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ConsciousnessLearn · Explore · Joseph Anady

KimiK25ForConditionalGeneration._merge_input_ids_with_image_features: left padding

In KimiK25ForConditionalGeneration._merge_input_ids_with_image_features, this statement defines or updates left_padding. The reduction aggregates values along the explicit source axis; preserving the axis and keep-dimension setting matters for the later broadcast.

Donor model implementationhistorical source recovered; expression not executed in this website audit
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donor-eq-e3f2b72000132asource-code mathematical expression
leftpadding=nottorch.sum(inputids[:,−1]==torch.tensor(padtokenid))left_padding = not torch.sum( input_ids[:, -1] == torch.tensor(pad_token_id))

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In KimiK25ForConditionalGeneration._merge_input_ids_with_image_features, this statement defines or updates left_padding. The reduction aggregates values along the explicit source axis; preserving the axis and keep-dimension setting matters for the later broadcast.

Terms & definitions

The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.

How it works

  1. Read left_padding as the quantity being defined or updated by this relation.

  2. Substitute the source-defined variables or recorded values and follow the displayed arithmetic and indexing exactly.

  3. Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.

Assumptions & limits

Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.

Original context

Read the surrounding source passage
ignore_index: int = self.config.ignore_index

        batch_size, sequence_length = input_ids.shape
        left_padding = not torch.sum(
            input_ids[:, -1] == torch.tensor(pad_token_id))

        # 1. Create a mask to know where special image tokens are
        _token_occupation_table = torch.ones_like(input_ids.flatten())
Exact original notation
left_padding = not torch.sum(
            input_ids[:, -1] == torch.tensor(pad_token_id))

Related builds

Sources & provenance

01
kimi source tree.txt
lines 1967–1968 · Aug 5, 2026
left_padding = not torch.sum( input_ids[:, -1] == torch.tensor(pad_token_id))

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