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move mask as sdpa input instead of attribute (#3036) #3114

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Merged
merged 1 commit into from
Apr 18, 2024
Merged

move mask as sdpa input instead of attribute (#3036) #3114

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5 changes: 2 additions & 3 deletions examples/models/llama2/export_llama_lib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,12 +96,10 @@ class SDPACustom(torch.nn.Module):
def __init__(
self,
kv_cache: KVCache,
mask,
dim: int,
):
super().__init__()
self.kv_cache = kv_cache
self.mask = mask
self.dim = dim

def forward(
Expand All @@ -112,6 +110,7 @@ def forward(
v: torch.Tensor,
bsz,
seqlen,
mask,
):
output = torch.ops.llama.sdpa_with_kv_cache(
q,
Expand All @@ -131,7 +130,7 @@ def _replace_sdpa_with_custom_op(module: torch.nn.Module):
setattr(
module,
name,
SDPACustom(child.kv_cache, child.mask, child.dim),
SDPACustom(child.kv_cache, child.dim),
)
else:
_replace_sdpa_with_custom_op(child)
Expand Down
19 changes: 10 additions & 9 deletions examples/models/llama2/llama_transformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -197,14 +197,14 @@ class SDPA(nn.Module):
def __init__(
self,
kv_cache: KVCache,
mask,
dim: int,
head_dim: int,
n_rep: int,
):
super().__init__()
self.kv_cache = kv_cache
self.mask = mask
self.dim = dim
self.head_dim = head_dim
self.n_rep = n_rep

def forward(
Expand All @@ -215,17 +215,18 @@ def forward(
v: torch.Tensor,
bsz,
seqlen,
mask: torch.Tensor,
) -> torch.Tensor:
q = q.transpose(1, 2) # (bs, n_local_heads, seqlen, head_dim)
k = k.transpose(1, 2)
v = v.transpose(1, 2)

k, v = self.kv_cache.update(input_pos, k, v)
mask = self.mask[None, None, input_pos]
attn_mask = mask[None, None, input_pos]

k = k.repeat_interleave(self.n_rep, dim=1)
v = v.repeat_interleave(self.n_rep, dim=1)
y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=0.0)

return y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)

Expand Down Expand Up @@ -271,10 +272,10 @@ def __init__(self, args: ModelArgs, layer_id: int):
not args.use_sdpa_with_kv_cache_op, # if we are using the custom op dont transpose the cache. Expect untransposed q k v
)
self.SDPA = SDPA(
self.kv_cache,
self.mask,
self.dim,
self.n_rep,
kv_cache=self.kv_cache,
dim=self.dim,
head_dim=self.head_dim,
n_rep=self.n_rep,
)

def forward(
Expand All @@ -298,7 +299,7 @@ def forward(

if self.use_kv_cache:
assert input_pos is not None
output = self.SDPA(input_pos, q, k, v, bsz, seqlen)
output = self.SDPA(input_pos, q, k, v, bsz, seqlen, self.mask)
return self.wo(output)

q = q.transpose(1, 2) # (bs, n_local_heads, seqlen, head_dim)
Expand Down