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Refactor latte.

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Former-commit-id: 304534269f105578097bc16ab497636ad59fd9b2
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sennnnn
2024-03-10 11:25:50 +08:00
parent bfe4f15694
commit 443bab1364
2 changed files with 739 additions and 0 deletions
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opensora/models/diffusion/latte/configuration_latte.py
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from typing import Union, Tuple
from ..configuration_base import BaseConfiguration
class LatteConfiguration(BaseConfiguration):
def __init__(
self,
input_size=32,
patch_size=2,
patch_size_t=1,
in_channels=4,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
num_frames=16,
class_dropout_prob=0.1,
num_classes=1000,
learn_sigma=True,
extras=1,
attention_mode='math',
attention_pe_mode=None,
pt_input_size: Union[int, Tuple[int, int]] = None, # (h, w)
intp_vfreq: bool = True, # vision position interpolation
**kwargs,
):
super().__init__(**kwargs)
self.input_size = input_size
self.patch_size = patch_size
self.patch_size_t = patch_size_t
self.in_channels = in_channels
self.hidden_size = hidden_size
self.depth = depth
self.num_heads = num_heads
self.mlp_ratio = mlp_ratio
self.num_frames = num_frames
self.class_dropout_prob = class_dropout_prob
self.num_classes = num_classes
self.learn_sigma = learn_sigma
self.extras = learn_sigma
self.attention_mode = attention_mode
self.attention_pe_mode = attention_pe_mode
self.pt_input_size = pt_input_size
self.intp_vfreq = intp_vfreq
def Latte_XL_122_Config(**kwargs):
return LatteConfiguration(depth=28, hidden_size=1152, patch_size_t=1, patch_size=2, num_heads=16, **kwargs)
def Latte_XL_144_Config(**kwargs):
return LatteConfiguration(depth=28, hidden_size=1152, patch_size_t=1, patch_size=4, num_heads=16, **kwargs)
def Latte_XL_188_Config(**kwargs):
return LatteConfiguration(depth=28, hidden_size=1152, patch_size_t=1, patch_size=8, num_heads=16, **kwargs)
def Latte_L_122_Config(**kwargs):
return LatteConfiguration(depth=24, hidden_size=1024, patch_size_t=1, patch_size=2, num_heads=16, **kwargs)
def Latte_L_144_Config(**kwargs):
return LatteConfiguration(depth=24, hidden_size=1024, patch_size_t=1, patch_size=4, num_heads=16, **kwargs)
def Latte_L_188_Config(**kwargs):
return LatteConfiguration(depth=24, hidden_size=1024, patch_size_t=1, patch_size=8, num_heads=16, **kwargs)
def Latte_B_122_Config(**kwargs):
return LatteConfiguration(depth=12, hidden_size=768, patch_size_t=1, patch_size=2, num_heads=12, **kwargs)
def Latte_B_144_Config(**kwargs):
return LatteConfiguration(depth=12, hidden_size=768, patch_size_t=1, patch_size=4, num_heads=12, **kwargs)
def Latte_B_188_Config(**kwargs):
return LatteConfiguration(depth=12, hidden_size=768, patch_size_t=1, patch_size=8, num_heads=12, **kwargs)
def Latte_S_122_Config(**kwargs):
return LatteConfiguration(depth=12, hidden_size=384, patch_size_t=1, patch_size=2, num_heads=6, **kwargs)
def Latte_S_144_Config(**kwargs):
return LatteConfiguration(depth=12, hidden_size=384, patch_size_t=1, patch_size=4, num_heads=6, **kwargs)
def Latte_S_188_Config(**kwargs):
return LatteConfiguration(depth=12, hidden_size=384, patch_size_t=1, patch_size=8, num_heads=6, **kwargs)
Latte_configs = {
"Latte-XL/122": Latte_XL_122_Config, "Latte-XL/144": Latte_XL_144_Config, "Latte-XL/188": Latte_XL_188_Config,
"Latte-L/122": Latte_L_122_Config, "Latte-L/144": Latte_L_144_Config, "Latte-L/188": Latte_L_188_Config,
"Latte-B/122": Latte_B_122_Config, "Latte-B/144": Latte_B_144_Config, "Latte-B/188": Latte_B_188_Config,
"Latte-S/122": Latte_S_122_Config, "Latte-S/144": Latte_S_144_Config, "Latte-S/188": Latte_S_188_Config,
}
opensora/models/diffusion/latte/modeling_latte.py
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# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# GLIDE: https://github.com/openai/glide-text2im
# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
# --------------------------------------------------------
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
import numpy as np
from torch.nn import functional as F
from einops import rearrange, repeat
from typing import Tuple, Union
from timm.layers import to_2tuple
from timm.models.vision_transformer import Mlp, PatchEmbed
from .VisionRoPE import VisionRotaryEmbeddingFast
try:
# needs to have https://github.com/corl-team/rebased/ installed
from fla.ops.triton.rebased_fast import parallel_rebased
except:
REBASED_IS_AVAILABLE = False
try:
# needs to have https://github.com/lucidrains/ring-attention-pytorch installed
from ring_attention_pytorch.ring_flash_attention_cuda import ring_flash_attn_cuda
except:
RING_ATTENTION_IS_AVAILABLE = False
from .configuration_latte import LatteConfiguration
# from timm.models.layers.helpers import to_2tuple
# from timm.models.layers.trace_utils import _assert
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
#################################################################################
# Attention Layers from TIMM #
#################################################################################
class Attention(nn.Module):
def __init__(self,
dim,
num_heads=8,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
use_lora=False,
attention_mode='math',
eps=1e-12,
causal=True,
ring_bucket_size=1024,
attention_pe_mode=None,
hw: Union[int, Tuple[int, int]] = 16, # (h, w)
pt_hw: Union[int, Tuple[int, int]] = 16, # (h, w)
intp_vfreq: bool = True, # vision position interpolation
):
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.attention_mode = attention_mode
self.attention_pe_mode = attention_pe_mode
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
self.eps = eps
self.causal = causal
self.ring_bucket_size = ring_bucket_size
if self.attention_pe_mode == '2d_rope':
half_head_dim = dim // num_heads // 2
self.hw = to_2tuple(hw)
self.rope = VisionRotaryEmbeddingFast(
dim=half_head_dim,
pt_hw=to_2tuple(pt_hw),
ft_hw=self.hw if intp_vfreq else None,
)
def forward(self, x, attn_mask):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4).contiguous()
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) b h n c
if attn_mask is not None:
attn_mask = attn_mask.repeat(1, self.num_heads, 1, 1).to(q.dtype)
if self.attention_pe_mode == '2d_rope':
q_t = q.view(B, self.num_heads, -1, self.hw[0] * self.hw[1], C // self.num_heads)
ro_q_t = self.rope(q_t)
q = ro_q_t.view(B, self.num_heads, N, C // self.num_heads)
k_t = k.view(B, self.num_heads, -1, self.hw[0] * self.hw[1], C // self.num_heads)
ro_k_t = self.rope(k_t)
k = ro_k_t.view(B, self.num_heads, N, C // self.num_heads)
if self.attention_mode == 'xformers': # cause loss nan while using with amp
with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=False, enable_mem_efficient=True):
x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask,
dropout_p=self.attn_drop.p, scale=self.scale).reshape(B, N, C) # require pytorch 2.0
elif self.attention_mode == 'flash':
# cause loss nan while using with amp
# Optionally use the context manager to ensure one of the fused kerenels is run
with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=True, enable_mem_efficient=False):
x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask,
dropout_p=self.attn_drop.p, scale=self.scale).reshape(B, N, C) # require pytorch 2.0
elif self.attention_mode == 'math':
attn = (q @ k.transpose(-2, -1)) * self.scale
if attn_mask is not None:
attn_bias = self.make_attn_bias(attn_mask)
attn = attn + attn_bias
attn = attn.softmax(dim=-1)
if torch.any(torch.isnan(attn)):
print('torch.any(torch.isnan(attn))')
attn = attn.masked_fill(torch.isnan(attn), float(0.))
