diff options
Diffstat (limited to 'src/core/GLES_COMPUTE/cs_shaders/convolution_layer.cs')
| -rw-r--r-- | src/core/GLES_COMPUTE/cs_shaders/convolution_layer.cs | 791 |
1 files changed, 0 insertions, 791 deletions
diff --git a/src/core/GLES_COMPUTE/cs_shaders/convolution_layer.cs b/src/core/GLES_COMPUTE/cs_shaders/convolution_layer.cs deleted file mode 100644 index 40b5a2beb0..0000000000 --- a/src/core/GLES_COMPUTE/cs_shaders/convolution_layer.cs +++ /dev/null @@ -1,791 +0,0 @@ -/* - * Copyright (c) 2017-2018 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. - */ - -layout(local_size_x = LOCAL_SIZE_X, local_size_y = LOCAL_SIZE_Y, local_size_z = LOCAL_SIZE_Z) in; - -#include "helpers_cs.h" - -#if defined(DATA_TYPE_FP16) -precision mediump float; -#endif // DATA_TYPE_FP16 - -#ifdef RESHAPE_TO_COLUMNS - -/** 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 "#define DATA_TYPE_NAME". e.g. "#define DATA_TYPE_FP32" - * @note In case biases will be added to the convolution "#define HAS_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: F16/F32 - * @param[in] src_attrs The attributes of the source tensor - * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_attrs The attributes of the destination tensor - * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr - * @param[in] biases_attrs The attributes of the biases tensor - * @param[in] width The width of the input tensor - * @param[in] height The height of the input tensor - * @param[in] depth The depth of the input tensor - * @param[in] total_filters Total number of filters. 4th dimension of the weights matrix - */ - -SHADER_PARAMS_DECLARATION -{ - Tensor3DAttributes src_attrs; - ImageAttributes dst_attrs; -#ifdef HAS_BIAS - VectorAttributes biases_attrs; -#endif /* HAS_BIAS */ - uint width; - uint height; - uint depth; - uint total_filters; -}; - -#if defined(DATA_TYPE_FP32) - -TENSOR_DECLARATION(1, srcBuffer, float, src_ptr, src_shift, 2, readonly); -TENSOR_DECLARATION(2, dstBuffer, float, dst_ptr, dst_shift, 2, writeonly); -#ifdef HAS_BIAS -TENSOR_DECLARATION(3, biasesBuffer, float, biases_ptr, biases_shift, 2, readonly); -#endif /* BIAS */ - -void main() -{ - Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR(src_attrs, src_shift); - ImageIterator dst_iter = CONVERT_TO_IMAGE_ITERATOR_NO_STEP(dst_attrs, dst_shift); -#ifdef HAS_BIAS - VectorIterator biases_iter = CONVERT_TO_VECTOR_ITERATOR_NO_STEP(biases_attrs, biases_shift); -#endif /* BIAS */ - - bool is_last_thread = (((int(gl_GlobalInvocationID.x)) == (int(gl_NumWorkGroups.x * gl_WorkGroupSize.x) - 1)) && ((int(gl_GlobalInvocationID.y)) == (int(gl_NumWorkGroups.y * gl_WorkGroupSize.y) - 1)) - && ((int(gl_GlobalInvocationID.z)) == (int(gl_NumWorkGroups.z * gl_WorkGroupSize.z) - 1))); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, ((uint(gl_GlobalInvocationID.x) * uint(dst_attrs.stride_y)) + (uint(gl_GlobalInvocationID.y) * uint(width) * uint(dst_attrs.stride_y)) + (uint( - gl_GlobalInvocationID.z) - * uint(width) * uint(height) * uint(dst_attrs.stride_y)))); - // Linearize convolution elements - if(is_last_thread) - { - for(uint i = 0u; i < uint(total_filters); ++i) - { - float s0 = LOAD_CURRENT_ITEM(src_ptr, src_iter); - STORE_CURRENT_ITEM(dst_ptr, dst_iter, s0); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, (depth * src_attrs.stride_z)); -#ifdef HAS_BIAS - float b = LOAD_CURRENT_ITEM(biases_ptr, biases_iter); - STORE(dst_ptr, TENSOR_OFFSET_ADVANCE_IN_BYTES(dst_iter, dst_attrs.stride_y), b); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(biases_iter, biases_attrs.stride_x); -#endif /* HAS_BIAS */ - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.stride_x); - } - } - else - { - for(uint i = 0u; i < uint(total_filters); ++i) - { - float s0 = LOAD_CURRENT_ITEM(src_ptr, src_iter); - STORE_CURRENT_ITEM(dst_ptr, dst_iter, s0); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, (depth * src_attrs.stride_z)); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.stride_x); - } - } -} - -#elif defined(DATA_TYPE_FP16) - -TENSOR_DECLARATION(1, srcBuffer, uint, src_ptr, src_shift, 2, readonly); -TENSOR_DECLARATION(2, dstBuffer, uint, dst_ptr, dst_shift, 2, writeonly); -#ifdef HAS_BIAS -TENSOR_DECLARATION(3, biasesBuffer, uint, biases_ptr, biases_shift, 2, readonly); -#endif /* BIAS */ - -void main() -{ - Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR(src_attrs, src_shift); - ImageIterator dst_iter = CONVERT_TO_IMAGE_ITERATOR_NO_STEP(dst_attrs, dst_shift); -#ifdef HAS_BIAS - VectorIterator biases_iter = CONVERT_TO_VECTOR_ITERATOR_NO_STEP(biases_attrs, biases_shift); -#endif /* BIAS */ - - bool is_last_thread = (((int(gl_GlobalInvocationID.x)) == (int(gl_NumWorkGroups.x * gl_WorkGroupSize.x) - 1)) && ((int(gl_GlobalInvocationID.y)) == (int(gl_NumWorkGroups.y * gl_WorkGroupSize.y) - 1)) - && ((int(gl_GlobalInvocationID.z)) == (int(gl_NumWorkGroups.z * gl_WorkGroupSize.z) - 1))); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, ((uint(gl_GlobalInvocationID.x) * uint(dst_attrs.stride_y)) + (uint(gl_GlobalInvocationID.y) * uint(width) * uint(dst_attrs.stride_y)) + (uint( - gl_GlobalInvocationID.z) - * uint(width) * uint(height) * uint(dst_attrs.stride_y)))); - // Linearize convolution elements - if(is_last_thread) - { - for(uint i = 0u; i < uint(total_filters); i = i + 2u) - { - vec2 s0 = LOAD_UNPACK2_CURRENT_ITEM_HALF(src_ptr, src_iter); - vec2 s; - if(int(CURRENT_ITEM_OFFSET_IN_BYTES(src_iter) >> 1u) % 2 == 0) - { - s.x = s0.x; - } - else - { - s.x = s0.y; - } - TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, (depth * src_attrs.stride_z)); - - vec2 s1 = LOAD_UNPACK2_CURRENT_ITEM_HALF(src_ptr, src_iter); - if(int(CURRENT_ITEM_OFFSET_IN_BYTES(src_iter) >> 1u) % 2 == 0) - { - s.y = s1.x; - } - else - { - s.y = s1.y; - } - STORE_PACK2_CURRENT_ITEM_HALF(dst_ptr, dst_iter, s); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, (depth * src_attrs.stride_z)); -#ifdef HAS_BIAS - vec2 b = LOAD_UNPACK2_CURRENT_ITEM_HALF(biases_ptr, biases_iter); - STORE_PACK2_HALF(dst_ptr, TENSOR_OFFSET_ADVANCE_IN_BYTES(dst_iter, dst_attrs.stride_y), b); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(biases_iter, (2u * biases_attrs.stride_x)); -#endif /* HAS_BIAS */ - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, (2u * dst_attrs.stride_x)); - } - } - else - { - for(uint i = 0u; i < uint(total_filters); i = i + 2u) - { - vec2 s0 = LOAD_UNPACK2_CURRENT_ITEM_HALF(src_ptr, src_iter); - vec2 s; - if(int(CURRENT_ITEM_OFFSET_IN_BYTES(src_iter) >> 1u) % 2 == 0) - { - s.x = s0.x; - } - else - { - s.x = s0.y; - } - TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, (depth * src_attrs.stride_z)); - - vec2 s1 = LOAD_UNPACK2_CURRENT_ITEM_HALF(src_ptr, src_iter); - if(int(CURRENT_ITEM_OFFSET_IN_BYTES(src_iter) >> 1u) % 2 == 0) - { - s.y = s1.x; - } - else - { - s.y = s1.y; - } - STORE_PACK2_CURRENT_ITEM_HALF(dst_ptr, dst_iter, s); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, (depth * src_attrs.stride_z)); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, (2u * dst_attrs.stride_x)); - } - } -} - -#endif /* DATA_TYPE_FP32 */ -#endif // RESHAPE_TO_COLUMNS - -#ifdef IM2COL_GENERIC - -/** 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 "#define DATA_TYPE_FP32" - * @note PAD_LEFT/PAD_RIGHT/PAD_TOP/PAD_BOTTOM must be passed for padding info, e.g. "#define PAD_LEFT xxx" - * @note KERNEL_WIDTH/KERNEL_HEIGHT/KERNEL_DEPTH must be passed for kernel dimension, e.g. "#define KERNEL_WIDTH xxx" - * @note STRIDE_X/STRIDE_Y must be passed for stride info, e.g. "#define STRIDE_X xxx" - * @note CONVOLVED_WIDTH/CONVOLVED_HEIGHT must be passed for convolved dimension, e.g. "#define CONVOLVED_WIDTH xxx" - * @note SRC_WIDTH/SRC_HEIGHT must be passed for input dimension, e.g. "#define SRC_WIDTH xxx" - * @note DILATION_X/DILATION_Y must be passed for dilation sizes, e.g. "#define DILATION_X xxx" - * @note In case biases will be added to the convolution "#define HAS_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: F16/F32 - * @param[in] src_attrs The attributes of the source tensor - * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_attrs The attributes of 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). - */ - -SHADER_PARAMS_DECLARATION -{ - Tensor3DAttributes src_attrs; - ImageAttributes dst_attrs; - uint src_stride_w; - uint dst_stride_w; -}; - -#ifdef DATA_TYPE_FP32 - -TENSOR_DECLARATION(1, srcBuffer, float, src_ptr, src_shift, 2, readonly); -TENSOR_DECLARATION(2, dstBuffer, float, dst_ptr, dst_shift, 2, restrict); - -void main(void) -{ - Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift); - ImageIterator dst_iter = CONVERT_TO_IMAGE_ITERATOR_NO_STEP(dst_attrs, dst_shift); - - int xc = int(gl_GlobalInvocationID.x); // x coordinate in the convolved tensor - int yc = int(gl_GlobalInvocationID.y); // y coordinate in the convolved tensor - int ch = int(gl_GlobalInvocationID.z) % KERNEL_DEPTH; // input feature map - int batch = int(gl_GlobalInvocationID.z) / KERNEL_DEPTH; // the batch - - // Calculate input indeces - int xi = xc * STRIDE_X - PAD_LEFT; - int yi = yc * STRIDE_Y - PAD_TOP; - TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, (ch * int(src_attrs.stride_z)) + (batch * int(src_stride_w))); - - // Calculate output indeces - int xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT; - int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution - // sizeof is not available in GLES, so we'll use stride_x - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, (yo * int(dst_attrs.stride_y)) + (batch * int(dst_stride_w)) + xo * int(dst_attrs.stride_x)); - - uint src_pos = 0u; - - // Linearize convolution elements - for(int y = yi, y_e = yi + KERNEL_HEIGHT * DILATION_Y; y < y_e; y += DILATION_Y) - { - for(int x = xi, x_e = xi + KERNEL_WIDTH * DILATION_X; x < x_e; x += DILATION_X, TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, int(dst_attrs.stride_x))) - { -#if PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 - src_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * int(src_attrs.stride_x) + y * int(src_attrs.stride_y)); - STORE_CURRENT_ITEM(dst_ptr, dst_iter, LOAD(src_ptr, src_pos)); -#else /* PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 */ - if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT) - { - STORE_CURRENT_ITEM(dst_ptr, dst_iter, 0.0f); - } - else - { - src_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * int(src_attrs.stride_x) + y * int(src_attrs.stride_y)); - STORE_CURRENT_ITEM(dst_ptr, dst_iter, LOAD(src_ptr, src_pos)); - } -#endif /* PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 */ - } - } - -#ifdef HAS_BIAS - if(ch == (KERNEL_DEPTH - 1)) - { - STORE_CURRENT_ITEM(dst_ptr, dst_iter, 1.0f); - } -#endif /* HAS_BIAS */ -} - -#elif defined(DATA_TYPE_FP16) - -TENSOR_DECLARATION(1, srcBuffer, uint, src_ptr, src_shift, 2, readonly); -TENSOR_DECLARATION(2, dstBuffer, uint, dst_ptr, dst_shift, 2, writeonly); - -#ifdef KERNEL_1x1 - -void main(void) -{ - Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift); - ImageIterator dst_iter = CONVERT_TO_IMAGE_ITERATOR_NO_STEP(dst_attrs, dst_shift); - - uint xc = gl_GlobalInvocationID.x; - uint yc = gl_GlobalInvocationID.y; - uint zc = gl_GlobalInvocationID.z; - uint ch = zc % uint(KERNEL_DEPTH); // input feature map - uint batch = zc / uint(KERNEL_DEPTH); // the batch - - // Calculate input indeces - uint xi = xc; - uint yi = yc; - TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, batch * src_stride_w + ch * src_attrs.step_z); - - // Calculate output indeces - uint dst_element_count = dst_attrs.step_x / dst_attrs.stride_x; - uint xo = ch * dst_element_count; - uint yo = xc + yc * uint(CONVOLVED_WIDTH); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, batch * dst_stride_w + yo * dst_attrs.stride_y + xo); - - bool x_start_even = ((xc % 2u) == 0u); - bool z_depth_even = ((uint(KERNEL_DEPTH) % 2u) == 0u); - uint input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, xi * src_attrs.stride_x + yi * src_attrs.stride_y); - uint tmp_left = 0u; - uint tmp_right = 0u; - - if(ch % 2u != 0u) - { - return; - } - - if(z_depth_even || (!z_depth_even && (int(ch) < (KERNEL_DEPTH - 1)))) - { - tmp_left = LOAD(src_ptr, input_pos); - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, xi * src_attrs.stride_x + yi * src_attrs.stride_y + src_attrs.stride_z); - tmp_right = LOAD(src_ptr, input_pos); - if(x_start_even) - { - tmp_right = (tmp_left & 0xffffu) + (tmp_right << 16u); - } - else - { - tmp_right = (tmp_left >> 16u) + (tmp_right & 0xffff0000u); - } - STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x); - -#ifdef HAS_BIAS - if(ch == (uint(KERNEL_DEPTH) - 2u)) - { - mediump vec2 bias_vec = vec2(1.f, 0.f); - uint bias_u = packHalf2x16(bias_vec); - STORE_CURRENT_ITEM(dst_ptr, dst_iter, bias_u); - } -#endif /* HAS_BIAS */ - } - else - { - tmp_left = LOAD(src_ptr, input_pos); - if(x_start_even) - { - tmp_right = (tmp_left & 0xffffu); - } - else - { - tmp_right = (tmp_left >> 16u); - } - -#ifdef HAS_BIAS - mediump vec2 bias_vec = vec2(0.f, 1.f); - uint bias_u = packHalf2x16(bias_vec); - tmp_right += (bias_u & 0xffff0000u); -#endif /* HAS_BIAS */ - - STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right); - } -} - -#else /* KERNEL_1x1 */ - -void main(void) -{ - uint xc = gl_GlobalInvocationID.x; - uint yc = gl_GlobalInvocationID.y; - uint zc = gl_GlobalInvocationID.z; - uint ch = zc % uint(KERNEL_DEPTH); // input feature map - uint batch = zc / uint(KERNEL_DEPTH); // the batch - - Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift); - Tensor3DIterator src_iter_b = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift); - ImageIterator dst_iter = CONVERT_TO_IMAGE_ITERATOR_NO_STEP(dst_attrs, dst_shift); - - // Calculate input indeces - uint src_element_count = src_attrs.step_x / src_attrs.stride_x; - uint xi = (xc * uint(STRIDE_X)) / src_element_count; - uint yi = yc * uint(STRIDE_Y); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, batch * src_stride_w + ch * src_attrs.stride_z); - - // Calculate output indeces - uint dst_element_count = dst_attrs.step_x / dst_attrs.stride_x; - uint xo = (ch * uint(KERNEL_WIDTH) * uint(KERNEL_HEIGHT)) * dst_element_count; - uint yo = xc + yc * uint(CONVOLVED_WIDTH); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, batch * dst_stride_w + yo * dst_attrs.stride_y + xo); - - bool x_start_even = ((xc * uint(STRIDE_X)) % 2u == 0u); - bool z_start_even = ((ch % 2u) == 0u); - uint input_pos = 0u; - uint tmp = 0u; - uint tmp_left = 0u; - uint tmp_right = 0u; - - // Linearize convolution elements - for(uint y = yi, y_e = yi + uint(KERNEL_HEIGHT); y < y_e; ++y) - { - uint xstart = 0u; - uint xend = 0u; - - // even col, even row - if(x_start_even) - { - if(((y - yi + ch) % 2u) == 0u) - { - for(uint x = xi, x_e = xi + (uint(KERNEL_WIDTH) / 2u); x < x_e; ++x, TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x)) - { - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * src_attrs.step_x + y * src_attrs.stride_y); - STORE_CURRENT_ITEM(dst_ptr, dst_iter, LOAD(src_ptr, input_pos)); - } - } - else - { - // 1st pair - if(!z_start_even && (y == yi)) - { - // cross 2d feature map - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter_b, (xi + (uint(KERNEL_WIDTH) / 2u)) * src_attrs.step_x + (yi + uint(KERNEL_HEIGHT) - 1u) * src_attrs.stride_y + batch * src_stride_w + - (ch - 1u) * src_attrs.stride_z); - } - else - { - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, - (xi + (uint(KERNEL_WIDTH) / 2u)) * src_attrs.step_x + (y - 1u) * src_attrs.stride_y); - } - tmp_right = LOAD(src_ptr, input_pos); - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, xi * src_attrs.step_x + y * src_attrs.stride_y); - tmp_left = LOAD(src_ptr, input_pos); - tmp_right = (tmp_right & 0xffffu) + (tmp_left << 16u); - STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x); - - // remaining - for(uint x = xi + 1u, x_e = xi + (uint(KERNEL_WIDTH) / 2u) + 1u; x < x_e; ++x, TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x)) - { - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, (x - 1u) * src_attrs.step_x + y * src_attrs.stride_y); - tmp_left = LOAD(src_ptr, input_pos); - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * src_attrs.step_x + y * src_attrs.stride_y); - tmp_right = LOAD(src_ptr, input_pos); - tmp_right = (tmp_left >> 16u) + (tmp_right << 16u); - STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right); - } - } - } - else - { - if((((y - yi) % 2u) == 0u && !z_start_even) || (((y - yi) % 2u) != 0u && z_start_even)) - { - // 1st pair - if(y == yi) - { - // cross 2d feature map - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter_b, (xi + (uint(KERNEL_WIDTH) / 2u)) * src_attrs.step_x + (yi + uint(KERNEL_HEIGHT) - 1u) * src_attrs.stride_y + batch * src_stride_w + - (ch - 1u) * src_attrs.stride_z); - } - else - { - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, - (xi + (uint(KERNEL_WIDTH) / 2u)) * src_attrs.step_x + (y - 1u) * src_attrs.stride_y); - } - - tmp_right = LOAD(src_ptr, input_pos); - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, xi * src_attrs.step_x + y * src_attrs.stride_y); - tmp_left = LOAD(src_ptr, input_pos); - tmp_right = (tmp_right >> 16u) + (tmp_left & 0xffff0000u); - STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x); - - // remaining - for(uint x = xi + 1u, x_e = xi + (uint(KERNEL_WIDTH) / 2u) + 1u; x < x_e; ++x, TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x)) - { - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * src_attrs.step_x + y * src_attrs.stride_y); - STORE_CURRENT_ITEM(dst_ptr, dst_iter, LOAD(src_ptr, input_pos)); - } - } - else if((((y - yi) % 2u) == 0u && z_start_even) || (((y - yi) % 2u) != 0u && !z_start_even)) - { - // 1st pair - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, xi * src_attrs.step_x + y * src_attrs.stride_y); - tmp_right = LOAD(src_ptr, input_pos); - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, (xi + 1u) * src_attrs.step_x + y * src_attrs.stride_y); - tmp_left = LOAD(src_ptr, input_pos); - tmp_right = (tmp_right >> 16u) + (tmp_left << 16u); - STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x); - - // remaining - for(uint x = xi + 1u, x_e = xi + (uint(KERNEL_WIDTH) / 2u); x < x_e; ++x, TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x)) - { - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * src_attrs.step_x + y * src_attrs.stride_y); - tmp_right = LOAD(src_ptr, input_pos); - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, (x + 1u) * src_attrs.step_x + y * src_attrs.stride_y); - tmp_left = LOAD(src_ptr, input_pos); - tmp_right = (tmp_right >> 16u) + (tmp_left << 16u); - STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right); - } - } - } - } - - // NOTE: must handle last element manually instead of in loops - // to avoid write conflict across 2d boundary - if(ch == uint(KERNEL_DEPTH) - 1u) - { - uint x = xi + (uint(KERNEL_WIDTH) / 2u); - uint y = yi + uint(KERNEL_HEIGHT) - 1u; - input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * src_attrs.step_x + y * src_attrs.stride_y); - tmp = LOAD(src_ptr, input_pos); - if(!x_start_even) - { - tmp = (tmp >> 16u) + (tmp << 16u); - } - -#ifdef HAS_BIAS - mediump vec2 bias_vec = vec2(1.f, 1.f); - uint bias_u = packHalf2x16(bias_vec); - if(z_start_even) - { - tmp = (tmp & 0xffffu) + (bias_u & 0xffff0000u); - } - else - { - tmp = (bias_u & 0xffffu); - } -#endif /* HAS_BIAS */ - - STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp); - } -} - -#endif /* KERNEL_1x1 */ -#else /* DATA_TYPE_FP32 */ -#error Data type not supported -#endif /* DATA_TYPE_FP32 */ -#endif /* IM2COL_GENERIC */ - -#ifdef IM2COL_REDUCED - -/** This kernel reshapes the tensor's low three dimensions to single row for GEMM operation - * - * @note The data type must be passed at compile time using "#define DATA_TYPE_FP16" - * @note In case biases will be added in late stage, "#define HAS_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: F16/F32 - * @param[in] src_attrs The attributes of the source tensor - * @param[out] dst_ptr Pointer to the destination tensor. Same as @p src_ptr - * @param[in] dst_attrs The attributes of the destination tensor - * @param[in] width The width of the input tensor - * @param[in] height The height of the input tensor - */ - -SHADER_PARAMS_DECLARATION -{ - Tensor3DAttributes src_attrs; - VectorAttributes dst_attrs; - uint width; - uint height; -}; - -#ifdef DATA_TYPE_FP32 - -TENSOR_DECLARATION(1, srcBuffer, float, src_ptr, src_shift, 2, readonly); -TENSOR_DECLARATION(2, dstBuffer, float, dst_ptr, dst_shift, 2, restrict); - -void main(void) -{ - Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR(src_attrs, src_shift); - VectorIterator dst_iter = CONVERT_TO_VECTOR_ITERATOR_NO_STEP(dst_attrs, dst_shift); - - uvec3 pos = uvec3(gl_GlobalInvocationID.xyz); - uvec3 size = uvec3(gl_WorkGroupSize.xyz); - uint image_size = width * height; - uint tmp_out_offset = VECTOR_OFFSET(dst_iter, pos.x + pos.y * width + pos.z * image_size); - - STORE(dst_ptr, tmp_out_offset, LOAD_CURRENT_ITEM(src_ptr, src_iter)); - -#ifdef HAS_BIAS - // If it is the last thread in the 3 dimensional workgroup - if(pos.x == (size.x - 1) && pos.y == (size.y - 1) && pos.z == (size.z - 1)) - { - tmp_out_offset += (dst_attrs.stride_x >> uint(2)); - STORE(dst_ptr, tmp_out_offset, 1.f); - } -#endif // HAS_BIAS -} - -#elif defined(DATA_TYPE_FP16) - -#if defined(IM2COL_REDUCED_8X) -TENSOR_DECLARATION(1, srcBuffer, uvec4, src_ptr, src_shift, 