inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/fused_conv_eltwise/fused_conv_eltwise_kernel_yxfb_yxio_b16.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 "fused_conv_eltwise_kernel_yxfb_yxio_b16.h"
  18
  19 namespace kernel_selector
  20 {
  21
  22     ParamsKey fused_conv_eltwise_kernel_yxfb_yxio_b16::GetSupportedKey() const
  23     {
  24         ParamsKey k;
  25         k.EnableInputDataType(Datatype::F16);
  26         k.EnableInputWeightsType(WeightsType::F16);
  27         k.EnableInputWeightsType(WeightsType::F32);
  28         k.EnableOutputDataType(Datatype::F16);
  29         k.EnableInputLayout(DataLayout::yxfb);
  30         k.EnableOutputLayout(DataLayout::yxfb);
  31         k.EnableTensorOffset();
  32         k.EnableTensorPitches();
  33         k.EnableBiasPerFeature();
  34         k.EnableNonBiasTerm();
  35         k.EnableBatching();
  36         k.EnableSplitSupport();
  37         k.EnableDilation();
  38         k.EnableSubGroup();
  39         k.EnableFusedConvEltwiseRWOutOpt();
  40         return k;
  41     }
  42
  43     std::string fused_conv_eltwise_kernel_yxfb_yxio_b16::GetKernelName(const fused_conv_eltwise_params& params) const
  44     {
  45         if (params.inputs[0].GetDType() == Datatype::F32)
  46         {
  47             return kernelName + "_fp32";
  48         }
  49         else
  50         {
  51             return kernelName + "_fp16";
  52         }
  53     }
  54
  55     namespace {
  56         // how many batches will a single work item compute
  57         size_t GetBatchesPerWorkItem(size_t batch_size, Datatype dataType)
  58         {
  59             if (dataType == Datatype::F16)
  60             {
  61                 const uint32_t min_batches_per_wi = 1;
  62                 const uint32_t min_lws = 16;
  63
  64                 if (batch_size % (4 * min_batches_per_wi * min_lws) == 0)
  65                 {
  66                     return 4 * min_batches_per_wi; // USE_BLOCK_READ_2 + as_half4
  67                 }
  68                 else if (batch_size % (2 * min_batches_per_wi * min_lws) == 0)
  69                 {
  70                     return 2 * min_batches_per_wi; // USE_BLOCK_READ_1 + as_half2
  71                 }
  72                 else
  73                 {
  74                     return min_batches_per_wi;
  75                 }
  76             }
  77             else
  78             {
  79                 return 2;
  80             }
  81         }
  82
  83         size_t GetOfmPerWorkitem(Datatype dataType)
  84         {
  85             if (dataType == Datatype::F16)
  86                 return 16;
  87             return 8;
  88         }
  89     }
  90
  91     fused_conv_eltwise_kernel_base::DispatchData fused_conv_eltwise_kernel_yxfb_yxio_b16::SetDefault(const fused_conv_eltwise_params& arg, int) const
  92     {
  93         DispatchData runInfo = fused_conv_eltwise_kernel_base::SetDefault(arg);
  94
  95         const auto filter_ofm_num = arg.weights.OFM().v;
  96         const auto batch_size = arg.output.Batch().v;
  97         const uint32_t min_lws = 16;
  98
  99         const size_t batchesPerWorkItem = GetBatchesPerWorkItem(batch_size, arg.inputs[0].GetDType());
 100         const size_t ofmPerWorkItem = GetOfmPerWorkitem(arg.inputs[0].GetDType());
 101
 102         if (arg.inputs[0].GetDType() == Datatype::F16)
 103         {
 104             runInfo.effiency = FORCE_PRIORITY_7;
 105         }
 106         else
 107         {
 108             runInfo.effiency = FORCE_PRIORITY_9;
 109         }
 110
 111         runInfo.lws0 = min_lws;
 112         runInfo.gws0 = filter_ofm_num * batch_size / (ofmPerWorkItem * batchesPerWorkItem);
 113
 114         return runInfo;
 115     }
 116
 117     bool fused_conv_eltwise_kernel_yxfb_yxio_b16::Validate(const Params& p, const optional_params& o) const
 118     {
 119         if (!fused_conv_eltwise_kernel_base::Validate(p, o))
 120         {
 121             return false;
 122         }
 123         const convolution_params& params = static_cast<const convolution_params&>(p);
 124
 125         const auto filter_ofm_num = params.weights.OFM().v;
 126         const auto batch_size = params.output.Batch().v;
 127         const uint32_t min_lws = 16;
 128
 129         const bool bInputValidated =
 130             (filter_ofm_num > 0) &&
 131             (batch_size > 0) &&
 132             (params.output.Feature().v == filter_ofm_num);
 133
 134         if (!bInputValidated)
 135         {
 136             return false;
 137         }
 138
 139         if (params.inputs[0].GetDType() == Datatype::F16)
 140         {
 141             const uint32_t min_ofm_per_wi = 16;
 142             const uint32_t min_batches_per_wi = 1;
 143
 144             const bool bFilterOK = filter_ofm_num % min_ofm_per_wi == 0;            // Number of output features dividable by minimum number of output features processed inside work item.
