Adding GPTNeoXBackbone

keras_nlp/layers/masked_lm_mask_generator.py

@@ -174,11 +174,7 @@ def call(self, inputs):
             # convert dense to ragged.
             inputs = tf.RaggedTensor.from_tensor(inputs)
 
-        (
-            token_ids,
-            mask_positions,
-            mask_ids,
-        ) = tf_text.mask_language_model(
+        (token_ids, mask_positions, mask_ids,) = tf_text.mask_language_model(
             inputs,
             item_selector=self._random_selector,
             mask_values_chooser=self._mask_values_chooser,

keras_nlp/models/gpt_neox/gpt_neox_attention.py

@@ -0,0 +1,185 @@
+# Copyright 2023 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import tensorflow as tf
+from tensorflow import keras
+
+from keras_nlp.models.gpt_neox.rotary_embedding import RotaryEmbedding
+from keras_nlp.utils.keras_utils import clone_initializer
+
+
+class GPTNeoXAttention(keras.layers.Layer):
+    def __init__(
+        self,
+        num_heads,
+        hidden_dim,
+        dropout=0.1,
+        max_position_embeddings=512,
+        kernel_initializer="glorot_uniform",
+        bias_initializer="zeros",
+        **kwargs,
+    ):
+
+        super().__init__()
+        self.num_heads = num_heads
+        self.hidden_dim = hidden_dim
+        #         self.rotary_pct = 4
+        self.dropout = dropout
+        self.attn_head_size = hidden_dim // num_heads
+        self.rotary_dim = self.attn_head_size * self.hidden_dim
+        self.max_position_embeddings = max_position_embeddings
+        self.rotary_embedding = RotaryEmbedding(self.attn_head_size)
+
+        self._kernel_initializer = keras.initializers.get(kernel_initializer)
+        self._bias_initializer = keras.initializers.get(bias_initializer)
+
+        self._query_dense = keras.layers.EinsumDense(
+            equation="abc,cde->abde",
+            output_shape=(None, self.num_heads, self.attn_head_size),
+            bias_axes="de",
+            **self._get_common_kwargs_for_sublayer(use_bias=True),
+            name="attention_output",
+        )
+        self._key_dense = keras.layers.EinsumDense(
+            equation="abc,cde->abde",
+            output_shape=(None, self.num_heads, self.attn_head_size),
+            bias_axes="de",
+            **self._get_common_kwargs_for_sublayer(use_bias=True),
+            name="key",
+        )
+        self._value_dense = keras.layers.EinsumDense(
+            equation="abc,cde->abde",
+            output_shape=(None, self.num_heads, self.attn_head_size),
+            bias_axes="de",
+            **self._get_common_kwargs_for_sublayer(use_bias=True),
+            name="value",
+        )
+
+        self._attn_dropout_layer = keras.layers.Dropout(
+            self.dropout, name="attention_dropout"
+        )
+
+        self._softmax = keras.layers.Softmax(axis=-1, name="attention_softmax")
+
+        # Output.
+        self._output_dense = keras.layers.EinsumDense(
+            equation="abc,cd->abd",
+            output_shape=(None, self.hidden_dim),
+            bias_axes="d",
+            **self._get_common_kwargs_for_sublayer(use_bias=True),
+            name="attention_output",
+        )
+
+    def _get_common_kwargs_for_sublayer(self, use_bias=True):
+        common_kwargs = {}
+
+        kernel_initializer = clone_initializer(self._kernel_initializer)
+        bias_initializer = clone_initializer(self._bias_initializer)
+
+        common_kwargs["kernel_initializer"] = kernel_initializer
+        if use_bias:
+            common_kwargs["bias_initializer"] = bias_initializer
