Forward Pass
The fundamental computation process of neural network inference, in which a model receives an input token sequence and produces a probability distribution through successive layer computations. Each API call to an LLM constitutes an independent forward pass and retains no state across requests.
Timeline (last 90 days)
A forward pass refers to a complete process in which input data flows from the first layer of the network to the last layer, passing through the computation of each layer in turn to produce an output.
For each token generated or processed, one forward pass must be executed, and its computational cost is roughly proportional to the number of model parameters.
A single forward pass of a neural network is self-contained: it can only access the current context, cannot query external databases, cannot know future outcomes, and cannot recall whether it answered correctly on a previous similar input.
Improper weight initialization causes signals to decay or explode layer by layer during forward propagation, and gradients to vanish or diverge during backpropagation.
A forward pass is the process of a neural network computing once completely from the input layer to the output layer; an autoregressive language model needs N forward passes to generate N tokens, with latency growing linearly with output length; a classifier-style model needs only one forward pass.
Drex 1.5 can return probabilities for all candidate options in a single forward pass
When using a generative model to evaluate K candidate options, the total cost grows linearly with the number of options, whereas a single forward pass can compress the inference cost to a single computation
All Facts (7)
A forward pass refers to a complete process in which input data flows from the first layer of the network to the last layer, passing through the computation of each layer in turn to produce an output.
50%UnverifiedFor each token generated or processed, one forward pass must be executed, and its computational cost is roughly proportional to the number of model parameters.
50%UnverifiedA single forward pass of a neural network is self-contained: it can only access the current context, cannot query external databases, cannot know future outcomes, and cannot recall whether it answered correctly on a previous similar input.
50%UnverifiedImproper weight initialization causes signals to decay or explode layer by layer during forward propagation, and gradients to vanish or diverge during backpropagation.
50%UnverifiedA forward pass is the process of a neural network computing once completely from the input layer to the output layer; an autoregressive language model needs N forward passes to generate N tokens, with latency growing linearly with output length; a classifier-style model needs only one forward pass.
50%UnverifiedDrex 1.5 can return probabilities for all candidate options in a single forward pass
50%UnverifiedWhen using a generative model to evaluate K candidate options, the total cost grows linearly with the number of options, whereas a single forward pass can compress the inference cost to a single computation
50%