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minor change to align with paper for better readliness #3

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minor change to align with paper for better readliness #3

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fbb9b4b
align with paper
chivee Jul 26, 2022
6ecc99f
delete unesscary change
chivee Jul 26, 2022
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15 changes: 7 additions & 8 deletions flash_pytorch/flash_pytorch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -264,7 +264,7 @@ def forward(
j - sequence dimension (target)
"""

b, n, device, g = x.shape[0], x.shape[-2], x.device, self.group_size
b, n, device, c = x.shape[0], x.shape[-2], x.device, self.group_size

# prenorm

Expand Down Expand Up @@ -299,24 +299,23 @@ def forward(

# padding for groups

padding = padding_to_multiple_of(n, g)
padding = padding_to_multiple_of(n, c)

if padding > 0:
quad_q, quad_k, lin_q, lin_k, v = map(lambda t: F.pad(t, (0, 0, 0, padding), value = 0.), (quad_q, quad_k, lin_q, lin_k, v))

mask = default(mask, torch.ones((b, n), device = device, dtype = torch.bool))
mask = F.pad(mask, (0, padding), value = False)

# group along sequence

quad_q, quad_k, lin_q, lin_k, v = map(lambda t: rearrange(t, 'b (g n) d -> b g n d', n = self.group_size), (quad_q, quad_k, lin_q, lin_k, v))
quad_q, quad_k, lin_q, lin_k, v = map(lambda t: rearrange(t, 'b (g c) d -> b g c d', c = c), (quad_q, quad_k, lin_q, lin_k, v))

if exists(mask):
mask = rearrange(mask, 'b (g j) -> b g 1 j', j = g)
mask = rearrange(mask, 'b (g c) -> b g 1 c', c = c)

# calculate quadratic attention output

sim = einsum('... i d, ... j d -> ... i j', quad_q, quad_k) / g
sim = einsum('... i d, ... j d -> ... i j', quad_q, quad_k) / c

sim = sim + self.rel_pos_bias(sim)

Expand All @@ -327,15 +326,15 @@ def forward(
attn = attn.masked_fill(~mask, 0.)

if self.causal:
causal_mask = torch.ones((g, g), dtype = torch.bool, device = device).triu(1)
causal_mask = torch.ones((c,c), dtype = torch.bool, device = device).triu(1)
attn = attn.masked_fill(causal_mask, 0.)

quad_out = einsum('... i j, ... j d -> ... i d', attn, v)

# calculate linear attention output

if self.causal:
lin_kv = einsum('b g n d, b g n e -> b g d e', lin_k, v) / g
lin_kv = einsum('b g n d, b g n e -> b g d e', lin_k, v) / c

# exclusive cumulative sum along group dimension

Expand Down