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/*
* Copyright (c) 2017 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "helpers.h"
#if defined(CONV_STRIDE_X)
#if CONV_STRIDE_X == 1
#define convolution1x3 convolution1x3_stride_1
#elif CONV_STRIDE_X == 2
#define convolution1x3 convolution1x3_stride_2
#elif CONV_STRIDE_X == 3
#define convolution1x3 convolution1x3_stride_3
#else /* CONV_STRIDE_X */
#error "Stride not supported"
#endif /* CONV_STRIDE_X */
/** Compute a 1D horizontal convolution of size 3 and stride 1 for floating point type.
*
* @param[in] left_pixel Pointer to the left pixel.
* @param[in] left_coeff Weight of the left pixel
* @param[in] middle_coeff Weight of the middle pixel
* @param[in] right_coeff Weight of the right pixel
*
* @return a float2 containing 2 convoluted values.
*/
inline float2 convolution1x3_stride_1(__global const uchar *left_pixel,
const float left_coeff,
const float middle_coeff,
const float right_coeff)
{
float4 temp = vload4(0, (__global float *)left_pixel);
float2 left = CONVERT(temp.s01, float2);
float2 middle = CONVERT(temp.s12, float2);
float2 right = CONVERT(temp.s23, float2);
return left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff;
}
/** Compute a 1D horizontal convolution of size 3 and stride 2 for floating point type.
*
* @param[in] left_pixel Pointer to the left pixel.
* @param[in] left_coeff Weight of the left pixel
* @param[in] middle_coeff Weight of the middle pixel
* @param[in] right_coeff Weight of the right pixel
*
* @return a float2 containing 2 convoluted values.
*/
inline float2 convolution1x3_stride_2(__global const uchar *left_pixel,
const float left_coeff,
const float middle_coeff,
const float right_coeff)
{
float4 temp0 = vload4(0, (__global float *)left_pixel);
float temp1 = *((__global float *)(left_pixel + 4 * sizeof(float)));
float2 left = CONVERT(temp0.s02, float2);
float2 middle = CONVERT(temp0.s13, float2);
float2 right = CONVERT((float2)(temp0.s2, temp1), float2);
return left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff;
}
/** Compute a 1D horizontal convolution of size 3 and stride 3 for floating point type.
*
* @param[in] left_pixel Pointer to the left pixel.
* @param[in] left_coeff Weight of the left pixel
* @param[in] middle_coeff Weight of the middle pixel
* @param[in] right_coeff Weight of the right pixel
*
* @return a float2 containing 2 convoluted values.
*/
inline float2 convolution1x3_stride_3(__global const uchar *left_pixel,
const float left_coeff,
const float middle_coeff,
const float right_coeff)
{
float4 temp0 = vload4(0, (__global float *)left_pixel);
float2 temp1 = vload2(0, (__global float *)(left_pixel + 4 * sizeof(float)));
float2 left = CONVERT(temp0.s03, float2);
float2 middle = CONVERT((float2)(temp0.s1, temp1.s0), float2);
float2 right = CONVERT((float2)(temp0.s2, temp1.s1), float2);
return left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff;
}
/** Apply a 3x3 convolution matrix to a single channel F32 input image and return the result.
*
* Convolution matrix layout:
*
* [ mat0, mat1, mat2 ]\n
* [ mat3, mat4, mat5 ]\n
* [ mat6, mat7, mat8 ]\n
*
* @param[in] src A pointer to source Image structure
* @param[in] mat0 Coefficient from the convolution matrix
* @param[in] mat1 Coefficient from the convolution matrix
* @param[in] mat2 Coefficient from the convolution matrix
* @param[in] mat3 Coefficient from the convolution matrix
* @param[in] mat4 Coefficient from the convolution matrix
* @param[in] mat5 Coefficient from the convolution matrix
* @param[in] mat6 Coefficient from the convolution matrix
* @param[in] mat0 Coefficient from the convolution matrix
* @param[in] mat7 Coefficient from the convolution matrix
* @param[in] mat8 Coefficient from the convolution matrix
*
* @return a float2 containing 2 convoluted values.
*/
inline float2 convolution3x3(
Image *src,
const float mat0, const float mat1, const float mat2,
const float mat3, const float mat4, const float mat5,
const float mat6, const float mat7, const float mat8)
{
float2 pixels;
pixels = convolution1x3(offset(src, 0, 0), mat0, mat1, mat2);
pixels += convolution1x3(offset(src, 0, 1), mat3, mat4, mat5);
pixels += convolution1x3(offset(src, 0, 2), mat6, mat7, mat8);
return pixels;
}
/** This function computes the horizontal integral of the image.
