PaliGemma

keras_nlp/api/models/__init__.py

@@ -159,6 +159,18 @@
 )
 from keras_nlp.src.models.opt.opt_preprocessor import OPTPreprocessor
 from keras_nlp.src.models.opt.opt_tokenizer import OPTTokenizer
+from keras_nlp.src.models.pali_gemma.pali_gemma_backbone import (
+    PaliGemmaBackbone,
+)
+from keras_nlp.src.models.pali_gemma.pali_gemma_causal_lm import (
+    PaliGemmaCausalLM,
+)
+from keras_nlp.src.models.pali_gemma.pali_gemma_causal_lm_preprocessor import (
+    PaliGemmaCausalLMPreprocessor,
+)
+from keras_nlp.src.models.pali_gemma.pali_gemma_tokenizer import (
+    PaliGemmaTokenizer,
+)
 from keras_nlp.src.models.phi3.phi3_backbone import Phi3Backbone
 from keras_nlp.src.models.phi3.phi3_causal_lm import Phi3CausalLM
 from keras_nlp.src.models.phi3.phi3_causal_lm_preprocessor import (

keras_nlp/src/layers/preprocessing/multi_segment_packer.py

@@ -239,7 +239,12 @@ def _trim_inputs(self, inputs):
         else:
             raise ValueError("Unsupported truncate: %s" % self.truncate)
 
-    def _combine_inputs(self, segments):
+    def _combine_inputs(
+        self,
+        segments,
+        add_start_value=True,
+        add_end_value=True,
+    ):
         """Combine inputs with start and end values added."""
         dtype = segments[0].dtype
         batch_size = segments[0].nrows()
@@ -259,10 +264,12 @@ def _combine_inputs(self, segments):
         ones_sep_columns = tf.ones_like(sep_columns, dtype="int32")
         ones_end_columns = tf.ones_like(end_columns, dtype="int32")
 
-        segments_to_combine = [start_columns]
-        segment_ids_to_combine = [
-            tf.ones_like(start_columns, dtype="int32") * 0
-        ]
+        segments_to_combine = []
+        segment_ids_to_combine = []
+        if add_start_value:
+            segments_to_combine.append(start_columns)
+            start_segment = tf.zeros_like(start_columns, dtype="int32")
+            segment_ids_to_combine.append(start_segment)
 
         for i, seg in enumerate(segments):
             # Combine all segments.
@@ -273,8 +280,9 @@ def _combine_inputs(self, segments):
 
             # Account for the sep/end tokens here.
             if i == len(segments) - 1:
-                segments_to_combine.append(end_columns)
-                segment_ids_to_combine.append(ones_end_columns * i)
+                if add_end_value:
+                    segments_to_combine.append(end_columns)
+                    segment_ids_to_combine.append(ones_end_columns * i)
             else:
                 segments_to_combine.append(sep_columns)
                 segment_ids_to_combine.append(ones_sep_columns * i)
@@ -283,13 +291,24 @@ def _combine_inputs(self, segments):
         segment_ids = tf.concat(segment_ids_to_combine, 1)
         return token_ids, segment_ids
 
-    def call(self, inputs):
+    def call(
+        self,
+        inputs,
+        sequence_length=None,
+        add_start_value=True,
+        add_end_value=True,
+    ):
         inputs, unbatched = self._sanitize_inputs(inputs)
 
         segments = self._trim_inputs(inputs)
-        token_ids, segment_ids = self._combine_inputs(segments)
+        token_ids, segment_ids = self._combine_inputs(
+            segments,
+            add_start_value=add_start_value,
+            add_end_value=add_end_value,
+        )
         # Pad to dense tensor output.
-        shape = tf.cast([-1, self.sequence_length], "int64")
+        sequence_length = sequence_length or self.sequence_length
+        shape = tf.cast([-1, sequence_length], "int64")
         token_ids = token_ids.to_tensor(
             shape=shape, default_value=self.pad_value
         )

keras_nlp/src/models/pali_gemma/__init__.py

@@ -0,0 +1,23 @@
+# Copyright 2024 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.pali_gemma.pali_gemma_backbone import (
+    PaliGemmaBackbone,
+)
+from keras_nlp.src.models.pali_gemma.pali_gemma_presets import backbone_presets
+from keras_nlp.src.models.pali_gemma.pali_gemma_tokenizer import (
+    PaliGemmaTokenizer,
+)
+from keras_nlp.src.utils.preset_utils import register_presets
+
+register_presets(backbone_presets, (PaliGemmaBackbone, PaliGemmaTokenizer))

