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Kv cache (#643)

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* in place kv_cache

* fix

* fix kv cache size

* partially fix kv cache dtype

* step kv cache

* multiple of step size

* more teests + kv cache

* more kv cache

* udpate all models to use kv cache
This commit is contained in:
Awni Hannun
2024-05-08 08:18:13 -07:00
committed by GitHub
parent bfbc0e434a
commit ee60e2a9d5
22 changed files with 534 additions and 298 deletions
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29
llms/mlx_lm/models/olmo.py
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@@ -78,11 +78,9 @@ class TransformerBlock(nn.Module):
values = values.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
if cache is not None:
key_cache, value_cache = cache
queries = self.rope(queries, offset=key_cache.shape[2])
keys = self.rope(keys, offset=key_cache.shape[2])
keys = mx.concatenate([key_cache, keys], axis=2)
values = mx.concatenate([value_cache, values], axis=2)
queries = self.rope(queries, offset=cache.offset)
keys = self.rope(keys, offset=cache.offset)
keys, values = cache.update_and_fetch(keys, values)
else:
queries = self.rope(queries)
keys = self.rope(keys)
@@ -92,7 +90,7 @@ class TransformerBlock(nn.Module):
scores += mask
scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
output = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
return self.attn_out(output), (keys, values)
return self.attn_out(output)
def __call__(
self,
@@ -100,13 +98,13 @@ class TransformerBlock(nn.Module):
mask: Optional[mx.array] = None,
cache: Optional[Tuple[mx.array, mx.array]] = None,
) -> mx.array:
r, cache = self.attend(self.att_norm(x), mask, cache)
r = self.attend(self.att_norm(x), mask, cache)
h = x + r
x1, x2 = mx.split(self.ff_proj(self.ff_norm(h)), 2, axis=-1)
out = h + self.ff_out(nn.silu(x2) * x1)
return out, cache
return out
class Transformer(nn.Module):
@@ -136,15 +134,15 @@ class Transformer(nn.Module):
if cache is None:
cache = [None] * len(self.blocks)
for e, block in enumerate(self.blocks):
h, cache[e] = block(h, mask, cache[e])
for block, c in zip(self.blocks, cache):
h = block(h, mask, c)
h = self.norm(h)
if self.weight_tying:
return self.wte.as_linear(h), cache
return self.ff_out(h), cache
return self.ff_out(h)
class OlmoModel(nn.Module):
@@ -165,6 +163,7 @@ class Model(nn.Module):
super().__init__()
self.model_type = args.model_type
self.model = OlmoModel(args)
self.args = args
def __call__(
self,
@@ -176,3 +175,11 @@ class Model(nn.Module):
@property
def layers(self):
return self.model.transformer.blocks
@property
def head_dim(self):
return self.args.d_model // self.args.n_heads
@property
def n_kv_heads(self):
return self.args.n_heads