Principle:Tensorflow Tfjs Causal Language Model Head

Summary

Causal language model head refers to the output projection layer that converts a transformer backbone's hidden states into next-token predictions. This is a library-agnostic concept: a causal LM head projects hidden representations back to vocabulary logits, typically using weight tying (reusing the token embedding weights transposed) to reduce parameters and improve performance.

Theory

The causal language model head is the final component in an autoregressive language model, converting contextualized hidden states into token probabilities.

The process works as follows:

1. Input: The backbone produces final hidden states of shape [batch, seq_len, hidden_dim].
2. Projection: The hidden states are projected to vocabulary logits via a reverse embedding (weight tying):

   logits = hidden_states × embedding_weights^T

3. Output: Logits of shape [batch, seq_len, vocab_size] where each position i predicts the token at position i+1.

Weight Tying

Weight tying is a technique where the output projection matrix shares weights with the input token embedding matrix (transposed). Given the token embedding matrix E of shape [vocab_size, hidden_dim]:

• Embedding: token_id → E[token_id] (row lookup)
• Reverse Embedding: hidden_state → hidden_state × E^T (matrix multiplication)

Aspect	Without Weight Tying	With Weight Tying
Parameters	Separate embedding + projection matrices	Single shared embedding matrix
Parameter Count	2 × V × dmodel	V × dmodel
Performance	Baseline	Generally improved (regularization effect)

Causal Masking

The term causal refers to the constraint that each position's prediction depends only on previous positions. The backbone enforces this via causal attention masking, and the LM head simply projects the already-causally-constrained hidden states to logits.

Key Properties

• Weight tying: Reduces model parameters by approximately V × d_model (e.g., ~38M parameters saved for GPT-2).
• Autoregressive: Each position predicts the next token, enabling left-to-right text generation.
• No additional learnable parameters: The reverse embedding introduces no new weights beyond those already in the embedding layer.
• Softmax-ready: Output logits can be converted to probabilities via softmax for sampling or loss computation.

Implementation

Implementation:Tensorflow_Tfjs_GPT2CausalLM_Constructor

Domains

NLP Language_Modeling

Sources

TensorFlow.js