Open KTibow opened 1 month ago
Code used:
v1 = loaded["P.weight"][36] / np.sqrt(np.sum(loaded["P.weight"][36] ** 2))
v2 = v1 * loaded["classifier.0.weight"][0]
v3 = v2 @ loaded["text_proj.0.weight"]
Hi there, thank you for raising this! You are right that currently, all the operations can be collapsed into a single matrix multiplication. The reason the operations are broken up is that we were experimenting with nonlinear variants as well when training the matrix factorization router.
The current matrix factorization router (
MFModel
) is unnecessarily complex. Given that all operations in the forward pass are linear with no activations, we can significantly simplify this model.Currently, we're doing several steps:
Since these are all linear operations, they can be collapsed into a single matrix multiplication, embedding * model. This would:
For an example of what this would look like, here's a flattened vector for the `mixtral-8x7b-instruct-v0.1` model:
``` [0.017317, 0.01118, 0.303653, 0.579448, -0.376634, -0.208742, -0.193392, 0.639659, -0.085909, 0.107312, 0.300785, -0.349391, -0.384368, -0.145022, 0.317397, -0.063074, -0.128751, 0.243364, -0.181707, 0.808825, 0.275169, 0.666149, -0.115858, 0.155953, 0.24292, -0.197154, -0.157491, 0.11632, 0.197647, 0.040279, 0.409797, -1.24056, -0.511287, -0.393113, -0.108808, 0.039914, 0.366597, 0.135737, 0.198802, 0.119974, 0.153426, -0.22505, 0.674797, 0.284063, -0.196429, 0.155066, -0.212335, -0.363016, 0.212736, 0.211674, -0.372157, 0.010955, 0.037939, -0.066029, -0.07933, -0.101132, -0.311588, 0.077285, -0.207608, 0.125983, 0.510143, -0.255973, -0.096116, 0.229892, -0.434007, 0.344456, -0.137472, 0.41125, -0.052777, 0.06959, -0.043151, -0.062137, 0.162818, 0.041656, 0.077479, 0.126347, 0.061875, 0.116124, 0.247373, 0.453157, -0.101855, 0.040579, -0.552021, 0.12112, -0.823787, -0.296899, -0.46667, 0.022095, -0.310721, -0.401873, 0.016014, 0.548683, -0.438079, 0.239599, 0.445288, 0.132415, 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