Add LLaMA 3 tokenizer and preset

keras_nlp/api/models/__init__.py

@@ -128,6 +128,12 @@
     GPTNeoXPreprocessor,
 )
 from keras_nlp.src.models.gpt_neo_x.gpt_neo_x_tokenizer import GPTNeoXTokenizer
+from keras_nlp.src.models.llama3.llama3_backbone import Llama3Backbone
+from keras_nlp.src.models.llama3.llama3_causal_lm_preprocessor import (
+    Llama3CausalLMPreprocessor,
+)
+from keras_nlp.src.models.llama3.llama3_preprocessor import Llama3Preprocessor
+from keras_nlp.src.models.llama3.llama3_tokenizer import Llama3Tokenizer
 from keras_nlp.src.models.llama.llama_backbone import LlamaBackbone
 from keras_nlp.src.models.llama.llama_causal_lm import LlamaCausalLM
 from keras_nlp.src.models.llama.llama_causal_lm_preprocessor import (

keras_nlp/src/models/llama/llama_causal_lm.py

@@ -19,7 +19,6 @@
 from keras_nlp.src.models.llama.llama_causal_lm_preprocessor import (
     LlamaCausalLMPreprocessor,
 )
-from keras_nlp.src.utils.python_utils import classproperty
 from keras_nlp.src.utils.tensor_utils import any_equal
 
 
@@ -46,6 +45,9 @@ class LlamaCausalLM(CausalLM):
             should be preprocessed before calling the model.
     """
 
+    backbone_cls = LlamaBackbone
+    preprocessor_cls = LlamaCausalLMPreprocessor
+
     def __init__(self, backbone, preprocessor=None, **kwargs):
         # === Layers ===
         self.backbone = backbone
@@ -61,14 +63,6 @@ def __init__(self, backbone, preprocessor=None, **kwargs):
             **kwargs,
         )
 
-    @classproperty
-    def backbone_cls(cls):
-        return LlamaBackbone
-
-    @classproperty
-    def preprocessor_cls(cls):
-        return LlamaCausalLMPreprocessor
-
     def call_with_cache(
         self,
         token_ids,

keras_nlp/src/models/llama/llama_tokenizer.py

@@ -11,14 +11,11 @@
 # 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 copy
 
 from keras_nlp.src.api_export import keras_nlp_export
-from keras_nlp.src.models.llama.llama_presets import backbone_presets
 from keras_nlp.src.tokenizers.sentence_piece_tokenizer import (
     SentencePieceTokenizer,
 )
-from keras_nlp.src.utils.python_utils import classproperty
 
 
 @keras_nlp_export("keras_nlp.models.LlamaTokenizer")
@@ -85,7 +82,3 @@ def set_proto(self, proto):
             self.start_token_id = None
             self.end_token_id = None
             self.pad_token_id = None
-
-    @classproperty
-    def presets(cls):
-        return copy.deepcopy(backbone_presets)

keras_nlp/src/models/llama3/__init__.py

@@ -0,0 +1,20 @@
+# 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.
+
+from keras_nlp.src.models.llama3.llama3_backbone import Llama3Backbone
+from keras_nlp.src.models.llama3.llama3_presets import backbone_presets
+from keras_nlp.src.models.llama3.llama3_tokenizer import Llama3Tokenizer
+from keras_nlp.src.utils.preset_utils import register_presets
+
+register_presets(backbone_presets, (Llama3Backbone, Llama3Tokenizer))

