tc comprehension integration Ref. SINGA-482

cmake/Dependencies.cmake

@@ -149,3 +149,47 @@ IF(USE_MKLDNN)
     INCLUDE_DIRECTORIES(${MKLDNN_INCLUDE_DIR})
     LIST(APPEND SINGA_LINKER_LIBS ${MKLDNN_LIBRARIES})
 ENDIF()
+
+
+### Tensor comprehensions
+### Tensor comprehensions
+### Tensor comprehensions
+# the path should be consistent with the inlcude path in src
+INCLUDE_DIRECTORIES(/root/TensorComprehensions)
+INCLUDE_DIRECTORIES(/root/TensorComprehensions/tc/version)
+INCLUDE_DIRECTORIES(/root/TensorComprehensions/build)
+
+# polyhedral model required
+INCLUDE_DIRECTORIES(/root/TensorComprehensions/isl_interface/include)
+
+# dlpack
+INCLUDE_DIRECTORIES(/root/TensorComprehensions/third-party/dlpack/include)
+# Halide
+INCLUDE_DIRECTORIES(/root/conda/envs/tc_build/include/Halide)
+
+# llvm
+INCLUDE_DIRECTORIES(/root/conda/envs/tc_build/include)
+
+# torch ATen header TO DELETE
+INCLUDE_DIRECTORIES(/root/conda/envs/tc_build/lib/python3.6/site-packages/torch/lib/include)
+
+# find Halide lib
+set(HALIDE_PREFIX "/root/conda/envs/tc_build")
+find_library(HALIDE_LIBRARIES REQUIRED NAMES Halide PATHS ${HALIDE_PREFIX} PATH_SUFFIXES lib lib64 NO_DEFAULT_PATH)
+message(STATUS "Found Halide.so file: ${HALIDE_LIBRARIES}")
+
+# find tc lib
+link_directories(/root/TensorComprehensions/build/tc/aten)
+link_directories(/root/TensorComprehensions/build/tc/lang)
+link_directories(/root/TensorComprehensions/build/tc/core)
+link_directories(/root/TensorComprehensions/build/tc/autotuner)
+link_directories(/root/TensorComprehensions/build/tc/proto)
+
+# torch(aten) lib to delete
+link_directories(/root/conda/envs/tc_build/lib/python3.6/site-packages/torch/lib)
+
+LIST(APPEND SINGA_LINKER_LIBS ${HALIDE_LIBRARIES} tc_aten tc_lang tc_core_cpu tc_cuda tc_core_cuda_no_sdk tc_core tc_autotuner tc_proto ATen)
+
+### Tensor comprehensions
+### Tensor comprehensions
+### Tensor comprehensions

include/singa/core/tensor.h

@@ -23,6 +23,14 @@
 #include <tuple>
 #include <memory>
 
+#include <dlpack/dlpack.h>
+#include <tc/core/tensor.h>
+#include <tc/utils/compiler_options.h>
+#include <tc/core/compiler.h>
+#include <tc/core/utils/time.h>
+#include <tc/core/cuda/cuda_backend.h>
+#include "tc/core/cuda/cuda_tc_executor.h"
+
 #include "singa/core/common.h"
 #include "singa/core/device.h"
 #include "singa/proto/core.pb.h"
@@ -147,6 +155,7 @@ class Tensor {
 
