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| import os | |
| from collections import OrderedDict | |
| import math | |
| from typing import Callable, Optional, Sequence, Tuple | |
| import torch | |
| from loguru import logger | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from torch.utils.checkpoint import checkpoint | |
| from .utils import to_2tuple | |
| try: | |
| import xformers.ops as xops | |
| except ImportError: | |
| xops = None | |
| print("Please 'pip install xformers'") | |
| class LayerNormFp32(nn.LayerNorm): | |
| """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).""" | |
| def forward(self, x: torch.Tensor): | |
| orig_type = x.dtype | |
| x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps) | |
| return x.to(orig_type) | |
| class LayerNorm(nn.LayerNorm): | |
| """Subclass torch's LayerNorm (with cast back to input dtype).""" | |
| def forward(self, x: torch.Tensor): | |
| orig_type = x.dtype | |
| x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) | |
| return x.to(orig_type) | |
| class QuickGELU(nn.Module): | |
| # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory | |
| def forward(self, x: torch.Tensor): | |
| return x * torch.sigmoid(1.702 * x) | |
| class LayerScale(nn.Module): | |
| def __init__(self, dim, init_values=1e-5, inplace=False): | |
| super().__init__() | |
| self.inplace = inplace | |
| self.gamma = nn.Parameter(init_values * torch.ones(dim)) | |
| def forward(self, x): | |
| return x.mul_(self.gamma) if self.inplace else x * self.gamma | |
| class PatchDropout(nn.Module): | |
| """ | |
| https://arxiv.org/abs/2212.00794 | |
| """ | |
| def __init__(self, prob, exclude_first_token=True): | |
| super().__init__() | |
| assert 0 <= prob < 1. | |
| self.prob = prob | |
| self.exclude_first_token = exclude_first_token # exclude CLS token | |
| def forward(self, x): | |
| if not self.training or self.prob == 0.: | |
| return x | |
| if self.exclude_first_token: | |
| cls_tokens, x = x[:, :1], x[:, 1:] | |
| else: | |
| cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1]) | |
| batch = x.size()[0] | |
| num_tokens = x.size()[1] | |
| batch_indices = torch.arange(batch) | |
| batch_indices = batch_indices[..., None] | |
| keep_prob = 1 - self.prob | |
| num_patches_keep = max(1, int(num_tokens * keep_prob)) | |
| rand = torch.randn(batch, num_tokens) | |
| patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices | |
| x = x[batch_indices, patch_indices_keep] | |
| if self.exclude_first_token: | |
| x = torch.cat((cls_tokens, x), dim=1) | |
| return x | |
| class Attention(nn.Module): | |
| def __init__( | |
| self, | |
| dim, | |
| num_heads=8, | |
| qkv_bias=True, | |
| scaled_cosine=False, | |
| scale_heads=False, | |
| logit_scale_max=math.log(1. / 0.01), | |
| attn_drop=0., | |
| proj_drop=0., | |
| xattn=False, | |
| ): | |
| super().__init__() | |
| self.scaled_cosine = scaled_cosine | |
| self.scale_heads = scale_heads | |
| assert dim % num_heads == 0, 'dim should be divisible by num_heads' | |
| self.num_heads = num_heads | |
| self.head_dim = dim // num_heads | |
| self.scale = self.head_dim ** -0.5 | |
| self.logit_scale_max = logit_scale_max | |
| # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original | |
| self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) | |
| if qkv_bias: | |
| self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) | |
| else: | |
| self.in_proj_bias = None | |
| if self.scaled_cosine: | |
| self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) | |
| else: | |
| self.logit_scale = None | |
| self.attn_drop = nn.Dropout(attn_drop) | |
| if self.scale_heads: | |
| self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) | |
| else: | |
| self.head_scale = None | |
| self.out_proj = nn.Linear(dim, dim) | |
| self.out_drop = nn.Dropout(proj_drop) | |
| self.xattn_drop = attn_drop | |
| self.xattn = xattn | |
| def forward(self, x, attn_mask: Optional[torch.Tensor] = None): | |
| L, N, C = x.shape | |
| q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1) | |
| if self.xattn: | |
| q = q.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) | |
| k = k.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) | |
| v = v.contiguous().view(L, N, self.num_heads, -1).transpose(0, 1) | |
| # logger.debug(f'using memory efficient attention') | |
| x = xops.memory_efficient_attention( | |
| q, k, v, | |
| p=self.xattn_drop, | |
| scale=self.scale if self.logit_scale is None else None, | |
| attn_bias=xops.LowerTriangularMask() if attn_mask is not None else None, | |
| # op=xops.MemoryEfficientAttentionFlashAttentionOp | |
| ) | |
| else: | |
| q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) | |
| k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) | |
| v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) | |
| if self.logit_scale is not None: | |
| attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) | |
| logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() | |
| attn = attn.view(N, self.num_heads, L, L) * logit_scale | |
| attn = attn.view(-1, L, L) | |
| else: | |
| q = q * self.scale | |
| attn = torch.bmm(q, k.transpose(-1, -2)) | |
| if attn_mask is not None: | |
| if attn_mask.dtype == torch.bool: | |
| new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) | |
| new_attn_mask.masked_fill_(attn_mask, float("-inf")) | |
| attn_mask = new_attn_mask | |
| attn += attn_mask | |
| attn = attn.softmax(dim=-1) | |
| attn = self.attn_drop(attn) | |
| x = torch.bmm(attn, v) | |
| if self.head_scale is not None: | |
| x = x.view(N, self.num_heads, L, C) * self.head_scale | |
| x = x.view(-1, L, C) | |
| x = x.transpose(0, 1).reshape(L, N, C) | |
| x = self.out_proj(x) | |
| x = self.out_drop(x) | |
| return x | |
| class AttentionalPooler(nn.Module): | |
| def __init__( | |
| self, | |
| d_model: int, | |
| context_dim: int, | |
| n_head: int = 8, | |
| n_queries: int = 256, | |
| norm_layer: Callable = LayerNorm | |
| ): | |
| super().__init__() | |
| self.query = nn.Parameter(torch.randn(n_queries, d_model)) | |
| self.attn = nn.MultiheadAttention(d_model, n_head, kdim=context_dim, vdim=context_dim) | |
| self.ln_q = norm_layer(d_model) | |
| self.ln_k = norm_layer(context_dim) | |
| def forward(self, x: torch.Tensor): | |
| x = self.ln_k(x).permute(1, 0, 2) # NLD -> LND | |
| N = x.shape[1] | |
| q = self.ln_q(self.query) | |
| out = self.attn(self._repeat(q, N), x, x, need_weights=False)[0] | |
| return out.permute(1, 0, 2) # LND -> NLD | |
| def _repeat(self, query, N: int): | |
| return query.unsqueeze(1).repeat(1, N, 1) | |
| class ResidualAttentionBlock(nn.Module): | |
| def __init__( | |
| self, | |
| d_model: int, | |
| n_head: int, | |
| mlp_ratio: float = 4.0, | |
| ls_init_value: float = None, | |
| act_layer: Callable = nn.GELU, | |
| norm_layer: Callable = LayerNorm, | |
| xattn: bool = False, | |
| ): | |
| super().__init__() | |
| self.ln_1 = norm_layer(d_model) | |
| if xattn: | |
| self.attn = Attention(d_model, n_head, xattn=True) | |
| else: | |
| self.attn = nn.MultiheadAttention(d_model, n_head) | |
| self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() | |
| self.ln_2 = norm_layer(d_model) | |
| mlp_width = int(d_model * mlp_ratio) | |
| self.mlp = nn.Sequential(OrderedDict([ | |
| ("c_fc", nn.Linear(d_model, mlp_width)), | |
| ("gelu", act_layer()), | |
| ("c_proj", nn.Linear(mlp_width, d_model)) | |
| ])) | |
| self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() | |
| self.xattn = xattn | |
| def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): | |
| attn_mask = attn_mask.to(x.dtype) if attn_mask is not None else None | |
| if self.xattn: | |
| return self.attn(x, attn_mask=attn_mask) | |
| return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0] | |
| def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): | |
| # t = time.time() | |
| x = x + self.ls_1(self.attention(self.ln_1(x), attn_mask=attn_mask)) | |
| x = x + self.ls_2(self.mlp(self.ln_2(x))) | |
| return x | |
| class CustomResidualAttentionBlock(nn.Module): | |
