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added_tokens.json

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+{
+  "[MASK]": 50257
+}

config.json

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+{
+  "architectures": [
+    "DIT"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_dit.DITConfig",
+    "AutoModelForMaskedLM": "modeling_dit.DIT"
+  },
+  "hidden_size": 1024,
+  "max_seq_len": 512,
+  "model_type": "dit",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "timestep_cond_dim": 128,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "vocab_size": 50258
+}

configuration_dit.py

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+from transformers import PretrainedConfig
+
+
+class DITConfig(PretrainedConfig):
+    model_type = "dit"
+
+    def __init__(
+        self,
+        vocab_size: int = 50258,
+        max_seq_len: int = 1024,
+        hidden_size: int = 768,
+        timestep_cond_dim: int = 128,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 12,
+        attention_dropout: float = 0.0,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.max_seq_len = max_seq_len
+        self.hidden_size = hidden_size
+        self.timestep_cond_dim = timestep_cond_dim
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.attention_dropout = attention_dropout

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:50ba67203027b21c39ebb9aeb5e32f92bfa77d1c1b81a5b0e951b3eb1abe8a8e
+size 1698719104

modeling_dit.py

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+# from: https://github.com/kuleshov-group/mdlm/blob/master/models/dit.py
+
+import math
+import typing
+
+import omegaconf
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PreTrainedModel
+from einops import rearrange
+
+from .configuration_dit import DITConfig
+
+try:
+  import flash_attn
+  import flash_attn.layers.rotary
+  has_flash_attn = True
+except ImportError:
+  torch.backends.cuda.enable_flash_sdp(enabled=True)
+  has_flash_attn = False
+
+# Flags required to enable jit fusion kernels
+torch._C._jit_set_profiling_mode(False)
+torch._C._jit_set_profiling_executor(False)
+torch._C._jit_override_can_fuse_on_cpu(True)
+torch._C._jit_override_can_fuse_on_gpu(True)
+
+
+def bias_dropout_add_scale(
+    x: torch.Tensor,
+    bias: typing.Optional[torch.Tensor],
+    scale: torch.Tensor,
+    residual: typing.Optional[torch.Tensor],
+    prob: float,
+    training: bool) -> torch.Tensor:
+  if bias is not None:
+    out = scale * F.dropout(x + bias, p=prob, training=training)
+  else:
+    out = scale * F.dropout(x, p=prob, training=training)
+
+  if residual is not None:
+    out = residual + out
+  return out
+
+
+def get_bias_dropout_add_scale(training):
+  def _bias_dropout_add(x, bias, scale, residual, prob):
+    return bias_dropout_add_scale(
+      x, bias, scale, residual, prob, training)
+
+  return _bias_dropout_add
+
+
+# function overload
+def modulate(x: torch.Tensor,
+             shift: torch.Tensor,
+             scale: torch.Tensor) -> torch.Tensor:
+  return x * (1 + scale) + shift
+
+
+# @torch.jit.script
+def bias_dropout_add_scale_fused_train(
+    x: torch.Tensor,
+    bias: typing.Optional[torch.Tensor],
+    scale: torch.Tensor,
+    residual: typing.Optional[torch.Tensor],
+    prob: float) -> torch.Tensor:
+  return bias_dropout_add_scale(
+    x, bias, scale, residual, prob, True)
+
+
+# @torch.jit.script
+def bias_dropout_add_scale_fused_inference(
+    x: torch.Tensor,
+    bias: typing.Optional[torch.Tensor],
+    scale: torch.Tensor,
+    residual: typing.Optional[torch.Tensor],
+    prob: float) -> torch.Tensor:
+  return bias_dropout_add_scale(
+    x, bias, scale, residual, prob, False)
+
+
+# @torch.jit.script
+def modulate_fused(x: torch.Tensor,
+                   shift: torch.Tensor,
+                   scale: torch.Tensor) -> torch.Tensor:
+  return modulate(x, shift, scale)
+
+
+class Rotary(torch.nn.Module):
+  def __init__(self, dim, base=10_000, max_seq_len=512):
+    super().__init__()
+    self.dim = dim
+    self.base = base
+    self.max_seq_len = max_seq_len
+    self.precompute()
+
+  def precompute(self):
+    inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float() / self.dim))
+    t = torch.arange(self.max_seq_len).type_as(inv_freq)
+    freqs = torch.einsum("i,j->ij", t, inv_freq.clone())
+    emb = torch.cat((freqs, freqs), dim=-1)
+    # dims are: batch, seq_len, qkv, head, dim
+    cos_cached = emb.cos()[None, :, None, None, :].repeat(1,1,3,1,1)
+    sin_cached = emb.sin()[None, :, None, None, :].repeat(1,1,3,1,1)
+    # This makes the transformation on v an identity.
+    cos_cached[:,:,2,:,:].fill_(1.)
+    sin_cached[:,:,2,:,:].fill_(0.)
+
+    self.register_buffer('cos_cached', cos_cached)
+    self.register_buffer('sin_cached', sin_cached)
+
+  def forward(self, x, seq_dim=1):
+    seq_len = x.shape[seq_dim]
+    return self.cos_cached[:, :, :seq_len], self.sin_cached[:, :, :seq_len]
+
+
+def rotate_half(x):
+  x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
