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[IE CLDNN] WA to use bfyx format if applicable for bf(w)zyx Gather input (#1056)

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Jedrzej Hajduczenia 2020-07-14 17:00:51 +02:00 committed by GitHub
parent 92a38b305f
commit 0607b7b0f7
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1 changed files with 121 additions and 8 deletions
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129
inference-engine/src/cldnn_engine/cldnn_program.cpp
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@ -3755,6 +3755,8 @@ void Program::CreateGatherPrimitive(cldnn::topology& topology, InferenceEngine::
}
};
auto gatherLayerName = layer_type_name_ID(layer);
std::vector<cldnn::primitive_id> reorderedInputs;
reorderedInputs.resize(inputPrimitives.size());
@ -3771,23 +3773,134 @@ void Program::CreateGatherPrimitive(cldnn::topology& topology, InferenceEngine::
targetFormat,
cldnn::data_types::i32);
topology.add(preprocessPrim);
AddInnerPrimitiveToProfiler(reorderPrimName, layer_type_name_ID(layer), layer);
reorderedInputs[portIndex] = (reorderPrimName);
AddInnerPrimitiveToProfiler(reorderPrimName, gatherLayerName, layer);
reorderedInputs[portIndex] = reorderPrimName;
} else {
reorderedInputs[portIndex] = inputPrimitives[portIndex];
}
}
std::string gatherLayerName = layer_type_name_ID(layer);
auto indicesDims = layer->insData[1].lock()->getTensorDesc().getDims();
auto indicesLayout = layer->insData[1].lock()->getTensorDesc().getLayout();
auto indicesFormat = FormatFromLayout(indicesLayout);
auto inputDims = layer->insData[0].lock()->getTensorDesc().getDims();
auto inputLayout = layer->insData[0].lock()->getTensorDesc().getLayout();
auto inputFormat = FormatFromLayout(inputLayout);
auto outDimsOriginal = layer->outData[0]->getTensorDesc().getDims();
auto outputLayoutOriginal = layer->outData[0]->getTensorDesc().getLayout();
auto outputFormatOriginal = FormatFromLayout(outputLayoutOriginal);
auto outDims = outDimsOriginal;
auto targetDatatype = DataTypeFromPrecision(layer->precision);
auto nonNegativeAxis = (axis >= 0) ? axis : axis + 3;
// following vector is needed just to check if we can apply bfyx WA
SizeVector originalRequiredDims;
for (size_t d = 0; d < inputDims.size(); d++) {
if ((d == nonNegativeAxis) || (inputDims[d] > 1)) {
originalRequiredDims.push_back(d);
}
}
if (originalRequiredDims.size() < 4) {
// make sure that we will have at least 4 required dimensions
auto originalAxesIt = originalRequiredDims.begin();
for (size_t i = 0; i < 4; i++) {
int dimFoundAtIndex = -1;
for (size_t j = 0; j < originalRequiredDims.size(); j++) {
if (originalRequiredDims[j] == i) {
dimFoundAtIndex = j;
}
}
if (dimFoundAtIndex == -1) {
originalAxesIt = originalRequiredDims.insert(originalAxesIt, i);
}
originalAxesIt++;
}
}
// clDNN primitive is missing proper support of 5d/6d inputs
// but we can still fall back to bfyx format in some cases
bool bfyx_wa = ((inputFormat == cldnn::format::bfzyx || inputFormat == cldnn::format::bfwzyx) &&
(originalRequiredDims.size() == 4) &&
(indicesFormat == cldnn::format::bfyx));
if (bfyx_wa) {
if (indicesDims.size() > 1) {
// reshape the indices dims to 1D (along batch axis)
size_t indDimAcc = std::accumulate(indicesDims.begin(), indicesDims.end(), 1, std::multiplies<size_t>());
SizeVector targetIndDims{ indDimAcc, 1, 1, 1 };
auto reshapeName = reorderedInputs[1] + "_" + layer->name + "_reshape";
auto targetTensor = CldnnTensorFromIEDims(targetIndDims);
auto reshapePrim = cldnn::reshape(reshapeName, reorderedInputs[1], CldnnTensorFromIEDims(targetIndDims));
topology.add(reshapePrim);
AddInnerPrimitiveToProfiler(reshapeName, gatherLayerName, layer);
reorderedInputs[1] = reshapeName;
// adjust expected output dims
outDims[nonNegativeAxis] = indDimAcc;
outDims.erase(outDims.begin() + nonNegativeAxis + 1, outDims.begin() + nonNegativeAxis + indicesDims.size());
}
// reorder input to bfyx
auto reorderName = reorderedInputs[0] + "_" + layer->name + "_format_reorder";
auto reorderPrim = cldnn::reorder(reorderName, reorderedInputs[0], cldnn::format::bfyx, targetDatatype);
topology.add(reorderPrim);
AddInnerPrimitiveToProfiler(reorderName, gatherLayerName, layer);
reorderedInputs[0] = reorderName;
// calculate new input/output dims in bfyx format
SizeVector targetInDims(4);
SizeVector targetOutDims(4);
for (size_t d = 0; d < 4; d++) {
targetInDims[d] = inputDims[originalRequiredDims[d]];
targetOutDims[d] = outDims[originalRequiredDims[d]];
}
outDims = targetOutDims;
// calculate new axis in bfyx format
for (size_t d = 0; d < originalRequiredDims.size(); d++) {
if (originalRequiredDims[d] == nonNegativeAxis) {
axis = d;
}
}
// reshape the input dims to the ones expected in bfyx format
auto reshapeName = reorderedInputs[0] + "_" + layer->name + "_reshape";
auto targetTensor = CldnnTensorFromIEDims(targetInDims);
auto reshapePrim = cldnn::reshape(reshapeName, reorderedInputs[0], CldnnTensorFromIEDims(targetInDims));
topology.add(reshapePrim);
AddInnerPrimitiveToProfiler(reshapeName, gatherLayerName, layer);
reorderedInputs[0] = reshapeName;
}
auto gatherPrim = cldnn::gather(
gatherLayerName,
reorderedInputs[0],
reorderedInputs[1],
cldnnAxisFromIE(axis),
CldnnTensorFromIEDims(gatherLayer->outData[0]->getTensorDesc().getDims()));
gatherLayerName,
reorderedInputs[0],
reorderedInputs[1],
cldnnAxisFromIE(axis),
CldnnTensorFromIEDims(outDims));
topology.add(gatherPrim);
AddPrimitiveToProfiler(gatherLayerName, layer);
if (bfyx_wa) {
// reorder output back to original format
auto reorderName = gatherLayerName + "_" + layer->name + "_format_reorder";
auto reorderPrim = cldnn::reorder(reorderName, gatherPrim, outputFormatOriginal, targetDatatype);
topology.add(reorderPrim);
AddInnerPrimitiveToProfiler(reorderName, gatherLayerName, layer);
// reshape output back to original dims
auto reshapeName = gatherLayerName + "_" + layer->name + "_reshape";
auto reshapePrim = cldnn::reshape(reshapeName, reorderName, CldnnTensorFromIEDims(outDimsOriginal));
topology.add(reshapePrim);
AddInnerPrimitiveToProfiler(reshapeName, gatherLayerName, layer);
}
}
void CLDNNPlugin::Program::CreateGatherTreePrimitive(cldnn::topology & topology, InferenceEngine::CNNLayerPtr & layer) {