Blame - src/core/CL/cl_kernels/direct_convolution3x3.cl - ml/ComputeLibrary

2017-07-18 17:37:43 +0100

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/*

Vidhya Sudhan Loganathan

7485d5a

2018-07-04 09:34:00 +0100

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steniu01

2017-07-18 17:37:43 +0100

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*

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* SPDX-License-Identifier: MIT

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*

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* Permission is hereby granted, free of charge, to any person obtaining a copy

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* of this software and associated documentation files (the "Software"), to

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* deal in the Software without restriction, including without limitation the

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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or

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* sell copies of the Software, and to permit persons to whom the Software is

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* furnished to do so, subject to the following conditions:

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*

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* The above copyright notice and this permission notice shall be included in all

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* copies or substantial portions of the Software.

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*

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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

* SOFTWARE.

*/

#include "helpers.h"

Moritz Pflanzer

2017-09-25 15:36:14 +0100

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#undef CONVERT_SAT

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Michalis Spyrou

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#define ADD_OP(a, b) ((a) + (b))

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#define MUL_OP(a, b) ((a) * (b))

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#define CONVERT_SAT(a, b) ((a))

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Gian Marco Iodice

2017-08-16 18:38:32 +0100

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#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)

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Gian Marco Iodice

2017-08-08 10:53:00 +0100

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#if STRIDE_X == 1

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#define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr)

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#elif STRIDE_X == 2 /* STRIDE_X == 1 */

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#define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr)

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#else /* STRIDE_X not equals 1 or 2 */

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#error "STRIDE_X larger than 2 is not supported"

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#endif /* STRIDE_X == 2 */

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Michalis Spyrou

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#define CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr) \

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({ \

steniu01

db00668

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VEC_DATA_TYPE(DATA_TYPE, 3) \

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weights_values0 = vload3(0, weights_row_ptr); \

Michalis Spyrou

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VEC_DATA_TYPE(DATA_TYPE, 8) \

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src0 = vload8(0, src_row_ptr); \

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VEC_DATA_TYPE(DATA_TYPE, 2) \

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src1 = vload2(0, src_row_ptr + 8); \

Gian Marco Iodice

2017-08-08 10:53:00 +0100

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\

Michalis Spyrou

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acc = ADD_OP(acc, MUL_OP(src0, (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0)); \

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acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1)); \

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acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2)); \

Gian Marco Iodice

2017-08-08 10:53:00 +0100

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})

steniu01

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Michalis Spyrou

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#define CONVOLUTION1x3_STRIDE2(acc, src_row_ptr, weights_row_ptr) \

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({ \

steniu01

db00668

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VEC_DATA_TYPE(DATA_TYPE, 3) \

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weights_values0 = vload3(0, weights_row_ptr); \

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VEC_DATA_TYPE(DATA_TYPE, 16) \

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src0 = vload16(0, src_row_ptr); \

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DATA_TYPE src1 = *(src_row_ptr + 16); \

Gian Marco Iodice

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\

Michalis Spyrou

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acc = ADD_OP(acc, MUL_OP(src0.even, (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0)); \

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acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1)); \

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acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2)); \

Gian Marco Iodice

2017-08-08 10:53:00 +0100

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})

steniu01

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Pablo Tello

2018-06-21 15:13:17 +0100

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#if defined(DATA_LAYOUT_NHWC)

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#define PTR_TO_VALUE(PTR, DATA_TYPE) *((__global DATA_TYPE *)(PTR))

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#if STRIDE_X == 1

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#define CONVOLUTION1x3_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x3_STRIDE_NHWC_STRIDE1(acc, row_ptr, weights_ptr)

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#elif STRIDE_X == 2 /* STRIDE_X == 1 */

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#define CONVOLUTION1x3_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x3_STRIDE_NHWC_STRIDE2(acc, row_ptr, weights_ptr)

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#else /* STRIDE_X not equals 1 or 2 */

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#error "STRIDE_X larger than 2 is not supported"

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#endif /* STRIDE_X == 2 */

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#define CONVOLUTION1x3_STRIDE_NHWC_STRIDE1(acc, row_ptr, weights_ptr) \

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{ \

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VEC_DATA_TYPE(DATA_TYPE, 8) \

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src0 = (VEC_DATA_TYPE(DATA_TYPE, 8))( \

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PTR_TO_VALUE(row_ptr + 0 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 1 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 2 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 3 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 4 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 5 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 6 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 7 * src_stride_y, DATA_TYPE)); \

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VEC_DATA_TYPE(DATA_TYPE, 2) \

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src1 = (VEC_DATA_TYPE(DATA_TYPE, 2))( \

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PTR_TO_VALUE(row_ptr + 8 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 9 * src_stride_y, DATA_TYPE)); \

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VEC_DATA_TYPE(DATA_TYPE, 3) \

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weights = (VEC_DATA_TYPE(DATA_TYPE, 3))( \

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PTR_TO_VALUE((weights_ptr) + 0 * weights_stride_y, DATA_TYPE), \

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PTR_TO_VALUE((weights_ptr) + 1 * weights_stride_y, DATA_TYPE), \

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PTR_TO_VALUE((weights_ptr) + 2 * weights_stride_y, DATA_TYPE)); \

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acc = ADD_OP(acc, MUL_OP(src0, (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s0)); \

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acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0), (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s1)); \

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acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01), (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s2)); \

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}

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#define CONVOLUTION1x3_STRIDE_NHWC_STRIDE2(acc, row_ptr, weights_ptr) \

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{ \

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VEC_DATA_TYPE(DATA_TYPE, 16) \

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src0 = (VEC_DATA_TYPE(DATA_TYPE, 16))( \

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PTR_TO_VALUE(row_ptr + 0 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 1 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 2 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 3 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 4 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 5 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 6 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 7 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 8 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 9 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 10 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 11 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 12 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 13 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 14 * src_stride_y, DATA_TYPE), \

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PTR_TO_VALUE(row_ptr + 15 * src_stride_y, DATA_TYPE)); \

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DATA_TYPE src1 = PTR_TO_VALUE(row_ptr + 16 * src_stride_y, DATA_TYPE); \

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VEC_DATA_TYPE(DATA_TYPE, 3) \

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weights = (VEC_DATA_TYPE(DATA_TYPE, 3))( \

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PTR_TO_VALUE((weights_ptr) + 0 * weights_stride_y, DATA_TYPE), \

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PTR_TO_VALUE((weights_ptr) + 1 * weights_stride_y, DATA_TYPE), \

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PTR_TO_VALUE((weights_ptr) + 2 * weights_stride_y, DATA_TYPE)); \

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\

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acc = ADD_OP(acc, MUL_OP(src0.s02468ACE, (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s0)); \

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acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF), (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s1)); \

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acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1), (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s2)); \

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}

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/** This kernel performs a direct convolution to convolve the low three dimensions.

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*

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* @note This OpenCL kernel works with stride_x = 1 and 2

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* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float

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* @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH

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* @note If biases are used then -DHAS_BIAS has to be passed at compile time

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*

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* @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32

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* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)

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* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)

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* @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)

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* @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)

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* @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor

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* @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr

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* @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)

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* @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)

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* @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)

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* @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)

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* @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor

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* @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr

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* @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)

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* @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)

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* @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)

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* @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)

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* @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor

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* @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr

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* @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)

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* @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor

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* @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension

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*/

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__kernel void direct_convolution3x3_nhwc(

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TENSOR3D_DECLARATION(src),

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TENSOR3D_DECLARATION(dst),

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TENSOR3D_DECLARATION(weights),

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#ifdef HAS_BIAS

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VECTOR_DECLARATION(biases),

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#endif /* defined(HAS_BIAS) */

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unsigned int weights_stride_w)

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{

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Image src = CONVERT_TO_IMAGE_STRUCT(src);

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Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);

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Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);

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VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)

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values0 = 0;

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const int id0 = get_global_id(0);

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const int id1 = get_global_id(1);

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const int id2 = get_global_id(2);

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__global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);

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__global uchar *src_addr = (__global uchar *)offset(&src, 0, 0) - src_stride_x * id0 + ((id2 * STRIDE_Y) - PAD_TOP) * (int)src_stride_z;

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weights_addr += id0 * weights_stride_w;

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const int coordy = ((id2 * STRIDE_Y) - PAD_TOP);

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for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)

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{

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#if PAD_TOP > 0

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if(coordy < 0) // special case Z = -1 doesn't exists

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{

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//skip first row and load the two next ones

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CONVOLUTION1x3_NHWC(values0, src_addr + 1 * (int)src_stride_z, (weights_addr + 1 * (int)weights_stride_z));

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CONVOLUTION1x3_NHWC(values0, src_addr + 2 * (int)src_stride_z, (weights_addr + 2 * (int)weights_stride_z));

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}

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else if(coordy == (SRC_HEIGHT - PAD_TOP - 1))

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{

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// special case when computing the last row of the output we must read the last three rows from the input buffer (including padding) but the

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// Z axis has no padding at all.

