465 lines
16 KiB
Python
465 lines
16 KiB
Python
"""
|
|
Dual-stream transformer for flow matching on hand pose sequences.
|
|
|
|
Derived from ShapeR/model/flow_matching/dualstream_transformer.py with changes:
|
|
- Import path adjusted from `from model import attention` (same, works as-is)
|
|
- Renamed internal variables: img→z (latent stream), txt→cond (condition stream)
|
|
- Added RoPE support: position IDs + rope_module passed through block calls
|
|
|
|
Architecture (Flux-style):
|
|
- DoubleStreamBlock: joint attention over (latent z_t, condition context)
|
|
- SingleStreamBlock: self-attention on concatenated tokens
|
|
- LastLayer: final projection to output dimension
|
|
"""
|
|
|
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# This source code is licensed under the CC BY-NC 4.0 license found in the
|
|
# LICENSE file in the root directory of this source tree.
|
|
#
|
|
# This file is inspired by the Flux dual stream transformer architecture.
|
|
# Original source: https://github.com/black-forest-labs/flux
|
|
# Original license: Apache License 2.0
|
|
|
|
import math
|
|
from dataclasses import dataclass
|
|
from typing import Optional, Tuple
|
|
|
|
import torch
|
|
from einops import rearrange
|
|
from model import attention
|
|
from model.pose_embedding import RotaryEmbedding
|
|
from torch import nn, Tensor
|
|
|
|
|
|
class FlowMatchingTransformer(nn.Module):
|
|
"""Transformer model for flow matching on sequences."""
|
|
|
|
def __init__(
|
|
self,
|
|
in_channels,
|
|
out_channels,
|
|
use_context_in,
|
|
context_in_dim,
|
|
use_txt_in,
|
|
vec_in_dim,
|
|
use_pre_text_attn,
|
|
config,
|
|
):
|
|
super().__init__()
|
|
|
|
self.config = config
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.use_txt_in = use_txt_in
|
|
self.use_context_in = use_context_in
|
|
self.use_pre_text_attn = use_pre_text_attn
|
|
if config.hidden_size % config.num_heads != 0:
|
|
raise ValueError(
|
|
f"Hidden size {config.hidden_size} must be divisible by num_heads {config.num_heads}"
|
|
)
|
|
self.hidden_size = config.hidden_size
|
|
self.num_heads = config.num_heads
|
|
self.pc_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
|
|
self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
|
|
if self.use_txt_in:
|
|
self.vector_in = MLPEmbedder(vec_in_dim, self.hidden_size)
|
|
|
|
self.dropout = getattr(config, 'dropout', 0.1)
|
|
|
|
if self.use_pre_text_attn:
|
|
self.text_attn_in = nn.Linear(context_in_dim, self.hidden_size)
|
|
self.text_blocks = nn.ModuleList(
|
|
[
|
|
DoubleStreamBlock(
|
|
self.hidden_size,
|
|
self.num_heads,
|
|
mlp_ratio=config.mlp_ratio,
|
|
qkv_bias=config.qkv_bias,
|
|
dropout=self.dropout,
|
|
)
|
|
for _ in range(4)
|
|
]
|
|
)
|
|
|
|
if self.use_context_in:
|
|
self.dino_in = nn.Linear(context_in_dim, self.hidden_size)
|
|
self.double_blocks = nn.ModuleList(
|
|
[
|
|
DoubleStreamBlock(
|
|
self.hidden_size,
|
|
self.num_heads,
|
|
mlp_ratio=config.mlp_ratio,
|
|
qkv_bias=config.qkv_bias,
|
|
dropout=self.dropout,
|
|
)
|
|
for _ in range(config.depth)
|
|
]
|
|
)
|
|
|
|
self.single_blocks = nn.ModuleList(
|
|
[
|
|
SingleStreamBlock(
|
|
self.hidden_size, self.num_heads, mlp_ratio=config.mlp_ratio,
|
|
dropout=self.dropout,
|
|
)
|
|
for _ in range(
|
|
config.depth_single_blocks
|
|
+ (config.depth if not self.use_context_in else 0)
|
|
)
|
|
]
|
|
)
|
|
|
|
self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
|
|
|
|
def forward(
|
|
self,
|
|
pc: Tensor,
|
|
dino: Tensor,
|
|
timesteps: Tensor,
|
|
y: Tensor,
|
|
txt_tokens: Tensor,
|
|
z_positions: Tensor = None,
|
|
cond_positions: Tensor = None,
|
|
rope_module=None,
|
|
) -> Tensor:
|
|
if pc.ndim != 3 or (dino is not None and dino.ndim != 3):
|
|
raise ValueError("Input pc and dino tensors must have 3 dimensions.")
