add nnom pack and example
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dkk0918
207
components/ai/nnom/src/layers/nnom_dense.c
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207
components/ai/nnom/src/layers/nnom_dense.c
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/*
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* Copyright (c) 2018-2020
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* Jianjia Ma
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* majianjia@live.com
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*
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* SPDX-License-Identifier: Apache-2.0
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*
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* Change Logs:
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* Date Author Notes
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* 2019-07-23 Jianjia Ma The first version
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*/
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#include <stdint.h>
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#include <string.h>
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#include <stdbool.h>
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#include "nnom.h"
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#include "nnom_local.h"
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#include "nnom_layers.h"
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#include "layers/nnom_dense.h"
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#ifdef NNOM_USING_CMSIS_NN
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#include "arm_math.h"
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#include "arm_nnfunctions.h"
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#endif
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nnom_layer_t *dense_s(const nnom_dense_config_t *config)
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{
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nnom_dense_layer_t *layer;
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nnom_buf_t *comp;
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nnom_layer_io_t *in, *out;
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// apply a block memory for all the sub handles.
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size_t mem_size = sizeof(nnom_dense_layer_t) + sizeof(nnom_layer_io_t) * 2 + sizeof(nnom_buf_t);
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layer = nnom_mem(mem_size);
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if (layer == NULL)
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return NULL;
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// distribut the memory to sub handles.
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in = (void *)((uint8_t*)layer + sizeof(nnom_dense_layer_t));
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out = (void *)((uint8_t*)in + sizeof(nnom_layer_io_t));
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comp = (void *)((uint8_t*)out + sizeof(nnom_layer_io_t));
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// set type in layer parent
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layer->super.type = NNOM_DENSE;
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// set buf state
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in->type = NNOM_TENSOR_BUF_TEMP;
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out->type = NNOM_TENSOR_BUF_TEMP;
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comp->type = NNOM_TENSOR_BUF_TEMP;
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// put in & out on the layer.
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layer->super.in = io_init(layer, in);
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layer->super.out = io_init(layer, out);
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layer->super.comp = comp;
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// set run and outshape methods
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layer->super.run = dense_run;
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layer->super.build = dense_build;
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layer->super.free = dense_free;
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// set parameters
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layer->output_unit = tensor_get_num_channel(config->weight);
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layer->bias = config->bias;
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layer->weight = config->weight;
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// set shifts
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layer->output_rshift = (nnom_qformat_param_t *)config->output_shift;
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layer->bias_lshift = (nnom_qformat_param_t *)config->bias_shift;
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// set config
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layer->super.config = (void*) config;
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return (nnom_layer_t *)layer;
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}
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nnom_layer_t *Dense(size_t output_unit, const nnom_weight_t *w, const nnom_bias_t *b)
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{
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nnom_dense_layer_t *layer;
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nnom_buf_t *comp;
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nnom_layer_io_t *in, *out;
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// apply a block memory for all the sub handles.
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size_t mem_size = sizeof(nnom_dense_layer_t) + sizeof(nnom_layer_io_t) * 2 + sizeof(nnom_buf_t);
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layer = nnom_mem(mem_size);
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if (layer == NULL)
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return NULL;
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// distribut the memory to sub handles.
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in = (void *)((uint8_t*)layer + sizeof(nnom_dense_layer_t));
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out = (void *)((uint8_t*)in + sizeof(nnom_layer_io_t));
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comp = (void *)((uint8_t*)out + sizeof(nnom_layer_io_t));
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// set type in layer parent
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layer->super.type = NNOM_DENSE;
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// set buf state
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in->type = NNOM_TENSOR_BUF_TEMP;
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out->type = NNOM_TENSOR_BUF_TEMP;
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comp->type = NNOM_TENSOR_BUF_TEMP;
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// put in & out on the layer.
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layer->super.in = io_init(layer, in);
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layer->super.out = io_init(layer, out);
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layer->super.comp = comp;
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// set run and outshape methods
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layer->super.run = dense_run;
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layer->super.build = dense_build;
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// set parameters
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layer->output_unit = output_unit; // this is no longer needed. the information is contained in the weight tensor.
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layer->weight = new_tensor(NNOM_QTYPE_PER_TENSOR, 2, output_unit);
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layer->bias = new_tensor(NNOM_QTYPE_PER_TENSOR, 1, output_unit);
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// configure weight tensor manually to support new tensor-based backends.