   /// Return average L2 norm
   float L2() const;
+
   // --------------------------------------------------------------------------
   // ---Following methods changes the internal data
   // --------------------------------------------------------------------------
@@ -603,6 +612,97 @@ Tensor ConcatRows(const vector<Tensor> &in);
 Tensor ConcatenateColumns(const vector<Tensor> &in);
 /// Alias name for function ConcatenateColumns
 Tensor ConcatColumns(const vector<Tensor> &in);
+
+
+
+
+/// tc integration start
+DLManagedTensor* toDLPack(const Tensor& src);
+//Tensor fromDLPack(const DLManagedTensor* src);
+
+inline std::vector<tc::DLTensorUPtr> makeDLTensors(
+    const std::vector<Tensor>& tensors);
+
+template <typename Backend>
+std::unique_ptr<typename Backend::ExecutorType> compileTC(
+    const std::string& tc,
+    const std::string& entryPoint,
+    const std::vector<Tensor>& inputs,
+    const typename Backend::MappingOptionsType& options,
+    const tc::CompilerOptions& compilerOptions = tc::CompilerOptions());
+
+
+std::vector<tc::DLTensorUPtr> inferOutputTensorInfo(
+    const std::string& tc,
+    const std::string& entryPoint,
+    const std::vector<Tensor>& inputs);
+
+std::vector<Tensor> prepareOutputs(
+    const std::string& tc,
+    const std::string& entryPoint,
+    const std::vector<Tensor>& inputs);
+
+template <typename Executor>
+void runTC(
+    const Executor& executor,
+    const std::vector<Tensor>& inputs,
+    std::vector<Tensor>& outputs);
+
+
+Tensor SoftMax_TC(const Tensor &in);
+
+
+
+
+// makeDLConstTensors implementation
+inline std::vector<tc::DLConstTensorUPtr> makeDLConstTensors(const std::vector<Tensor>& tensors) {
+  std::vector<tc::DLConstTensorUPtr> dlTensors;
+  for (auto tensor : tensors) {
+    auto dlMTensor = toDLPack(tensor);
+    dlTensors.push_back(tc::makeDLConstTensor(&(dlMTensor->dl_tensor)));
+    dlMTensor->deleter(dlMTensor);
+  }
+  return dlTensors;
+}
+
+// makeDLTensors implementation
+inline std::vector<tc::DLTensorUPtr> makeDLTensors( const std::vector<Tensor>& tensors) {
+  std::vector<tc::DLTensorUPtr> dlTensors;
+  for (auto tensor : tensors) {
+    auto dlMTensor = toDLPack(tensor);
+    dlTensors.push_back(tc::makeDLTensor(&(dlMTensor->dl_tensor)));
+    dlMTensor->deleter(dlMTensor);
+  }
+  return dlTensors;
+}
+
+
+// compile implementation
+template <typename Backend>
+std::unique_ptr<typename Backend::ExecutorType> compileTC(
+    const std::string& tc,
+    const std::string& entryPoint,
+    const std::vector<Tensor>& inputs,
+    const typename Backend::MappingOptionsType& options,
+    const tc::CompilerOptions& compilerOptions) {
+  auto inputDLTensors = makeDLConstTensors(inputs);
+  return tc::compile<Backend>(
+      tc, entryPoint, extractRawPtrs(inputDLTensors), options, compilerOptions);
+}
+
+// run implementation
+template <typename Executor>
+void runTC(
+    const Executor& executor,
+    const std::vector<Tensor>& inputs,
+    std::vector<Tensor>& outputs) {
+  auto inputDLTensors = makeDLConstTensors(inputs);
+  auto outputDLTensors = makeDLTensors(outputs);
+  return executor.run( extractRawPtrs(inputDLTensors), extractRawPtrs(outputDLTensors));
+}
+
+/// tc integration end
+
 }  // namespace singa
 
 #endif  // SINGA_CORE_TENSOR_H_

src/api/core_tensor.i

@@ -345,4 +345,26 @@ namespace singa{
 
   Tensor CrossEntropyFwd(const Tensor& p, const Tensor& t);
   Tensor SoftmaxCrossEntropyBwd(const Tensor& p, const Tensor& t);
+
+  /* ============ Tensor Comprehensions ============ */
+  /* /root/incubator-singa/build/src/api/singa_wrap.cxx:14938:166: error: use of deleted function */
+  /* due to below issue, abort this approach
+  std::vector<Tensor> prepareOutputs(
+      const std::string& tc,
+      const std::string& entryPoint,
+      const std::vector<Tensor>& inputs);
+
+  template <typename Executor>
+  void runTC( const Executor& executor, const std::vector<Tensor>& inputs, std::vector<Tensor>& outputs);
+  %template(runTCCuda) runTC<tc::CudaTcExecutor>;
+
+  template <typename Backend>
+  std::unique_ptr<typename Backend::ExecutorType> compileTC(
+      const std::string& tc,
+      const std::string& entryPoint,
+      const std::vector<Tensor>& inputs,
+      const typename Backend::MappingOptionsType& options,
+      const tc::CompilerOptions& compilerOptions = tc::CompilerOptions());
+  %template(compileTCCuda) compileTC<tc::CudaBackend>;
+  */
 }

