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[PyTorch] Adjusted the logic of MHA and DPA to enable speculative decoding #668
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Signed-off-by: Oleg Goncharov <[email protected]>
Signed-off-by: Oleg Goncharov <[email protected]>
Signed-off-by: Oleg Goncharov <[email protected]>
Signed-off-by: Oleg Goncharov <[email protected]>
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[Pytorch] Adjusted the logic of MHA and DPA to enable speculative decoding
[PyTorch] Adjusted the logic of MHA and DPA to enable speculative decoding
Feb 16, 2024
/te-ci |
/te-ci pytorch |
timmoon10
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Feb 17, 2024
Co-authored-by: Tim Moon <[email protected]> Signed-off-by: Oleg Goncharov <[email protected]>
Co-authored-by: Tim Moon <[email protected]> Signed-off-by: Oleg Goncharov <[email protected]>
Co-authored-by: Tim Moon <[email protected]> Signed-off-by: Oleg Goncharov <[email protected]>
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Signed-off-by: Oleg Goncharov <[email protected]>
…ode review Signed-off-by: Oleg Goncharov <[email protected]>
Signed-off-by: Oleg Goncharov <[email protected]>
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LGTM, pending CI.
/te-ci pytorch |
Signed-off-by: Oleg Goncharov <[email protected]>
/te-ci pytorch |
/te-ci pytorch |
/te-ci pytorch |
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This PR modifies the logic of MHA and DPA by using InferenceParams (KV-cache), and extends the supported shapes of matrices in unfused softmax to rectangular to enable speculative decoding.
test_numerics.py
to check if the outputs of TransformerLayer or MultiheadAttention generated in a single full run match with those generated incrementally using KV-cache