EncoderDecoderModel for Machine Translation
Hello! I am trying to initialize the encoder in the EncoderDecoderModel with AraBERT (a BERT model trained on Arabic data), and the decoder with GPT-2 for Arabic to English Machine Translation. Here’s the model config after some changes I’ve done on it:EncoderDecoderConfig {
  "decoder": {
    "_name_or_path": "gpt2",
    "activation_function": "gelu_new",
    "add_cross_attention": true,
    "architectures": [
      "GPT2LMHeadModel"
    ],
    "attn_pdrop": 0.1,
    "bad_words_ids": null,
    "bos_token_id": 50256,
    "chunk_size_feed_forward": 0,
    "cross_attention_hidden_size": null,
    "decoder_start_token_id": null,
    "diversity_penalty": 0.0,
    "do_sample": false,
    "early_stopping": false,
    "embd_pdrop": 0.1,
    "encoder_no_repeat_ngram_size": 0,
    "eos_token_id": 50256,
    "exponential_decay_length_penalty": null,
    "finetuning_task": null,
    "forced_bos_token_id": null,
    "forced_eos_token_id": null,
    "id2label": {
      "0": "LABEL_0",
      "1": "LABEL_1"
    },
    "initializer_range": 0.02,
    "is_decoder": true,
    "is_encoder_decoder": false,
    "label2id": {
      "LABEL_0": 0,
      "LABEL_1": 1
    },
    "layer_norm_epsilon": 1e-05,
    "length_penalty": 1.0,
    "max_length": 20,
    "min_length": 0,
    "model_type": "gpt2",
    "n_ctx": 1024,
    "n_embd": 512,
    "n_head": 12,
    "n_inner": null,
    "n_layer": 12,
    "n_positions": 1024,
    "no_repeat_ngram_size": 0,
    "num_beam_groups": 1,
    "num_beams": 1,
    "num_return_sequences": 1,
    "output_attentions": false,
    "output_hidden_states": false,
    "output_scores": false,
    "pad_token_id": null,
    "prefix": null,
    "problem_type": null,
    "pruned_heads": {},
    "remove_invalid_values": false,
    "reorder_and_upcast_attn": false,
    "repetition_penalty": 1.0,
    "resid_pdrop": 0.1,
    "return_dict": true,
    "return_dict_in_generate": false,
    "scale_attn_by_inverse_layer_idx": false,
    "scale_attn_weights": true,
    "sep_token_id": null,
    "summary_activation": null,
    "summary_first_dropout": 0.1,
    "summary_proj_to_labels": true,
    "summary_type": "cls_index",
    "summary_use_proj": true,
    "task_specific_params": {
      "text-generation": {
        "do_sample": true,
        "max_length": 50
      }
    },
    "temperature": 1.0,
    "tie_encoder_decoder": false,
    "tie_word_embeddings": true,
    "tokenizer_class": null,
    "top_k": 50,
    "top_p": 1.0,
    "torch_dtype": null,
    "torchscript": false,
    "transformers_version": "4.19.2",
    "typical_p": 1.0,
    "use_bfloat16": false,
    "use_cache": true,
    "vocab_size": 64000
  },
  "decoder_start_token_id": 2,
  "encoder": {
    "_name_or_path": "aubmindlab/bert-base-arabertv02",
    "add_cross_attention": false,
    "architectures": [
      "BertForMaskedLM"
    ],
    "attention_probs_dropout_prob": 0.1,
    "bad_words_ids": null,
    "bos_token_id": null,
    "chunk_size_feed_forward": 0,
    "classifier_dropout": null,
    "cross_attention_hidden_size": null,
    "decoder_start_token_id": null,
    "diversity_penalty": 0.0,
    "do_sample": false,
    "early_stopping": false,
    "encoder_no_repeat_ngram_size": 0,
    "eos_token_id": 50256,
    "exponential_decay_length_penalty": null,
    "finetuning_task": null,
    "forced_bos_token_id": null,
    "forced_eos_token_id": null,
    "hidden_act": "gelu",
    "hidden_dropout_prob": 0.1,
    "hidden_size": 768,
    "id2label": {
      "0": "LABEL_0",
      "1": "LABEL_1"
    },
    "initializer_range": 0.02,
    "intermediate_size": 3072,
    "is_decoder": false,
    "is_encoder_decoder": false,
    "label2id": {
      "LABEL_0": 0,
      "LABEL_1": 1
    },
    "layer_norm_eps": 1e-12,
    "length_penalty": 1.0,
    "max_length": 20,
    "max_position_embeddings": 512,
    "min_length": 0,
    "model_type": "bert",
    "no_repeat_ngram_size": 0,
    "num_attention_heads": 12,
    "num_beam_groups": 1,
    "num_beams": 1,
    "num_hidden_layers": 12,
    "num_return_sequences": 1,
    "output_attentions": false,
    "output_hidden_states": false,
    "output_scores": false,
    "pad_token_id": null,
    "position_embedding_type": "absolute",
    "prefix": null,
    "problem_type": null,
    "pruned_heads": {},
    "remove_invalid_values": false,
    "repetition_penalty": 1.0,
    "return_dict": true,
    "return_dict_in_generate": false,
    "sep_token_id": null,
    "task_specific_params": null,
    "temperature": 1.0,
    "tie_encoder_decoder": false,
    "tie_word_embeddings": true,
    "tokenizer_class": null,
    "top_k": 50,
    "top_p": 1.0,
    "torch_dtype": null,
    "torchscript": false,
    "transformers_version": "4.19.2",
    "type_vocab_size": 2,
    "typical_p": 1.0,
    "use_bfloat16": false,
    "use_cache": true,
    "vocab_size": 64000
  },
  "eos_token_id": 50256,
  "is_encoder_decoder": true,
  "max_length": 512,
  "model_type": "encoder-decoder",
  "pad_token_id": 0,
  "transformers_version": null,
  "vocab_size": 64000
}The configurations I’ve changed are as follows:model.config.decoder_start_token_id = ar_tokenizer.cls_token_id
model.config.vocab_size = model.config.decoder.vocab_size
model.config.eos_token_id = model.config.decoder.eos_token_id
model.config.encoder.pad_token_id = model.config.pad_token_id
model.config.encoder.eos_token_id = model.config.eos_token_id
model.config.decoder.n_embd = 512
model.config.decoder.vocab_size = 64000
model.config.pad_token_id = 0My pre-processing function:max_input_length = 128
max_target_length = 128
source_lang = "ar"
target_lang = "en"

def preprocess_function(examples):
    inputs = [ex[source_lang] for ex in examples["translation"]]
    targets = [ex[target_lang] for ex in examples["translation"]]
    model_inputs = ar_tokenizer(inputs, max_length=max_input_length, truncation=True, padding=True)

    # Setup the tokenizer for targets
    with ar_tokenizer.as_target_tokenizer():
        labels = en_tokenizer(targets, max_length=max_target_length, truncation=True, padding=True)

    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

tokenized_dataset = train_test.map(preprocess_function, batched=True)However, when I start training, I get this error message:ValueError: expected sequence of length 128 at dim 1 (got 118)What should I change in my code for it to work?