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Diffstat (limited to 'src/core/CL/cl_kernels/depthwise_convolution.cl')
| -rw-r--r-- | src/core/CL/cl_kernels/depthwise_convolution.cl | 1847 |
1 files changed, 0 insertions, 1847 deletions
diff --git a/src/core/CL/cl_kernels/depthwise_convolution.cl b/src/core/CL/cl_kernels/depthwise_convolution.cl deleted file mode 100644 index c050175798..0000000000 --- a/src/core/CL/cl_kernels/depthwise_convolution.cl +++ /dev/null @@ -1,1847 +0,0 @@ -/* - * Copyright (c) 2017-2019 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" - -#include "activation_float_helpers.h" - -/** Get the pointer position at a certain offset in x and y direction. - * - * @param[in] ptr Pointer to the starting position of the buffer - * @param[in] x Relative X position - * @param[in] y Relative Y position - * @param[in] stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] stride_y Stride of the source tensor in Y dimension (in bytes) - * - * @return a uchar - */ -inline __global uchar *ptr_offset(__global uchar *ptr, const int x, const int y, const int stride_x, const int stride_y) -{ - return ptr + x * stride_x + y * stride_y; -} - -#if(DILATION_X == 1 && DILATION_Y == 1) - -#define CONVOLUTION1x3_BIFROST2X1_STRIDE1(acc, src0, weights_row0) \ - ({ \ - acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0.s1, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0.s2, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0.s3, weights_row0.s2, acc.s1); \ - }) - -#define CONVOLUTION1x3_BIFROST4X1_STRIDE1(acc, src0, weights_row0) \ - ({ \ - acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0.s1, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0.s2, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0.s3, weights_row0.s2, acc.s1); \ - acc.s2 = fma(src0.s2, weights_row0.s0, acc.s2); \ - acc.s2 = fma(src0.s3, weights_row0.s1, acc.s2); \ - acc.s2 = fma(src0.s4, weights_row0.s2, acc.s2); \ - acc.s3 = fma(src0.s3, weights_row0.s0, acc.s3); \ - acc.s3 = fma(src0.s4, weights_row0.s1, acc.s3); \ - acc.s3 = fma(src0.s5, weights_row0.s2, acc.s3); \ - }) - -#define CONVOLUTION1x3_BIFROST2X1_STRIDE2(acc, src0, src1, weights_row0) \ - ({ \ - acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0.s2, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0.s3, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src1.s0, weights_row0.s2, acc.s1); \ - }) - -#define CONVOLUTION1x3_BIFROST4X1_STRIDE2(acc, src0, src1, weights_row0) \ - ({ \ - acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0.s2, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0.s3, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0.s4, weights_row0.s2, acc.s1); \ - acc.s2 = fma(src0.s4, weights_row0.s0, acc.s2); \ - acc.s2 = fma(src0.s5, weights_row0.s1, acc.s2); \ - acc.s2 = fma(src0.s6, weights_row0.s2, acc.s2); \ - acc.s3 = fma(src0.s6, weights_row0.s0, acc.s3); \ - acc.s3 = fma(src0.s7, weights_row0.s1, acc.s3); \ - acc.s3 = fma(src1.s0, weights_row0.s2, acc.s3); \ - }) - -#else /* DILATION_X==1 && DILATION_Y==1 */ - -#define CONVOLUTION1x3_BIFROST2X1_STRIDE1(acc, src0_left, src0_mid, src0_right, weights_row0) \ - ({ \ - acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0_left.s1, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0_mid.s1, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0_right.s1, weights_row0.s2, acc.s1); \ - }) - -#define CONVOLUTION1x3_BIFROST2X1_STRIDE2(acc, src0_left, src0_mid, src0_right, weights_row0) \ - ({ \ - acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0_left.s2, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0_mid.s2, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0_right.s2, weights_row0.s2, acc.s1); \ - }) - -#define CONVOLUTION1x3_BIFROST4X1_STRIDE1(acc, src0_left, src0_mid, src0_right, weights_row0) \ - ({ \ - acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0_left.s1, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0_mid.s1, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0_right.s1, weights_row0.s2, acc.s1); \ - acc.s2 = fma(src0_left.s2, weights_row0.s0, acc.s2); \ - acc.s2 = fma(src0_mid.s2, weights_row0.s1, acc.s2); \ - acc.s2 = fma(src0_right.s2, weights_row0.s2, acc.s2); \ - acc.s3 = fma(src0_left.s3, weights_row0.s0, acc.s3); \ - acc.s3 = fma(src0_mid.s3, weights_row0.s1, acc.s3); \ - acc.s3 = fma(src0_right.s3, weights_row0.s2, acc.s3); \ - }) - -#define CONVOLUTION1x3_BIFROST4X1_STRIDE2(acc, src0_left, src0_mid, src0_right, weights_row0) \ - ({ \ - acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \ - acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \ - acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \ - acc.s1 = fma(src0_left.s2, weights_row0.s0, acc.s1); \ - acc.s1 = fma(src0_mid.s2, weights_row0.s1, acc.s1); \ - acc.s1 = fma(src0_right.s2, weights_row0.s2, acc.s1); \ - acc.s2 = fma(src0_left.s4, weights_row0.s0, acc.s2); \ - acc.s2 = fma(src0_mid.s4, weights_row0.s1, acc.s2); \ - acc.s2 = fma(src0_right.s4, weights_row0.s2, acc.s2); \ - acc.s3 = fma(src0_left.s6, weights_row0.s0, acc.s3); \ - acc.s3 = fma(src0_mid.s6, weights_row0.s1, acc.s3); \ - acc.s3 = fma(src0_right.s6, weights_row0.s2, acc.s3); \ - }) - -#endif /* DILATION_X==1 && DILATION_Y==1 */ - -#if defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F32) -#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) -{ -#if(DILATION_X == 1 && DILATION_Y == 1) - 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; -#else /* DILATION_X==1 && DILATION_Y==1 */ - return vload2(0, (__global float *)left_pixel) * (float2)left_coeff - + vload2(0, (__global float *)(left_pixel) + DILATION_X) * (float2)middle_coeff - + vload2(0, (__global float *)(left_pixel) + 2 * DILATION_X) * (float2)right_coeff; -#endif /* DILATION_X==1 && DILATION_Y==1 */ -} - -/** 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) -{ -#if(DILATION_X == 1 && DILATION_Y == 1) - 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; -#else /* DILATION_X==1 && DILATION_Y==1 */ - __global float *left_pixel_float = (__global float *)left_pixel; - - return vload4(0, left_pixel_float).s02 * (float2)left_coeff - + vload4(0, left_pixel_float + DILATION_X).s02 * (float2)middle_coeff - + vload4(0, left_pixel_float + DILATION_X * 2).s02 * (float2)right_coeff; - -#endif /* DILATION_X==1 && DILATION_Y==1 */ -} - -/** 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) -{ -#if(DILATION_X == 1 && DILATION_Y == 1) - 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; -#else /* DILATION_X==1 && DILATION_Y==1 */ - __global float *left_pixel_float = (__global float *)left_pixel; - - return (float2)(*left_pixel_float, *(left_pixel_float + 3)) * (float2)left_coeff - + (float2)(*(left_pixel_float + DILATION_X), *(left_pixel_float + DILATION_X + 3)) * (float2)middle_coeff - + (float2)(*(left_pixel_float + DILATION_X * 2), *(left_pixel_float + DILATION_X * 2 + 3)) * (float2)right_coeff; -#endif /* DILATION_X==1 && DILATION_Y==1 */ -} - -/** 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( - __global const uchar *src, - unsigned int src_stride_y, - 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((src + 0 * DILATION_Y * src_stride_y), mat0, mat1, mat2); - pixels += convolution1x3((src + 1 * DILATION_Y * src_stride_y), mat3, mat4, mat5); - pixels += convolution1x3((src + 2 * DILATION_Y * src_stride_y), mat6, mat7, mat8); - - return pixels; -} - -/** This OpenCL kernel computes the depthwise convolution 3x3 - * - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: 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_offset_first_element_in_bytes The offset of the first element in the source tensor - * @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: 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: 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_NO_STEP(weights); - - float2 pixels = 0.0f; - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - // Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - - __global uchar *src_addr = src.