[content] jasmine/utils/nn.py (python)

def modulate(x, shift, scale):
    return x * (1 + scale) + shift


# TODO mihir clean this up
class TimestepEmbedder(nnx.Module):
    """
    Embeds scalar timesteps into vector representations.
    """

    def __init__(self, hidden_size: int, rngs, bias: bool = True):
        self.hidden_size = hidden_size
        self.bias = bias
        self.dense1 = nnx.Linear(
            self.hidden_size,
            self.hidden_size,
            kernel_init=nnx.initializers.normal(0.02),
            bias_init=nnx.initializers.normal(0.02),
            rngs=rngs,
        )
        self.dense2 = nnx.Linear(
            self.hidden_size,
            self.hidden_size,
            kernel_init=nnx.initializers.normal(0.02),
            bias_init=nnx.initializers.normal(0.02),
            rngs=rngs,
        )

    def __call__(self, t):
        x = self.timestep_embedding(t)
        x = self.dense1(x)
        x = nnx.silu(x)
        x = self.dense2(x)
        return x

    # t is between [0, 1].
    def timestep_embedding(self, t, max_period=10000):
        """
        Create sinusoidal timestep embeddings.
        :param t: a 1-D Tensor of N indices, one per batch element.
                            These may be fractional.
        :param dim: the dimension of the output.
        :param max_period: controls the minimum frequency of the embeddings.
        :return: an (N, D) Tensor of positional embeddings.
        """
        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
        t = jax.lax.convert_element_type(t, jnp.float32)  # [B, T]
        dim = self.hidden_size
        half = dim // 2
        freqs = jnp.exp(
            -math.log(max_period)
            * jnp.arange(start=0, stop=half, dtype=jnp.float32)
            / half
        )
        args = t[:, :, None] * freqs[None, None]  # TODO verify this
        embedding = jnp.concatenate([jnp.cos(args), jnp.sin(args)], axis=-1)
        return embedding


class DiTBlock(nnx.Module):
    """DiT block"""

    def __init__(
        self,
        model_dim: int,
        ffn_dim: int,
        num_heads: int,
        dropout: float,
        param_dtype: jnp.dtype,
        dtype: jnp.dtype,
        rngs: nnx.Rngs,
        use_flash_attention: bool = False,
        decode: bool = False,
        sow_weights: bool = False,
        sow_activations: bool = False,
    ):
        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.decode = decode
        self.sow_weights = sow_weights
        self.sow_activations = sow_activations
        self.condition_up = nnx.Linear(
            self.model_dim,
            9 * self.model_dim,
            kernel_init=nnx.initializers.normal(0.02),
            bias_init=nnx.initializers.normal(0.02),
            rngs=rngs,
        )

        self.temporal_norm = nnx.LayerNorm(
            num_features=self.model_dim,
            param_dtype=self.param_dtype,
            dtype=self.param_dtype,  # layer norm in full precision
            rngs=rngs,
        )
        self.spatial_norm = nnx.LayerNorm(
            num_features=self.model_dim,
            param_dtype=self.param_dtype,
            dtype=self.param_dtype,  # layer norm in full precision
            rngs=rngs,
        )
        self.ffn_norm = nnx.LayerNorm(
            num_features=self.model_dim,
            param_dtype=self.param_dtype,
            dtype=self.param_dtype,  # layer norm in full precision
            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=self.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=False
            ),
            rngs=rngs,
            decode=self.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,
        )

    def __call__(self, x_BTNM, c_BTM):
        B, T, N, M = x_BTNM.shape

        # Calculate adaLn modulation parameters.
        c_BTM = nnx.silu(c_BTM)
        c = self.condition_up(c_BTM)  # (B, T, 6*M)
        (
            shift_spatial,
            scale_spatial,
            gate_spatial,
            shift_temporal,
            scale_temporal,
            gate_temporal,
            shift_mlp,
            scale_mlp,
            gate_mlp,
        ) = jnp.split(c, 9, axis=-1)

        # TODO find a better way to do this
        """
        We adapt adaLN conditioning to the spatio-temporal block. 

        For spatial attention:
        Each frame gets its noise level conditioning. Frame level conditioning is broadcasted to all patches in the frame.

