Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Decouple custom ops in llama_transformer.py Part 1/N #3005

Closed
wants to merge 1 commit into from
Closed

Decouple custom ops in llama_transformer.py Part 1/N #3005

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 1 addition & 5 deletions examples/models/llama2/builder.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -206,11 +206,7 @@ def source_transform(
def _get_dynamic_shape(self) -> Any:
dim = torch.export.Dim("token_dim", max=self.model.params.max_seq_len - 1)
if self.use_kv_cache:
if self.use_sdpa_with_kv_cache:
return None
else:
# return {1: dim}, {0: dim}} TODO update xnnpack to be able to handle dynamic shape kv cache
return None
return None
else:
return ({1: dim},)

Expand Down
4 changes: 4 additions & 0 deletions examples/models/llama2/export_llama_lib.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -492,6 +492,10 @@ def _prepare_for_llama_export(modelname: str, args) -> LlamaEdgeManager:
if args.expand_rope_table:
transforms.append(materialze_broadcast_of_rope_freq_cis)

if args.use_sdpa_with_kv_cache:
pass
# TODO: Next diff transforms.append()

return (
load_llama_model(
checkpoint=checkpoint_path,
Expand Down
134 changes: 98 additions & 36 deletions examples/models/llama2/llama_transformer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -209,6 +209,95 @@ def update(
return k_out, v_out


class SDPA(nn.Module):
def __init__(
self,
kv_cache: KVCache,
mask,
use_sdpa_with_kv_cache_op: bool,
dim: int,
n_rep: int,
):
super().__init__()
self.kv_cache = kv_cache
self.mask = mask
self.use_sdpa_with_kv_cache_op = use_sdpa_with_kv_cache_op
self.dim = dim
self.n_rep = n_rep

def forward(
self,
input_pos: torch.Tensor,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
bsz,
seqlen,
) -> torch.Tensor:
if not self.use_sdpa_with_kv_cache_op:
return self._forward_default(
input_pos,
q,
k,
v,
bsz,
seqlen,
)
else:
return self._forward_custom(
input_pos,
q,
k,
v,
bsz,
seqlen,
)

def _forward_custom(
self,
input_pos: torch.Tensor,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
bsz,
seqlen,
):
from .custom_ops import sdpa_with_kv_cache # noqa

output = torch.ops.llama.sdpa_with_kv_cache(
q,
k,
v,
self.kv_cache.k_cache,
self.kv_cache.v_cache,
input_pos[-1].item(),
seqlen,
)
return output.view(bsz, seqlen, self.dim)

def _forward_default(
self,
input_pos: torch.Tensor,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
bsz,
seqlen,
) -> 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]

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)

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


class Attention(nn.Module):
def __init__(self, args: ModelArgs, layer_id: int):
super().__init__()
Expand All @@ -229,7 +318,6 @@ def __init__(self, args: ModelArgs, layer_id: int):
self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)

self.use_sdpa_with_kv_cache_op = args.use_sdpa_with_kv_cache_op
self.layer_id = layer_id

causal_mask = torch.tril(
Expand All @@ -250,6 +338,13 @@ def __init__(self, args: ModelArgs, layer_id: int):
self.head_dim,
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,
args.use_sdpa_with_kv_cache_op,
self.dim,
self.n_rep,
)

def forward(
self,
Expand All @@ -272,41 +367,8 @@ def forward(

if self.use_kv_cache:
assert input_pos is not None

if not self.use_sdpa_with_kv_cache_op:

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]

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

y = self.wo(y)
return y
else:
from .custom_ops import sdpa_with_kv_cache # noqa

output = torch.ops.llama.sdpa_with_kv_cache(
q,
k,
v,
self.kv_cache.k_cache,
self.kv_cache.v_cache,
input_pos[-1].item(),
seqlen,
)
output = output.view(bsz, seqlen, -1)
output = self.wo(output)
return output
output = self.SDPA(input_pos, q, k, v, bsz, seqlen)
return self.wo(output)

q = q.transpose(1, 2) # (bs, n_local_heads, seqlen, head_dim)
k = k.transpose(1, 2)
Expand Down
16 changes: 1 addition & 15 deletions examples/models/llama2/model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -209,11 +209,7 @@ def get_eager_model(self):

def get_example_inputs(self):
if self.use_kv_cache:
if self.use_sdpa_with_kv_cache_op:
return self.get_example_inputs_kvcache_sdpa()
else:
# return self.get_example_inputs_kvcache() TODO xnnpack does not handle forwarding symints, update partitioner to not partition symints
return self.get_example_inputs_kvcache_sdpa()
return self.get_example_inputs_kvcache_sdpa()
else:
return (
torch.tensor(
Expand All @@ -231,13 +227,3 @@ def get_example_inputs_kvcache_sdpa(self):
[0], dtype=torch.long
), # start_pos, what token of output are we on.)
)

def get_example_inputs_kvcache(self):
return (
torch.tensor(
[[1, 2, 3]], dtype=torch.long
), # tokens, with kv cache our input token length is always just 1 token.
torch.tensor(
[0, 1, 2], dtype=torch.long
), # start_pos, what token of output are we on.
)
Loading