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PD heterogenous TP #77
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Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
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Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
@@ -340,6 +369,7 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]): | |||
# MLA case. | |||
self.num_blocks = first_kv_cache.shape[0] | |||
block_rank = 2 # [block_size, latent_dim] | |||
# TODO does this include tp dependent size? |
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For MLA we replicate the KV cache across TP ranks, so in this case the prefiller would need to send the same blocks to all decoders. This is the same when TP size is greater than the num kv heads
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
Signed-off-by: nicklucche <[email protected]>
What this PR does:
TODOs: