renamed SoAMetadata class to SoAWrapper

Author: chrisizeh

PhysicsTools/PyTorch/README.md

@@ -64,11 +64,11 @@ The interface provides a converter to dynamically wrap SoA data into one or more
 
 #### Metadata
 
-The structual information of the input and output SoA are stored in an `SoAMetadata`. These two objects are then combined to a `ModelMetadata`, to be used by the `Converter`.
+The structual information of the input and output SoA are stored in an `SoAWrapper`. These two objects are then combined to a `ModelMetadata`, to be used by the `Converter`.
 
 #### Defining Metadata
 
-The `SoAMetadata' can be defined by first initialising the object and then adding blocks to the metadata. Each block is transformed into a tensor whose size and type are derived from the columns provided.
+The `SoAWrapper' can be defined by first initialising the object and then adding blocks to the metadata. Each block is transformed into a tensor whose size and type are derived from the columns provided.
 
 Example SOA Template for Model Input:
 ```cpp
@@ -97,14 +97,14 @@ fill(queue, deviceCollection);
 auto records = deviceCollection.view().records();
 auto result_records = deviceResultCollection.view().records();
 
-SoAMetadata<SoA> input(batch_size);
+SoAWrapper<SoA> input(batch_size);
 input.append_block("eigen_vector", records.a(), records.b());
 input.append_block("eigen_matrix", records.c());
 input.append_block("column", records.x(), records.y(), records.z());
 input.append_block("scalar", view.type());
 input.change_order({"column", "scalar", "eigen_matrix", "eigen_vector"});
 
-SoAMetadata<SoA> output(batch_size);
+SoAWrapper<SoA> output(batch_size);
 output.append_block("result", result_view.cluster());
 ModelMetadata metadata(input, output);
 ```
@@ -150,9 +150,9 @@ alpaka::wait(queue);
     // metadata for automatic tensor conversion
     auto input_records = inputs_device.view().records();
     auto output_records = outputs_device.view().records();
-    cms::torch::alpaka::SoAMetadata<SoAInputs> inputs_metadata(batch_size);
+    cms::torch::alpaka::SoAWrapper<SoAInputs> inputs_metadata(batch_size);
     inputs_metadata.append_block("features", input_records.x(), input_records.y(), input_records.z());
-    cms::torch::alpaka::SoAMetadata<SoAOutputs> outputs_metadata(batch_size); 
+    cms::torch::alpaka::SoAWrapper<SoAOutputs> outputs_metadata(batch_size); 
     outputs_metadata.append_block("preds", output_records.m(), output_records.n());
     cms::torch::alpaka::ModelMetadata<SoAInputs, SoAOutputs> metadata(inputs_metadata, outputs_metadata);
     
@@ -180,4 +180,5 @@ The function `change_order()` in the allows specifying the order in which the bl
     - #9786 Pytorch with ROCm (temp): https://github.com/cms-sw/cmsdist/pull/9312
     - #9312 [WIP] Build PyTorch with ROCm https://github.com/cms-sw/cmsdist/pull/9786
 - AOT support is under active development and subject to changes that obey CMSSW releasing rules.
-- On more complex models with multiple output branches extra copy is needed 
+- On more complex models with multiple output branches extra copy is needed 
+- The alignment of the SoA has to be a multiple of the types used, as no bytewise padding is currently supported.

PhysicsTools/PyTorch/interface/Converter.h

@@ -4,7 +4,7 @@
 #include <torch/torch.h>
 
 #include "DataFormats/SoATemplate/interface/SoALayout.h"
-#include "PhysicsTools/PyTorch/interface/SoAMetadata.h"
+#include "PhysicsTools/PyTorch/interface/SoAWrapper.h"
 
 
 namespace cms::torch::alpaka {
@@ -13,14 +13,14 @@ namespace cms::torch::alpaka {
 template <typename SOA_Input, typename SOA_Output>
 class ModelMetadata {
  public:
-  SoAMetadata<SOA_Input> input;
-  SoAMetadata<SOA_Output> output;
+  SoAWrapper<SOA_Input> input;
+  SoAWrapper<SOA_Output> output;
 
   // Used in AOT model class to correctly choose multi or single output conversion
   // Default value true, as single value can be parsed with multi output
   bool multi_output;
 
-  ModelMetadata(const SoAMetadata<SOA_Input>& input_, const SoAMetadata<SOA_Output>& output_, bool multi_output_=true)
+  ModelMetadata(const SoAWrapper<SOA_Input>& input_, const SoAWrapper<SOA_Output>& output_, bool multi_output_=true)
       : input(input_), output(output_), multi_output(multi_output_) {}
 };
 

…icsTools/PyTorch/interface/SoAMetadata.h …sicsTools/PyTorch/interface/SoAWrapper.hPhysicsTools/PyTorch/interface/SoAMetadata.h renamed to PhysicsTools/PyTorch/interface/SoAWrapper.h PhysicsTools/PyTorch/interface/SoAMetadata.h renamed to PhysicsTools/PyTorch/interface/SoAWrapper.h

@@ -1,5 +1,5 @@
-#ifndef PHYSICS_TOOLS__PYTORCH__INTERFACE__SOAMETADATA_H_
-#define PHYSICS_TOOLS__PYTORCH__INTERFACE__SOAMETADATA_H_
+#ifndef PHYSICS_TOOLS__PYTORCH__INTERFACE__SoAWrapper_H_
+#define PHYSICS_TOOLS__PYTORCH__INTERFACE__SoAWrapper_H_
 
 #include <iostream>
 
@@ -107,7 +107,7 @@ struct Block {
 // Metadata for SOA split into multiple blocks.
 // An order for the resulting tensors can be defined.
 template <typename SOA_Layout>
-struct SoAMetadata {
+struct SoAWrapper {
  private:
   std::map<std::string, Block<SOA_Layout>> blocks;
 
@@ -145,7 +145,7 @@ struct SoAMetadata {
   int nElements;
   int nBlocks;
 
-  SoAMetadata(int nElements_) : nElements(nElements_), nBlocks(0) {}
+  SoAWrapper(int nElements_) : nElements(nElements_), nBlocks(0) {}
 
   // TODO: Check columns are contiguous
   template <typename T, typename... Others>
@@ -198,4 +198,4 @@ struct SoAMetadata {
 
 }  // namespace cms::torch::alpaka
 
-#endif  // PHYSICS_TOOLS__PYTORCH__INTERFACE__SOAMETADATA_H_
+#endif  // PHYSICS_TOOLS__PYTORCH__INTERFACE__SoAWrapper_H_

PhysicsTools/PyTorch/plugins/alpaka/AotRegressionProducer.cc

@@ -15,7 +15,7 @@
 #include "HeterogeneousCore/AlpakaInterface/interface/config.h"
 #include "PhysicsTools/PyTorch/interface/AlpakaConfig.h"
 #include "PhysicsTools/PyTorch/interface/Model.h"
-#include "PhysicsTools/PyTorch/interface/SoAMetadata.h"
+#include "PhysicsTools/PyTorch/interface/SoAWrapper.h"
 #include "PhysicsTools/PyTorch/interface/Nvtx.h"
 #include "PhysicsTools/PyTorch/plugins/alpaka/Kernels.h"
 
@@ -96,9 +96,9 @@ void AotRegressionProducer::produce(device::Event &event, const device::EventSet
   // metadata for automatic tensor conversion
   auto input_records = inputs.view().records();
   auto output_records = outputs.view().records();
-  cms::torch::alpaka::SoAMetadata<torchportable::ParticleSoA> inputs_metadata(batch_size);
+  cms::torch::alpaka::SoAWrapper<torchportable::ParticleSoA> inputs_metadata(batch_size);
   inputs_metadata.append_block("features", input_records.pt(), input_records.eta(), input_records.phi());
-  cms::torch::alpaka::SoAMetadata<torchportable::RegressionSoA> outputs_metadata(batch_size); 
+  cms::torch::alpaka::SoAWrapper<torchportable::RegressionSoA> outputs_metadata(batch_size); 
   outputs_metadata.append_block("preds", output_records.reco_pt());
   cms::torch::alpaka::ModelMetadata<torchportable::ParticleSoA, torchportable::RegressionSoA> metadata(inputs_metadata, outputs_metadata);
 

PhysicsTools/PyTorch/plugins/alpaka/JitClassificationProducer.cc

@@ -15,7 +15,7 @@
 #include "HeterogeneousCore/AlpakaInterface/interface/config.h"
 #include "PhysicsTools/PyTorch/interface/AlpakaConfig.h"
 #include "PhysicsTools/PyTorch/interface/Model.h"
-#include "PhysicsTools/PyTorch/interface/SoAMetadata.h"
+#include "PhysicsTools/PyTorch/interface/SoAWrapper.h"
 #include "PhysicsTools/PyTorch/interface/Nvtx.h"
 #include "PhysicsTools/PyTorch/plugins/alpaka/Kernels.h"
 
