diff options
Diffstat (limited to 'src/core/CL/cl_kernels/convolution_layer.cl')
| -rw-r--r-- | src/core/CL/cl_kernels/convolution_layer.cl | 320 |
1 files changed, 3 insertions, 317 deletions
diff --git a/src/core/CL/cl_kernels/convolution_layer.cl b/src/core/CL/cl_kernels/convolution_layer.cl index 77b9b64945..f8e0c27724 100644 --- a/src/core/CL/cl_kernels/convolution_layer.cl +++ b/src/core/CL/cl_kernels/convolution_layer.cl @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -27,6 +27,7 @@ #include "fixed_point.h" #endif // FIXED_POINT_POSITION +#if defined(DATA_TYPE) /** This kernel reshapes the tensor's low three dimensions to single column * * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short @@ -96,319 +97,4 @@ __kernel void reshape_to_columns( } } } - -#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(PAD_VALUE) -/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM. - * - * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The value to use for the paddings must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0 - * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes). - * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes). - */ -__kernel void im2col_generic( - TENSOR3D_DECLARATION(src), - IMAGE_DECLARATION(dst), - uint src_stride_w, - uint dst_stride_w) -{ - const int xc = get_global_id(0); // x coordinate in the convolved tensor - const int yc = get_global_id(1); // y coordinate in the convolved tensor - const int ch = get_global_id(2) % KERNEL_DEPTH; // input feature map - const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size - - // Calculate input indices - const int xi = xc * STRIDE_X - PAD_LEFT; - const int yi = yc * STRIDE_Y - PAD_TOP; - - // Calculate output indices - const int xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT; - const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution - - __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * src_stride_z + batch * src_stride_w; - __global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w)) + xo; - - // Linearize convolution elements - for(int y = yi, y_e = yi + KERNEL_HEIGHT; y < y_e; ++y) - { - for(int x = xi, x_e = xi + KERNEL_WIDTH; x < x_e; ++x, ++output_ptr) - { -#if PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 - *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y)); -#else // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 - if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT) - { - *output_ptr = PAD_VALUE; - } - else - { - *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y)); - } -#endif // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 - } - } - -#ifdef HAS_BIAS - if(ch == (KERNEL_DEPTH - 1)) - { -#ifdef FIXED_POINT_POSITION - *output_ptr = (DATA_TYPE)(1 << FIXED_POINT_POSITION); -#else // FIXED_POINT_POSITION - *output_ptr = 1.0f; -#endif // FIXED_POINT_POSITION - } -#endif // HAS_BIAS -} - -/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 3x3 and pad_x = pad_y = 0 - * - * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes). - * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes). - */ -__kernel void im2col_kernel3x3_padx0_pady0( - TENSOR3D_DECLARATION(src), - IMAGE_DECLARATION(dst), - uint src_stride_w, - uint dst_stride_w) -{ - const int xc = get_global_id(0); // x coordinate in the convolved tensor - const int yc = get_global_id(1); // y coordinate in the convolved tensor - const int ch = get_global_id(2) % KERNEL_DEPTH; // input feature map - const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size - - // Calculate input indices - const int xi = xc * STRIDE_X; - const int yi = yc * STRIDE_Y; - - // Calculate output indices - const int xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT; - const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution - - // Get input and output address - __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * src_stride_x + yi * src_stride_y + ch * src_stride_z + batch * src_stride_w; - - __global DATA_TYPE *output_ptr = (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w) + xo; - - VEC_DATA_TYPE(DATA_TYPE, 3) - row0 = vload3(0, (__global DATA_TYPE *)(input_ptr + 0 * src_stride_y)); - VEC_DATA_TYPE(DATA_TYPE, 3) - row1 = vload3(0, (__global DATA_TYPE *)(input_ptr + 1 * src_stride_y)); - VEC_DATA_TYPE(DATA_TYPE, 3) - row2 = vload3(0, (__global DATA_TYPE *)(input_ptr + 2 * src_stride_y)); - - vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row0.s012, row1.s012, row2.s01), 0, output_ptr); - *(output_ptr + 8) = row2.s2; - -#ifdef HAS_BIAS - if(ch == (KERNEL_DEPTH - 1)) - { -#ifdef FIXED_POINT_POSITION - *(output_ptr + 9) = (DATA_TYPE)(1 << FIXED_POINT_POSITION); -#else // FIXED_POINT_POSITION - *(output_ptr + 9) = 1.0f; -#endif // FIXED_POINT_POSITION - } -#endif // HAS_BIAS -} -#endif //defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) - -#if defined(WIDTH_OUTPUT) -/** This kernel performs a reshaping of the output of the convolution layer. - * - * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) - */ -__kernel void col2im( - TENSOR3D_DECLARATION(src), - TENSOR3D_DECLARATION(dst), - uint dst_stride_w) -{ - Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); - Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(dst); - - // Compute output offset - int idx = get_global_id(0) * dst.stride_z + (get_global_id(1) / WIDTH_OUTPUT) * dst_stride_y + (get_global_id(1) % WIDTH_OUTPUT) * dst_stride_x + get_global_id(2) * dst_stride_w; - - // Store value - *((__global DATA_TYPE *)(dst.ptr + idx)) = *((__global DATA_TYPE *)(src.ptr)); -} -#endif // defined(WIDTH_OUTPUT) - -/** This kernel reshapes the tensor's low three dimensions to single row for GEMM operation - * - * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float - * @note In case biases will be added in late stage, -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] dst_ptr Pointer to the destination tensor. Same as @p src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] width The width of the input tensor - * @param[in] height The height of the input tensor - */ -__kernel void im2col_reduced( - TENSOR3D_DECLARATION(src), - VECTOR_DECLARATION(dst), - uint width, uint height) -{ - Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src); - - const uint image_size = width * height; - - __global uchar *tmp_out_ptr = dst_ptr + dst_offset_first_element_in_bytes + (get_global_id(0) + get_global_id(1) * width + get_global_id(2) * image_size) * dst_stride_x; - - *((__global DATA_TYPE *)tmp_out_ptr) = *((__global DATA_TYPE *)src.ptr); - -#ifdef HAS_BIAS - // If it is the last thread in the 3 dimensional workgroup - if(get_global_id(0) == (get_global_size(0) - 1) && get_global_id(1) == (get_global_size(1) - 1) && get_global_id(2) == (get_global_size(2) - 1)) - { - tmp_out_ptr += dst_stride_x; -#ifdef FIXED_POINT_POSITION - *((__global DATA_TYPE *)tmp_out_ptr) = (DATA_TYPE)(1 << FIXED_POINT_POSITION); -#else // FIXED_POINT_POSITION - *((__global DATA_TYPE *)tmp_out_ptr) = (DATA_TYPE)1; -#endif // FIXED_POINT_POSITION - } -#endif // HAS_BIAS -} - -#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE) -/** This kernel reshapes the input tensor to a tensor used to perform convolution using GEMM when - * the kernel width is greater than 1 (except when the kernel size is 3x3) and pad_x == pad_y == 0. - * - * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. - * @note The vector size must be passed at compile time using -DVECTOR_SIZE e.g. -DVECTOR_SIZE=4. - * @note The width modulo vector size must be passed at compile time using -DWIDTH_MOD_VECTOR_SIZE e.g. -DWIDTH_MOD_VECTOR_SIZE=3. - * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32 - * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor - * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes). - * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes). - */ -__kernel void im2col_generic_padx0_pady0( - TENSOR3D_DECLARATION(src), - IMAGE_DECLARATION(dst), - uint src_stride_w, - uint dst_stride_w) -{ - const int xc = get_global_id(0); // x coordinate in the convolved tensor - const int yc = get_global_id(1); // y coordinate in the convolved tensor - const int ch = get_global_id(2) % KERNEL_DEPTH; // input feature map - const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size - - // Calculate input indices - const int xi = xc * STRIDE_X; - const int yi = yc * STRIDE_Y; - // Calculate output indices - const int xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT; - const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution - __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * src_stride_z + batch * src_stride_w; - __global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w)) + xo; - // Linearize convolution elements - for(int y = yi, y_e = yi + KERNEL_HEIGHT; y < y_e; ++y) - { - int last_x = 0; - for(int x = xi, x_e = xi + KERNEL_WIDTH; x + VECTOR_SIZE <= x_e; x += VECTOR_SIZE, output_ptr += VECTOR_SIZE) - { - VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE) - row = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y)); - VSTORE(VECTOR_SIZE) - (row, 0, output_ptr); - last_x = x; - } - // Copy the remainder of the row by doing VLOAD(WIDTH_MOD_VECTOR_SIZE) and VSTORE(WIDTH_MOD_VECTOR_SIZE). - // Note that x and output_ptr have already been incremented by VECTOR_SIZE by the loop just before exit. -#if WIDTH_MOD_VECTOR_SIZE == 1 - *output_ptr = *((__global DATA_TYPE *)(input_ptr + (last_x + VECTOR_SIZE) * src_stride_x + y * src_stride_y)); -#elif WIDTH_MOD_VECTOR_SIZE > 1 - VEC_DATA_TYPE(DATA_TYPE, WIDTH_MOD_VECTOR_SIZE) - row = VLOAD(WIDTH_MOD_VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + (last_x + VECTOR_SIZE) * src_stride_x + y * src_stride_y)); - VSTORE(WIDTH_MOD_VECTOR_SIZE) - (row, 0, output_ptr); -#endif /* WIDTH_MOD_VECTOR_SIZE */ - output_ptr += WIDTH_MOD_VECTOR_SIZE; - } /* End of loop over KERNEL_HEIGHT */ - -#ifdef HAS_BIAS - if(ch == (KERNEL_DEPTH - 1)) - { -#ifdef FIXED_POINT_POSITION - *output_ptr = (DATA_TYPE)(1 << FIXED_POINT_POSITION); -#else // FIXED_POINT_POSITION - *output_ptr = 1.0f; -#endif // FIXED_POINT_POSITION - } -#endif // HAS_BIAS -} -#endif //defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE) +#endif // defined(DATA_TYPE)
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