Function

class tensorplay.autograd.function.Function[source]

Records operation history and defines formulas for differentiating ops.

1. Legacy style: forward(ctx, ...) / backward(ctx, ...) (forward receives a context object).

2. Combined-forward style: define forward(*args, **kwargs), setup_context(ctx, inputs, output) and use save_for_backward/save_for_forward inside setup_context instead of receiving a ctx argument in forward.

classmethod apply(*args, **kwargs)[source]

Runs the operation and attaches gradient bookkeeping to outputs.

flat arguments computes needs_input_grad and wires next-edges BEFORE forward; outputs are marked and attached AFTER setup_context. When the fused C++ helpers are present the hot path makes two pybind crossings total (graph setup + output attach); otherwise a generic Python fallback runs.

static backward(ctx, *grad_outputs)[source]

Defines a formula for differentiating the operation.

static forward(ctx, *args, **kwargs)[source]

Performs the operation.

This function is to be overridden by all subclasses. There are two ways to define forward:

Usage 1 (Combined forward and ctx):

@staticmethod
def forward(ctx, input1, input2):
    ...
    return output

Usage 2 (Separated forward and ctx):

@staticmethod
def forward(input1, input2):
    ...
    return output

@staticmethod
def setup_context(ctx, inputs, output):
    ...

static jvp(ctx, *grad_inputs)[source]

Defines a formula for computing the jacobian-vector product.

Not yet supported by this engine; provided for API compatibility.

static setup_context(ctx, inputs, output)[source]

Sets up the context object (Usage 2 above).

Parameters:

• ctx (_Context) – context object to modify in-place

• inputs (tuple) – inputs to forward()

• output (Any) – output of forward()

static vmap(info, in_dims, *args)[source]

Defines a formula for vectorizing the operation.

Not yet supported by this engine; provided for API compatibility.