keras_nlp/src/models/llama3/llama3_backbone.py

@@ -0,0 +1,84 @@
+# 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.
+
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.llama.llama_backbone import LlamaBackbone
+
+
+# LLaMA 3 shares the same architecture as its predecessors
+# So, we simply create an alias for API consistency
+@keras_nlp_export("keras_nlp.models.Llama3Backbone")
+class Llama3Backbone(LlamaBackbone):
+    """
+    The Llama Transformer core architecture with hyperparameters.
+
+    This network implements a Transformer-based decoder network,
+    Llama, as described in
+    ["Llama 7B"](https://arxiv.org/pdf/2310.06825.pdf).
+    It includes the embedding lookups and transformer layers.
+
+    The default constructor gives a fully customizable, randomly initialized
+    Llama model with any number of layers, heads, and embedding
+    dimensions. To load preset architectures and weights, use the `from_preset`
+    constructor.
+
+    Args:
+        vocabulary_size (int): The size of the token vocabulary.
+        num_layers (int): The number of transformer layers.
+        num_query_heads (int): The number of query attention heads for
+            each transformer.
+        hidden_dim (int): The size of the transformer encoding and pooling layers.
+        intermediate_dim (int): The output dimension of the first Dense layer in a
+            three-layer feedforward network for each transformer.
+        num_key_value_heads (int): The number of key and value attention heads for
+            each transformer.
+        rope_max_wavelength (int, optional): The maximum angular wavelength of the
+            sine/cosine curves, for rotary embeddings. Defaults to `10000`.
+        rope_scaling_factor (float, optional): The scaling factor for calculation
+            of roatary embedding. Defaults to `1.0`.
+        layer_norm_epsilon (float, optional): Epsilon for the layer normalization
+            layers in the transformer decoder. Defaults to `1e-6`.
+        dtype: string or `keras.mixed_precision.DTypePolicy`. The dtype to use
+            for model computations and weights. Note that some computations,
+            such as softmax and layer normalization, will always be done at
+            float32 precision regardless of dtype.
+
+    Examples:
+
+    ```python
+    input_data = {
+        "token_ids": np.ones(shape=(1, 12), dtype="int32"),
+        "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
+    }
+
+    # Pretrained Llama decoder.
+    model = keras_nlp.models.Llama3Backbone.from_preset("llama3_8b_en")
+    model(input_data)
+
+    # Randomly initialized Llama decoder with custom config.
+    model = keras_nlp.models.Llama3Backbone(
+        vocabulary_size=10,
+        hidden_dim=512,
+        num_layers=2,
+        num_query_heads=32,
+        num_key_value_heads=8,
+        intermediate_dim=1024,
+        layer_norm_epsilon=1e-6,
+        dtype="float32"
+    )
+    model(input_data)
+    ```
+    """
+
+    pass

keras_nlp/src/models/llama3/llama3_causal_lm.py

@@ -0,0 +1,44 @@
+# 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.
+from keras_nlp.src.models.llama3.llama3_backbone import Llama3Backbone
+from keras_nlp.src.models.llama3.llama3_causal_lm_preprocessor import (
+    Llama3CausalLMPreprocessor,
+)
+from keras_nlp.src.models.llama.llama_causal_lm import LlamaCausalLM
+
+
+class Llama3CausalLM(LlamaCausalLM):
+    """An end-to-end Llama 3 model for causal language modeling.
+
+    A causal language model (LM) predicts the next token based on previous
+    tokens. This task setup can be used to train the model unsupervised on
+    plain text input, or to autoregressively generate plain text similar to
+    the data used for training. This task can be used for pre-training or
+    fine-tuning a LLaMA 3 model, simply by calling `fit()`.
+
+    This model has a `generate()` method, which generates text based on a
+    prompt. The generation strategy used is controlled by an additional
+    `sampler` argument on `compile()`. You can recompile the model with
+    different `keras_nlp.samplers` objects to control the generation. By
+    default, `"top_k"` sampling will be used.
+
+    Args:
+        backbone: A `keras_nlp.models.Llama3Backbone` instance.
+        preprocessor: A `keras_nlp.models.Llama3CausalLMPreprocessor` or `None`.
+            If `None`, this model will not apply preprocessing, and inputs
+            should be preprocessed before calling the model.
+    """
+
+    backbone_cls = Llama3Backbone
+    preprocessor_cls = Llama3CausalLMPreprocessor