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Fix tree mask conflict #62

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Merged
liusong1222 merged 2 commits into from
Sep 3, 2025
Merged

Fix tree mask conflict #62

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f3dd751
fix tree mask conflict
RuBing-Yang Sep 2, 2025
88f47ee
fix typo
RuBing-Yang Sep 3, 2025
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101 changes: 56 additions & 45 deletions angelslim/compressor/speculative/inference/models/eagle3/target/modeling_qwen3_kv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -765,7 +765,7 @@ def _update_causal_mask(
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
else past_seen_tokens + sequence_length
)

# In case the provided `attention` mask is 2D, we generate a
Expand Down Expand Up @@ -849,50 +849,61 @@ def _prepare_4d_causal_attention_mask_with_cache_position(
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(
(sequence_length, target_length),
fill_value=min_dtype,
dtype=dtype,
device=device,
)
diagonal_attend_mask = torch.arange(
target_length, device=device
) > cache_position.reshape(-1, 1)
if config.sliding_window is not None:
# if we have sliding window, we should not attend to tokens beyond
# sliding window length, so we mask them out also the check is needed
# to verify is current checkpoint was trained with sliding window or not
if (
not isinstance(past_key_values, SlidingWindowCache)
or sequence_length > target_length
):
sliding_attend_mask = torch.arange(
target_length, device=device
) <= (cache_position.reshape(-1, 1) - config.sliding_window)
diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
causal_mask *= diagonal_attend_mask
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = (
causal_mask.clone()
) # copy to contiguous memory for in-place edit
if attention_mask.shape[-1] > target_length:
attention_mask = attention_mask[:, :target_length]
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[
:, None, None, :
].to(causal_mask.device)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[
:, :, :, :mask_length
].masked_fill(padding_mask, min_dtype)

if hasattr(self, "tree_mask") and self.tree_mask is not None:
tree_mask = self.tree_mask
tree_len = tree_mask.size(-1)
causal_mask[:, :, -tree_len:, -tree_len:][
tree_mask == 0
] = causal_mask.min()
if sequence_length == target_length:
causal_mask = torch.full(
(sequence_length, target_length),
fill_value=min_dtype,
dtype=dtype,
device=device,
)
diagonal_attend_mask = torch.arange(
target_length, device=device
) > cache_position.reshape(-1, 1)
if config.sliding_window is not None:
# if we have sliding window, we should not attend to tokens
# beyond sliding window length, so we mask them out also the
# check is needed to verify is current checkpoint was
# trained with sliding window or not
if (
not isinstance(past_key_values, SlidingWindowCache)
or sequence_length > target_length
):
sliding_attend_mask = torch.arange(
target_length, device=device
) <= (cache_position.reshape(-1, 1) - config.sliding_window)
diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
causal_mask *= diagonal_attend_mask
causal_mask = causal_mask[None, None, :, :].expand(
batch_size, 1, -1, -1
)
if attention_mask is not None:
causal_mask = (
causal_mask.clone()
) # copy to contiguous memory for in-place edit
if attention_mask.shape[-1] > target_length:
attention_mask = attention_mask[:, :target_length]
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[
:, None, None, :
].to(causal_mask.device)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[
:, :, :, :mask_length
].masked_fill(padding_mask, min_dtype)
else:
causal_mask = torch.zeros(
(sequence_length, target_length), dtype=dtype, device=device
)
causal_mask = causal_mask[None, None, :, :].expand(
batch_size, 1, -1, -1
)

if hasattr(self, "tree_mask") and self.tree_mask is not None:
tree_mask = self.tree_mask
tree_len = tree_mask.size(-1)
causal_mask[:, :, -tree_len:, -tree_len:][
tree_mask == 0
] = min_dtype
return causal_mask


Expand Down