283 lines
9.8 KiB
Python
283 lines
9.8 KiB
Python
from dataclasses import dataclass
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import torch
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from torch import Tensor, nn
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import torch.utils.checkpoint as ckpt
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from .layers import (
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DoubleStreamBlock,
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EmbedND,
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LastLayer,
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SingleStreamBlock,
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timestep_embedding,
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Approximator,
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distribute_modulations,
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)
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@dataclass
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class ChromaParams:
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in_channels: int
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context_in_dim: int
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hidden_size: int
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mlp_ratio: float
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num_heads: int
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depth: int
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depth_single_blocks: int
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axes_dim: list[int]
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theta: int
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qkv_bias: bool
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guidance_embed: bool
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approximator_in_dim: int
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approximator_depth: int
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approximator_hidden_size: int
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_use_compiled: bool
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chroma_params = ChromaParams(
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in_channels=64,
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context_in_dim=4096,
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hidden_size=3072,
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mlp_ratio=4.0,
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num_heads=24,
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depth=19,
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depth_single_blocks=38,
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axes_dim=[16, 56, 56],
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theta=10_000,
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qkv_bias=True,
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guidance_embed=True,
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approximator_in_dim=64,
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approximator_depth=5,
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approximator_hidden_size=5120,
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_use_compiled=False,
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)
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def modify_mask_to_attend_padding(mask, max_seq_length, num_extra_padding=8):
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"""
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Modifies attention mask to allow attention to a few extra padding tokens.
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Args:
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mask: Original attention mask (1 for tokens to attend to, 0 for masked tokens)
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max_seq_length: Maximum sequence length of the model
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num_extra_padding: Number of padding tokens to unmask
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Returns:
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Modified mask
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"""
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# Get the actual sequence length from the mask
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seq_length = mask.sum(dim=-1)
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batch_size = mask.shape[0]
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modified_mask = mask.clone()
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for i in range(batch_size):
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current_seq_len = int(seq_length[i].item())
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# Only add extra padding tokens if there's room
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if current_seq_len < max_seq_length:
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# Calculate how many padding tokens we can unmask
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available_padding = max_seq_length - current_seq_len
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tokens_to_unmask = min(num_extra_padding, available_padding)
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# Unmask the specified number of padding tokens right after the sequence
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modified_mask[i, current_seq_len : current_seq_len + tokens_to_unmask] = 1
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return modified_mask
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class Chroma(nn.Module):
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"""
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Transformer model for flow matching on sequences.
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"""
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def __init__(self, params: ChromaParams):
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super().__init__()
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self.params = params
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self.in_channels = params.in_channels
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self.out_channels = self.in_channels
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self.gradient_checkpointing = False
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if params.hidden_size % params.num_heads != 0:
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raise ValueError(
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f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
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)
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pe_dim = params.hidden_size // params.num_heads
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if sum(params.axes_dim) != pe_dim:
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raise ValueError(
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f"Got {params.axes_dim} but expected positional dim {pe_dim}"
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)
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self.hidden_size = params.hidden_size
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self.num_heads = params.num_heads
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self.pe_embedder = EmbedND(
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dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim
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)
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self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
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# TODO: need proper mapping for this approximator output!
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# currently the mapping is hardcoded in distribute_modulations function
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self.distilled_guidance_layer = Approximator(
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params.approximator_in_dim,
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self.hidden_size,
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params.approximator_hidden_size,
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params.approximator_depth,
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)
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self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
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self.double_blocks = nn.ModuleList(
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[
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DoubleStreamBlock(
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self.hidden_size,
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self.num_heads,
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mlp_ratio=params.mlp_ratio,
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qkv_bias=params.qkv_bias,
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use_compiled=params._use_compiled,
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)
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for _ in range(params.depth)
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]
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)
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self.single_blocks = nn.ModuleList(
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[
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SingleStreamBlock(
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self.hidden_size,
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self.num_heads,
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mlp_ratio=params.mlp_ratio,
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use_compiled=params._use_compiled,
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)
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for _ in range(params.depth_single_blocks)
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]
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)
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self.final_layer = LastLayer(
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self.hidden_size,
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1,
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self.out_channels,
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use_compiled=params._use_compiled,
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)
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# TODO: move this hardcoded value to config
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# single layer has 3 modulation vectors
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# double layer has 6 modulation vectors for each expert
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# final layer has 2 modulation vectors
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self.mod_index_length = 3 * params.depth_single_blocks + 2 * 6 * params.depth + 2
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self.depth_single_blocks = params.depth_single_blocks
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self.depth_double_blocks = params.depth
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# self.mod_index = torch.tensor(list(range(self.mod_index_length)), device=0)
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self.register_buffer(
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"mod_index",
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torch.tensor(list(range(self.mod_index_length)), device="cpu"),
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persistent=False,
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)
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self.approximator_in_dim = params.approximator_in_dim
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@property
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def device(self):
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# Get the device of the module (assumes all parameters are on the same device)
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return next(self.parameters()).device
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def enable_gradient_checkpointing(self, enable: bool = True):
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self.gradient_checkpointing = enable
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def forward(
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self,
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img: Tensor,
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img_ids: Tensor,
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txt: Tensor,
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txt_ids: Tensor,
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txt_mask: Tensor,
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timesteps: Tensor,
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guidance: Tensor,
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attn_padding: int = 1,
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) -> Tensor:
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if img.ndim != 3 or txt.ndim != 3:
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raise ValueError("Input img and txt tensors must have 3 dimensions.")
