/*
 * 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.
 */
#include "arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h"

#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/PixelValue.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#include "support/ToolchainSupport.h"

using namespace arm_compute;
using namespace arm_compute::misc;
using namespace arm_compute::misc::shape_calculator;

CLDepthwiseConvolutionLayer3x3::CLDepthwiseConvolutionLayer3x3()
    : _kernel(), _border_handler()
{
}

void CLDepthwiseConvolutionLayer3x3::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, ActivationLayerInfo act_info)
{
    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);

    _kernel.set_target(CLScheduler::get().target());
    _kernel.configure(input, weights, biases, output, conv_info, act_info);

    // Configure border handler
    PixelValue &&zero_value(0.f);
    if(is_data_type_quantized_asymmetric(input->info()->data_type()))
    {
        zero_value = PixelValue(static_cast<uint8_t>(input->info()->quantization_info().offset));
    }
    _border_handler.configure(input, _kernel.border_size(), BorderMode::CONSTANT, zero_value);
}

void CLDepthwiseConvolutionLayer3x3::run()
{
    CLScheduler::get().enqueue(_border_handler);
    CLScheduler::get().enqueue(_kernel);
}

CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer()
    : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _vector_to_tensor_kernel(), _output_stage_kernel(), _v2mm_input_fill_border(), _v2mm_weights_fill_border(), _input_reshaped(),
      _weights_reshaped(), _v2mm_output(), _output_reshaped(), _is_first_run(true), _is_quantized(false), _original_weights(nullptr)
{
}

void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info)
{
    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32);
    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
    ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != weights->info()->dimension(2));

    const size_t weights_w = weights->info()->dimension(0);
    const size_t weights_h = weights->info()->dimension(1);
    const size_t weights_z = weights->info()->dimension(2);

    _is_first_run     = true;
    _original_weights = weights;
    _is_quantized     = is_data_type_quantized_asymmetric(input->info()->data_type());

    bool            append_bias = (biases != nullptr) && !_is_quantized;
    const GPUTarget gpu_target  = CLScheduler::get().target();

    // Calculate output shape
    TensorShape dwc_output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info);

    // Output width and height
    const unsigned int conv_w = dwc_output_shape.x();
    const unsigned int conv_h = dwc_output_shape.y();

    // Set up intermediate tensors
    const size_t patch_size = weights_w * weights_h + ((append_bias) ? 1 : 0);
    const size_t conv_size  = conv_w * conv_h;

    // Im2Col configuration
    TensorShape shape_im2col = input->info()->tensor_shape();
    shape_im2col.set(0, patch_size);
    shape_im2col.set(1, conv_size);
    shape_im2col.set(2, weights_z);
    _input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
    _im2col_kernel.set_target(gpu_target);
    _im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias);

    // Weights reshape configuration
    const TensorShape shape_weights_reshape(patch_size, weights_z);
    _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape));
    _weights_reshape_kernel.configure(weights, &_weights_reshaped, append_bias ? biases : nullptr);

    // GEMV configuration
    DataType    v2mm_dt        = (input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : input->info()->data_type();
    TensorShape shape_v2mm_out = input->info()->tensor_shape();
    shape_v2mm_out.set(0, conv_size * weights_z);
    shape_v2mm_out.set(1, 1);
    shape_v2mm_out.set(2, 1);
    _v2mm_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out));
    _v2mm_kernel.set_target(gpu_target);
    _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output);
    _output_reshaped.allocator()->init(_v2mm_output.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(dwc_output_shape));
    _vector_to_tensor_kernel.configure(&_v2mm_output, (_is_quantized) ? &_output_reshaped : output, conv_w, conv_h);

    // Output staged configuration
    if(_is_quantized)
    {
        const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();

        float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
        int   output_multiplier, output_shift;
        quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
        _output_stage_kernel.configure(&_output_reshaped, biases, output, output_multiplier, output_shift, output_quant_info.offset);
        _output_reshaped.allocator()->allocate();
    }

    // Fill borders on inputs
    PixelValue zero_in(static_cast<int32_t>(0));
    PixelValue zero_w(static_cast<int32_t>(0));
    if(_is_quantized)
    {
        zero_in = PixelValue(static_cast<int32_t>(input->info()->quantization_info().offset));
        zero_w  = PixelValue(static_cast<int32_t>(weights->info()->quantization_info().offset));
    }
    BorderSize border_size = _v2mm_kernel.border_size();
    _v2mm_input_fill_border.configure(&_input_reshaped, border_size, BorderMode::CONSTANT, zero_in);

    border_size.bottom = 0;
    _v2mm_weights_fill_border.configure(&_weights_reshaped, border_size, BorderMode::CONSTANT, zero_w);

    // Allocate intermediate tensors
    _input_reshaped.allocator()->allocate();
    _weights_reshaped.allocator()->allocate();
    _v2mm_output.allocator()->allocate();
}

void CLDepthwiseConvolutionLayer::run()
{
    // Run weights reshaping (Runs once for every configure)
    if(_is_first_run)
    {
        ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());

        CLScheduler::get().enqueue(_weights_reshape_kernel);
        CLScheduler::get().enqueue(_v2mm_weights_fill_border);
        _is_first_run = false;

        // Mark original weights tensor as unused
        _original_weights->mark_as_unused();
    }

    CLScheduler::get().enqueue(_im2col_kernel);
    CLScheduler::get().enqueue(_v2mm_input_fill_border);
    CLScheduler::get().enqueue(_v2mm_kernel);
    CLScheduler::get().enqueue(_vector_to_tensor_kernel);
    if(_is_quantized)
    {
        CLScheduler::get().enqueue(_output_stage_kernel);
    }
}