|
|
|
|
pc = self.pc_in(pc)
|
|
vec = self.time_in(timestep_embedding(timesteps, 256))
|
|
intermediates = None
|
|
if self.use_txt_in and y is not None:
|
|
vec = vec + self.vector_in(y)
|
|
|
|
if self.use_pre_text_attn:
|
|
txt_tokens = self.text_attn_in(txt_tokens)
|
|
for block in self.text_blocks:
|
|
pc, txt_tokens = block(z=pc, cond=txt_tokens, vec=vec)
|
|
|
|
cond_len = 0
|
|
if self.use_context_in:
|
|
dino = self.dino_in(dino)
|
|
cond_len = dino.shape[1]
|
|
for block in self.double_blocks:
|
|
pc, dino = block(
|
|
z=pc, cond=dino, vec=vec,
|
|
z_positions=z_positions,
|
|
cond_positions=cond_positions,
|
|
rope_module=rope_module,
|
|
)
|
|
# Build combined positions (matching concatenation order: [cond, z])
|
|
if z_positions is not None and cond_positions is not None:
|
|
combined_positions = torch.cat([cond_positions, z_positions], dim=0)
|
|
else:
|
|
combined_positions = None
|
|
pc = torch.cat((dino, pc), 1)
|
|
|
|
for block_idx, block in enumerate(self.single_blocks):
|
|
pc = block(pc, vec=vec, positions=combined_positions, rope_module=rope_module)
|
|
if block_idx == 3:
|
|
intermediates = pc
|
|
|
|
if self.use_context_in:
|
|
pc = pc[:, cond_len:, ...]
|
|
|
|
pc = self.final_layer(pc, vec)
|
|
return pc, intermediates
|
|
|
|
|
|
def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
|
|
"""Create sinusoidal timestep embeddings."""
|
|
t = time_factor * t
|
|
half = dim // 2
|
|
freqs = torch.exp(
|
|
-math.log(max_period)
|
|
* torch.arange(start=0, end=half, dtype=torch.float32)
|
|
/ half
|
|
).to(t.device)
|
|
|
|
args = t[:, None].float() * freqs[None]
|
|
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
|
if dim % 2:
|
|
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
|
if torch.is_floating_point(t):
|
|
embedding = embedding.to(t)
|
|
return embedding
|
|
|
|
|
|
class MLPEmbedder(nn.Module):
|
|
"""Two-layer MLP for embedding."""
|
|
|
|
def __init__(self, in_dim: int, hidden_dim: int):
|
|
super().__init__()
|
|
self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
|
|
self.silu = nn.SiLU()
|
|
self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
return self.out_layer(self.silu(self.in_layer(x)))
|
|
|
|
|
|
class RMSNorm(torch.nn.Module):
|
|
"""Root Mean Square Layer Normalization."""
|
|
|
|
def __init__(self, dim: int):
|
|
super().__init__()
|
|
self.scale = nn.Parameter(torch.ones(dim))
|
|
|
|
def forward(self, x: Tensor):
|
|
x_dtype = x.dtype
|
|
x = x.float()
|
|
rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
|
|
return (x * rrms).to(dtype=x_dtype) * self.scale
|
|
|
|
|
|
class QKNorm(torch.nn.Module):
|
|
"""Query-Key normalization for stable attention."""
|
|
|
|
def __init__(self, dim: int):
|
|
super().__init__()
|
|
self.query_norm = RMSNorm(dim)
|
|
self.key_norm = RMSNorm(dim)
|
|
|
|
def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tuple[Tensor, Tensor]:
|
|
q = self.query_norm(q)
|
|
k = self.key_norm(k)
|
|
return q.to(v), k.to(v)
|
|
|
|
|
|
class SelfAttention(nn.Module):
|
|
"""Standard self-attention with QK normalization."""
