step1x3d_geometry/models/conditional_encoders/clip/modeling_conditional_clip.py

# coding=utf-8
# Copyright 2023 Meta AI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Reference:
# * transformers/models/dinov2/modeling_dinov2.py
# * https://github.com/facebookresearch/DiT/blob/main/models.py#L101
# * https://github.com/3DTopia/OpenLRM/tree/main/openlrm/models/encoders/dinov2
"""PyTorch CLIP model."""

from typing import Dict, List, Optional, Set, Tuple, Union

import torch
import torch.nn as nn

from .modeling_clip import (
    CLIPConfig,
    CLIPTextConfig,
    CLIPVisionConfig,
    CLIPEncoderLayer,
    CLIPTextTransformer,
    CLIPVisionTransformer,
    CLIPModel,
    CLIPVisionEmbeddings,
    CLIPVisionModel,
    CLIPOutput,
    BaseModelOutput,
    BaseModelOutputWithPooling,
)


class ModLN(nn.Module):
    def __init__(self, inner_dim: int, mod_dim: int = 32):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.SiLU(),
            nn.Linear(mod_dim, inner_dim * 2),
        )

        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.zeros_(m.weight)
                nn.init.zeros_(m.bias)

    def forward(self, x: torch.Tensor, condition: torch.Tensor):
        """
        x: [N, M, C_in], M: num of tokens
        condition: [N, C_mod]
        """
        shift, scale = self.mlp(condition).unsqueeze(1).chunk(2, dim=-1)
        return x * (1 + scale) + shift


class ConditionalCLIPVisionConfig(CLIPVisionConfig):
    def __init__(self, modulation_dim: int = 32, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.modulation_dim = modulation_dim


class ConditionalCLIPEncoderLayer(CLIPEncoderLayer):
    """This corresponds to the Block class in the original implementation."""

    def __init__(self, config: ConditionalCLIPVisionConfig) -> None:
        super().__init__(config)
        self.mod_norm1 = ModLN(config.hidden_size, config.modulation_dim)
        self.mod_norm2 = ModLN(config.hidden_size, config.modulation_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        causal_attention_mask: torch.Tensor,
        condition: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        residual = hidden_states

        hidden_states = self.mod_norm1(self.layer_norm1(hidden_states), condition)
        hidden_states, attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            causal_attention_mask=causal_attention_mask,
            output_attentions=output_attentions,
        )
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.mod_norm2(self.layer_norm2(hidden_states), condition)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class ConditionalCLIPEncoder(nn.Module):
    def __init__(self, config: CLIPConfig) -> None:
        super().__init__()
        self.config = config
        self.layers = nn.ModuleList(
            [
                ConditionalCLIPEncoderLayer(config)
                for _ in range(config.num_hidden_layers)
            ]
        )
        self.gradient_checkpointing = False

    def forward(
        self,
        inputs_embeds,
        attention_mask: Optional[torch.Tensor] = None,
        causal_attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        condition: Optional[torch.Tensor] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BaseModelOutput]:
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        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
        all_attentions = () if output_attentions 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 = self._gradient_checkpointing_func(
                    encoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    causal_attention_mask,
                    condition=condition,
                    output_attentions=output_attentions,
                )
            else:
                layer_outputs = encoder_layer(
                    hidden_states,
                    attention_mask,
                    causal_attention_mask,
                    condition=condition,
                    output_attentions=output_attentions,
                )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states,)

        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, encoder_states, all_attentions]
                if v is not None
            )
        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=encoder_states,
            attentions=all_attentions,
        )


class ConditionalCLIPVisionTransformer(CLIPVisionTransformer):
    def __init__(self, config: ConditionalCLIPVisionConfig):
        super().__init__(config)
        self.config = config
        embed_dim = config.hidden_size

        self.embeddings = CLIPVisionEmbeddings(config)
        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
        self.encoder = ConditionalCLIPEncoder(config)
        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)

    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        condition: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPooling]:
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        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:
            raise ValueError("You have to specify pixel_values")

        hidden_states = self.embeddings(pixel_values)
        hidden_states = self.pre_layrnorm(hidden_states)

        encoder_outputs = self.encoder(
            inputs_embeds=hidden_states,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            condition=condition,
            return_dict=return_dict,
        )

        last_hidden_state = encoder_outputs[0]
        pooled_output = last_hidden_state[:, 0, :]
        pooled_output = self.post_layernorm(pooled_output)

        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,
        )


class ConditionalCLIPVisionModel(CLIPVisionModel):
    config_class = ConditionalCLIPVisionConfig

    def __init__(self, config: ConditionalCLIPVisionConfig):
        super().__init__(config)
        self.vision_model = ConditionalCLIPVisionTransformer(config)
        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        condition: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPooling]:
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        return self.vision_model(
            pixel_values=pixel_values,
            condition=condition,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )


class ConditionalCLIPModel(CLIPModel):
    config_class = CLIPConfig

    def __init__(self, config: CLIPConfig):
        super().__init__(config)

        if not isinstance(config.text_config, CLIPTextConfig):
            raise ValueError(
                "config.text_config is expected to be of type CLIPTextConfig but is of type"
                f" {type(config.text_config)}."
            )

        if not isinstance(config.vision_config, CLIPVisionConfig):
            raise ValueError(
                "config.vision_config is expected to be of type CLIPVisionConfig but is of type"
                f" {type(config.vision_config)}."
            )

        text_config = config.text_config
        vision_config = config.vision_config

        self.projection_dim = config.projection_dim
        self.text_embed_dim = text_config.hidden_size
        self.vision_embed_dim = vision_config.hidden_size

        self.text_model = CLIPTextTransformer(text_config)
        self.vision_model = ConditionalCLIPVisionTransformer(vision_config)

        self.visual_projection = nn.Linear(
            self.vision_embed_dim, self.projection_dim, bias=False
        )
        self.text_projection = nn.Linear(
            self.text_embed_dim, self.projection_dim, bias=False
        )
        self.logit_scale = nn.Parameter(
            torch.tensor(self.config.logit_scale_init_value)
        )

        # Initialize weights and apply final processing
        self.post_init()

    def get_image_features(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        condition: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> torch.FloatTensor:
        # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        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
        )

        vision_outputs = self.vision_model(
            pixel_values=pixel_values,
            condition=condition,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = vision_outputs[1]  # pooled_output
        image_features = self.visual_projection(pooled_output)

        return image_features

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        condition: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        return_loss: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CLIPOutput]:
        # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        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
        )

        vision_outputs = self.vision_model(
            pixel_values=pixel_values,
            condition=condition,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        text_outputs = self.text_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        image_embeds = vision_outputs[1]
        image_embeds = self.visual_projection(image_embeds)

        text_embeds = text_outputs[1]
        text_embeds = self.text_projection(text_embeds)

        # normalized features
        image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
        text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)

        # cosine similarity as logits
        logit_scale = self.logit_scale.exp()
        logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
        logits_per_image = logits_per_text.t()

        loss = None
        if return_loss:
            loss = clip_loss(logits_per_text)

        if not return_dict:
            output = (
                logits_per_image,
                logits_per_text,
                text_embeds,
                image_embeds,
                text_outputs,
                vision_outputs,
            )
            return ((loss,) + output) if loss is not None else output

        return CLIPOutput(
            loss=loss,
            logits_per_image=logits_per_image,
            logits_per_text=logits_per_text,
            text_embeds=text_embeds,
            image_embeds=image_embeds,
            text_model_output=text_outputs,
            vision_model_output=vision_outputs,
        )