wehub-resource-sync
480 lines
18 KiB
Python
Executable File
480 lines
18 KiB
Python
Executable File
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
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import os
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import numpy as np
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import torch
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import torch.nn as nn
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from termcolor import colored
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from timm.models.layers import DropPath
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from diffusion.model.builder import MODELS
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from diffusion.model.nets.basic_modules import DWMlp, GLUMBConv, MBConvPreGLU, Mlp
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from diffusion.model.nets.sana_blocks import (
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Attention,
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CaptionEmbedder,
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FlashAttention,
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LiteLA,
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MultiHeadCrossAttention,
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MultiHeadCrossVallinaAttention,
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PatchEmbed,
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RopePosEmbed,
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T2IFinalLayer,
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TimestepEmbedder,
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t2i_modulate,
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)
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from diffusion.model.norms import RMSNorm
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from diffusion.model.utils import auto_grad_checkpoint, to_2tuple
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from diffusion.utils.dist_utils import get_rank
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from diffusion.utils.import_utils import is_triton_module_available
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from diffusion.utils.logger import get_root_logger
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_triton_modules_available = False
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if is_triton_module_available():
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from diffusion.model.nets.fastlinear.modules import TritonLiteMLA, TritonMBConvPreGLU
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_triton_modules_available = True
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class SanaBlock(nn.Module):
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"""
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A Sana block with global shared adaptive layer norm (adaLN-single) conditioning.
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"""
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def __init__(
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self,
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hidden_size,
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num_heads,
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mlp_ratio=4.0,
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drop_path=0,
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qk_norm=False,
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cross_norm=False,
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attn_type="flash",
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ffn_type="mlp",
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mlp_acts=("silu", "silu", None),
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linear_head_dim=32,
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cross_attn_type="flash",
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**block_kwargs,
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):
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super().__init__()
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self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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if attn_type == "flash":
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# flash self attention
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self.attn = FlashAttention(
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hidden_size,
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num_heads=num_heads,
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qkv_bias=True,
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qk_norm=qk_norm,
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**block_kwargs,
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)
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elif attn_type == "linear":
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# linear self attention
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# TODO: Here the num_heads set to 36 for tmp used
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self_num_heads = hidden_size // linear_head_dim
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self.attn = LiteLA(hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm)
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elif attn_type == "triton_linear":
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if not _triton_modules_available:
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raise ValueError(
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f"{attn_type} type is not available due to _triton_modules_available={_triton_modules_available}."
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)
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# linear self attention with triton kernel fusion
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# TODO: Here the num_heads set to 36 for tmp used
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self_num_heads = hidden_size // linear_head_dim
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self.attn = TritonLiteMLA(hidden_size, num_heads=self_num_heads, eps=1e-8)
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elif attn_type == "vanilla":
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# vanilla self attention
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self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True)
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else:
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self.attn = None
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if cross_attn_type in ["flash", "linear"]:
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self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, qk_norm=cross_norm, **block_kwargs)
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elif cross_attn_type == "vanilla":
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self.cross_attn = MultiHeadCrossVallinaAttention(hidden_size, num_heads, qk_norm=cross_norm, **block_kwargs)
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else:
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raise ValueError(f"{cross_attn_type} type is not defined.")
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self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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# to be compatible with lower version pytorch
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if ffn_type == "dwmlp":
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approx_gelu = lambda: nn.GELU(approximate="tanh")
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self.mlp = DWMlp(
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in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
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)
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elif ffn_type == "glumbconv":
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self.mlp = GLUMBConv(
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in_features=hidden_size,
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hidden_features=int(hidden_size * mlp_ratio),
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use_bias=(True, True, False),
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norm=(None, None, None),
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act=mlp_acts,
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)
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elif ffn_type == "glumbconv_dilate":
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self.mlp = GLUMBConv(
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in_features=hidden_size,
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hidden_features=int(hidden_size * mlp_ratio),
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use_bias=(True, True, False),
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norm=(None, None, None),
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act=mlp_acts,
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dilation=2,
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)
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elif ffn_type == "mlp":
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approx_gelu = lambda: nn.GELU(approximate="tanh")
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self.mlp = Mlp(
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in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
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)
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else:
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self.mlp = None
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self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
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self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5)
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def forward(self, x, y, t, mask=None, **kwargs):
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B, N, C = x.shape
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
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self.scale_shift_table[None] + t.reshape(B, 6, -1)
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).chunk(6, dim=1)
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x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)).reshape(B, N, C))
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x = x + self.cross_attn(x, y, mask)
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x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
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return x
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#############################################################################
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# Core Sana Model #
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#################################################################################
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@MODELS.register_module()
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class Sana(nn.Module):
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"""
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Diffusion model with a Transformer backbone.
