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Improve future compatibility of CLIPMultiHeadAttention #1975

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Nov 11, 2024
Merged

Improve future compatibility of CLIPMultiHeadAttention #1975

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Improve future compatibility of CLIPMultiHeadAttention
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@james77777778
james77777778 committed Nov 7, 2024
commit 5787fcd45c43ab84ba3953fac8c814e0789f03c0
23 changes: 3 additions & 20 deletions keras_hub/src/models/clip/clip_encoder_block.py
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Expand Up @@ -10,28 +10,11 @@ def quick_gelu(x):
# TODO: Deprecate this in favor of `keras.layers.MultiHeadAttention` once the
# dtype compatibility issue is resolved.
class CLIPMultiHeadAttention(layers.MultiHeadAttention):
def _compute_attention(
self, query, key, value, attention_mask=None, training=None
):
query = ops.multiply(
query, ops.cast(self._inverse_sqrt_key_dim, query.dtype)
)
attention_scores = ops.einsum(self._dot_product_equation, key, query)
attention_scores = self._masked_softmax(
def _masked_softmax(self, attention_scores, attention_mask=None):
attention_scores = super()._masked_softmax(
attention_scores, attention_mask
)
# Fix the dtype compatibility.
attention_scores = ops.cast(attention_scores, value.dtype)
if self.dropout:
final_attn_scores = self._dropout_layer(
attention_scores, training=training
)
else:
final_attn_scores = attention_scores
attention_output = ops.einsum(
self._combine_equation, final_attn_scores, value
)
return attention_output, attention_scores
return ops.cast(attention_scores, self._value_dense.compute_dtype)


class CLIPEncoderBlock(layers.Layer):
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