ADD RWKV7

keras_hub/api/models/__init__.py

@@ -605,6 +605,15 @@
 from keras_hub.src.models.roformer_v2.roformer_v2_tokenizer import (
     RoformerV2Tokenizer as RoformerV2Tokenizer,
 )
+from keras_hub.src.models.rwkv7.rwkv7_backbone import (
+    RWKV7Backbone as RWKV7Backbone,
+)
+from keras_hub.src.models.rwkv7.rwkv7_causal_lm import (
+    RWKV7CausalLM as RWKV7CausalLM,
+)
+from keras_hub.src.models.rwkv7.rwkv7_causal_lm_preprocessor import (
+    RWKV7CausalLMPreprocessor as RWKV7CausalLMPreprocessor,
+)
 from keras_hub.src.models.sam.sam_backbone import SAMBackbone as SAMBackbone
 from keras_hub.src.models.sam.sam_image_segmenter import (
     SAMImageSegmenter as SAMImageSegmenter,

keras_hub/api/tokenizers/__init__.py

@@ -90,6 +90,9 @@
 from keras_hub.src.models.roformer_v2.roformer_v2_tokenizer import (
     RoformerV2Tokenizer as RoformerV2Tokenizer,
 )
+from keras_hub.src.models.rwkv7.rwkv7_tokenizer import (
+    RWKVTokenizer as RWKVTokenizer,
+)
 from keras_hub.src.models.siglip.siglip_tokenizer import (
     SigLIPTokenizer as SigLIPTokenizer,
 )