src/core/tensor/tensor.cc

@@ -21,12 +21,23 @@
 #include "./tensor_math_cpp.h"
 #include "./tensor_math_cuda.h"
 #include "./tensor_math_opencl.h"
+
 #include <utility>
 #include <algorithm>
 
+//#include <tc/lang/error_report.h>
+//#include <tc/core/compiler.h>
+#include "tc/core/check.h"
+#include "tc/core/compiler.h"
+#include "tc/core/tc_executor.h"
+#include "tc/core/tensor.h"
 
 #define Noaxis 9999
 
+// namespace is already exist in singa
+// aliasing to avoid duplicates
+namespace tclang = lang;
+
 namespace singa {
 
 Tensor::~Tensor() {
@@ -1334,4 +1345,129 @@ Tensor Reshape(const Tensor &in, const Shape &s) {
   return out.Reshape(s);
 }
 
+
+/// tc integration start
+struct SingaDLManagedTensor {
+  Tensor handle;
+  DLManagedTensor tensor;
+};
+
+void deleter(DLManagedTensor* arg) {
+  delete static_cast<SingaDLManagedTensor*>(arg->manager_ctx);
+}
+
+static DLDataType getDLDataType(const Tensor& t) {
+  DLDataType dtype;
+  dtype.lanes = 1;
+  dtype.bits = t.Size() * 8;
+  switch (t.data_type()) {
+    case kFloat32:
+      dtype.code = DLDataTypeCode::kDLFloat;
+      break;
+    default:
+      throw std::logic_error("only kFloat32 is supported for dlpack conversion");
+      break;
+  }
+  return dtype;
+}
+
+static DLContext getDLContext(const Tensor& tensor, const int64_t& device_id) {
+  DLContext ctx;
+  ctx.device_id = device_id;
+  ctx.device_type = DLDeviceType::kDLGPU;
+  //TODO: fix this
+  //if (tensor.is_cuda()) {
+  //  ctx.device_type = DLDeviceType::kDLGPU;
+  //} else {
+  //  ctx.device_type = DLDeviceType::kDLCPU;
+  //}
+  return ctx;
+}
+
+// This function returns a shared_ptr to memory managed DLpack tensor
+// constructed out of ATen tensor
+DLManagedTensor* toDLPack(const Tensor& src) {
+  SingaDLManagedTensor* singaDLManagedTensor(new SingaDLManagedTensor);
+  singaDLManagedTensor->handle = src;
+  singaDLManagedTensor->tensor.manager_ctx = singaDLManagedTensor;
+  singaDLManagedTensor->tensor.deleter = &deleter;
+  singaDLManagedTensor->tensor.dl_tensor.data = src.block()->mutable_data();
+  int64_t device_id = 0;
+  // TODO: fix this
+  //if (src.is_cuda()) {
+  //  device_id = src.get_device();
+  //}
+  singaDLManagedTensor->tensor.dl_tensor.ctx = getDLContext(src, device_id);
+  singaDLManagedTensor->tensor.dl_tensor.ndim = src.nDim();
+  singaDLManagedTensor->tensor.dl_tensor.dtype = getDLDataType(src);
+
+  auto shapeVec = new std::vector<int64_t>(src.shape().begin(),src.shape().end());
+  singaDLManagedTensor->tensor.dl_tensor.shape = shapeVec->data();
+
+  auto strideVec = new std::vector<int64_t>(src.stride().begin(),src.stride().end());
+  singaDLManagedTensor->tensor.dl_tensor.strides = strideVec->data();
+
+  singaDLManagedTensor->tensor.dl_tensor.byte_offset = 0;