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CONVOLUTION1x3_NHWC(values0, src_addr, (weights_addr + 0 * (int)weights_stride_z));

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CONVOLUTION1x3_NHWC(values0, src_addr + 1 * (int)src_stride_z, (weights_addr + 1 * (int)weights_stride_z));

}

else

{

CONVOLUTION1x3_NHWC(values0, src_addr, (weights_addr + 0 * (int)weights_stride_z));

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CONVOLUTION1x3_NHWC(values0, src_addr + 1 * (int)src_stride_z, (weights_addr + 1 * (int)weights_stride_z));

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CONVOLUTION1x3_NHWC(values0, src_addr + 2 * (int)src_stride_z, (weights_addr + 2 * (int)weights_stride_z));

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}

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#else // PAD_TOP > 0

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CONVOLUTION1x3_NHWC(values0, src_addr, (weights_addr + 0 * (int)weights_stride_z));

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CONVOLUTION1x3_NHWC(values0, src_addr + 1 * (int)src_stride_z, (weights_addr + 1 * (int)weights_stride_z));

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CONVOLUTION1x3_NHWC(values0, src_addr + 2 * (int)src_stride_z, (weights_addr + 2 * (int)weights_stride_z));

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#endif // PAD_TOP > 0

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src_addr += src_stride_x;

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weights_addr += weights_stride_x;

}

#ifdef HAS_BIAS

Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);

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values0 = ADD_OP(values0, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, id0))));

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#endif /* defined(HAS_BIAS) */

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*((__global DATA_TYPE *)(dst.ptr + 0 * dst_stride_y)) = values0.s0;

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*((__global DATA_TYPE *)(dst.ptr + 1 * dst_stride_y)) = values0.s1;

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*((__global DATA_TYPE *)(dst.ptr + 2 * dst_stride_y)) = values0.s2;

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*((__global DATA_TYPE *)(dst.ptr + 3 * dst_stride_y)) = values0.s3;

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*((__global DATA_TYPE *)(dst.ptr + 4 * dst_stride_y)) = values0.s4;

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*((__global DATA_TYPE *)(dst.ptr + 5 * dst_stride_y)) = values0.s5;

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*((__global DATA_TYPE *)(dst.ptr + 6 * dst_stride_y)) = values0.s6;

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*((__global DATA_TYPE *)(dst.ptr + 7 * dst_stride_y)) = values0.s7;

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}

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#endif // defined(DATA_LAYOUT_NHWC)

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steniu01

2017-07-18 17:37:43 +0100

[diff] [blame]

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/** This kernel performs a direct convolution to convolve the low three dimensions.

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*

Gian Marco Iodice

2017-08-16 18:38:32 +0100

[diff] [blame]

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* @note This OpenCL kernel works with stride_x = 1 and 2

steniu01

2017-07-18 17:37:43 +0100

[diff] [blame]

251

* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float

Gian Marco Iodice

2017-08-08 10:53:00 +0100

[diff] [blame]

252

* @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH

Gian Marco Iodice

2017-08-16 18:38:32 +0100

[diff] [blame]

253

* @note If biases are used then -DHAS_BIAS has to be passed at compile time

steniu01

2017-07-18 17:37:43 +0100

[diff] [blame]

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*

Vidhya Sudhan Loganathan

7485d5a

2018-07-04 09:34:00 +0100

[diff] [blame]

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* @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32

steniu01

2017-07-18 17:37:43 +0100

[diff] [blame]

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* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)

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* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)

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* @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)

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* @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)

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* @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor

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* @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr

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* @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)

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* @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)

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* @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)

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* @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)

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* @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor

Joel Liang

f1f3ebd

2017-11-10 09:59:19 +0800

[diff] [blame]

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* @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr

steniu01

2017-07-18 17:37:43 +0100

[diff] [blame]

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* @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)

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* @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)

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* @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)

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* @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)

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* @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor

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* @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr

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* @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)

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* @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor

Gian Marco Iodice

2017-08-08 10:53:00 +0100

[diff] [blame]

283

* @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension

steniu01

2017-07-18 17:37:43 +0100

[diff] [blame]

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*/

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__kernel void direct_convolution3x3(

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TENSOR3D_DECLARATION(src),

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TENSOR3D_DECLARATION(dst),

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TENSOR3D_DECLARATION(weights),

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#ifdef HAS_BIAS

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VECTOR_DECLARATION(biases),

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#endif /* defined(HAS_BIAS) */

Gian Marco Iodice

2017-08-08 10:53:00 +0100

[diff] [blame]

292

unsigned int weights_stride_w)

steniu01

2017-07-18 17:37:43 +0100

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{

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Image src = CONVERT_TO_IMAGE_STRUCT(src);

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Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);

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Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);

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Michalis Spyrou

2017-08-14 11:26:37 +0100

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VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)

Pablo Tello

2018-06-21 15:13:17 +0100

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values0 = 0;

steniu01

2017-07-18 17:37:43 +0100

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Gian Marco Iodice

2017-08-08 10:53:00 +0100

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__global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);

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__global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);

steniu01

2017-07-18 17:37:43 +0100

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Gian Marco Iodice

2017-08-08 10:53:00 +0100

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const int kernel_index = get_global_id(2);

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weights_addr += kernel_index * weights_stride_w;

steniu01

2017-07-18 17:37:43 +0100

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306

Gian Marco Iodice

744b5ed

2017-10-06 15:44:27 +0100

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for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)

steniu01

2017-07-18 17:37:43 +0100

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{

Pablo Tello

2018-06-21 15:13:17 +0100

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CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y));

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CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));

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CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));

steniu01

2017-07-18 17:37:43 +0100

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Gian Marco Iodice

2017-08-08 10:53:00 +0100

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src_addr += src_stride_z;

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weights_addr += weights_stride_z;

steniu01

2017-07-18 17:37:43 +0100

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}

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#ifdef HAS_BIAS

Gian Marco Iodice

2017-08-08 10:53:00 +0100

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Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);

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Pablo Tello

2018-06-21 15:13:17 +0100

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320

values0 = ADD_OP(values0, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index))));

steniu01

2017-07-18 17:37:43 +0100

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#endif /* defined(HAS_BIAS) */

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Pablo Tello

2018-06-21 15:13:17 +0100

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vstore8(CONVERT_SAT(values0, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)dst.ptr);

steniu01

2017-07-18 17:37:43 +0100

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324

}

Gian Marco Iodice

2017-08-16 18:38:32 +0100

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#endif //defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)

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#if defined(WEIGHTS_DEPTH)

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#define CONVOLUTION1x3_BIFROST(acc, src0, src1, weights_row0) \

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({ \

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acc.s0 = mad(src0.s0, weights_row0.s0, acc.s0); \

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acc.s1 = mad(src0.s1, weights_row0.s0, acc.s1); \

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acc.s2 = mad(src0.s2, weights_row0.s0, acc.s2); \

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acc.s3 = mad(src0.s3, weights_row0.s0, acc.s3); \

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acc.s0 = mad(src0.s1, weights_row0.s1, acc.s0); \

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acc.s1 = mad(src0.s2, weights_row0.s1, acc.s1); \

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acc.s2 = mad(src0.s3, weights_row0.s1, acc.s2); \

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acc.s3 = mad(src1.s0, weights_row0.s1, acc.s3); \

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acc.s0 = mad(src0.s2, weights_row0.s2, acc.s0); \

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acc.s1 = mad(src0.s3, weights_row0.s2, acc.s1); \

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acc.s2 = mad(src1.s0, weights_row0.s2, acc.s2); \

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acc.s3 = mad(src1.s1, weights_row0.s2, acc.s3); \

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})

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/** An optimized direct convolution 3x3 OpenCL kernel for Bifrost architectures when the data type is F32

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*

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* @note This OpenCL kernel works only with stride_x and stride_y equal to 1

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* @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH

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* @note In case biases, -DHAS_BIAS must to be passed at compile

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*

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* @param[in] src_ptr Pointer to the source tensor. Supported data types: F32

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* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)

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* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)

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* @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)

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* @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)

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* @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor

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* @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr

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* @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)

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* @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)

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* @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)

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* @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)

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* @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor

Joel Liang

f1f3ebd

2017-11-10 09:59:19 +0800

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* @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr

Gian Marco Iodice

2017-08-16 18:38:32 +0100

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* @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)

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* @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)

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* @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)

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* @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)

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* @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)

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* @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor

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* @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr

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* @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)

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* @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)

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* @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor

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* @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension

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*/

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__kernel void direct_convolution3x3_f32_bifrost(

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TENSOR3D_DECLARATION(src),

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TENSOR3D_DECLARATION(dst),

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TENSOR3D_DECLARATION(weights),

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#ifdef HAS_BIAS

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VECTOR_DECLARATION(biases),

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#endif /* defined(HAS_BIAS) */

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unsigned int weights_stride_w)

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{

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// Get the kernel index

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const int kernel_index = get_global_id(2);

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Image src = CONVERT_TO_IMAGE_STRUCT(src);

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Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);

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Pablo Tello

2018-06-21 15:13:17 +0100

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float4 values0 = 0;

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float4 values1 = 0;

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float4 values2 = 0;

Gian Marco Iodice

2017-08-16 18:38:32 +0100

[diff] [blame]

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__global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + kernel_index * weights_stride_w);

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__global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);

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// Note: Since each work-item computes 4x3 elements, we need to load 5 rows from the input tensor

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for(ushort d = 0; d < (ushort)WEIGHTS_DEPTH; ++d)

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{

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// Load the weights

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float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y));

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float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y));

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float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y));

float4 src0;

float2 src1;

// Load values from row0 of input tensor

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src0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));

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src1 = vload2(0, (__global float *)(src_addr + 0 * src_stride_y) + 4);

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Pablo Tello

2018-06-21 15:13:17 +0100

[diff] [blame]

418

CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row0);

Gian Marco Iodice

2017-08-16 18:38:32 +0100

[diff] [blame]

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// Load values from row1 of input tensor

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src0 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));

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src1 = vload2(0, (__global float *)(src_addr + 1 * src_stride_y) + 4);

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// Accumulate

Pablo Tello

2018-06-21 15:13:17 +0100

[diff] [blame]

425

CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row1);

426

CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row0);

Gian Marco Iodice

2017-08-16 18:38:32 +0100

[diff] [blame]

427

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// Load values from row2 of input tensor

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src0 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));

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src1 = vload2(0, (__global float *)(src_addr + 2 * src_stride_y) + 4);

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// Accumulate

Pablo Tello

2018-06-21 15:13:17 +0100

[diff] [blame]

433

CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row2);

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CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row1);

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CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row0);

Gian Marco Iodice

2017-08-16 18:38:32 +0100

[diff] [blame]

436

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// Load values from row3 of input tensor

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src0 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));

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src1 = vload2(0, (__global float *)(src_addr + 3 * src_stride_y) + 4);

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// Accumulate

Pablo Tello

2018-06-21 15:13:17 +0100

[diff] [blame]

442

CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row2);

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CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row1);

Gian Marco Iodice

2017-08-16 18:38:32 +0100

[diff] [blame]

444

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// Row4

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src0 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y));

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src1 = vload2(0, (__global float *)(src_addr + 4 * src_stride_y) + 4);

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// Accumulate

Pablo Tello

2018-06-21 15:13:17 +0100

[diff] [blame]

450

CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row2);

Gian Marco Iodice

2017-08-16 18:38:32 +0100

[diff] [blame]

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src_addr += src_stride_z;

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weights_addr += weights_stride_z;

}

#ifdef HAS_BIAS

Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);

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Gian Marco Iodice

1c8409d

2017-09-06 17:24:25 +0100

[diff] [blame]

459

float bias = (float) * ((__global float *)(vector_offset(&biases, kernel_index)));

Gian Marco Iodice

2017-08-16 18:38:32 +0100

[diff] [blame]

460

Pablo Tello

2018-06-21 15:13:17 +0100

[diff] [blame]

461

values0 += (float4)bias;

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values1 += (float4)bias;

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values2 += (float4)bias;

Gian Marco Iodice

2017-08-16 18:38:32 +0100

[diff] [blame]

464

#endif /* defined(HAS_BIAS) */

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Pablo Tello

2018-06-21 15:13:17 +0100

[diff] [blame]

466

vstore4(values0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y));

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vstore4(values1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y));

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vstore4(values2, 0, (__global float *)(dst.ptr + 2 * dst_stride_y));

Gian Marco Iodice