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| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2018-05-31 15:11:26 -0700 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-05-31 15:13:58 -0700 |
| commit | 269a4ed1c27251b55cffe578b7bd969ec5975487 (patch) | |
| tree | 83dd602a71ad69b3fcb7b5ff5adc59c7adac3758 /tensorflow/contrib/lite/kernels/internal/kernel_utils.cc | |
| parent | f21816ecefe3f6e554d3b7daae3bb7f7a03bad20 (diff) | |
Internal change.
PiperOrigin-RevId: 198787391
Diffstat (limited to 'tensorflow/contrib/lite/kernels/internal/kernel_utils.cc')
| -rw-r--r-- | tensorflow/contrib/lite/kernels/internal/kernel_utils.cc | 7 |
1 files changed, 2 insertions, 5 deletions
diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc index 3bbaaa6a9d..67e3810479 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc @@ -52,7 +52,8 @@ void RnnBatchStep(const float* input_ptr_batch, const int8_t* input_weights_ptr, TfLiteFusedActivation activation, int8_t* quantized_input_ptr_batch, int8_t* quantized_hidden_state_ptr_batch, - float* hidden_state_ptr_batch, float* output_ptr_batch) { + float* scaling_factors, float* hidden_state_ptr_batch, + float* output_ptr_batch) { // Output = bias tensor_utils::VectorBatchVectorAssign(bias_ptr, num_units, batch_size, output_ptr_batch); @@ -62,7 +63,6 @@ void RnnBatchStep(const float* input_ptr_batch, const int8_t* input_weights_ptr, // Quantize input from float to uint8 + quantization params (scaling // factor). float unused_min, unused_max; - float* scaling_factors = new float[batch_size]; for (int b = 0; b < batch_size; ++b) { const int offset = b * input_size; tensor_utils::SymmetricQuantizeFloats( @@ -76,7 +76,6 @@ void RnnBatchStep(const float* input_ptr_batch, const int8_t* input_weights_ptr, tensor_utils::MatrixBatchVectorMultiplyAccumulate( input_weights_ptr, num_units, input_size, quantized_input_ptr_batch, scaling_factors, batch_size, output_ptr_batch, /*result_stride=*/1); - delete[] scaling_factors; } // Save quantization and matmul computation for all zero input. @@ -84,7 +83,6 @@ void RnnBatchStep(const float* input_ptr_batch, const int8_t* input_weights_ptr, batch_size * num_units)) { // Quantize hidden_state float unused_min, unused_max; - float* scaling_factors = new float[batch_size]; for (int b = 0; b < batch_size; ++b) { const int offset = b * num_units; tensor_utils::SymmetricQuantizeFloats( @@ -99,7 +97,6 @@ void RnnBatchStep(const float* input_ptr_batch, const int8_t* input_weights_ptr, recurrent_weights_ptr, num_units, num_units, quantized_hidden_state_ptr_batch, scaling_factors, batch_size, output_ptr_batch, /*result_stride=*/1); - delete[] scaling_factors; } // Output = activation(Output) and update hidden_state |