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handle BN with sequence axis #3814

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handle BN with sequence axis #3814

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92 changes: 61 additions & 31 deletions Source/CNTKv2LibraryDll/proto/onnx/CNTKToONNX.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,8 @@
#include "Internals/ComputationGraphAlgorithms.h"
#include "ControlFlowHelper.h"

#include <functional>

using namespace Microsoft::MSR::CNTK;
using namespace CNTK::ONNX;
using namespace CNTK;
Expand Down Expand Up @@ -845,6 +847,10 @@ class CNTKToONNXHelper
std::unordered_map<Variable, onnxruntime::Node*>& variableNodes,
std::vector<ScanLoop>& scanLoops, int createLoopIndex);

static onnxruntime::Node* WrapSequenceOpWithReshape(const FunctionPtr& src,
onnxruntime::Graph* graph, onnxruntime::NodeArg *input, onnxruntime::NodeArg *output,
std::function<onnxruntime::Node*(NodeArg *inputArg, NodeArg *outputArg)>& node_creator);

static onnxruntime::Node* CreatePoolingNode(const FunctionPtr& src,
onnxruntime::Graph* graph,
std::unordered_map<FunctionPtr, onnxruntime::Node*>& functionNodes,
Expand Down Expand Up @@ -3817,7 +3823,7 @@ onnxruntime::Node *CNTKToONNXHelper::AddIdentityOp(onnxruntime::NodeArg &nodeArg

onnxruntime::NodeArg &outputArg = graph->GetOrCreateNodeArg(out_arg_name, &outputTypeProto);
onnxruntime::Node* identityNode = &graph->AddNode(
nodeArg.Name() + string("_identity"), "Identity", "", {&nodeArg}, {&outputArg});
nodeArg.Name() + string("_identity_") + out_arg_name, "Identity", "", {&nodeArg}, {&outputArg});
return identityNode;
}

Expand Down Expand Up @@ -8422,8 +8428,20 @@ onnxruntime::Node* CNTKToONNXHelper::CreateBatchNormalization(const FunctionPtr
onnxruntime::Node *node = nullptr;
if (spatial)
{
// input and output are in correct shape.
node = &graph->AddNode(nodeName, "BatchNormalization", "", inputs, outputs);
if (src->Inputs()[0].DynamicAxes().size() == 2)
{
std::function<onnxruntime::Node*(NodeArg*, NodeArg*)> node_creator = [&](NodeArg *inputArg, NodeArg *outputArg)
{
inputs[0] = inputArg;
outputs[0] = outputArg;
return &graph->AddNode(nodeName, "BatchNormalization", "", inputs, outputs);
};
node = WrapSequenceOpWithReshape(src, graph, inputs[0], outputs[0], node_creator);
} else
{
// input and output are in correct shape.
node = &graph->AddNode(nodeName, "BatchNormalization", "", inputs, outputs);
}
}
else
{
Expand Down Expand Up @@ -8625,6 +8643,40 @@ onnxruntime::Node* ApplyActivationToSequenceConvolution(Node* convNode, const Fu
return activationNode;
}

onnxruntime::Node* CNTKToONNXHelper::WrapSequenceOpWithReshape(const FunctionPtr& src,
onnxruntime::Graph* graph, onnxruntime::NodeArg *input, onnxruntime::NodeArg *output,
std::function<onnxruntime::Node*(NodeArg *inputArg, NodeArg *outputArg)>& node_creator)
{
// Max/AveragePool/BN takes input of shape [N, C, H, W] or [N, C, D1, D2, ..., Dn]. CNTK input needs to be reshaped to match it.
// reshape [#, *][C, H, W] to [-1, C, H, W]
// onnx Max/AveragePool/BN
// reshape [-1, C_out, H_out, W_out] to [#, *][C_out, H_out, W_out]
vector<int64_t> newDimInputToOpNode;
// collapse extra dims into one axis as N for ONNX Conv
newDimInputToOpNode.push_back(-1);
for (int i = 2; i < input->Shape()->dim_size(); i++)
{
// copy C, H, W
if (!input->Shape()->dim(i).has_dim_value())
LogicError("wrapped_op: feature dimensions need to have dim value.");
newDimInputToOpNode.push_back(input->Shape()->dim(i).dim_value());
}

onnxruntime::Node* preReshape = AddReshapeNode(*input, newDimInputToOpNode, input->Name() + "_reshaped_for_wrapped_op", graph);
const std::vector<onnxruntime::NodeArg *> pooling_inputs({ const_cast<NodeArg *>(preReshape->OutputDefs()[0]) });
TypeProto nodeOutputTypeProto;
UpdateONNXType(src->Outputs()[0].GetDataType(), nodeOutputTypeProto);