keras_nlp/src/models/pali_gemma/pali_gemma_backbone.py

@@ -0,0 +1,279 @@
+# 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.backend import config
+from keras_nlp.src.backend import keras
+from keras_nlp.src.backend import ops
+from keras_nlp.src.layers.modeling.reversible_embedding import (
+    ReversibleEmbedding,
+)
+from keras_nlp.src.models.backbone import Backbone
+from keras_nlp.src.models.gemma.rms_normalization import RMSNormalization
+from keras_nlp.src.models.pali_gemma.pali_gemma_decoder_block import (
+    PaliGemmaDecoderBlock,
+)
+from keras_nlp.src.models.pali_gemma.pali_gemma_vit import PaliGemmaVit
+
+
+@keras_nlp_export("keras_nlp.models.PaliGemmaBackbone")
+class PaliGemmaBackbone(Backbone):
+    """PaliGemma core network with hyperparameters.
+
+    This backbone implements the mixed-modality PaliGemma architecture. It
+    contains a Visual Transformer network, as well as text token embedding
+    layer, followed by a backend-agnostic concatenation operation to
+    construct a sequence of representations of mixed type embeddings (visual
+    and textual). Then, the concatenated sequence is passed through a series
+    of Mixed Modality Decoder Blocks. The returned value from calling this model
+    represents probabilistic values for output tokens.
+
+    For a higher-level object for text-generation,
+    see `keras_nlp.models.PaliGemmaCausalLM`.
+
+    The default constructor gives a fully customizable, randomly initialized
+    PaliGemma model with any number of vit layers, heads, embedding
+    dimensions, and equivalent configuration for Paligemma Decoder layers. To
+    load preset architectures and weights, use the `from_preset` constructor.
+
+    Args:
+        vocabulary_size: int. The size of the token vocabulary.
+        image_size: int. The resolution of the image in both width and height.
+            Note: input images must be square.
+        num_layers: int. The number of transformer mixed decoder layers.
+        num_query_heads: int. The number of heads for the query projections in
+            the mixed decoder attention layer.
+        num_key_value_heads: int. The number of heads for the key and value
+            projections in the mixed decoder attention layers.
+        hidden_dim: int. The size of the transformer hidden state at the end
+            of each mixed transformer layer.
+        intermediate_dim: int. The output dimension of the first Dense layer in
+            a two-layer feedforward network for each transformer decoder block.
+        head_dim: int. The size of each attention head in the mixed decoder.
+        vit_patch_size: int. The size of each square patch in the input image.
+        vit_num_heads: int. The number of attention heads for the vision(image)
+            transformer encoder.
+        vit_hidden_dim: int. The size of the transformer hidden state at the end
+            of each vision transformer layer.
+        vit_num_layers: int. The number of vision transformer layers.
+        vit_intermediate_dim: int. The output dimension of the first Dense layer
+            in a two-layer feedforward network for vision transformer.
+        vit_pooling: string. The encoded vision embeddings are pooled using the
+            specified polling setting. The accepted values are `"map"`, `"gap"`,
+            `"0"` or `"none"`. Defaults to `"none"`.
+        vit_classifier_activation: activation function. The activation that
+            is used for final output classification in the vision transformer.
+        vit_name: string. The name used for vision transformer layers.
+        layer_norm_epsilon: float. The epsilon value user for every layer norm
+            in all transformer blocks.
+        dropout: float. Dropout probability for the Transformer decoder blocks.
+        dtype: string or `keras.mixed_precision.DTypePolicy`. The dtype to use
+            for the models computations and weights. Note that some
+            computations, such as softmax and layer normalization will always
+            be done a float32 precision regardless of dtype.
+
+    Example:
+    ```python
+    input_data = {
+        "token_ids": np.ones(shape=(1, 12), dtype="int32"),
+        "images": np.random.uniform(size=(1, 224, 224, 3)),
+        "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
+    }
+
+    # Pretrained PaliGemma decoder.
+    model = keras_nlp.models.PaliGemmaBackbone.from_preset("pali_gemma_mix_224")
+    model(input_data)
+
+    # Randomly initialized PaliGemma decoder with custom config.
+    model = keras_nlp.models.PaliGemmaBackbone(
+        vocabulary_size=50257,
+        images_size=224,
+        num_layers=12,
+        num_query_heads=12,
+        num_key_value_heads=1,
+        hidden_dim=768,
+        intermediate_dim=3072,
+        head_dim=64,
+        vit_patch_size=14,
+        vit_num_heads=8,
+        vit_hidden_dim=768,
+        vit_intermediate_dim=3072,
+        vit_num_layers=2,
+    )
+    model(input_data)
+    ```
+    """
+
+    def __init__(
+        self,
+        vocabulary_size,
+        image_size,
+        num_layers,
+        num_query_heads,
+        num_key_value_heads,
+        hidden_dim,
+        intermediate_dim,
+        head_dim,
+        vit_patch_size,
+        vit_num_heads,
+        vit_hidden_dim,
+        vit_num_layers,
+        vit_intermediate_dim=None,  # TODO remove default
+        vit_pooling=None,
+        vit_classifier_activation=None,
+        vit_name=None,
+        layer_norm_epsilon=1e-6,
+        dropout=0,
+        dtype=None,
+        **kwargs,
+    ):
