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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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21
llms/mlx_lm/models/phi3small.py
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@@ -3,12 +3,12 @@
import math
from dataclasses import dataclass
from functools import partial
from typing import Dict, Optional, Tuple, Union
from typing import Any, Optional
import mlx.core as mx
import mlx.nn as nn
from .base import BaseModelArgs, KVCache, create_attention_mask
from .base import BaseModelArgs, create_attention_mask
@dataclass
@@ -22,14 +22,14 @@ class ModelArgs(BaseModelArgs):
num_attention_heads: int
layer_norm_epsilon: float
vocab_size: int
num_key_value_heads: Optional[int] = None
num_key_value_heads: int
mup_attn_multiplier: float = 1.0
mup_use_scaling: bool = True
mup_embedding_multiplier: float = 10.0
mup_width_multiplier: float = 8.0
rope_embedding_base: float = 1000000
rope_position_scale: float = 1.0
blocksparse_block_size: Tuple[int] = (64,)
blocksparse_block_size: int = 64
blocksparse_num_local_blocks: int = 16
blocksparse_vert_stride: int = 8
@@ -61,7 +61,6 @@ class Attention(nn.Module):
dim = args.hidden_size
self.n_heads = n_heads = args.num_attention_heads
assert args.num_key_value_heads is not None
self.n_kv_heads = n_kv_heads = args.num_key_value_heads
self.n_q_per_kv = n_heads // n_kv_heads
@@ -161,7 +160,7 @@ class Attention(nn.Module):
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[KVCache] = None,
cache: Optional[Any] = None,
) -> mx.array:
B, L, D = x.shape
@@ -230,7 +229,7 @@ class TransformerBlock(nn.Module):
self,
x: mx.array,
mask: Optional[mx.array] = None,
cache: Optional[KVCache] = None,
cache: Optional[Any] = None,
) -> mx.array:
r = self.self_attn(self.input_layernorm(x), mask, cache)
h = x + r
@@ -304,16 +303,8 @@ class Model(nn.Module):
def layers(self):
return self.model.layers
@property
def head_dim(self):
return self.args.hidden_size // self.args.num_attention_heads
def sanitize(self, weights):
# Remove unused precomputed rotary freqs
return {
k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
}
@property
def n_kv_heads(self):
return self.args.num_key_value_heads