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - - // Load the weights - float3 weights_values0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); - float3 weights_values1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); - float3 weights_values2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); - - pixels = convolution3x3(src_addr, src_stride_y, - 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) - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - float bias = *((__global float *)(vector_offset(&biases, channel))); - - pixels += (float2)bias; -#endif //defined(HAS_BIAS) - - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels, A_VAL, B_VAL), 0, (__global float *)dst.ptr); -} -#endif //defined(CONV_STRIDE_X) - -#if(DILATION_X > 1 || DILATION_Y > 1) - -/** Perform 3x3 convolution for stride_x=1 and stride_y=1 when DILATION_X>1 or DILATION_Y>1 for F32 - * - * @param[in] src_addr Pointer to the starting position of where to perform the convolution - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] y_offset Offset from the source tensor from which to start convolution - * @param[in] weights_addr Pointer from where to get weights - * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension - */ -inline float2 convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(__global uchar *src_addr, const int stride_x_bytes, const int stride_y_bytes, - const int y_offset, __global uchar *weights_addr, const int weights_stride_y) -{ - // Load the weights - float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); - float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); - float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); - - float2 pixels0 = 0.0f; - - float2 src00_left = vload2(0, (__global float *)ptr_offset(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); // Row0 - float2 src00_mid = vload2(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - float2 src00_right = vload2(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - - float2 src10_left = vload2(0, (__global float *)ptr_offset(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); // Row1 - float2 src10_mid = vload2(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - float2 src10_right = vload2(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - - float2 src20_left = vload2(0, (__global float *)ptr_offset(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); // Row2 - float2 src20_mid = vload2(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - float2 src20_right = vload2(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels0, src00_left, src00_mid, src00_right, weights_row0); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels0, src10_left, src10_mid, src10_right, weights_row1); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels0, src20_left, src20_mid, src20_right, weights_row2); - - return pixels0; -} - -/** Perform 3x3 convolution for stride_x=2 and stride_y=2 when DILATION_X>1 or DILATION_Y>1 for F32 - * - * @param[in] src_addr Pointer to the starting position of where to perform the convolution - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] y_offset Offset from the source tensor from which to start convolution - * @param[in] weights_addr Pointer from where to get weights - * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension - */ -inline float2 convolution_3x3_dilation_stridex2_stridey2_bifrost_f32(__global uchar *src_addr, const int stride_x_bytes, const int stride_y_bytes, - const int y_offset, __global uchar *weights_addr, const int weights_stride_y) -{ - // Load the weights - float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); - float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); - float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); - - float2 pixels0 = 0.0f; - - float3 src00_left = vload3(0, (__global float *)ptr_offset(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); // Row0 - float3 src00_mid = vload3(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - float3 src00_right = vload3(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - - float3 src10_left = vload3(0, (__global float *)ptr_offset(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); // Row1 - float3 src10_mid = vload3(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - float3 src10_right = vload3(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - - float3 src20_left = vload3(0, (__global float *)ptr_offset(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); // Row2 - float3 src20_mid = vload3(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - float3 src20_right = vload3(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - - CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels0, src00_left, src00_mid, src00_right, weights_row0); - CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels0, src10_left, src10_mid, src10_right, weights_row1); - CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels0, src20_left, src20_mid, src20_right, weights_row2); - - return pixels0; -} - -#endif /* (DILATION_X > 1 || DILATION_Y > 1) */ - -/** This OpenCL kernel is optimized for Bifrost architectures and computes the depthwise convolution 3x3 when both - * stride_x and stride_y are equal to 1 - * - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float. - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: 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_offset_first_element_in_bytes The offset of the first element in the source tensor - * @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: 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: 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: 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_stridex1_stridey1_bifrost_f32( - 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_NO_STEP(weights); - - float2 pixels0 = 0.0f; - float2 pixels1 = 0.0f; - float2 pixels2 = 0.0f; - float2 pixels3 = 0.0f; - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - // Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - __global uchar *src_addr = src.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - -#if(DILATION_X == 1 && DILATION_Y == 1) - // Load the weights - float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); - float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); - float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); - - // Note: Since each work-item computes 4x2 elements, we need to load 6 rows from the input tensor - float4 src00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y)); // Row0 - float4 src10 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y)); // Row1 - float4 src20 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y)); // Row2 - float4 src30 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y)); // Row3 - float4 src40 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y)); // Row4 - float4 src50 = vload4(0, (__global float *)(src_addr + 5 * src_stride_y)); // Row5 - - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels0, src00, weights_row0); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels0, src10, weights_row1); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels0, src20, weights_row2); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels1, src10, weights_row0); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels1, src20, weights_row1); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels1, src30, weights_row2); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels2, src20, weights_row0); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels2, src30, weights_row1); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels2, src40, weights_row2); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels3, src30, weights_row0); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels3, src40, weights_row1); - CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels3, src50, weights_row2); - -#else /* DILATION_X==1 && DILATION_Y==1 */ - - //3x3 Convolution of elements starting in 0th row - pixels0 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(src_addr, src.stride_x, src.stride_y, 0, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 1st row - pixels1 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(src_addr, src.stride_x, src.stride_y, 1, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 2nd row - pixels2 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(src_addr, src.stride_x, src.stride_y, 2, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 