| def __init__( | |
| self, | |
| d_model: int, | |
| n_head: int, | |
| mlp_ratio: float = 4.0, | |
| ls_init_value: float = None, | |
| act_layer: Callable = nn.GELU, | |
| norm_layer: Callable = LayerNorm, | |
| scale_cosine_attn: bool = False, | |
| scale_heads: bool = False, | |
| scale_attn: bool = False, | |
| scale_fc: bool = False, | |
| ): | |
| super().__init__() | |
| self.ln_1 = norm_layer(d_model) | |
| self.attn = Attention( | |
| d_model, n_head, | |
| scaled_cosine=scale_cosine_attn, | |
| scale_heads=scale_heads, | |
| ) | |
| self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity() | |
| self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() | |
| self.ln_2 = norm_layer(d_model) | |
| mlp_width = int(d_model * mlp_ratio) | |
| self.mlp = nn.Sequential(OrderedDict([ | |
| ("c_fc", nn.Linear(d_model, mlp_width)), | |
| ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()), | |
| ("gelu", act_layer()), | |
| ("c_proj", nn.Linear(mlp_width, d_model)) | |
| ])) | |
| self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() | |
| def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): | |
| x = x + self.ls_1(self.ln_attn(self.attn(self.ln_1(x), attn_mask=attn_mask))) | |
| x = x + self.ls_2(self.mlp(self.ln_2(x))) | |
| return x | |
| class Transformer(nn.Module): | |
| def __init__( | |
| self, | |
| width: int, | |
| layers: int, | |
| heads: int, | |
| mlp_ratio: float = 4.0, | |
| ls_init_value: float = None, | |
| act_layer: Callable = nn.GELU, | |
| norm_layer: Callable = LayerNorm, | |
| xattn=False, | |
| ): | |
| super().__init__() | |
| self.width = width | |
| self.layers = layers | |
| self.grad_checkpointing = False | |
| logger.debug(f'xattn in transformer of CLIP is {xattn}') | |
| self.resblocks = nn.ModuleList([ | |
| ResidualAttentionBlock( | |
| width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer, | |
| xattn=xattn) | |
| for _ in range(layers) | |
| ]) | |
| def get_cast_dtype(self) -> torch.dtype: | |
| return self.resblocks[0].mlp.c_fc.weight.dtype | |
| def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): | |
| for r in self.resblocks: | |
| if self.grad_checkpointing and not torch.jit.is_scripting(): | |
| # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 | |
| x = checkpoint(r, x, None, None, attn_mask) | |
| else: | |
| x = r(x, attn_mask=attn_mask) | |
| return x | |
| class VisionTransformer(nn.Module): | |
| output_tokens: torch.jit.Final[bool] | |
| def __init__( | |
| self, | |
| image_size: int, | |
| patch_size: int, | |
| width: int, | |
| layers: int, | |
| heads: int, | |
| mlp_ratio: float, | |
| ls_init_value: float = None, | |
| global_average_pool: bool = False, | |
| attentional_pool: bool = False, | |
| n_queries: int = 256, | |
| attn_pooler_heads: int = 8, | |
| output_dim: int = 512, | |
| patch_dropout: float = 0., | |
| input_patchnorm: bool = False, | |
| act_layer: Callable = nn.GELU, | |
| norm_layer: Callable = LayerNorm, | |
| output_tokens: bool = False | |
| ): | |
| super().__init__() | |
| self.output_tokens = output_tokens | |
| image_height, image_width = self.image_size = to_2tuple(image_size) | |
| patch_height, patch_width = self.patch_size = to_2tuple(patch_size) | |
| self.grid_size = (image_height // patch_height, image_width // patch_width) | |
| self.output_dim = output_dim | |
| # whether to layernorm each patch, as done in dual patchnorm paper - https://arxiv.org/abs/2302.01327v1 | |
| self.input_patchnorm = input_patchnorm | |
| if input_patchnorm: | |
| patch_input_dim = patch_height * patch_width * 3 | |
| self.patchnorm_pre_ln = LayerNorm(patch_input_dim) | |
| self.conv1 = nn.Linear(patch_input_dim, width) | |
| else: | |
| self.patchnorm_pre_ln = nn.Identity() | |
| self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) | |
| # class embeddings and positional embeddings | |
| scale = width ** -0.5 | |
| self.class_embedding = nn.Parameter(scale * torch.randn(width)) | |
| self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width)) | |
| # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn | |
| self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() | |
| self.ln_pre = norm_layer(width) | |
| self.transformer = Transformer( | |
| width, | |
| layers, | |
| heads, | |
| mlp_ratio, | |
| ls_init_value=ls_init_value, | |
| act_layer=act_layer, | |
| norm_layer=norm_layer, | |
| ) | |
| self.global_average_pool = global_average_pool | |
| if attentional_pool: | |
| self.attn_pool = AttentionalPooler(output_dim, width, n_head=attn_pooler_heads, n_queries=n_queries) | |
| self.ln_post = norm_layer(output_dim) | |
| self.proj = nn.Parameter(scale * torch.randn(output_dim, output_dim)) | |
| else: | |
| self.attn_pool = None | |
| self.ln_post = norm_layer(width) | |
| self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) | |
| self.init_parameters() | |
| def lock(self, unlocked_groups=0, freeze_bn_stats=False): | |
| for param in self.parameters(): | |
| param.requires_grad = False | |
| if unlocked_groups != 0: | |
| groups = [ | |
| [ | |
| self.conv1, | |
| self.class_embedding, | |
| self.positional_embedding, | |
| self.ln_pre, | |
| ], | |
| *self.transformer.resblocks[:-1], | |
| [ | |
| self.transformer.resblocks[-1], | |
| self.ln_post, | |
| ], | |
| self.proj, | |
| ] | |
| def _unlock(x): | |
| if isinstance(x, Sequence): | |
| for g in x: | |
| _unlock(g) | |
| else: | |
| if isinstance(x, torch.nn.Parameter): | |
| x.requires_grad = True | |
| else: | |
| for p in x.parameters(): | |
| p.requires_grad = True | |
| _unlock(groups[-unlocked_groups:]) | |
| def init_parameters(self): | |
| # FIXME OpenAI CLIP did not define an init for the VisualTransformer | |
| # TODO experiment if default PyTorch init, below, or alternate init is best. | |
| # nn.init.normal_(self.class_embedding, std=self.scale) | |
| # nn.init.normal_(self.positional_embedding, std=self.scale) | |
| # | |
| # proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) | |
| # attn_std = self.transformer.width ** -0.5 | |
| # fc_std = (2 * self.transformer.width) ** -0.5 | |
| # for block in self.transformer.resblocks: | |
| # nn.init.normal_(block.attn.in_proj_weight, std=attn_std) | |
| # nn.init.normal_(block.attn.out_proj.weight, std=proj_std) | |
| # nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) | |
| # nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) | |
| # | |
| # if self.text_projection is not None: | |
| # nn.init.normal_(self.text_projection, std=self.scale) | |
| pass | |
| def set_grad_checkpointing(self, enable=True): | |
| self.transformer.grad_checkpointing = enable | |
| def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: | |
| if self.global_average_pool: | |
| return x.mean(dim=1), x | |
| else: | |
| return x[:, 0], x[:, 1:] | |
| def forward(self, x: torch.Tensor): | |
| # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1 | |
| if self.input_patchnorm: | |
| # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)') | |
| x = x.reshape(x.shape[0], x.shape[1], self.grid_size[0], self.patch_size[0], self.grid_size[1], self.patch_size[1]) | |
| x = x.permute(0, 2, 4, 1, 3, 5) | |
| x = x.reshape(x.shape[0], self.grid_size[0] * self.grid_size[1], -1) | |
| x = self.patchnorm_pre_ln(x) | |
| x = self.conv1(x) | |
| else: | |
| x = self.conv1(x) # shape = [*, width, grid, grid] | |
| x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] | |
| x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] | |
| # class embeddings and positional embeddings | |
| x = torch.cat( | |
| [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), | |
| x], dim=1) # shape = [*, grid ** 2 + 1, width] | |
| x = x + self.positional_embedding.to(x.dtype) | |
| # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in | |
| x = self.patch_dropout(x) | |
| x = self.ln_pre(x) | |
| x = x.permute(1, 0, 2) # NLD -> LND | |
| x = self.transformer(x) | |
| x = x.permute(1, 0, 2) # LND -> NLD | |
| if self.attn_pool is not None: | |
| x = self.attn_pool(x) | |
| x = self.ln_post(x) | |
| pooled, tokens = self._global_pool(x) | |