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
elif self.attention_mode == 'rebased':
x = parallel_rebased(q, k, v, self.eps, True, True).reshape(B, N, C)
elif self.attention_mode == 'ring':
x = ring_flash_attn_cuda(q, k, v, causal=self.causal, bucket_size=self.ring_bucket_size).reshape(B, N, C)
else:
raise NotImplemented
x = self.proj(x)
x = self.proj_drop(x)
return x
def make_attn_bias(self, attn_mask):
attn_bias = torch.where(attn_mask == 0, -1e8 if attn_mask.dtype == torch.float32 else 1e-4, attn_mask)
attn_bias = torch.where(attn_mask == 1, 0., attn_bias)
return attn_bias
#################################################################################
# Embedding Layers for Timesteps and Class Labels #
#################################################################################
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, 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
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=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)
return embedding
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_emb = self.mlp(t_freq)
return t_emb
class LabelEmbedder(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
"""
def __init__(self, num_classes, hidden_size, dropout_prob):
super().__init__()
use_cfg_embedding = dropout_prob > 0
self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
self.num_classes = num_classes
self.dropout_prob = dropout_prob
def token_drop(self, labels, force_drop_ids=None):
"""
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
else:
drop_ids = force_drop_ids == 1
labels = torch.where(drop_ids, self.num_classes, labels)
return labels
def forward(self, labels, train, force_drop_ids=None):
use_dropout = self.dropout_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
labels = self.token_drop(labels, force_drop_ids)
embeddings = self.embedding_table(labels)
return embeddings
#################################################################################
# Core Latte Model #
#################################################################################
class TransformerBlock(nn.Module):
"""
A Latte tansformer block with adaptive layer norm zero (adaLN-Zero) conditioning.
"""
def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
super().__init__()
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
def forward(self, x, c, attn_bias):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), attn_bias)
x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
class FinalLayer(nn.Module):
"""
The final layer of Latte.
"""
def __init__(self, hidden_size, patch_size_t, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(hidden_size, patch_size_t * 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, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class Latte(nn.Module):
"""
Diffusion model with a Transformer backbone.
"""
def __init__(self, config: LatteConfiguration):
super().__init__()
input_size = config.input_size
patch_size = config.patch_size
patch_size_t = config.patch_size_t
in_channels = config.in_channels
hidden_size = config.hidden_size
depth = config.depth
num_heads = config.num_heads
mlp_ratio = config.mlp_ratio
num_frames = config.num_frames
class_dropout_prob = config.class_dropout_prob
num_classes = config.num_classes
learn_sigma = config.learn_sigma
extras = config.learn_sigma
attention_mode = config.attention_mode
attention_pe_mode = config.attention_pe_mode
pt_input_size = config.pt_input_size
intp_vfreq = config.intp_vfreq
self.config = config
self.learn_sigma = learn_sigma
self.in_channels = in_channels
self.out_channels = in_channels * 2 if learn_sigma else in_channels
self.patch_size = patch_size
self.patch_size_t = patch_size_t
self.num_heads = num_heads
self.extras = extras
self.num_frames = num_frames
self.hidden_size = hidden_size
self.gradient_checkpointing = False
self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
self.t_embedder = TimestepEmbedder(hidden_size)
if self.extras == 2:
self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)
if self.extras == 78: # timestep + text_embedding
self.text_embedding_projection = nn.Sequential(
nn.SiLU(),
nn.Linear(77 * 768, hidden_size, bias=True)
)
num_patches = self.x_embedder.num_patches
# Will use fixed sin-cos embedding:
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)
self.temp_embed = nn.Parameter(torch.zeros(1, num_frames, hidden_size), requires_grad=False)
self.hidden_size = hidden_size
if pt_input_size is None:
pt_input_size = input_size
self.blocks = nn.ModuleList([
TransformerBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, attention_mode=attention_mode,
attention_pe_mode=attention_pe_mode, hw=input_size, pt_hw=pt_input_size,
intp_vfreq=intp_vfreq
) for _ in range(depth)
])
self.final_layer = FinalLayer(hidden_size, patch_size_t, patch_size, self.out_channels)
self.initialize_weights()
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Initialize (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5))
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
temp_embed = get_1d_sincos_temp_embed(self.temp_embed.shape[-1], self.temp_embed.shape[-2])
self.temp_embed.data.copy_(torch.from_numpy(temp_embed).float().unsqueeze(0))
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
w = self.x_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
nn.init.constant_(self.x_embedder.proj.bias, 0)
if self.extras == 2:
# Initialize label embedding table:
nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
# Zero-out adaLN modulation layers in Latte blocks:
for block in self.blocks:
nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
# Zero-out output layers:
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def unpatchify(self, x):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0]
h = w = int(x.shape[1] ** 0.5)
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
x = torch.einsum('nhwpqc->nchpwq', x)
imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
return imgs
def ckpt_wrapper(self, module):
def ckpt_forward(*inputs):
outputs = module(*inputs)
return outputs
return ckpt_forward
def make_mask(self, attention_mask):
attention_mask = attention_mask.flatten(1).unsqueeze(-1) # bs t h w -> bs thw 1
attention_mask = attention_mask @ attention_mask.transpose(1, 2) # bs thw 1 @ bs 1 thw = bs thw thw
attention_mask = attention_mask.unsqueeze(1)
return attention_mask
# @torch.cuda.amp.autocast()
# @torch.compile
def forward(self,
x,
t,
y=None,
text_embedding=None,
attention_mask=None):
"""
Forward pass of Latte.
x: (N, F, C, H, W) tensor of video inputs
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
attention_mask: (N, F, H, W)
"""
attention_mask_temproal, attention_mask_spatial = None, None
if attention_mask is not None:
attention_mask_spatial = rearrange(attention_mask, 'b t h w -> (b t) h w')
attention_mask_spatial = self.make_mask(attention_mask_spatial)
attention_mask_temproal = rearrange(attention_mask, 'b t h w -> (b h w) t')
attention_mask_temproal = self.make_mask(attention_mask_temproal)
batches, frames, channels, high, weight = x.shape
x = rearrange(x, 'b f c h w -> (b f) c h w')
x = self.x_embedder(x) + self.pos_embed
t = self.t_embedder(t)
timestep_spatial = repeat(t, 'n d -> (n c) d', c=self.temp_embed.shape[1])
timestep_temp = repeat(t, 'n d -> (n c) d', c=self.pos_embed.shape[1])
if self.extras == 2:
y = self.y_embedder(y, self.training)
y_spatial = repeat(y, 'n d -> (n c) d', c=self.temp_embed.shape[1])
y_temp = repeat(y, 'n d -> (n c) d', c=self.pos_embed.shape[1])
elif self.extras == 78:
text_embedding = self.text_embedding_projection(text_embedding.reshape(batches, -1))
text_embedding_spatial = repeat(text_embedding, 'n d -> (n c) d', c=self.temp_embed.shape[1])
text_embedding_temp = repeat(text_embedding, 'n d -> (n c) d', c=self.pos_embed.shape[1])
for i in range(0, len(self.blocks), 2):
spatial_block, temp_block = self.blocks[i:i+2]
if self.extras == 2:
c = timestep_spatial + y_spatial
elif self.extras == 78:
c = timestep_spatial + text_embedding_spatial
else:
c = timestep_spatial
if self.gradient_checkpointing and self.training:
x = torch.utils.checkpoint.checkpoint(self.ckpt_wrapper(spatial_block), x, c, attention_mask_spatial)
else:
x = spatial_block(x, c, attention_mask_spatial)
x = rearrange(x, '(b f) t d -> (b t) f d', b=batches)
# Add Time Embedding
if i == 0:
x = x + self.temp_embed
if self.extras == 2:
c = timestep_temp + y_temp
elif self.extras == 78:
c = timestep_temp + text_embedding_temp
else:
c = timestep_temp
if self.gradient_checkpointing and self.training:
x = torch.utils.checkpoint.checkpoint(self.ckpt_wrapper(temp_block), x, c, attention_mask_temproal)
else:
x = temp_block(x, c, attention_mask_temproal)
x = rearrange(x, '(b t) f d -> (b f) t d', b=batches)
if self.extras == 2:
c = timestep_spatial + y_spatial
else:
c = timestep_spatial
x = self.final_layer(x, c)
x = self.unpatchify(x)
x = rearrange(x, '(b f) c h w -> b f c h w', b=batches)
return x
def forward_with_cfg(self, x, t, y=None, cfg_scale=7.0, text_embedding=None, attention_mask=None):
"""
Forward pass of Latte, but also batches the unconditional forward pass for classifier-free guidance.
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
half = x[: len(x) // 2]
combined = torch.cat([half, half], dim=0)
model_out = self.forward(combined, t, y=y, text_embedding=text_embedding, attention_mask=attention_mask)
# For exact reproducibility reasons, we apply classifier-free guidance on only
# three channels by default. The standard approach to cfg applies it to all channels.
# This can be done by uncommenting the following line and commenting-out the line following that.
eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
# eps, rest = model_out[:, :3], model_out[:, 3:]
# eps, rest = model_out[:, :, :4, ...], model_out[:, :, 4:, ...]
cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
eps = torch.cat([half_eps, half_eps], dim=0)
return torch.cat([eps, rest], dim=2)
#################################################################################
# Sine/Cosine Positional Embedding Functions #
#################################################################################
# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
def get_1d_sincos_temp_embed(embed_dim, length):
pos = torch.arange(0, length).unsqueeze(1)
return get_1d_sincos_pos_embed_from_grid(embed_dim, pos)
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])
emb = np.concatenate([emb_h, emb_w], axis=1)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.
omega = 1. / 10000**omega
pos = pos.reshape(-1)
out = np.einsum('m,d->md', pos, omega)
emb_sin = np.sin(out)
emb_cos = np.cos(out)
emb = np.concatenate([emb_sin, emb_cos], axis=1)
return emb
#################################################################################
# Latte Configs #
#################################################################################
from .configuration_latte import (
Latte_XL_122_Config, Latte_XL_144_Config, Latte_XL_188_Config,
Latte_L_122_Config, Latte_L_144_Config, Latte_L_188_Config,
Latte_B_122_Config, Latte_B_144_Config, Latte_B_188_Config,
Latte_S_122_Config, Latte_S_144_Config, Latte_S_188_Config,
)
def Latte_XL_122(**kwargs):
return Latte(Latte_XL_122_Config(**kwargs))
def Latte_XL_144(**kwargs):
return Latte(Latte_XL_144_Config(**kwargs))
def Latte_XL_188(**kwargs):
return Latte(Latte_XL_188_Config(**kwargs))
def Latte_L_122(**kwargs):
return Latte(Latte_L_122_Config(**kwargs))
def Latte_L_144(**kwargs):
return Latte(Latte_L_144_Config(**kwargs))
def Latte_L_188(**kwargs):
return Latte(Latte_L_188_Config(**kwargs))
def Latte_B_122(**kwargs):
return Latte(Latte_B_122_Config(**kwargs))
def Latte_B_144(**kwargs):
return Latte(Latte_B_144_Config(**kwargs))
def Latte_B_188(**kwargs):
return Latte(Latte_B_188_Config(**kwargs))
def Latte_S_122(**kwargs):
return Latte(Latte_S_122_Config(**kwargs))
def Latte_S_144(**kwargs):
return Latte(Latte_S_144_Config(**kwargs))
def Latte_S_188(**kwargs):
return Latte(Latte_S_188_Config(**kwargs))
Latte_models = {
"Latte-XL/122": Latte_XL_122, "Latte-XL/144": Latte_XL_144, "Latte-XL/188": Latte_XL_188,
"Latte-L/122": Latte_L_122, "Latte-L/144": Latte_L_144, "Latte-L/188": Latte_L_188,
"Latte-B/122": Latte_B_122, "Latte-B/144": Latte_B_144, "Latte-B/188": Latte_B_188,
"Latte-S/122": Latte_S_122, "Latte-S/144": Latte_S_144, "Latte-S/188": Latte_S_188,
}
Latte_models = {
'Latte-XL/122': Latte_XL_122, 'Latte-XL/144': Latte_XL_144, 'Latte-XL/188': Latte_XL_188,
'Latte-L/122': Latte_L_122, 'Latte-L/144': Latte_L_144, 'Latte-L/188': Latte_L_188,
'Latte-B/122': Latte_B_122, 'Latte-B/144': Latte_B_144, 'Latte-B/188': Latte_B_188,
'Latte-S/122': Latte_S_122, 'Latte-S/144': Latte_S_144, 'Latte-S/188': Latte_S_188,
}
if __name__ == '__main__':
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
img = torch.randn(3, 16, 4, 32, 32).to(device)
t = torch.tensor([1, 2, 3]).to(device)
y = torch.tensor([1, 2, 3]).to(device)
network = Latte_XL_122().to(device)
from thop import profile
flops, params = profile(network, inputs=(img, t))
print('FLOPs = ' + str(flops/1000**3) + 'G')
print('Params = ' + str(params/1000**2) + 'M')
# y_embeder = LabelEmbedder(num_classes=101, hidden_size=768, dropout_prob=0.5).to(device)
# lora.mark_only_lora_as_trainable(network)
# out = y_embeder(y, True)
# out = network(img, t, y)
# print(out.shape)