Update gather elements implementation

builtin_op_importers.cpp

@@ -1206,85 +1206,73 @@ DEFINE_BUILTIN_OP_IMPORTER(Gather)
 
 DEFINE_BUILTIN_OP_IMPORTER(GatherElements)
 {
+
+    // We can treat GatherElements as a regular Gather operation with transformed input and indices tensors.
+    // Consider a simple example of a 3D tensor with axis = 1.
+    // The regular forumla of out[i][j][k] = in[i][idx[i][j][k]][k] can be rewritten as out[i][j][k] = in'[idx'[i,j,k]]
+    // Where in' is a squeezed down 1D representation of the data and idx' is calculated from the following formula:
+    // idx' = idx[i,j,k] * pitch[1] + bias. The bias is calculated as i*pitch[0] + k*pitch[2].
+
+    // clang-format off
+    /* Example: Data is 3D tensor of shape [2,2,2] with values [[[1,2], [3,4]], [[5,6], [7,8]]]
+                Indices is a 3D tensor of shape [2,2,1] with values [[[0], [1]], [[0], [1]]]
+                From the original formula, the output is [[[1], [3]], [[5], [7]]],
+
+                Pitch vector of data is [4,2,1].
+
+                idx` calculation:
+                    idx`[0, 0, 0] = [idx[0,0,0]](0) * [pitch[axis]](2) + [i(0)*pitch[0](4)](0) + [k(0)*pitch[2](1)](0) = 0
+                    idx`[0, 1, 0] = [idx[0,1,0]](1) * [pitch[axis]](2) + [i(0)*pitch[0](4)](0) + [k(0)*pitch[2](1)](0) = 2
+                    idx`[1, 0, 0] = [idx[1,0,0]](0) * [pitch[axis]](2) + [i(1)*pitch[0](4)](4) + [k(0)*pitch[2](1)](0) = 4
+                    idx`[1, 1, 0] = [idx[1,1,0]](1) * [pitch[axis]](2) + [i(1)*pitch[0](4)](4) + [k(0)*pitch[2](1)](0) = 6
+                    = [[[0], [2]], [[4], [6]]]
+
+                After linearizing data to 1D: [1,2,3,4,5,6,7,8], gathering on axis 0 with the new indices gives the same results.
+    */
+    // clang-format on
+
     nvinfer1::ITensor& data = convertToTensor(inputs.at(0), ctx);
     nvinfer1::ITensor& index = convertToTensor(inputs.at(1), ctx);
 
     const nvinfer1::Dims& idxDims = index.getDimensions();
-    const nvinfer1::Dims& dataDims = data.getDimensions();
+    const nvinfer1::Dims& daDims = data.getDimensions();
+
+    // Note the above tranformation requires dimensions to be known at parse time, so check for dynamic shapes
+    ASSERT(!isDynamic(daDims) && !isDynamic(idxDims)
+            && "This version of TenosrRT does not support GatherElements on dynamic shapes!",
+        ErrorCode::kUNSUPPORTED_NODE);
 
     OnnxAttrs attrs(node, ctx);
     int32_t axis = attrs.get<int32_t>("axis", 0);
-    int32_t dataNbDims = dataDims.nbDims;
+    int32_t dataNbDims = daDims.nbDims;
 
     TRT_CHECK(convertAxis(axis, dataNbDims));
     LOG_VERBOSE("Using Gather axis: " << axis);
 
-    // Calculate how many indices
+    // Calculate data pitches vector, and create axisPitch vector
     int64_t nIndx = volume(idxDims);
+    std::vector<int32_t> pitches = calculatePitches(daDims);
+    std::vector<int32_t> axisPitch(nIndx, pitches[axis]);
 
-    // Calculate pitches of input tensor
-    int32_t nDataElements = volume(dataDims), pitch = 1;
-    int32_t pitches[nvinfer1::Dims::MAX_DIMS] = {0};
-    pitches[dataDims.nbDims-1] = pitch;
-    for (int32_t i = dataDims.nbDims-2; i >= 0 ; i--)
-    {
-        pitch *= dataDims.d[i];
-        pitches[i] = pitch;
-    }
-
-    // Generate constants based on axis
-    std::vector<int32_t> sCoeff(nIndx, pitches[axis]);
-    std::vector<int32_t> aCoeff;
-
-    // Transform a 1-d index back to the nDims
-    for (int32_t i = 0; i < nIndx; i++)
-    {
-        std::vector<int32_t> nDimsIdx; //this can be an array
-        int32_t currI = i;
-
-        for (int32_t j = 0; j < dataDims.nbDims; j++)
-        {
-            int32_t currIdxVal = currI / pitches[j];
-            nDimsIdx.push_back(currIdxVal);
-            currI = currI % pitches[j];
-        }
-
-        int32_t bias = 0;
-        //calculate the aCoeff
-        for (size_t j = 0; j < nDimsIdx.size(); j++)
-        {
-
-            if (j == (size_t)axis)
-            {
-                continue;
-            }
-            bias += nDimsIdx[j] * pitches[j];
-        }
-        aCoeff.push_back(bias);
-    }
-
-    auto* sCoeffLayer = addConstant(ctx, sCoeff, ::ONNX_NAMESPACE::TensorProto::INT32, idxDims);
-    auto* aCoeffLayer = addConstant(ctx, aCoeff, ::ONNX_NAMESPACE::TensorProto::INT32, idxDims);
-
-    nvinfer1::ITensor* sCoeffTensor = sCoeffLayer->getOutput(0);
-    nvinfer1::ITensor* aCoeffTensor = aCoeffLayer->getOutput(0);
-    auto* mul = ctx->network()->addElementWise(index, *sCoeffTensor, nvinfer1::ElementWiseOperation::kPROD);
-
-    nvinfer1::ITensor* mulTensor = mul->getOutput(0);
-    auto* add = ctx->network()->addElementWise(*mulTensor, *aCoeffTensor, nvinfer1::ElementWiseOperation::kSUM);
+    // Calculate bias vector
+    std::vector<int32_t> biasVector = calculateBias(daDims, idxDims, pitches, axis);
 