|
|
|
|
def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
|
|
super().__init__()
|
|
self.num_heads = num_heads
|
|
head_dim = dim // num_heads
|
|
|
|
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
|
self.norm = QKNorm(head_dim)
|
|
self.proj = nn.Linear(dim, dim)
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
qkv = self.qkv(x)
|
|
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
|
q, k = self.norm(q, k, v)
|
|
x = attention(q, k, v)
|
|
x = self.proj(x)
|
|
return x
|
|
|
|
|
|
@dataclass
|
|
class ModulationOut:
|
|
shift: Tensor
|
|
scale: Tensor
|
|
gate: Tensor
|
|
|
|
|
|
class Modulation(nn.Module):
|
|
"""Adaptive layer norm modulation from timestep embedding."""
|
|
|
|
def __init__(self, dim: int, double: bool):
|
|
super().__init__()
|
|
self.is_double = double
|
|
self.multiplier = 6 if double else 3
|
|
self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
|
|
|
|
def forward(self, vec: Tensor) -> Tuple[ModulationOut, Optional[ModulationOut]]:
|
|
out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(
|
|
self.multiplier, dim=-1
|
|
)
|
|
return (
|
|
ModulationOut(*out[:3]),
|
|
ModulationOut(*out[3:]) if self.is_double else None,
|
|
)
|
|
|
|
|
|
class DoubleStreamBlock(nn.Module):
|
|
"""Double-stream attention block with separate z (latent) and cond (condition) streams."""
|
|
|
|
def __init__(
|
|
self, hidden_size: int, num_heads: int, mlp_ratio: float,
|
|
qkv_bias: bool = False, dropout: float = 0.1,
|
|
):
|
|
super().__init__()
|
|
|
|
mlp_hidden_dim = int(hidden_size * mlp_ratio)
|
|
self.num_heads = num_heads
|
|
self.hidden_size = hidden_size
|
|
# z stream (latent) modules
|
|
self.z_mod = Modulation(hidden_size, double=True)
|
|
self.z_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.z_attn = SelfAttention(
|
|
dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias
|
|
)
|
|
self.z_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.z_mlp = nn.Sequential(
|
|
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
|
|
nn.GELU(approximate="tanh"),
|
|
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
|
|
)
|
|
self.z_attn_drop = nn.Dropout(dropout)
|
|
self.z_mlp_drop = nn.Dropout(dropout)
|
|
|
|
# cond stream (condition) modules
|
|
self.cond_mod = Modulation(hidden_size, double=True)
|
|
self.cond_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.cond_attn = SelfAttention(
|
|
dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias
|
|
)
|
|
self.cond_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.cond_mlp = nn.Sequential(
|
|
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
|
|
nn.GELU(approximate="tanh"),
|
|
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
|
|
)
|
|
self.cond_attn_drop = nn.Dropout(dropout)
|
|
self.cond_mlp_drop = nn.Dropout(dropout)
|
|
|
|
def forward(
|
|
self,
|
|
z: Tensor,
|
|
cond: Tensor,
|
|
vec: Tensor,
|
|
z_positions: Tensor = None,
|
|
cond_positions: Tensor = None,
|
|
rope_module=None,
|
|
) -> Tuple[Tensor, Tensor]:
|
|
z_mod1, z_mod2 = self.z_mod(vec)
|
|
cond_mod1, cond_mod2 = self.cond_mod(vec)
|
|
|
|
# prepare z stream for attention
|
|
z_modulated = self.z_norm1(z)
|
|
z_modulated = (1 + z_mod1.scale) * z_modulated + z_mod1.shift
|
|
z_qkv = self.z_attn.qkv(z_modulated)
|
|
z_q, z_k, z_v = rearrange(
|
|
z_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads
|
|
)
|
|
z_q, z_k = self.z_attn.norm(z_q, z_k, z_v)
|
|
|
|
# prepare cond stream for attention
|
|
cond_modulated = self.cond_norm1(cond)
|
|
cond_modulated = (1 + cond_mod1.scale) * cond_modulated + cond_mod1.shift
|
|
cond_qkv = self.cond_attn.qkv(cond_modulated)
|
|
cond_q, cond_k, cond_v = rearrange(
|
|
cond_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads
|
|
)
|
|
cond_q, cond_k = self.cond_attn.norm(cond_q, cond_k, cond_v)
|
|
|
|
# Apply RoPE (after QK-norm, before concatenation)
|
|
if rope_module is not None and z_positions is not None:
