inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_mmad_batched_block.cpp

   1 /*
   2 // Copyright (c) 2018 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 "convolution_kernel_mmad_batched_block.h"
  18 #include "kernel_selector_utils.h"
  19
  20 namespace kernel_selector {
  21
  22     ParamsKey ConvolutionKernel_mmad_batched_block::GetSupportedKey() const
  23     {
  24         ParamsKey k;
  25         k.EnableInputDataType(Datatype::INT8);
  26         k.EnableOutputDataType(Datatype::INT8);
  27         k.EnableInputWeightsType(WeightsType::INT8);
  28         k.EnableInputLayout(DataLayout::fs_bs_yx_bsv4_fsv32);
  29         k.EnableOutputLayout(DataLayout::fs_bs_yx_bsv4_fsv32);
  30         k.EnableTensorOffset();
  31         k.EnableTensorPitches();
  32         k.EnableBiasPerFeature();
  33         k.EnableBatching();
  34         k.EnableInt8Quantization();
  35         k.EnableOutputCalibration();
  36         k.DisableTuning();
  37         return k;
  38     }
  39
  40     struct block_params
  41     {
  42         int32_t out_width;
  43         int32_t out_height;
  44         int32_t out_depth;
  45     };
  46
  47     static block_params get_out_block_size(const convolution_params& p)
  48     {
  49         if (p.filterSize.x == 3 && p.filterSize.y == 3)
  50         {
  51             if (p.output.X().v == 7)
  52                 return { 7, 1, 4 };
  53             else if (p.output.X().v == 14)
  54                 return { 7, 1, 4 };
  55             else if (p.output.X().v == 28)
  56                 return { 7, 1, 4 };
  57             else if (p.output.X().v == 56)
  58                 return { 8, 1, 4 };
  59         }
  60
  61         return { 1,1,1 };
  62     }
  63
  64     std::vector<WeightsLayout> ConvolutionKernel_mmad_batched_block::GetSupportedWeightLayouts(const convolution_params& cp) const
  65     {
  66         auto block = get_out_block_size(cp);
  67         if (block.out_depth == 4)
  68             return { WeightsLayout::os_is_yx_isa8_osv8_isv4_swizzled_by_4 };
  69         else
  70             return { WeightsLayout::os_is_yx_isa8_osv8_isv4 };
  71     }
  72
  73     bool ConvolutionKernel_mmad_batched_block::Validate(const Params& p, const optional_params& o) const
  74     {
  75         if (!ConvolutionKernelBase::Validate(p, o) ||
  76             !CovolutionCheckInput(p, o))
  77         {
  78             return false;
  79         }
  80         const convolution_params& cp = static_cast<const convolution_params&>(p);
  81
  82         // if block sizes are 1x1, then this algorithm is probably not the best
  83         auto block = get_out_block_size(cp);
  84         if (block.out_width == 1 && block.out_height == 1)
  85             return false;
  86
  87         if (cp.output.X().v % block.out_width != 0)
  88             return false;
  89         if (cp.output.Y().v % block.out_height != 0)
  90             return false;
  91
  92         if (cp.filterSize.x == 1)
  93             return false;
  94
  95         return true;
  96     }
  97
  98     ConvolutionKernelBase::DispatchData ConvolutionKernel_mmad_batched_block::SetDefault(const convolution_params& arg, int) const
  99     {
 100         DispatchData runInfo = ConvolutionKernelBase::SetDefault(arg);
 101
 102         constexpr size_t sub_group_size = 8;
 103
 104         runInfo.effiency = FORCE_PRIORITY_5;
 105
 106         auto block = get_out_block_size(arg);
 107
 108         runInfo.gws0 = arg.output.X().v / block.out_width;
 109         runInfo.gws1 = arg.output.Y().v / block.out_height;
 110         runInfo.gws2 = (arg.output.Feature().v) * ((arg.output.Batch().v+3) / 4) / block.out_depth; // process 4 output channels per Workitem
 111
 112         runInfo.lws0 = 1;
 113         runInfo.lws1 = 1;
 114         runInfo.lws2 = sub_group_size;
 115
 116         return runInfo;
 117     }
 118
 119     JitConstants ConvolutionKernel_mmad_batched_block::GetJitConstants(const convolution_params& params, const DispatchData& runInfo) const
 120     {
 121         auto jit = Parent::GetJitConstants(params, runInfo);
 122
 123         jit.AddConstant(MakeJitConstant("SUB_GROUP_SIZE", runInfo.lws2));
 124
 125         // pitch for special block format used in this kernel
 126         const size_t ifm_32_aligned = Align(params.weights.IFM().v, 32);
 127         const size_t filter_ofm_block_pitch = (ifm_32_aligned / 32) * params.weights.X().v * params.weights.Y().v * 4 * 8 * 8;
 128         jit.AddConstant(MakeJitConstant("FILTER_OFM_BLOCK_PITCH", filter_ofm_block_pitch));
 129
 130         const size_t in_x_pitch = 32 * 4;
 131         const size_t in_y_pitch = 32 * 4 * params.inputs[0].X().LogicalDimPadded();
 132         const size_t in_b_block_pitch = in_y_pitch * params.inputs[0].Y().LogicalDimPadded();
 133         const size_t in_f_block_pitch = in_b_block_pitch * ((params.inputs[0].Batch().v + 3) / 4);
 134         const size_t in_offset = in_x_pitch * params.inputs[0].X().pad.before + in_y_pitch * params.inputs[0].Y().pad.before;
 135
 136         jit.AddConstant(MakeJitConstant("IN_X_PITCH", in_x_pitch));
 137         jit.AddConstant(MakeJitConstant("IN_Y_PITCH", in_y_pitch));
 138         jit.AddConstant(MakeJitConstant("IN_B_BLOCK_PITCH", in_b_block_pitch));
 139         jit.AddConstant(MakeJitConstant("IN_F_BLOCK_PITCH", in_f_block_pitch));
 140         jit.AddConstant(MakeJitConstant("IN_OFFSET", in_offset));
 141
 142         auto block = get_out_block_size(params);
 143         jit.AddConstant(MakeJitConstant("OUT_BLOCK_WIDTH", block.out_width));
 144         jit.AddConstant(MakeJitConstant("OUT_BLOCK_HEIGHT", block.out_height));
 145         jit.AddConstant(MakeJitConstant("WEIGHTS_PER_WORKITEM", block.out_depth));
 146
 147         return jit;
 148     }
 149
 150     KernelsData ConvolutionKernel_mmad_batched_block::GetKernelsData(const Params& params, const optional_params& options) const
 151     {
 152         KernelsData kd = GetCommonKernelsData(params, options);
 153         if(!kd.empty())
 154             kd[0].estimatedTime = FORCE_PRIORITY_5;
 155         return kd;
 156     }
 157 }