NodeArg *opOutputArg = &graph->GetOrCreateNodeArg(output->Name() + "_wrapped_op_of_reshaped", &nodeOutputTypeProto);

onnxruntime::Node* node = node_creator(const_cast<NodeArg *>(preReshape->OutputDefs()[0]), opOutputArg);

vector<int64_t> newDimOutputFromPooling = ToINTS(*output->TypeAsProto());
onnxruntime::Node* postReshape = AddReshapeNode(*opOutputArg, newDimOutputFromPooling, output->Name(), graph);

return node;
}

// insert reshape before and after a Pooling op when the CNTK op has both sequence and batch axes.
onnxruntime::Node* CNTKToONNXHelper::CreatePoolingNode(const FunctionPtr& src,
onnxruntime::Graph* graph,
Expand All @@ -8642,35 +8694,13 @@ onnxruntime::Node* CNTKToONNXHelper::CreatePoolingNode(const FunctionPtr& src,
std::vector<onnxruntime::NodeArg *> outputs;
ProcessOutputs(src, inputs, outputs, graph);

// Max/AveragePool takes input of shape [N, C, H, W] or [N, C, D1, D2, ..., Dn]. CNTK input needs to be reshaped to match it.
// reshape [#, *][C, H, W] to [-1, C, H, W]
// onnx Max/AveragePool
// reshape [-1, C_out, H_out, W_out] to [#, *][C_out, H_out, W_out]
vector<int64_t> newDimInputToPooling;
// collapse extra dims into one axis as N for ONNX Conv
newDimInputToPooling.push_back(-1);
for (int i = 2; i < inputs[0]->Shape()->dim_size(); i++)
{
// copy C, H, W
if (!inputs[0]->Shape()->dim(i).has_dim_value())
LogicError("Max/AveragePool: feature dimensions need to have dim value.");
newDimInputToPooling.push_back(inputs[0]->Shape()->dim(i).dim_value());
}

onnxruntime::Node* preReshape = AddReshapeNode(*inputs[0], newDimInputToPooling, inputs[0]->Name() + "_reshaped_for_max_pool", graph);
const std::vector<onnxruntime::NodeArg *> pooling_inputs({const_cast<NodeArg *>(preReshape->OutputDefs()[0])});
TypeProto poolingOutputTypeProto;
UpdateONNXType(src->Outputs()[0].GetDataType(), poolingOutputTypeProto);

NodeArg *poolingOutputArg = &graph->GetOrCreateNodeArg(outputs[0]->Name() + "_pooling_of_reshaped", &poolingOutputTypeProto);

onnxruntime::Node* poolingNode = AddNode(src, graph, pooling_inputs, { poolingOutputArg });

vector<int64_t> newDimOutputFromPooling = ToINTS(*outputs[0]->TypeAsProto());
onnxruntime::Node* postReshape = AddReshapeNode(*poolingOutputArg, newDimOutputFromPooling, outputs[0]->Name(), graph);
std::function<onnxruntime::Node*(NodeArg*, NodeArg*)> node_creator = [&](NodeArg *inputArg, NodeArg *outputArg) {
return AddNode(src, graph, { inputArg }, { outputArg });
};
onnxruntime::Node* poolingNode = WrapSequenceOpWithReshape(src, graph, inputs[0], outputs[0], node_creator);

functionNodes.emplace(src, poolingNode);
return postReshape;
return poolingNode;
}

onnxruntime::Node* CNTKToONNXHelper::CreateConvolutionNode(const FunctionPtr& src,
Expand Down Expand Up @@ -8706,7 +8736,7 @@ onnxruntime::Node* CNTKToONNXHelper::CreateConvolutionNode(const FunctionPtr& sr
newDimInputToConv.push_back(inputs[1]->Shape()->dim(i).dim_value());
}

onnxruntime::Node* preReshape = AddReshapeNode(*inputs[1], newDimInputToConv, inputs[1]->Name() + "_reshaped_for_conv", graph);
onnxruntime::Node* preReshape = AddReshapeNode(*inputs[1], newDimInputToConv, inputs[1]->Name() + "_reshaped_for_conv_" + ToLegacyString(ToUTF8(src->Name())), graph);
std::vector<onnxruntime::NodeArg *> conv_inputs = inputs;
conv_inputs[1] = const_cast<NodeArg *>(preReshape->OutputDefs()[0]);
TypeProto convOutputTypeProto;
Expand Down