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bfdae48 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | # Adapted from https://github.com/louaaron/Score-Entropy-Discrete-Diffusion/blob/main/model/transformer.py
# Not used in this repo but implemented for future reference using flash attention
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from einops import rearrange
from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
from huggingface_hub import PyTorchModelHubMixin
from . import rotary
from .fused_add_dropout_scale import (
bias_dropout_add_scale_fused_train,
bias_dropout_add_scale_fused_inference,
modulate_fused,
)
from .dit import LayerNorm, TimestepEmbedder
#################################################################################
# Core Model #
#################################################################################
class DiTBlock(nn.Module):
def __init__(self, dim, n_heads, cond_dim, mlp_ratio=4, dropout=0.1):
super().__init__()
self.n_heads = n_heads
self.norm1 = LayerNorm(dim)
self.attn_qkv = nn.Linear(dim, 3 * dim, bias=False)
self.attn_out = nn.Linear(dim, dim, bias=False)
self.dropout1 = nn.Dropout(dropout)
self.norm2 = LayerNorm(dim)
self.mlp = nn.Sequential(
nn.Linear(dim, mlp_ratio * dim, bias=True),
nn.GELU(approximate="tanh"),
nn.Linear(mlp_ratio * dim, dim, bias=True)
)
self.dropout2 = nn.Dropout(dropout)
self.dropout = dropout
self.adaLN_modulation = nn.Linear(cond_dim, 6 * dim, bias=True)
self.adaLN_modulation.weight.data.zero_()
self.adaLN_modulation.bias.data.zero_()
def _get_bias_dropout_scale(self):
return (
bias_dropout_add_scale_fused_train
if self.training
else bias_dropout_add_scale_fused_inference
)
def forward(self, x, rotary_cos_sin, c, seqlens=None):
batch_size, seq_len = x.shape[0], x.shape[1]
bias_dropout_scale_fn = self._get_bias_dropout_scale()
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c)[:, None].chunk(6, dim=2)
# attention operation
x_skip = x
x = modulate_fused(self.norm1(x), shift_msa, scale_msa)
# dtype0 = x.dtype
qkv = self.attn_qkv(x)
qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.n_heads)
with torch.cuda.amp.autocast(enabled=False):
cos, sin = rotary_cos_sin
qkv = rotary.apply_rotary_pos_emb_flash(
qkv, cos.to(qkv.dtype), sin.to(qkv.dtype)
)
qkv = rearrange(qkv, 'b s ... -> (b s) ...')
if seqlens is None:
cu_seqlens = torch.arange(
0, (batch_size + 1) * seq_len, step=seq_len,
dtype=torch.int32, device=qkv.device
)
else:
cu_seqlens = seqlens.cumsum(-1)
x = flash_attn_varlen_qkvpacked_func(
qkv, cu_seqlens, seq_len, 0., causal=False)
x = rearrange(x, '(b s) h d -> b s (h d)', b=batch_size)
x = bias_dropout_scale_fn(self.attn_out(x), None, gate_msa, x_skip, self.dropout)
# mlp operation
x = bias_dropout_scale_fn(self.mlp(modulate_fused(self.norm2(x), shift_mlp, scale_mlp)), None, gate_mlp, x, self.dropout)
return x
class EmbeddingLayer(nn.Module):
def __init__(self, dim, vocab_dim):
"""
Mode arg: 0 -> use a learned layer, 1 -> use eigenvectors,
2-> add in eigenvectors, 3 -> use pretrained embedding matrix
"""
super().__init__()
# self.embedding = nn.Parameter(torch.empty((vocab_dim, dim)))
self.embedding = nn.Linear(vocab_dim, dim)
torch.nn.init.kaiming_uniform_(self.embedding.weight, a=math.sqrt(5))
def forward(self, x):
# return self.embedding[x]
return self.embedding(x)
class DiTFinalLayer(nn.Module):
def __init__(self, hidden_size, out_channels, cond_dim):
super().__init__()
self.norm_final = LayerNorm(hidden_size)
self.linear = nn.Linear(hidden_size, out_channels)
self.linear.weight.data.zero_()
self.linear.bias.data.zero_()
self.adaLN_modulation = nn.Linear(cond_dim, 2 * hidden_size, bias=True)
self.adaLN_modulation.weight.data.zero_()
self.adaLN_modulation.bias.data.zero_()
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c)[:, None].chunk(2, dim=2)
x = modulate_fused(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class DiT(nn.Module, PyTorchModelHubMixin):
def __init__(
self,
input_dim: int,
output_dim: int,
hidden_size: int,
n_heads: int,
cond_dim: int,
dropout: float,
n_blocks: int,
**kwargs
):
super().__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.vocab_embed = EmbeddingLayer(hidden_size, input_dim)
self.sigma_map = TimestepEmbedder(cond_dim)
self.rotary_emb = rotary.Rotary(hidden_size // n_heads)
self.blocks = nn.ModuleList([
DiTBlock(hidden_size, n_heads, cond_dim, dropout=dropout)
for _ in range(n_blocks)
])
self.output_layer = DiTFinalLayer(hidden_size, output_dim, cond_dim)
self.kwargs = kwargs
def _get_bias_dropout_scale(self):
return (
bias_dropout_add_scale_fused_train
if self.training
else bias_dropout_add_scale_fused_inference
)
def forward(self, input_vector, t):
x = self.vocab_embed(input_vector)
c = F.silu(self.sigma_map(t))
rotary_cos_sin = self.rotary_emb(x)
with torch.cuda.amp.autocast(dtype=torch.bfloat16):
for i in range(len(self.blocks)):
x = self.blocks[i](x, rotary_cos_sin, c, seqlens=None)
x = self.output_layer(x, c) # BxCxD
return x |