Add Mixtral

keras_hub/api/models/__init__.py

@@ -234,6 +234,12 @@
 from keras_hub.src.models.mit.mit_image_classifier_preprocessor import (
     MiTImageClassifierPreprocessor,
 )
+from keras_hub.src.models.mixtral.mixtral_backbone import MixtralBackbone
+from keras_hub.src.models.mixtral.mixtral_causal_lm import MixtralCausalLM
+from keras_hub.src.models.mixtral.mixtral_causal_lm_preprocessor import (
+    MixtralCausalLMPreprocessor,
+)
+from keras_hub.src.models.mixtral.mixtral_tokenizer import MixtralTokenizer
 from keras_hub.src.models.mobilenet.mobilenet_backbone import MobileNetBackbone
 from keras_hub.src.models.mobilenet.mobilenet_image_classifier import (
     MobileNetImageClassifier,

keras_hub/api/tokenizers/__init__.py

@@ -25,6 +25,7 @@
 from keras_hub.src.models.llama.llama_tokenizer import LlamaTokenizer
 from keras_hub.src.models.llama3.llama3_tokenizer import Llama3Tokenizer
 from keras_hub.src.models.mistral.mistral_tokenizer import MistralTokenizer
+from keras_hub.src.models.mixtral.mixtral_tokenizer import MixtralTokenizer
 from keras_hub.src.models.opt.opt_tokenizer import OPTTokenizer
 from keras_hub.src.models.pali_gemma.pali_gemma_tokenizer import (
     PaliGemmaTokenizer,

