diff options
Diffstat (limited to 'Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_s8.c')
| -rw-r--r-- | Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_s8.c | 335 |
1 files changed, 335 insertions, 0 deletions
diff --git a/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_s8.c b/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_s8.c new file mode 100644 index 0000000..e884b31 --- /dev/null +++ b/Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_s8.c @@ -0,0 +1,335 @@ +/* + * Copyright (C) 2010-2021 Arm Limited or its affiliates. + * + * SPDX-License-Identifier: Apache-2.0 + * + * 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 + * + * 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. + */ + +/* ---------------------------------------------------------------------- + * Project: CMSIS NN Library + * Title: arm_convolve_s8.c + * Description: s8 version of convolution using symmetric quantization. + * + * $Date: December 14, 2021 + * $Revision: V.2.1.0 + * + * Target Processor: Cortex-M cores + * + * -------------------------------------------------------------------- */ + +#include "arm_nnfunctions.h" +#include "arm_nnsupportfunctions.h" + +/** + * @ingroup groupNN + */ + +/** + * @addtogroup NNConv + * @{ + */ + +/* + * Basic s8 convolution function. + * + * Refer header file for details. Optimal use case for the DSP/MVE implementation is when input and output channels + * are multiples of 4 or atleast greater than 4. + * + */ + +arm_status arm_convolve_s8(const cmsis_nn_context *ctx, + const cmsis_nn_conv_params *conv_params, + const cmsis_nn_per_channel_quant_params *quant_params, + const cmsis_nn_dims *input_dims, + const q7_t *input_data, + const cmsis_nn_dims *filter_dims, + const q7_t *filter_data, + const cmsis_nn_dims *bias_dims, + const int32_t *bias_data, + const cmsis_nn_dims *output_dims, + q7_t *output_data) +{ + (void)bias_dims; + + if (ctx->buf == NULL && arm_convolve_s8_get_buffer_size(input_dims, filter_dims) > 0) + { + return ARM_MATH_ARGUMENT_ERROR; + } + q15_t *buffer_a = (q15_t *)ctx->buf; + + const int32_t input_batches = input_dims->n; + const uint16_t input_x = input_dims->w; + const uint16_t input_y = input_dims->h; + const uint16_t input_ch = input_dims->c; + const uint16_t kernel_x = filter_dims->w; + const uint16_t kernel_y = filter_dims->h; + const uint16_t output_x = output_dims->w; + const uint16_t output_y = output_dims->h; + const uint16_t output_ch = output_dims->c; + + const uint16_t pad_x = conv_params->padding.w; + const uint16_t pad_y = conv_params->padding.h; + const uint16_t stride_x = conv_params->stride.w; + const uint16_t stride_y = conv_params->stride.h; + + const int32_t input_offset = conv_params->input_offset; + const int32_t out_offset = conv_params->output_offset; + const int32_t out_activation_min = conv_params->activation.min; + const int32_t out_activation_max = conv_params->activation.max; + int32_t *output_mult = quant_params->multiplier; + int32_t *output_shift = quant_params->shift; + + int i_batch; + for (i_batch = 0; i_batch < input_batches; i_batch++) + { +#if defined(ARM_MATH_MVEI) + /* Generate upto four columns from the input tensor a GEMM computation */ + q7_t *im2col_buf = (q7_t *)buffer_a; + q7_t *out = output_data; + int32_t buffer_fill_cnt = 0; + int32_t padded = 0; + const int32_t num_elem = kernel_x * kernel_y * input_ch; + const int32_t dilation_x = conv_params->dilation.w; + const int32_t dilation_y = conv_params->dilation.h; + + /* This part implements the im2col function */ + for (int i_out_y = 0; i_out_y < output_y; i_out_y++) + { + for (int i_out_x = 0; i_out_x < output_x; i_out_x++) + { + const int32_t base_idx_x = stride_x * i_out_x - pad_x; + const int32_t base_idx_y = stride_y * i_out_y - pad_y; + + for (int32_t i_ker_y = 0; i_ker_y < kernel_y; i_ker_y++) + { + for (int32_t i_ker_x = 0; i_ker_x < kernel_x; i_ker_x++) + { + const int32_t k_y = base_idx_y + dilation_y * i_ker_y; + const int32_t k_x = base_idx_x + dilation_x * i_ker_x; + + if (k_y < 0 || k_y >= input_y || k_x < 0 || k_x >= input_x) + { + memset(im2col_buf, (int8_t)-input_offset, sizeof(q7_t) * input_ch); + padded = 1; + } + else + { + arm_memcpy_q7(im2col_buf, input_data + (k_y * input_x + k_x) * input_ch, input_ch); + } + im2col_buf += input_ch; + } + } + + buffer_fill_cnt++; + + /* Computation is filed for every 4 columns */ + if (buffer_fill_cnt == 4 && (padded == 0)) + { + buffer_fill_cnt = 0; + out = arm_nn_mat_mul_core_4x_s8(num_elem, + num_elem, + (q7_t *)buffer_a, + filter_data, + output_ch, + conv_params, + quant_params, + bias_data, + out); + im2col_buf = (q7_t *)buffer_a; + } + else if (buffer_fill_cnt == 4 && (padded != 0)) + { + buffer_fill_cnt = 0; + out = arm_nn_mat_mult_s8(filter_data, + (q7_t *)buffer_a, + output_ch, + 4, + output_shift, + output_mult, + out_offset, + input_offset, + 0, + out_activation_min, + out_activation_max, + num_elem, + bias_data, + out); + + im2col_buf = (q7_t *)buffer_a; + padded = 0; + } + } + } + /* Handle left over columns */ + if (buffer_fill_cnt != 0) + { + out = arm_nn_mat_mult_s8(filter_data, + (q7_t *)buffer_a, + output_ch, + buffer_fill_cnt, + output_shift, + output_mult, + out_offset, + input_offset, + 0, + out_activation_min, + out_activation_max, + num_elem, + bias_data, + out); + } +#else // #if defined(ARM_MATH_MVEI) + const uint16_t dilation_x = conv_params->dilation.w; + const uint16_t dilation_y = conv_params->dilation.h; + + int32_t i_out_y, i_out_x, i_ker_y, i_ker_x; + + /* Generate two columns from the input tensor a GEMM computation */ + q15_t *two_column_buf = buffer_a; + q7_t *out = output_data; + + /* This part implements the im2col function */ + for (i_out_y = 0; i_out_y < output_y; i_out_y++) + { + for (i_out_x = 0; i_out_x < output_x; i_out_x++) + { + const int32_t base_idx_y = stride_y * i_out_y - pad_y; + const int32_t base_idx_x = stride_x * i_out_x - pad_x; + + for (i_ker_y = 0; i_ker_y < kernel_y; i_ker_y++) + { + for (i_ker_x = 0; i_ker_x < kernel_x; i_ker_x++) + { + const int32_t k_y = base_idx_y + dilation_y * i_ker_y; + const int32_t k_x = base_idx_x + dilation_x * i_ker_x; + + if (k_y < 0 || k_y >= input_y || k_x < 0 || k_x >= input_x) + { + /* Filling 0 for out-of-bound paddings */ + memset(two_column_buf, 0, sizeof(q15_t) * input_ch); + } + else + { + /* Copying the pixel data to column */ + arm_q7_to_q15_with_offset( + input_data + (k_y * input_x + k_x) * input_ch, two_column_buf, input_ch, input_offset); + } + two_column_buf += input_ch; + } + } + + /* Computation is filed for every 2 columns */ + if (two_column_buf == buffer_a + 2 * input_ch * kernel_y * kernel_x) + { + out = arm_nn_mat_mult_kernel_s8_s16(filter_data, + buffer_a, + output_ch, + output_shift, + output_mult, + out_offset, + out_activation_min, + out_activation_max, + input_ch * kernel_y * kernel_x, + bias_data, + out); + + /* counter reset */ + two_column_buf = buffer_a; + } + } + } + + /* left-over because odd number of output pixels */ + if (two_column_buf != buffer_a) + { + const q7_t *ker_a = filter_data; + int i; + + for (i = 0; i < output_ch; i++) + { + /* Load the accumulator with bias first */ + q31_t sum = 0; + if (bias_data) + { + sum = bias_data[i]; + } + + /* Point to the beginning of the im2col buffer where the input is available as a rearranged column */ + const q15_t *ip_as_col = buffer_a; + + /* 4 multiply and accumulates are done in one loop. */ +#if defined(ARM_MATH_DSP) + uint16_t col_count = (input_ch * kernel_y * kernel_x) >> 2; + + while (col_count) + { + q31_t ker_a1, ker_a2; + q31_t ip_b1, ip_b2; + + ker_a = read_and_pad(ker_a, &ker_a1, &ker_a2); + + ip_b1 = arm_nn_read_q15x2_ia(&ip_as_col); + sum = __SMLAD(ker_a1, ip_b1, sum); + ip_b2 = arm_nn_read_q15x2_ia(&ip_as_col); + sum = __SMLAD(ker_a2, ip_b2, sum); + + col_count--; + } + /* Handle left over mac */ + col_count = input_ch * kernel_y * kernel_x & 0x3; +#else + uint16_t col_count = input_ch * kernel_y * kernel_x; +#endif + while (col_count) + { + q7_t ker_a1 = *ker_a++; + q15_t ip_b1 = *ip_as_col++; + sum += ker_a1 * ip_b1; + col_count--; + } + + sum = arm_nn_requantize(sum, output_mult[i], output_shift[i]); + sum += out_offset; + sum = MAX(sum, out_activation_min); + sum = MIN(sum, out_activation_max); + *out++ = (q7_t)sum; + } + } +#endif // #if defined(ARM_MATH_MVEI) + /* Advance to the next batch */ + input_data += (input_x * input_y * input_ch); + output_data += (output_x * output_y * output_ch); + } + + /* Return to application */ + return ARM_MATH_SUCCESS; +} + +int32_t arm_convolve_s8_get_buffer_size(const cmsis_nn_dims *input_dims, const cmsis_nn_dims *filter_dims) +{ +#if defined(ARM_MATH_MVEI) + int32_t col_length = input_dims->c * filter_dims->w * filter_dims->h; + // Get number of complete int16 lanes(multiple of 8) for given col_length. This is dependent on + // implementation of arm_nn_mat_mult_s8 + col_length = (col_length + 7) / 8; + // 4 -> number of im2col buffers, 8 -> 8 elements per Q register + return 4 * col_length * 8 * (int32_t)sizeof(int8_t); +#else + return (2 * input_dims->c * filter_dims->w * filter_dims->h) * (int32_t)sizeof(int16_t); +#endif +} + +/** + * @} end of NNConv group + */ |