rohitg00/ai-engineering-from-scratch · error · ValueError
max_context_length must be >= 1, got {max_context_length}
Error message
max_context_length must be >= 1, got {max_context_length} What it means
Error "max_context_length must be >= 1, got {max_context_length}" thrown in rohitg00/ai-engineering-from-scratch.
Source
Thrown at phases/19-capstone-projects/32-token-positional-embeddings/code/main.py:65
if ids.dtype != torch.long:
raise TypeError(f"ids must be long tensor, got {ids.dtype}")
if ids.dim() != 2:
raise ValueError(f"ids must be (B, T), got shape {tuple(ids.shape)}")
return self.embedding(ids)
class LearnedPositionalEmbedding(nn.Module):
"""Position-id to vector lookup with learned parameters."""
def __init__(
self,
max_context_length: int,
d_model: int,
init_std: float = DEFAULT_INIT_STD,
) -> None:
super().__init__()
if max_context_length < 1:
raise ValueError(f"max_context_length must be >= 1, got {max_context_length}")
if d_model < 1:
raise ValueError(f"d_model must be >= 1, got {d_model}")
self.max_context_length = max_context_length
self.d_model = d_model
self.embedding = nn.Embedding(max_context_length, d_model)
_init_normal(self.embedding.weight, std=init_std)
def forward(self, seq_len: int) -> torch.Tensor:
if seq_len < 1:
raise ValueError(f"seq_len must be >= 1, got {seq_len}")
if seq_len > self.max_context_length:
raise ValueError(
f"seq_len {seq_len} exceeds max_context_length {self.max_context_length}"
)
positions = torch.arange(seq_len, device=self.embedding.weight.device)
return self.embedding(positions)
View on GitHub (pinned to 39ea8a1c6d)
When it happens
Trigger: Thrown at phases/19-capstone-projects/32-token-positional-embeddings/code/main.py:65 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/341342ce76c401fa.
Report an issue: GitHub.