t5: add conversion script for T5X to FLAX (#16853)

* t5: add conversion script for T5X to FLAX

* t5: make flake happy

* t5: add copyright message to t5x conversion script

* t5: fix lm head for v1.0 checkpoints

src/transformers/models/t5/convert_t5x_checkpoint_to_flax.py

+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# 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
+#
+#     http://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.
+
+"""Convert T5X checkpoints from the original repository to JAX/FLAX model."""
+
+import argparse
+
+from t5x import checkpoints
+from transformers import FlaxT5ForConditionalGeneration, T5Config
+
+
+def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_folder_path):
+    config = T5Config.from_pretrained(config_name)
+    flax_model = FlaxT5ForConditionalGeneration(config=config)
+    t5x_model = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path)
+
+    split_mlp_wi = "wi_0" in t5x_model["target"]["encoder"]["layers_0"]["mlp"]
+
+    # Encoder
+    for layer_index in range(config.num_layers):
+        layer_name = f"layers_{str(layer_index)}"
+
+        # Self-Attention
+        t5x_attention_key = t5x_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
+        t5x_attention_out = t5x_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
+        t5x_attention_query = t5x_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
+        t5x_attention_value = t5x_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]
+
+        # Layer Normalization
+        t5x_attention_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]
+
+        if split_mlp_wi:
+            t5x_mlp_wi_0 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
+            t5x_mlp_wi_1 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
+        else:
+            t5x_mlp_wi = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]
+
+        t5x_mlp_wo = t5x_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]
+
+        # Layer Normalization
+        t5x_mlp_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]
+
+        # Assigning
+        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["k"][
+            "kernel"
+        ] = t5x_attention_key
+        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["o"][
+            "kernel"
+        ] = t5x_attention_out
+        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["q"][
+            "kernel"
+        ] = t5x_attention_query
+        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["v"][
+            "kernel"
+        ] = t5x_attention_value
+
+        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["0"]["layer_norm"][
+            "weight"
+        ] = t5x_attention_layer_norm
+
+        if split_mlp_wi:
+            flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["DenseReluDense"]["wi_0"][
+                "kernel"
+            ] = t5x_mlp_wi_0
+            flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["DenseReluDense"]["wi_1"][
+                "kernel"
+            ] = t5x_mlp_wi_1
+        else:
+            flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["DenseReluDense"]["wi"][
+                "kernel"
+            ] = t5x_mlp_wi
+
+        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["DenseReluDense"]["wo"][
+            "kernel"
+        ] = t5x_mlp_wo
+        flax_model.params["encoder"]["block"][str(layer_index)]["layer"]["1"]["layer_norm"][
+            "weight"
+        ] = t5x_mlp_layer_norm
+
+    # Only for layer 0:
+    t5x_encoder_rel_embedding = t5x_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
+    flax_model.params["encoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"][
+        "embedding"
+    ] = t5x_encoder_rel_embedding
+
+    # Assigning
+    t5x_encoder_norm = t5x_model["target"]["encoder"]["encoder_norm"]["scale"]
+    flax_model.params["encoder"]["final_layer_norm"]["weight"] = t5x_encoder_norm
+
+    # Decoder
+    for layer_index in range(config.num_layers):
+        layer_name = f"layers_{str(layer_index)}"
+
+        # Self-Attention
+        t5x_attention_key = t5x_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
+        t5x_attention_out = t5x_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
+        t5x_attention_query = t5x_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
+        t5x_attention_value = t5x_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]
+
+        # Layer Normalization
+        t5x_pre_attention_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
+            "scale"
+        ]
+
+        # Encoder-Decoder-Attention
+        t5x_enc_dec_attention_key = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
+            "kernel"
+        ]
+        t5x_enc_dec_attention_out = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
+            "kernel"
+        ]
+        t5x_enc_dec_attention_query = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
+            "kernel"
+        ]
+        t5x_enc_dec_attention_value = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
+            "kernel"
+        ]
+
+        # Layer Normalization
+        t5x_cross_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]
+
+        # MLP
+        if split_mlp_wi:
+            t5x_mlp_wi_0 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
+            t5x_mlp_wi_1 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
+        else:
+            t5x_mlp_wi = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]
+
+        t5x_mlp_wo = t5x_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]
+
+        # Layer Normalization
+        tx5_mlp_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]
+
+        # Assigning
+        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["k"][
+            "kernel"
+        ] = t5x_attention_key
+        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["o"][
+            "kernel"
+        ] = t5x_attention_out
+        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["q"][
+            "kernel"
+        ] = t5x_attention_query
+        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["SelfAttention"]["v"][
+            "kernel"
+        ] = t5x_attention_value
+
+        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["0"]["layer_norm"][
+            "weight"
+        ] = t5x_pre_attention_layer_norm
+
+        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["EncDecAttention"]["k"][
+            "kernel"
+        ] = t5x_enc_dec_attention_key
+        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["EncDecAttention"]["o"][
+            "kernel"
+        ] = t5x_enc_dec_attention_out
+        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["EncDecAttention"]["q"][
+            "kernel"
+        ] = t5x_enc_dec_attention_query
+        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["EncDecAttention"]["v"][
+            "kernel"
+        ] = t5x_enc_dec_attention_value
+
+        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["1"]["layer_norm"][
+            "weight"
+        ] = t5x_cross_layer_norm
+
+        if split_mlp_wi:
+            flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["DenseReluDense"]["wi_0"][
+                "kernel"
+            ] = t5x_mlp_wi_0
+            flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["DenseReluDense"]["wi_1"][
+                "kernel"
+            ] = t5x_mlp_wi_1
+        else:
+            flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["DenseReluDense"]["wi"][
+                "kernel"
+            ] = t5x_mlp_wi
+
+        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["DenseReluDense"]["wo"][
+            "kernel"
+        ] = t5x_mlp_wo
+
+        flax_model.params["decoder"]["block"][str(layer_index)]["layer"]["2"]["layer_norm"][
+            "weight"
+        ] = tx5_mlp_layer_norm
+
+    # Decoder Normalization
+    tx5_decoder_norm = t5x_model["target"]["decoder"]["decoder_norm"]["scale"]
+    flax_model.params["decoder"]["final_layer_norm"]["weight"] = tx5_decoder_norm
+
+    # Only for layer 0:
+    t5x_decoder_rel_embedding = t5x_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
+    flax_model.params["decoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"][
+        "embedding"
+    ] = t5x_decoder_rel_embedding
+
+    # Token Embeddings
+    tx5_token_embeddings = t5x_model["target"]["token_embedder"]["embedding"]
+    flax_model.params["shared"]["embedding"] = tx5_token_embeddings
+
+    # LM Head (only in v1.1 checkpoints)
+    if "logits_dense" in t5x_model["target"]["decoder"]:
+        flax_model.params["lm_head"]["kernel"] = t5x_model["target"]["decoder"]["logits_dense"]["kernel"]
+
+    flax_model.save_pretrained(flax_dump_folder_path)
+    print("T5X Model was sucessfully converted!")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Required parameters
+    parser.add_argument(
+        "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
+    )
+    parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
+    parser.add_argument(
+        "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
+    )
+    args = parser.parse_args()
+    convert_t5x_checkpoint_to_flax(args.t5x_checkpoint_path, args.config_name, args.flax_dump_folder_path)