Memory Error After Increasing Context Window Size
Recently, we encountered a memory allocation error after increasing the context window size from 4K to 8K on a 16GB RAM laptop:
Error message:
(Memory Error) Unable to allocate 7.81 GiB for an array with shape (8192, 256000) and data type float32
This raises the question: Why does it attempt to allocate such a large amount of memory—more than the model itself?
Examining the error details:
8192 likely represents the context window size. 256000 corresponds to the vocabulary size. These values can be confirmed in the model's metadata:
'gemma2.context_length': '8192'
...
llm_load_print_meta: n_vocab = 256000
The error message indicates an array with shape (8192, 256000) and data type float32.
Memory calculation: 8192×256000×4 bytes=7.8125 GiB
This matches the exact memory allocation request. This array is likely the logits matrix, a crucial component in the model’s prediction process.
The logits matrix is a 2D array where:
Each row represents a token in the input/output sequence (bounded by the context window size).
Each column corresponds to a token in the model's vocabulary (which remains fixed for a given model).
Essentially, the logits matrix stores the unnormalized final scores used for predicting the next token. It is where the actual prediction of the next token takes place. The memory is allocated to optimize performance.