*
* @param[in] src_ptr Pointer to the source image. Supported data types: U8
* @param[in] src_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source image in Y dimension (in bytes)
* @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image
* @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)
* @param[in] dst_ptr Pointer to the destination tensor. Supported data types: F16/F32
* @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
* @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
* @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
* @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F16/F32
* @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
* @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
* @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
* @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes)
* @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector
* @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: F16/F32
* @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)
* @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector
*/
__kernel void depthwise_convolution_3x3(
TENSOR3D_DECLARATION(src),
TENSOR3D_DECLARATION(dst),
TENSOR3D_DECLARATION(weights)
#if defined(HAS_BIAS)
,
VECTOR_DECLARATION(biases)
#endif //defined(HAS_BIAS)
)
{
Image src = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(src);
Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst);
Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT(weights);
#if defined(HAS_BIAS)
Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
#endif //defined(HAS_BIAS)
uchar3 offset = (uchar3)(0, 1, 2) * (uchar3)weights_stride_y;
float3 weights_values0 = vload3(0, (__global float *)(weights.ptr + offset.s0));
float3 weights_values1 = vload3(0, (__global float *)(weights.ptr + offset.s1));
float3 weights_values2 = vload3(0, (__global float *)(weights.ptr + offset.s2));
float2 pixels = convolution3x3(&src, weights_values0.s0, weights_values0.s1, weights_values0.s2,
weights_values1.s0, weights_values1.s1, weights_values1.s2,
weights_values2.s0, weights_values2.s1, weights_values2.s2);
#if defined(HAS_BIAS)
pixels += (float2)(*((__global float *)(biases.ptr + get_global_id(2) * biases_stride_x)));
#endif //defined(HAS_BIAS)
vstore2(pixels, 0, (__global float *)dst.ptr);
}
#endif //defined(CONV_STRIDE_X)
#if defined(SRC_WIDTH) && defined(DATA_TYPE)
/** This kernel reshapes each of the tensor's low three dimensions to single rows.
*
* @note Datatype and source width should be given as a preprocessor argument using -DDATA_TYPE=type and -DSRC_WIDTH=width. e.g. -DSRC_WIDTH=128
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
* @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)
* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
* @param[out] dst_ptr Pointer to the destination tensor. Same as @p src_ptr
* @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
* @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
* @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: F16/F32
* @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)
* @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector
*/
__kernel void depthwise_weights_reshape(
TENSOR3D_DECLARATION(src),
IMAGE_DECLARATION(dst)
#ifdef HAS_BIAS
,
VECTOR_DECLARATION(biases)
#endif /* HAS_BIAS */
)
{
Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
#ifdef HAS_BIAS
Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
#endif /* HAS_BIAS */
__global DATA_TYPE *input_ptr = (__global DATA_TYPE *)src.ptr;
__global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + get_global_id(1) * SRC_WIDTH * dst_stride_x + get_global_id(2) * dst_stride_y;
for(int i = 0; i < SRC_WIDTH; ++i, ++input_ptr)
{
*((__global DATA_TYPE *)(output_ptr + i * dst_stride_x)) = *input_ptr;
}
#if defined(HAS_BIAS)
if(get_global_id(1) == 0)
{
*((__global DATA_TYPE *)(output_ptr + SRC_WIDTH * get_global_size(1) * dst_stride_x)) = *((__global float *)(biases.ptr + get_global_id(2) * biases_stride_x));
}
#endif // defined(HAS_BIAS)
}
#endif //defined(SRC_WIDTH) && defined(DATA_TYPE)
#if defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DATA_TYPE)
/** This kernel performs a reshaping of the input tensor to a tensor used to perform depthwise convolution using vector to matrix multiplication.
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The convolution information must be passed at compile time using -DSTRIDE_X, -DSTRIDE_Y, -DPAD_LEFT, -DPAD_TOP, -DPAD_RIGHT, -DPAD_BOTTOM, -DKERNEL_WIDHT, -DKERNEL_HEIGHT, -DSRC_WIDTH, -DSRC_HEIGHT
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
* @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
* @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
* @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
* @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
* @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
*/
__kernel void depthwise_im2col(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst))
{
Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
const int src_pixel_linear = get_global_id(1) * STRIDE_X;
const int full_length = SRC_WIDTH + PAD_LEFT + PAD_RIGHT;
const int max_initial_x = STRIDE_X * (((full_length - KERNEL_WIDTH) / STRIDE_X) + 1);
const int src_x = -PAD_LEFT + src_pixel_linear % max_initial_x;
const int src_y = -PAD_TOP + src_pixel_linear / max_initial_x * STRIDE_Y;
const int src_z = get_global_id(2);
__global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + src_z * src_stride_z;
__global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst.ptr));
for(int y = src_y; y < src_y + KERNEL_HEIGHT; ++y)
{
for(int x = src_x; x < src_x + KERNEL_WIDTH; ++x, ++output_ptr)
{
if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT)
{
*output_ptr = 0;
}
else
{
*output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
}
}
}
#if defined(HAS_BIAS)
*output_ptr = (DATA_TYPE)(1);
#endif // defined(HAS_BIAS)
}
#endif //defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_WIDTH) && defined(DATA_TYPE)
#if defined(CONV_WIDTH) && defined(CONV_HEIGHT) && defined(DATA_TYPE)
/** This kernel performs a reshaping of the output of the depthwise generic convolution.
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The convolution information must be passed at compile time using -DCONV_WIDTH, -DCONV_HEIGHT, e.g -DCONV_WIDTH=32, -DCONV_HEIGHT=42
*
* @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
* @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
* @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
* @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
* @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
*/
__kernel void depthwise_vector_to_tensor(
VECTOR_DECLARATION(src),
TENSOR3D_DECLARATION(dst))
{
Vector src = CONVERT_TO_VECTOR_STRUCT(src);
const int patch_size = CONV_WIDTH * CONV_HEIGHT;
const int id0 = get_global_id(0);
const int z = id0 / patch_size;
const int index2D = id0 - z * patch_size;
__global uchar *out_ptr = dst_ptr + dst_offset_first_element_in_bytes + index2D % CONV_WIDTH * dst_stride_x + index2D / CONV_WIDTH * dst_stride_y + z * dst_stride_z;
*((__global DATA_TYPE *)out_ptr) = *((__global DATA_TYPE *)src.ptr);
}
#endif //defined(CONV_WIDTH) && defined(CONV_HEIGHT) && defined(DATA_TYPE)
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