@@ -96,9 +96,9 @@ void JitClassificationProducer::produce(device::Event &event, const device::Even
   // metadata for automatic tensor conversion
   auto input_records = inputs.view().records();
   auto output_records = outputs.view().records();
-  cms::torch::alpaka::SoAMetadata<torchportable::ParticleSoA> inputs_metadata(batch_size);
+  cms::torch::alpaka::SoAWrapper<torchportable::ParticleSoA> inputs_metadata(batch_size);
   inputs_metadata.append_block("features", input_records.pt(), input_records.eta(), input_records.phi());
-  cms::torch::alpaka::SoAMetadata<torchportable::ClassificationSoA> outputs_metadata(batch_size); 
+  cms::torch::alpaka::SoAWrapper<torchportable::ClassificationSoA> outputs_metadata(batch_size); 
   outputs_metadata.append_block("preds", output_records.c1(), output_records.c2());
   cms::torch::alpaka::ModelMetadata<torchportable::ParticleSoA, torchportable::ClassificationSoA> metadata(inputs_metadata, outputs_metadata);
 

PhysicsTools/PyTorch/test/BuildFile.xml

@@ -115,6 +115,7 @@
 </bin>
 
 <bin file="testModel.cc testRunner.cc" name="testModel">
+  <use name="boost_filesystem"/>
   <use name="cppunit"/>
   <use name="pytorch"/>
   <use name="pytorch-cuda"/>

PhysicsTools/PyTorch/test/alpaka/testPortableInferenceAOT.dev.cc

@@ -12,7 +12,7 @@
 #include "HeterogeneousCore/AlpakaInterface/interface/workdivision.h"
 #include "PhysicsTools/PyTorch/interface/AlpakaConfig.h"
 #include "PhysicsTools/PyTorch/interface/Model.h"
-#include "PhysicsTools/PyTorch/interface/SoAMetadata.h"
+#include "PhysicsTools/PyTorch/interface/SoAWrapper.h"
 #include "PhysicsTools/PyTorch/test/testUtilities.h"
 
 
@@ -97,9 +97,9 @@ void TestPortableInferenceAOT::test() {
     // metadata for automatic tensor conversion
     auto input_records = inputs_device.view().records();
     auto output_records = outputs_device.view().records();
-    cms::torch::alpaka::SoAMetadata<SoAInputs> inputs_metadata(batch_size);
+    cms::torch::alpaka::SoAWrapper<SoAInputs> inputs_metadata(batch_size);
     inputs_metadata.append_block("features", input_records.x(), input_records.y(), input_records.z());
-    cms::torch::alpaka::SoAMetadata<SoAOutputs> outputs_metadata(batch_size); 
+    cms::torch::alpaka::SoAWrapper<SoAOutputs> outputs_metadata(batch_size); 
     outputs_metadata.append_block("prob", output_records.prob());
     cms::torch::alpaka::ModelMetadata<SoAInputs, SoAOutputs> metadata(inputs_metadata, outputs_metadata);
     // inference

PhysicsTools/PyTorch/test/alpaka/testPortableInferenceJIT.cc

@@ -12,7 +12,7 @@
 #include "HeterogeneousCore/AlpakaInterface/interface/workdivision.h"
 #include "PhysicsTools/PyTorch/interface/AlpakaConfig.h"
 #include "PhysicsTools/PyTorch/interface/Model.h"
-#include "PhysicsTools/PyTorch/interface/SoAMetadata.h"
+#include "PhysicsTools/PyTorch/interface/SoAWrapper.h"
 #include "PhysicsTools/PyTorch/test/testUtilities.h"
 
 
@@ -84,9 +84,9 @@ void TestPortableInferenceJIT::test() {
     // metadata for automatic tensor conversion
     auto input_records = inputs_device.view().records();
     auto output_records = outputs_device.view().records();
-    cms::torch::alpaka::SoAMetadata<SoAInputs> inputs_metadata(batch_size);
+    cms::torch::alpaka::SoAWrapper<SoAInputs> inputs_metadata(batch_size);
     inputs_metadata.append_block("features", input_records.x(), input_records.y(), input_records.z());
-    cms::torch::alpaka::SoAMetadata<SoAOutputs> outputs_metadata(batch_size); 
+    cms::torch::alpaka::SoAWrapper<SoAOutputs> outputs_metadata(batch_size); 
     outputs_metadata.append_block("preds", output_records.m(), output_records.n());
     cms::torch::alpaka::ModelMetadata<SoAInputs, SoAOutputs> metadata(inputs_metadata, outputs_metadata);
     // inference

