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More cache improvements (#1015)

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* fix rotating kv cache for chat use case

* reorg + fixes to caching, unify prompt caching across types and use cases for e.g. caching during a chat

* nit in chat

* fix tests

* fix tests

* fix tests

* docs

* chat command

* comments + docs

* Define meta_state on all Cache implementations

* fixes + trim_prompt_cache api

* fix default model

---------

Co-authored-by: Angelos Katharopoulos <a_katharopoulos@apple.com>
This commit is contained in:
Awni Hannun
2024-10-07 20:45:51 -07:00
committed by GitHub
parent 9bc53fc210
commit fca087be49
43 changed files with 1151 additions and 691 deletions
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26
llms/mlx_lm/models/plamo.py
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@@ -1,7 +1,7 @@
# Copyright © 2023-2024 Apple Inc.
from dataclasses import dataclass
from typing import Any, List, Optional, Tuple, Union
from typing import Any, Optional
import mlx.core as mx
import mlx.nn as nn
@@ -62,8 +62,8 @@ class Attention(nn.Module):
self,
hidden_states: mx.array,
attention_mask: Optional[mx.array] = None,
cache: Optional[Tuple[mx.array, mx.array]] = None,
) -> Tuple[mx.array, Tuple[mx.array, mx.array]]:
cache: Optional[Any] = None,
) -> mx.array:
bsz, q_len, _ = hidden_states.shape
queries = self.q_proj(hidden_states)
@@ -127,8 +127,8 @@ class PlamoDecoderLayer(nn.Module):
self,
hidden_states: mx.array,
attention_mask: Optional[mx.array] = None,
cache: Optional[Tuple[mx.array, mx.array]] = None,
) -> Tuple[Any, ...]:
cache: Optional[Any] = None,
):
# from LlamaDecoder
residual = hidden_states
@@ -169,8 +169,8 @@ class PlamoModel(nn.Module):
def __call__(
self,
inputs: mx.array,
cache: Optional[List[Union[Tuple[mx.array, mx.array], None]]] = None,
) -> Tuple[mx.array, Optional[List[Union[Tuple[mx.array, mx.array], None]]]]:
cache: Optional[Any] = None,
) -> mx.array:
h = self.embed_tokens(inputs)
mask = create_attention_mask(h, cache)
@@ -197,19 +197,11 @@ class Model(nn.Module):
def __call__(
self,
inputs: mx.array,
cache: Optional[List[Tuple[mx.array, mx.array]]] = None,
) -> Tuple[mx.array, mx.array]:
cache: Optional[Any] = None,
) -> mx.array:
out = self.model(inputs, cache)
return self.lm_head(out)
@property
def layers(self):
return self.model.layers.layers
@property
def head_dim(self):
return self.args.hidden_size // self.args.num_attention_heads
@property
def n_kv_heads(self):
return self.args.num_attention_heads // self.args.n_shared_head