Dependencies for MDLM

models/dit.py

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+import math
+import typing
+
+import flash_attn
+import flash_attn.layers.rotary
+import huggingface_hub
+import omegaconf
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+# 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):
+    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(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                                    #
+#################################################################################
+
+
+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.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)
+    with torch.cuda.amp.autocast(enabled=False):
+      cos, sin = rotary_cos_sin
+      qkv = apply_rotary_pos_emb(
+        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.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)
+
+    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(nn.Module, huggingface_hub.PyTorchModelHubMixin):
+  def __init__(self, config, vocab_size: int):
+    super().__init__()
+    if type(config) == dict:
+      config = omegaconf.OmegaConf.create(config)
+
+    self.config = config
+    self.vocab_size = vocab_size
+
+    self.vocab_embed = EmbeddingLayer(config.model.hidden_size,
+                                      vocab_size)
+    self.sigma_map = TimestepEmbedder(config.model.cond_dim)
+    self.rotary_emb = Rotary(
+      config.model.hidden_size // config.model.n_heads)
+
+    blocks = []
+    for _ in range(config.model.n_blocks):
+      blocks.append(DDiTBlock(config.model.hidden_size,
+                              config.model.n_heads,
+                              config.model.cond_dim,
+                              dropout=config.model.dropout))
+    self.blocks = nn.ModuleList(blocks)
+
+    self.output_layer = DDitFinalLayer(
+      config.model.hidden_size,
+      vocab_size,
+      config.model.cond_dim)
+    self.scale_by_sigma = config.model.scale_by_sigma
+
+  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, indices, sigma):
+    x = self.vocab_embed(indices)
+    c = F.silu(self.sigma_map(sigma))
+
+    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)
+
+    return x

models/ema.py

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+import torch
+
+
+class ExponentialMovingAverage:
+  """
+  Maintains (exponential) moving average of a set of parameters.
+  """
+
+  def __init__(self, parameters, decay, use_num_updates=True):
+    """
+    Args:
+        parameters: Iterable of `torch.nn.Parameter`; usually the result of
+            `model.parameters()`.
+        decay: The exponential decay.
+        use_num_updates: Whether to use number of updates when computing
+            averages.
+    """
+    if decay < 0.0 or decay > 1.0:
+      raise ValueError('Decay must be between 0 and 1')
+    self.decay = decay
+    self.num_updates = 0 if use_num_updates else None
+    self.shadow_params = [p.clone().detach()
+                          for p in parameters if p.requires_grad]
+    self.collected_params = []
+
+  def move_shadow_params_to_device(self, device):
+    self.shadow_params = [i.to(device) for i in self.shadow_params]
+
+  def update(self, parameters):
+    """
+    Update currently maintained parameters.
+
+    Call this every time the parameters are updated, such as the result of
+    the `optimizer.step()` call.
+
+    Args:
+        parameters: Iterable of `torch.nn.Parameter`; usually the same set of
+            parameters used to initialize this object.
+    """
+    decay = self.decay
+    if self.num_updates is not None:
+      self.num_updates += 1
+      decay = min(decay, (1 + self.num_updates) /
+                  (10 + self.num_updates))
+    one_minus_decay = 1.0 - decay
+    with torch.no_grad():
+      parameters = [p for p in parameters if p.requires_grad]
+      for s_param, param in zip(self.shadow_params, parameters):
+        s_param.sub_(one_minus_decay * (s_param - param))
+
+  def copy_to(self, parameters):
+    """
+    Copy current parameters into given collection of parameters.
+
+    Args:
+        parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            updated with the stored moving averages.
+    """
+    parameters = [p for p in parameters if p.requires_grad]
+    for s_param, param in zip(self.shadow_params, parameters):
+      if param.requires_grad:
+        param.data.copy_(s_param.data)
+
+  def store(self, parameters):
+    """
+    Save the current parameters for restoring later.
+
+    Args:
+        parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            temporarily stored.
+    """
+    self.collected_params = [param.clone() for param in parameters]
+
+  def restore(self, parameters):
+    """
+    Restore the parameters stored with the `store` method.
+    Useful to validate the model with EMA parameters without affecting the
+    original optimization process. Store the parameters before the
+    `copy_to` method. After validation (or model saving), use this to
+    restore the former parameters.
+
+    Args:
+        parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+            updated with the stored parameters.
+    """
+    for c_param, param in zip(self.collected_params, parameters):
+      param.data.copy_(c_param.data)
+
+  def state_dict(self):
+    return dict(decay=self.decay,
+                num_updates=self.num_updates,
+                shadow_params=self.shadow_params)
+
+  def load_state_dict(self, state_dict):
+    self.decay = state_dict['decay']
+    self.num_updates = state_dict['num_updates']
+    self.shadow_params = state_dict['shadow_params']