3rd row - pixels3 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(src_addr, src.stride_x, src.stride_y, 3, weights_addr, weights_stride_y); - -#endif /* DILATION_X==1 && DILATION_Y==1 */ - -#ifdef HAS_BIAS - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - float bias = *((__global float *)(vector_offset(&biases, channel))); - - pixels0 += (float2)bias; - pixels1 += (float2)bias; - pixels2 += (float2)bias; - pixels3 += (float2)bias; -#endif /* defined(HAS_BIAS) */ - - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels0, A_VAL, B_VAL), 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels1, A_VAL, B_VAL), 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels2, A_VAL, B_VAL), 0, (__global float *)(dst.ptr + 2 * dst_stride_y)); - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels3, A_VAL, B_VAL), 0, (__global float *)(dst.ptr + 3 * dst_stride_y)); -} - -/** This OpenCL kernel is optimized for Bifrost architectures and computes the depthwise convolution 3x3 when both - * stride_x and stride_y are equal to 2 - * - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float. - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: 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_offset_first_element_in_bytes The offset of the first element in the source tensor - * @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: 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: 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: 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_stridex2_stridey2_bifrost_f32( - 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_NO_STEP(weights); - - float2 pixels0 = 0.0f; - float2 pixels1 = 0.0f; - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - // Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - __global uchar *src_addr = src.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - -#if(DILATION_X == 1 && DILATION_Y == 1) - - // Load the weights - float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); - float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); - float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); - - // Note: Since each work-item computes 4x2 elements, we need to load 5 rows from the input tensor - float4 src00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y)); // Row0 - float2 src01 = vload2(2, (__global float *)(src_addr + 0 * src_stride_y)); // Row0 - float4 src10 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y)); // Row1 - float2 src11 = vload2(2, (__global float *)(src_addr + 1 * src_stride_y)); // Row1 - float4 src20 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y)); // Row2 - float2 src21 = vload2(2, (__global float *)(src_addr + 2 * src_stride_y)); // Row2 - float4 src30 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y)); // Row3 - float2 src31 = vload2(2, (__global float *)(src_addr + 3 * src_stride_y)); // Row3 - float4 src40 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y)); // Row4 - float2 src41 = vload2(2, (__global float *)(src_addr + 4 * src_stride_y)); // Row4 - - CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels0, src00, src01, weights_row0); - CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels0, src10, src11, weights_row1); - CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels0, src20, src21, weights_row2); - CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels1, src20, src21, weights_row0); - CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels1, src30, src31, weights_row1); - CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels1, src40, src41, weights_row2); - -#else /* DILATION_X==1 && DILATION_Y==1 */ - - //3x3 Convolution of elements starting in 0th row - pixels0 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f32(src_addr, src.stride_x, src.stride_y, 0, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 2nd row - pixels1 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f32(src_addr, src.stride_x, src.stride_y, 2, weights_addr, weights_stride_y); -#endif /* DILATION_X==1 && DILATION_Y==1 */ - -#ifdef HAS_BIAS - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - float bias = *((__global float *)(vector_offset(&biases, channel))); - - pixels0 += (float2)bias; - pixels1 += (float2)bias; -#endif /* defined(HAS_BIAS) */ - - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels0, A_VAL, B_VAL), 0, (__global float *)(dst.ptr + 0 * dst_stride_y)); - vstore2(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels1, A_VAL, B_VAL), 0, (__global float *)(dst.ptr + 1 * dst_stride_y)); -} - -#endif // defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F32) - -#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(DST_WIDTH) -/** Reshape the weights for quantized depthwise convolution - * - * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type, e.g. -DDATA_TYPE=uint8 - * @note Output width should be given as a preprocessor argument using -DDST_WIDTH=width, e.g. -DDST_WIDTH=128 - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=vec_size, e.g., -DVEC_SIZE=4 - * @attention Input's height and width should be 3 - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8 - * @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. 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_offset_first_element_in_bytes The offset of the first element in the destination tensor - */ -__kernel void depthwise_convolution_reshape_weights( - TENSOR3D_DECLARATION(src), - IMAGE_DECLARATION(dst)) -{ - Vector src = CONVERT_TO_VECTOR_STRUCT(src); - const int x = get_global_id(0); - - // Load 3x3xVEC_SIZE weights - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - w0 = VLOAD(VEC_SIZE)(0, src.ptr + 0 * src_stride_y + 0 * src_stride_z); - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - w1 = VLOAD(VEC_SIZE)(0, src.ptr + 1 * src_stride_y + 0 * src_stride_z); - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - w2 = VLOAD(VEC_SIZE)(0, src.ptr + 2 * src_stride_y + 0 * src_stride_z); - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - w3 = VLOAD(VEC_SIZE)(0, src.ptr + 0 * src_stride_y + 1 * src_stride_z); - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - w4 = VLOAD(VEC_SIZE)(0, src.ptr + 1 * src_stride_y + 1 * src_stride_z); - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - w5 = VLOAD(VEC_SIZE)(0, src.ptr + 2 * src_stride_y + 1 * src_stride_z); - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - w6 = VLOAD(VEC_SIZE)(0, src.ptr + 0 * src_stride_y + 2 * src_stride_z); - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - w7 = VLOAD(VEC_SIZE)(0, src.ptr + 1 * src_stride_y + 2 * src_stride_z); - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - w8 = VLOAD(VEC_SIZE)(0, src.ptr + 2 * src_stride_y + 2 * src_stride_z); - - __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * DST_WIDTH * sizeof(DATA_TYPE); - -#if defined(TRANSPOSE) -#if VEC_SIZE != 4 -#error "VEC_SIZE not supported" -#else // VEC_SIZE != 4 - VSTORE(VEC_SIZE) - ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(w0.s0, w1.s0, w2.s0, w3.s0), 0, dst_addr + 0); - VSTORE(VEC_SIZE) - ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(w4.s0, w5.s0, w6.s0, w7.s0), 0, dst_addr + 1 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(w8.s0, w0.s1, w1.s1, w2.s1), 0, dst_addr + 2 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(w3.s1, w4.s1, w5.s1, w6.s1), 0, dst_addr + 3 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(w7.s1, w8.s1, w0.s2, w1.s2), 0, dst_addr + 4 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(w2.s2, w3.s2, w4.s2, w5.s2), 0, dst_addr + 5 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(w6.s2, w7.s2, w8.s2, w0.s3), 0, dst_addr + 6 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(w1.s3, w2.s3, w3.s3, w4.s3), 0, dst_addr + 7 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))(w5.s3, w6.s3, w7.s3, w8.s3), 0, dst_addr + 8 * sizeof(DATA_TYPE) * VEC_SIZE); -#endif // VEC_SIZE != 4 -#else // !defined(TRANSPOSE) - VSTORE(VEC_SIZE) - (w0, 0, dst_addr + 0); - VSTORE(VEC_SIZE) - (w1, 0, dst_addr + 1 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - (w2, 0, dst_addr + 2 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - (w3, 0, dst_addr + 3 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - (w4, 0, dst_addr + 4 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - (w5, 0, dst_addr + 5 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - (w6, 0, dst_addr + 6 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - (w7, 0, dst_addr + 7 * sizeof(DATA_TYPE) * VEC_SIZE); - VSTORE(VEC_SIZE) - (w8, 0, dst_addr + 8 * sizeof(DATA_TYPE) * VEC_SIZE); -#endif // defined(TRANSPOSE) -} -#endif // defined(VEC_SIZE) && defined(DATA_TYPE) && defined(DST_WIDTH) - -#if defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F16) -#if defined(CONV_STRIDE_X) -#if CONV_STRIDE_X == 1 -#define convolution1x3_f16 convolution1x3_stride_1_f16 -#elif CONV_STRIDE_X == 2 -#define convolution1x3_f16 convolution1x3_stride_2_f16 -#elif CONV_STRIDE_X == 3 -#define convolution1x3_f16 convolution1x3_stride_3_f16 -#else /* CONV_STRIDE_X */ -#error "Stride not supported" -#endif /* CONV_STRIDE_X */ - -#if(DILATION_X > 1 || DILATION_Y > 1) - -/** Perform 3x3 convolution for stride_x=1 and stride_y=1 when DILATION_X>1 or DILATION_Y>1 for f16 - * - * @param[in] src_addr Pointer to the starting position of where to perform the convolution - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] y_offset Offset from the source tensor from which to start convolution - * @param[in] weights_addr Pointer from where to get weights - * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension - */ -inline half4 convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(__global uchar *src_addr, const int stride_x_bytes, const int stride_y_bytes, - const int y_offset, __global uchar *weights_addr, const int weights_stride_y) -{ - // Load the weights - half3 weights_row0 = vload3(0, (__global half *)(weights_addr + 0 * weights_stride_y)); - half3 weights_row1 = vload3(0, (__global half *)(weights_addr + 1 * weights_stride_y)); - half3 weights_row2 = vload3(0, (__global half *)(weights_addr + 2 * weights_stride_y)); - - half4 pixels0 = 0.0f; - - half4 src00_left = vload4(0, (__global half *)ptr_offset(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); // Row0 - half4 src00_mid = vload4(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - half4 src00_right = vload4(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - - half4 src10_left = vload4(0, (__global half *)ptr_offset(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); // Row1 - half4 src10_mid = vload4(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - half4 src10_right = vload4(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - - half4 src20_left = vload4(0, (__global half *)ptr_offset(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); // Row2 - half4 src20_mid = vload4(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - half4 src20_right = vload4(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels0, src00_left, src00_mid, src00_right, weights_row0); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels0, src10_left, src10_mid, src10_right, weights_row1); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels0, src20_left, src20_mid, src20_right, weights_row2); - - return pixels0; -} - -/** Perform 3x3 convolution for stride_x=2 and stride_y=2 when DILATION_X>1 or DILATION_Y>1 for F16 - * - * @param[in] src_addr Pointer to the starting position of where to perform the convolution - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] y_offset Offset from the source tensor from which to start convolution - * @param[in] weights_addr Pointer from where to get weights - * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension - */ -inline half4 convolution_3x3_dilation_stridex2_stridey2_bifrost_f16(__global uchar *src_addr, const int stride_x_bytes, const int stride_y_bytes, - const int y_offset, __global uchar *weights_addr, const int weights_stride_y) -{ - // Load the weights - half3 weights_row0 = vload3(0, (__global half *)(weights_addr + 0 * weights_stride_y)); - half3 weights_row1 = vload3(0, (__global half *)(weights_addr + 1 * weights_stride_y)); - half3 weights_row2 = vload3(0, (__global half *)(weights_addr + 2 * weights_stride_y)); - - half4 pixels0 = 0.0f; - - half8 src00_left = vload8(0, (__global half *)ptr_offset(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); // Row0 - half8 src00_mid = vload8(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - half8 src00_right = vload8(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); - - half8 src10_left = vload8(0, (__global half *)ptr_offset(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); // Row1 - half8 src10_mid = vload8(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - half8 src10_right = vload8(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); - - half8 src20_left = vload8(0, (__global half *)ptr_offset(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); // Row2 - half8 src20_mid = vload8(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - half8 src20_right = vload8(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); - - CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels0, src00_left, src00_mid, src00_right, weights_row0); - CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels0, src10_left, src10_mid, src10_right, weights_row1); - CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels0, src20_left, src20_mid, src20_right, weights_row2); - - return pixels0; -} - -#endif // (DILATION_X > 1 && DILATION_Y > 1) - -/** Compute a 1D horizontal convolution of size 3 and stride 1 for 16bit 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 half4 containing 4 convoluted values. - */ -inline half4 convolution1x3_stride_1_f16(__global const uchar *left_pixel, - const half left_coeff, - const half middle_coeff, - const half right_coeff) -{ -#if(DILATION_X == 1 && DILATION_Y == 1) - - half8 temp = vload8(0, (__global half *)left_pixel); - - half4 left = CONVERT(temp.s0123, half4); - half4 middle = CONVERT(temp.s1234, half4); - half4 right = CONVERT(temp.s2345, half4); - - return left * (half4)left_coeff + middle * (half4)middle_coeff + right * (half4)right_coeff; -#else /* DILATION_X==1 && DILATION_Y==1 */ - return vload4(0, (__global half *)left_pixel) * (half4)left_coeff - + vload4(0, (__global half *)(left_pixel) + DILATION_X) * (half4)middle_coeff - + vload4(0, (__global half *)(left_pixel) + 2 * DILATION_X) * (half4)right_coeff; - -#endif /* DILATION_X==1 && DILATION_Y==1 */ -} - -/** Compute a 1D horizontal convolution of size 3 and stride 2 for 16bit 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 half4 containing 4 convoluted values. - */ -inline half4 convolution1x3_stride_2_f16(__global const uchar *left_pixel, - const half left_coeff, - const half middle_coeff, - const half right_coeff) -{ -#if(DILATION_X == 1 && DILATION_Y == 1) - - half8 temp0 = vload8(0, (__global half *)left_pixel); - half temp1 = *((__global half *)(left_pixel + 8 * sizeof(half))); - - half4 left = CONVERT(temp0.s0246, half4); - half4 middle = CONVERT(temp0.s1357, half4); - half4 right = CONVERT((half4)(temp0.s246, temp1), half4); - - return left * (half4)left_coeff + middle * (half4)middle_coeff + right * (half4)right_coeff; -#else /* DILATION_X==1 && DILATION_Y==1 */ - - __global half *left_pixel_float = (__global half *)left_pixel; - - return (half4)(*left_pixel_float, *(left_pixel_float + 2), *(left_pixel_float + 4), *(left_pixel_float + 6)) * (half4)left_coeff - + (half4)(*(left_pixel_float + DILATION_X), *(left_pixel_float + DILATION_X + 2), *(left_pixel_float + DILATION_X + 4), *(left_pixel_float + DILATION_X + 6)) * (half4)middle_coeff - + (half4)(*(left_pixel_float + DILATION_X * 2), *(left_pixel_float + DILATION_X * 2 + 2), *(left_pixel_float + DILATION_X * 2 + 4), *(left_pixel_float + DILATION_X * 2 + 6)) * (half4)right_coeff; - -#endif /* DILATION_X==1 && DILATION_Y==1 */ -} - -/** Compute a 1D horizontal convolution of size 3 and stride 3 for 16bit 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 half4 containing 4 convoluted values. - */ -inline half4 convolution1x3_stride_3_f16(__global const uchar *left_pixel, - const half left_coeff, - const half middle_coeff, - const half right_coeff) -{ -#if(DILATION_X == 1 && DILATION_Y == 1) - - half16 temp0 = vload16(0, (__global half *)left_pixel); - - half4 left = CONVERT(temp0.s0369, half4); - half4 middle = CONVERT(temp0.s147A, half4); - half4 