-    nvinfer1::ITensor* addTensor = add->getOutput(0);
+    // Perform idx` = idx * pitch[axis] + bias calculation.
+    auto* axisPitchTensor = addConstant(ctx, axisPitch, ::ONNX_NAMESPACE::TensorProto::INT32, idxDims)->getOutput(0);
+    auto* biasTensor = addConstant(ctx, biasVector, ::ONNX_NAMESPACE::TensorProto::INT32, idxDims)->getOutput(0);
 
-    nvinfer1::Dims flattenDataDims{1};
+    auto* mul
+        = ctx->network()->addElementWise(index, *axisPitchTensor, nvinfer1::ElementWiseOperation::kPROD)->getOutput(0);
+    auto* newIndices
+        = ctx->network()->addElementWise(*mul, *biasTensor, nvinfer1::ElementWiseOperation::kSUM)->getOutput(0);
 
-    flattenDataDims.nbDims = 1;
-    flattenDataDims.d[0] = nDataElements;
+    nvinfer1::Dims flattenDataDims{1, {static_cast<int32_t>(volume(daDims))}};
     auto* reshape = ctx->network()->addShuffle(data);
     reshape->setReshapeDimensions(flattenDataDims);
     reshape->setZeroIsPlaceholder(false);
 
     nvinfer1::ITensor* flattenData = reshape->getOutput(0);
-    auto* layer = ctx->network()->addGather(*flattenData, *addTensor, 0);
+    auto* layer = ctx->network()->addGather(*flattenData, *newIndices, 0);
     ctx->registerLayer(layer, getNodeName(node));
     RETURN_FIRST_OUTPUT(layer);
 }

onnx2trt_utils.cpp

@@ -136,6 +136,68 @@ Status broadcastTensors(IImporterContext* ctx, nvinfer1::ITensor*& t1, nvinfer1:
     return Status::success();
 }
 
+// Helper functions for calculateBias:
+int32_t getBias(const std::vector<int32_t>& dimension_count, const std::vector<int32_t>& pitches, int32_t axis)
+{
+    int32_t result{0};
+    for (int32_t i = 0; i < static_cast<int32_t>(dimension_count.size()); i++)
+    {
+        if (i != axis)
+        {
+            result += dimension_count[i] * pitches[i];
+        }
+    }
+    return result;
+}
+
+void incrementOuterDimension(std::vector<int32_t>& dimensionCount, nvinfer1::Dims idxDims)
+{
+    // Start at [x,x,0]. Increment starting from the outer dimension.
+    int32_t rank = dimensionCount.size();
+
+    for (int32_t i = rank - 1; i >= 0; i--)
+    {
+        int dimLimit = idxDims.d[i];
+        // If we're not at the limit, increment current axis and return
+        if (++dimensionCount[i] != dimLimit)
+        {
+            break;
+        }
+        // Else, we increment on the next dimension and reset current one
+        dimensionCount[i] = 0;
+    }
+}
+
+std::vector<int32_t> calculateBias(
+    const nvinfer1::Dims& daDims, const nvinfer1::Dims& idxDims, const std::vector<int32_t>& pitches, int32_t axis)
+{
+    std::vector<int32_t> biasVector;
+    std::vector<int32_t> dimensionCount(daDims.nbDims, 0);
+    int64_t total = volume(idxDims);
+
+    for (int64_t i = 0; i < total; i++)
+    {
+        int32_t bias = getBias(dimensionCount, pitches, axis);
+        biasVector.push_back(bias);
+        incrementOuterDimension(dimensionCount, idxDims);
+    }
+    return biasVector;
+}
+
+std::vector<int32_t> calculatePitches(const nvinfer1::Dims& inputDims)
+{
+    int32_t pitch = 1;
+    int32_t nbDims = inputDims.nbDims;
+    std::vector<int32_t> pitches(nbDims);
+    pitches[nbDims - 1] = pitch;
+    for (int32_t i = nbDims - 2; i >= 0; i--)
+    {
+        pitch *= inputDims.d[i + 1];
+        pitches[i] = pitch;
+    }
+    return pitches;
+}
+
 bool canUseLinearResize(const size_t scaleSize, const float* scaleFactors)
 {
     // Linear resize supports up to 3D resize on the outermost dimensions.

onnx2trt_utils.hpp

@@ -156,6 +156,13 @@ Status broadcastTensors(IImporterContext* ctx, nvinfer1::ITensor*& t1, nvinfer1:
 // Helper function to broadcast three tensors to the largest one's shape
 Status broadcastTensors(IImporterContext* ctx, nvinfer1::ITensor*& t1, nvinfer1::ITensor*& t2, nvinfer1::ITensor*& t3);
 
+// Helper function to calculate the bias tensor for GatherElements.
+std::vector<int32_t> calculateBias(
+    const nvinfer1::Dims& daDims, const nvinfer1::Dims& idxDims, const std::vector<int32_t>& pitches, int32_t axis);
+
+// Helper function to calculate and return a vector representation of the pitches of a given shape
+std::vector<int32_t> calculatePitches(const nvinfer1::Dims& inputDims);
+
 // Helper function to check that linear resize can be used
 bool canUseLinearResize(const size_t scaleSize, const float* scaleFactors);