Update modeling_intern_vit.py

modeling_intern_vit.py

@@ -12,123 +12,22 @@ from einops import rearrange
 from timm.models.layers import DropPath
 from torch import nn
 from transformers.activations import ACT2FN
-from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
+from transformers.modeling_outputs import (BaseModelOutput,
+ BaseModelOutputWithPooling)
 from transformers.modeling_utils import PreTrainedModel
 from transformers.utils import logging
 
 from .configuration_intern_vit import InternVisionConfig
 
-
 try:
- from triton_flash_atn import _attention
-
- from triton_bert_pading import pad_input, unpad_input
-
+ from .flash_attention import FlashAttention
  has_flash_attn = True
 except:
- print("FlashAttention is not installed.")
+ print('FlashAttention is not installed.')
  has_flash_attn = False
 
-logger = logging.get_logger(__name__)
-
-
-class FlashAttention(nn.Module):
- """Implement the scaled dot product attention with softmax.
- Arguments
- ---------
- softmax_scale: The temperature to use for the softmax attention.
- (default: 1/sqrt(d_keys) where d_keys is computed at
- runtime)
- attention_dropout: The dropout rate to apply to the attention
- (default: 0.0)
- """
-
- def __init__(
- self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None
- ):
- super().__init__()
- self.softmax_scale = softmax_scale
- self.dropout_p = attention_dropout
 
- def forward(
- self,
- qkv,
- key_padding_mask=None,
- causal=False,
- cu_seqlens=None,
- max_s=None,
- need_weights=False,
- ):
- """Implements the multihead softmax attention.
- Arguments
- ---------
- qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
- if unpadded: (nnz, 3, h, d)
- key_padding_mask: a bool tensor of shape (B, S)
- """
- assert not need_weights
- assert qkv.dtype in [torch.float16, torch.bfloat16]
- assert qkv.is_cuda
-
- if cu_seqlens is None:
- batch_size = qkv.shape[0]
- seqlen = qkv.shape[1]
- if key_padding_mask is None:
- qkv = rearrange(qkv, "b s ... -> (b s) ...")
- max_s = seqlen
- cu_seqlens = torch.arange(
- 0,
- (batch_size + 1) * seqlen,
- step=seqlen,
- dtype=torch.int32,
- device=qkv.device,
- )
- output = _attention.apply(
- qkv,
- cu_seqlens,
- max_s,
- self.dropout_p if self.training else 0.0,
- sm_scale=self.softmax_scale,
- causal=causal,
- )
- output = rearrange(output, "(b s) ... -> b s ...", b=batch_size)
- else:
- nheads = qkv.shape[-2]
- x = rearrange(qkv, "b s three h d -> b s (three h d)")
- x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
- x_unpad = rearrange(
- x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads
- )
- output_unpad = _attention.apply(
- x_unpad,
- cu_seqlens,
- max_s,
- self.dropout_p if self.training else 0.0,
- sm_scale=self.softmax_scale,
- causal=causal,
- )
- output = rearrange(
- pad_input(
- rearrange(output_unpad, "nnz h d -> nnz (h d)"),
- indices,
- batch_size,
- seqlen,
- ),
- "b s (h d) -> b s h d",
- h=nheads,
- )
- else:
- assert max_s is not None
- output = _attention.apply(
- qkv,
- cu_seqlens,
- max_s,
- self.dropout_p if self.training else 0.0,
- sm_scale=self.softmax_scale,
- causal=causal,
- )
-
- return output, None
+logger = logging.get_logger(__name__)
 
 
 class InternRMSNorm(nn.Module):
@@ -150,25 +49,15 @@ try:
 
  InternRMSNorm = FusedRMSNorm # noqa
 
- logger.info(
- "Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm"
- )
+ logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
 except ImportError:
  # using the normal InternRMSNorm
  pass
 except Exception:
- logger.warning(
- "discovered apex but it failed to load, falling back to InternRMSNorm"
- )
+ logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
  pass
 
 
-NORM2FN = {
- "rms_norm": InternRMSNorm,
- "layer_norm": nn.LayerNorm,
-}
-
-
 class InternVisionEmbeddings(nn.Module):
  def __init__(self, config: InternVisionConfig):
  super().__init__()
@@ -182,55 +71,22 @@ class InternVisionEmbeddings(nn.Module):
  )
 
  self.patch_embedding = nn.Conv2d(
- in_channels=3,
- out_channels=self.embed_dim,
- kernel_size=self.patch_size,
- stride=self.patch_size,
+ in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
  )
 
  self.num_patches = (self.image_size // self.patch_size) ** 2
  self.num_positions = self.num_patches + 1
 
