76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
import torch
|
|
from torch import nn
|
|
import triton
|
|
import triton.language as tl
|
|
|
|
from flash_attn.flash_attn_interface import flash_attn_varlen_func, flash_attn_with_kvcache
|
|
from nanovllm.utils.context import get_context
|
|
|
|
|
|
@triton.jit
|
|
def store_kvcache_kernel(
|
|
key_ptr,
|
|
key_stride,
|
|
value_ptr,
|
|
value_stride,
|
|
k_cache_ptr,
|
|
v_cache_ptr,
|
|
slot_mapping_ptr,
|
|
D: tl.constexpr,
|
|
):
|
|
idx = tl.program_id(0)
|
|
slot = tl.load(slot_mapping_ptr + idx)
|
|
if slot == -1: return
|
|
key_offsets = idx * key_stride + tl.arange(0, D)
|
|
value_offsets = idx * value_stride + tl.arange(0, D)
|
|
key = tl.load(key_ptr + key_offsets)
|
|
value = tl.load(value_ptr + value_offsets)
|
|
cache_offsets = slot * D + tl.arange(0, D)
|
|
tl.store(k_cache_ptr + cache_offsets, key)
|
|
tl.store(v_cache_ptr + cache_offsets, value)
|
|
|
|
|
|
def store_kvcache(key: torch.Tensor, value: torch.Tensor, k_cache: torch.Tensor, v_cache: torch.Tensor, slot_mapping: torch.Tensor):
|
|
N, num_heads, head_dim = key.shape
|
|
D = num_heads * head_dim
|
|
assert key.stride(-1) == 1 and value.stride(-1) == 1
|
|
assert key.stride(1) == head_dim and value.stride(1) == head_dim
|
|
assert k_cache.stride(1) == D and v_cache.stride(1) == D
|
|
assert slot_mapping.numel() == N
|
|
store_kvcache_kernel[(N,)](key, key.stride(0), value, value.stride(0), k_cache, v_cache, slot_mapping, D)
|
|
|
|
|
|
class Attention(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
num_heads,
|
|
head_dim,
|
|
scale,
|
|
num_kv_heads,
|
|
):
|
|
super().__init__()
|
|
self.num_heads = num_heads
|
|
self.head_dim = head_dim
|
|
self.scale = scale
|
|
self.num_kv_heads = num_kv_heads
|
|
self.k_cache = self.v_cache = torch.tensor([])
|
|
|
|
def forward(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor):
|
|
context = get_context()
|
|
k_cache, v_cache = self.k_cache, self.v_cache
|
|
if k_cache.numel() and v_cache.numel():
|
|
store_kvcache(k, v, k_cache, v_cache, context.slot_mapping)
|
|
if context.is_prefill:
|
|
if context.block_tables is not None: # prefix cache
|
|
k, v = k_cache, v_cache
|
|
o = flash_attn_varlen_func(q, k, v,
|
|
max_seqlen_q=context.max_seqlen_q, cu_seqlens_q=context.cu_seqlens_q,
|
|
max_seqlen_k=context.max_seqlen_k, cu_seqlens_k=context.cu_seqlens_k,
|
|
softmax_scale=self.scale, causal=True, block_table=context.block_tables)
|
|
else: # decode
|
|
o = flash_attn_with_kvcache(q.unsqueeze(1), k_cache, v_cache,
|
|
cache_seqlens=context.context_lens, block_table=context.block_tables,
|
|
softmax_scale=self.scale, causal=True)
|
|
return o
|