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"""
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def __init__(
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self,
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input_size=32,
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patch_size=2,
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in_channels=4,
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hidden_size=1152,
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depth=28,
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num_heads=16,
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mlp_ratio=4.0,
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class_dropout_prob=0.1,
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pred_sigma=True,
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drop_path: float = 0.0,
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caption_channels=2304,
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pe_interpolation=1.0,
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config=None,
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model_max_length=120,
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qk_norm=False,
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y_norm=False,
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norm_eps=1e-5,
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attn_type="flash",
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cross_attn_type="flash",
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ffn_type="mlp",
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use_pe=True,
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y_norm_scale_factor=1.0,
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patch_embed_kernel=None,
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mlp_acts=("silu", "silu", None),
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linear_head_dim=32,
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cross_norm=False,
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pos_embed_type="sincos",
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cfg_embed=False,
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timestep_norm_scale_factor=1.0,
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null_embed_path=None,
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**kwargs,
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):
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super().__init__()
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self.pred_sigma = pred_sigma
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self.in_channels = in_channels
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self.out_channels = in_channels * 2 if pred_sigma else in_channels
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self.hidden_size = hidden_size
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self.patch_size = patch_size[0] if isinstance(patch_size, tuple) else patch_size
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self.num_heads = num_heads
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self.linear_head_dim = linear_head_dim
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self.pe_interpolation = pe_interpolation
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self.depth = depth
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self.use_pe = use_pe
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self.pos_embed_type = pos_embed_type
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self.y_norm = y_norm
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self.config = config
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self.fp32_attention = kwargs.get("use_fp32_attention", False)
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self.null_embed_path = null_embed_path
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self.timestep_norm_scale_factor = timestep_norm_scale_factor
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kernel_size = patch_embed_kernel or patch_size
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self.x_embedder = PatchEmbed(
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input_size, patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True
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)
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self.t_embedder = TimestepEmbedder(hidden_size)
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self.cfg_embedder = None
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if cfg_embed:
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self.cfg_embedder = TimestepEmbedder(hidden_size)
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num_patches = self.x_embedder.num_patches
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self.base_size = input_size // self.patch_size
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# Will use fixed sin-cos embedding:
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self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
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approx_gelu = lambda: nn.GELU(approximate="tanh")
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self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
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self.y_embedder = CaptionEmbedder(
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in_channels=caption_channels,
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hidden_size=hidden_size,
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uncond_prob=class_dropout_prob,
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act_layer=approx_gelu,
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token_num=model_max_length,
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)
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if self.y_norm:
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self.attention_y_norm = RMSNorm(hidden_size, scale_factor=y_norm_scale_factor, eps=norm_eps)
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drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule
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if attn_type == "flash":
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attention_head_dim = hidden_size // num_heads
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else:
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attention_head_dim = linear_head_dim
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self.blocks = nn.ModuleList(
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[
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SanaBlock(
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hidden_size,
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num_heads,
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mlp_ratio=mlp_ratio,
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drop_path=drop_path[i],
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qk_norm=qk_norm,
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cross_norm=cross_norm,
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attn_type=attn_type,
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ffn_type=ffn_type,
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mlp_acts=mlp_acts,
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linear_head_dim=linear_head_dim,
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cross_attn_type=cross_attn_type,
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)
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for i in range(depth)
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]
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)
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self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
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if config and config.work_dir:
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self.logger = get_root_logger(os.path.join(config.work_dir, "train_log.log"))
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self.logger = self.logger.info
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else:
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self.logger = print
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# Fixed image size pos embed
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if self.use_pe and self.pos_embed_type in ["sincos", "flux_rope"]:
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if self.pos_embed_type == "sincos":
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# Initialize (and freeze) pos_embed by sin-cos embedding:
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pos_embed = get_2d_sincos_pos_embed(
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self.pos_embed.shape[-1],
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int(self.x_embedder.num_patches**0.5),
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pe_interpolation=self.pe_interpolation,
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base_size=self.base_size,
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)
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self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
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elif self.pos_embed_type == "flux_rope":
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# Initialize (and freeze) pos_embed by 3D-Rope embedding:
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self.pos_embed = RopePosEmbed(theta=10000, axes_dim=[0, 16, 16])
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self.initialize_weights()
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if get_rank() == 0:
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self.logger(
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f"use pe: {use_pe}, pos embed type: {pos_embed_type}, "
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f"position embed interpolation: {self.pe_interpolation}, base size: {self.base_size}"
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)
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self.logger(
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f"attention type: {attn_type}; ffn type: {ffn_type}; self-attn head dim: {attention_head_dim}; self-attn qk norm: {qk_norm}; "
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f"cross-attn type: {cross_attn_type}; cross-attn qk norm: {cross_norm}; "
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f"autocast linear attn: {os.environ.get('AUTOCAST_LINEAR_ATTN', False)}"
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)
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def forward(self, x, timestep, y, mask=None, data_info=None, **kwargs):
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"""
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Forward pass of Sana.