right = CONVERT(temp0.s258B, half4); - - return left * (half4)left_coeff + middle * (half4)middle_coeff + right * (half4)right_coeff; -#else /* DILATION_X==1 && DILATION_Y==1 */ - - __global half *left_pixel_float = (__global half *)left_pixel; - - return (half4)(*left_pixel_float, *(left_pixel_float + 3), *(left_pixel_float + 6), *(left_pixel_float + 9)) * (half4)left_coeff - + (half4)(*(left_pixel_float + DILATION_X), *(left_pixel_float + DILATION_X + 3), *(left_pixel_float + DILATION_X + 6), *(left_pixel_float + DILATION_X + 9)) * (half4)middle_coeff - + (half4)(*(left_pixel_float + DILATION_X * 2), *(left_pixel_float + DILATION_X * 2 + 3), *(left_pixel_float + DILATION_X * 2 + 6), *(left_pixel_float + DILATION_X * 2 + 9)) * (half4)right_coeff; - -#endif /* DILATION_X==1 && DILATION_Y==1 */ -} - -/** Apply a 3x3 convolution matrix to a single channel F16 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 half4 containing 4 convoluted values. - */ -inline half4 convolution3x3_f16( - Image *src, - const half mat0, const half mat1, const half mat2, - const half mat3, const half mat4, const half mat5, - const half mat6, const half mat7, const half mat8) -{ - half4 pixels; - - pixels = convolution1x3_f16(offset(src, 0, 0), mat0, mat1, mat2); - pixels += convolution1x3_f16(offset(src, 0, DILATION_Y), mat3, mat4, mat5); - pixels += convolution1x3_f16(offset(src, 0, DILATION_Y * 2), mat6, mat7, mat8); - - return pixels; -} - -#if defined(DEPTH_MULTIPLIER) - -/** This OpenCL kernel computes the depthwise convolution 3x3 - * - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types: half. - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16 - * @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[in] 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 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: same as @p src_ptr - * @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 - * @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_f16( - 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_NO_STEP(weights); -#if defined(HAS_BIAS) - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); -#endif //defined(HAS_BIAS) - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - // Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - src.ptr -= batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z + (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - - uchar3 offset = (uchar3)(0, 1, 2) * (uchar3)weights_stride_y; - half3 weights_values0 = vload3(0, (__global half *)(weights_addr + offset.s0)); - half3 weights_values1 = vload3(0, (__global half *)(weights_addr + offset.s1)); - half3 weights_values2 = vload3(0, (__global half *)(weights_addr + offset.s2)); - - half4 pixels = convolution3x3_f16(&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 += (half4)(*((__global half *)(biases.ptr + channel * biases_stride_x))); -#endif //defined(HAS_BIAS) - - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels, A_VAL, B_VAL), 0, (__global half *)dst.ptr); -} -#endif // defined(DEPTH_MULTIPLIER) -#endif // defined(CONV_STRIDE_X) - -/** This OpenCL kernel is optimized for Bifrost architectures and computes the 16bit floating point depthwise convolution 3x3 - * when both stride_x and stride_y are equal to 1 - * - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types: half. - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16 - * @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[in] 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 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: same as @p src_ptr - * @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: same as @p src_ptr - * @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_stridex1_stridey1_bifrost_f16( - 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_NO_STEP(weights); - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - -#ifdef HAS_BIAS - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - half bias = *((__global half *)(vector_offset(&biases, channel))); -#endif /* defined(HAS_BIAS) */ - - half4 pixels0 = 0.0f; - half4 pixels1 = 0.0f; - half4 pixels2 = 0.0f; - half4 pixels3 = 0.0f; - - // Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - __global uchar *src_addr = src.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - -#if(DILATION_X == 1 && DILATION_Y == 1) - // Load the weights - half3 weights_row0 = vload3(0, (__global half *)(weights_addr + 0 * weights_stride_y)); - half3 weights_row1 = vload3(0, (__global half *)(weights_addr + 1 * weights_stride_y)); - half3 weights_row2 = vload3(0, (__global half *)(weights_addr + 2 * weights_stride_y)); - - // Note: Since each work-item computes 4x4 elements, we need to load 6 rows from the input tensor - half8 src00 = vload8(0, (__global half *)(src_addr + 0 * src_stride_y)); // Row0 - half8 src10 = vload8(0, (__global half *)(src_addr + 1 * src_stride_y)); // Row1 - half8 src20 = vload8(0, (__global half *)(src_addr + 2 * src_stride_y)); // Row2 - half8 src30 = vload8(0, (__global half *)(src_addr + 3 * src_stride_y)); // Row3 - half8 src40 = vload8(0, (__global half *)(src_addr + 4 * src_stride_y)); // Row4 - half8 src50 = vload8(0, (__global half *)(src_addr + 5 * src_stride_y)); // Row5 - - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels0, src00, weights_row0); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels0, src10, weights_row1); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels0, src20, weights_row2); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels1, src10, weights_row0); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels1, src20, weights_row1); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels1, src30, weights_row2); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels2, src20, weights_row0); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels2, src30, weights_row1); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels2, src40, weights_row2); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels3, src30, weights_row0); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels3, src40, weights_row1); - CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels3, src50, weights_row2); - -#else /* DILATION_X==1 && DILATION_Y==1 */ - - //3x3 Convolution of elements starting in 0th row - pixels0 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(src_addr, src.stride_x, src.stride_y, 0, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 1st row - pixels1 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(src_addr, src.stride_x, src.stride_y, 1, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 2nd row - pixels2 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(src_addr, src.stride_x, src.stride_y, 2, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 3rd row - pixels3 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(src_addr, src.stride_x, src.stride_y, 3, weights_addr, weights_stride_y); - -#endif /* DILATION_X==1 && DILATION_Y==1 */ - -#ifdef HAS_BIAS - pixels0 += (half4)bias; - pixels1 += (half4)bias; - pixels2 += (half4)bias; - pixels3 += (half4)bias; -#endif /* defined(HAS_BIAS) */ - - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels0, A_VAL, B_VAL), 0, (__global half *)(dst.ptr + 0 * dst_stride_y)); - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels1, A_VAL, B_VAL), 0, (__global half *)(dst.ptr + 1 * dst_stride_y)); - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels2, A_VAL, B_VAL), 0, (__global half *)(dst.ptr + 2 * dst_stride_y)); - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels3, A_VAL, B_VAL), 0, (__global half *)(dst.ptr + 3 * dst_stride_y)); -} - -/** This OpenCL kernel is optimized for Bifrost architectures and computes 16bit floating point the depthwise convolution 3x3 - * when both stride_x and stride_y are equal to 2 - * - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types: half. - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16 - * @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_y * 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[in] 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 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: same as @p src_ptr - * @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: same as @p src_ptr - * @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_stridex2_stridey2_bifrost_f16( - 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_NO_STEP(weights); - - // Extract channel and linearized batch indices - const int channel = get_global_id(2) % DST_CHANNELS; - const int batch = get_global_id(2) / DST_CHANNELS; - -#ifdef HAS_BIAS - Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); - - half bias = *((__global half *)(vector_offset(&biases, channel))); -#endif /* defined(HAS_BIAS) */ - - half4 pixels0 = 0.0f; - half4 pixels1 = 0.0f; - - // Load relevant input and weights data ( Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER) - __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; - __global uchar *src_addr = src.