| else: | |
| pooled, tokens = self._global_pool(x) | |
| pooled = self.ln_post(pooled) | |
| if self.proj is not None: | |
| pooled = pooled @ self.proj | |
| if self.output_tokens: | |
| return pooled, tokens | |
| return pooled | |
| class TextTransformer(nn.Module): | |
| output_tokens: torch.jit.Final[bool] | |
| def __init__( | |
| self, | |
| context_length: int = 77, | |
| vocab_size: int = 49408, | |
| width: int = 512, | |
| heads: int = 8, | |
| layers: int = 12, | |
| ls_init_value: float = None, | |
| output_dim: int = 512, | |
| act_layer: Callable = nn.GELU, | |
| norm_layer: Callable = LayerNorm, | |
| embed_cls: bool = False, | |
| pad_id: int = 0, | |
| output_tokens: bool = False, | |
| ): | |
| super().__init__() | |
| self.output_tokens = output_tokens | |
| self.num_pos = self.context_length = context_length | |
| self.vocab_size = vocab_size | |
| self.width = width | |
| self.output_dim = output_dim | |
| self.heads = heads | |
| self.pad_id = pad_id | |
| self.text_projection = nn.Parameter(torch.empty(width, output_dim)) | |
| if embed_cls: | |
| self.cls_emb = nn.Parameter(torch.empty(width)) | |
| self.num_pos += 1 | |
| else: | |
| self.cls_emb = None | |
| self.token_embedding = nn.Embedding(vocab_size, width) | |
| self.positional_embedding = nn.Parameter(torch.empty(self.num_pos, width)) | |
| xattn = (os.getenv('FLASH_TXT', 'f') == 't') | |
| self.transformer = Transformer( | |
| width=width, | |
| layers=layers, | |
| heads=heads, | |
| ls_init_value=ls_init_value, | |
| act_layer=act_layer, | |
| norm_layer=norm_layer, | |
| xattn=xattn | |
| ) | |
| self.xattn = xattn | |
| self.ln_final = norm_layer(width) | |
| self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) | |
| self.init_parameters() | |
| def init_parameters(self): | |
| nn.init.normal_(self.token_embedding.weight, std=0.02) | |
| nn.init.normal_(self.positional_embedding, std=0.01) | |
| if self.cls_emb is not None: | |
| nn.init.normal_(self.cls_emb, std=0.01) | |
| proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) | |
| attn_std = self.transformer.width ** -0.5 | |
| fc_std = (2 * self.transformer.width) ** -0.5 | |
| for block in self.transformer.resblocks: | |
| nn.init.normal_(block.attn.in_proj_weight, std=attn_std) | |
| nn.init.normal_(block.attn.out_proj.weight, std=proj_std) | |
| nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) | |
| nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) | |
| if self.text_projection is not None: | |
| nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) | |
| def set_grad_checkpointing(self, enable=True): | |
| self.transformer.grad_checkpointing = enable | |
| def build_attention_mask(self): | |
| # lazily create causal attention mask, with full attention between the tokens | |
| # pytorch uses additive attention mask; fill with -inf | |
| mask = torch.empty(self.num_pos, self.num_pos) | |
| mask.fill_(float("-inf")) | |
| mask.triu_(1) # zero out the lower diagonal | |
| return mask | |
| def build_cls_mask(self, text, cast_dtype: torch.dtype): | |
| cls_mask = (text != self.pad_id).unsqueeze(1) | |
| cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=1.0) | |
| additive_mask = torch.empty(cls_mask.shape, dtype=cast_dtype, device=cls_mask.device) | |
| additive_mask.fill_(0) | |
| additive_mask.masked_fill_(~cls_mask, float("-inf")) | |
| additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0) | |
| return additive_mask | |
| def _repeat(self, t, N: int): | |
| return t.reshape(1, 1, -1).repeat(N, 1, 1) | |
| def forward(self, text): | |
| cast_dtype = self.transformer.get_cast_dtype() | |
| seq_len = text.shape[1] | |
| x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] | |
| attn_mask = self.attn_mask | |
| if self.cls_emb is not None: | |
| seq_len += 1 | |
| x = torch.cat([x, self._repeat(self.cls_emb, x.shape[0])], dim=1) | |
| cls_mask = self.build_cls_mask(text, cast_dtype) | |
| attn_mask = attn_mask[None, :seq_len, :seq_len] + cls_mask[:, :seq_len, :seq_len] | |
