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Merge pull request #45 from tum-ei-eda/sync
Complete Sync with CMSIS-NN
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Source/ConvolutionFunctions/muriscv_nn_convolve_even_s4.c
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| // Modifications copyright (C) 2024 Chair of Electronic Design Automation, TUM | ||
| /* | ||
| * SPDX-FileCopyrightText: Copyright 2023-2024 Arm Limited and/or its affiliates <[email protected]> | ||
| * | ||
| * 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: muriscv_nn_convolve_even_s4.c | ||
| * Description: s8 version of convolution using symmetric quantization with 4 bit weights. | ||
| * | ||
| * $Date: 05 Jun 2024 | ||
| * $Revision: V.1.0.0 | ||
| * | ||
| * Target : Arm(R) M-Profile Architecture | ||
| * | ||
| * -------------------------------------------------------------------- */ | ||
|
|
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| #include "muriscv_nn_functions.h" | ||
| #include "muriscv_nn_support_functions.h" | ||
|
|
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| /** | ||
| * @ingroup Public | ||
| */ | ||
|
|
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| /** | ||
| * @addtogroup NNConv | ||
| * @{ | ||
| */ | ||
|
|
||
| /* | ||
| * Basic s8 convolution function with int4 packed RHS (weights) and even RHS columns, | ||
| * | ||
| * Refer header file for details. | ||
| * | ||
| */ | ||
| muriscv_nn_status muriscv_nn_convolve_even_s4(const muriscv_nn_context *ctx, | ||
| const muriscv_nn_conv_params *conv_params, | ||
| const muriscv_nn_per_channel_quant_params *quant_params, | ||
| const muriscv_nn_dims *input_dims, | ||
| const int8_t *input_data, | ||
| const muriscv_nn_dims *filter_dims, | ||
| const int8_t *packed_filter_data, | ||
| const muriscv_nn_dims *bias_dims, | ||
| const int32_t *bias_data, | ||
| const muriscv_nn_dims *output_dims, | ||
| int8_t *output_data) | ||
| { | ||
| (void)bias_dims; | ||
|
|
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| //#if defined(USE_VEXT) | ||
| // | ||
| // if (ctx->buf == NULL) | ||
| // { | ||
| // return MURISCV_NN_ARG_ERROR; | ||
| // } | ||
| // | ||
| // int16_t *buffer_a = (int16_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 dilation_x = conv_params->dilation.w; | ||
| // const int32_t dilation_y = conv_params->dilation.h; | ||
| // 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; | ||
| // const int32_t rhs_cols = kernel_x * kernel_y * input_ch; | ||
| // const int32_t input_offset = conv_params->input_offset; | ||
| // | ||
| // if (rhs_cols & 0x1) | ||
| // { | ||
| // return MURISCV_NN_ARG_ERROR; | ||
| // } | ||
| // | ||
| // const int32_t blk_cnt = rhs_cols >> 5; | ||
| // | ||
| // 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++) | ||
| // { | ||
| // /* Generate up to four columns from the input tensor a GEMM computation */ | ||
| // int8_t *im2col_buf = (int8_t *)buffer_a; | ||
| // const int32_t rhs_rows = output_dims->c; | ||
| // int8_t *out = output_data; | ||
| // int32_t lhs_rows = 0; | ||
| // | ||
| // /* 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) | ||
| // { | ||
| // muriscv_nn_memset_s8(im2col_buf, (int8_t)-input_offset, sizeof(int8_t) * input_ch); | ||
| // } | ||
| // else | ||
| // { | ||
| // muriscv_nn_memcpy_s8(im2col_buf, input_data + (k_y * input_x + k_x) * input_ch, input_ch); | ||
| // } | ||
| // im2col_buf += input_ch; | ||
| // } | ||
| // } | ||
| // | ||
| // /* Reformat most of the buffer by interleaving it */ | ||
| // int8_t *im2col_buf_interleaved = (int8_t *)buffer_a + lhs_rows * rhs_cols; | ||
| // for (int j = blk_cnt; j > 0; --j) | ||
| // { | ||
| // int8x16x2_t x2 = vld2q_s8(im2col_buf_interleaved); | ||
| // | ||
| // vstrbq_s8(im2col_buf_interleaved, x2.val[1]); | ||
| // im2col_buf_interleaved += 16; | ||
| // | ||
| // vstrbq_s8(im2col_buf_interleaved, x2.val[0]); | ||
| // im2col_buf_interleaved += 16; | ||
| // } | ||
| // | ||
| // lhs_rows++; | ||
| // | ||
| // /* Computation is filed for every 4 columns */ | ||
| // if (lhs_rows == 4) | ||
| // { | ||
| // muriscv_nn_mat_mult_nt_interleaved_t_even_s4((int8_t *)buffer_a, | ||
| // packed_filter_data, | ||
| // bias_data, | ||
| // out, | ||
| // output_mult, | ||
| // output_shift, | ||
| // lhs_rows, | ||
| // rhs_rows, | ||
| // rhs_cols, | ||
| // input_offset, | ||
| // out_offset, | ||
| // out_activation_min, | ||
| // out_activation_max, | ||
| // rhs_cols); | ||
| // | ||
| // out += lhs_rows * rhs_rows; | ||
| // | ||
| // lhs_rows = 0; | ||
| // im2col_buf = (int8_t *)buffer_a; | ||
| // } | ||
| // } | ||
| // } | ||
| // | ||
| // /* Handle left over columns */ | ||
| // if (lhs_rows != 0) | ||
| // { | ||
| // muriscv_nn_mat_mult_nt_interleaved_t_even_s4((int8_t *)buffer_a, | ||
| // packed_filter_data, | ||
| // bias_data, | ||
| // out, | ||
| // output_mult, | ||
| // output_shift, | ||
| // lhs_rows, | ||
| // rhs_rows, | ||
| // rhs_cols, | ||
| // input_offset, | ||
| // out_offset, | ||
| // out_activation_min, | ||
| // out_activation_max, | ||
| // rhs_cols); | ||
| // out += lhs_rows * rhs_rows; | ||
| // lhs_rows = 0; | ||
| // im2col_buf = (int8_t *)buffer_a; | ||
| // } | ||
| // | ||
| // /* Advance to the next batch */ | ||
| // input_data += (input_x * input_y * input_ch); | ||
| // output_data += (output_x * output_y * output_ch); | ||
| // } | ||
| //#else | ||
| (void)ctx; | ||
| (void)conv_params; | ||
| (void)quant_params; | ||
| (void)input_dims; | ||
| (void)input_data; | ||
| (void)filter_dims; | ||
| (void)packed_filter_data; | ||
| (void)bias_data; | ||
| (void)output_dims; | ||
| (void)output_data; | ||
|
|
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| return MURISCV_NN_NO_IMPL_ERROR; | ||
|
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| //#endif // #if defined(USE_VEXT) | ||
| // | ||
| // /* Return to application */ | ||
| // return MURISCV_NN_SUCCESS; | ||
| } | ||
| // | ||
| ///** | ||
| // * @} end of NNConv group | ||
| // */ |
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