Convert T5 to Keras 3

keras_nlp/models/t5/t5_backbone.py

@@ -19,7 +19,6 @@
 from keras_nlp.models.t5.t5_layer_norm import T5LayerNorm
 from keras_nlp.models.t5.t5_transformer_layer import T5TransformerLayer
 from keras_nlp.utils.python_utils import classproperty
-from keras_nlp.utils.tensor_utils import assert_tf_backend
 
 
 @keras_nlp_export("keras_nlp.models.T5Backbone")
@@ -81,8 +80,6 @@ def __init__(
         tie_embedding_weights=False,
         **kwargs,
     ):
-        assert_tf_backend(self.__class__.__name__)
-
         # Encoder inputs
         encoder_token_ids = keras.Input(
             shape=(None,), dtype="int32", name="encoder_token_ids"
@@ -121,7 +118,7 @@ def __init__(
 
         position_bias = None
         for i in range(num_layers):
-            x, position_bias = T5TransformerLayer(
+            output = T5TransformerLayer(
                 is_decoder=False,
                 hidden_dim=hidden_dim,
                 intermediate_dim=intermediate_dim,
@@ -138,6 +135,8 @@ def __init__(
                 position_bias=position_bias,
                 use_causal_mask=False,
             )
+            if isinstance(output, tuple):
+                x, position_bias = output
 
         x = T5LayerNorm(
             epsilon=layer_norm_epsilon,
@@ -162,7 +161,7 @@ def __init__(
 
         position_bias = None
         for i in range(num_layers):
-            x, position_bias = T5TransformerLayer(
+            output = T5TransformerLayer(
                 is_decoder=True,
                 hidden_dim=hidden_dim,
                 intermediate_dim=intermediate_dim,
@@ -181,6 +180,8 @@ def __init__(
                 encoder_attention_mask=encoder_attention_mask,
                 use_causal_mask=True,
             )
+            if isinstance(output, tuple):
+                x, position_bias = output
 
         x = T5LayerNorm(
             epsilon=layer_norm_epsilon,

keras_nlp/models/t5/t5_backbone_test.py

@@ -19,7 +19,6 @@
 from keras_nlp.tests.test_case import TestCase
 
 
-@pytest.mark.tf_only
 class T5BackboneTest(TestCase):
     def setUp(self):
         self.init_kwargs = {

keras_nlp/models/t5/t5_layer_norm.py

@@ -12,9 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import tensorflow as tf
-
 from keras_nlp.backend import keras
+from keras_nlp.backend import ops
 
 
 class T5LayerNorm(keras.layers.Layer):
@@ -31,8 +30,6 @@ def build(self, input_shape):
         self.built = True
 
     def call(self, hidden_states):
-        variance = tf.math.reduce_mean(
-            tf.math.square(hidden_states), axis=-1, keepdims=True
-        )
-        hidden_states = hidden_states * tf.math.rsqrt(variance + self.epsilon)
+        variance = ops.mean(ops.square(hidden_states), axis=-1, keepdims=True)
+        hidden_states = hidden_states * ops.rsqrt(variance + self.epsilon)
         return self.weight * hidden_states

keras_nlp/models/t5/t5_multi_head_attention.py

@@ -12,18 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import tensorflow as tf
-from tensorflow.compiler.tf2xla.python.xla import dynamic_slice
+import numpy as np
 
 from keras_nlp.backend import keras
-
-
-def shape_list(tensor):
-    dynamic = tf.shape(tensor)
-    if tensor.shape == tf.TensorShape(None):
-        return dynamic
-    static = tensor.shape.as_list()
-    return [dynamic[i] if s is None else s for i, s in enumerate(static)]
+from keras_nlp.backend import ops
 
