector, output_vector, v_size, n_batch);
}
} // namespace tensor_utils

3
tensorflow/lite/kernels/internal/reference/portable_tensor_utils_impl.h
View File

@ -200,10 +200,9 @@ void PortableReductionSumVector(const float* input_vector, float* output_vector,
int output_size, int reduction_size);
// Layer norm for each batch.
// normalization_epsilon is added to avoid divergence.
void PortableMeanStddevNormalization(const float* input_vector,
float* output_vector, int v_size,
int n_batch, float normalization_epsilon);
int n_batch);
} // namespace tensor_utils
} // namespace tflite

4
tensorflow/lite/kernels/internal/tensor_utils.h
View File

@ -439,10 +439,8 @@ void ReductionSumVector(const float* input_vector, float* output_vector,
int output_size, int reduction_size);
// Layer norm for each batch.
// normalization_epsilon is added to avoid divergence.
void MeanStddevNormalization(const float* input_vector, float* output_vector,
int v_size, int n_batch,
float normalization_epsilon);
int v_size, int n_batch);
} // namespace tensor_utils
} // namespace tflite

4
tensorflow/lite/kernels/internal/tensor_utils_test.cc
View File

@ -1466,7 +1466,6 @@ TEST(uKernels, ReductionSumVectorTest) {
TEST(uKernels, MeanStddevNormalization) {
constexpr int kVectorSize = 4;
constexpr int kBatchSize = 8; // 9, but large mean, small variance fails
constexpr float kNormalizationEpsilon = 1e-8;
// None-zero input.
static float input[kVectorSize * kBatchSize] = {
@ -1480,8 +1479,7 @@ TEST(uKernels, MeanStddevNormalization) {
-100.0f, 0.0f, 200.0f, 300.0f, // large mean, large variance
};
float output[kVectorSize * kBatchSize];
MeanStddevNormalization(input, output, kVectorSize, kBatchSize,
kNormalizationEpsilon);
MeanStddevNormalization(input, output, kVectorSize, kBatchSize);
const float ksqrt16 = std::sqrt(1.6f);
const float ksqrt04 = std::sqrt(0.4f);
const std::vector<float> expected_output = {

31
tensorflow/lite/kernels/lstm_eval.cc
View File

@ -38,11 +38,6 @@ namespace builtin {
namespace lstm_eval {
namespace {
// Small float to avoid divergence during calculation of deviation for layer
// norm lstm.
const float kLayerNormEpsilon = 1e-8;
// Performs an LSTM batch inference step for input specified by input_ptr_batch.
// The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and
// biases (*_bias_ptr), and buffers (*_scratch), along with additional
@ -224,9 +219,8 @@ inline void LstmStepWithAuxInput(
input_gate_scratch);
}
if (is_layer_norm_lstm) {
tensor_utils::MeanStddevNormalization(input_gate_scratch,
input_gate_scratch, n_cell, n_batch,
kLayerNormEpsilon);
tensor_utils::MeanStddevNormalization(
input_gate_scratch, input_gate_scratch, n_cell, n_batch);
tensor_utils::VectorBatchVectorCwiseProduct(
input_layer_norm_coefficients_ptr, n_cell, input_gate_scratch,
n_batch, input_gate_scratch);
@ -245,8 +239,7 @@ inline void LstmStepWithAuxInput(
}
if (is_layer_norm_lstm) {
tensor_utils::MeanStddevNormalization(forget_gate_scratch,
forget_gate_scratch, n_cell, n_batch,
kLayerNormEpsilon);
forget_gate_scratch, n_cell, n_batch);
tensor_utils::VectorBatchVectorCwiseProduct(
forget_layer_norm_coefficients_ptr, n_cell, forget_gate_scratch,
n_batch, forget_gate_scratch);
@ -261,7 +254,7 @@ inline void LstmStepWithAuxInput(
n_batch * n_cell, cell_state_ptr);
if (is_layer_norm_lstm) {
tensor_utils::MeanStddevNormalization(cell_scratch, cell_scratch, n_cell,
n_batch, kLayerNormEpsilon);
n_batch);
tensor_utils::VectorBatchVectorCwiseProduct(
cell_layer_norm_coefficients_ptr, n_cell, cell_scratch, n_batch,
cell_scratch);
@ -292,8 +285,7 @@ inline void LstmStepWithAuxInput(
}
if (is_layer_norm_lstm) {
tensor_utils::MeanStddevNormalization(output_gate_scratch,
output_gate_scratch, n_cell, n_batch,
kLayerNormEpsilon);
output_gate_scratch, n_cell, n_batch);
tensor_utils::VectorBatchVectorCwiseProduct(
output_layer_norm_coefficients_ptr, n_cell, output_gate_scratch,
n_batch, output_gate_scratch);
@ -699,9 +691,8 @@ inline void LstmStepWithAuxInput(
input_gate_scratch);
}
if (is_layer_norm_lstm) {
tensor_utils::MeanStddevNormalization(input_gate_scratch,
input_gate_scratch, n_cell, n_batch,
kLayerNormEpsilon);
tensor_utils::MeanStddevNormalization(
input_gate_scratch, input_gate_scratch, n_cell, n_batch);
tensor_utils::VectorBatchVectorCwiseProduct(
input_layer_norm_coefficients_ptr, n_cell, input_gate_scratch,
n_batch, input_gate_scratch);
@ -723,8 +714,7 @@ inline void LstmStepWithAuxInput(
}
if (is_layer_norm_lstm) {
tensor_utils::MeanStddevNormalization(forget_gate_scratch,
forget_gate_scratch, n_cell, n_batch,
kLayerNormEpsilon);
forget_gate_scratch, n_cell, n_batch);
tensor_utils::VectorBatchVectorCwiseProduct(
forget_layer_norm_coefficients_ptr, n_cell, forget_gate_scratch,
n_batch, forget_gate_scratch);
@ -739,7 +729,7 @@ inline void LstmStepWithAuxInput(
n_batch * n_cell, cell_state_ptr);
if (is_layer_norm_lstm) {
tensor_utils::MeanStddevNormalization(cell_scratch, cell_scratch, n_cell,
n_batch, kLayerNormEpsilon);
n_batch);
tensor_utils::VectorBatchVectorCwiseProduct(
cell_layer_norm_coefficients_ptr, n_cell, cell_scratch, n_batch,
cell_scratch);
@ -775,8 +765,7 @@ inline void LstmStepWithAuxInput(
}
if (is_layer_norm_lstm) {
tensor_utils::MeanStddevNormalization(output_gate_scratch,
output_gate_scratch, n_cell, n_batch,
kLayerNormEpsilon);
output_gate_scratch, n_cell, n_batch);
tensor_utils::VectorBatchVectorCwiseProduct(
output_layer_norm_coefficients_ptr, n_cell, output_gate_scratch,
n_batch, output_gate_scratch);