keras_hub/src/models/mixtral/mixtral_attention.py

@@ -0,0 +1,263 @@
+import inspect
+import math
+
+import keras
+from keras import ops
+
+from keras_hub.src.layers.modeling.rotary_embedding import RotaryEmbedding
+from keras_hub.src.utils.keras_utils import clone_initializer
+from keras_hub.src.utils.keras_utils import fused_attention_op_available
+from keras_hub.src.utils.keras_utils import gpu_supports_fused_attention_op
+from keras_hub.src.utils.keras_utils import running_on_gpu
+from keras_hub.src.utils.keras_utils import running_on_tpu
+
+
+class CachedMixtralAttention(keras.layers.Layer):
+    """A cached grounded query attention layer with sliding window."""
+
+    def __init__(
+        self,
+        num_query_heads,
+        num_key_value_heads,
+        rope_max_wavelength=10000,
+        rope_scaling_factor=1.0,
+        kernel_initializer="glorot_uniform",
+        sliding_window=512,
+        dropout=0,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self._num_query_heads = num_query_heads
+        self._num_key_value_heads = num_key_value_heads
+        self._sliding_window = sliding_window
+        self._dropout = dropout
+
+        self._num_key_value_groups = num_query_heads // num_key_value_heads
+        self._rope_max_wavelength = rope_max_wavelength
+
+        self._kernel_initializer = keras.initializers.get(
+            clone_initializer(kernel_initializer)
+        )
+
+        self._rope_scaling_factor = rope_scaling_factor
+
+    def build(self, inputs_shape):
+        # Einsum variables:
+        # b = batch size
+        # q = query length
+        # k = key/value length
+        # m = model dim
+        # u = num query heads
+        # v = num key/value heads
+        # h = head dim
+        self._hidden_dim = inputs_shape[-1]
+        self._head_dim = self._hidden_dim // self._num_query_heads
+        self._inv_norm_factor = 1.0 / math.sqrt(self._head_dim)
+
+        self._query_dense = keras.layers.EinsumDense(
+            equation="bqm,muh->bquh",
+            output_shape=(None, self._num_query_heads, self._head_dim),
+            kernel_initializer=self._kernel_initializer,
+            dtype=self.dtype_policy,
+            name="query",
+        )
+        self._query_dense.build(inputs_shape)
+
+        self._key_dense = keras.layers.EinsumDense(
+            equation="bkm,mvh->bkvh",
+            output_shape=(
+                None,
+                self._num_key_value_heads,
+                self._head_dim,
+            ),
+            kernel_initializer=self._kernel_initializer,
+            dtype=self.dtype_policy,
+            name="key",
+        )
+        self._key_dense.build(inputs_shape)
+
+        self._value_dense = keras.layers.EinsumDense(
+            equation="bkm,mvh->bkvh",
+            output_shape=(
+                None,
+                self._num_key_value_heads,
+                self._head_dim,
+            ),
+            kernel_initializer=self._kernel_initializer,
+            dtype=self.dtype_policy,
+            name="value",
+        )
+        self._value_dense.build(inputs_shape)
+
+        self._softmax = keras.layers.Softmax(
+            axis=-1,
+            dtype="float32",
+            name="attention_softmax",
+        )
+
+        self._dropout_layer = keras.layers.Dropout(
+            rate=self._dropout,
+            dtype=self.dtype_policy,
+        )
+
+        self._output_dense = keras.layers.EinsumDense(
+            equation="bquh,uhm->bqm",
+            output_shape=(None, self._hidden_dim),
+            kernel_initializer=self._kernel_initializer,
+            dtype=self.dtype_policy,
+            name="attention_output",
+        )
+        self._output_dense.build(
+            (None, None, self._num_query_heads, self._head_dim)
+        )
+
+        self.rotary_embedding_layer = RotaryEmbedding(
+            max_wavelength=self._rope_max_wavelength,
+            scaling_factor=self._rope_scaling_factor,
+            dtype=self.dtype_policy,
+        )
+
+        self._dot_product_equation = "bquh,bkuh->buqk"
+        self._combine_equation = "buqk,bkuh->bquh"
+
+        self.built = True
+
+    def call(
+        self,
+        hidden_states,
+        attention_mask=None,
+        cache=None,
+        cache_update_index=None,
+        training=None,
+    ):
+        start_index = (
+            cache_update_index if cache_update_index is not None else 0
+        )
+
+        query = self._query_dense(hidden_states)
+
+        # Compute RoPE for queries
+        query = self.rotary_embedding_layer(query, start_index=start_index)
+
+        def _compute_key_value(x):
+            key, value = self._key_dense(x), self._value_dense(x)
+            # Compute RoPE for keys
+            key = self.rotary_embedding_layer(key, start_index=start_index)
+            return key, value
+
+        if cache is not None:
+            key_cache = cache[:, 0, ...]
+            value_cache = cache[:, 1, ...]
+            if cache_update_index is None:
+                key = key_cache
+                value = value_cache
+            else:
+                key_update, value_update = _compute_key_value(hidden_states)
+                start = [0, cache_update_index, 0, 0]
+                key = ops.slice_update(key_cache, start, key_update)
+                value = ops.slice_update(value_cache, start, value_update)
+                cache = ops.stack((key, value), axis=1)
+        else:
+            if cache_update_index is not None:
+                raise ValueError(
+                    "`cache_update_index` should not be set if `cache` is "
+                    f"`None`. Received: cache={cache}, "
+                    f"cache_update_index={cache_update_index}"
+                )
+            key, value = _compute_key_value(hidden_states)
+
+        # [batch_shape, seq_len, num_key_value_heads, head_dim]
+        # -> [batch_shape, seq_len, num_heads, head_dim]
+        key = ops.repeat(key, repeats=self._num_key_value_groups, axis=2)
+        value = ops.repeat(value, repeats=self._num_key_value_groups, axis=2)
+
+        attention_output = self._compute_attention(
+            query, key, value, attention_mask
+        )
+
+        attention_output = self._dropout_layer(
+            attention_output, training=training
+        )
+
+        attention_output = self._output_dense(attention_output)
+
+        if cache is not None:
+            return attention_output, cache
+        return attention_output
+
+    def _masked_softmax(self, attention_scores, attention_mask=None):
+        if attention_mask is not None:
+            return self._softmax(
+                attention_scores, attention_mask[:, None, :, :]
+            )
+        return self._softmax(attention_scores)
+
+    def _use_fused_attention_op(self):
+        if not fused_attention_op_available():
+            return False
+        if self.dropout > 0.0:
+            return False
+        if running_on_gpu():
+            # GPU never supports softcap in the fused op.
+            if self.logit_soft_cap is not None:
+                return False
+            return gpu_supports_fused_attention_op()
+        elif running_on_tpu():
+            # TPU supports softcap with on keras >= 3.10.
+            sig = inspect.signature(ops.dot_product_attention)
+            return "attn_logits_soft_cap" in sig.parameters
+        else:
+            return False
+
+    def _compute_attention(self, query, key, value, attention_mask=None):
+        if self._use_fused_attention_op():
+            if attention_mask is not None:
+                attention_mask = ops.expand_dims(attention_mask, axis=1)
+                attention_mask = ops.cast(attention_mask, dtype="bool")
+
+            if self.logit_soft_cap:
+                kwargs = {"attn_logits_soft_cap": self.logit_soft_cap}
+            else:
+                kwargs = {}
+
+            attention_output = ops.dot_product_attention(
+                query,
+                key,
+                value,
+                mask=attention_mask,
+                scale=self._inv_norm_factor,
+                **kwargs,
+            )
+            return attention_output
+
+        attention_scores = ops.einsum(self._dot_product_equation, query, key)
+        attention_scores = ops.multiply(
+            attention_scores,
+            ops.cast(self._inv_norm_factor, self.compute_dtype),
+        )
+        attention_scores = self._masked_softmax(
+            attention_scores, attention_mask
+        )
+        attention_scores = ops.cast(attention_scores, self.compute_dtype)
+        attention_output = ops.einsum(
+            self._combine_equation, attention_scores, value
+        )
+
+        return attention_output
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "num_query_heads": self._num_query_heads,
+                "num_key_value_heads": self._num_key_value_heads,
+                "rope_max_wavelength": self._rope_max_wavelength,
+                "rope_scaling_factor": self._rope_scaling_factor,
+                "kernel_initializer": keras.initializers.serialize(
+                    self._kernel_initializer
+                ),
+                "sliding_window": self._sliding_window,
+                "dropout": self._dropout,
+            }
+        )
+        return config