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NN.Runtime.Autograd.Torch.Core.Ops.ShapeReduction

Eager Tensor Operations #

PyTorch-style tensor operations backed by the eager CPU/CUDA tapes. These wrappers record runtime nodes, dispatch CUDA kernels when requested, and preserve the typed TensorRef surface.

Shape and reduction operations #

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def Runtime.Autograd.Torch.Internal.EagerSession.sum {α : Type} [TorchLean.Storage α] (s : EagerSession α) [Add α] [Zero α] {sh : Spec.Shape} (x : TensorRef α sh) :
IO (TensorRef α Spec.Shape.scalar)

Sum-reduce all elements to a scalar. PyTorch: x.sum().

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    def Runtime.Autograd.Torch.Internal.EagerSession.flatten {α : Type} [TorchLean.Storage α] (s : EagerSession α) [Inhabited α] {sh : Spec.Shape} (x : TensorRef α sh) :
    IO (TensorRef α [sh.size])

    Flatten a tensor to a 1D vector. PyTorch: torch.flatten.

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      def Runtime.Autograd.Torch.Internal.EagerSession.reshape {α : Type} [TorchLean.Storage α] (s : EagerSession α) [Inhabited α] {sh1 sh2 : Spec.Shape} (x : TensorRef α sh1) (h : sh1.size = sh2.size) :
      IO (TensorRef α sh2)

      Reshape a tensor while preserving total number of elements.

      PyTorch comparison: torch.reshape / view (when valid).

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        def Runtime.Autograd.Torch.Internal.EagerSession.swapAdjacentAtDepth {α : Type} [TorchLean.Storage α] (s : EagerSession α) {sh : Spec.Shape} (depth : ℕ) (x : TensorRef α sh) :
        IO (TensorRef α (sh.swapAdjacentAtDepth depth))

        Swap two adjacent axes at a given depth. PyTorch analogue: x.transpose(dim, dim+1).

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          def Runtime.Autograd.Torch.Internal.EagerSession.broadcastTo {α : Type} [TorchLean.Storage α] (s : EagerSession α) [Inhabited α] [Add α] [Zero α] {sh1 sh2 : Spec.Shape} (cb : sh1.CanBroadcastTo sh2) (x : TensorRef α sh1) :
          IO (TensorRef α sh2)

          Broadcast a tensor to a larger shape. PyTorch: implicit broadcasting / expand.

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            def Runtime.Autograd.Torch.Internal.EagerSession.reduceSum {α : Type} [TorchLean.Storage α] (s : EagerSession α) [Add α] [Zero α] [Inhabited α] {sh : Spec.Shape} (axis : ℕ) [valid : Spec.Shape.HasNonemptyAxis axis sh] [wf : sh.WellFormed] (x : TensorRef α sh) :
            IO (TensorRef α (TorchLean.Tensor.shapeAfterSum sh axis))

            Sum-reduce along axis. PyTorch: torch.sum(x, dim=axis).

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              def Runtime.Autograd.Torch.Internal.EagerSession.reduceMean {α : Type} [TorchLean.Storage α] (s : EagerSession α) [Context α] {sh : Spec.Shape} (axis : ℕ) [valid : Spec.Shape.HasNonemptyAxis axis sh] [wf : sh.WellFormed] (x : TensorRef α sh) :
              IO (TensorRef α (TorchLean.Tensor.shapeAfterSum sh axis))

              Mean-reduce along axis. PyTorch: torch.mean(x, dim=axis).

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