76 lines
1.5 KiB
Python
76 lines
1.5 KiB
Python
import intel_extension_for_pytorch as ipex
|
|
import torch
|
|
from text_generation_server.models.flash_causal_lm import BLOCK_SIZE
|
|
from text_generation_server.layers.attention import Seqlen
|
|
|
|
SUPPORTS_WINDOWING = False
|
|
|
|
|
|
def attention(
|
|
q,
|
|
k,
|
|
v,
|
|
out,
|
|
cu_seqlens,
|
|
max_s,
|
|
softmax_scale,
|
|
window_size_left=-1,
|
|
causal=True,
|
|
):
|
|
# We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
|
|
return ipex.llm.functional.varlen_attention(
|
|
q,
|
|
k,
|
|
v,
|
|
out,
|
|
cu_seqlens,
|
|
cu_seqlens,
|
|
max_s,
|
|
max_s,
|
|
0.0,
|
|
softmax_scale,
|
|
False,
|
|
causal,
|
|
False,
|
|
None,
|
|
)
|
|
|
|
|
|
def reshape_and_cache(
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
key_cache: torch.Tensor,
|
|
value_cache: torch.Tensor,
|
|
slots: torch.Tensor,
|
|
):
|
|
ipex.llm.modules.PagedAttention.reshape_and_cache(
|
|
key, value, key_cache, value_cache, slots
|
|
)
|
|
|
|
|
|
def paged_attention(
|
|
out: torch.Tensor,
|
|
query: torch.Tensor,
|
|
key_cache: torch.Tensor,
|
|
value_cache: torch.Tensor,
|
|
kv_head_mapping: torch.Tensor,
|
|
softmax_scale: float,
|
|
block_tables: torch.Tensor,
|
|
seqlen: Seqlen,
|
|
max_s: int,
|
|
):
|
|
ipex.llm.modules.PagedAttention.single_query_cached_kv_attention(
|
|
out,
|
|
query,
|
|
key_cache,
|
|
value_cache,
|
|
kv_head_mapping,
|
|
softmax_scale,
|
|
block_tables,
|
|
seqlen.input_lengths,
|
|
BLOCK_SIZE,
|
|
max_s,
|
|
None,
|
|
)
|
|
return out
|