+
+        return common_kwargs
+
+    def _masked_softmax(self, attention_scores, attention_mask=None):
+        #         print(attention_scores[0].shape, attention_scores[1].shape)
+        # print(attention_mask.shape, attention_scores.shape)
+        if attention_mask is not None:
+            mask_expansion_axis = -3
+            for _ in range(
+                attention_scores.shape.rank - attention_mask.shape.rank
+            ):
+                attention_mask = tf.expand_dims(
+                    attention_mask, axis=mask_expansion_axis
+                )
+        return self._softmax(attention_scores, attention_mask)
+
+    def _compute_attention(
+        self, query, key, value, attention_mask=None, training=None
+    ):
+
+        attention_scores = tf.einsum("aecd,abcd->acbe", key, query)
+
+        #         batch_size, _, key_len, _ = tf.shape(key)
+        #         causal_mask = compute_causal_mask(batch_size, key_len, key_len)
+        #         attention_mask = attention_mask & causal_mask
+        #         mask_value = tf.constant(float('-inf'), dtype=attention_scores.dtype)
+        #         attention_scores = tf.where(causal_mask, attention_scores, mask_value)
+
+        #         print(attention_scores[0].shape, attention_scores[1].shape)
+        #         if attention_mask is not None:
+        #             attention_scores += attention_mask
+
+        attention_scores = self._masked_softmax(
+            attention_scores, attention_mask
+        )
+        attention_scores = self._attn_dropout_layer(
+            attention_scores, training=training
+        )
+        attention_output = tf.einsum("acbe,aecd->abcd", attention_scores, value)
+
+        return attention_output, attention_scores
+
+    def call(
+        self,
+        hidden_states,
+        attention_mask,
+        return_attention_scores=False,
+        training=None,
+    ):
+
+        query = self._query_dense(hidden_states)
+        key = self._key_dense(hidden_states)
+        value = self._value_dense(hidden_states)
+
+        # query = tf.transpose(query, (0, 2, 1, 3))
+        # key = tf.transpose(key, (0, 2, 1, 3))
+        # value = tf.transpose(value, (0, 2, 1, 3))
+
+        query_rot, query_pass = (
+            query[..., : self.rotary_dim],
+            query[..., self.rotary_dim :],
+        )
+        key_rot, key_pass = (
+            key[..., : self.rotary_dim],
+            key[..., self.rotary_dim :],
+        )
+
+        query, key = self.rotary_embedding(query_rot, key_rot)
+        query = tf.concat((query, query_pass), axis=-1)
+        key = tf.concat((key, key_pass), axis=-1)
+
+        #         if layer_past is not None:
+        #             past_key, past_value = layer_past
+        #             key = tf.concat((past_key, key), axis=-2)
+        #             value = tf.concat((past_value, value), axis=-2)
+
+        attention_output, attention_scores = self._compute_attention(
+            query, key, value, attention_mask, training
+        )
+
+        # Reshape `attention_output` to `(batch_size, sequence_length, hidden_dim)`.
+        attention_output = tf.reshape(
+            attention_output,
+            [
+                tf.shape(attention_output)[0],
+                tf.shape(attention_output)[1],
+                self.hidden_dim,
+            ],
+        )
+        attention_output = self._output_dense(attention_output)
+
+        if return_attention_scores:
+            return attention_output, attention_scores
+        return attention_output

