llama : differentiate the KV dims in the attention (#4657)

* Add n_key_dim and n_value_dim

Some models use values that are not derived from `n_embd`.
Also remove `n_embd_head` and `n_embd_gqa` because it is not clear
which "head" is referred to (key or value).

Fix issue #4648.

* Fix `llm_build_kqv` to use `n_value_gqa`

* Rebase

* Rename variables

* Fix llm_build_kqv to be more generic wrt n_embd_head_k

* Update default values for n_embd_head_k and n_embd_head_v

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Fix llm_load_tensors: the asserts were not backcompat

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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postmasters 2024-01-02 03:51:28 -08:00 committed by GitHub
parent 32866c5edd
commit 83e633c27e
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3 changed files with 201 additions and 78 deletions

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@ -46,6 +46,8 @@ class Keys:
HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
CLAMP_KQV = "{arch}.attention.clamp_kqv"
KEY_LENGTH = "{arch}.attention.key_length"
VALUE_LENGTH = "{arch}.attention.value_length"
LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"

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@ -333,6 +333,12 @@ class GGUFWriter:
def add_head_count_kv(self, count: int) -> None:
self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count)
def add_key_length(self, length: int) -> None:
self.add_uint32(Keys.Attention.KEY_LENGTH.format(arch=self.arch), length)
def add_value_length(self, length: int) -> None:
self.add_uint32(Keys.Attention.VALUE_LENGTH.format(arch=self.arch), length)
def add_max_alibi_bias(self, bias: float) -> None:
self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias)