[content] utils/nn.py (python)
<<<<<<< Updated upstream
        z_BTNF = self.ffn_dense1(z_BTNM)
        z_BTNF = jax.nn.gelu(z_BTNF)
        z_BTNM = self.ffn_dense2(z_BTNF)
=======
        z_BTND = self.ffn_dense1(z_BTNM)
        z_BTND = jax.nn.gelu(z_BTND)
        z_BTNM = self.ffn_dense2(z_BTND)
>>>>>>> Stashed changes
        x_BTNM = x_BTNM + z_BTNM

        return x_BTNM


class STTransformer(nnx.Module):
    """
    Dimension keys:
        B: batch size
        T: number of frames
        N: number of patches per frame
        I: number of input features
        M: model dimension
<<<<<<< Updated upstream
        F: FFN dimension
=======
        D: FFN dimension
>>>>>>> Stashed changes
        O: number of output features
    """
    def __init__(
        self,
        input_dim: int,
        model_dim: int,
        ffn_dim: int,
        out_dim: int,
        num_blocks: int,
        num_heads: int,
        dropout: float,
        param_dtype: jnp.dtype,
        dtype: jnp.dtype,
        use_flash_attention: bool,
        rngs: nnx.Rngs,
    ):
        self.input_dim = input_dim
        self.model_dim = model_dim
        self.ffn_dim = ffn_dim
        self.out_dim = out_dim
        self.num_blocks = num_blocks
        self.num_heads = num_heads
        self.dropout = dropout
        self.param_dtype = param_dtype
        self.dtype = dtype
        self.use_flash_attention = use_flash_attention

        self.input_norm1 = nnx.LayerNorm(
            num_features=self.input_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )
        self.input_dense = nnx.Linear(
            in_features=self.input_dim,
            out_features=self.model_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )
        self.input_norm2 = nnx.LayerNorm(
            num_features=self.model_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )

        self.blocks = []
        for _ in range(self.num_blocks):
            self.blocks.append(
                STBlock(
                    dim=self.model_dim,
                    ffn_dim=self.ffn_dim,
                    num_heads=self.num_heads,
                    dropout=self.dropout,
                    param_dtype=self.param_dtype,
                    dtype=self.dtype,
                    use_flash_attention=self.use_flash_attention,
                    rngs=rngs,
                )
            )

        self.output_dense = nnx.Linear(
            in_features=self.model_dim,
            out_features=self.out_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )

    def __call__(self, x_BTNI: jax.Array) -> jax.Array:
        x_BTNI = self.input_norm1(x_BTNI)
        x_BTNM = self.input_dense(x_BTNI)
        x_BTNM = self.input_norm2(x_BTNM)

        for block in self.blocks:
            x_BTNM = block(x_BTNM)

        x_BTNO = self.output_dense(x_BTNM)
        return x_BTNO

class TransformerBlock(nnx.Module):
    def __init__(
        self,
        model_dim: int,
        ffn_dim: int,
        num_heads: int,
        dropout: float,
        param_dtype: jnp.dtype,
        dtype: jnp.dtype,
        use_flash_attention: bool,
        decode: bool,
        rngs: nnx.Rngs,
    ):
        self.model_dim = model_dim
        self.ffn_dim = ffn_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.param_dtype = param_dtype
        self.dtype = dtype
        self.use_flash_attention = use_flash_attention

        self.temporal_pos_enc = PositionalEncoding(self.model_dim)
        self.spatial_pos_enc = PositionalEncoding(self.model_dim)
        self.temporal_norm = nnx.LayerNorm(
            num_features=self.model_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )
        self.spatial_norm = nnx.LayerNorm(
            num_features=self.model_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )
        self.ffn_norm = nnx.LayerNorm(
            num_features=self.model_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )
        self.temporal_attention = nnx.MultiHeadAttention(
            num_heads=self.num_heads,
            in_features=self.model_dim,
            qkv_features=self.model_dim,
            dropout_rate=self.dropout,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            attention_fn=_create_flash_attention_fn(
                self.use_flash_attention, is_causal=True
            ),
            rngs=rngs,
            decode=decode,
        )
        self.spatial_attention = nnx.MultiHeadAttention(
            num_heads=self.num_heads,
            in_features=self.model_dim,
            qkv_features=self.model_dim,
            dropout_rate=self.dropout,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            attention_fn=_create_flash_attention_fn(
                self.use_flash_attention, is_causal=True
            ),
            rngs=rngs,
            decode=decode,
        )
        self.ffn_dense1 = nnx.Linear(
            in_features=self.model_dim,
            out_features=self.ffn_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )
        self.ffn_dense2 = nnx.Linear(
            in_features=self.ffn_dim,
            out_features=self.model_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )

    @nnx.remat
    def __call__(self, x_BTNM: jax.Array) -> jax.Array:
        # FIXME (f.srambical): this is exactly the same as STBlock (except for the positional encoding)
        # --- Spatial attention ---
        _, T, N, _ = x_BTNM.shape
        z_FNM = einops.rearrange(x_BTNM, "b t n m -> (b t) n m")
        z_FNM = self.spatial_norm(z_FNM)
        z_FNM = self.spatial_attention(z_FNM)
        z_BTNM = einops.rearrange(z_FNM, "(b t) n m -> b t n m", t=T)
        x_BTNM = x_BTNM + z_BTNM
        # --- Temporal attention ---
        z_PTM = einops.rearrange(x_BTNM, "b t n m -> (b n) t m")
        z_PTM = self.temporal_norm(z_PTM)
        z_PTM = self.temporal_attention(z_PTM)
        z_BTNM = einops.rearrange(z_PTM, "(b n) t m -> b t n m", n=N)
        x_BTNM = x_BTNM + z_BTNM
        # --- Feedforward ---
        z_BTNM = self.ffn_norm(x_BTNM)
        z_BTND = self.ffn_dense1(z_BTNM)
        z_BTND = jax.nn.gelu(z_BTND)
        z_BTNM = self.ffn_dense2(z_BTND)
        x_BTNM = x_BTNM + z_BTNM

        return x_BTNM

class Transformer(nnx.Module):
    """
    Dimension keys:
        B: batch size
        T: number of frames
        N: number of patches per frame
        I: number of input features
        M: model dimension
        D: FFN dimension
        O: number of output features
        F: number of frames in batch
        P: number of patch positions in batch
    """
    def __init__(
        self,
        input_dim: int,
        model_dim: int,
        ffn_dim: int,
        out_dim: int,
        num_blocks: int,
        num_heads: int,
        dropout: float,
        param_dtype: jnp.dtype,
        dtype: jnp.dtype,
        use_flash_attention: bool,
        decode: bool,
        rngs: nnx.Rngs,
    ):
        self.input_dim = input_dim
        self.model_dim = model_dim
        self.ffn_dim = ffn_dim
        self.out_dim = out_dim
        self.num_blocks = num_blocks
        self.num_heads = num_heads
        self.dropout = dropout
        self.param_dtype = param_dtype
        self.dtype = dtype
        self.use_flash_attention = use_flash_attention

        self.pos_enc = PositionalEncoding(self.model_dim)
        self.input_norm1 = nnx.LayerNorm(
            num_features=self.input_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )
        self.input_dense = nnx.Linear(
            in_features=self.input_dim,
            out_features=self.model_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )
        self.input_norm2 = nnx.LayerNorm(
            num_features=self.model_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )

        self.blocks = []
        for _ in range(self.num_blocks):
            self.blocks.append(
                TransformerBlock(
                    model_dim=self.model_dim,
                    ffn_dim=self.ffn_dim,
                    num_heads=self.num_heads,
                    dropout=self.dropout,
                    param_dtype=self.param_dtype,
                    dtype=self.dtype,
                    use_flash_attention=self.use_flash_attention,
                    decode=decode,
                    rngs=rngs,
                )
            )
        self.output_dense = nnx.Linear(
            in_features=self.model_dim,
            out_features=self.out_dim,
            param_dtype=self.param_dtype,
            dtype=self.dtype,
            rngs=rngs,
        )

    def __call__(self, x_BTNI: jax.Array) -> jax.Array:
        x_BTNI = self.input_norm1(x_BTNI)
        x_BTNM = self.input_dense(x_BTNI)
        x_BTNM = self.input_norm2(x_BTNM)
        x_BTNM = self.pos_enc(x_BTNM)

        for block in self.blocks:
            x_BTNM = block(x_BTNM)

        x_BTNV = self.output_dense(x_BTNM)
        return x_BTNV