/* * Copyright (c) 2017 ARM Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ layout(local_size_x = LOCAL_SIZE_X, local_size_y = LOCAL_SIZE_Y, local_size_z = LOCAL_SIZE_Z) in; #include "helpers.h" layout(std140) uniform shader_params { TENSOR3D_PARAM_DECLARATION(src); TENSOR3D_PARAM_DECLARATION(dst); TENSOR3D_PARAM_DECLARATION(weights); #ifdef BIAS VECTOR_PARAM_DECLARATION(biases); #endif /* BIAS */ uint weights_stride_w; uint weights_depth; }; #if defined(DATA_TYPE_FP32) precision highp float; BUFFER_DECLARATION(src, 1, float, readonly); BUFFER_DECLARATION(dst, 2, float, writeonly); BUFFER_DECLARATION(weights, 3, float, readonly); #ifdef BIAS BUFFER_DECLARATION(biases, 4, float, readonly); #endif /* BIAS */ /** This kernel performs a direct convolution to convolve the low three dimensions. * * @note The data type must be passed at compile time using "#define DATA_TYPE_FP32" * @note The convolution stride x must be passed at compile time using "#define STRIDE_X" e.g. "#define STRIDE_X 1" * @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: 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 Z 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[out] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes) * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension * @param[in] weights_depth The third dimensions of the weights tensors */ void main() { Image src = CONVERT_TO_IMAGE_STRUCT(src); Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); #ifdef BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); #endif /* BIAS */ float pixels = CONVERT(0, float); uint z_index = gl_GlobalInvocationID.z; weights.current_offset += z_index * weights_stride_w >> 2; float temp; float temp_weight; for(int d = 0; d < int(weights_depth); ++d) { temp = LOAD4(src, CURRENT_OFFSET(src)); temp_weight = LOAD4(weights, CURRENT_OFFSET(weights)); pixels += temp * temp_weight; src.current_offset += (src_stride_z >> 2); weights.current_offset += (weights_stride_z >> 2); } #ifdef BIAS pixels += LOAD4(biases, vector_offset(biases, int(z_index))); #endif /* BIAS */ STORE4(dst, CURRENT_OFFSET(dst), pixels); } #elif defined(DATA_TYPE_FP16) precision mediump float; BUFFER_DECLARATION(src, 1, uvec4, readonly); BUFFER_DECLARATION(dst, 2, uvec4, writeonly); BUFFER_DECLARATION(weights, 3, uint, readonly); #ifdef BIAS BUFFER_DECLARATION(biases, 4, uint, readonly); #endif /* BIAS */ #if STRIDE_X == 2 #define CONVOLVE(s, w) convolve_stride2(s, w) #elif STRIDE_X == 1 /* STRIDE_X == 1 */ #define CONVOLVE(s, w) convolve_stride1(s, w) #else /* STRIDE_X not equals 1 or 2 */ #error STRIDE_X larger than 2 is not supported #endif /* STRIDE_X == 2 */ vec4[2] convolve_stride1(Image src, float w) { uvec4 packed_s; vec4 s[2]; GC_LOAD1_2D_OFFSET(packed_s, src, 0, 0); s[0] = vec4(unpackHalf2x16(packed_s.x), unpackHalf2x16(packed_s.y)); s[1] = vec4(unpackHalf2x16(packed_s.z), unpackHalf2x16(packed_s.w)); s[0] *= w; s[1] *= w; return s; } vec4[2] convolve_stride2(Image src, float w) { uvec4 packed_s; vec4 s[2]; vec4 r[2]; GC_LOAD1_2D_OFFSET(packed_s, src, 0, 0); s[0] = vec4(unpackHalf2x16(packed_s.x), unpackHalf2x16(packed_s.y)); s[1] = vec4(unpackHalf2x16(packed_s.z), unpackHalf2x16(packed_s.w)); r[0] = vec4(s[0].xz, s[1].xz); GC_LOAD1_2D_OFFSET(packed_s, src, 8, 0); s[0] = vec4(unpackHalf2x16(packed_s.x), unpackHalf2x16(packed_s.y)); s[1] = vec4(unpackHalf2x16(packed_s.z), unpackHalf2x16(packed_s.w)); r[1] = vec4(s[0].xz, s[1].xz); r[0] *= w; r[1] *= w; return r; } /** This kernel performs a direct convolution to convolve the low three dimensions. * * @note The data type must be passed at compile time using "#define DATA_TYPE_FP16" * @note The convolution stride x must be passed at compile time using "#define STRIDE_X" e.g. "#define STRIDE_X 1" * @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 * @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 Z 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[out] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes) * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes) * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes) * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes) * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes) * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension * @param[in] weights_depth The third dimensions of the weights tensors */ void main() { Image src = GC_CONVERT_TO_IMAGE_STRUCT(src); Tensor3D weights = GC_CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights); Tensor3D dst = GC_CONVERT_TO_TENSOR3D_STRUCT(dst); #ifdef BIAS Vector biases = GC_CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); #endif /* BIAS */ vec4 pixels[2]; pixels[0] = vec4(0.f); pixels[1] = vec4(0.f); uint z_index = gl_GlobalInvocationID.z; weights.current_offset += z_index * weights_stride_w; uint packed_w; float w; for(int d = 0; d < int(weights_depth); ++d) { GC_LOAD1_3D_OFFSET(packed_w, weights, 0, 0, 0); w = unpackHalf2x16(packed_w).x; vec4 r[2] = CONVOLVE(src, w); pixels[0] += r[0]; pixels[1] += r[1]; src.current_offset += src_stride_z; weights.current_offset += weights_stride_z; } #ifdef BIAS uint packed_b; float b; GC_LOAD1_1D_OFFSET(packed_b, biases, z_index); if(z_index % uint(2) == uint(0)) { b = unpackHalf2x16(packed_b).x; } else { b = unpackHalf2x16(packed_b).y; } pixels[0] += vec4(b); pixels[1] += vec4(b); #endif /* BIAS */ uvec4 packed_d; packed_d = uvec4(packHalf2x16(pixels[0].xy), packHalf2x16(pixels[0].zw), packHalf2x16(pixels[1].xy), packHalf2x16(pixels[1].zw)); GC_STORE1_3D_OFFSET(packed_d, dst, 0, 0, 0); } #else /* DATA_TYPE_FP32 */ #error Data type not supported #endif /* DATA_TYPE_FP32 */