15
tensorflow/lite/tools/optimize/calibration/builtin_logging_ops/lstm.cc
View File

@ -32,8 +32,6 @@ namespace builtin {
namespace {
const float kLayerNormEpsilon = 1e-8;
inline void LstmStepWithAuxInput(
const float* input_ptr_batch, const float* input_to_input_weights_ptr,
const float* input_to_forget_weights_ptr,
@ -157,9 +155,8 @@ inline void LstmStepWithAuxInput(
if (is_layer_norm_lstm) {
logger->LogTensorValue(intemediate_tensor_indexes[0], input_gate_scratch,
n_cell * n_batch);
tensor_utils::MeanStddevNormalization(input_gate_scratch,
input_gate_scratch, n_cell, n_batch,
kLayerNormEpsilon);
tensor_utils::MeanStddevNormalization(
input_gate_scratch, input_gate_scratch, n_cell, n_batch);
tensor_utils::VectorBatchVectorCwiseProduct(
input_layer_norm_coefficients_ptr, n_cell, input_gate_scratch,
n_batch, input_gate_scratch);
@ -180,8 +177,7 @@ inline void LstmStepWithAuxInput(
logger->LogTensorValue(intemediate_tensor_indexes[1], forget_gate_scratch,
n_cell * n_batch);
tensor_utils::MeanStddevNormalization(forget_gate_scratch,
forget_gate_scratch, n_cell, n_batch,
kLayerNormEpsilon);
forget_gate_scratch, n_cell, n_batch);
tensor_utils::VectorBatchVectorCwiseProduct(
forget_layer_norm_coefficients_ptr, n_cell, forget_gate_scratch,
n_batch, forget_gate_scratch);
@ -198,7 +194,7 @@ inline void LstmStepWithAuxInput(
logger->LogTensorValue(intemediate_tensor_indexes[2], cell_scratch,
n_cell * n_batch);
tensor_utils::MeanStddevNormalization(cell_scratch, cell_scratch, n_cell,
n_batch, kLayerNormEpsilon);
n_batch);
tensor_utils::VectorBatchVectorCwiseProduct(
cell_layer_norm_coefficients_ptr, n_cell, cell_scratch, n_batch,
cell_scratch);
@ -231,8 +227,7 @@ inline void LstmStepWithAuxInput(
logger->LogTensorValue(intemediate_tensor_indexes[3], output_gate_scratch,
n_cell * n_batch);
tensor_utils::MeanStddevNormalization(output_gate_scratch,
output_gate_scratch, n_cell, n_batch,
kLayerNormEpsilon);
output_gate_scratch, n_cell, n_batch);
tensor_utils::VectorBatchVectorCwiseProduct(
output_layer_norm_coefficients_ptr, n_cell, output_gate_scratch,
n_batch, output_gate_scratch);