| x = x + self.positional_embedding[:seq_len].to(cast_dtype) | |
| x = x.permute(1, 0, 2) # NLD -> LND | |
| x = self.transformer(x, attn_mask=attn_mask) | |
| x = x.permute(1, 0, 2) # LND -> NLD | |
| # x.shape = [batch_size, n_ctx, transformer.width] | |
| # take features from the eot embedding (eot_token is the highest number in each sequence) | |
| if self.cls_emb is not None: | |
| pooled, tokens = x[:, -1], x[:, :-1] | |
| pooled = self.ln_final(pooled) | |
| else: | |
| x = self.ln_final(x) | |
| pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x | |
| if self.text_projection is not None: | |
| pooled = pooled @ self.text_projection | |
| if self.output_tokens: | |
| return pooled, tokens | |
| return pooled | |
| class MultimodalTransformer(Transformer): | |
| def __init__( | |
| self, | |
| width: int, | |
| layers: int, | |
| heads: int, | |
| context_length: int = 77, | |
| mlp_ratio: float = 4.0, | |
| ls_init_value: float = None, | |
| act_layer: Callable = nn.GELU, | |
| norm_layer: Callable = LayerNorm, | |
| output_dim: int = 512, | |
| ): | |
| super().__init__( | |
| width=width, | |
| layers=layers, | |
| heads=heads, | |
| mlp_ratio=mlp_ratio, | |
| ls_init_value=ls_init_value, | |
| act_layer=act_layer, | |
| norm_layer=norm_layer, | |
| ) | |
| self.context_length = context_length | |
| self.cross_attn = nn.ModuleList([ | |
| ResidualAttentionBlock( | |
| width, | |
| heads, | |
| mlp_ratio, | |
| ls_init_value=ls_init_value, | |
| act_layer=act_layer, | |
| norm_layer=norm_layer, | |
| is_cross_attention=True, | |
| ) | |
| for _ in range(layers) | |
| ]) | |
| self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) | |
| self.ln_final = norm_layer(width) | |
| self.text_projection = nn.Parameter(torch.empty(width, output_dim)) | |
| def init_parameters(self): | |
| proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) | |
| attn_std = self.transformer.width ** -0.5 | |
| fc_std = (2 * self.transformer.width) ** -0.5 | |
| for block in self.transformer.resblocks: | |
| nn.init.normal_(block.attn.in_proj_weight, std=attn_std) | |
| nn.init.normal_(block.attn.out_proj.weight, std=proj_std) | |
| nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) | |
| nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) | |
| for block in self.transformer.cross_attn: | |
| nn.init.normal_(block.attn.in_proj_weight, std=attn_std) | |
| nn.init.normal_(block.attn.out_proj.weight, std=proj_std) | |
| nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) | |
| nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) | |
| if self.text_projection is not None: | |
| nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) | |
| def build_attention_mask(self): | |
| # lazily create causal attention mask, with full attention between the tokens | |
| # pytorch uses additive attention mask; fill with -inf | |
| mask = torch.empty(self.context_length, self.context_length) | |
| mask.fill_(float("-inf")) | |
| mask.triu_(1) # zero out the lower diagonal | |
| return mask | |
| def forward(self, image_embs, text_embs): | |
| text_embs = text_embs.permute(1, 0, 2) # NLD -> LNDsq | |
| image_embs = image_embs.permute(1, 0, 2) # NLD -> LND | |
| seq_len = text_embs.shape[0] | |
| for resblock, cross_attn in zip(self.resblocks, self.cross_attn): | |
| if self.grad_checkpointing and not torch.jit.is_scripting(): | |
| # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 | |
| text_embs = checkpoint(resblock, text_embs, None, None, self.attn_mask[:seq_len, :seq_len]) | |
| text_embs = checkpoint(cross_attn, text_embs, image_embs, image_embs, None) | |
| else: | |
| text_embs = resblock(text_embs, attn_mask=self.attn_mask[:seq_len, :seq_len]) | |
| text_embs = cross_attn(text_embs, k_x=image_embs, v_x=image_embs) | |
| x = text_embs.permute(1, 0, 2) # LND -> NLD | |
| x = self.ln_final(x) | |
| if self.text_projection is not None: | |
| x = x @ self.text_projection | |
| return x | |
| def set_grad_checkpointing(self, enable=True): | |
| self.grad_checkpointing = enable | |