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

Eager Tensor Operations #

PyTorch-style tensor operations recorded on the session's tape. Each one runs the CPU or the CUDA kernel of the operation, as the session's device selects, and preserves 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) {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) {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) [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] {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) [Add α] [Zero α] [Div α] [Mul α] [One α] [NatCast 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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