4, readonly); -TENSOR_DECLARATION(2, dstBuffer, uvec4, dst_ptr, dst_shift, 4, restrict); -#elif defined(IM2COL_REDUCED_4X) /* IM2COL_REDUCED_8X */ -TENSOR_DECLARATION(1, srcBuffer, uvec2, src_ptr, src_shift, 3, readonly); -TENSOR_DECLARATION(2, dstBuffer, uvec2, dst_ptr, dst_shift, 3, restrict); -#else /* IM2COL_REDUCED_8X */ -TENSOR_DECLARATION(1, srcBuffer, uint, src_ptr, src_shift, 2, readonly); -TENSOR_DECLARATION(2, dstBuffer, uint, dst_ptr, dst_shift, 2, restrict); -#endif /* IM2COL_REDUCED_8X */ - -#if defined(IM2COL_REDUCED_GENERIC) - -void main(void) -{ - Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR(src_attrs, src_shift); - Tensor3DIterator src_nostep_iter = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift); - VectorIterator dst_iter = CONVERT_TO_VECTOR_ITERATOR_NO_STEP(dst_attrs, dst_shift); - - uvec3 pos = uvec3(gl_GlobalInvocationID.xyz); - uvec3 size = uvec3(gl_WorkGroupSize.xyz); - uint image_size = width * height; - uint element_count = src_attrs.step_x / src_attrs.stride_x; - uint tmp_out_offset = VECTOR_OFFSET(dst_iter, pos.x * element_count + pos.y * width + pos.z * image_size); - uint width_fp16 = (width + uint(1)) >> uint(1); - uint tmp; - - // odd width - if(width % uint(2) != uint(0)) - { - // even row - if((pos.y + pos.z * height) % uint(2) == uint(0)) - { - // skip last element of each line to avoid write conflict except for last line - if((pos.x < (width / element_count)) || ((pos.y == gl_NumWorkGroups.y - 1u) && (pos.z == gl_NumWorkGroups.z - 1u))) - { - tmp = LOAD_CURRENT_ITEM(src_ptr, src_iter); - STORE(dst_ptr, tmp_out_offset, tmp); - } - } - else - { - // special op - uint tmp_left = uint(0); - uint tmp_right = uint(0); - tmp_right = LOAD_CURRENT_ITEM(src_ptr, src_iter); //right half - if(pos.x == uint(0)) - { - tmp_left = LOAD(src_ptr, TENSOR3D_OFFSET(src_nostep_iter, int(width), int(pos.y) - 1, int(pos.z))); //left half - tmp_right = (tmp_left & uint(0xffff)) + (tmp_right << uint(16)); - } - else - { - tmp_left = LOAD(src_ptr, TENSOR3D_OFFSET(src_nostep_iter, (int(pos.x) - 1) * int(element_count), int(pos.y), int(pos.z))); - tmp_right = ((tmp_left >> uint(16)) + (tmp_right << uint(16))); - } - STORE(dst_ptr, tmp_out_offset, tmp_right); - } - } - else - { - tmp = LOAD_CURRENT_ITEM(src_ptr, src_iter); - STORE(dst_ptr, tmp_out_offset, tmp); - } - -#ifdef HAS_BIAS - // If it is the last thread in the 3 dimensional workgroup - if(pos.x == (size.x - 1u) && pos.y == (size.y - 1u) && pos.z == (size.z - 1u)) - { - tmp_out_offset += (dst_attrs.stride_x >> dst_shift); - - // FIXME: need odd/even detection for tmp_out_offset? - mediump vec2 bias_vec = vec2(1.0f, 1.0f); - STORE_PACK2_HALF(dst_ptr, tmp_out_offset, bias_vec); - } -#endif // HAS_BIAS -} - -#else /* IM2COL_REDUCED_GENERIC */ - -void main(void) -{ - Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR(src_attrs, src_shift); - VectorIterator dst_iter = CONVERT_TO_VECTOR_ITERATOR_NO_STEP(dst_attrs, dst_shift); - - uvec3 pos = uvec3(gl_GlobalInvocationID.xyz); -#if defined(IM2COL_REDUCED_8X) - uint tmp_out_offset = VECTOR_OFFSET(dst_iter, pos.x * uint(8) + pos.y * width + pos.z * uint(IMAGE_SIZE)); - uvec4 tmp = LOAD_CURRENT_ITEM(src_ptr, src_iter); - STORE(dst_ptr, tmp_out_offset, tmp); -#elif defined(IM2COL_REDUCED_4X) /* IM2COL_REDUCED_8X */ - uint tmp_out_offset = VECTOR_OFFSET(dst_iter, pos.x * uint(4) + pos.y * width + pos.z * uint(IMAGE_SIZE)); - uvec2 tmp = LOAD_CURRENT_ITEM(src_ptr, src_iter); - STORE(dst_ptr, tmp_out_offset, tmp); -#else /* IM2COL_REDUCED_8X */ - uint tmp_out_offset = VECTOR_OFFSET(dst_iter, pos.x * uint(2) + pos.y * width + pos.z * uint(IMAGE_SIZE)); - uint tmp = LOAD_CURRENT_ITEM(src_ptr, src_iter); - STORE(dst_ptr, tmp_out_offset, tmp); -#endif /* IM2COL_REDUCED_8X */ -} - -#endif /* IM2COL_REDUCED_GENERIC */ -#else /* DATA_TYPE_FP32 */ -#error Data type not supported -#endif /* DATA_TYPE_FP32 */ -#endif /* IM2COL_REDUCED */ - -#ifdef COL2IM -#ifdef 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 "#define DATA_TYPE_FP32" - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32 - * @param[in] src_attrs The attributes of the source tensor - * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr - * @param[in] dst_attrs The attributes of the destination tensor - * @param[in] dst_depth The length of the destination tensor in Z dimension - * @param[in] dst_strideZ The actual stride of the destination tensor in Z dimension - */ - -SHADER_PARAMS_DECLARATION -{ - Tensor3DAttributes src_attrs; - Tensor3DAttributes dst_attrs; - uint dst_depth; - uint dst_strideZ; -}; - -#ifdef DATA_TYPE_FP32 - -TENSOR_DECLARATION(1, srcBuffer, float, src_ptr, src_shift, 2, readonly); -TENSOR_DECLARATION(2, dstBuffer, float, dst_ptr, dst_shift, 2, restrict); - -void main(void) -{ - Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift); - Tensor3DIterator dst_iter = CONVERT_TO_TENSOR3D_ITERATOR(dst_attrs, dst_shift); - - uvec3 pos = uvec3(gl_GlobalInvocationID.xyz); - TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, pos.x * src_attrs.step_y + pos.y * uint(WIDTH_OUTPUT) * src_attrs.step_y + (pos.z % dst_depth) * src_attrs.stride_x + (pos.z / dst_depth) * dst_strideZ); - - STORE_CURRENT_ITEM(dst_ptr, dst_iter, LOAD_CURRENT_ITEM(src_ptr, src_iter)); -} - -#elif defined(DATA_TYPE_FP16) - -TENSOR_DECLARATION(1, srcBuffer, uint, src_ptr, src_shift, 2, readonly); -TENSOR_DECLARATION(2, dstBuffer, uint, dst_ptr, dst_shift, 2, restrict); - -void main(void) -{ - Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift); - Tensor3DIterator dst_iter = CONVERT_TO_TENSOR3D_ITERATOR(dst_attrs, dst_shift); - - uvec3 pos = uvec3(gl_GlobalInvocationID.xyz); - - if((pos.z % dst_depth) % 2u == 0u) - { - uint common_offset_in_bytes = pos.x * src_attrs.step_y * 2u + pos.y * uint(WIDTH_OUTPUT) * src_attrs.step_y + (pos.z % dst_depth) * src_attrs.stride_x + (pos.z / dst_depth) * dst_strideZ; - uint tmp1_in_offset = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, common_offset_in_bytes); - uint tmp2_in_offset = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, common_offset_in_bytes + src_attrs.step_y); - vec2 tmp1 = LOAD_UNPACK2_HALF(src_ptr, tmp1_in_offset); - vec2 tmp2 = LOAD_UNPACK2_HALF(src_ptr, tmp2_in_offset); - vec2 result = vec2(tmp1.x, tmp2.x); - STORE_PACK2_CURRENT_ITEM_HALF(dst_ptr, dst_iter, result); - } - else - { - uint common_offset_in_bytes = pos.x * src_attrs.step_y * 2u + pos.y * uint(WIDTH_OUTPUT) * src_attrs.step_y + (pos.z % dst_depth) * src_attrs.stride_x + (pos.z / dst_depth) * dst_strideZ - 2u; - uint tmp1_in_offset = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, common_offset_in_bytes); - uint tmp2_in_offset = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, common_offset_in_bytes + src_attrs.step_y); - vec2 tmp1 = LOAD_UNPACK2_HALF(src_ptr, tmp1_in_offset); - vec2 tmp2 = LOAD_UNPACK2_HALF(src_ptr, tmp2_in_offset); - vec2 result = vec2(tmp1.y, tmp2.y); - STORE_PACK2_CURRENT_ITEM_HALF(dst_ptr, dst_iter, result); - } -} - -#else /* DATA_TYPE_FP32 */ -#error Data type not supported -#endif /* DATA_TYPE_FP32 */ -#endif /* WIDTH_OUTPUT */ -#endif /* COL2IM */ |