Add 7B presets for Mistral

keras_nlp/models/mistral/mistral_backbone.py

@@ -11,6 +11,8 @@
 # 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.api_export import keras_nlp_export
 from keras_nlp.backend import keras
 from keras_nlp.backend import ops
@@ -19,9 +21,11 @@
 from keras_nlp.models.mistral.mistral_layer_norm import (
     MistralLayerNormalization,
 )
+from keras_nlp.models.mistral.mistral_presets import backbone_presets
 from keras_nlp.models.mistral.mistral_transformer_decoder import (
     MistralTransformerDecoder,
 )
+from keras_nlp.utils.python_utils import classproperty
 
 
 def _mistral_kernel_initializer(stddev=0.02):
@@ -196,3 +200,7 @@ def get_config(self):
             }
         )
         return config
+
+    @classproperty
+    def presets(cls):
+        return copy.deepcopy(backbone_presets)

keras_nlp/models/mistral/mistral_backbone_test.py

@@ -54,3 +54,29 @@ def test_num_parameters(self):
         model = MistralBackbone(**self.init_kwargs)
         # Reference value calculated using the PyTorch model
         self.assertEqual(model.count_params(), 2704)
+
+    @pytest.mark.large
+    def test_smallest_preset(self):
+        self.run_preset_test(
+            cls=MistralBackbone,
+            preset="mistral_7b_en",
+            input_data={
+                "token_ids": ops.array([[1, 1824, 349, 524, 11234, 28804]]),
+                "padding_mask": ops.ones((1, 6), dtype="int32"),
+            },
+            expected_output_shape=(1, 6, 4096),
+            # The forward pass from a preset should be stable!
+            # Reference values computed using PyTorch HF model.
+            expected_partial_output=ops.array(
+                [-1.6875, 0.5117, -1.7188, 2.3125, -0.0996]
+            ),
+        )
+
+    @pytest.mark.extra_large
+    def test_all_presets(self):
+        for preset in MistralBackbone.presets:
+            self.run_preset_test(
+                cls=MistralBackbone,
+                preset=preset,
+                input_data=self.input_data,
+            )

keras_nlp/models/mistral/mistral_causal_lm.py

@@ -11,6 +11,7 @@
 # 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.api_export import keras_nlp_export
 from keras_nlp.backend import keras
@@ -20,6 +21,7 @@
 from keras_nlp.models.mistral.mistral_causal_lm_preprocessor import (
     MistralCausalLMPreprocessor,
 )
+from keras_nlp.models.mistral.mistral_presets import backbone_presets
 from keras_nlp.utils.python_utils import classproperty
 
 
@@ -211,3 +213,7 @@ def next(prompt, cache, index):
             "token_ids": token_ids,
             "padding_mask": padding_mask,
         }
+
+    @classproperty
+    def presets(cls):
+        return copy.deepcopy(backbone_presets)

keras_nlp/models/mistral/mistral_causal_lm_preprocessor.py

@@ -131,6 +131,20 @@ def generate_preprocess(
         x,
         sequence_length=None,
     ):
+        """Covert strings to integer token input for generation.
+
+        Similar to calling the layer for training, this method takes in strings
+        or tensor strings, tokenizes and packs the input, and computes a padding
+        mask masking all inputs not filled in with a padded value.
+
+        Unlike calling the layer for training, this method does not compute
+        labels and will never append a `tokenizer.end_token_id` to the end of
+        the sequence (as generation is expected to continue at the end of the
+        inputted prompt).
+        """
+        if not self.built:
+            self.build(None)
+
         x = convert_inputs_to_list_of_tensor_segments(x)[0]
         x = self.tokenizer(x)
         token_ids, padding_mask = self.packer(

keras_nlp/models/mistral/mistral_causal_lm_preprocessor_test.py

@@ -14,6 +14,8 @@
 
 import os
 
+import pytest
+
 from keras_nlp.models.mistral.mistral_causal_lm_preprocessor import (
     MistralCausalLMPreprocessor,
 )
@@ -79,3 +81,12 @@ def test_generate_postprocess(self):
         preprocessor = MistralCausalLMPreprocessor(**self.init_kwargs)
         x = preprocessor.generate_postprocess(input_data)
         self.assertAllEqual(x, "the quick brown fox")
+
+    @pytest.mark.extra_large
+    def test_all_presets(self):
+        for preset in MistralCausalLMPreprocessor.presets:
+            self.run_preset_test(
+                cls=MistralCausalLMPreprocessor,
+                preset=preset,
+                input_data=self.input_data,
+            )