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; - -#if(DILATION_X == 1 && DILATION_Y == 1) - - // Load the weights - half3 weights_row0 = vload3(0, (__global half *)(weights_addr + 0 * weights_stride_y)); - half3 weights_row1 = vload3(0, (__global half *)(weights_addr + 1 * weights_stride_y)); - half3 weights_row2 = vload3(0, (__global half *)(weights_addr + 2 * weights_stride_y)); - - // Note: Since each work-item computes 2x4 elements, we need to load 5 rows from the input tensor - half8 src00 = vload8(0, (__global half *)(src_addr + 0 * src_stride_y)); // Row0 - half2 src01 = vload2(4, (__global half *)(src_addr + 0 * src_stride_y)); // Row0 - half8 src10 = vload8(0, (__global half *)(src_addr + 1 * src_stride_y)); // Row1 - half2 src11 = vload2(4, (__global half *)(src_addr + 1 * src_stride_y)); // Row1 - half8 src20 = vload8(0, (__global half *)(src_addr + 2 * src_stride_y)); // Row2 - half2 src21 = vload2(4, (__global half *)(src_addr + 2 * src_stride_y)); // Row2 - half8 src30 = vload8(0, (__global half *)(src_addr + 3 * src_stride_y)); // Row3 - half2 src31 = vload2(4, (__global half *)(src_addr + 3 * src_stride_y)); // Row3 - half8 src40 = vload8(0, (__global half *)(src_addr + 4 * src_stride_y)); // Row4 - half2 src41 = vload2(4, (__global half *)(src_addr + 4 * src_stride_y)); // Row4 - - CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels0, src00, src01, weights_row0); - CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels0, src10, src11, weights_row1); - CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels0, src20, src21, weights_row2); - CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels1, src20, src21, weights_row0); - CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels1, src30, src31, weights_row1); - CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels1, src40, src41, weights_row2); - -#else /* DILATION_X==1 && DILATION_Y==1 */ - //3x3 Convolution of elements starting in 0th row - pixels0 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f16(src_addr, src.stride_x, src.stride_y, 0, weights_addr, weights_stride_y); - //3x3 Convolution of elements starting in 2nd row - pixels1 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f16(src_addr, src.stride_x, src.stride_y, 2, weights_addr, weights_stride_y); -#endif /* DILATION_X==1 && DILATION_Y==1 */ - -#ifdef HAS_BIAS - pixels0 += (half4)bias; - pixels1 += (half4)bias; -#endif /* defined(HAS_BIAS) */ - - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels0, A_VAL, B_VAL), 0, (__global half *)(dst.ptr + 0 * dst_stride_y)); - vstore4(ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, pixels1, A_VAL, B_VAL), 0, (__global half *)(dst.ptr + 1 * dst_stride_y)); -} -#endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F16) - -#if defined(SRC_DIM1) && defined(SRC_DIM2) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(N0) && defined(DATA_TYPE) && defined(DILATION_X) && defined(DILATION_Y) && defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) && defined(CONV_PAD_LEFT) && defined(CONV_PAD_TOP) -/** This function computes the depthwise convolution for NHWC data layout. This kernel assumes that the weights tensor is NOT reshaped - * - * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float - * @note The number of elements processed must be passed at compile time using -DN0 (e.g. -DN0=2) - * @note The depth multiplier must be passed at compile time using -DDEPTH_MULTIPLIER (e.g. -DDEPTH_MULTIPLIER=1) - * @note The first dimension of the input tensor must be passed at compile time using -DSRC_DIM1 (e.g. -DSRC_DIM1=112) - * @note The second dimension of the input tensor must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM2=80) - * @note The kernel width must be passed at compile time using -DKERNEL_WIDTH (e.g. -DKERNEL_WIDTH=5) - * @note The kernel height must be passed at compile time using -DKERNEL_HEIGHT (e.g. -DKERNEL_HEIGHT=5) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1) - * @note The convolution stride along the width must be passed at compile time using -DCONV_STRIDE_X (e.g. -DCONV_STRIDE_Y=X) - * @note The convolution stride along the height must be passed at compile time using -DCONV_STRIDE_Y (e.g. -DCONV_STRIDE_Y=1) - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * - * @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_y * number of elements along Z processed per workitem(in bytes) - * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) - * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as 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 Y processed per workitem(in bytes) - * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) - * @param[in] dst_step_w dst_stride_w * number of elements along W 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 weights tensor - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr - * @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 dwc_MxN_native_fp_nhwc( - TENSOR4D_DECLARATION(src), - TENSOR4D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights) -#if defined(HAS_BIAS) - , - VECTOR_DECLARATION(biases) -#endif // defined(HAS_BIAS) -) -{ - int x = get_global_id(0); // channels - int y = get_global_id(1); // spatial coordinate x -#if defined(DST_DEPTH) - int z = get_global_id(2) % (int)DST_DEPTH; // spatial coordinate y - int b = get_global_id(2) / (int)DST_DEPTH; // batch -#else // defined(DST_DEPTH) - int z = get_global_id(2); // spatial coordinate y -#endif // defined(DST_DEPTH) - - __global uchar *s_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(DATA_TYPE) * (int)N0; - - __global uchar *d_addr = dst_ptr + dst_offset_first_element_in_bytes + x * sizeof(DATA_TYPE) * (int)DEPTH_MULTIPLIER * (int)N0 + y * dst_stride_y + z * dst_stride_z; - - __global uchar *w_addr = weights_ptr + weights_offset_first_element_in_bytes + x * sizeof(DATA_TYPE) * (int)DEPTH_MULTIPLIER * (int)N0; - -#if defined(HAS_BIAS) - __global uchar *b_addr = biases_ptr + biases_offset_first_element_in_bytes + x * sizeof(DATA_TYPE) * (int)DEPTH_MULTIPLIER * (int)N0; -#endif // defined(HAS_BIAS) - -#if defined(DST_DEPTH) - s_addr += b * src_stride_w; - d_addr += b * dst_stride_w; -#endif // defined(DST_DEPTH) - - for(int d = 0; d < (int)DEPTH_MULTIPLIER; ++d) - { - // Each work-item computes N0x1x1 elements - VEC_DATA_TYPE(DATA_TYPE, N0) - res = 0; - - int x_coord = y * CONV_STRIDE_X - (int)CONV_PAD_LEFT; - int y_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP; - - for(int yk = 0; yk < KERNEL_HEIGHT; ++yk) - { - if(y_coord >= 0 && y_coord < SRC_DIM2) - { - int x_coord_tmp = x_coord; - - for(int xk = 0; xk < KERNEL_WIDTH; ++xk) - { - if(x_coord_tmp >= 0 && x_coord_tmp < SRC_DIM1) - { - int s_offset = x_coord_tmp * (int)src_stride_y + y_coord * (int)src_stride_z; - int w_offset = xk * weights_stride_y + yk * weights_stride_z; - - // Load input and weights values - VEC_DATA_TYPE(DATA_TYPE, N0) - i = VLOAD(N0)(0, (__global DATA_TYPE *)(s_addr + s_offset)); - VEC_DATA_TYPE(DATA_TYPE, N0) - w = VLOAD(N0)(0, (__global DATA_TYPE *)(w_addr + w_offset)); - -#if GPU_ARCH == GPU_ARCH_MIDGARD - res += i * w; -#else // GPU_ARCH == GPU_ARCH_MIDGARD - res = fma(i, w, res); -#endif // GPU_ARCH == GPU_ARCH_MIDGARD - } - x_coord_tmp += DILATION_X; - } - } - y_coord += DILATION_Y; - } - -#if defined(HAS_BIAS) - res += VLOAD(N0)(0, (__global DATA_TYPE *)(b_addr)); -#endif // defined(HAS_BIAS) - - res = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, res, A_VAL, B_VAL); - - VSTORE(N0) - (res, 