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Refactor set_multi_step_attn_mask for arbitrary step #348

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Refactor set_multi_step_attn_mask for arbitrary step #348

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8d81046
refactor_multi_step_attn_mask_for_arbitrary_step
yeyu-nvidia Sep 19, 2025
9e398c8
minor
yeyu-nvidia Sep 19, 2025
57f9143
make new mask the same dtype and device as attn_mask
yeyu-nvidia Sep 19, 2025
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66 changes: 14 additions & 52 deletions modelopt/torch/speculative/plugins/megatron_eagle.py
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Original file line number Diff line number Diff line change
Expand Up @@ -305,65 +305,27 @@ def set_multi_step_attention_mask(attn_mask, step):
=======================================================================================================================
""" # noqa: E501
assert step > 1, "step should be larger than 1 in multi-step attention mask."
assert step <= 4, "Currently only a step of 4 or smaller is supported!"

s = attn_mask.shape[-1]
zero_mask = torch.ones_like(attn_mask).bool()
mask_2_1 = attn_mask.clone().detach()
mask_2_1[:, :, :, :-1] = mask_2_1[:, :, :, 1:]
mask_2_2 = torch.ones_like(attn_mask).bool()
for i in range(1, s - 1):
mask_2_2[:, :, i, i] = False

if step == 2:
attn_mask = torch.cat(
(
torch.cat((attn_mask, zero_mask), dim=-1),
torch.cat((mask_2_1, mask_2_2), dim=-1),
),
dim=-2,
)
return attn_mask

mask_3_1 = mask_2_1.clone().detach()
mask_3_1[:, :, :, :-1] = mask_3_1[:, :, :, 1:]
mask_3_2 = mask_2_2.clone().detach()
mask_3_2[:, :, :, :-1] = mask_3_2[:, :, :, 1:]
mask_3_2[:, :, 1, 0] = True
mask_3_3 = mask_2_2.clone().detach()
mask_3_3[:, :, 1, 1] = True
for iter in range(2, step + 1):
# iter starts from 2nd step
zero_mask = attn_mask.new_ones(
attn_mask.shape[0], attn_mask.shape[1], attn_mask.shape[2], s
).bool()
mask_0 = attn_mask.clone().detach()[:, :, -s:, :]
mask_0[:, :, iter - 2, :] = True
mask_0[:, :, :, :-1] = mask_0[:, :, :, 1:]
mask_1 = attn_mask.new_ones(attn_mask.shape[0], attn_mask.shape[1], s, s).bool()
for i in range(iter - 1, s - 1):
mask_1[:, :, i, i] = False

if step == 3:
attn_mask = torch.cat(
(
torch.cat((attn_mask, zero_mask, zero_mask), dim=-1),
torch.cat((mask_2_1, mask_2_2, zero_mask), dim=-1),
torch.cat((mask_3_1, mask_3_2, mask_3_3), dim=-1),
torch.cat((attn_mask, zero_mask), dim=-1),
torch.cat((mask_0, mask_1), dim=-1),
),
dim=-2,
)
return attn_mask

mask_4_1 = mask_3_1.clone().detach()
mask_4_1[:, :, :, :-1] = mask_4_1[:, :, :, 1:]
mask_4_2 = mask_3_2.clone().detach()
mask_4_2[:, :, :, :-1] = mask_4_2[:, :, :, 1:]
mask_4_2[:, :, 2, 0] = True
mask_4_3 = mask_3_3.clone().detach()
mask_4_3[:, :, :, :-1] = mask_4_3[:, :, :, 1:]
mask_4_3[:, :, 2, 1] = True
mask_4_4 = mask_3_3.clone().detach()
mask_4_4[:, :, 2, 2] = True

attn_mask = torch.cat(
(
torch.cat((attn_mask, zero_mask, zero_mask, zero_mask), dim=-1),
torch.cat((mask_2_1, mask_2_2, zero_mask, zero_mask), dim=-1),
torch.cat((mask_3_1, mask_3_2, mask_3_3, zero_mask), dim=-1),
torch.cat((mask_4_1, mask_4_2, mask_4_3, mask_4_4), dim=-1),
),
dim=-2,
)
return attn_mask


Expand Down Expand Up @@ -883,7 +845,7 @@ def _get_eagle_module_inputs(
rotary_pos_emb = self.eagle_module.rotary_pos_emb(padded_input_ids.shape[-1])

attn_mask = attention_mask.clone().detach()
attn_mask[:, :, :-1, :-1] = attention_mask[:, :, 1:, 1:]
attn_mask[:, :, :-1, :-1] = attn_mask[:, :, 1:, 1:]
attn_mask[:, :, -1, :] = True
attn_mask[:, :, :, -1] = True

Expand Down
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