Upload BD3LM

config.json

@@ -1,12 +1,12 @@
 {
-  "_name_or_path": "kuleshov-group/bamdlm-owt-block_size8",
+  "_name_or_path": "kuleshov-group/bd3lm-owt-block_size8",
   "architectures": [
-    "BAMDLM"
+    "BD3LM"
   ],
   "attn_backend": "sdpa",
   "auto_map": {
-    "AutoConfig": "configuration_bamdlm.BAMDLMConfig",
-    "AutoModelForMaskedLM": "modeling_bamdlm.BAMDLM"
+    "AutoConfig": "configuration_bd3lm.BD3LMConfig",
+    "AutoModelForMaskedLM": "modeling_bd3lm.BD3LM"
   },
   "block_size": 8,
   "cond_dim": 128,
@@ -14,7 +14,7 @@
   "dropout": 0.1,
   "hidden_dim": 768,
   "model_length": 1024,
-  "model_type": "bamdlm",
+  "model_type": "bd3lm",
   "n_blocks": 12,
   "n_heads": 12,
   "return_dict": false,

configuration_bd3lm.py

@@ -0,0 +1,43 @@
+"""BD3LM config for Hugging Face.
+
+"""
+
+import transformers
+
+
+class BD3LMConfig(transformers.PretrainedConfig):
+  """Hugging Face configuration class for BD3LM."""
+  model_type = "bd3lm"
+
+  def __init__(
+    self,
+    block_size: int = 1,
+    vocab_size: int = 50258,
+    model_length: int = 1024,
+    cross_attn: bool = True,
+    attn_backend: str = 'sdpa',
+    hidden_dim: int = 768,
+    cond_dim: int = 129,
+    n_blocks: int = 12,
+    n_heads: int = 12,
+    dropout: float = 0.1,
+    time_conditioning: bool = False,
+    var_min: bool = True,
+    sampling_eps_min: float = 1e-3,
+    sampling_eps_max: float = 0.999,
+    ** kwargs):
+    super().__init__(**kwargs)
+    self.block_size = block_size
+    self.cross_attn = cross_attn
+    self.attn_backend = attn_backend
+    self.vocab_size = vocab_size
+    self.model_length = model_length
+    self.hidden_dim = hidden_dim
+    self.cond_dim = cond_dim
+    self.n_blocks = n_blocks
+    self.n_heads = n_heads
+    self.dropout = dropout
+    self.time_conditioning = time_conditioning
+    self.var_min = var_min
+    self.sampling_eps_min = sampling_eps_min
+    self.sampling_eps_max = sampling_eps_max

modeling_bd3lm.py

@@ -0,0 +1,560 @@
+"""BD3LM model for Hugging Face.
+
+"""
+import math
+import typing
+
+import einops
+import flash_attn
+import flash_attn.layers.rotary
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import transformers
+from transformers import modeling_outputs
+
+from .configuration_bd3lm import BD3LMConfig
+
+# 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 block_causal_mask(num_rows, block_size, mode='full', offset=0):
+  mask = block_size * torch.arange(
+    1, num_rows // block_size + 1).unsqueeze(1).tile(block_size).flatten().unsqueeze(1)
+  if mode == 'full':
+    mask = (mask >= mask.T + offset)
+  elif mode == 'diag':
+    mask = (mask + offset == mask.T)
+  elif mode == 'triu_diag':
+    mask = torch.zeros(num_rows, num_rows)
+    rows = torch.arange(0, num_rows)
+    group_indices = rows // (block_size)
+    column_indices = group_indices * (block_size) + block_size + offset
+    valid_rows = column_indices < num_rows
+    mask[rows[valid_rows].unsqueeze(1), column_indices[valid_rows].unsqueeze(1)] = 1
+  return mask.int()
+
+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):
+    super().__init__()
+    inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+    self.register_buffer('inv_freq', inv_freq)
+    self.seq_len_cached = None
+    self.cos_cached = None
+    self.sin_cached = None
+
+  def forward(self, x, seq_dim=1):
+    seq_len = x.shape[seq_dim]
+    if seq_len != self.seq_len_cached:
+      self.seq_len_cached = seq_len
+      t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq)
+      freqs = torch.einsum("i,j->ij", t, self.inv_freq.clone())
+      emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+      # dims are: batch, seq_len, qkv, head, dim
+      self.cos_cached = emb.cos()[None, :, None, None, :].repeat(1,1,3,1,1)
+      self.sin_cached = emb.sin()[None, :, None, None, :].repeat(1,1,3,1,1)
+      # This makes the transformation on v an identity.
+      self.cos_cached[:,:,2,:,:].fill_(1.)
+      self.sin_cached[:,:,2,:,:].fill_(0.)
+
+    return self.cos_cached, self.sin_cached
+
+
+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_torchscript(qkv, cos, sin):
+    return (qkv * cos) + (rotate_half(qkv) * sin)
+
+def apply_rotary_pos_emb(qkv, cos, sin):
+  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)
+
+
+# function overload
+def modulate(x, shift, scale):
+  return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+#################################################################################
+#                                  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):
+    with torch.cuda.amp.autocast(enabled=False):
+      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                                    #
+#################################################################################
+
+def regular_attention_multi_headed(qkv):
+  # Assuming qkv is a tensor with shape [batch, seq_len, 3, num_heads, head_dim]
+  # where the 3 represents Q, K, V packed in that order
+  batch_size, seq_len, _, num_heads, head_dim = qkv.shape
+  # Separate Q, K, V from the packed qkv tensor
+  # [batch_size, seq_len, num_heads, head_dim]
+  q = qkv[:, :, 0, :, :]
+  k = qkv[:, :, 1, :, :]
+  v = qkv[:, :, 2, :, :]  
+
+  # Transpose and reshape Q and K for batched matrix multiplication:
+  # [batch_size, num_heads, seq_len, head_dim]
+  q = q.transpose(1, 2)
+  k = k.transpose(1, 2)
+  v = v.transpose(1, 2)
+
+  # Compute scaled dot-product attention
+  # [batch_size, num_heads, seq_len, seq_len]
+  attention_scores = torch.matmul(
+    q, k.transpose(-2, -1)) / math.sqrt(head_dim)
+
+  # Apply softmax to calculate the attention weights
+  attention_probs = F.softmax(attention_scores, dim=-1)
+
+  # [batch_size, num_heads, seq_len, head_dim]
+  attention_output = torch.matmul(attention_probs, v)
+
+  # [batch_size, seq_len, num_heads, head_dim]
+  attention_output = attention_output.transpose(1, 2)
+  return einops.rearrange(attention_output,
+                          'b s h d -> b s (h d)')
+
+
+class DDiTBlock(nn.Module):
+  def __init__(self, n, dim, n_heads, cond_dim, mlp_ratio=4,
+               dropout=0.1, attn_backend='flash_attn'):
+    super().__init__()
+    self.n = n
+    self.n_heads = n_heads
+    self.attn_backend = attn_backend
+    self.kv_cache = None
+
+    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 get_qkv(self, x, rotary_cos_sin, save_kv=False):
+    # compute qkv (potentially use cache)
+    if self.kv_cache is not None:
+      block_len = x.shape[1] - self.kv_cache.shape[1]
+      new_qkv = self.attn_qkv(x[:, -block_len:])
+      qkv = torch.cat((self.kv_cache, new_qkv), dim=1)
+    else:
+      qkv = self.attn_qkv(x)
+    
+    # save kv cache in a sliding window (can't exceed context len)
+    if save_kv:
+      if self.kv_cache is not None:
+        cache_len = min(x.shape[1], self.n - block_len)
+        self.kv_cache = qkv[:, -cache_len:]
+      else:
+        self.kv_cache = qkv
+    qkv = einops.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
+      if self.attn_backend == 'flash_attn':
+        qkv = apply_rotary_pos_emb(
+          qkv, cos.to(qkv.dtype), sin.to(qkv.dtype))
+      else:
+        qkv = apply_rotary_pos_emb_torchscript(
+          qkv, cos.to(qkv.dtype), sin.to(qkv.dtype))
+    return qkv
+
+  def cross_attn(self, x, qkv, cross_attn_mask=None):
+    scale = qkv.shape[-1]
+    qkv = qkv.transpose(1, 3)
+    attn_dropout = self.attn_dropout if self.training else 0.0
+    cross_attn_mask = cross_attn_mask.bool() if cross_attn_mask is not None else None
+    x = F.scaled_dot_product_attention(
+      query=qkv[:, :, 0],
+      key=qkv[:, :, 1],
+      value=qkv[:, :, 2],
