Flax T5 (#12150)

* copy pytorch-t5

* init

* boom boom

* forward pass same

* make generation work

* add more tests

* make test work

* finish normal tests

* make fix-copies

* finish quality

* correct slow example

* correct slow test

* version table

* upload models

* Update tests/test_modeling_flax_t5.py

* correct incorrectly deleted line

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Patrick von Platen <patrick@huggingface.co>

docs/source/index.rst

@@ -396,7 +396,7 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |            TAPAS            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

docs/source/model_doc/t5.rst

@@ -160,3 +160,15 @@ TFT5EncoderModel
 
 .. autoclass:: transformers.TFT5EncoderModel
     :members: call
+
+FlaxT5Model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxT5Model
+    :members: __call__, encode, decode
+
+FlaxT5ForConditionalGeneration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxT5ForConditionalGeneration
+    :members: __call__, encode, decode

setup.py

@@ -114,6 +114,7 @@ _deps = [
     "onnxruntime-tools>=1.4.2",
     "onnxruntime>=1.4.0",
     "optuna",
+    "optax>=0.0.8",
     "packaging",
     "parameterized",
     "protobuf",
@@ -234,7 +235,7 @@ if os.name == "nt":  # windows
     extras["flax"] = []  # jax is not supported on windows
 else:
     extras["retrieval"] = deps_list("faiss-cpu", "datasets")
-    extras["flax"] = deps_list("jax", "jaxlib", "flax")
+    extras["flax"] = deps_list("jax", "jaxlib", "flax", "optax")
 
 extras["tokenizers"] = deps_list("tokenizers")
 extras["onnxruntime"] = deps_list("onnxruntime", "onnxruntime-tools")
@@ -325,7 +326,7 @@ install_requires = [
     deps["huggingface-hub"],
     deps["numpy"],
     deps["packaging"],  # utilities from PyPA to e.g., compare versions
-    deps["pyyaml"], # used for the model cards metadata
+    deps["pyyaml"],  # used for the model cards metadata
     deps["regex"],  # for OpenAI GPT
     deps["requests"],  # for downloading models over HTTPS
     deps["sacremoses"],  # for XLM

src/transformers/__init__.py

@@ -1597,6 +1597,7 @@ if is_flax_available():
             "FlaxRobertaPreTrainedModel",
         ]
     )
+    _import_structure["models.t5"].extend(["FlaxT5ForConditionalGeneration", "FlaxT5Model"])
     _import_structure["models.vit"].extend(["FlaxViTForImageClassification", "FlaxViTModel"])
 else:
     from .utils import dummy_flax_objects
@@ -2920,6 +2921,7 @@ if TYPE_CHECKING:
             FlaxRobertaModel,
             FlaxRobertaPreTrainedModel,
         )
+        from .models.t5 import FlaxT5ForConditionalGeneration, FlaxT5Model
         from .models.vit import FlaxViTForImageClassification, FlaxViTModel
     else:
         # Import the same objects as dummies to get them in the namespace.

src/transformers/dependency_versions_table.py

@@ -31,6 +31,7 @@ deps = {
     "onnxruntime-tools": "onnxruntime-tools>=1.4.2",
     "onnxruntime": "onnxruntime>=1.4.0",
     "optuna": "optuna",
+    "optax": "optax>=0.0.8",
     "packaging": "packaging",
     "parameterized": "parameterized",
     "protobuf": "protobuf",

src/transformers/models/auto/modeling_flax_auto.py

@@ -62,6 +62,7 @@ from ..roberta.modeling_flax_roberta import (
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )
+from ..t5.modeling_flax_t5 import FlaxT5ForConditionalGeneration, FlaxT5Model
 from ..vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
 from .auto_factory import auto_class_factory
 from .configuration_auto import (
@@ -72,6 +73,7 @@ from .configuration_auto import (
     ElectraConfig,
     GPT2Config,
     RobertaConfig,
+    T5Config,
     ViTConfig,
 )
 
@@ -90,6 +92,7 @@ FLAX_MODEL_MAPPING = OrderedDict(
         (ElectraConfig, FlaxElectraModel),
         (CLIPConfig, FlaxCLIPModel),
         (ViTConfig, FlaxViTModel),
+        (T5Config, FlaxT5Model),
     ]
 )
 
@@ -101,6 +104,7 @@ FLAX_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
         (BigBirdConfig, FlaxBigBirdForPreTraining),
         (BartConfig, FlaxBartForConditionalGeneration),
         (ElectraConfig, FlaxElectraForPreTraining),
+        (T5Config, FlaxT5ForConditionalGeneration),
     ]
 )
 