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// needs to be very careful
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{
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// config weight
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nnom_shape_data_t dim[2] = {0, output_unit}; // the first dim doesnt matter here. will be file in later.
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*(layer->weight->q_offset) = 0; // we have no support of offset here
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*(layer->weight->q_dec) = 0; // this is not even correct
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layer->weight->p_data = (void*)w->p_value;
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layer->weight->bitwidth = 8;
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layer->weight->qtype = NNOM_QTYPE_PER_TENSOR;
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nnom_memcpy(layer->weight->dim, dim, layer->weight->num_dim * sizeof(nnom_shape_data_t));
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// config bias
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dim[0] = output_unit;
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*(layer->bias->q_offset) = 0; // we have no support of offset here
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*(layer->bias->q_dec) = 0; // this is not even correct
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layer->bias->p_data = (void*)b->p_value;
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layer->bias->bitwidth = 8;
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layer->weight->qtype = NNOM_QTYPE_PER_TENSOR;
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nnom_memcpy(layer->bias->dim, dim, layer->bias->num_dim * sizeof(nnom_shape_data_t));
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}
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// set output shifts
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layer->output_rshift = (nnom_qformat_param_t *)&w->shift;
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layer->bias_lshift = (nnom_qformat_param_t *)&b->shift;
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return (nnom_layer_t *)layer;
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}
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nnom_status_t dense_build(nnom_layer_t *layer)
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{
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nnom_dense_layer_t *cl = (nnom_dense_layer_t *)layer;
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// get the tensor from last layer's output
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layer->in->tensor = layer->in->hook.io->tensor;
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// create new tensor for output
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layer->out->tensor = new_tensor(NNOM_QTYPE_PER_TENSOR, 1, tensor_get_num_channel(layer->in->tensor));
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// setup new tensor
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nnom_shape_data_t dim[1] = {cl->output_unit};
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tensor_set_attr(layer->out->tensor, cl->weight->q_dec, cl->weight->q_offset, dim, 1, 8); // test, this is not correct
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// calculate the output tensor q format, only support per tensor quantise now
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layer->out->tensor->q_dec[0] = layer->in->tensor->q_dec[0] + cl->weight->q_dec[0] - cl->output_rshift[0];
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// see if the activation will change the q format
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if(layer->actail)
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layer->out->tensor->q_dec[0] = act_get_dec_bit(layer->actail->type, layer->out->tensor->q_dec[0]);
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// vec_buffer size: dim_vec (*2, q7->q15) ? I am not sure this is right
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layer->comp->size = tensor_size(layer->in->tensor)*2;
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// computational cost: In * out
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layer->stat.macc = tensor_size(layer->in->tensor) * tensor_size(layer->out->tensor);
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return NN_SUCCESS;
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}
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nnom_status_t dense_free(nnom_layer_t *layer)
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{
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// free weight and bias tensor when we are not initialised from structured configuration.
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if(!layer->config)
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{
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nnom_dense_layer_t* cl = (nnom_dense_layer_t*)layer;
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delete_tensor(cl->weight);
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delete_tensor(cl->bias);
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}
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return NN_SUCCESS;
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}
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nnom_status_t dense_run(nnom_layer_t *layer)
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{
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nnom_status_t result = NN_SUCCESS;
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nnom_dense_layer_t *cl = (nnom_dense_layer_t *)(layer);
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nnom_qformat_param_t bias_shift = cl->bias_lshift[0]; // this is not correct but a temporary fix solution for backward compatibility.
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nnom_qformat_param_t output_shift = cl->output_rshift[0];
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#if !(DENSE_WEIGHT_OPT)
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#ifdef NNOM_USING_CMSIS_NN
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result = (nnom_status_t)arm_fully_connected_q7(
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#else
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local_fully_connected_q7(
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#endif
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#else
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#ifdef NNOM_USING_CMSIS_NN
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result = (nnom_status_t)arm_fully_connected_q7_opt(
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#else
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local_fully_connected_q7_opt(
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#endif
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#endif
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layer->in->tensor->p_data,
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cl->weight->p_data,
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tensor_size(layer->in->tensor), layer->out->tensor->dim[0],
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bias_shift, output_shift,
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cl->bias->p_data,
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layer->out->tensor->p_data, (q15_t *)(layer->comp->mem->blk));
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return result;
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}
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