For the complete documentation index, see llms.txt. Markdown versions of all pages are available by appending .md to any URL (e.g. /max/get-started.md).
Python function
causal_attention_mask_with_token_mask
causal_attention_mask_with_token_mask()
max.pipelines.modeling.dataprocessing.causal_attention_mask_with_token_mask(original_start_pos, token_mask, *, mask_name='token_mask')
Builds a causal attention mask and additionally masks invalid tokens.
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Parameters:
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- original_start_pos (list[int]) – Per-example start position (context length) in the batch.
- token_mask (Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str]) – Per-example validity mask for tokens in the current pass. Shape [seq_len] or [batch, seq_len]. True marks a valid token and False marks padding or any token that should be hidden.
- mask_name (str) – Name used in validation errors.
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Returns:
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Float32 additive mask array where visible positions are 0 and masked positions are a large negative value.
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Return type:
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