concrete/compiler/lib/Support/Jit.cpp

// Part of the Concrete Compiler Project, under the BSD3 License with Zama
// Exceptions. See
// https://github.com/zama-ai/concrete-compiler-internal/blob/master/LICENSE.txt
// for license information.

#include "llvm/Support/Error.h"
#include <llvm/ADT/ArrayRef.h>
#include <llvm/ADT/SmallVector.h>
#include <llvm/ADT/StringRef.h>
#include <llvm/Support/TargetSelect.h>

#include <mlir/Dialect/LLVMIR/LLVMDialect.h>
#include <mlir/Target/LLVMIR/Dialect/LLVMIR/LLVMToLLVMIRTranslation.h>

#include "concretelang/Common/BitsSize.h"
#include <concretelang/Support/Error.h>
#include <concretelang/Support/Jit.h>
#include <concretelang/Support/logging.h>

namespace mlir {
namespace concretelang {

llvm::Expected<std::unique_ptr<JITLambda>>
JITLambda::create(llvm::StringRef name, mlir::ModuleOp &module,
                  llvm::function_ref<llvm::Error(llvm::Module *)> optPipeline,
                  llvm::Optional<llvm::StringRef> runtimeLibPath) {

  // Looking for the function
  auto rangeOps = module.getOps<mlir::LLVM::LLVMFuncOp>();
  auto funcOp = llvm::find_if(rangeOps, [&](mlir::LLVM::LLVMFuncOp op) {
    return op.getName() == name;
  });
  if (funcOp == rangeOps.end()) {
    return llvm::make_error<llvm::StringError>(
        "cannot find the function to JIT", llvm::inconvertibleErrorCode());
  }

  llvm::InitializeNativeTarget();
  llvm::InitializeNativeTargetAsmPrinter();

  mlir::registerLLVMDialectTranslation(*module->getContext());

  // Create an MLIR execution engine. The execution engine eagerly
  // JIT-compiles the module. If runtimeLibPath is specified, it's passed as a
  // shared library to the JIT compiler.
  std::vector<llvm::StringRef> sharedLibPaths;
  if (runtimeLibPath.hasValue())
    sharedLibPaths.push_back(runtimeLibPath.getValue());
  auto maybeEngine = mlir::ExecutionEngine::create(
      module, /*llvmModuleBuilder=*/nullptr, optPipeline,
      /*jitCodeGenOptLevel=*/llvm::None, sharedLibPaths);
  if (!maybeEngine) {
    return StreamStringError("failed to construct the MLIR ExecutionEngine");
  }
  auto &engine = maybeEngine.get();
  auto lambda = std::make_unique<JITLambda>((*funcOp).getType(), name);
  lambda->engine = std::move(engine);

  return std::move(lambda);
}

llvm::Error JITLambda::invokeRaw(llvm::MutableArrayRef<void *> args) {
  auto found = std::find(args.begin(), args.end(), nullptr);
  if (found == args.end()) {
    return this->engine->invokePacked(this->name, args);
  }
  int pos = found - args.begin();
  return StreamStringError("invoke: argument at pos ")
         << pos << " is null or missing";
}

// memref is a struct which is flattened aligned, allocated pointers, offset,
// and two array of rank size for sizes and strides.
uint64_t numArgOfRankedMemrefCallingConvention(uint64_t rank) {
  return 3 + 2 * rank;
}

llvm::Expected<std::unique_ptr<clientlib::PublicResult>>
JITLambda::call(clientlib::PublicArguments &args) {
  // invokeRaw needs to have pointers on arguments and a pointers on the result
  // as last argument.
  // Prepare the outputs vector to store the output value of the lambda.
  auto numOutputs = 0;
  for (auto &output : args.clientParameters.outputs) {
    if (output.shape.dimensions.empty()) {
      // scalar gate
      if (output.encryption.hasValue()) {
        // encrypted scalar : memref<lweSizexi64>
        numOutputs += numArgOfRankedMemrefCallingConvention(1);
      } else {
        // clear scalar
        numOutputs += 1;
      }
    } else {
      // memref gate : rank+1 if the output is encrypted for the lwe size
      // dimension
      auto rank = output.shape.dimensions.size() +
                  (output.encryption.hasValue() ? 1 : 0);
      numOutputs += numArgOfRankedMemrefCallingConvention(rank);
    }
  }
  std::vector<void *> outputs(numOutputs);
  // Prepare the raw arguments of invokeRaw, i.e. a vector with pointer on
  // inputs and outputs.
  std::vector<void *> rawArgs(args.preparedArgs.size() + 1 /*runtime context*/ +
                              outputs.size());
  size_t i = 0;
  // Pointers on inputs
  for (auto &arg : args.preparedArgs) {
    rawArgs[i++] = &arg;
  }
  // Pointer on runtime context, the rawArgs take pointer on actual value that
  // is passed to the compiled function.
  auto rtCtxPtr = &args.runtimeContext;
  rawArgs[i++] = &rtCtxPtr;
  // Pointers on outputs
  for (auto &out : outputs) {
    rawArgs[i++] = &out;
  }

  // Invoke
  if (auto err = invokeRaw(rawArgs)) {
    return std::move(err);
  }

  // Store the result to the PublicResult
  std::vector<clientlib::TensorData> buffers;
  {
    size_t outputOffset = 0;
    for (auto &output : args.clientParameters.outputs) {
      if (output.shape.dimensions.empty() && !output.encryption.hasValue()) {
        // clear scalar
        buffers.push_back(
            clientlib::tensorDataFromScalar((uint64_t)outputs[outputOffset++]));
      } else {
        // encrypted scalar, and tensor gate are memref
        auto rank = output.shape.dimensions.size() +
                    (output.encryption.hasValue() ? 1 : 0);
        auto allocated = (uint64_t *)outputs[outputOffset++];
        auto aligned = (uint64_t *)outputs[outputOffset++];
        auto offset = (size_t)outputs[outputOffset++];
        size_t *sizes = (size_t *)&outputs[outputOffset];
        outputOffset += rank;
        size_t *strides = (size_t *)&outputs[outputOffset];
        outputOffset += rank;
        buffers.push_back(clientlib::tensorDataFromMemRef(
            rank, allocated, aligned, offset, sizes, strides));
      }
    }
  }
  return clientlib::PublicResult::fromBuffers(args.clientParameters, buffers);
}

} // namespace concretelang
} // namespace mlir