+        if not config.keras_3():
+            raise ValueError(
+                "`PaliGemmaBackbone` requires Keras 3. Run "
+                "`pip install -U keras` to upgrade your Keras version, or see "
+                "https://keras.io/getting_started/ "
+                "for more info on Keras versions and installation."
+            )
+
+        # === Layers ===
+        self.token_embedding = ReversibleEmbedding(
+            input_dim=vocabulary_size,
+            output_dim=hidden_dim,
+            tie_weights=True,
+            embeddings_initializer=keras.initializers.VarianceScaling(
+                scale=1.0,
+                mode="fan_in",
+                distribution="untruncated_normal",
+                seed=None,
+            ),
+            dtype=dtype,
+            name="token_embedding",
+        )
+        # TODO Remove this. Work around for previous serialization bug.
+        vit_intermediate_dim = vit_intermediate_dim or 4304
+        self.vit_encoder = PaliGemmaVit(
+            image_size=image_size,
+            patch_size=vit_patch_size,
+            num_heads=vit_num_heads,
+            hidden_dim=vit_hidden_dim,
+            num_layers=vit_num_layers,
+            intermediate_dim=vit_intermediate_dim,
+            pooling=vit_pooling,
+            num_classes=hidden_dim,
+            classifier_activation=vit_classifier_activation,
+            dtype=dtype,
+            name=vit_name,
+        )
+        self.transformer_layers = []
+        for i in range(num_layers):
+            layer = PaliGemmaDecoderBlock(
+                hidden_dim=hidden_dim,
+                intermediate_dim=intermediate_dim,
+                num_query_heads=num_query_heads,
+                head_dim=head_dim,
+                num_key_value_heads=num_key_value_heads,
+                dropout=dropout,
+                dtype=dtype,
+                name=f"decoder_block_{i}",
+            )
+            self.transformer_layers.append(layer)
+        self.layer_norm = RMSNormalization(
+            epsilon=layer_norm_epsilon,
+            dtype=dtype,
+            name="final_normalization",
+        )
+
+        # === Functional Model ===
+        image_input = self.vit_encoder.inputs[0]
+        token_id_input = keras.Input(
+            shape=(None,), dtype="int32", name="token_ids"
+        )
+        padding_mask_input = keras.Input(
+            shape=(None,), dtype="int32", name="padding_mask"
+        )
+        response_mask_input = keras.Input(
+            shape=(None,), dtype="int32", name="response_mask"
+        )
+        img_embeddings = self.vit_encoder(image_input)
+        text_embeddings = self.token_embedding(token_id_input)
+        text_embeddings = text_embeddings * ops.cast(
+            ops.sqrt(hidden_dim), text_embeddings.dtype
+        )
+        x = ops.concatenate((img_embeddings, text_embeddings), axis=1)
+        for transformer_layer in self.transformer_layers:
+            x = transformer_layer(
+                x,
+                padding_mask=padding_mask_input,
+                response_mask=response_mask_input,
+            )
+        sequence_output = self.layer_norm(x)
+        super().__init__(
+            inputs={
+                "images": image_input,
+                "token_ids": token_id_input,
+                "padding_mask": padding_mask_input,
+                "response_mask": response_mask_input,
+            },
+            outputs=sequence_output,
+            dtype=dtype,
+            **kwargs,
+        )
+
+        # === Config ===
+        self.vocabulary_size = vocabulary_size
+        self.image_size = image_size
+        self.num_layers = num_layers
+        self.num_query_heads = num_query_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_dim = hidden_dim
+        self.intermediate_dim = intermediate_dim
+        self.head_dim = head_dim
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.dropout = dropout
+        # VIT Params
+        self.vit_patch_size = vit_patch_size
+        self.vit_num_heads = vit_num_heads
+        self.vit_hidden_dim = vit_hidden_dim
+        self.vit_num_layers = vit_num_layers
+        self.vit_intermediate_dim = vit_intermediate_dim
+        self.vit_pooling = vit_pooling
+        self.vit_classifier_activation = vit_classifier_activation
+        self.vit_name = vit_name
+        # Keep the image_sequence_length as a backbone property for easy access.
+        self.image_sequence_length = self.vit_encoder.image_sequence_length
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "vocabulary_size": self.vocabulary_size,
+                "image_size": self.image_size,
+                "num_layers": self.num_layers,
+                "num_query_heads": self.num_query_heads,
+                "num_key_value_heads": self.num_key_value_heads,
+                "hidden_dim": self.hidden_dim,
+                "intermediate_dim": self.intermediate_dim,
+                "head_dim": self.head_dim,
+                "layer_norm_epsilon": self.layer_norm_epsilon,
+                "dropout": self.dropout,
+                "vit_patch_size": self.vit_patch_size,
+                "vit_num_heads": self.vit_num_heads,
+                "vit_hidden_dim": self.vit_hidden_dim,
+                "vit_num_layers": self.vit_num_layers,
+                "vit_intermediate_dim": self.vit_intermediate_dim,
+                "vit_pooling": self.vit_pooling,
+                "vit_classifier_activation": self.vit_classifier_activation,
+                "vit_name": self.vit_name,
+            }
+        )
+        return config