PhysicsTools/PyTorch/test/alpaka/testSOADataTypes.dev.cc

@@ -254,14 +254,14 @@ void TestSOADataTypes::testInterfaceVerbose() {
   fill(queue, deviceCollection);
   SoAMetaRecords records = deviceCollection.view().records();
 
-  SoAMetadata<SoA> input(batch_size);
+  SoAWrapper<SoA> input(batch_size);
   input.append_block("vector", records.a(), records.b());
   input.append_block("matrix", records.c());
   input.append_block("column", records.x(), records.y(), records.z());
   input.append_block("scalar", records.type());
   input.change_order({"column", "scalar", "matrix", "vector"});
 
-  SoAMetadata<SoA> output(batch_size);
+  SoAWrapper<SoA> output(batch_size);
   output.append_block("result", records.v());
   ModelMetadata metadata(input, output);
 
@@ -289,11 +289,11 @@ void TestSOADataTypes::testMultiOutput() {
   fill(queue, deviceCollection);
 
   auto records = deviceCollection.view().records();
-  SoAMetadata<SoA> input(batch_size);
+  SoAWrapper<SoA> input(batch_size);
   input.append_block("x", records.x());
   input.append_block("y", records.y());
 
-  SoAMetadata<SoA> output(batch_size);
+  SoAWrapper<SoA> output(batch_size);
   output.append_block("v", records.v());
   output.append_block("w", records.w());
   ModelMetadata metadata(input, output);
@@ -322,14 +322,14 @@ void TestSOADataTypes::testSingleElement() {
   SoAMetaRecords records = deviceCollection.view().records();
 
   // Run Converter for single tensor
-  SoAMetadata<SoA> input(batch_size);
+  SoAWrapper<SoA> input(batch_size);
   input.append_block("vector", records.a(), records.b());
   input.append_block("matrix", records.c());
   input.append_block("column", records.x(), records.y(), records.z());
   input.append_block("scalar", records.type());
   input.change_order({"column", "scalar", "matrix", "vector"});
 
-  SoAMetadata<SoA> output(batch_size);
+  SoAWrapper<SoA> output(batch_size);
   output.append_block("result", records.v());
   ModelMetadata metadata(input, output);
 
@@ -355,14 +355,14 @@ void TestSOADataTypes::testNoElement() {
   SoAMetaRecords records = deviceCollection.view().records();
 
   // Run Converter
-  SoAMetadata<SoA> input(batch_size);
+  SoAWrapper<SoA> input(batch_size);
   input.append_block("vector", records.a(), records.b());
   input.append_block("matrix", records.c());
   input.append_block("column", records.x(), records.y(), records.z());
   input.append_block("scalar", records.type());
   input.change_order({"column", "scalar", "matrix", "vector"});
 
-  SoAMetadata<SoA> output(batch_size);
+  SoAWrapper<SoA> output(batch_size);
   output.append_block("result", records.v());
   ModelMetadata metadata(input, output);
 
@@ -392,8 +392,8 @@ void TestSOADataTypes::testEmptyMetadata() {
   fill(queue, deviceCollection);
 
   // Run Converter for empty metadata
-  SoAMetadata<SoA> input(batch_size);
-  SoAMetadata<SoA> output(batch_size);
+  SoAWrapper<SoA> input(batch_size);
+  SoAWrapper<SoA> output(batch_size);
   ModelMetadata metadata(input, output);
 
   alpaka::wait(queue);

PhysicsTools/PyTorch/test/alpaka/testSOAtoTorch.dev.cc

@@ -125,11 +125,11 @@ void testSOAToTorch::test() {
   }
 
   // Create SoA Metadata
-  cms::torch::alpaka::SoAMetadata<SoAPosition> input(batch_size);
+  cms::torch::alpaka::SoAWrapper<SoAPosition> input(batch_size);
   auto posview = positionCollection.view().records();
   input.append_block("main", posview.x(), posview.y(), posview.z());
 
-  cms::torch::alpaka::SoAMetadata<SoAResult> output(batch_size);
+  cms::torch::alpaka::SoAWrapper<SoAResult> output(batch_size);
   auto view = resultCollection.view().records();
   output.append_block("result", view.x(), view.y());
   cms::torch::alpaka::ModelMetadata metadata(input, output);