Add preset tests and weights URLs

keras_nlp/models/backbone.py

@@ -112,12 +112,14 @@ def from_preset(
         if not load_weights:
             return model
 
+        filename = os.path.basename(metadata["weights_url"])
         weights = keras.utils.get_file(
-            "model.h5",
+            filename,
             metadata["weights_url"],
             cache_subdir=os.path.join("models", preset),
             file_hash=metadata["weights_hash"],
         )
+
         model.load_weights(weights)
         return model
 

keras_nlp/models/t5/t5_backbone_test.py

@@ -53,3 +53,32 @@ def test_saved_model(self):
             init_kwargs=self.init_kwargs,
             input_data=self.input_data,
         )
+
+    @pytest.mark.large
+    def test_smallest_preset(self):
+        self.run_preset_test(
+            cls=T5Backbone,
+            preset="t5_small_multi",
+            input_data=self.input_data,
+            expected_output_shape={
+                "encoder_sequence_output": (2, 3, 512),
+                "decoder_sequence_output": (2, 3, 512),
+            },
+            expected_partial_output={
+                "encoder_sequence_output": ops.array(
+                    [-0.0034, 0.0293, -0.0827, -0.1076]
+                ),
+                "decoder_sequence_output": ops.array(
+                    [0.0097, 0.3576, -0.1508, 0.0150]
+                ),
+            },
+        )
+
+    @pytest.mark.extra_large
+    def test_all_presets(self):
+        for preset in T5Backbone.presets:
+            self.run_preset_test(
+                cls=T5Backbone,
+                preset=preset,
+                input_data=self.input_data,
+            )

keras_nlp/models/t5/t5_presets.py

@@ -38,6 +38,10 @@
             "layer_norm_epsilon": 1e-06,
         },
         "preprocessor_config": {},
+        "weights_url": "https://storage.googleapis.com/keras-nlp/models/t5_small_multi/v1/model.weights.h5",
+        "weights_hash": "5a241ea61142eaf96ac1805898a2f2d1",
+        "vocabulary_url": "https://storage.googleapis.com/keras-nlp/models/t5_small_multi/v1/vocab.spm",
+        "vocabulary_hash": "9d15ef55d09d5a425ceb63fa31f7cae3",
     },
     "t5_base_multi": {
         "metadata": {
@@ -62,6 +66,10 @@
             "layer_norm_epsilon": 1e-06,
         },
         "preprocessor_config": {},
+        "weights_url": "https://storage.googleapis.com/keras-nlp/models/t5_base_multi/v1/model.weights.h5",
+        "weights_hash": "9bef4c6650d91d1ea438ee4a2bea47ad",
+        "vocabulary_url": "https://storage.googleapis.com/keras-nlp/models/t5_base_multi/v1/vocab.spm",
+        "vocabulary_hash": "9d15ef55d09d5a425ceb63fa31f7cae3",
     },
     "t5_large_multi": {
         "metadata": {
@@ -86,6 +94,10 @@
             "layer_norm_epsilon": 1e-06,
         },
         "preprocessor_config": {},
+        "weights_url": "https://storage.googleapis.com/keras-nlp/models/t5_large_multi/v1/model.weights.h5",
+        "weights_hash": "eab8eee1bad033e65324a71cd6e5a8e9",
+        "vocabulary_url": "https://storage.googleapis.com/keras-nlp/models/t5_large_multi/v1/vocab.spm",
+        "vocabulary_hash": "9d15ef55d09d5a425ceb63fa31f7cae3",
     },
     "flan_small_multi": {
         "metadata": {
@@ -111,6 +123,10 @@
             "layer_norm_epsilon": 1e-06,
         },
         "preprocessor_config": {},
+        "weights_url": "https://storage.googleapis.com/keras-nlp/models/flan_small_multi/v1/model.weights.h5",
+        "weights_hash": "4e39b0bab56606a9ab2b8e52a6bc7a9f",
+        "vocabulary_url": "https://storage.googleapis.com/keras-nlp/models/flan_small_multi/v1/vocab.spm",
+        "vocabulary_hash": "9d15ef55d09d5a425ceb63fa31f7cae3",
     },
     "flan_base_multi": {
         "metadata": {
@@ -135,6 +151,10 @@
             "layer_norm_epsilon": 1e-06,
         },
         "preprocessor_config": {},
+        "weights_url": "https://storage.googleapis.com/keras-nlp/models/flan_base_multi/v1/model.weights.h5",
+        "weights_hash": "b529270c5361db36d359a46403532b5c",
+        "vocabulary_url": "https://storage.googleapis.com/keras-nlp/models/flan_base_multi/v1/vocab.spm",
+        "vocabulary_hash": "9d15ef55d09d5a425ceb63fa31f7cae3",
     },
     "flan_large_multi": {
         "metadata": {
@@ -159,5 +179,9 @@
             "layer_norm_epsilon": 1e-06,
         },
         "preprocessor_config": {},
+        "weights_url": "https://storage.googleapis.com/keras-nlp/models/flan_large_multi/v1/model.weights.h5",
+        "weights_hash": "50b8d3c88fc10db07e495d79ff29a1b6",
+        "vocabulary_url": "https://storage.googleapis.com/keras-nlp/models/flan_large_multi/v1/vocab.spm",
+        "vocabulary_hash": "9d15ef55d09d5a425ceb63fa31f7cae3",
     },
 }

keras_nlp/models/t5/t5_tokenizer_test.py

@@ -14,6 +14,7 @@
 
 import io
 
+import pytest
 import sentencepiece
 import tensorflow as tf
 
@@ -64,3 +65,22 @@ def test_errors_missing_special_tokens(self):
         )
         with self.assertRaises(ValueError):
             T5Tokenizer(proto=bytes_io.getvalue())
+
+    @pytest.mark.large
+    def test_smallest_preset(self):
+        for preset in T5Tokenizer.presets:
+            self.run_preset_test(
+                cls=T5Tokenizer,
+                preset=preset,
+                input_data=["The quick brown fox."],
+                expected_output=[[1996, 4248, 2829, 4419, 1012]],
+            )
+
+    @pytest.mark.extra_large
+    def test_all_presets(self):
+        for preset in T5Tokenizer.presets:
+            self.run_preset_test(
+                cls=T5Tokenizer,
+                preset=preset,
+                input_data=self.input_data,
+            )