Repository: Project-MONAI/MONAI
File: monai/networks/blocks/transformerblock.py (Python)

Code before:

class TransformerBlock(nn.Module):
    """
    A transformer block, based on: "Dosovitskiy et al.,
    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"
    """

    def __init__(
        self,
        hidden_size: int,
        mlp_dim: int,
        num_heads: int,
        dropout_rate: float = 0.0,
        qkv_bias: bool = False,
        save_attn: bool = False,
        causal: bool = False,
        sequence_length: int | None = None,
        with_cross_attention: bool = False,
        use_flash_attention: bool = False,
    ) -> None:
        """
        Args:
            hidden_size (int): dimension of hidden layer. mlp_dim (int): dimension of feedforward layer.
            num_heads (int): number of attention heads.
            dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0. qkv_bias
             (bool, optional): apply bias term for the qkv linear layer. Defaults to False.
            save_attn (bool, optional): to make accessible the attention matrix. Defaults to False.
            use_flash_attention: if True, use Pytorch's inbuilt flash attention for a memory efficient attention mechanism
             (see https://pytorch.org/docs/2.2/generated/torch.nn.functional.scaled_dot_product_attention.html).

        """

        super().__init__()

        if not (0 <= dropout_rate <= 1):
            raise ValueError("dropout_rate should be between 0 and 1.")

        if hidden_size % num_heads != 0:
            raise ValueError("hidden_size should be divisible by num_heads.")

        self.mlp = MLPBlock(hidden_size, mlp_dim, dropout_rate)
        self.norm1 = nn.LayerNorm(hidden_size)
        self.attn = SABlock(
            hidden_size,
            num_heads,
            dropout_rate,
            qkv_bias=qkv_bias,
            save_attn=save_attn,
            causal=causal,
            sequence_length=sequence_length,
            use_flash_attention=use_flash_attention,

Review comment (suggestion):
```suggestion
            dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0.
            qkv_bias (bool, optional): apply bias term for the qkv linear layer. Defaults to False.
```

Code after:

class TransformerBlock(nn.Module):
    """
    A transformer block, based on: "Dosovitskiy et al.,
    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"
    """

    def __init__(
        self,
        hidden_size: int,
        mlp_dim: int,
        num_heads: int,
        dropout_rate: float = 0.0,
        qkv_bias: bool = False,
        save_attn: bool = False,
        causal: bool = False,
        sequence_length: int | None = None,
        with_cross_attention: bool = False,
        use_flash_attention: bool = False,
    ) -> None:
        """
        Args:
            hidden_size (int): dimension of hidden layer.
            mlp_dim (int): dimension of feedforward layer.
            num_heads (int): number of attention heads.
            dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0.
            qkv_bias(bool, optional): apply bias term for the qkv linear layer. Defaults to False.
            save_attn (bool, optional): to make accessible the attention matrix. Defaults to False.
            use_flash_attention: if True, use Pytorch's inbuilt flash attention for a memory efficient attention mechanism
             (see https://pytorch.org/docs/2.2/generated/torch.nn.functional.scaled_dot_product_attention.html).

        """

        super().__init__()

        if not (0 <= dropout_rate <= 1):
            raise ValueError("dropout_rate should be between 0 and 1.")

        if hidden_size % num_heads != 0:
            raise ValueError("hidden_size should be divisible by num_heads.")

        self.mlp = MLPBlock(hidden_size, mlp_dim, dropout_rate)
        self.norm1 = nn.LayerNorm(hidden_size)
        self.attn = SABlock(
            hidden_size,
            num_heads,
            dropout_rate,
            qkv_bias=qkv_bias,
            save_attn=save_attn,
            causal=causal,
            sequence_length=sequence_length,