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.

Elementwise operations

def Runtime.Autograd.Torch.Internal.EagerSession.add {α : Type} (s : EagerSession α) [Add α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (a b : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise addition a + b. PyTorch: torch.add.

def Runtime.Autograd.Torch.Internal.EagerSession.sub {α : Type} (s : EagerSession α) [Sub α] [Zero α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (a b : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise subtraction a - b. PyTorch: torch.sub.

def Runtime.Autograd.Torch.Internal.EagerSession.mul {α : Type} (s : EagerSession α) [Mul α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (a b : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise multiplication a * b. PyTorch: torch.mul.

def Runtime.Autograd.Torch.Internal.EagerSession.scale {α : Type} [CudaBridge.TensorConv α] (s : EagerSession α) [Mul α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) (c : α) : IO (TensorRef α sh)

Record scaling by a scalar constant. PyTorch: x * c.

def Runtime.Autograd.Torch.Internal.EagerSession.abs {α : Type} (s : EagerSession α) [Context α] [DecidableRel fun (x1 x2 : α) => x1 > x2] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise absolute value. PyTorch: torch.abs.

def Runtime.Autograd.Torch.Internal.EagerSession.sqrt {α : Type} (s : EagerSession α) [Context α] [DecidableRel fun (x1 x2 : α) => x1 > x2] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise square root. PyTorch: torch.sqrt.

def Runtime.Autograd.Torch.Internal.EagerSession.clamp {α : Type} [CudaBridge.TensorConv α] (s : EagerSession α) [Context α] [DecidableRel fun (x1 x2 : α) => x1 > x2] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) (minVal maxVal : α) : IO (TensorRef α sh)

Record elementwise clamp to [minVal,maxVal]. PyTorch: torch.clamp.

def Runtime.Autograd.Torch.Internal.EagerSession.max {α : Type} (s : EagerSession α) [Context α] [DecidableRel fun (x1 x2 : α) => x1 > x2] [DecidableEq Spec.Shape] {sh : Spec.Shape} (a b : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise maximum. PyTorch: torch.maximum.

def Runtime.Autograd.Torch.Internal.EagerSession.min {α : Type} (s : EagerSession α) [Context α] [DecidableRel fun (x1 x2 : α) => x1 > x2] [DecidableEq Spec.Shape] {sh : Spec.Shape} (a b : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise minimum. PyTorch: torch.minimum.

def Runtime.Autograd.Torch.Internal.EagerSession.relu {α : Type} (s : EagerSession α) [Mul α] [Zero α] [Max α] [One α] [LT α] [DecidableRel fun (x1 x2 : α) => x1 > x2] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise ReLU. PyTorch: torch.relu / torch.nn.functional.relu.

def Runtime.Autograd.Torch.Internal.EagerSession.sigmoid {α : Type} (s : EagerSession α) [Context α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise sigmoid. PyTorch: torch.sigmoid.

def Runtime.Autograd.Torch.Internal.EagerSession.tanh {α : Type} (s : EagerSession α) [Context α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise tanh. PyTorch: torch.tanh.

def Runtime.Autograd.Torch.Internal.EagerSession.gelu {α : Type} (s : EagerSession α) [Context α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) : IO (TensorRef α sh)

Record tanh-approximate GELU as one tape operation.

def Runtime.Autograd.Torch.Internal.EagerSession.softmax {α : Type} (s : EagerSession α) [Context α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) : IO (TensorRef α sh)

Record softmax (shape-preserving).

PyTorch comparison: torch.softmax(x, dim=...) (dimension convention is chosen by the underlying tape op).

def Runtime.Autograd.Torch.Internal.EagerSession.logSoftmax {α : Type} (s : EagerSession α) [Context α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) : IO (TensorRef α sh)

Record stable log-softmax (shape-preserving, last-axis convention).

PyTorch comparison: torch.nn.functional.log_softmax(x, dim=-1).

def Runtime.Autograd.Torch.Internal.EagerSession.softplus {α : Type} (s : EagerSession α) [Context α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise softplus. PyTorch: torch.nn.functional.softplus.

def Runtime.Autograd.Torch.Internal.EagerSession.exp {α : Type} (s : EagerSession α) [Context α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise exponential. PyTorch: torch.exp.

def Runtime.Autograd.Torch.Internal.EagerSession.log {α : Type} (s : EagerSession α) [Context α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise log. PyTorch: torch.log.

def Runtime.Autograd.Torch.Internal.EagerSession.inv {α : Type} (s : EagerSession α) [Context α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) : IO (TensorRef α sh)

Record elementwise inverse 1/x. PyTorch: torch.reciprocal.

def Runtime.Autograd.Torch.Internal.EagerSession.safeLog {α : Type} [CudaBridge.TensorConv α] (s : EagerSession α) [Context α] [DecidableEq Spec.Shape] {sh : Spec.Shape} (x : TensorRef α sh) (ε : α := Numbers.epsilon) : IO (TensorRef α sh)

Record elementwise log with epsilon guard.

PyTorch comparison: torch.log(torch.clamp(x, min=ε)).