[skip ci] ViT TTNN Tech Report #12800
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mbahnasTT
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bbradelTT
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tech_reports/ViT-TTNN/vit.md
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| ` seqL × head_count × head_size` | ||
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| This step aggregates the outputs from the different heads into a single vector representation for each position in the sequence. The followin step is the Linear OP to calculate the self output, which is the output of the self multi-head attention module. |
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tech_reports/ViT-TTNN/vit.md
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| ``` | ||
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| #### 4.4.1 Q,K,V Generation using the Fused Linear OP | ||
| The encoder input is matrix-mutiplied by the Q,K,V weights to generate the individual Query, Key, Value tensors. In the TT-NN implementation, the input is multipled by the pre-fused weights to generate the merged 3 tensors that will be split in a following step. The fused linear operation obective is to maximize the utilization by increasing the workload that is computed simultaneously on the Tensic core grid. |
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fix: multipled, obective
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