summaryrefslogtreecommitdiff
path: root/onert-micro/luci-interpreter/pal/mcu/PALSVDF.h
diff options
context:
space:
mode:
Diffstat (limited to 'onert-micro/luci-interpreter/pal/mcu/PALSVDF.h')
-rw-r--r--onert-micro/luci-interpreter/pal/mcu/PALSVDF.h258
1 files changed, 0 insertions, 258 deletions
diff --git a/onert-micro/luci-interpreter/pal/mcu/PALSVDF.h b/onert-micro/luci-interpreter/pal/mcu/PALSVDF.h
deleted file mode 100644
index d39a2f109..000000000
--- a/onert-micro/luci-interpreter/pal/mcu/PALSVDF.h
+++ /dev/null
@@ -1,258 +0,0 @@
-/*
- * Copyright (c) 2022 Samsung Electronics Co., Ltd. All Rights Reserved
- * Copyright 2020 The TensorFlow Authors. All Rights Reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LUCI_INTERPRETER_PAL_SVDF_H
-#define LUCI_INTERPRETER_PAL_SVDF_H
-
-#include <tensorflow/lite/kernels/internal/reference/svdf.h>
-
-namespace luci_interpreter_pal
-{
-static inline void
-IntegerSVDF(const TfLiteSVDFParams &params, const tflite::RuntimeShape &input_shape,
- const int8_t *input_data, const tflite::RuntimeShape &weight_feature_shape,
- const int8_t *weight_feature_data, const tflite::RuntimeShape &weight_time_shape,
- const int16_t *weight_time_data, const tflite::RuntimeShape &bias_shape,
- const int32_t *bias_data, int16_t *activation_state_data,
- const tflite::RuntimeShape &output_shape, int8_t *output_data, int32_t *scratchpad_data,
- int32_t *output_temp_data, int32_t scale_1_a, int scale_1_b, int32_t scale_2_a,
- int scale_2_b, int32_t input_zp, int32_t output_zp)
-{
- const int n_rank = params.rank;
- const int n_batch = input_shape.Dims(0);
- const int n_input = input_shape.Dims(1);
- const int n_filter = weight_feature_shape.Dims(0);
- const int n_unit = n_filter / n_rank;
- const int n_memory = weight_time_shape.Dims(1);
-
- // Left shift the activation_state.
- {
- int16_t *new_state_start = activation_state_data;
- const int16_t *old_state_start = activation_state_data + 1;
- const int16_t *old_state_end = activation_state_data + n_batch * n_filter * n_memory;
- while (old_state_start != old_state_end)
- {
- *new_state_start++ = *old_state_start++;
- }
- }
-
- // Note: no need to clear the latest activation, matmul is not accumulative.
-
- // Feature matmul.
- {
- const int32_t output_max = std::numeric_limits<int16_t>::max();
- const int32_t output_min = std::numeric_limits<int16_t>::min();
- int16_t *result_in_batch = activation_state_data + (n_memory - 1);
- for (int b = 0; b < n_batch; b++)
- {
- const int8_t *matrix_ptr = weight_feature_data;
- for (int r = 0; r < n_filter; r++)
- {
- int32_t dot_prod = 0;
- const int8_t *vector_in_batch = input_data + b * n_input;
- for (int c = 0; c < n_input; c++)
- {
- dot_prod += *matrix_ptr++ * (*vector_in_batch++ - input_zp);
- }
- dot_prod = tflite::MultiplyByQuantizedMultiplier(dot_prod, scale_1_a, scale_1_b);
- dot_prod = std::min(std::max(output_min, dot_prod), output_max);
- // This assumes state is symmetrically quantized. Otherwise last bit of
- // state should be initialized to its zero point and accumulate the
- // dot_prod.
- // Equivalent as the following:
- // result_in_batch = zero point, which happens to be zero.
- // result_in_batch += dot_prod_56.
- *result_in_batch = dot_prod;
- result_in_batch += n_memory;
- }
- }
- }
-
- // Time.
- {
- for (int b = 0; b < n_batch; ++b)
- {
- int32_t *scratch_ptr_batch = scratchpad_data + b * n_filter;
-
- // Perform batched vector dot product:
- const int16_t *vector1_ptr = weight_time_data;
- const int16_t *vector2_ptr = activation_state_data + b * n_memory * n_filter;
-
- for (int i = 0; i < n_filter; i++)
- {
- *scratch_ptr_batch = 0;
- for (int j = 0; j < n_memory; j++)
- {
- *scratch_ptr_batch += *vector1_ptr++ * *vector2_ptr++;
- }
- scratch_ptr_batch++;
- }
- }
- }
-
- // Reduce, add bias, rescale, activation.
- {
- // Add bias.
- if (bias_data)
- {
- // Vector batch assign:
- for (int i = 0; i < n_batch; ++i)
- {
- int32_t *output_ptr = output_temp_data + i * n_unit;
- const int32_t *bias_ptr = bias_data;
- for (int j = 0; j < n_unit; ++j)
- {
- *output_ptr++ = *bias_ptr++;
- }
- }
- }
- else
- {
- int32_t *output_ptr = output_temp_data;
- for (int i = 0; i < n_batch * n_unit; ++i)
- {
- *output_ptr++ = 0;
- }
- }
-
- // Reduce.