 
 class T5MultiHeadAttention(keras.layers.Layer):
@@ -123,39 +115,39 @@ def _relative_position_bucket(
         if bidirectional:
             num_buckets //= 2
             relative_buckets += (
-                tf.cast(
-                    tf.math.greater(relative_position, 0),
+                ops.cast(
+                    ops.greater(relative_position, 0),
                     dtype=relative_position.dtype,
                 )
                 * num_buckets
             )
-            relative_position = tf.math.abs(relative_position)
+            relative_position = ops.abs(relative_position)
         else:
-            relative_position = -tf.math.minimum(relative_position, 0)
+            relative_position = -ops.minimum(relative_position, 0)
         # now n is in the range [0, inf)
         max_exact = num_buckets // 2
-        is_small = tf.math.less(relative_position, max_exact)
-        relative_position_if_large = max_exact + tf.cast(
-            tf.math.log(
-                tf.cast(relative_position, "float32")
-                / tf.cast(max_exact, "float32")
+        is_small = ops.less(relative_position, max_exact)
+        relative_position_if_large = max_exact + ops.cast(
+            ops.log(
+                ops.cast(relative_position, "float32")
+                / ops.cast(max_exact, "float32")
             )
-            / tf.math.log(max_distance / max_exact)
+            / ops.cast(ops.log(max_distance / max_exact), "float32")
             * (num_buckets - max_exact),
             dtype=relative_position.dtype,
         )
-        relative_position_if_large = tf.math.minimum(
+        relative_position_if_large = ops.minimum(
             relative_position_if_large, num_buckets - 1
         )
-        relative_buckets += tf.where(
+        relative_buckets += ops.where(
             is_small, relative_position, relative_position_if_large
         )
         return relative_buckets
 
     def compute_bias(self, query_length, key_length):
         """Compute binned relative position bias"""
-        context_position = tf.range(query_length)[:, None]
-        memory_position = tf.range(key_length)[None, :]
+        context_position = ops.arange(query_length)[:, None]
+        memory_position = ops.arange(key_length)[None, :]
         relative_position = (
             memory_position - context_position
         )  # shape (query_length, key_length)
@@ -165,11 +157,11 @@ def compute_bias(self, query_length, key_length):
             num_buckets=self.relative_attention_buckets,
             max_distance=self.relative_attention_max_distance,
         )
-        values = tf.gather(
-            self.relative_attention_bias, relative_position_bucket
+        values = ops.take(
+            self.relative_attention_bias, relative_position_bucket, axis=0
         )  # shape (query_length, key_length, num_heads)
-        values = tf.expand_dims(
-            tf.transpose(values, [2, 0, 1]), axis=0
+        values = ops.expand_dims(
+            ops.transpose(values, axes=(2, 0, 1)), axis=0
         )  # shape (1, num_heads, query_length, key_length)
         return values
 
@@ -186,7 +178,7 @@ def call(
     ):
         # Input is (batch_size, query_length, dim)
         # past_key_value[0] is (batch_size, num_heads, q_len - 1, dim_per_head)
-        batch_size, seq_length = shape_list(hidden_states)[:2]
+        batch_size, seq_length = ops.shape(hidden_states)[:2]
 
         real_seq_length = seq_length
 
@@ -197,29 +189,29 @@ def call(
                     f"keys and values. Got {len(past_key_value)} past states."
                 )
             real_seq_length += (
-                shape_list(past_key_value[0])[2]
+                ops.shape(past_key_value[0])[2]
                 if query_length is None
                 else query_length
             )
 
         key_length = (
             real_seq_length
             if key_value_states is None
-            else shape_list(key_value_states)[1]
+            else ops.shape(key_value_states)[1]
         )
 
         def shape(hidden_states):
-            return tf.transpose(
-                tf.reshape(
+            return ops.transpose(
+                ops.reshape(
                     hidden_states,
                     (batch_size, -1, self.num_heads, self.key_value_dim),
                 ),
-                perm=(0, 2, 1, 3),
+                axes=(0, 2, 1, 3),
             )
 
         def unshape(hidden_states):
-            return tf.reshape(
-                tf.transpose(hidden_states, perm=(0, 2, 1, 3)),
+            return ops.reshape(
+                ops.transpose(hidden_states, axes=(0, 2, 1, 3)),
                 (batch_size, -1, self.inner_dim),
             )
 