|
|
sin, cos = RotaryEmbedding.get_sin_cos_from_positions(
|
|
z_positions, rope_module.inv_freq
|
|
)
|
|
z_q = RotaryEmbedding.apply_rope(z_q, sin, cos)
|
|
z_k = RotaryEmbedding.apply_rope(z_k, sin, cos)
|
|
if rope_module is not None and cond_positions is not None:
|
|
sin, cos = RotaryEmbedding.get_sin_cos_from_positions(
|
|
cond_positions, rope_module.inv_freq
|
|
)
|
|
cond_q = RotaryEmbedding.apply_rope(cond_q, sin, cos)
|
|
cond_k = RotaryEmbedding.apply_rope(cond_k, sin, cos)
|
|
|
|
# run actual attention (concat [cond, z] streams)
|
|
q = torch.cat((cond_q, z_q), dim=2)
|
|
k = torch.cat((cond_k, z_k), dim=2)
|
|
v = torch.cat((cond_v, z_v), dim=2)
|
|
|
|
attn = attention(q, k, v)
|
|
cond_attn, z_attn = attn[:, : cond.shape[1]], attn[:, cond.shape[1] :]
|
|
|
|
# calculate the z stream blocks
|
|
z = z + z_mod1.gate * self.z_attn_drop(self.z_attn.proj(z_attn))
|
|
z = z + z_mod2.gate * self.z_mlp_drop(self.z_mlp(
|
|
(1 + z_mod2.scale) * self.z_norm2(z) + z_mod2.shift
|
|
))
|
|
|
|
# calculate the cond stream blocks
|
|
cond = cond + cond_mod1.gate * self.cond_attn_drop(self.cond_attn.proj(cond_attn))
|
|
cond = cond + cond_mod2.gate * self.cond_mlp_drop(self.cond_mlp(
|
|
(1 + cond_mod2.scale) * self.cond_norm2(cond) + cond_mod2.shift
|
|
))
|
|
return z, cond
|
|
|
|
|
|
class SingleStreamBlock(nn.Module):
|
|
"""
|
|
A DiT block with parallel linear layers as described in
|
|
https://arxiv.org/abs/2302.05442 and adapted modulation interface.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size: int,
|
|
num_heads: int,
|
|
mlp_ratio: float = 4.0,
|
|
qk_scale: Optional[float] = None,
|
|
dropout: float = 0.1,
|
|
):
|
|
super().__init__()
|
|
self.hidden_dim = hidden_size
|
|
self.num_heads = num_heads
|
|
head_dim = hidden_size // num_heads
|
|
self.scale = qk_scale or head_dim**-0.5
|
|
|
|
self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
|
|
self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
|
|
self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
|
|
|
|
self.norm = QKNorm(head_dim)
|
|
|
|
self.hidden_size = hidden_size
|
|
self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
|
|
self.mlp_act = nn.GELU(approximate="tanh")
|
|
self.modulation = Modulation(hidden_size, double=False)
|
|
self.attn_drop = nn.Dropout(dropout)
|
|
self.mlp_drop = nn.Dropout(dropout)
|
|
|
|
def forward(
|
|
self,
|
|
x: Tensor,
|
|
vec: Tensor,
|
|
positions: Tensor = None,
|
|
rope_module=None,
|
|
) -> Tensor:
|
|
mod, _ = self.modulation(vec)
|
|
x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
|
|
qkv, mlp = torch.split(
|
|
self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1
|
|
)
|
|
|
|
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
|
q, k = self.norm(q, k, v)
|
|
|
|
# Apply RoPE (after QK-norm, before attention)
|
|
if rope_module is not None and positions is not None:
|
|
sin, cos = RotaryEmbedding.get_sin_cos_from_positions(
|
|
positions, rope_module.inv_freq
|
|
)
|
|
q = RotaryEmbedding.apply_rope(q, sin, cos)
|
|
k = RotaryEmbedding.apply_rope(k, sin, cos)
|
|
|
|
attn = self.attn_drop(attention(q, k, v))
|
|
output = self.mlp_drop(self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)))
|
|
return x + mod.gate * output
|
|
|
|
|
|
class LastLayer(nn.Module):
|
|
"""Final projection layer with adaptive layer norm."""
|
|
|
|
def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
|
|
super().__init__()
|
|
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.linear = nn.Linear(
|
|
hidden_size, patch_size * patch_size * out_channels, bias=True
|
|
)
|
|
self.adaLN_modulation = nn.Sequential(
|
|
nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)
|
|
)
|
|
|
|
def forward(self, x: Tensor, vec: Tensor) -> Tensor:
|
|
shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
|
|
x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
|
|
x = self.linear(x)
|
|
return x
|