File size: 4,336 Bytes
84a4b93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | """Inference lifecycle for GDN2 state + convolution state + attention KV cache.
Use eval() and no_grad()/inference_mode(), enable once, then reset for each
new batch of equal-length prompts. No global OLMo or FLA monkey patches.
"""
from __future__ import annotations
import torch
# Optional accelerated-path dependency; see nova.architecture for the rationale.
try:
from olmo_core.nn.attention import Attention
from olmo_core.nn.attention.kv_cache import KVCacheManager
except ImportError as error: # pragma: no cover - exercised only without .[flash]
raise ImportError(
"nova.fast_decode requires the flash extras (OLMo-core). Install `.[flash]` "
"or load the model with inference_backend='torch'."
) from error
from .gdn2 import GatedDeltaNet2
__all__ = ["enable", "reset", "disable", "is_enabled"]
class _HostPositionKVCache(KVCacheManager):
"""Track a common prompt position without synchronizing a GPU scalar.
This subclass is attached only to the model passed to enable(); the base
OLMo-core KVCacheManager is left unchanged.
"""
def __init__(self, **kwargs):
# Keep mutable KV buffers usable when reset() is called outside the
# inference_mode context in which enable() originally ran.
with torch.inference_mode(False):
super().__init__(**kwargs)
self._host_pos = 0
def update_seqlen(self, seqlen: int):
super().update_seqlen(seqlen)
self._host_pos += int(seqlen)
def current_position(self):
return self._host_pos
def zero_cache(self):
super().zero_cache()
self._host_pos = 0
def reallocate(self, *args, **kwargs):
with torch.inference_mode(False):
super().reallocate(*args, **kwargs)
self._host_pos = 0
def _modules(model):
attn, gdn = [], []
for module in model.modules():
if isinstance(module, GatedDeltaNet2):
gdn.append(module)
elif isinstance(module, Attention):
attn.append(module)
return attn, gdn
def enable(model, max_seq_len: int, batch_size: int = 1) -> None:
"""Enable empty caches. Existing prompts are discarded, including on reuse."""
if model.training:
raise ValueError("Call model.eval() before fast_decode.enable()")
if batch_size <= 0 or max_seq_len <= 0:
raise ValueError("batch_size and max_seq_len must be positive")
attn, gdn = _modules(model)
if not gdn:
raise ValueError("No GatedDeltaNet2 layers found")
# Validate all backends before allocating or mutating any caches.
for module in attn:
module.backend.assert_supports_kv_cache()
try:
for module in attn:
cache = module.kv_cache_manager
if isinstance(cache, _HostPositionKVCache):
cache.reset(batch_size, max_seq_len)
else:
dtype = module.w_k.weight.dtype
if dtype not in (torch.float16, torch.bfloat16):
dtype = torch.bfloat16
module.kv_cache_manager = _HostPositionKVCache(
batch_size=batch_size, max_seq_len=max_seq_len,
num_kv_heads=module.n_kv_heads, head_dim=module.head_dim,
device=module.w_k.weight.device, dtype=dtype,
)
for module in gdn:
module.enable_decode(batch_size, max_seq_len)
except Exception:
disable(model)
raise
def reset(model, batch_size: int = 1, max_seq_len: int | None = None) -> None:
"""Start an independent prompt batch, resetting every type of state."""
_, gdn = _modules(model)
if not gdn or not all(module.decode_enabled for module in gdn):
raise ValueError("Call fast_decode.enable() before reset()")
length = max_seq_len if max_seq_len is not None else gdn[0]._decode_max_seq_len
enable(model, max_seq_len=length, batch_size=batch_size)
def disable(model) -> None:
"""Release inference caches before training or uncached evaluation."""
attn, gdn = _modules(model)
for module in attn:
module.kv_cache_manager = None
for module in gdn:
module.disable_decode()
def is_enabled(model) -> bool:
_, gdn = _modules(model)
return bool(gdn) and all(module.decode_enabled for module in gdn)
|