+      attn_mask=cross_attn_mask,
+      dropout_p=attn_dropout,
+      is_causal=False,
+      scale=1 / math.sqrt(scale))
+    x = x.transpose(1, 2)
+    x = einops.rearrange(x, 'b s h d -> b s (h d)')
+    return x
+
+  def forward(self, x, rotary_cos_sin, c, cross_attn_mask=None, save_kv=False):
+    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)
+
+    # get qkvs
+    if cross_attn_mask is not None and not save_kv:
+      qkv_x = self.get_qkv(x[:,:self.n], rotary_cos_sin)
+      qkv_x0 = self.get_qkv(x[:,self.n:], rotary_cos_sin)
+      qkv = torch.cat((qkv_x, qkv_x0), dim=1)
+    else:
+      qkv = self.get_qkv(x, rotary_cos_sin, save_kv=save_kv)
+
+    if cross_attn_mask is None and self.attn_backend == 'flash_attn':
+      x = regular_attention_multi_headed(qkv)
+    else:
+      x = self.cross_attn(x, qkv, cross_attn_mask=cross_attn_mask)
+
+    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 DITBackbone(nn.Module):
+  def __init__(
+      self,
+      config: BD3LMConfig):
+    super().__init__()
+
+    self.config = config
+    self.cross_attn = config.cross_attn
+    self.block_size = config.block_size
+    self.vocab_size = config.vocab_size
+    self.n = config.model_length
+
+    self.vocab_embed = EmbeddingLayer(
+      config.hidden_dim,
+      config.vocab_size)
+    self.sigma_map = TimestepEmbedder(
+      config.cond_dim)
+    self.rotary_emb = Rotary(
+      config.hidden_dim // config.n_heads)
+
+    blocks = []
+    for _ in range(config.n_blocks):
+      blocks.append(DDiTBlock(self.n,
+                              config.hidden_dim,
+                              config.n_heads,
+                              config.cond_dim,
+                              dropout=config.dropout,
+                              attn_backend=config.attn_backend,))
+    self.blocks = nn.ModuleList(blocks)
+
+    self.output_layer = DDitFinalLayer(
+      config.hidden_dim,
+      config.vocab_size,
+      config.cond_dim)
+    if self.cross_attn:
+      self.gen_mask(config.model_length, self.block_size)
+    self.precision = torch.float32
+
+  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 gen_mask(self, seqlen, block_size):
+    self_attn_mask = block_causal_mask(seqlen, block_size, mode='diag')
+    x0_attn_mask = block_causal_mask(seqlen, block_size, mode='full')
+    cross_attn_mask = x0_attn_mask.clone()
+    cross_attn_mask.masked_fill_(self_attn_mask == 1, 0)
+
+    cross_attn_mask = torch.cat((self_attn_mask, cross_attn_mask), dim=1)
+    x0_attn_mask = torch.cat((torch.zeros_like(self_attn_mask), x0_attn_mask), dim=1)
+    self.cross_attn_mask = torch.cat((cross_attn_mask, x0_attn_mask), dim=0)
+
+  def forward(self, indices, sigma, disable_cross_attn=False,
+              output_hidden_states=False, save_kv=False):
+    cross_attn = self.cross_attn and not disable_cross_attn
+    if not self.config.time_conditioning:
+      sigma = torch.zeros_like(sigma)
+    all_hidden_states = []
+    x = self.vocab_embed(indices)
+    if output_hidden_states:
+      all_hidden_states.append(x)
+    c = F.silu(self.sigma_map(sigma))
+    if cross_attn:
+      cross_attn_mask = self.cross_attn_mask.to(x.device)
+      if save_kv:
+        cross_attn_mask = cross_attn_mask[:x.shape[1], :x.shape[1]]
+      rotary_cos_sin = self.rotary_emb(x[:, :self.n])
+    else:
+      cross_attn_mask = None
+      rotary_cos_sin = self.rotary_emb(x)
+
+    with torch.cuda.amp.autocast(dtype=self.precision):
+      for i in range(len(self.blocks)):
+        x = self.blocks[i](x, 
+                           rotary_cos_sin,
+                           c,
+                           cross_attn_mask=cross_attn_mask,
+                           save_kv=save_kv)
+        if output_hidden_states:
+          all_hidden_states.append(x)
+      logits = self.output_layer(x, c)
+    if cross_attn and not save_kv:
+      logits = logits[:, :self.n]
+      all_hidden_states = [hidden_states[:, :self.n] for hidden_states in all_hidden_states]
+    return logits, all_hidden_states
+
+class BD3LM(transformers.PreTrainedModel):
+  """HF-compatible model."""
+  config_class = BD3LMConfig
+  base_model_prefix = "bd3lm"
+
+  def __init__(
+    self,
+    config: BD3LMConfig):
+    super().__init__(config)
+    self.backbone = DITBackbone(config)
+    if config.var_min:
+      self.register_buffer(
+        'sampling_eps_min',
+        torch.tensor(config.sampling_eps_min))
+      self.register_buffer(
+        'sampling_eps_max',
+        torch.tensor(config.sampling_eps_max))
+
+  def forward(
+      self,
+      input_ids: torch.LongTensor = None,
+      timesteps: torch.FloatTensor = None,
+      disable_cross_attn: typing.Optional[bool] = None,
+      output_hidden_states: typing.Optional[bool] = None,
+      return_dict: typing.Optional[bool] = None,
+  ) -> typing.Union[
+    torch.Tensor, typing.Tuple,
+    modeling_outputs.MaskedLMOutput]:
+    """HF-compatible forward method."""
+    output_hidden_states = (
+      output_hidden_states
+      if output_hidden_states is not None
+      else self.config.output_hidden_states
+    )
+    return_dict = return_dict \
+      if return_dict is not None \
+      else self.config.use_return_dict
+    
+    logits, all_hidden_states = self.backbone(
+      indices=input_ids,
+      sigma=timesteps,
+      disable_cross_attn=disable_cross_attn,
+      output_hidden_states=output_hidden_states
+    )
+    if return_dict:
+      return modeling_outputs.MaskedLMOutput(
+        logits=logits,
+        hidden_states=all_hidden_states if output_hidden_states else None,
+        loss=None
+      )
+    elif output_hidden_states:
+      return logits, all_hidden_states
+    else:
+      return logits