from typing import Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint 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_utils import PreTrainedModel from transformers.utils import logging from .configuration_intern_vit import InternVisionConfig try: try: # v1 from flash_attn.flash_attn_interface import \ flash_attn_unpadded_qkvpacked_func except: # v2 from flash_attn.flash_attn_interface import \ flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func from flash_attn.bert_padding import pad_input, unpad_input has_flash_attn = True except: 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 = flash_attn_unpadded_qkvpacked_func( qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0, softmax_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 = flash_attn_unpadded_qkvpacked_func( x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0, softmax_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 = flash_attn_unpadded_qkvpacked_func( qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0, softmax_scale=self.softmax_scale, causal=causal ) return output, None class InternRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) try: from apex.normalization import FusedRMSNorm InternRMSNorm = FusedRMSNorm # noqa 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') pass NORM2FN = { 'rms_norm': InternRMSNorm, 'layer_norm': nn.LayerNorm, } class InternVisionEmbeddings(nn.Module): def __init__(self, config: InternVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.image_size = config.image_size self.patch_size = config.patch_size self.class_embedding = nn.Parameter( torch.randn(1, 1, self.embed_dim), ) self.patch_embedding = nn.Conv2d( 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 def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: target_dtype = self.patch_embedding.weight.dtype patch_embeds = self.patch_embedding(pixel_values) # shape = [*, channel, width, height] batch_size, _, height, width = patch_embeds.shape 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) return embeddings class InternAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: InternVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size 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.') 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}).' ) 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) self.qk_normalization = config.qk_normalization if self.qk_normalization: self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps) self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps) if self.use_flash_attn: self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout) self.proj = nn.Linear(self.embed_dim, self.embed_dim) 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) 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) attn = ((q * self.scale) @ k.transpose(-2, -1)) attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x 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) if self.qk_normalization: q, k, v = qkv.unbind(2) q = self.q_norm(q.flatten(-2, -1)).view(q.shape) k = self.k_norm(k.flatten(-2, -1)).view(k.shape) 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 ) 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) return x class InternMLP(nn.Module): def __init__(self, config: InternVisionConfig): super().__init__() self.config = config self.act = ACT2FN[config.hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states class InternVisionEncoderLayer(nn.Module): def __init__(self, config: InternVisionConfig, drop_path_rate: float): 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.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. 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]]]: """ 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_path2(self.mlp(self.norm2(hidden_states)) * self.ls2) return hidden_states class InternVisionEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`InternEncoderLayer`]. Args: config (`InternConfig`): The corresponding vision configuration for the `InternEncoder`. """ def __init__(self, config: InternVisionConfig): 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)]) self.gradient_checkpointing = False def forward( self, inputs_embeds, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Embedded representation of the inputs. Should be float, not int tokens. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): 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 encoder_states = () if output_hidden_states else None hidden_states = inputs_embeds for idx, encoder_layer in enumerate(self.layers): if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = torch.utils.checkpoint.checkpoint( encoder_layer, hidden_states) else: layer_outputs = encoder_layer( hidden_states, ) hidden_states = layer_outputs if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states ) class InternVisionModel(PreTrainedModel): main_input_name = 'pixel_values' _supports_flash_attn_2 = True config_class = InternVisionConfig _no_split_modules = ['InternVisionEncoderLayer'] def __init__(self, config: InternVisionConfig): super().__init__(config) self.config = config self.embeddings = InternVisionEmbeddings(config) self.encoder = InternVisionEncoder(config) def resize_pos_embeddings(self, old_size, new_size, patch_size): 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.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)) 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, ) -> 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 if pixel_values is None and pixel_embeds is None: raise ValueError('You have to specify pixel_values or pixel_embeds') if pixel_embeds is not None: hidden_states = pixel_embeds else: if len(pixel_values.shape) == 4: hidden_states = self.embeddings(pixel_values) else: raise ValueError(f'wrong pixel_values size: {pixel_values.shape}') encoder_outputs = self.encoder( inputs_embeds=hidden_states, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs.last_hidden_state pooled_output = last_hidden_state[:, 0, :] if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, )