libtorch-ffi 2.0.1.9 → 2.0.1.10

raw patch · 4 files changed

+18/−11 lines, 4 filesdep −optparse-applicativedep ~template-haskellsetup-changedPVP: minor bump suggested

API additions: PVP suggests at least a minor version bump

Dependencies removed: optparse-applicative

Dependency ranges changed: template-haskell

API changes (from Hackage documentation)

Files

• Setup.hs +4/−4
• libtorch-ffi.cabal +5/−3
• src/Torch/Internal/Managed/Autograd.hs +2/−0
• src/Torch/Internal/Unmanaged/Autograd.hs +7/−4

@@ -70,7 +70,7 @@ getLibtorchVersion = do   mVersion <- lookupEnv "LIBTORCH_VERSION"   case mVersion of-    Nothing -> return "2.5.0"+    Nothing -> return "2.9.1"     Just other -> return other  getLocalUserLibtorchDir :: IO FilePath@@ -97,7 +97,7 @@  getCudaFlavor :: IO String getCudaFlavor = do-  fromMaybe "cpu" <$> lookupEnv "LIBTORCH_CUDA_VERSION"  -- "cpu" | "cu117" | "cu118" | "cu121"+  fromMaybe "cpu" <$> lookupEnv "LIBTORCH_CUDA_VERSION"  -- "cpu" | "cu126" | "cu128" | "cu130"  ensureLibtorch :: IO (Maybe FilePath) ensureLibtorch = do@@ -188,9 +188,9 @@                  , "libtorch-macos-x86_64.zip" )       _       -> error "Unsupported macOS arch"     Linux -> case flavor of-      "cpu"  -> ( "https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-" ++ v ++ "%2Bcpu.zip"+      "cpu"  -> ( "https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-" ++ v ++ "%2Bcpu.zip"                 , "libtorch-linux.zip" )-      cudaVersion -> ( "https://download.pytorch.org/libtorch/" ++ cudaVersion ++"/libtorch-cxx11-abi-shared-with-deps-" ++ v ++ "%2B" ++ cudaVersion ++ ".zip"+      cudaVersion -> ( "https://download.pytorch.org/libtorch/" ++ cudaVersion ++"/libtorch-shared-with-deps-" ++ v ++ "%2B" ++ cudaVersion ++ ".zip"                 , "libtorch-linux-" ++ cudaVersion ++ ".zip" )     Windows -> error "Windows not supported by this setup" 

@@ -1,6 +1,6 @@ cabal-version:       3.0 name:                libtorch-ffi-version:             2.0.1.9+version:             2.0.1.10 -- The prefix(2.0) of this version("2.0.0.0") is the same as libtorch's one. synopsis:            Haskell bindings for PyTorch description:         This package provides Haskell bindings to libtorch, the C++ library underlying PyTorch, specifically designed for the Hasktorch ecosystem.@@ -155,9 +155,8 @@  build-depends:       base >= 4.7 && < 5                     , inline-c-cpp >= 0.5.0.2 && < 0.6.0.0                     , inline-c >= 0.9.1.10 && < 0.10-                    , optparse-applicative >= 0.14.3.0 && < 0.19                     , containers >= 0.6.7 && < 0.8-                    , template-haskell >= 2.20.0 && < 2.23+                    , template-haskell >= 2.20.0 && < 2.24                     , bytestring >= 0.11.5 && < 0.13                     , safe-exceptions >= 0.1.7 && < 0.2                     , sysinfo >= 0.1.1 && < 0.2@@ -193,6 +192,9 @@   if !flag(gcc)     cxx-options:       -std=c++17     ghc-options:       -optcxx-std=c++17+ else+  cxx-options:       -std=c++17+  ghc-options:       -optcxx-std=c++17  default-extensions:          Strict                             , StrictData  cxx-sources:          csrc/hasktorch_finalizer.cpp

@@ -14,6 +14,8 @@ grad :: ForeignPtr Tensor -> ForeignPtr TensorList -> IO (ForeignPtr TensorList) grad = _cast2 Unmanaged.grad +gradWithOptions :: Bool -> Bool -> Bool -> ForeignPtr Tensor -> ForeignPtr TensorList -> IO (ForeignPtr TensorList)+gradWithOptions = _cast5 Unmanaged.gradWithOptions  makeIndependent :: ForeignPtr Tensor -> CBool -> IO (ForeignPtr Tensor) makeIndependent = _cast2 Unmanaged.makeIndependent

@@ -25,7 +25,10 @@ C.include "<ATen/core/functional.h>"  grad :: Ptr Tensor -> Ptr TensorList -> IO (Ptr TensorList)-grad y inputs = [C.throwBlock| std::vector<at::Tensor>* {+grad = gradWithOptions 1 0 0++gradWithOptions :: CBool -> CBool -> CBool -> Ptr Tensor -> Ptr TensorList -> IO (Ptr TensorList)+gradWithOptions keepGraph createGraph accumulateGrad y inputs = [C.throwBlock| std::vector<at::Tensor>* {     torch::autograd::Variable y = *$(at::Tensor* y);     const auto & inputs = *$(std::vector<at::Tensor>* inputs); @@ -59,9 +62,9 @@      auto & engine = torch::autograd::Engine::get_default_engine();     auto outputs = engine.execute(roots, grads,-                                  /*keep_graph=*/true,-                                  /*create_graph=*/false,-                                  /*accumulate_grad=*/false, // https://github.com/pytorch/pytorch/pull/46855+                                  /*keep_graph=*/$(bool keepGraph),+                                  /*create_graph=*/$(bool createGraph),+                                  /*accumulate_grad=*/$(bool accumulateGrad), // https://github.com/pytorch/pytorch/pull/46855                                                              // https://github.com/pytorch/pytorch/issues/46373                                   output_edges);