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No scaling, no encoder mask

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Juarez Bochi 2023-12-16 14:24:13 -05:00
parent 64e7eaccb8
commit d12db65eeb
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1 changed files with 5 additions and 3 deletions
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t5/t5.py
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@ -125,8 +125,8 @@ class MultiHeadAttention(nn.Module):
values = values.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3) values = values.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3)
# Dimensions are [batch x num heads x sequence x hidden dim] # Dimensions are [batch x num heads x sequence x hidden dim]
scale = math.sqrt(1 / queries.shape[-1]) scores = queries @ keys
scores = (queries * scale) @ keys
if mask is not None: if mask is not None:
scores = scores + mask.astype(scores.dtype) scores = scores + mask.astype(scores.dtype)
@ -274,12 +274,14 @@ class T5(nn.Module):
) -> tuple[mx.array, mx.array]: ) -> tuple[mx.array, mx.array]:
x = self.wte(inputs) x = self.wte(inputs)
y = self.encoder(x, mask=None) #, cache)
mask = None mask = None
if x.shape[1] > 1: if x.shape[1] > 1:
mask = MultiHeadAttention.create_additive_causal_mask(x.shape[1]) mask = MultiHeadAttention.create_additive_causal_mask(x.shape[1])
mask = mask.astype(x.dtype) mask = mask.astype(x.dtype)
y = self.encoder(x, mask) #, cache)
# y, cache = self.decoder(x, mask, cache) # y, cache = self.decoder(x, mask, cache)
# return self.lm_head(y), cache # return self.lm_head(y), cache
return y #, cache return y #, cache