0, (__global DATA_TYPE *)(d_addr)); - - w_addr += sizeof(DATA_TYPE); - d_addr += sizeof(DATA_TYPE); -#if defined(HAS_BIAS) - b_addr += sizeof(DATA_TYPE); -#endif // defined(HAS_BIAS) - } -} -#endif // defined(SRC_DIM1) && defined(SRC_DIM2) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defiend(N0) && defined(DATA_TYPE) && defined(DILATION_X) && defined(DILATION_Y) && defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) && defined(CONV_PAD_LEFT) && defined(CONV_PAD_TOP) - -#if defined(VEC_SIZE) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT) && defined(DATA_TYPE) - -#if DATA_TYPE != float || DATA_TYPE != half -#error "Unsupported data type" -#endif // DATA_TYPE != float || DATA_TYPE != half - -#define VEC_FLOAT VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) - -#if defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) - -/** This function computes the depthwise convolution for NHWC data layout when the stride along the width or height is not 1. - * - * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float - * @note The number of elements read per thread must be passed at compile time using -DVEC_SIZE (e.g. -DVEC_SIZE=2) - * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1) - * @note The convolution stride along the width must be passed at compile time using -DCONV_STRIDE_X (e.g. -DCONV_STRIDE_Y=X) - * @note The convolution stride along the height must be passed at compile time using -DCONV_STRIDE_Y (e.g. -DCONV_STRIDE_Y=1) - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * - * @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_y * number of elements along Z processed per workitem(in bytes) - * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) - * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as 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 Y processed per workitem(in bytes) - * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) - * @param[in] dst_step_w dst_stride_w * number of elements along W 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 weights tensor - * @param[in] max_offset Max offset for the input tensor - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr - * @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_nhwc( - TENSOR4D_DECLARATION(src), - TENSOR4D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights), -#if defined(HAS_BIAS) - VECTOR_DECLARATION(biases), -#endif /* defined(HAS_BIAS) */ - int max_offset) -{ - int x = get_global_id(0); // channels - int y = get_global_id(1); // spatial coordinate x -#if defined(DST_DEPTH) - int z = get_global_id(2) % (int)DST_DEPTH; // spatial coordinate y - int b = get_global_id(2) / (int)DST_DEPTH; // batch -#else // defined(DST_DEPTH) - int z = get_global_id(2); // spatial coordinate y -#endif // defined(DST_DEPTH) - - Vector weights = CONVERT_TO_VECTOR_STRUCT(weights); - -#if defined(DST_DEPTH) - __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(DATA_TYPE) * VEC_SIZE + b * src_stride_w; -#else /* defined(DST_DEPTH) */ - __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(DATA_TYPE) * VEC_SIZE; -#endif /* defined(DST_DEPTH) */ - - int z_coord = 0; - int4 offset = 0; - int4 y_offset = ((int4)(y * CONV_STRIDE_X) + (int4)(0, DILATION_X * 1, DILATION_X * 2, DILATION_X * 3) - CONV_PAD_LEFT) * (int4)src_stride_y; - - // We compute 2x1x1 [C,W,H] elements - VEC_FLOAT acc = 0; - - // Load weights - VEC_FLOAT w0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 0 * weights_stride_y + 0 * weights_stride_z)); - VEC_FLOAT w1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 1 * weights_stride_y + 0 * weights_stride_z)); - VEC_FLOAT w2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 2 * weights_stride_y + 0 * weights_stride_z)); - VEC_FLOAT w3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 0 * weights_stride_y + 1 * weights_stride_z)); - VEC_FLOAT w4 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 1 * weights_stride_y + 1 * weights_stride_z)); - VEC_FLOAT w5 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 2 * weights_stride_y + 1 * weights_stride_z)); - VEC_FLOAT w6 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 0 * weights_stride_y + 2 * weights_stride_z)); - VEC_FLOAT w7 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 1 * weights_stride_y + 2 * weights_stride_z)); - VEC_FLOAT w8 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 2 * weights_stride_y + 2 * weights_stride_z)); - - // Load input values - // z == 0 - // Clamp z_coord as for z = 0, it can be negative - // z_coord is casted to unsigned int in order to use just a min() operation - // A "-1" 32 bit signed variable converted to unsigned gives 4294967295 - z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP; - z_coord = min((uint)z_coord, (uint)SRC_DIM_2); - offset = y_offset + (int4)(z_coord * src_stride_z); - offset = min(offset, (int4)max_offset); - - VEC_FLOAT values0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s0)); - VEC_FLOAT values1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s1)); - VEC_FLOAT values2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s2)); - - // z == 1 - // z_coord can be only negative for z = 0 so we do not need to clamp it - // Moreover z_coord cannot be out-of-bound for z = 1 so we do not need to clamp the offset - z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP + DILATION_Y; - offset = y_offset + (int4)(z_coord * src_stride_z); - VEC_FLOAT values3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s0)); - VEC_FLOAT values4 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s1)); - VEC_FLOAT values5 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s2)); - - // z == 2 - // Offset can be out-of-bound so we need to check if it is greater than max_offset - z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP + DILATION_Y * 2; - offset = y_offset + (int4)(z_coord * src_stride_z); - offset = min(offset, (int4)max_offset); - VEC_FLOAT values6 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s0)); - VEC_FLOAT values7 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s1)); - VEC_FLOAT values8 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s2)); - - acc = fma(values0, w0, acc); - acc = fma(values1, w1, acc); - acc = fma(values2, w2, acc); - - acc = fma(values3, w3, acc); - acc = fma(values4, w4, acc); - acc = fma(values5, w5, acc); - - acc = fma(values6, w6, acc); - acc = fma(values7, w7, acc); - acc = fma(values8, w8, acc); - -#if defined(HAS_BIAS) - Vector biases = CONVERT_TO_VECTOR_STRUCT(biases); - VEC_FLOAT bias_values = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)biases.ptr); - acc += bias_values; -#endif // defined(HAS_BIAS) - -#if defined(DST_DEPTH) - __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * dst_step_x + y * dst_step_y + z * dst_step_z + b * dst_stride_w; -#else /* defined(DST_DEPTH) */ - __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * dst_step_x + y * dst_step_y + z * dst_step_z; -#endif /* defined(DST_DEPTH) */ - - VSTORE(VEC_SIZE) - (ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, acc, A_VAL, B_VAL), 0, (__global DATA_TYPE *)(dst_addr)); -} -#endif // defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) - -#if defined(NUM_ROWS_PROCESSED) && defined(NUM_PLANES_PROCESSED) -/** This function computes the depthwise convolution for NHWC data layout when the stride along the width and height is 1. - * - * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float - * @note The number of elements read per thread must be passed at compile time using -DVEC_SIZE (e.g. -DVEC_SIZE=2) - * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112) - * @note The number of rows processed per thread must be passed at compile time using -DNUM_ROWS_PROCESSED (i.e. -DNUM_ROWS_PROCESSED=2) - * @note The number of planes processed per thread must be passed at compile time using -DNUM_PLANES_PROCESSED (i.e. -DNUM_PLANES_PROCESSED=2) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1) - * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1) - * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu - * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size - * - * @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_y * number of elements along Z processed per workitem(in