inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.cpp

   1 /*
   2 // Copyright (c) 2016 Intel Corporation
   3 //
   4 // Licensed under the Apache License, Version 2.0 (the "License");
   5 // you may not use this file except in compliance with the License.
   6 // You may obtain a copy of the License at
   7 //
   8 //      http://www.apache.org/licenses/LICENSE-2.0
   9 //
  10 // Unless required by applicable law or agreed to in writing, software
  11 // distributed under the License is distributed on an "AS IS" BASIS,
  12 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13 // See the License for the specific language governing permissions and
  14 // limitations under the License.
  15 */
  16
  17 #include "deconvolution_kernel_base.h"
  18 #include "kernel_selector_utils.h"
  19
  20 namespace kernel_selector
  21 {
  22     std::string deconvolution_params::to_string() const
  23     {
  24         std::stringstream s;
  25
  26         s << base_params::to_string() << "_";
  27         if (bias.empty())
  28         {
  29             s << "no_bias" << "_";
  30         }
  31         else
  32         {
  33             s << "bias_size:" << bias[0].PhysicalSize() << "_";
  34         }
  35         s << filterSize.x << "_" << filterSize.y << "_";
  36         s << stride.x << "_" << stride.y << "_";
  37         s << dilation.x << "_" << dilation.y << "_";
  38         s << padding.x << "_" << padding.y << "_";
  39         s << split;
  40
  41         return s.str();
  42     }
  43
  44     JitConstants DeconvolutionKernelBase::GetJitConstants(const deconvolution_params& dp) const
  45     {
  46         JitConstants jit = WeightBiasKernelBase::GetJitConstants(dp);
  47         const auto& padding = dp.padding;
  48         const auto& input = dp.inputs[0];
  49
  50         int64_t input_offset_with_padding = (int64_t)input.GetFirstElementOffset() - (dp.filterSize.x - 1 + padding.x)*input.X().pitch - (dp.filterSize.y - 1 + padding.y)*input.Y().pitch;
  51         input_offset_with_padding = std::max(input_offset_with_padding, (int64_t)0);
  52
  53         jit.AddConstants({
  54             MakeJitConstant("STRIDE",                       dp.stride),
  55             MakeJitConstant("PADDING",                      dp.padding),
  56             MakeJitConstant("DILATION",                     dp.dilation),
  57             MakeJitConstant("FILTER_ARRAY_NUM",             dp.split),
  58             MakeJitConstant("INPUT0_OFFSET_WITH_PADDING",   input_offset_with_padding),
  59             MakeJitConstant("DEPTHWISE_SEPARABLE_OPT",      dp.depthwise_separable_opt),
  60             MakeJitConstant("FUSED_ELTWISE",                dp.fused_eltwise),
  61             MakeJitConstant("GROUPED",                      (dp.groups > 1) ? 1 : 0)
  62         });
  63
  64         return jit;
  65     }
  66
  67     DeconvolutionKernelBase::DispatchData DeconvolutionKernelBase::SetDefault(const deconvolution_params& params) const
  68     {
  69         auto batch_size = params.output.Batch().v;
  70         auto output_features = params.output.Feature().v;
  71
  72         DispatchData kd;
  73
  74         kd.fp16UnitUsed = params.inputs[0].GetDType() == Datatype::F16;
  75         size_t gws0 = output_features * batch_size;
  76         size_t lws0 = std::min(gws0, static_cast<size_t>(32));
  77         while (gws0 % lws0)
  78         {
  79             lws0--;
  80         }
  81         kd.gws0 = gws0;
  82         kd.gws1 = params.output.X().v;
  83         kd.gws2 = params.output.Y().v;
  84         kd.lws0 = lws0;
  85         kd.lws1 = 1;
  86         kd.lws2 = 1;
  87         kd.effiency = DONT_USE_IF_HAVE_SOMETHING_ELSE;
  88         return kd;
  89     }
  90
  91     KernelsData DeconvolutionKernelBase::GetKernelsData(const Params& params, const optional_params& options) const
  92     {
  93         assert(params.GetType() == KernelType::DECONVOLUTION);
  94
  95         const deconvolution_params& orgParams = static_cast<const deconvolution_params&>(params);
  96
  97         const std::vector<WeightsLayout> weightsLayouts = {
  98             WeightsLayout::oiyx,
  99             WeightsLayout::iyxo,
 100             WeightsLayout::yxio,
 101             WeightsLayout::oyxi
 102         };
 103
 104         DispatchData runInfo = SetDefault(orgParams);
 105         KernelData kd = KernelData::Default<deconvolution_params>(params);
 106         deconvolution_params& newParams = *static_cast<deconvolution_params*>(kd.params.get());
 107
 108         bool succeed = UpdateWeightsParams(
 109             newParams,
 110             options,
 111             weightsLayouts,
 112             kd.weightsReorderParams);
 113
 114         if (!succeed)
 115         {
 116             return{};
 117         }
 118
 119         auto cldnn_jit = GetJitConstants(newParams);
 120         auto entry_point = GetEntryPoint(kernelName, newParams.layerID, options);
 121         auto jit = CreateJit(kernelName, cldnn_jit, entry_point);
 122
 123         auto& kernel = kd.kernels[0];
 124         FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point, DEFAULT, true, !newParams.bias.empty());
 125         kernel.arguments.push_back({ ArgumentDescriptor::Types::SPLIT, 0 });
 126         if (orgParams.fused_eltwise)
 127             kernel.arguments.push_back({ ArgumentDescriptor::Types::INPUT, 1 });
 128
 129         kd.estimatedTime = runInfo.effiency;
 130
 131         return{ kd };
 132     }
 133 }