hasktorch 0.2.1.7 → 0.2.1.8

raw patch · 4 files changed

+48/−10 lines, 4 filesdep ~ghc-typelits-extradep ~ghc-typelits-knownnatdep ~ghc-typelits-natnormalisePVP: major bump suggested

API removals or changes: PVP suggests a major version bump

Dependency ranges changed: ghc-typelits-extra, ghc-typelits-knownnat, ghc-typelits-natnormalise, libtorch-ffi, lifted-async, random, template-haskell

API changes (from Hackage documentation)

Files

• hasktorch.cabal +8/−8
• src/Torch/Autograd.hs +16/−0
• src/Torch/Typed/Autograd.hs +22/−0
• test/Torch/Typed/FunctionalSpec2.hs +2/−2

@@ -1,6 +1,6 @@ cabal-version:       3.0 name:                hasktorch-version:             0.2.1.7+version:             0.2.1.8 synopsis:            Haskell bindings to libtorch, supporting both typed and untyped tensors. description:         Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the core C++ libraries shared by PyTorch. homepage:            https://github.com/hasktorch/hasktorch#readme@@ -107,15 +107,15 @@   cpp-options:        -D__APPLE__  build-depends:       async >= 2.2.5 && < 2.3                     , base >= 4.7 && < 5-                    , libtorch-ffi == 2.0.1.*+                    , libtorch-ffi >= 2.0.1.10 && < 2.0.2                     , libtorch-ffi-helper == 2.0.0.*                     , finite-typelits >= 0.1 && < 0.3-                    , ghc-typelits-extra >= 0.4.6 && < 0.5-                    , ghc-typelits-knownnat >= 0.7.9 && < 0.8-                    , ghc-typelits-natnormalise >= 0.7.9 && < 0.8+                    , ghc-typelits-extra >= 0.4.6 && < 0.6+                    , ghc-typelits-knownnat >= 0.7.9 && < 0.9+                    , ghc-typelits-natnormalise >= 0.7.9 && < 0.10                     , mtl >= 2.3.1 && < 2.4                     , safe-exceptions >= 0.1.7 && < 0.2-                    , random >= 1.2.1 && < 1.3+                    , random >= 1.2.1 && < 1.4                     , reflection >= 2.1 && < 2.2                     , stm >= 2.5.1 && < 2.6                     , JuicyPixels >= 3.3 && < 3.4@@ -131,7 +131,7 @@                     , pipes-csv >= 1.4.3 && < 1.5                     , lens-family-core >= 2.1.3 && < 2.2                     , cassava >= 0.5.3 && < 0.6-                    , lifted-async >= 0.10.2 && < 0.11+                    , lifted-async >= 0.10.2 && < 0.12                     , monad-control >= 1.0.3 && < 1.1                     , foldl >= 1.4 && < 1.5                     , transformers-base >= 0.4.6 && < 0.5@@ -140,7 +140,7 @@                     , containers >= 0.6.7 && < 0.8                     , inline-c >= 0.9.1 && < 0.10                     , vector-sized >= 1.5 && < 1.7-                    , template-haskell >= 2.20.0 && < 2.23+                    , template-haskell >= 2.20.0 && < 2.24                     , megaparsec >= 9.5 && < 9.8                     , half >= 0.3 && < 0.4                     , constraints >= 0.14 && < 0.15

@@ -2,6 +2,7 @@ {-# LANGUAGE FlexibleInstances #-} {-# LANGUAGE MultiParamTypeClasses #-} {-# LANGUAGE ScopedTypeVariables #-}+{-# LANGUAGE RecordWildCards #-}  module Torch.Autograd where @@ -14,7 +15,9 @@ import qualified Torch.Internal.Managed.Type.Tensor as ATen import qualified Torch.Internal.Type as ATen import Torch.Tensor+import Data.Default.Class + -- | Note: to create an `IndependentTensor` use `makeIndependent`; -- | otherwise, Torch will complain the parameter does not require a gradient. newtype IndependentTensor = IndependentTensor@@ -22,8 +25,21 @@   }   deriving (Show, Generic) +data GradOptions = GradOptions+  { keepGraph :: Bool+  , createGraph :: Bool+  , accumulateGrad :: Bool+  }+  deriving (Show)++instance Default GradOptions where+  def = GradOptions True False False+ grad :: Tensor -> [IndependentTensor] -> [Tensor] grad y inputs = unsafePerformIO $ cast2 Torch.Internal.Managed.Autograd.grad y (map toDependent inputs)++gradWithOptions :: GradOptions -> Tensor -> [IndependentTensor] -> [Tensor]+gradWithOptions GradOptions{..} y inputs = unsafePerformIO $ cast5 Torch.Internal.Managed.Autograd.gradWithOptions keepGraph createGraph accumulateGrad y (map toDependent inputs)  requiresGrad :: Tensor -> Bool requiresGrad t = unsafePerformIO $ cast1 ATen.tensor_requires_grad t

@@ -9,6 +9,7 @@ {-# LANGUAGE TypeOperators #-} {-# LANGUAGE UndecidableInstances #-} {-# LANGUAGE NoStarIsType #-}+{-# LANGUAGE RecordWildCards #-}  module Torch.Typed.Autograd   ( Torch.Typed.Autograd.HasGrad,@@ -28,10 +29,13 @@ import qualified Torch.Tensor as D import Torch.Typed.Parameter import Torch.Typed.Tensor+import Torch.Autograd (GradOptions(..)) + class HasGrad a b | a -> b where   -- | calculate gradients of a zero-dimensional tensor with respect to a list of parameters   grad :: forall dtype device. Tensor device dtype '[] -> a -> b+  gradWithOptions :: forall dtype device. GradOptions -> Tensor device dtype '[] -> a -> b    toDependent :: a -> b @@ -49,6 +53,15 @@         LibTorch.grad         loss         [Torch.Typed.Autograd.toDependent input]+  gradWithOptions GradOptions{..} loss input =+    head . unsafePerformIO $+      ATen.cast5+        LibTorch.gradWithOptions+        keepGraph+        createGraph+        accumulateGrad+        loss+        [Torch.Typed.Autograd.toDependent input]   toDependent = Torch.Typed.Parameter.toDependent  instance HasGrad (HList ('[] :: [Type])) (HList ('[] :: [Type])) where@@ -66,6 +79,15 @@     unsafePerformIO $       ATen.cast2         LibTorch.grad+        loss+        (Torch.Typed.Autograd.toDependent inputs)+  gradWithOptions GradOptions{..} loss inputs =+    unsafePerformIO $+      ATen.cast5+        LibTorch.gradWithOptions+        keepGraph+        createGraph+        accumulateGrad         loss         (Torch.Typed.Autograd.toDependent inputs)   toDependent (a :. as) =

@@ -635,8 +635,8 @@       it "solve" $ do         let solveShapes =               hzip-                (Proxy @'[1, 0] :. Proxy @'[1, 2] :. Proxy @'[2, 1] :. Proxy @'[3, 1, 2] :. HNil)-                (Proxy @'[1, 1] :. Proxy @'[1, 1] :. Proxy @'[2, 2] :. Proxy @'[3, 1, 1] :. HNil)+                (Proxy @'[1, 2] :. Proxy @'[2, 1] :. Proxy @'[3, 1, 2] :. HNil)+                (Proxy @'[1, 1] :. Proxy @'[2, 2] :. Proxy @'[3, 1, 1] :. HNil)         case device of           Device {deviceType = CPU, deviceIndex = 0} ->             hfoldrM @IO SolveSpec () (hattach cpu (hproduct standardFloatingPointDTypes solveShapes))