/* * Copyright (c) 2017-2021 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "helpers.h" #define ADD_OP(a, b) ((a) + (b)) #define SUB_OP(a, b) ((a) - (b)) #define MUL_OP(a, b) ((a) * (b)) #define INVSQRT_OP(a) rsqrt((a)) #define SQCVT_SAT(a) (a) #if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(ACTIVATION_TYPE) #include "activation_float_helpers.h" /** Apply batch normalization. * * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively * * @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32 * @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes) * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes) * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] input_stride_z Stride of the first source tensor in Z dimension (in bytes) * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] input_offset_first_element_in_bytes The offset of the first element in the first source tensor * @param[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor * @param[in] mean_ptr Pointer to the mean source tensor. Supported data types: same as @p input_ptr * @param[in] mean_stride_x Stride of the mean source tensor in X dimension (in bytes) * @param[in] mean_step_x mean_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] mean_offset_first_element_in_bytes The offset of the first element in the mean source tensor * @param[in] var_ptr Pointer to the var tensor. Supported data types: same as @p input_ptr * @param[in] var_stride_x Stride of the var tensor in X dimension (in bytes) * @param[in] var_step_x var_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] var_offset_first_element_in_bytes The offset of the first element in the var source tensor * @param[in] beta_ptr Pointer to the beta source tensor. Supported data types: same as @p input_ptr * @param[in] beta_stride_x Stride of the beta source tensor in X dimension (in bytes) * @param[in] beta_step_x beta_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] beta_offset_first_element_in_bytes The offset of the first element in the beta source tensor * @param[in] gamma_ptr Pointer to the gamma source tensor. Supported data types: same as @p input_ptr * @param[in] gamma_stride_x Stride of the gamma source tensor in X dimension (in bytes) * @param[in] gamma_step_x gamma_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] gamma_offset_first_element_in_bytes The offset of the first element in the gamma source tensor * @param[in] epsilon Epsilon parameter in the batch normalization equation */ __kernel void batchnormalization_layer_nchw(TENSOR3D_DECLARATION(input), #ifndef IN_PLACE TENSOR3D_DECLARATION(output), #endif /* not IN_PLACE */ VECTOR_DECLARATION(mean), VECTOR_DECLARATION(var), #ifndef USE_DEFAULT_BETA VECTOR_DECLARATION(beta), #endif /* USE_DEFAULT_BETA */ #ifndef USE_DEFAULT_GAMMA VECTOR_DECLARATION(gamma), #endif /* USE_DEFAULT_GAMMA */ float epsilon) { Tensor3D in = CONVERT_TO_TENSOR3D_STRUCT(input); #ifdef IN_PLACE Tensor3D out = in; #else /* IN_PLACE */ Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(output); #endif /* IN_PLACE */ Vector mean = CONVERT_TO_VECTOR_STRUCT(mean); Vector var = CONVERT_TO_VECTOR_STRUCT(var); #ifndef USE_DEFAULT_BETA Vector beta = CONVERT_TO_VECTOR_STRUCT(beta); #endif /* USE_DEFAULT_BETA */ #ifndef USE_DEFAULT_GAMMA Vector gamma = CONVERT_TO_VECTOR_STRUCT(gamma); #endif /* USE_DEFAULT_GAMMA */ VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) data = 0; VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) denominator = 0; VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) numerator = 0; VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) x_bar = 0; VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) res = 0; const int current_slice = get_global_id(2); data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)in.ptr); denominator = *((__global DATA_TYPE *)(var.ptr + current_slice * var.stride_x)); denominator = INVSQRT_OP(ADD_OP(denominator, ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))SQCVT_SAT(epsilon)))); // Calculate x bar and store results numerator = *((__global DATA_TYPE *)(mean.ptr + current_slice * mean.stride_x)); numerator = SUB_OP(data, numerator); x_bar = MUL_OP(numerator, denominator); #ifndef USE_DEFAULT_GAMMA VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) gamma_vec = *((__global DATA_TYPE *)(gamma.ptr + current_slice * gamma.stride_x)); res = MUL_OP(gamma_vec, x_bar); #else /* USE_DEFAULT_GAMMA */ // gamma is equal to 1, no need to perform multiplications res = x_bar; #endif /* USE_DEFAULT_GAMMA */ #ifndef USE_DEFAULT_BETA VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) beta_vec = *((__global DATA_TYPE *)(beta.ptr + current_slice * beta.stride_x)); // beta is not zero, hence we need to perform the addition res = ADD_OP(res, beta_vec); #endif /* USE_DEFAULT_BETA */ res = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, VEC_SIZE, res, A_VAL, B_VAL); VSTORE(VEC_SIZE) (res, 0, (__global DATA_TYPE *)out.ptr); } #endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(DATA_TYPE)*/