bytes) - * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) - * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as 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 Y processed per workitem(in bytes) - * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) - * @param[in] dst_step_w dst_stride_w * number of elements along W 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 weights tensor - * @param[in] max_offset Max offset for the input tensor - * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr - * @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_nhwc_stride1( - TENSOR4D_DECLARATION(src), - TENSOR4D_DECLARATION(dst), - TENSOR3D_DECLARATION(weights), -#if defined(HAS_BIAS) - VECTOR_DECLARATION(biases), -#endif /* defined(HAS_BIAS) */ - int max_offset) -{ - int x = get_global_id(0); // channels - int y = get_global_id(1); // spatial coordinate x -#if defined(DST_DEPTH) - int z = get_global_id(2) % (int)DST_DEPTH; // spatial coordinate y - int b = get_global_id(2) / (int)DST_DEPTH; // batch -#else // defined(DST_DEPTH) - int z = get_global_id(2); // spatial coordinate y -#endif // defined(DST_DEPTH) - - Vector weights = CONVERT_TO_VECTOR_STRUCT(weights); - -#if defined(DST_DEPTH) - __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(DATA_TYPE) * VEC_SIZE + b * src_stride_w; -#else /* defined(DST_DEPTH) */ - __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * sizeof(DATA_TYPE) * VEC_SIZE; -#endif /* defined(DST_DEPTH) */ - - int z_coord = 0; - int4 offset = 0; - int4 y_offset = ((int4)(y * NUM_ROWS_PROCESSED) + (int4)(0, 1, 2, 3) - (int)CONV_PAD_LEFT) * (int4)src_stride_y; - - // We compute 2x2x2 [C,W,H] elements - VEC_FLOAT acc0 = 0; - VEC_FLOAT acc1 = 0; - VEC_FLOAT acc2 = 0; - VEC_FLOAT acc3 = 0; - - // Load weights - VEC_FLOAT w0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 0 * weights_stride_y + 0 * weights_stride_z)); - VEC_FLOAT w1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 1 * weights_stride_y + 0 * weights_stride_z)); - VEC_FLOAT w2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 2 * weights_stride_y + 0 * weights_stride_z)); - VEC_FLOAT w3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 0 * weights_stride_y + 1 * weights_stride_z)); - VEC_FLOAT w4 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 1 * weights_stride_y + 1 * weights_stride_z)); - VEC_FLOAT w5 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 2 * weights_stride_y + 1 * weights_stride_z)); - VEC_FLOAT w6 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 0 * weights_stride_y + 2 * weights_stride_z)); - VEC_FLOAT w7 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 1 * weights_stride_y + 2 * weights_stride_z)); - VEC_FLOAT w8 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights.ptr + 2 * weights_stride_y + 2 * weights_stride_z)); - - // Load input values - // z == 0 - // Clamp z_coord as for z = 0, it can be negative - // z_coord is casted to unsigned int in order to use just a min() operation - // A "-1" 32 bit signed variable converted to unsigned gives 4294967295 - z_coord = z * (int)NUM_PLANES_PROCESSED - (int)CONV_PAD_TOP; - z_coord = min((uint)z_coord, (uint)SRC_DIM_2); - offset = y_offset + (int4)(z_coord * src_stride_z); - offset = min(offset, (int4)max_offset); - - VEC_FLOAT values0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s0)); - VEC_FLOAT values1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s1)); - VEC_FLOAT values2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s2)); - VEC_FLOAT values3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s3)); - - // z == 1 - // z_coord can be only negative for z = 0 so we do not need to clamp it - // Moreover z_coord cannot be out-of-bound for z = 1 so we do not need to clamp the offset - z_coord = z * (int)NUM_PLANES_PROCESSED - (int)CONV_PAD_TOP + 1; - offset = y_offset + (int4)(z_coord * src_stride_z); - VEC_FLOAT values4 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s0)); - VEC_FLOAT values5 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s1)); - VEC_FLOAT values6 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s2)); - VEC_FLOAT values7 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s3)); - - // z == 2 - // After z = 1 we can simply add src_stride_z to offset without updating z_coord - // However offset can be out-of-bound so we need to check if it is greater than max_offset - offset += (int4)src_stride_z; - offset = min(offset, (int4)max_offset); - VEC_FLOAT values8 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s0)); - VEC_FLOAT values9 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s1)); - VEC_FLOAT values10 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s2)); - VEC_FLOAT values11 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s3)); - - // z == 3 - // After z = 1 we can simply add src_stride_z to offset without updating z_coord - // However offset can be out-of-bound so we need to check if it is greater than max_offset - offset += (int4)src_stride_z; - offset = min(offset, (int4)max_offset); - VEC_FLOAT values12 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s0)); - VEC_FLOAT values13 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s1)); - VEC_FLOAT values14 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s2)); - VEC_FLOAT values15 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s3)); - - acc0 = fma(values0, w0, acc0); - acc0 = fma(values1, w1, acc0); - acc0 = fma(values2, w2, acc0); - acc1 = fma(values1, w0, acc1); - acc1 = fma(values2, w1, acc1); - acc1 = fma(values3, w2, acc1); - - acc0 = fma(values4, w3, acc0); - acc0 = fma(values5, w4, acc0); - acc0 = fma(values6, w5, acc0); - acc1 = fma(values5, w3, acc1); - acc1 = fma(values6, w4, acc1); - acc1 = fma(values7, w5, acc1); - - acc0 = fma(values8, w6, acc0); - acc0 = fma(values9, w7, acc0); - acc0 = fma(values10, w8, acc0); - acc1 = fma(values9, w6, acc1); - acc1 = fma(values10, w7, acc1); - acc1 = fma(values11, w8, acc1); - - acc2 = fma(values4, w0, acc2); - acc2 = fma(values5, w1, acc2); - acc2 = fma(values6, w2, acc2); - acc3 = fma(values5, w0, acc3); - acc3 = fma(values6, w1, acc3); - acc3 = fma(values7, w2, acc3); - - acc2 = fma(values8, w3, acc2); - acc2 = fma(values9, w4, acc2); - acc2 = fma(values10, w5, acc2); - acc3 = fma(values9, w3, acc3); - acc3 = fma(values10, w4, acc3); - acc3 = fma(values11, w5, acc3); - - acc2 = fma(values12, w6, acc2); - acc2 = fma(values13, w7, acc2); - acc2 = fma(values14, w8, acc2); - acc3 = fma(values13, w6, acc3); - acc3 = fma(values14, w7, acc3); - acc3 = fma(values15, w8, acc3); - -#if defined(HAS_BIAS) - Vector biases = CONVERT_TO_VECTOR_STRUCT(biases); - - VEC_FLOAT bias_values = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)biases.ptr); - - acc0 += bias_values; - acc1 += bias_values; - acc2 += bias_values; - acc3 += bias_values; -#endif // defined(HAS_BIAS) - -#if defined(DST_DEPTH) - __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * dst_step_x + y * dst_step_y + (z * NUM_PLANES_PROCESSED) * dst_step_z + b * dst_stride_w; -#else /* defined(DST_DEPTH) */ - __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * dst_step_x + y * dst_step_y + (z * NUM_PLANES_PROCESSED) * dst_step_z; -#endif /* defined(DST_DEPTH) */ - - VSTORE(VEC_SIZE) - (ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, acc0, A_VAL, B_VAL), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)); - VSTORE(VEC_SIZE) - (ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, acc1, A_VAL, B_VAL), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)); - -#if((DST_DIM_2 % NUM_PLANES_PROCESSED) != 0) - if((z * NUM_PLANES_PROCESSED + 1) < DST_DIM_2) -#endif // ((DST_DIM_2 % NUM_PLANES_PROCESSED) != 0) - { - VSTORE(VEC_SIZE) - (ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, acc2, A_VAL, B_VAL), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y + 1 * dst_stride_z)); - VSTORE(VEC_SIZE) - (ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, acc3, A_VAL, B_VAL), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y + 1 * dst_stride_z)); - } -} - -#endif // defined(NUM_ROWS_PROCESSED) && defined(NUM_PLANES_PROCESSED) -#endif // defined(VEC_SIZE) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT) && defined(DATA_TYPE)
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