From 5a7e776eee2e9147eab12631f5717847fb6cac5c Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Fri, 1 Dec 2017 16:27:29 +0000 Subject: COMPMID-556: Rename CPP folder to reference Change-Id: I147644349547c4e3804a80b564a9ad95131ad2d0 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/111560 Reviewed-by: Michalis Spyrou Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com --- tests/validation/reference/FullyConnectedLayer.cpp | 201 +++++++++++++++++++++ 1 file changed, 201 insertions(+) create mode 100644 tests/validation/reference/FullyConnectedLayer.cpp (limited to 'tests/validation/reference/FullyConnectedLayer.cpp') diff --git a/tests/validation/reference/FullyConnectedLayer.cpp b/tests/validation/reference/FullyConnectedLayer.cpp new file mode 100644 index 0000000000..c24881e2b4 --- /dev/null +++ b/tests/validation/reference/FullyConnectedLayer.cpp @@ -0,0 +1,201 @@ +/* + * 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. + */ +#include "FullyConnectedLayer.h" + +#include "arm_compute/core/Types.h" +#include "tests/validation/FixedPoint.h" +#include "tests/validation/reference/UtilsQuantizedAsymm.h" + +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" + +#include + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +namespace +{ +// Vector matrix multiply for floating point +template < typename T, typename TB, typename std::enable_if < is_floating_point::value &&is_floating_point::value, int >::type = 0 > +void vector_matrix_multiply(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, SimpleTensor &dst, int offset_src, int offset_dst, int cols_weights, + int rows_weights, uint8_t fixed_point_position) +{ + ARM_COMPUTE_UNUSED(fixed_point_position); + + const T *src_ptr = src.data() + offset_src; + const T *weights_ptr = weights.data(); + const TB *bias_ptr = bias.data(); + T *dst_ptr = dst.data() + offset_dst; + + for(int y = 0; y < rows_weights; ++y) + { + dst_ptr[y] = std::inner_product(src_ptr, src_ptr + cols_weights, weights_ptr, static_cast(0)) + bias_ptr[y]; + weights_ptr += cols_weights; + } +} + +// Vector matrix multiply for fixed point type +template < typename T, typename TB, typename std::enable_if < std::is_integral::value &&std::is_integral::value, int >::type = 0 > +void vector_matrix_multiply(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, SimpleTensor &dst, int offset_src, int offset_dst, int cols_weights, + int rows_weights, uint8_t fixed_point_position) +{ + const T *src_ptr = src.data() + offset_src; + const T *weights_ptr = weights.data(); + const TB *bias_ptr = bias.data(); + T *dst_ptr = dst.data() + offset_dst; + + using namespace fixed_point_arithmetic; + using promoted_type = fixed_point_arithmetic::traits::promote_t; + + for(int y = 0; y < rows_weights; ++y) + { + // Reset accumulator + fixed_point acc(0, fixed_point_position); + + for(int x = 0; x < cols_weights; ++x) + { + const fixed_point i_value(src_ptr[x], fixed_point_position, true); + const fixed_point w_value(weights_ptr[x], fixed_point_position, true); + acc = acc + i_value * w_value; + } + + // Get the bias + const fixed_point b(bias_ptr[y], fixed_point_position, true); + + // Convert back and accumulate the bias + fixed_point res(acc); + res = res + b; + + // Store the result + dst_ptr[y] = res.raw(); + + weights_ptr += cols_weights; + } +} + +// Vector matrix multiply for quantized type +template <> +void vector_matrix_multiply(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, SimpleTensor &dst, int offset_src, int offset_dst, + int cols_weights, int rows_weights, uint8_t fixed_point_position) +{ + ARM_COMPUTE_UNUSED(fixed_point_position); + + const uint8_t *src_ptr = src.data() + offset_src; + const uint8_t *weights_ptr = weights.data(); + const int32_t *bias_ptr = bias.data(); + uint8_t *dst_ptr = dst.data() + offset_dst; + + const int input_offset = -src.quantization_info().offset; + const float input_scale = src.quantization_info().scale; + const int weights_offset = -weights.quantization_info().offset; + const float weights_scale = weights.quantization_info().scale; + const int output_offset = dst.quantization_info().offset; + const float output_scale = dst.quantization_info().scale; + + int output_multiplier = 0; + int output_shift = 0; + const float multiplier = input_scale * weights_scale / output_scale; + arm_compute::quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + + for(int y = 0; y < rows_weights; ++y) + { + // Reset accumulator + int32_t acc = 0; + + for(int x = 0; x < cols_weights; ++x) + { + acc += (src_ptr[x] + input_offset) * (weights_ptr[x] + weights_offset); + } + + // Accumulate the bias + acc += bias_ptr[y]; + + acc = asymm_rounding_divide_by_pow2(asymm_int_mult(acc, output_multiplier), output_shift); + acc += output_offset; + acc = clamp(acc, 0, 255); + + // Store the result + dst_ptr[y] = static_cast(acc); + + weights_ptr += cols_weights; + } +} +} // namespace + +template +SimpleTensor fully_connected_layer(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, const TensorShape &dst_shape) +{ + // Create reference + SimpleTensor dst{ TensorShape{ dst_shape }, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() }; + + // Sanity checks + const int num_batch_dimensions = std::max(0, static_cast(dst_shape.num_dimensions()) - 1); + const int num_input_dimensions = src.shape().num_dimensions() - num_batch_dimensions; + const unsigned int linear_input_size = src.shape().total_size_lower(num_input_dimensions); + + ARM_COMPUTE_UNUSED(num_batch_dimensions); + ARM_COMPUTE_UNUSED(num_input_dimensions); + ARM_COMPUTE_UNUSED(linear_input_size); + ARM_COMPUTE_ERROR_ON(weights.shape().x() != linear_input_size); + ARM_COMPUTE_ERROR_ON(weights.shape().y() != bias.shape().x()); + ARM_COMPUTE_ERROR_ON(weights.shape().y() != dst.shape().x()); + + // Compute reference + const int cols_weights = weights.shape().x(); + const int rows_weights = weights.shape().y(); + const int num_batches = dst_shape.total_size_upper(1); + + for(int k = 0; k < num_batches; ++k) + { + const int offset_in = k * cols_weights; + const int offset_out = k * rows_weights; + + vector_matrix_multiply(src, + weights, + bias, + dst, + offset_in, + offset_out, + cols_weights, + rows_weights, + src.fixed_point_position()); + } + + return dst; +} + +template SimpleTensor fully_connected_layer(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, const TensorShape &dst_shape); +template SimpleTensor fully_connected_layer(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, const TensorShape &dst_shape); +template SimpleTensor fully_connected_layer(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, const TensorShape &dst_shape); +template SimpleTensor fully_connected_layer(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, const TensorShape &dst_shape); +template SimpleTensor fully_connected_layer(const SimpleTensor &src, const SimpleTensor &weights, const SimpleTensor &bias, const TensorShape &dst_shape); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute -- cgit v1.2.1