keras_nlp/models/mistral/mistral_preprocessor.py

@@ -11,9 +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.api_export import keras_nlp_export
 from keras_nlp.layers.preprocessing.start_end_packer import StartEndPacker
+from keras_nlp.models.mistral.mistral_presets import backbone_presets
 from keras_nlp.models.mistral.mistral_tokenizer import MistralTokenizer
 from keras_nlp.models.preprocessor import Preprocessor
 from keras_nlp.utils.keras_utils import (
@@ -121,15 +123,21 @@ def __init__(
     ):
         super().__init__(**kwargs)
         self.tokenizer = tokenizer
+        self.packer = None
+        self.add_start_token = add_start_token
+        self.add_end_token = add_end_token
+        self.sequence_length = sequence_length
+
+    def build(self, input_shape):
+        # Defer packer creation to `build()` so that we can be sure tokenizer
+        # assets have loaded when restoring a saved model.
         self.packer = StartEndPacker(
             start_value=self.tokenizer.start_token_id,
             end_value=self.tokenizer.end_token_id,
-            sequence_length=sequence_length,
+            sequence_length=self.sequence_length,
             return_padding_mask=True,
         )
-        self.add_start_token = add_start_token
-        self.add_end_token = add_end_token
-        self.sequence_length = sequence_length
+        self.built = True
 
     def get_config(self):
         config = super().get_config()
@@ -184,3 +192,7 @@ def sequence_length(self, value):
     @classproperty
     def tokenizer_cls(cls):
         return MistralTokenizer
+
+    @classproperty
+    def presets(cls):
+        return copy.deepcopy(backbone_presets)

keras_nlp/models/mistral/mistral_preprocessor_test.py

@@ -14,6 +14,8 @@
 
 import os
 
+import pytest
+
 from keras_nlp.models.mistral.mistral_preprocessor import MistralPreprocessor
 from keras_nlp.models.mistral.mistral_tokenizer import MistralTokenizer
 from keras_nlp.tests.test_case import TestCase
@@ -57,3 +59,12 @@ def test_errors_for_2d_list_input(self):
         ambiguous_input = [["one", "two"], ["three", "four"]]
         with self.assertRaises(ValueError):
             preprocessor(ambiguous_input)
+
+    @pytest.mark.extra_large
+    def test_all_presets(self):
+        for preset in MistralPreprocessor.presets:
+            self.run_preset_test(
+                cls=MistralPreprocessor,
+                preset=preset,
+                input_data=self.input_data,
+            )

keras_nlp/models/mistral/mistral_presets.py

@@ -0,0 +1,38 @@
+# 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.
+"""Mistral model preset configurations."""
+
+# Metadata for loading pretrained model weights.
+backbone_presets = {
+    "mistral_7b_en": {
+        "metadata": {
+            "description": "Mistral 7B base model",
+            "params": 7241732096,
+            "official_name": "Mistral",
+            "path": "mistral",
+            "model_card": "https://github.com/mistralai/mistral-src/blob/main/README.md",
+        },
+        "kaggle_handle": "kaggle://keras/mistral/keras/mistral_7b_en/3",
+    },
+    "mistral_instruct_7b_en": {
+        "metadata": {
+            "description": "Mistral 7B instruct model",
+            "params": 7241732096,
+            "official_name": "Mistral",
+            "path": "mistral",
+            "model_card": "https://github.com/mistralai/mistral-src/blob/main/README.md",
+        },
+        "kaggle_handle": "kaggle://keras/mistral/keras/mistral_instruct_7b_en/3",
+    },
+}

keras_nlp/models/mistral/mistral_tokenizer.py

@@ -11,8 +11,12 @@
 # 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.api_export import keras_nlp_export
+from keras_nlp.models.mistral.mistral_presets import backbone_presets
 from keras_nlp.tokenizers.sentence_piece_tokenizer import SentencePieceTokenizer
+from keras_nlp.utils.python_utils import classproperty
 
 
 @keras_nlp_export("keras_nlp.models.MistralTokenizer")
@@ -77,3 +81,7 @@ def set_proto(self, proto):
         else:
             self.start_token_id = None
             self.end_token_id = None
+
+    @classproperty
+    def presets(cls):
+        return copy.deepcopy(backbone_presets)

keras_nlp/models/mistral/mistral_tokenizer_test.py

@@ -14,6 +14,8 @@
 
 import os
 
+import pytest
+
 from keras_nlp.models.mistral.mistral_tokenizer import MistralTokenizer
 from keras_nlp.tests.test_case import TestCase
 
@@ -44,3 +46,21 @@ def test_errors_missing_special_tokens(self):
                     self.get_test_data_dir(), "no_special_token_vocab.spm"
                 )
             )
+
+    @pytest.mark.large
+    def test_smallest_preset(self):
+        self.run_preset_test(
+            cls=MistralTokenizer,
+            preset="mistral_7b_en",
+            input_data=["The quick brown fox."],
+            expected_output=[[415, 2936, 9060, 285, 1142, 28723]],
+        )
+
+    @pytest.mark.extra_large
+    def test_all_presets(self):
+        for preset in MistralTokenizer.presets:
+            self.run_preset_test(
+                cls=MistralTokenizer,
+                preset=preset,
+                input_data=self.input_data,
+            )