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x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
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t: (N,) tensor of diffusion timesteps
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y: (N, 1, 120, C) tensor of class labels
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"""
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x = x.to(self.dtype)
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timestep = timestep.to(self.dtype)
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y = y.to(self.dtype)
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pos_embed = self.pos_embed.to(self.dtype)
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self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size
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x = self.x_embedder(x)
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image_pos_embed = None
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if self.use_pe:
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if self.pos_embed_type == "sincos":
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x = x + pos_embed # (N, T, D), where T = H * W / patch_size ** 2
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elif self.pos_embed_type == "flux_rope":
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image_pos_embed = pos_embed
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x += image_pos_embed
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t = self.t_embedder(timestep.to(x.dtype)) # (N, D)
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t0 = self.t_block(t)
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y = self.y_embedder(y, self.training) # (N, 1, L, D)
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if self.y_norm:
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y = self.attention_y_norm(y)
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if mask is not None:
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if mask.shape[0] != y.shape[0]:
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mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
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mask = mask.squeeze(1).squeeze(1)
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y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
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y_lens = mask.sum(dim=1).tolist()
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else:
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y_lens = [y.shape[2]] * y.shape[0]
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y = y.squeeze(1).view(1, -1, x.shape[-1])
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for block in self.blocks:
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x = auto_grad_checkpoint(block, x, y, t0, y_lens, image_pos_embed) # (N, T, D) #support grad checkpoint
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x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
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x = self.unpatchify(x) # (N, out_channels, H, W)
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return x
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def __call__(self, *args, **kwargs):
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"""
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This method allows the object to be called like a function.
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It simply calls the forward method.
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"""
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return self.forward(*args, **kwargs)
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def forward_with_dpmsolver(self, x, timestep, y, mask=None, **kwargs):
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"""
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dpm solver donnot need variance prediction
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"""
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# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
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model_out = self.forward(x, timestep, y, mask)
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return model_out.chunk(2, dim=1)[0] if self.pred_sigma else model_out
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def unpatchify(self, x):
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"""
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x: (N, T, patch_size**2 * C)
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imgs: (N, H, W, C)
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"""
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c = self.out_channels
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p = self.x_embedder.patch_size[0]
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h = w = int(x.shape[1] ** 0.5)
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assert h * w == x.shape[1]
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x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
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x = torch.einsum("nhwpqc->nchpwq", x)
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imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
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return imgs
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def initialize_weights(self):
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# Initialize transformer layers:
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def _basic_init(module):
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if isinstance(module, nn.Linear):
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torch.nn.init.xavier_uniform_(module.weight)
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if module.bias is not None:
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nn.init.constant_(module.bias, 0)
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self.apply(_basic_init)
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# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
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w = self.x_embedder.proj.weight.data
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nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
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# Initialize timestep embedding MLP:
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nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
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nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
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nn.init.normal_(self.t_block[1].weight, std=0.02)
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# Initialize caption embedding MLP:
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nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02)
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nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02)
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# load null embed
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try:
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null_embed = torch.load(self.null_embed_path, map_location="cpu")
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self.y_embedder.y_embedding.data = null_embed["uncond_prompt_embeds"][0]
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if get_rank() == 0:
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self.logger(colored(f"Load null embed from {self.null_embed_path}....", "green"))
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except Exception as e:
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if get_rank() == 0:
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self.logger(
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colored(
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f"Failed to load null embed from {self.null_embed_path}....{e}. Ignore the error during inference",
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"red",
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)
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)
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@property
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def dtype(self):
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return next(self.parameters()).dtype
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def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, pe_interpolation=1.0, base_size=16):
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"""
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grid_size: int of the grid height and width
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return:
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pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
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"""
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if isinstance(grid_size, int):
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grid_size = to_2tuple(grid_size)
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grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / pe_interpolation
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grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / pe_interpolation
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grid = np.meshgrid(grid_w, grid_h) # here w goes first
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grid = np.stack(grid, axis=0)
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grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
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pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
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if cls_token and extra_tokens > 0:
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pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
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return pos_embed
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|
|
|
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def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
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assert embed_dim % 2 == 0
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|
|
|
# use half of dimensions to encode grid_h
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|
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
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emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
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|
|
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emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
|
|
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 / 2.0
|
|
omega = 1.0 / 10000**omega # (D/2,)
|
|
|
|
pos = pos.reshape(-1) # (M,)
|
|
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
|
|
|
|
emb_sin = np.sin(out) # (M, D/2)
|
|
emb_cos = np.cos(out) # (M, D/2)
|
|
|
|
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
|
return emb
|
|
|
|
|
|
#################################################################################
|
|
# Sana Configs #
|
|
#################################################################################
|
|
@MODELS.register_module()
|
|
def Sana_600M_P1_D28(**kwargs):
|
|
return Sana(depth=28, hidden_size=1152, patch_size=1, num_heads=16, **kwargs)
|
|
|
|
|
|
@MODELS.register_module()
|
|
def Sana_1600M_P1_D20(**kwargs):
|
|
# 20 layers, 1648.48M
|
|
return Sana(depth=20, hidden_size=2240, patch_size=1, num_heads=20, **kwargs)
|