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ml: follow nxstyle
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Signed-off-by: jihandong <[email protected]>
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@jihandong @xiaoxiang781216
jihandong authored and xiaoxiang781216 committed Oct 18, 2024
1 parent 7d87768 commit 2593728
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330 changes: 176 additions & 154 deletions mlearning/tflite-micro/operators/neon/arm_convolve_s8.c
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@@ -1,5 +1,8 @@
/*
* SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or its affiliates <[email protected]>
/****************************************************************************
* apps/mlearning/tflite-micro/operators/neon/arm_convolve_s8.c
*
* SPDX-FileCopyrightText: Copyright 2010-2023 Arm Limited and/or
* its affiliates <[email protected]>
*
* SPDX-License-Identifier: Apache-2.0
*
Expand All @@ -14,191 +17,210 @@
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
****************************************************************************/

/****************************************************************************
* Included Files
****************************************************************************/

#include <arm_neon.h>
#include "arm_nnfunctions.h"
#include "arm_nnsupportfunctions.h"

/**
* @ingroup Public
*/

/**
* @addtogroup NNConv
* @{
*/
/****************************************************************************
* Public Functions
****************************************************************************/

/*
* 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.
/* 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_cmsis_nn_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 int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data)

arm_cmsis_nn_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 int8_t *input_data,
const cmsis_nn_dims *filter_dims,
const int8_t *filter_data,
const cmsis_nn_dims *bias_dims,
const int32_t *bias_data,
const cmsis_nn_dims *output_dims,
int8_t *output_data)
{
(void)bias_dims;
(void)bias_dims;

if (ctx->buf == NULL)
if (ctx->buf == NULL)
{
return ARM_CMSIS_NN_ARG_ERROR;
return ARM_CMSIS_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;

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++)

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;

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++)
{
const int32_t remainder = rhs_cols % 4;
const int32_t aligned_rhs_cols = remainder != 0 ? rhs_cols + 4 - remainder : rhs_cols;
/**
* Use Im2col to speed up conv2d calculations.
* Use as a ping-pong buffer for unordered elements.
*/
int8_t *im2col_buf = (int8_t *)buffer_a + aligned_rhs_cols * 2;
int16_t *im2col_buf_start_s16 = buffer_a;
int8_t *out = output_data;
int32_t lhs_rows = 0;
/* This part implements the im2col function */
for (int i_out_x = 0; i_out_x < output_x; i_out_x++)
const int32_t remainder = rhs_cols % 4;
const int32_t aligned_rhs_cols = remainder != 0 ?
rhs_cols + 4 - remainder : rhs_cols;

/**
* Use Im2col to speed up conv2d calculations.
* Use as a ping-pong buffer for unordered elements.
*/

int8_t *im2col_buf = (int8_t *)buffer_a + aligned_rhs_cols * 2;
int16_t *im2col_buf_start_s16 = buffer_a;
int8_t *out = output_data;
int32_t lhs_rows = 0;

/* This part implements the im2col function */

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;
for (int i_out_y = 0; i_out_y < output_y; i_out_y++)
const int32_t base_idx_x = stride_x * i_out_x - pad_x;
for (int i_out_y = 0; i_out_y < output_y; i_out_y++)
{
const int32_t base_idx_y = stride_y * i_out_y - pad_y;
for (int32_t i_ker_x = 0; i_ker_x < kernel_x; i_ker_x++)
const int32_t base_idx_y = stride_y * i_out_y - pad_y;
for (int32_t i_ker_x = 0; i_ker_x < kernel_x; i_ker_x++)
{
int32_t k_x = base_idx_x + dilation_x * i_ker_x;
int32_t k_y = base_idx_y - dilation_y;
for (int32_t i_ker_y = 0; i_ker_y < kernel_y; i_ker_y++)
int32_t k_x = base_idx_x + dilation_x * i_ker_x;
int32_t k_y = base_idx_y - dilation_y;
for (int32_t i_ker_y = 0; i_ker_y < kernel_y; i_ker_y++)
{
k_y += dilation_y;
arm_memcpy_s8(im2col_buf,
input_data + (k_y * input_x + k_x) * input_ch,
input_ch);
im2col_buf += input_ch;
k_y += dilation_y;
arm_memcpy_s8(im2col_buf,
input_data + (k_y * input_x + k_x) * input_ch,
input_ch);
im2col_buf += input_ch;
}
}
lhs_rows++;
/* Extend the input data from int8 to int16, and add offset. */
arm_q7_to_q15_with_offset(im2col_buf - rhs_cols,
im2col_buf_start_s16,
rhs_cols,
(int16_t)input_offset);
im2col_buf_start_s16 += aligned_rhs_cols;
if (lhs_rows & 2)

lhs_rows++;