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# running on sequences img
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img = self.img_in(img)
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txt = self.txt_in(txt)
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# TODO:
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# need to fix grad accumulation issue here for now it's in no grad mode
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# besides, i don't want to wash out the PFP that's trained on this model weights anyway
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# the fan out operation here is deleting the backward graph
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# alternatively doing forward pass for every block manually is doable but slow
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# custom backward probably be better
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with torch.no_grad():
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distill_timestep = timestep_embedding(timesteps, 16)
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# TODO: need to add toggle to omit this from schnell but that's not a priority
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distil_guidance = timestep_embedding(guidance, 16)
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# get all modulation index
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modulation_index = timestep_embedding(self.mod_index, 32)
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# we need to broadcast the modulation index here so each batch has all of the index
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modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1)
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# and we need to broadcast timestep and guidance along too
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timestep_guidance = (
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torch.cat([distill_timestep, distil_guidance], dim=1)
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.unsqueeze(1)
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.repeat(1, self.mod_index_length, 1)
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)
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# then and only then we could concatenate it together
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input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1)
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mod_vectors = self.distilled_guidance_layer(input_vec.requires_grad_(True))
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mod_vectors_dict = distribute_modulations(mod_vectors, self.depth_single_blocks, self.depth_double_blocks)
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ids = torch.cat((txt_ids, img_ids), dim=1)
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pe = self.pe_embedder(ids)
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# compute mask
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# assume max seq length from the batched input
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max_len = txt.shape[1]
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# mask
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with torch.no_grad():
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txt_mask_w_padding = modify_mask_to_attend_padding(
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txt_mask, max_len, attn_padding
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)
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txt_img_mask = torch.cat(
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[
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txt_mask_w_padding,
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torch.ones([img.shape[0], img.shape[1]], device=txt_mask.device),
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],
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dim=1,
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)
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txt_img_mask = txt_img_mask.float().T @ txt_img_mask.float()
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txt_img_mask = (
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txt_img_mask[None, None, ...]
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.repeat(txt.shape[0], self.num_heads, 1, 1)
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.int()
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.bool()
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)
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# txt_mask_w_padding[txt_mask_w_padding==False] = True
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for i, block in enumerate(self.double_blocks):
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# the guidance replaced by FFN output
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img_mod = mod_vectors_dict[f"double_blocks.{i}.img_mod.lin"]
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txt_mod = mod_vectors_dict[f"double_blocks.{i}.txt_mod.lin"]
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double_mod = [img_mod, txt_mod]
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if torch.is_grad_enabled() and self.gradient_checkpointing:
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img.requires_grad_(True)
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img, txt = ckpt.checkpoint(
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block, img, txt, pe, double_mod, txt_img_mask
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)
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else:
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img, txt = block(
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img=img, txt=txt, pe=pe, distill_vec=double_mod, mask=txt_img_mask
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)
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img = torch.cat((txt, img), 1)
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for i, block in enumerate(self.single_blocks):
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single_mod = mod_vectors_dict[f"single_blocks.{i}.modulation.lin"]
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if torch.is_grad_enabled() and self.gradient_checkpointing:
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img.requires_grad_(True)
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img = ckpt.checkpoint(block, img, pe, single_mod, txt_img_mask)
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else:
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img = block(img, pe=pe, distill_vec=single_mod, mask=txt_img_mask)
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img = img[:, txt.shape[1] :, ...]
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final_mod = mod_vectors_dict["final_layer.adaLN_modulation.1"]
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img = self.final_layer(
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img, distill_vec=final_mod
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) # (N, T, patch_size ** 2 * out_channels)
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return img
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