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NN.Runtime.Autograd.Torch.Core.Ops.LinearAlgebra

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.

Linear algebra and concatenation #

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def Runtime.Autograd.Torch.Internal.EagerSession.matmul {α : Type} (s : EagerSession α) [Context α] [DecidableRel fun (x1 x2 : α) => x1 > x2] [DecidableEq Spec.Shape] {m n p : ℕ} (a : TensorRef α (Spec.Shape.dim m (Spec.Shape.dim n Spec.Shape.scalar))) (b : TensorRef α (Spec.Shape.dim n (Spec.Shape.dim p Spec.Shape.scalar))) :
IO (TensorRef α (Spec.Shape.dim m (Spec.Shape.dim p Spec.Shape.scalar)))

2D matrix multiplication. PyTorch: torch.matmul for 2D tensors.

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    def Runtime.Autograd.Torch.Internal.EagerSession.bmm {α : Type} (s : EagerSession α) [Add α] [Mul α] [Zero α] [DecidableEq Spec.Shape] {batch m n p : ℕ} (a : TensorRef α (Spec.Shape.dim batch (Spec.Shape.dim m (Spec.Shape.dim n Spec.Shape.scalar)))) (b : TensorRef α (Spec.Shape.dim batch (Spec.Shape.dim n (Spec.Shape.dim p Spec.Shape.scalar)))) :
    IO (TensorRef α (Spec.Shape.dim batch (Spec.Shape.dim m (Spec.Shape.dim p Spec.Shape.scalar))))

    Batched matrix multiplication. PyTorch: torch.bmm.

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      def Runtime.Autograd.Torch.Internal.EagerSession.concatVectors {α : Type} (s : EagerSession α) [Context α] [DecidableRel fun (x1 x2 : α) => x1 > x2] [DecidableEq Spec.Shape] {n m : ℕ} (a : TensorRef α (Spec.Shape.dim n Spec.Shape.scalar)) (b : TensorRef α (Spec.Shape.dim m Spec.Shape.scalar)) :
      IO (TensorRef α (Spec.Shape.dim (n + m) Spec.Shape.scalar))

      Concatenate two vectors along dim 0. PyTorch: torch.cat([a,b], dim=0).

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        def Runtime.Autograd.Torch.Internal.EagerSession.concatLeadingAxis {α : Type} (s : EagerSession α) [DecidableEq Spec.Shape] {n m : ℕ} {sh : Spec.Shape} (a : TensorRef α (Spec.Shape.dim n sh)) (b : TensorRef α (Spec.Shape.dim m sh)) :
        IO (TensorRef α (Spec.Shape.dim (n + m) sh))

        Concatenate along dim 0 for tensors with leading dimension. PyTorch: torch.cat(..., dim=0).

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          def Runtime.Autograd.Torch.Internal.EagerSession.sliceLeadingAxisRange {α : Type} (s : EagerSession α) [Zero α] [DecidableEq Spec.Shape] {n : ℕ} {sh : Spec.Shape} (x : TensorRef α (Spec.Shape.dim n sh)) (start len : ℕ) (h : len + start ≤ n) :
          IO (TensorRef α (Spec.Shape.dim len sh))

          Slice along dim 0: x[start:start+len]. PyTorch: standard slicing.

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