@@ -240,7 +232,7 @@ def project(
                 if key_value_states is None:
                     # self-attention
                     # (batch_size, num_heads, key_length, dim_per_head)
-                    hidden_states = tf.concat(
+                    hidden_states = ops.concat(
                         [past_key_value, hidden_states], axis=2
                     )
                 else:
@@ -267,13 +259,13 @@ def project(
             past_key_value[1] if past_key_value is not None else None,
         )
 
-        scores = tf.einsum(
+        scores = ops.einsum(
             "bnqd,bnkd->bnqk", query_states, key_states
         )  # (batch_size, num_heads, query_length, key_length)
 
         if position_bias is None:
             if not self.use_relative_attention_bias:
-                position_bias = tf.zeros(
+                position_bias = ops.zeros(
                     (1, self.num_heads, real_seq_length, key_length),
                     self.compute_dtype,
                 )
@@ -289,24 +281,24 @@ def project(
                     # we might have a padded past structure,
                     # in which case we want to fetch the position bias slice
                     # right after the most recently filled past index
-                    most_recently_filled_past_index = tf.reduce_max(
-                        tf.where(past_key_value[0][0, 0, :, 0] != 0.0)
+                    most_recently_filled_past_index = ops.amax(
+                        ops.where(past_key_value[0][0, 0, :, 0] != 0.0)
                     )
-                    position_bias = dynamic_slice(
+                    position_bias = ops.slice(
                         position_bias,
                         (0, 0, most_recently_filled_past_index + 1, 0),
                         (1, self.num_heads, seq_length, real_seq_length),
                     )
 
             if mask is not None:
                 # Add a new mask axis for the head dim.
-                mask = mask[:, tf.newaxis, :, :]
+                mask = mask[:, np.newaxis, :, :]
                 # Add a very large negative position bias for masked positions.
-                mask = (1.0 - tf.cast(mask, position_bias.dtype)) * -1e9
+                mask = (1.0 - ops.cast(mask, position_bias.dtype)) * -1e9
                 position_bias = position_bias + mask
 
         scores += position_bias
-        weights = tf.nn.softmax(
+        weights = ops.nn.softmax(
             scores, axis=-1
         )  # (batch_size, num_heads, query_length, key_length)
         weights = self.dropout_layer(
@@ -315,9 +307,9 @@ def project(
 
         # Optionally mask heads
         if layer_head_mask is not None:
-            weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * weights
+            weights = ops.reshape(layer_head_mask, (1, -1, 1, 1)) * weights
 
-        attention_output = tf.matmul(
+        attention_output = ops.matmul(
             weights, value_states
         )  # (batch_size, num_heads, query_length, dim_per_head)
 

keras_nlp/models/t5/t5_transformer_layer.py

@@ -12,9 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import tensorflow as tf
-
 from keras_nlp.backend import keras
+from keras_nlp.backend import ops
 from keras_nlp.layers.modeling.transformer_layer_utils import (
     compute_causal_mask,
 )
@@ -103,10 +102,10 @@ def call(
         training=False,
     ):
         if use_causal_mask:
-            shape = tf.shape(hidden_states)
+            shape = ops.shape(hidden_states)
             batch_size, length = shape[0], shape[1]
             causal_mask = compute_causal_mask(batch_size, length, length)
-            attention_mask = tf.cast(attention_mask, "int32")
+            attention_mask = ops.cast(attention_mask, "int32")
             attention_mask = causal_mask & attention_mask
 
         x = hidden_states  # Intermediate result.
@@ -147,4 +146,7 @@ def call(
         x = self.dropout_layer(x, training=training)
         x = x + residual
 
-        return x, position_bias
+        if position_bias is not None:
+            return x, position_bias
+        else:
+            return x