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Update the int8 quant logic in ReversibleEmbedding #2250

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Merged
merged 1 commit into from
May 12, 2025
Merged

Update the int8 quant logic in ReversibleEmbedding #2250

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60 changes: 25 additions & 35 deletions keras_hub/src/layers/modeling/reversible_embedding.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
import inspect

import keras
from keras import ops

Expand Down Expand Up @@ -184,31 +186,33 @@ def quantized_call(self, inputs, reverse=False):
else:
self._quantization_mode_error(self.quantization_mode)

def _int8_build(
self,
embeddings_initializer="zeros",
embeddings_scale_initializer="ones",
reverse_embeddings_initializer="zeros",
reverse_embeddings_scale_initializer="ones",
):
super()._int8_build(
embeddings_initializer, embeddings_scale_initializer
)
def _int8_build(self, embeddings_shape=None):
if (
"embeddings_shape"
in inspect.signature(super()._int8_build).parameters
):
if embeddings_shape is None:
embeddings_shape = (self.input_dim, self.output_dim)
super()._int8_build(embeddings_shape=embeddings_shape)
else:
# Backward compatibility for older versions of Keras.
super()._int8_build()
self.inputs_quantizer = keras.quantizers.AbsMaxQuantizer(axis=-1)
if not self.tie_weights:
self.reverse_embeddings = self.add_weight(
name="reverse_embeddings",
shape=(self.output_dim, self.input_dim),
initializer=reverse_embeddings_initializer,
initializer="zeros",
dtype="int8",
trainable=False,
)
self.reverse_embeddings_scale = self.add_weight(
name="reverse_embeddings_scale",
shape=(self.input_dim,),
initializer=reverse_embeddings_scale_initializer,
initializer="ones",
trainable=False,
)
self._is_quantized = True

def _int8_call(self, inputs, reverse=False):
if reverse:
Expand All @@ -232,27 +236,20 @@ def _int8_call(self, inputs, reverse=False):
return super()._int8_call(inputs)

def quantize(self, mode, type_check=True):
import gc

if type_check and type(self) is not ReversibleEmbedding:
raise NotImplementedError(
f"Layer {self.__class__.__name__} does not have a `quantize()` "
"method implemented."
)
self._check_quantize_args(mode, self.compute_dtype)
raise self._not_implemented_error(self.quantize)

def abs_max_quantize(inputs, axis):
return keras.quantizers.abs_max_quantize(
inputs, axis=axis, to_numpy=True
)

self._tracker.unlock()
embeddings_shape = (self.input_dim, self.output_dim)
if mode == "int8":
embeddings, embeddings_scale = abs_max_quantize(
self._embeddings, axis=-1
)
embeddings_scale = ops.squeeze(embeddings_scale, axis=-1)
self._untrack_variable(self._embeddings)
del self._embeddings
if not self.tie_weights:
reverse_embeddings, reverse_embeddings_scale = abs_max_quantize(
Expand All @@ -261,24 +258,17 @@ def abs_max_quantize(inputs, axis):
reverse_embeddings_scale = ops.squeeze(
reverse_embeddings_scale, axis=0
)
self._untrack_variable(self.reverse_embeddings)
del self.reverse_embeddings
else:
reverse_embeddings = None
reverse_embeddings_scale = None
self._int8_build(
lambda shape, dtype: embeddings,
lambda shape, dtype: embeddings_scale,
lambda shape, dtype: reverse_embeddings,
lambda shape, dtype: reverse_embeddings_scale,
)
else:
raise self._quantization_mode_error(mode)
self._tracker.lock()
self.quantized_build(embeddings_shape, mode)
if mode == "int8":
self._embeddings.assign(embeddings)
self.embeddings_scale.assign(embeddings_scale)
if not self.tie_weights:
self.reverse_embeddings.assign(reverse_embeddings)
self.reverse_embeddings_scale.assign(reverse_embeddings_scale)

if self.dtype_policy.quantization_mode is None:
policy = keras.dtype_policies.get(
f"{mode}_from_{self.dtype_policy.name}"
)
self.dtype_policy = policy
gc.collect()
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