keras_hub/src/models/rwkv7/rwkv7_backbone.py

@@ -0,0 +1,179 @@
+import keras
+from keras import ops
+
+from keras_hub.src.api_export import keras_hub_export
+from keras_hub.src.models.backbone import Backbone
+from keras_hub.src.models.rwkv7.rwkv7_layer import RWKV7_Block
+
+
+def rwkv7_kernel_initializer(stddev=0.02):
+    return keras.initializers.TruncatedNormal(stddev=stddev)
+
+
+@keras_hub_export("keras_hub.models.RWKV7Backbone")
+class RWKV7Backbone(Backbone):
+    """
+    The RWKV7 Transformer core architecture with hyperparameters.
+
+    This network implements a RNN-based decoder network,
+    Goose, as described in
+    [RWKV-7](https://arxiv.org/abs/2503.14456).
+
+    This network implements a Modern RNN architecture based on linear
+    attention mechanisms with recurrent processing, as described in the
+    RWKV papers. It includes the embedding lookups and RWKV-7 blocks.
+
+    The default constructor gives a fully customizable, randomly initialized
+    RWKV-7 model with any number of layers, heads, and embedding dimensions.
+    To load preset architectures and weights, use the `from_preset`
+    constructor.
+
+    Args:
+        hidden_size: int. The size of the transformer encoding and pooling
+            layers.
+        head_size: int. The size of each attention head.
+        num_layers: int. The number of transformer layers.
+        vocabulary_size: int. The size of the token vocabulary.
+        intermediate_dim: int. The output dimension of the first Dense layer in
+            a two-layer feedforward network for each transformer.
+        gate_lora: int. LoRA dimension for gating.Defaults to 128.
+        mv_lora: int. LoRA dimension for value mixing.Defaults to 32.
+        aaa_lora: int. LoRA dimension for alpha parameters.Defaults to 64.
+        decay_lora: int. LoRA dimension for decay parameters.Defaults to 64.
+        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.
+        dropout_rate: float. Dropout rate for the dropout layer.
+
+    Examples:
+
+    ```python
+    input_data = np.ones(shape=(1, 12), dtype="int32")
+
+
+    # Randomly initialized RWKV-7 decoder with custom config.
+    model = keras_hub.models.RWKV7Backbone(
+        vocabulary_size=10,
+        hidden_size=512,
+        num_layers=2,
+        head_size=64,
+        intermediate_dim=1024,
+        dtype="float32"
+    )
+    model(input_data)
+    ```
+    """
+
+    def __init__(
+        self,
+        hidden_size,
+        head_size,
+        num_layers,
+        vocabulary_size,
+        intermediate_dim,
+        gate_lora=128,
+        mv_lora=32,
+        aaa_lora=64,
+        decay_lora=64,
+        dtype=None,
+        dropout_rate=0,
+        **kwargs,
+    ):
+        # === Layers ===
+        self.token_embedding = keras.layers.Embedding(
+            input_dim=vocabulary_size,
+            output_dim=hidden_size,
+            embeddings_initializer=rwkv7_kernel_initializer(),
+            dtype=dtype,
+            name="token_embedding",
+        )
+
+        self.output_layer_norm = keras.layers.LayerNormalization(
+            epsilon=1e-5,
+            name="output_norm",
+            dtype=dtype,
+        )
+        self.dropout = keras.layers.Dropout(
+            dropout_rate,
+            dtype=dtype,
+            name="dropout",
+        )
+        self.rwkv_layers = []
+        for i in range(num_layers):
+            layer = RWKV7_Block(
+                hidden_size,
+                head_size,
+                intermediate_dim,
+                gate_lora,
+                mv_lora,
+                aaa_lora,
+                decay_lora,
+                use_initial_norm=i == 0,
+                kernel_initializer=rwkv7_kernel_initializer(),
+                dtype=dtype,
+                name=f"rwkv_layer_{i}",
+            )
+
+            self.rwkv_layers.append(layer)
+        self.head = keras.layers.Dense(
+            units=vocabulary_size,
+            kernel_initializer=rwkv7_kernel_initializer(),
+            use_bias=False,
+            name="head",
+            dtype=dtype,
+        )
+        # === Functional Model ===
+        token_id_input = keras.Input(
+            shape=(None,), dtype="int32", name="token_ids"
+        )
+
+        padding_mask_input = keras.Input(
+            shape=(None,), dtype="int32", name="padding_mask"
+        )
+
+        x = self.token_embedding(token_id_input)
+        padding_mask = ops.cast(padding_mask_input, dtype=x.dtype)
+        v_first = None
+        for rwkv_layer in self.rwkv_layers:
+            x, v_first = rwkv_layer(x, v_first, padding_mask)
+            x = self.dropout(x)
+        sequence_output = self.output_layer_norm(x)
+        sequence_output = self.head(sequence_output)
+
+        super().__init__(
+            inputs={
+                "token_ids": token_id_input,
+                "padding_mask": padding_mask_input,
+            },
+            outputs=sequence_output,
+            dtype=dtype,
+            **kwargs,
+        )
+
+        self.num_layers = num_layers
+        self.head_size = head_size
+        self.hidden_size = hidden_size
+        self.gate_lora = gate_lora
+        self.mv_lora = mv_lora
+        self.aaa_lora = aaa_lora
+        self.decay_lora = decay_lora
+        self.vocabulary_size = vocabulary_size
+        self.dropout_rate = dropout_rate
+        self.intermediate_dim = intermediate_dim
+
+    def get_config(self):
+        config = {
+            "hidden_size": self.hidden_size,
+            "head_size": self.head_size,
+            "gate_lora": self.gate_lora,
+            "mv_lora": self.mv_lora,
+            "aaa_lora": self.aaa_lora,
+            "decay_lora": self.decay_lora,
+            "vocabulary_size": self.vocabulary_size,
+            "dropout_rate": self.dropout_rate,
+            "intermediate_dim": self.intermediate_dim,
+            "num_layers": self.num_layers,
+        }
+        base_config = super().get_config()
+        return dict(list(base_config.items()) + list(config.items()))

keras_hub/src/models/rwkv7/rwkv7_backbone_test.py

@@ -0,0 +1,38 @@
+import pytest
+from keras import ops
+
+from keras_hub.src.models.rwkv7.rwkv7_backbone import RWKV7Backbone
+from keras_hub.src.tests.test_case import TestCase
+
+
+class RWKV7BackboneTest(TestCase):
+    def setUp(self):
+        self.init_kwargs = {
+            "vocabulary_size": 10,
+            "hidden_size": 16,
+            "num_layers": 2,
+            "head_size": 4,
+            "intermediate_dim": 32,
+            "gate_lora": 32,
+            "mv_lora": 16,
+            "aaa_lora": 16,
+            "decay_lora": 16,
+        }
+        t = ops.ones((2, 16), dtype="int32")
+        self.input_data = {"token_ids": t, "padding_mask": t}
+
+    def test_backbone_basics(self):
+        self.run_backbone_test(
+            cls=RWKV7Backbone,
+            init_kwargs=self.init_kwargs,
+            input_data=self.input_data,
+            expected_output_shape=(2, 16, 10),
+        )
+
+    @pytest.mark.large
+    def test_saved_model(self):
+        self.run_model_saving_test(
+            cls=RWKV7Backbone,
+            init_kwargs=self.init_kwargs,
+            input_data=self.input_data,
+        )