def __init__()

in optimum/bettertransformer/models/encoder_models.py [0:0]

• 57 lines of code
• 1 McCabe index (conditional complexity)

    def __init__(self, prophetnet_layer, config):
        r"""
        A simple conversion of the ProphetNet Encoder layer to its `BetterTransformer` implementation.

        Args:
            prophet_net_layer (`torch.nn.Module`):
                The original ProphetNet Layer where the weights needs to be retrieved.
        """
        super().__init__(config)
        super(BetterTransformerBaseLayer, self).__init__()
        self.config = config
        # In_proj layer
        self.in_proj_weight = nn.Parameter(
            torch.cat(
                [
                    prophetnet_layer.self_attn.query_proj.weight,
                    prophetnet_layer.self_attn.key_proj.weight,
                    prophetnet_layer.self_attn.value_proj.weight,
                ]
            )
        )
        self.in_proj_bias = nn.Parameter(
            torch.cat(
                [
                    prophetnet_layer.self_attn.query_proj.bias,
                    prophetnet_layer.self_attn.key_proj.bias,
                    prophetnet_layer.self_attn.value_proj.bias,
                ]
            )
        )

        # Out proj layer
        self.out_proj_weight = prophetnet_layer.self_attn.out_proj.weight
        self.out_proj_bias = prophetnet_layer.self_attn.out_proj.bias

        # Linear layer 1
        self.linear1_weight = prophetnet_layer.feed_forward.intermediate.weight
        self.linear1_bias = prophetnet_layer.feed_forward.intermediate.bias

        # Linear layer 2
        self.linear2_weight = prophetnet_layer.feed_forward.output.weight
        self.linear2_bias = prophetnet_layer.feed_forward.output.bias

        # Layer norm 1
        self.norm1_eps = prophetnet_layer.self_attn_layer_norm.eps
        self.norm1_weight = prophetnet_layer.self_attn_layer_norm.weight
        self.norm1_bias = prophetnet_layer.self_attn_layer_norm.bias

        # Layer norm 2
        self.norm2_eps = prophetnet_layer.feed_forward_layer_norm.eps
        self.norm2_weight = prophetnet_layer.feed_forward_layer_norm.weight
        self.norm2_bias = prophetnet_layer.feed_forward_layer_norm.bias

        # Model hyper parameters
        self.num_heads = prophetnet_layer.self_attn.num_attn_heads
        self.embed_dim = prophetnet_layer.self_attn.head_dim * self.num_heads

        # Last step: set the last layer to `False` -> this will be set to `True` when converting the model
        self.is_last_layer = False

        self.original_layers_mapping = {
            "in_proj_weight": [
                "self_attn.query_proj.weight",
                "self_attn.key_proj.weight",
                "self_attn.value_proj.weight",
            ],
            "in_proj_bias": ["self_attn.query_proj.bias", "self_attn.key_proj.bias", "self_attn.value_proj.bias"],
            "out_proj_weight": "self_attn.out_proj.weight",
            "out_proj_bias": "self_attn.out_proj.bias",
            "linear1_weight": "feed_forward.intermediate.weight",
            "linear1_bias": "feed_forward.intermediate.bias",
            "linear2_weight": "feed_forward.output.weight",
            "linear2_bias": "feed_forward.output.bias",
            "norm1_weight": "self_attn_layer_norm.weight",
            "norm1_bias": "self_attn_layer_norm.bias",
            "norm2_weight": "feed_forward_layer_norm.weight",
            "norm2_bias": "feed_forward_layer_norm.bias",
        }

        self.validate_bettertransformer()