/* Extend the input data from int8 to int16, and add offset. */

arm_q7_to_q15_with_offset(im2col_buf - rhs_cols,
im2col_buf_start_s16,
rhs_cols,
(int16_t)input_offset);
im2col_buf_start_s16 += aligned_rhs_cols;
if (lhs_rows & 2)
{
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,
rhs_cols,
aligned_rhs_cols,
bias_data,
out);
/* counter reset */
im2col_buf_start_s16 = buffer_a;
im2col_buf = (int8_t *)buffer_a + (aligned_rhs_cols << 1);
lhs_rows = 0;
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,
rhs_cols,
aligned_rhs_cols,
bias_data,
out);

/* counter reset */

im2col_buf_start_s16 = buffer_a;
im2col_buf = (int8_t *)buffer_a + (aligned_rhs_cols << 1);
lhs_rows = 0;
}
}
}
if (lhs_rows != 0)

if (lhs_rows != 0)
{
const int8_t *ker_a = filter_data;
int i;
for (i = 0; i < output_ch; i++)
const int8_t *ker_a = filter_data;
int i;

for (i = 0; i < output_ch; i++)
{
/* Load the accumulator with bias first */
uint16_t col_count = rhs_cols / 8;
int32_t sum = 0;
const int16_t *ip_as_col = buffer_a;
int32x4_t res_s32 = vdupq_n_s32(0);
if (bias_data)
/* Load the accumulator with bias first */

uint16_t col_count = rhs_cols / 8;
int32_t sum = 0;
const int16_t *ip_as_col = buffer_a;
int32x4_t res_s32 = vdupq_n_s32(0);
if (bias_data)
{
sum = bias_data[i];
sum = bias_data[i];
}
while (col_count)

while (col_count)
{
int8x8_t filter_s8 = vld1_s8(ker_a);
int16x8_t input_s16 = vld1q_s16(ip_as_col);
int16x8_t filter_s16 = vmovl_s8(filter_s8);
ker_a += 8;
ip_as_col += 8;
res_s32 = vmlal_s16(res_s32,
vget_low_s16(input_s16),
vget_low_s16(filter_s16));
res_s32 = vmlal_s16(res_s32,
vget_high_s16(input_s16),
vget_high_s16(filter_s16));
col_count --;
int8x8_t filter_s8 = vld1_s8(ker_a);
int16x8_t input_s16 = vld1q_s16(ip_as_col);
int16x8_t filter_s16 = vmovl_s8(filter_s8);
ker_a += 8;
ip_as_col += 8;
res_s32 = vmlal_s16(res_s32,
vget_low_s16(input_s16),
vget_low_s16(filter_s16));
res_s32 = vmlal_s16(res_s32,
vget_high_s16(input_s16),
vget_high_s16(filter_s16));
col_count--;
}
sum += vgetq_lane_s32(res_s32, 0);
sum += vgetq_lane_s32(res_s32, 1);
sum += vgetq_lane_s32(res_s32, 2);
sum += vgetq_lane_s32(res_s32, 3);
col_count = rhs_cols % 8;
while (col_count)

sum += vgetq_lane_s32(res_s32, 0);
sum += vgetq_lane_s32(res_s32, 1);
sum += vgetq_lane_s32(res_s32, 2);
sum += vgetq_lane_s32(res_s32, 3);
col_count = rhs_cols % 8;
while (col_count)
{
int8_t ker_a1 = *ker_a++;
int16_t ip_b1 = *ip_as_col++;
sum += ker_a1 * ip_b1;
col_count--;
int8_t ker_a1 = *ker_a++;
int16_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++ = (int8_t)sum;

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++ = (int8_t)sum;
}
}
/* Advance to the next batch */
input_data += (input_x * input_y * input_ch);
output_data += (output_x * output_y * output_ch);

/* 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_CMSIS_NN_SUCCESS;

/* Return to application */

return ARM_CMSIS_NN_SUCCESS;
}

/**
* @} end of NNConv group
*/
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