src/transformers/models/bort/convert_bort_original_gluonnlp_checkpoint_to_pytorch.py/convert_bort_checkpoint_to_pytorch
def convert_bort_checkpoint_to_pytorch(bort_checkpoint_path: str, pytorch_dump_folder_path: str):
    """
    Convert the original Bort checkpoint (based on MXNET and Gluonnlp) to our BERT structure-
    """

    # Original Bort configuration
    bort_4_8_768_1024_hparams = {
        "attention_cell": "multi_head",
        "num_layers": 4,
        "units": 1024,
        "hidden_size": 768,
        "max_length": 512,
        "num_heads": 8,
        "scaled": True,
        "dropout": 0.1,
        "use_residual": True,
        "embed_size": 1024,
        "embed_dropout": 0.1,
        "word_embed": None,
        "layer_norm_eps": 1e-5,
        "token_type_vocab_size": 2,
    }

    predefined_args = bort_4_8_768_1024_hparams

    # Let's construct the original Bort model here
    # Taken from official BERT implementation, see:
    # https://github.com/alexa/bort/blob/master/bort/bort.py
    encoder = BERTEncoder(
        attention_cell=predefined_args["attention_cell"],
        num_layers=predefined_args["num_layers"],
        units=predefined_args["units"],
        hidden_size=predefined_args["hidden_size"],
        max_length=predefined_args["max_length"],
        num_heads=predefined_args["num_heads"],
        scaled=predefined_args["scaled"],
        dropout=predefined_args["dropout"],
        output_attention=False,
        output_all_encodings=False,
        use_residual=predefined_args["use_residual"],
        activation=predefined_args.get("activation", "gelu"),
        layer_norm_eps=predefined_args.get("layer_norm_eps", None),
    )

    # Vocab information needs to be fetched first
    # It's the same as RoBERTa, so RobertaTokenizer can be used later
    vocab_name = "openwebtext_ccnews_stories_books_cased"

    # Specify download folder to Gluonnlp's vocab
    gluon_cache_dir = os.path.join(get_home_dir(), "models")
    bort_vocab = _load_vocab(vocab_name, None, gluon_cache_dir, cls=Vocab)

    original_bort = nlp.model.BERTModel(
        encoder,
        len(bort_vocab),
        units=predefined_args["units"],
        embed_size=predefined_args["embed_size"],
        embed_dropout=predefined_args["embed_dropout"],
        word_embed=predefined_args["word_embed"],
        use_pooler=False,
        use_token_type_embed=False,
        token_type_vocab_size=predefined_args["token_type_vocab_size"],
        use_classifier=False,
        use_decoder=False,
    )

    original_bort.load_parameters(bort_checkpoint_path, cast_dtype=True, ignore_extra=True)
    params = original_bort._collect_params_with_prefix()

    # Build our config 🤗
    hf_bort_config_json = {
        "architectures": ["BertForMaskedLM"],
        "attention_probs_dropout_prob": predefined_args["dropout"],
        "hidden_act": "gelu",
        "hidden_dropout_prob": predefined_args["dropout"],
        "hidden_size": predefined_args["embed_size"],
        "initializer_range": 0.02,
        "intermediate_size": predefined_args["hidden_size"],
        "layer_norm_eps": predefined_args["layer_norm_eps"],
        "max_position_embeddings": predefined_args["max_length"],
        "model_type": "bort",
        "num_attention_heads": predefined_args["num_heads"],
        "num_hidden_layers": predefined_args["num_layers"],
        "pad_token_id": 1,  # 2 = BERT, 1 = RoBERTa
        "type_vocab_size": 1,  # 2 = BERT, 1 = RoBERTa
        "vocab_size": len(bort_vocab),
    }

    hf_bort_config = BertConfig.from_dict(hf_bort_config_json)
    hf_bort_model = BertForMaskedLM(hf_bort_config)
    hf_bort_model.eval()

    # Parameter mapping table (Gluonnlp to Transformers)
    # * denotes layer index
    #
    # | Gluon Parameter                                                | Transformers Parameter
    # | -------------------------------------------------------------- | ----------------------
    # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
    # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
    # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
    # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
    # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
    # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
    # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
    # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
    # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
    # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
    # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

    # Helper function to convert MXNET Arrays to PyTorch
    def to_torch(mx_array) -> nn.Parameter:
        return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()))

    # Check param shapes and map new HF param back
    def check_and_map_params(hf_param, gluon_param):
        shape_hf = hf_param.shape

        gluon_param = to_torch(params[gluon_param])
        shape_gluon = gluon_param.shape

        assert (
            shape_hf == shape_gluon
        ), f"The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"

        return gluon_param

    hf_bort_model.bert.embeddings.word_embeddings.weight = check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight, "word_embed.0.weight"
    )
    hf_bort_model.bert.embeddings.position_embeddings.weight = check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight, "encoder.position_weight"
    )
    hf_bort_model.bert.embeddings.LayerNorm.bias = check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias, "encoder.layer_norm.beta"
    )
    hf_bort_model.bert.embeddings.LayerNorm.weight = check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.weight, "encoder.layer_norm.gamma"
    )

    # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data = torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data
    )

    for i in range(hf_bort_config.num_hidden_layers):
        layer: BertLayer = hf_bort_model.bert.encoder.layer[i]

        # self attention
        self_attn: BertSelfAttention = layer.attention.self

        self_attn.key.bias.data = check_and_map_params(
            self_attn.key.bias.data, f"encoder.transformer_cells.{i}.attention_cell.proj_key.bias"
        )

        self_attn.key.weight.data = check_and_map_params(
            self_attn.key.weight.data, f"encoder.transformer_cells.{i}.attention_cell.proj_key.weight"
        )
        self_attn.query.bias.data = check_and_map_params(
            self_attn.query.bias.data, f"encoder.transformer_cells.{i}.attention_cell.proj_query.bias"
        )
        self_attn.query.weight.data = check_and_map_params(
            self_attn.query.weight.data, f"encoder.transformer_cells.{i}.attention_cell.proj_query.weight"
        )
        self_attn.value.bias.data = check_and_map_params(
            self_attn.value.bias.data, f"encoder.transformer_cells.{i}.attention_cell.proj_value.bias"
        )
        self_attn.value.weight.data = check_and_map_params(
            self_attn.value.weight.data, f"encoder.transformer_cells.{i}.attention_cell.proj_value.weight"
        )

        # self attention output
        self_output: BertSelfOutput = layer.attention.output

        self_output.dense.bias = check_and_map_params(
            self_output.dense.bias, f"encoder.transformer_cells.{i}.proj.bias"
        )
        self_output.dense.weight = check_and_map_params(
            self_output.dense.weight, f"encoder.transformer_cells.{i}.proj.weight"
        )
        self_output.LayerNorm.bias = check_and_map_params(
            self_output.LayerNorm.bias, f"encoder.transformer_cells.{i}.layer_norm.beta"
        )
        self_output.LayerNorm.weight = check_and_map_params(
            self_output.LayerNorm.weight, f"encoder.transformer_cells.{i}.layer_norm.gamma"
        )

        # intermediate
        intermediate: BertIntermediate = layer.intermediate

        intermediate.dense.bias = check_and_map_params(
            intermediate.dense.bias, f"encoder.transformer_cells.{i}.ffn.ffn_1.bias"
        )
        intermediate.dense.weight = check_and_map_params(
            intermediate.dense.weight, f"encoder.transformer_cells.{i}.ffn.ffn_1.weight"
        )

        # output
        bert_output: BertOutput = layer.output

        bert_output.dense.bias = check_and_map_params(
            bert_output.dense.bias, f"encoder.transformer_cells.{i}.ffn.ffn_2.bias"
        )
        bert_output.dense.weight = check_and_map_params(
            bert_output.dense.weight, f"encoder.transformer_cells.{i}.ffn.ffn_2.weight"
        )
        bert_output.LayerNorm.bias = check_and_map_params(
            bert_output.LayerNorm.bias, f"encoder.transformer_cells.{i}.ffn.layer_norm.beta"
        )
        bert_output.LayerNorm.weight = check_and_map_params(
            bert_output.LayerNorm.weight, f"encoder.transformer_cells.{i}.ffn.layer_norm.gamma"
        )

    # Save space and energy 🎄
    hf_bort_model.half()

    # Compare output of both models
    tokenizer = RobertaTokenizer.from_pretrained("roberta-base")

    input_ids = tokenizer.encode_plus(SAMPLE_TEXT)["input_ids"]

    # Get gluon output
    gluon_input_ids = mx.nd.array([input_ids])
    output_gluon = original_bort(inputs=gluon_input_ids, token_types=[])

    # Get Transformer output (save and reload model again)
    hf_bort_model.save_pretrained(pytorch_dump_folder_path)
    hf_bort_model = BertModel.from_pretrained(pytorch_dump_folder_path)
    hf_bort_model.eval()

    input_ids = tokenizer.encode_plus(SAMPLE_TEXT, return_tensors="pt")
    output_hf = hf_bort_model(**input_ids)[0]

    gluon_layer = output_gluon[0].asnumpy()
    hf_layer = output_hf[0].detach().numpy()

    max_absolute_diff = np.max(np.abs(hf_layer - gluon_layer)).item()
    success = np.allclose(gluon_layer, hf_layer, atol=1e-3)

    if success:
        print("✔️ Both model do output the same tensors")
    else:
        print("❌ Both model do **NOT** output the same tensors")
        print("Absolute difference is:", max_absolute_diff)