rohitg00/ai-engineering-from-scratch · error · TypeError
token_embedding must be a TokenEmbedding
Error message
token_embedding must be a TokenEmbedding
What it means
Error "token_embedding must be a TokenEmbedding" thrown in rohitg00/ai-engineering-from-scratch.
Source
Thrown at phases/19-capstone-projects/32-token-positional-embeddings/code/main.py:139
f"seq_len {seq_len} exceeds max_context_length {self.max_context_length}"
)
return self.pe[:seq_len]
class EmbeddingComposer(nn.Module):
"""Sums a token embedding with a positional embedding.
The positional embedding may be learned or sinusoidal.
"""
def __init__(
self,
token_embedding: TokenEmbedding,
positional_embedding: nn.Module,
) -> None:
super().__init__()
if not isinstance(token_embedding, TokenEmbedding):
raise TypeError("token_embedding must be a TokenEmbedding")
if not isinstance(
positional_embedding,
(LearnedPositionalEmbedding, SinusoidalPositionalEmbedding),
):
raise TypeError(
"positional_embedding must be Learned or Sinusoidal Positional Embedding"
)
if token_embedding.d_model != getattr(positional_embedding, "d_model", None):
raise ValueError("token and positional embeddings must share d_model")
self.token_embedding = token_embedding
self.positional_embedding = positional_embedding
@property
def d_model(self) -> int:
return self.token_embedding.d_model
def forward(self, ids: torch.Tensor) -> torch.Tensor:
if ids.dim() != 2:View on GitHub (pinned to 39ea8a1c6d)
When it happens
Trigger: Thrown at phases/19-capstone-projects/32-token-positional-embeddings/code/main.py:139 when the library encounters an invalid state.
Common situations: See trigger scenarios.
AI-assisted analysis of rohitg00/ai-engineering-from-scratch@39ea8a1c6d (2026-08-26).
Data as JSON: /api/errors/d0f3ffd39d704001.
Report an issue: GitHub.