        For temporal attention:
        Each patch needs to be aware of its noise level. Thus we replicate the frame level conditioning to all patches in the frame.
        """
        shift_spatial = einops.rearrange(shift_spatial, "b t m -> (b t) 1 m")
        scale_spatial = einops.rearrange(scale_spatial, "b t m -> (b t) 1 m")
        gate_spatial = einops.rearrange(gate_spatial, "b t m -> (b t) 1 m")
        shift_temporal = einops.repeat(shift_temporal, "b t m -> (tile b) t m", tile=N)
        scale_temporal = einops.repeat(scale_temporal, "b t m -> (tile b) t m", tile=N)
        gate_temporal = einops.repeat(gate_temporal, "b t m -> (tile b) t m", tile=N)
        shift_mlp = einops.rearrange(shift_mlp, "b t m -> b t 1 m")
        scale_mlp = einops.rearrange(scale_mlp, "b t m -> b t 1 m")
        gate_mlp = einops.rearrange(gate_mlp, "b t m -> b t 1 m")

        # --- Spatial attention ---
        z_FNM = einops.rearrange(x_BTNM, "b t n m -> (b t) n m")
        z_FNM = self.spatial_norm(z_FNM)
        z_FNM = modulate(z_FNM, shift_spatial, scale_spatial)
        z_FNM = self.spatial_attention(z_FNM, sow_weights=self.sow_weights)
        z_FNM = z_FNM * gate_spatial
        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 = modulate(z_PTM, shift_temporal, scale_temporal)
        z_PTM = self.temporal_attention(z_PTM, sow_weights=self.sow_weights)
        z_PTM = z_PTM * gate_temporal
        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_BTNM = modulate(z_BTNM, shift_mlp, scale_mlp)
        z_BTND = self.ffn_dense1(z_BTNM)
        z_BTND = jax.nn.gelu(z_BTND)
        z_BTNM = self.ffn_dense2(z_BTND)
        z_BTNM = z_BTNM * gate_mlp
        x_BTNM = x_BTNM + z_BTNM
        if self.sow_activations:
            self.sow(nnx.Intermediate, "activations", x_BTNM)

        return x_BTNM


class DiffusionTransformer(nnx.Module):
    """Diffusion transformer"""

    def __init__(
        self,
        model_dim: int,
        ffn_dim: int,
        num_blocks: int,
        num_heads: int,
        dropout: float,
        param_dtype: jnp.dtype,
        dtype: jnp.dtype,
        rngs: nnx.Rngs,
        decode: bool = False,
    ):
        self.model_dim = model_dim
        self.ffn_dim = ffn_dim
        self.num_blocks = num_blocks
        self.num_heads = num_heads
        self.dropout = dropout
        self.param_dtype = param_dtype
        self.dtype = dtype
        self.pos_enc = _get_spatiotemporal_positional_encoding(self.model_dim)
        self.blocks: nnx.List[DiTBlock] = nnx.List([])
        for _ in range(self.num_blocks):
            self.blocks.append(
                DiTBlock(
                    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,
                    rngs=rngs,
                    decode=decode,
                )
            )
        self.patch_size = 4
        self.time_step_embedder = TimestepEmbedder(self.model_dim, rngs=rngs)
        self.step_size_embedder = TimestepEmbedder(self.model_dim, rngs=rngs)

        self.patch_dim = self.patch_size * self.patch_size * 3
        self.patch_up = nnx.Linear(
            self.patch_dim,
            self.model_dim,
            kernel_init=nnx.initializers.normal(0.02),
            bias_init=nnx.initializers.normal(0.02),
            rngs=rngs,
        )
        self.condition_up = nnx.Linear(
            self.model_dim,
            2 * self.model_dim,
            kernel_init=nnx.initializers.normal(0.02),
            bias_init=nnx.initializers.normal(0.02),
            rngs=rngs,
        )
        self.layer_norm = nnx.LayerNorm(
            self.model_dim, use_bias=False, use_scale=False, rngs=rngs
        )
        self.dense2 = nnx.Linear(
            self.model_dim,
            self.patch_dim,
            kernel_init=nnx.initializers.normal(0.02),
            bias_init=nnx.initializers.normal(0.02),
            rngs=rngs,
        )

    def __call__(
        self, x_BTHWC: jax.Array, t: jax.Array, dt: jax.Array, act_embed_BTM: jax.Array
    ) -> jax.Array:
        B, T, H, W, C = x_BTHWC.shape
        x_BTNP = patchify(x_BTHWC, self.patch_size)
        x_BTNM = self.patch_up(x_BTNP)
        x_BTNM = self.pos_enc(x_BTNM)
        te = self.time_step_embedder(t)  # (B, T, hidden_size)
        dte = self.step_size_embedder(dt)  # (B, T, hidden_size)
        # TODO add action conditioning here
        # TODO maybe prepending later?
        c = te + dte + act_embed_BTM  # (B, T, hidden_size)
        for block in self.blocks:
            x_BTNM = block(x_BTNM, c)

        c = nnx.silu(c)
        c = self.condition_up(c)
        shift, scale = jnp.split(c, 2, axis=-1)
        shift = einops.rearrange(shift, "b t m -> b t 1 m")
        scale = einops.rearrange(scale, "b t m -> b t 1 m")
        x_BTNM = self.layer_norm(x_BTNM)
        x_BTNM = modulate(x_BTNM, shift, scale)