modeling_japanese_clip.py

import collections.abc
import math
from collections import OrderedDict
from itertools import repeat
from typing import Callable, Optional, Sequence, Tuple

import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils.checkpoint import checkpoint

from transformers import AutoModel, PreTrainedModel

from .configuration_japanese_clip import JapaneseCLIPConfig


class LayerNorm(nn.LayerNorm):
    """Subclass torch's LayerNorm (with cast back to input dtype)."""

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        orig_dtype = x.dtype
        x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        return x.to(dtype=orig_dtype)


class LayerScale(nn.Module):
    def __init__(self, dim, init_values=1e-5, inplace=False):
        super().__init__()
        self.inplace = inplace
        self.gamma = nn.Parameter(torch.ones(dim) * init_values)
    
    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 < 1.0
        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 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(q.unsqueeze(1).expand(-1, N, -1), x, x, need_weights=False)[0]
        return out.permute(1, 0, 2)  # LND -> NLD


class ResidualAttentionBlock(nn.Module):
    def __init__(
        self,
        d_model: int,
        n_head: int,
        mlp_ratio: float = 4.0,
        ls_init_value: Optional[float] = None,
        act_layer: Callable = nn.GELU,
        norm_layer: Callable = LayerNorm,
        is_cross_attention: bool = False,
    ):
        super().__init__()

        self.ln_1 = norm_layer(d_model)
        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()
        if is_cross_attention:
            self.ln_1_kv = norm_layer(d_model)
        
        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()

    def attention(
            self,
            q_x: torch.Tensor,
            k_x: Optional[torch.Tensor] = None,
            v_x: Optional[torch.Tensor] = None,
            attn_mask: Optional[torch.Tensor] = None,
    ):
        k_x = k_x if k_x is not None else q_x
        v_x = v_x if v_x is not None else q_x

        attn_mask = attn_mask.to(q_x.dtype) if attn_mask is not None else None
        return self.attn(
            q_x, k_x, v_x, need_weights=False, attn_mask=attn_mask
        )[0]

    def forward(
            self,
            q_x: torch.Tensor,
            k_x: Optional[torch.Tensor] = None,
            v_x: Optional[torch.Tensor] = None,
            attn_mask: Optional[torch.Tensor] = None,
    ):
        k_x = self.ln_1_kv(k_x) if hasattr(self, "ln_1_kv") and k_x is not None else None
        v_x = self.ln_1_kv(v_x) if hasattr(self, "ln_1_kv") and v_x is not None else None

        x = q_x + self.ls_1(self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask))
        x = x + self.ls_2(self.mlp(self.ln_2(x)))
        return x


# From PyTorch internals
def _ntuple(n):
    def parse(x):
        if isinstance(x, collections.abc.Iterable):
            return x
        return tuple(repeat(x, n))
    return parse

to_2tuple = _ntuple(2)


def _expand_token(token, batch_size: int):
    return token.view(1, 1, -1).expand(batch_size, -1, -1)


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,
    ):
        super().__init__()
        self.width = width
        self.layers = layers
        self.grad_checkpointing = False

        self.resblocks = nn.ModuleList([
            ResidualAttentionBlock(
                width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer)
            for _ in range(layers)
        ])

    def get_cast_dtype(self) -> torch.dtype:
        if hasattr(self.resblocks[0].mlp.c_fc, 'int8_original_dtype'):
            return self.resblocks[0].mlp.c_fc.int8_original_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 JapaneseCLIPVisionTransformer(PreTrainedModel):
    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,
            attentional_pool: bool = False,
            attn_pooler_queries: int = 256,
            attn_pooler_heads: int = 8,
            output_dim: int = 512,
            patch_dropout: float = 0.,
            no_ln_pre: bool = False,
            pool_type: str = 'tok',
            final_ln_after_pool: bool = False,
            act_layer: Callable = nn.GELU,
            norm_layer: Callable = LayerNorm,
            output_tokens: bool = False,
            **kwargs,
    ):
        super().__init__()
        assert pool_type in ('tok', 'avg', 'none')
        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.final_ln_after_pool = final_ln_after_pool  # currently ignored w/ attn pool enabled
        self.output_dim = output_dim