@@ -115,6 +119,14 @@ FLAX_MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
     ]
 )
 
+FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = OrderedDict(
+    [
+        # Model for Seq2Seq Causal LM mapping
+        (BartConfig, FlaxBartForConditionalGeneration),
+        (T5Config, FlaxT5ForConditionalGeneration),
+    ]
+)
+
 FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = OrderedDict(
     [
         # Model for Image-classsification
@@ -234,3 +246,9 @@ FlaxAutoModelForNextSentencePrediction = auto_class_factory(
     FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
     head_doc="next sentence prediction",
 )
+
+FlaxAutoModelForSeq2SeqLM = auto_class_factory(
+    "FlaxAutoModelForSeq2SeqLM",
+    FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+    head_doc="sequence-to-sequence language modeling",
+)

src/transformers/models/bart/modeling_flax_bart.py

@@ -229,7 +229,6 @@ class FlaxBartAttention(nn.Module):
     embed_dim: int
     num_heads: int
     dropout: float = 0.0
-    is_decoder: bool = False
     causal: bool = False
     bias: bool = True
     dtype: jnp.dtype = jnp.float32  # the dtype of the computation
@@ -510,7 +509,6 @@ class FlaxBartDecoderLayer(nn.Module):
             embed_dim=self.embed_dim,
             num_heads=self.config.decoder_attention_heads,
             dropout=self.config.attention_dropout,
-            is_decoder=True,
             causal=True,
         )
         self.dropout_layer = nn.Dropout(rate=self.config.dropout)
@@ -523,7 +521,6 @@ class FlaxBartDecoderLayer(nn.Module):
             embed_dim=self.embed_dim,
             num_heads=self.config.decoder_attention_heads,
             dropout=self.config.attention_dropout,
-            is_decoder=True,
         )
         self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype)
         self.fc1 = nn.Dense(

src/transformers/models/t5/__init__.py

@@ -20,6 +20,7 @@ from typing import TYPE_CHECKING
 
 from ...file_utils import (
     _BaseLazyModule,
+    is_flax_available,
     is_sentencepiece_available,
     is_tf_available,
     is_tokenizers_available,
@@ -56,6 +57,13 @@ if is_tf_available():
         "TFT5PreTrainedModel",
     ]
 
+if is_flax_available():
+    _import_structure["modeling_flax_t5"] = [
+        "FlaxT5ForConditionalGeneration",
+        "FlaxT5Model",
+        "FlaxT5PreTrainedModel",
+    ]
+
 
 if TYPE_CHECKING:
     from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
@@ -85,6 +93,10 @@ if TYPE_CHECKING:
             TFT5PreTrainedModel,
         )
 
+    if is_flax_available():
+        from .modeling_flax_t5 import FlaxT5ForConditionalGeneration, FlaxT5Model, FlaxT5PreTrainedModel
+
+
 else:
     import importlib
     import os

src/transformers/utils/dummy_flax_objects.py

@@ -570,6 +570,24 @@ class FlaxRobertaPreTrainedModel:
         requires_backends(cls, ["flax"])
 
 
+class FlaxT5ForConditionalGeneration:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
+class FlaxT5Model:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["flax"])
+
+
 class FlaxViTForImageClassification:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])

tests/test_modeling_flax_bart.py

@@ -72,7 +72,7 @@ def prepare_bart_inputs_dict(
     }
 
 
-class FlaxBartModelTester(unittest.TestCase):
+class FlaxBartModelTester:
     def __init__(
         self,
         parent,

tests/test_modeling_t5.py

@@ -794,6 +794,21 @@ class T5ModelIntegrationTests(unittest.TestCase):
     def tokenizer(self):
         return T5Tokenizer.from_pretrained("t5-base")
 
+    @slow
+    def test_small_generation(self):
+        model = T5ForConditionalGeneration.from_pretrained("t5-small").to(torch_device)
+        model.config.max_length = 8
+        model.config.num_beams = 1
+        model.config.do_sample = False
+        tokenizer = T5Tokenizer.from_pretrained("t5-small")
+
+        input_ids = tokenizer("summarize: Hello there", return_tensors="pt").input_ids
+
+        sequences = model.generate(input_ids)
+
+        output_str = tokenizer.batch_decode(sequences, skip_special_tokens=True)[0]
+        self.assertTrue(output_str == "Hello there!")
+
     @slow
     def test_small_integration_test(self):
         """