Skip to content

fix cross attention mask #230

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

Merged
buttercrab merged 1 commit into from
May 28, 2025
Merged

fix cross attention mask #230

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
3 changes: 2 additions & 1 deletion dia/layers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -333,7 +333,7 @@ def __init__(
self.kv_output_dim = num_kv_heads * kv_head_dim
self.linear = nn.Linear(in_features, out_features, bias=bias)

def forward(self, inputs: torch.Tensor) -> torch.Tensor:
def forward(self, inputs: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
x = self.linear(inputs)

q, k, v = x.split([self.q_output_dim, self.kv_output_dim, self.kv_output_dim], dim=-1)
Expand Down Expand Up @@ -723,6 +723,7 @@ def forward(
Xq=x_norm,
q_positions=state.dec_positions,
kv_positions=state.enc_positions,
attn_mask=state.cross_attn_mask,
cache=cross_attn_cache,
)
x = residual + ca_out
Expand Down
13 changes: 8 additions & 5 deletions dia/state.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,9 @@ def new(cls, config: DiaConfig, cond_src: torch.Tensor) -> "EncoderInferenceStat


class KVCache(torch.nn.Module):
k: torch.Tensor
v: torch.Tensor

def __init__(
self,
batch_size: int,
Expand All @@ -83,7 +86,6 @@ def __init__(
v = torch.zeros((2 * batch_size, num_heads, max_len, head_dim), dtype=dtype, device=device) if v is None else v
super().__init__()

self.current_idx = torch.tensor(0)
self.register_buffer("k", k)
self.register_buffer("v", v)

Expand All @@ -104,15 +106,12 @@ def update(self, k: torch.Tensor, v: torch.Tensor, current_idx: torch.Tensor) ->
k_out, v_out = self.k, self.v
k_out[:, :, current_idx, :] = k
v_out[:, :, current_idx, :] = v
# self.current_idx += 1
# return self.k[:, :, : self.current_idx, :], self.v[:, :, : self.current_idx, :]
return self.k, self.v

def prefill(self, k: torch.Tensor, v: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
def prefill(self, k: torch.Tensor, v: torch.Tensor):
prefill_len = k.shape[2]
self.k[:, :, :prefill_len, :] = k
self.v[:, :, :prefill_len, :] = v
self.current_idx = prefill_len - 1


@dataclass
Expand All @@ -127,6 +126,7 @@ class DecoderInferenceState:
self_attn_cache: list[KVCache]
cross_attn_cache: list[KVCache]
casual_attn_mask: torch.Tensor
cross_attn_mask: torch.Tensor

@classmethod
def new(
Expand All @@ -145,6 +145,8 @@ def new(

dec_positions = torch.full((2 * batch_size, 1), fill_value=0, dtype=torch.int32, device=device)
causal_mask = torch.tril(torch.ones(max_audio_len, max_audio_len, dtype=torch.bool, device=device))
dec_mask = torch.ones((2 * batch_size, 1), dtype=torch.bool, device=device)
cross_attn_mask = create_attn_mask(dec_mask, enc_state.padding_mask, device, is_causal=False)

self_attn_cache = [
KVCache(
Expand All @@ -167,6 +169,7 @@ def new(
self_attn_cache=self_attn_cache,
cross_attn_cache=dec_cross_attn_cache,
casual_attn_mask=causal_mask,
cross_attn_mask=cross_attn_mask,
)

def prepare_step(self, step_from: int, step_to: int | None = None) -> None:
Expand Down