- for (int b = 0; b < n_batch; ++b)
- {
- int32_t *output_temp_ptr = output_temp_data + b * n_unit;
- int32_t *scratch_ptr_batch = scratchpad_data + b * n_filter;
-
- // Reduction sum vector
- for (int i = 0; i < n_unit; ++i)
- {
- for (int j = 0; j < n_rank; ++j)
- {
- output_temp_ptr[i] += *scratch_ptr_batch++;
- }
- }
- }
-
- // Rescale.
- const int32_t output_max = std::numeric_limits<int8_t>::max();
- const int32_t output_min = std::numeric_limits<int8_t>::min();
- for (int i = 0; i < n_batch * n_unit; ++i)
- {
- int32_t x1 = output_temp_data[i];
- int32_t x2 = tflite::MultiplyByQuantizedMultiplier(x1, scale_2_a, scale_2_b);
- int32_t x3 = x2 + output_zp;
- int32_t x4 = std::min(std::max(output_min, x3), output_max);
- output_data[i] = static_cast<int8_t>(x4);
- }
- }
-}
-static inline void
-FloatSVDF(const TfLiteSVDFParams &params, const tflite::RuntimeShape &input_shape,
- const float *input_data, const tflite::RuntimeShape &weight_feature_shape,
- const float *weight_feature_data, const tflite::RuntimeShape &weight_time_shape,
- const float *weight_time_data, const tflite::RuntimeShape &bias_shape,
- const float *bias_data, float *scratchpad_data, float *activation_state_data,
- const tflite::RuntimeShape &output_shape, float *output_data)
-{
- const int32_t rank = params.rank;
- const int32_t batch_size = input_shape.Dims(0);
- const int32_t input_size = input_shape.Dims(1);
- const int32_t num_filters = weight_feature_shape.Dims(0);
- const int32_t num_units = num_filters / rank;
- const int32_t memory_size = weight_time_shape.Dims(1);
-
- // Left shift the activation_state.
- {
- float *new_state_start = activation_state_data;
- const float *old_state_start = activation_state_data + 1;
- const float *old_state_end = activation_state_data + batch_size * num_filters * memory_size;
- while (old_state_start != old_state_end)
- {
- *new_state_start++ = *old_state_start++;
- }
- }
-
- // Note: no need to clear the latest activation, matmul is not accumulative.
-
- // Compute conv1d(inputs, weights_feature).
- // The activation_state's rightmost column is used to save current cycle
- // activation. This is achieved by starting at state_ptr[memory_size - 1] and
- // having the stride equal to memory_size.
-
- // Perform batched matrix vector multiply operation:
- {
- const float *matrix = weight_feature_data;
- const float *vector = input_data;
- float *result = &activation_state_data[memory_size - 1];
- float *result_in_batch = result;
- for (int i = 0; i < batch_size; ++i)
- {
- const float *matrix_ptr = matrix;
- for (int j = 0; j < num_filters; ++j)
- {
- float dot_prod = 0.0f;
- const float *vector_in_batch = vector + i * input_size;
- for (int k = 0; k < input_size; ++k)
- {
- dot_prod += *matrix_ptr++ * *vector_in_batch++;
- }
- *result_in_batch = dot_prod;
- result_in_batch += memory_size;
- }
- }
- }
-
- tflite::reference_ops::ApplyTimeWeightsBiasAndActivation(
- batch_size, memory_size, num_filters, num_units, rank, weight_time_data, bias_data,
- params.activation, activation_state_data, scratchpad_data, output_data);
-}
-
-static inline void SetupScratchpadTensor(
- const luci_interpreter::DataType &input_data_type,
- const luci_interpreter::DataType &weight_feature_data_type,
- luci_interpreter::Tensor *scratchpad_1, luci_interpreter::Tensor *scratchpad_2,
- luci_interpreter::Tensor *scratchpad_3, luci_interpreter::Tensor *scratchpad_4,
- luci_interpreter::Tensor *scratchpad_5, luci_interpreter::Tensor *scratchpad_6,
- const luci_interpreter::Shape input_shape, const luci_interpreter::Shape weight_time_shape,
- const int32_t batch_size, const int32_t num_filters, const int32_t num_units)
-{
-
- if (input_data_type == luci_interpreter::DataType::FLOAT32 &&
- (weight_feature_data_type == luci_interpreter::DataType::S8 ||
- weight_feature_data_type == luci_interpreter::DataType::U8))
- {
- (void)input_shape;
- (void)weight_time_shape;
- (void)scratchpad_3;
- (void)scratchpad_4;
- (void)scratchpad_5;
- (void)scratchpad_6;
-
- assert(false && "Hybrid type is not currently supported for mcu platform");
- }
-
- // Resize scratchpad_1 tensor
- scratchpad_1->resize({batch_size, num_filters});
-
- if (input_data_type == luci_interpreter::DataType::S8)
- {
- // Resize scratchpad_2 for full_integer op
- scratchpad_2->resize({batch_size, num_units});
- }
-}
-
-} // namespace luci_interpreter_pal
-
-#endif // LUCI_INTERPRETER_PAL_SVDF_H
generated by cgit v1.2.3 (git 2.25.1) at 2024-05-19 04:43:38 +0000