Add MistralAI's 7B Transformer as a backbone in KerasNLP Models

keras_nlp/layers/modeling/rotary_embedding.py

@@ -97,7 +97,7 @@ def _apply_rotary_pos_emb(self, tensor, cos_emb, sin_emb):
         return (tensor * cos_emb) + (half_rot_tensor * sin_emb)
 
     def _compute_cos_sin_embedding(self, x, rotary_dim, start_index):
-        freq_range = ops.arange(0, rotary_dim, 2, dtype="float32")
+        freq_range = ops.arange(0, rotary_dim, 2)
         freq_range = ops.cast(freq_range, self.compute_dtype)
         freq_range = freq_range / ops.cast(
             self.scaling_factor, self.compute_dtype
@@ -107,7 +107,7 @@ def _compute_cos_sin_embedding(self, x, rotary_dim, start_index):
             ** (freq_range / ops.cast(rotary_dim, self.compute_dtype))
         )
         seq_len = ops.shape(x)[self.sequence_axis]
-        tensor = ops.arange(seq_len, dtype="float32") + start_index
+        tensor = ops.cast(ops.arange(seq_len), self.compute_dtype) + start_index
         tensor = ops.cast(tensor, dtype=inverse_freq.dtype)
         freq = ops.einsum("i, j -> ij", tensor, inverse_freq)
         embedding = ops.concatenate((freq, freq), axis=self.feature_axis)

keras_nlp/models/__init__.py

@@ -89,6 +89,7 @@
     GPTNeoXPreprocessor,
 )
 from keras_nlp.models.gpt_neo_x.gpt_neo_x_tokenizer import GPTNeoXTokenizer
+from keras_nlp.models.mistral.mistral_backbone import MistralBackbone
 from keras_nlp.models.opt.opt_backbone import OPTBackbone
 from keras_nlp.models.opt.opt_causal_lm import OPTCausalLM
 from keras_nlp.models.opt.opt_causal_lm_preprocessor import (

keras_nlp/models/mistral/__init__.py

@@ -0,0 +1,13 @@
+# 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.