 145             const bool bBatchOK = batch_size % (min_batches_per_wi * min_lws) == 0; // Batch size dividable by minimum number of batches processed when smallest local work size is used.
 146
 147             if (!bFilterOK || !bBatchOK)
 148             {
 149                 return false;
 150             }
 151         }
 152         else
 153         {
 154             if ((filter_ofm_num * batch_size) % min_lws != 0 ||
 155                 batch_size < 32) // TODO: check why it's not supported
 156             {
 157                 return false;
 158             }
 159         }
 160
 161         return true;
 162     }
 163
 164     JitConstants fused_conv_eltwise_kernel_yxfb_yxio_b16::GetJitConstants(const fused_conv_eltwise_params& params, const DispatchData& kd) const
 165     {
 166         auto jit = Parent::GetJitConstants(params, kd);
 167
 168         const auto local_work_group_size = kd.lws0;
 169         const auto batch_size = params.output.Batch().v;
 170
 171         if (params.inputs[0].GetDType() == Datatype::F32)
 172         {
 173             // A LITTLE HACK, for convolutions with low number of input features don't use block reads, and it will speed up by 25%
 174             // TODO - investigate why is this happening
 175             if (params.inputs[0].Feature().v > 4)
 176             {
 177                 jit.AddConstant(MakeJitConstant("USE_BLOCK_READ_2", ""));
 178             }
 179         }
 180         else
 181         {
 182             const auto batch_pad_before = params.output.Batch().pad.before;
 183             const auto feature_pitch = params.output.Feature().pitch;
 184
 185             if (batch_size >= 64 && (feature_pitch % 2 == 0) && (batch_pad_before % 2 == 0))
 186             {
 187                 jit.AddConstant(MakeJitConstant("USE_BLOCK_READ_2", ""));
 188             }
 189             else if (batch_size >= 32 && (feature_pitch % 2 == 0) && (batch_pad_before % 2 == 0))
 190             {
 191                 jit.AddConstant(MakeJitConstant("USE_BLOCK_READ_1", ""));
 192             }
 193         }
 194
 195         const size_t batchesPerWorkItem = GetBatchesPerWorkItem(batch_size, params.inputs[0].GetDType());
 196         const size_t ofmPerWorkItem = GetOfmPerWorkitem(params.inputs[0].GetDType());
 197
 198         jit.AddConstants({
 199             MakeJitConstant("LOCAL_WORK_GROUP_SIZE", kd.lws0),
 200             MakeJitConstant("OFM_PER_WORK_ITEM", ofmPerWorkItem),
 201             MakeJitConstant("BATCHES_PER_WORK_ITEM",                            batchesPerWorkItem), // how many batches will a single work item compute
 202             MakeJitConstant("LOCAL_WORK_GROUPS_PER_SINGLE_BATCHES_ELEMENTS",    std::max(batch_size / batchesPerWorkItem / local_work_group_size, static_cast<size_t>(1))), // how many local work groups we need to compute single element for each batch
 203             MakeJitConstant("WORK_ITEMS_PER_SINGLE_BATCHES_ELEMENTS", batch_size / batchesPerWorkItem), // how many work items we need to compute single element for each batch
 204         });
 205
 206         if (!params.eltw.stride.empty())
 207         {
 208             jit.AddConstant(MakeJitConstant("ELTW_STRIDE_X", params.eltw.stride[0].x));
 209             jit.AddConstant(MakeJitConstant("ELTW_STRIDE_Y", params.eltw.stride[0].y));
 210         }
 211         else
 212         {
 213             jit.AddConstant(MakeJitConstant("ELTW_STRIDE_X", 1));
 214             jit.AddConstant(MakeJitConstant("ELTW_STRIDE_Y", 1));
 215         }
 216
 217         return jit;
 218     }
 219
 220     KernelsData fused_conv_eltwise_kernel_yxfb_yxio_b16::GetKernelsData(const Params& params, const optional_params& options) const
 221     {
 222         return GetTunedKernelsDataByIndex(params, options);
 223     }
 224 }