        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 = nn.Identity() if no_ln_pre else 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,
        )

        if attentional_pool:
            if isinstance(attentional_pool, str):
                self.attn_pool_type = attentional_pool
                self.pool_type = 'none'
                if attentional_pool in ('parallel', 'cascade'):
                    self.attn_pool = AttentionalPooler(
                        output_dim,
                        width,
                        n_head=attn_pooler_heads,
                        n_queries=attn_pooler_queries,
                    )
                    self.attn_pool_contrastive = AttentionalPooler(
                        output_dim,
                        width,
                        n_head=attn_pooler_heads,
                        n_queries=1,
                    )
                else:
                    assert False
            else:
                self.attn_pool_type = ''
                self.pool_type = pool_type
                self.attn_pool = AttentionalPooler(
                    output_dim,
                    width,
                    n_head=attn_pooler_heads,
                    n_queries=attn_pooler_queries,
                )
                self.attn_pool_contrastive = None
            pool_dim = output_dim
        else:
            self.attn_pool = None
            pool_dim = width
            self.pool_type = pool_type

        self.ln_post = norm_layer(pool_dim)
        self.proj = nn.Parameter(scale * torch.randn(pool_dim, 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

    @torch.jit.ignore
    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.pool_type == 'avg':
            pooled, tokens = x[:, 1:].mean(dim=1), x[:, 1:]
        elif self.pool_type == 'tok':
            pooled, tokens = x[:, 0], x[:, 1:]
        else:
            pooled = tokens = x

        return pooled, tokens

    def forward(self, x: torch.Tensor):
        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([_expand_token(self.class_embedding, x.shape[0]).to(x.dtype), x], dim=1)
        # shape = [*, grid ** 2 + 1, width]
        x = x + self.positional_embedding.to(x.dtype)

        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:
            if self.attn_pool_contrastive is not None:
                # This is untested, WIP pooling that should match paper
                x = self.ln_post(x)  # TBD LN first or separate one after each pool?
                tokens = self.attn_pool(x)
                if self.attn_pool_type == 'parallel':
                    pooled = self.attn_pool_contrastive(x)
                else:
                    assert self.attn_pool_type == 'cascade'
                    pooled = self.attn_pool_contrastive(tokens)
            else:
                # this is the original OpenCLIP CoCa setup, does not match paper
                x = self.attn_pool(x)
                x = self.ln_post(x)
                pooled, tokens = self._global_pool(x)
        elif self.final_ln_after_pool:
            pooled, tokens = self._global_pool(x)
            pooled = self.ln_post(pooled)
        else:
            x = self.ln_post(x)
            pooled, tokens = self._global_pool(x)

        if self.proj is not None:
            pooled = pooled @ self.proj

        if self.output_tokens:
            return pooled, tokens
        
        return pooled


class JapaneseCLIPModel(PreTrainedModel):
    config_class = JapaneseCLIPConfig

    def __init__(self, config: JapaneseCLIPConfig):
        super().__init__(config)
        text_config = config.text_config
        vision_config = config.vision_config

        self.image_encoder = JapaneseCLIPVisionTransformer(
            **vision_config.to_dict()
        )
        self.text_encoder = AutoModel.from_config(text_config, add_pooling_layer=False)
        hidden_size = text_config.hidden_size
        self.projection_dim = self.image_encoder.output_dim
        self.text_projection = nn.Linear(hidden_size, self.projection_dim, bias=False)
        self.logit_scale = nn.Parameter(torch.ones([]) * math.log(1 / 0.07))
        self.max_length = config.max_length
        self.position_ids = list(range(0, self.max_length))
    
    def _create_position_id_tensor(self, batch_size: int) -> torch.LongTensor:
        # rinna/japanese-roberta-base requires providing custom position ids
        # see: https://huggingface.co./rinna/japanese-roberta-base#note-3-provide-position_ids-as-an-argument-explicitly
        return torch.LongTensor([self.position_ids for _ in range(batch_size)])

    def get_image_features(self, pixel_values: torch.FloatTensor) -> torch.FloatTensor:
        return self.image_encoder(pixel_values)  # (batch_size, hidden_dim)

    def get_text_features(
        self, input_ids: torch.Tensor, position_ids: torch.Tensor = None
    ) -> torch.FloatTensor:
        if position_ids is None:
            position_ids = self._create_position_id_tensor(input_ids.size(0)).to(
                input_ids.device
            )
        last_hidden_state = self.text_encoder(
            input_ids=input_ids,
            position_ids=position_ids,
            output_hidden_states=True,
            return_dict=True,
        ).hidden_states[
            -1
        ]  # (batch_size, tokens, embed_dim)
        pooled_output = last_hidden_state[:, 0, :]  # (batch_size, embed_dim)
        return self.text_projection(pooled_output)  # (batch_size, hidden_dim)
    
    def forward(
        self,
        pixel_values: torch.FloatTensor,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor = None,
    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
        """
        DDPを使うときはこのメソッドを経由しなければならない
        他のメソッドで得られた勾配はGPU間で同期されない
        """
        image_features = self.get_image_features(pixel_values)
        text_features = self.get_text_features(input_ids, position_ids)
        return image_features, text_features, self.logit_scale