keras_nlp/models/mistral/mistral_attention.py

@@ -0,0 +1,293 @@
+# 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.backend import keras
+from keras_nlp.backend import ops
+from keras_nlp.layers.modeling.rotary_embedding import RotaryEmbedding
+from keras_nlp.utils.keras_utils import clone_initializer
+
+
+# This is just a self-attention layer in Mistral. But it can be generalized
+# to use the `keras_nlp.layers.CachedMultiHeadAttention` API. Since this layer
+# implements grouped-query attention and sliding window attention, it might be
+# useful outside of Mistral itself.
+# TODO(tirthasheshpatel): Generalize the attention layer
+# TODO(tirthasheshpatel): Merge `LlamaAttention` with this layer
+# TODO(tirthasheshpatel): Use flash attention
+class CachedMistralAttention(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._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.compute_dtype,
+            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.compute_dtype,
+            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.compute_dtype,
+            name="value",
+        )
+        self._value_dense.build(inputs_shape)
+
+        self._softmax = keras.layers.Softmax(axis=-1, name="attention_softmax")
+
+        self._dropout_layer = keras.layers.Dropout(
+            rate=self._dropout, dtype=self.compute_dtype
+        )
+
+        self._output_dense = keras.layers.EinsumDense(
+            equation="bquh,uhm->bqm",
+            output_shape=(None, self._hidden_dim),
+            kernel_initializer=self._kernel_initializer,
+            dtype=self.compute_dtype,
+            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.compute_dtype,
+        )
+
+        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,
+    ):
+        seq_len = ops.shape(hidden_states)[1]
+        start_index = (
+            cache_update_index if cache_update_index is not None else 0
+        )
+        # If `cache_update_index` is a tensor, RotaryEmbedding expects it
+        # to have dtype `self.compute_dtype`.
+        start_index = ops.cast(
+            start_index, self.rotary_embedding_layer.compute_dtype
+        )
+
+        query = self._query_dense(hidden_states)
+
+        # Note that the original PyTorch implementation uses
+        # view_as_complex/view_as_real while we use split/concatenate to
+        # convert to/from complex numbers. The transformations below make
+        # the rope computation numerically equivalent to the original
+        # implementation.
+        def _mistral_rope(x):
+            x = ops.concatenate([x[..., ::2], x[..., 1::2]], axis=-1)
+            x = self.rotary_embedding_layer(x, start_index=start_index)
+            x = ops.reshape(
+                ops.stack(ops.split(x, 2, axis=-1), axis=-1), ops.shape(x)
+            )
+            return x
+
+        # Compute RoPE for queries
+        query = _mistral_rope(query)
+
+        def _compute_key_value(x):
+            key, value = self._key_dense(x), self._value_dense(x)
+            key = _mistral_rope(key)
+            return key, value
+
+        if cache is not None:
+            cache_k = cache[:, 0, ...]
+            cache_v = cache[:, 1, ...]
+
+            if cache_update_index is not None:
+                # Compute the new keys and values
+                key, value = _compute_key_value(hidden_states)
+
+                # Cache is a rotating buffer, we want to warp around if
+                # the sequence length exceeds the sliding window.
+                update_end_index = (
+                    cache_update_index + seq_len - 1
+                ) % self._sliding_window + 1
+                update_end_index = ops.cast(update_end_index, "int32")
+                cache_update_index = cache_update_index % self._sliding_window
+                update_start_index = ops.cond(
+                    update_end_index > cache_update_index,
+                    lambda: ops.cast(cache_update_index, "int32"),
+                    lambda: ops.cast(0, "int32"),
+                )
+                # Also note that the update step below assumes that the
+                # sequence length is always one when `cache_update_index != 0`.
+                # This is necessary to support XLA compilation. Ideally, we
+                # would want to use
+                # `key[:, -(update_end_index - update_start_index):, ...]`
+                # as the update but updating using a dynamic slice gives an
+                # XLA compilation error in TensorFlow.
+                # Passing a sequence of length > 1 with cache update might give
+                # incorrect results (since there is no way to determine how
+                # many most recent tokens are to be saved if the tokens exceed
+                # the sliding window length).
+                cache_k = ops.slice_update(
+                    cache_k,
+                    [0, update_start_index, 0, 0],
+                    # We slice the keys and values since if the user has passed
+                    # a sequence of length > `self._sliding_window`. We want to
+                    # prefill the cache using just the most recent values in the
+                    # sliding window.
+                    ops.cast(
+                        key[:, -self._sliding_window :, ...], cache_k.dtype
+                    ),
+                )
+                cache_v = ops.slice_update(
+                    cache_v,
+                    [0, update_start_index, 0, 0],
+                    ops.cast(
+                        value[:, -self._sliding_window :, ...], cache_v.dtype
+                    ),
+                )
+                cache = ops.stack([cache_k, cache_v], axis=1)
+
+                # Get the required keys and values from the cache.
+                # Since we expect the user to pass a fixed-size cache, we just
+                # pick the first few slices up-to and including the newly computed
+                # keys and values.
+                cache_k = cache_k[:, :update_end_index, ...]
+                cache_v = cache_v[:, :update_end_index, ...]
+
+            key = ops.cast(cache_k, dtype=self.compute_dtype)
+            value = ops.cast(cache_v, dtype=self.compute_dtype)
+        else:
+            # Compute keys and values
+            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 _compute_attention(self, query, key, value, attention_mask=None):
+        attention_scores = ops.einsum(self._dot_product_equation, key, query)
+
+        norm_factor = ops.sqrt(ops.cast(self._head_dim, self.compute_dtype))
+
+        attention_scores = attention_scores / norm_factor
+
+        attention_scores = self._masked_softmax(
+            attention_scores, attention_mask
+        )
+        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