ETP Operators & Core Types

This page documents the user-facing ETP operators — the ops you call inside a model’s update to make a weight participate in online learning — together with the small set of core types every algorithm consumes: input wrappers, the eligibility-trace state, gradient utilities, and the error hierarchy.

To add your own ETP primitive (with custom trace-propagation rules), see Custom ETP Primitives.

ETP Primitive Operators

These functions mark weight operations for inclusion in online learning. Use braintrace.matmul(x, w) instead of x @ w to include a weight in eligibility-trace computation; a parameter used through a regular JAX op is automatically excluded. There is no special parameter class — every brainstate.ParamState is eligible, and participation is decided purely by whether an ETP operator consumed it.

All operators accept physical-unit quantities (mantissa/unit are split, computed, and recombined) and come in batched and unbatched forms selected by input rank.

matmul	ETP-aware matrix multiplication.
element_wise	ETP-aware element-wise operation.
conv	ETP-aware convolution.
sparse_matmul	ETP-aware sparse matrix multiplication.
lora_matmul	ETP-aware LoRA (Low-Rank Adaptation) matrix multiplication.

Controlling Parameter Participation

import jax
import braintrace
import brainstate

class MyRNN(brainstate.nn.Module):
    def __init__(self):
        super().__init__()
        self.w_rec = brainstate.ParamState(...)   # want ETP
        self.w_in = brainstate.ParamState(...)     # do NOT want ETP
        self.h = brainstate.ShortTermState(...)

    def update(self, x):
        # regular matmul -> w_in excluded from ETP
        inp = x @ self.w_in.value
        # ETP matmul -> w_rec included in ETP
        self.h.value = jax.nn.tanh(inp + braintrace.matmul(self.h.value, self.w_rec.value))
        return self.h.value

Table 1 Parameter Selection Rules

Goal	How
Include a parameter in online learning	Use a braintrace.* ETP operator (e.g. braintrace.matmul(x, w)).
Exclude a parameter from online learning	Use a regular JAX op (e.g. x @ w).
Selection mechanism	The operation’s primitive type — not the parameter’s class. Every brainstate.ParamState is eligible; participation depends solely on whether an ETP primitive consumed it.

Input Data

Wrappers that tell an online-learning algorithm whether a step receives a single time step or a whole sequence of time steps.

SingleStepData	A container marking input data as belonging to a single time step.
MultiStepData	A container marking input data as spanning multiple time steps.

Eligibility Trace State

The state object that stores the eligibility trace carried forward across time steps during online learning.

EligibilityTrace

The state for storing the eligibility trace during the computation of online learning algorithms.

Gradient Utilities

Helpers used when combining per-step gradients into the running online gradient.

GradExpon

Accumulate gradients with an exponential (leaky) running sum.

Errors

Exceptions raised by the compilation and execution machinery.

NotSupportedError	Exception raised for operations that are not supported.
CompilationError	Exception raised for errors that occur during compilation.