+  return &(singaDLManagedTensor->tensor);
+}
+
+// prepare output
+std::vector<tc::DLTensorUPtr> inferOutputTensorInfo(
+    const std::string& tc,
+    const std::string& entryPoint,
+    const std::vector<Tensor>& inputs) {
+  auto parsedTcs = tc::detail::parse(tc);
+  if (parsedTcs.count(entryPoint) != 1u) {
+    TC_CHECK_GE(parsedTcs.size(), 1u)
+        << "No TC was parsed, should have thrown earlier";
+    throw tclang::ErrorReport(parsedTcs.begin()->second)
+        << "\nattempting to access undefined entryPoint: " << entryPoint;
+  }
+  auto inputDLTensors = makeDLConstTensors(inputs);
+  return makeDLTensorVector(tc::detail::inferOutputTensorInfo(parsedTcs.at(entryPoint), extractRawPtrs(inputDLTensors)));
+}
+
+std::vector<Tensor> prepareOutputs(
+    const std::string& tc,
+    const std::string& entryPoint,
+    const std::vector<Tensor>& inputs) {
+  std::vector<Tensor> outputs;
+  auto outTensorInfo = inferOutputTensorInfo(tc, entryPoint, inputs);
+  if (outTensorInfo.size() == 0) {
+    return outputs;
+  }
+  TC_CHECK_GE(inputs.size(), 1u)
+      << "NYI: Need >= 1 input tensors to determine "
+      << "backend and prepare ATen outputs. Add an overload with just an ATen "
+      << "backend";
+
+  auto dev = inputs[0].device();
+  auto dtype = inputs[0].data_type();
+  for (size_t i = 0; i < outTensorInfo.size(); ++i) {
+    tc::TensorInfo info(outTensorInfo[i]);
+    Shape shape(info.shape.begin(), info.shape.end());
+
+    Tensor tmp(shape, dev, dtype);
+    outputs.push_back(tmp);
+  }
+  return outputs;
+}
+
+
+Tensor SoftMax_TC(const Tensor &in) {
+  std::string tc= R"TC(
+def softmax(float(N, D) I) -> (O, expsum) {
+  expsum(n) +=! exp(I(n, d))
+  O(n, d) = exp(I(n, d)) / expsum(n)
+}
+)TC";
+  auto naiveOptions = tc::CudaBackend::MappingOptionsType::makeNaiveMappingOptions();
+  auto pExecutor = singa::compileTC<tc::CudaBackend>(tc, "softmax", {in}, {naiveOptions});
+  auto outputs = singa::prepareOutputs(tc, "softmax", {in});
+  singa::runTC(*pExecutor, {in}, outputs);
+  return outputs[0];
+}
+/// tc integration end
+
+
 }  // namespace singa

test/CMakeLists.txt

@@ -19,6 +19,8 @@
 INCLUDE_DIRECTORIES(${CMAKE_CURRENT_SOURCE_DIR})
 INCLUDE_DIRECTORIES(${CMAKE_BINARY_DIR}/include)
 
+
+
 IF(ENABLE_DIST)
   ADD_EXECUTABLE(test_ep "singa/test_ep.cc")
   ADD_DEPENDENCIES(test_ep singa)
@@ -33,7 +35,7 @@ LIST(REMOVE_ITEM singa_test_source "singa/test_ep.cc")
 ADD_EXECUTABLE(test_singa "gtest/gtest_main.cc" ${singa_test_source})
 ADD_DEPENDENCIES(test_singa singa)
 #MESSAGE(STATUS "link libs" ${singa_linker_libs})
-TARGET_LINK_LIBRARIES(test_singa gtest singa )
+TARGET_LINK_LIBRARIES(test_singa gtest singa ${SINGA_LINKER_LIBS})
 IF(UNIX AND (NOT APPLE))
     LIST(APPEND LINK_FLAGS "-pthread")
 ENDIF()