- self.position_embedding = nn.Parameter(
- torch.randn(1, self.num_positions, self.embed_dim)
- )
-
- def _get_pos_embed(self, pos_embed, H, W):
- target_dtype = pos_embed.dtype
- pos_embed = (
- pos_embed.float()
- .reshape(
- 1,
- self.image_size // self.patch_size,
- self.image_size // self.patch_size,
- -1,
- )
- .permute(0, 3, 1, 2)
- )
- pos_embed = (
- F.interpolate(pos_embed, size=(H, W), mode="bicubic", align_corners=False)
- .reshape(1, -1, H * W)
- .permute(0, 2, 1)
- .to(target_dtype)
- )
- return pos_embed
+ self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
 
  def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+ batch_size = pixel_values.shape[0]
  target_dtype = self.patch_embedding.weight.dtype
- # shape = [*, channel, width, height]
- patch_embeds = self.patch_embedding(pixel_values)
- batch_size, _, height, width = patch_embeds.shape
+ patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid]
  patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
  class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
  embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
- position_embedding = torch.cat(
- [
- self.position_embedding[:, :1, :],
- self._get_pos_embed(self.position_embedding[:, 1:, :], height, width),
- ],
- dim=1,
- )
- embeddings = embeddings + position_embedding.to(target_dtype)
+ embeddings = embeddings + self.position_embedding.to(target_dtype)
  return embeddings
 
 
@@ -244,17 +100,15 @@ class InternAttention(nn.Module):
  self.num_heads = config.num_attention_heads
  self.use_flash_attn = config.use_flash_attn and has_flash_attn
  if config.use_flash_attn and not has_flash_attn:
- print(
- "Warning: Flash Attention is not available, use_flash_attn is set to False."
- )
+ print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
  self.head_dim = self.embed_dim // self.num_heads
  if self.head_dim * self.num_heads != self.embed_dim:
  raise ValueError(
- f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
- f" {self.num_heads})."
+ f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
+ f' {self.num_heads}).'
  )
 
- self.scale = self.head_dim**-0.5
+ self.scale = self.head_dim ** -0.5
  self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
  self.attn_drop = nn.Dropout(config.attention_dropout)
  self.proj_drop = nn.Dropout(config.dropout)
@@ -271,28 +125,15 @@ class InternAttention(nn.Module):
 
  def _naive_attn(self, x):
  B, N, C = x.shape
- qkv = (
- self.qkv(x)
- .reshape(B, N, 3, self.num_heads, C // self.num_heads)
- .permute(2, 0, 3, 1, 4)
- )
- # make torchscript happy (cannot use tensor as tuple)
- q, k, v = qkv.unbind(0)
+ qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+ q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
 
  if self.qk_normalization:
  B_, H_, N_, D_ = q.shape
- q = (
- self.q_norm(q.transpose(1, 2).flatten(-2, -1))
- .view(B_, N_, H_, D_)
- .transpose(1, 2)
- )
- k = (
- self.k_norm(k.transpose(1, 2).flatten(-2, -1))
- .view(B_, N_, H_, D_)
- .transpose(1, 2)
- )
+ q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+ k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
 
- attn = (q * self.scale) @ k.transpose(-2, -1)
+ attn = ((q * self.scale) @ k.transpose(-2, -1))
  attn = attn.softmax(dim=-1)
  attn = self.attn_drop(attn)
 
@@ -303,9 +144,7 @@ class InternAttention(nn.Module):
 
  def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
  qkv = self.qkv(x)
- qkv = rearrange(
- qkv, "b s (three h d) -> b s three h d", three=3, h=self.num_heads
- )
+ qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
 
  if self.qk_normalization:
  q, k, v = qkv.unbind(2)
@@ -314,21 +153,14 @@ class InternAttention(nn.Module):
  qkv = torch.stack([q, k, v], dim=2)
 
  context, _ = self.inner_attn(
- qkv,
- key_padding_mask=key_padding_mask,
- need_weights=need_weights,
- causal=False,
+ qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
  )
- outs = self.proj(rearrange(context, "b s h d -> b s (h d)"))
+ outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
  outs = self.proj_drop(outs)
  return outs
 
  def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
- x = (
- self._naive_attn(hidden_states)
- if not self.use_flash_attn
- else self._flash_attn(hidden_states)
- )
+ x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
  return x
 