keras_nlp/models/gpt_neox/gpt_neox_backbone.py

@@ -0,0 +1,168 @@
+# Copyright 2023 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.experimental import dtensor
+from tensorflow.experimental.dtensor import Layout
+
+from keras_nlp.api_export import keras_nlp_export
+from keras_nlp.layers.position_embedding import PositionEmbedding
+from keras_nlp.models.backbone import Backbone
+from keras_nlp.models.gpt_neox.gpt_neox_decoder import GPTNeoXDecoder
+
+
+def _gpt_neox_kernel_initializer(stddev=0.02):
+    return keras.initializers.RandomNormal(stddev=stddev)
+
+
+@keras_nlp_export("keras_nlp.models.GPTNeoXBackbone")
+class GPTNeoXBackbone(Backbone):
+    def __init__(
+        self,
+        vocabulary_size,
+        num_layers,
+        num_heads,
+        hidden_dim,
+        intermediate_dim,
+        dropout=0.1,
+        max_sequence_length=512,
+        **kwargs,
+    ):
+        # Inputs
+        token_ids = keras.Input(shape=(None,), dtype="int32", name="token_ids")
+        padding_mask = keras.Input(
+            shape=(None,), dtype="int32", name="padding_mask"
+        )
+
+        # Embed tokens, positions.
+        token_embedding = keras.layers.Embedding(
+            input_dim=vocabulary_size,
+            output_dim=hidden_dim,
+            embeddings_initializer=_gpt_neox_kernel_initializer(stddev=0.01),
+            name="token_embedding",
+        )(token_ids)
+
+        # Can't use `TokenAndPositionEmbedding` layer here because of different
+        # initializers.
+        position_embedding = PositionEmbedding(
+            initializer=_gpt_neox_kernel_initializer(stddev=0.02),
+            sequence_length=max_sequence_length,
+            name="position_embedding",
+        )(token_embedding)
+
+        # Sum and apply dropout to embeddings.
+        x = keras.layers.Add(name="embeddings_add")(
+            (token_embedding, position_embedding)
+        )
+        x = keras.layers.Dropout(
+            dropout,
+            name="embeddings_dropout",
+        )(x)
+
+        # Apply successive transformer decoder blocks.
+        for i in range(num_layers):
+            x = GPTNeoXDecoder(
+                intermediate_dim=intermediate_dim,
+                num_heads=num_heads,
+                dropout=dropout,
+                layer_norm_epsilon=1e-05,
+                activation=lambda x: keras.activations.gelu(
+                    x, approximate=True
+                ),
+                kernel_initializer=_gpt_neox_kernel_initializer(stddev=0.02),
+                normalize_first=True,
+                name=f"transformer_layer_{i}",
+            )(x, decoder_padding_mask=padding_mask)
+
+        sequence_output = keras.layers.LayerNormalization(
+            name="layer_norm",
+            axis=-1,
+            epsilon=1e-05,
+            dtype=tf.float32,
+        )(x)
+
+        # Instantiate using Functional API Model constructor
+        super().__init__(
+            inputs={
+                "token_ids": token_ids,
+                "padding_mask": padding_mask,
+            },
+            outputs=sequence_output,
+            **kwargs,
+        )
+        # All references to `self` below this line
+        self.vocabulary_size = vocabulary_size
+        self.num_layers = num_layers
+        self.num_heads = num_heads
+        self.hidden_dim = hidden_dim
+        self.intermediate_dim = intermediate_dim
+        self.dropout = dropout
+        self.max_sequence_length = max_sequence_length
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "vocabulary_size": self.vocabulary_size,
+                "num_layers": self.num_layers,
+                "num_heads": self.num_heads,
+                "hidden_dim": self.hidden_dim,
+                "intermediate_dim": self.intermediate_dim,
+                "dropout": self.dropout,
+                "max_sequence_length": self.max_sequence_length,
+            }
+        )
+        return config
+
+    @property
+    def token_embedding(self):
+        return self.get_layer("token_embedding")
+
+    @classmethod
+    def create_layout_map(cls, mesh):
+
+        # We assert the mesh is 2D, and assume the first mesh dim is for data
+        # parallel and the second dim is for model parallel.
+        mesh_shape = mesh.shape()
+        if len(mesh_shape) != 2:
+            raise ValueError(
+                f"Expect to create layout based on 2D mesh, received {mesh}"
+            )
+        _, model_dim = mesh.dim_names
+        unshard_dim = dtensor.UNSHARDED
+
+        layout_map = keras.dtensor.experimental.LayoutMap(mesh=mesh)
+        # Embedding sharding
+        layout_map[r".*embeddings"] = Layout([unshard_dim, model_dim], mesh)
+
+        # Transformer block sharding
+        layout_map[r".*_(query|key|value)_dense.kernel"] = Layout(
+            [unshard_dim, unshard_dim, model_dim], mesh
+        )
+        layout_map[r".*_(query|key|value)_dense.bias"] = Layout(
+            [model_dim, unshard_dim], mesh
+        )
+        layout_map[r".*_feedforward_intermediate_dense.kernel"] = Layout(
+            [unshard_dim, model_dim], mesh
+        )
+        layout_map[r".*_feedforward_intermediate_dense.bias"] = Layout(
+            [model_dim], mesh
+        )
+        layout_map[r".*_feedforward_output_dense.kernel"] = Layout(
+            [model_dim, unshard_dim], mesh
+        )
+        layout_map[r".*_feedforward_output_dense.bias"] = Layout(
+            [unshard_dim], mesh
+        )
+        return layout_map