+  return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(qkv, cos, sin):
+  if has_flash_attn:
+    cos = cos[0,:,0,0,:cos.shape[-1]//2]
+    sin = sin[0,:,0,0,:sin.shape[-1]//2]
+    return flash_attn.layers.rotary.apply_rotary_emb_qkv_(qkv, cos, sin)
+  else:
+    return (qkv * cos) + (rotate_half(qkv) * sin)
+
+
+# function overload
+def modulate(x, shift, scale):
+  return x * (1 + scale) + shift
+
+
+#################################################################################
+#                                  Layers                                       #
+#################################################################################
+class LayerNorm(nn.Module):
+  def __init__(self, dim):
+    super().__init__()
+    self.weight = nn.Parameter(torch.ones([dim]))
+    self.dim = dim
+  def forward(self, x):
+    x = F.layer_norm(x.float(), [self.dim])
+    return x * self.weight[None,None,:]
+
+
+def residual_linear(x, W, x_skip, residual_scale):
+  """x_skip + residual_scale * W @ x"""
+  dim_out, dim_in = W.shape[0], W.shape[1]
+  return torch.addmm(
+    x_skip.view(-1, dim_out),
+    x.view(-1, dim_in),
+    W.T,
+    alpha=residual_scale).view(*x.shape[:-1], dim_out)
+
+
+#################################################################################
+#               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, cond_size):
+    super().__init__()
+    self.embedding_table = nn.Embedding(num_classes + 1, cond_size)
+    self.num_classes = num_classes
+
+    # TODO think of initializing with 0.02 std deviation like in original DiT paper
+
+  def forward(self, labels):
+    embeddings = self.embedding_table(labels)
+    return embeddings
+    
+
+#################################################################################
+#                                 Core Model                                    #
+#################################################################################
+
+
+class DDiTBlock(nn.Module):
+  def __init__(self, dim, n_heads, cond_dim, mlp_ratio=4, dropout=0.1):
+    super().__init__()
+    self.n_heads = n_heads
+    self.dim = dim
+    self.cond_dim = cond_dim
+    self.mlp_ratio = mlp_ratio
+
+    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):
+    if self.training:
+      return bias_dropout_add_scale_fused_train
+    else:
+      return 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)
+
+    qkv = self.attn_qkv(x)
+    qkv = rearrange(qkv,
+                    'b s (three h d) -> b s three h d',
+                    three=3,
+                    h=self.n_heads)
+    cos, sin = rotary_cos_sin
+    qkv = apply_rotary_pos_emb(
+      qkv, cos.to(qkv.dtype), sin.to(qkv.dtype))
+    
+    if has_flash_attn:
+      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.flash_attn_interface.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)
+    else:
+      q, k, v = qkv[:, :, 0].transpose(1, 2), qkv[:, :, 1].transpose(1, 2), qkv[:, :, 2].transpose(1, 2)
+      x = F.scaled_dot_product_attention(q, k, v)
+      
+      x = rearrange(x, 'b h s 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):
+    super().__init__()
+    self.embedding = nn.Parameter(torch.empty((vocab_dim, dim)))
+    torch.nn.init.kaiming_uniform_(self.embedding, a=math.sqrt(5))
+
+  def forward(self, x):
+    return self.embedding[x]
+
+
+class DDitFinalLayer(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(PreTrainedModel):
+  config_class = DITConfig
+  base_model_prefix = "dit"
+
+  def __init__(self, config: DITConfig):
+    super().__init__(config)
+
+    self.config = config
+    self.vocab_size = config.vocab_size
+
+    self.vocab_embed = EmbeddingLayer(config.hidden_size, config.vocab_size)
+    self.sigma_map = TimestepEmbedder(config.timestep_cond_dim)
+    self.rotary_emb = Rotary(
+      config.hidden_size // config.num_attention_heads,
+      max_seq_len=config.max_seq_len,
+    )
+
+    blocks = []
+    for _ in range(config.num_hidden_layers):
+      blocks.append(DDiTBlock(config.hidden_size,
+                              config.num_attention_heads,
+                              config.timestep_cond_dim,
+                              dropout=config.attention_dropout))
+    self.blocks = nn.ModuleList(blocks)
+
+    self.output_layer = DDitFinalLayer(
+      config.hidden_size,
+      config.vocab_size,
+      config.timestep_cond_dim)
+
+  def _get_bias_dropout_scale(self):
+    if self.training:
+      return bias_dropout_add_scale_fused_train
+    else:
+      return bias_dropout_add_scale_fused_inference
+
+  def forward(self, input_ids, timesteps):
+    x = self.vocab_embed(input_ids)
+    c = F.silu(self.sigma_map(timesteps))
+
+    rotary_cos_sin = self.rotary_emb(x)
+
+    for i in range(len(self.blocks)):
+      x = self.blocks[i](x, rotary_cos_sin, c, seqlens=None)
+    x = self.output_layer(x, c)
+
+    return x

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50257": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}