 
@@ -352,41 +184,28 @@ class InternVisionEncoderLayer(nn.Module):
  super().__init__()
  self.embed_dim = config.hidden_size
  self.intermediate_size = config.intermediate_size
- self.norm_type = config.norm_type
 
  self.attn = InternAttention(config)
  self.mlp = InternMLP(config)
- self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
- self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
+ self.norm1 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+ self.norm2 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
 
  self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
  self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
- self.drop_path1 = (
- DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
- )
- self.drop_path2 = (
- DropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
- )
+ self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+ self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
 
  def forward(
- self,
- hidden_states: torch.Tensor,
- ) -> Tuple[
- torch.FloatTensor,
- Optional[torch.FloatTensor],
- Optional[Tuple[torch.FloatTensor]],
- ]:
+ self,
+ hidden_states: torch.Tensor,
+ ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
  """
  Args:
  hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
  """
- hidden_states = hidden_states + self.drop_path1(
- self.attn(self.norm1(hidden_states)) * self.ls1
- )
+ hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states)) * self.ls1)
 
- hidden_states = hidden_states + self.drop_path2(
- self.mlp(self.norm2(hidden_states)) * self.ls2
- )
+ hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states)) * self.ls2)
 
  return hidden_states
 
@@ -405,23 +224,16 @@ class InternVisionEncoder(nn.Module):
  super().__init__()
  self.config = config
  # stochastic depth decay rule
- dpr = [
- x.item()
- for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)
- ]
- self.layers = nn.ModuleList(
- [
- InternVisionEncoderLayer(config, dpr[idx])
- for idx in range(config.num_hidden_layers)
- ]
- )
+ dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
+ self.layers = nn.ModuleList([
+ InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
  self.gradient_checkpointing = True
 
  def forward(
- self,
- inputs_embeds,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
+ self,
+ inputs_embeds,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
  ) -> Union[Tuple, BaseModelOutput]:
  r"""
  Args:
@@ -434,13 +246,9 @@ class InternVisionEncoder(nn.Module):
  Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
  """
  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
+ 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
 
  encoder_states = () if output_hidden_states else None
  hidden_states = inputs_embeds
@@ -450,8 +258,8 @@ class InternVisionEncoder(nn.Module):
  encoder_states = encoder_states + (hidden_states,)
  if self.gradient_checkpointing and self.training:
  layer_outputs = torch.utils.checkpoint.checkpoint(
- encoder_layer, hidden_states
- )
+ encoder_layer,
+ hidden_states)
  else:
  layer_outputs = encoder_layer(
  hidden_states,
@@ -469,9 +277,9 @@ class InternVisionEncoder(nn.Module):
 
 
 class InternVisionModel(PreTrainedModel):
- main_input_name = "pixel_values"
+ main_input_name = 'pixel_values'
  config_class = InternVisionConfig
- _no_split_modules = ["InternVisionEncoderLayer"]
+ _no_split_modules = ['InternVisionEncoderLayer']
 
  def __init__(self, config: InternVisionConfig):
  super().__init__(config)
@@ -484,46 +292,30 @@ class InternVisionModel(PreTrainedModel):
  pos_emb = self.embeddings.position_embedding
  _, num_positions, embed_dim = pos_emb.shape
  cls_emb = pos_emb[:, :1, :]
- pos_emb = (
- pos_emb[:, 1:, :]
- .reshape(1, old_size // patch_size, old_size // patch_size, -1)
- .permute(0, 3, 1, 2)
- )
- pos_emb = F.interpolate(
- pos_emb.float(),
- size=new_size // patch_size,
- mode="bicubic",
- align_corners=False,
- )
+ pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
+ pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
  pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
  pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
  self.embeddings.position_embedding = nn.Parameter(pos_emb)
- self.embeddings.image_size = new_size
- logger.info(
- "Resized position embeddings from {} to {}".format(old_size, new_size)
- )
+ logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
 
  def get_input_embeddings(self):
  return self.embeddings
 
  def forward(
- self,
- pixel_values: Optional[torch.FloatTensor] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- pixel_embeds: Optional[torch.FloatTensor] = None,
+ self,
+ pixel_values: Optional[torch.FloatTensor] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ pixel_embeds: Optional[torch.FloatTensor] = None,
  ) -> Union[Tuple, BaseModelOutputWithPooling]:
  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
+ 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
 
  if pixel_values is None and pixel_embeds is None:
- raise ValueError("You have to specify pixel_values or pixel_embeds")
+ raise ValueError('You have to specify pixel_values or pixel_embeds')
 
  if pixel_embeds is not None:
  hidden_states = pixel_embeds
@@ -531,7 +323,7 @@ class InternVisionModel(PreTrainedModel):
  if len(pixel_values.shape) == 4:
  hidden_states = self.embeddings(pixel_values)
  else:
- raise ValueError(f"wrong pixel_values size: {pixel_values.shape}")
+ raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
  encoder_outputs = self.encoder(
  inputs_embeds=hidden_states,
  output_hidden_states=output_hidden_states,