modeling.py

import torch
import torch.nn as nn

from typing import Optional, Union, Tuple

from transformers.models.vision_encoder_decoder.modeling_vision_encoder_decoder import (
    shift_tokens_right,
    VisionEncoderDecoderModel
)
from transformers.modeling_outputs import Seq2SeqLMOutput
from transformers import PreTrainedModel
from transformers.models.pixtral.modeling_pixtral import apply_rotary_pos_emb, PixtralAttention, PixtralVisionModel
from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
from transformers.modeling_outputs import BaseModelOutput

from pixtral_encoder_decoder.configuration import PixtralVisionModelBatchConfig, VisionPixtralEncoderDecoderConfig


def position_ids_in_meshgrid_batch(patch_embeds, max_width):
    """get the position ids of the batch. """
    # unlike flattened patch_embeds, we use the padded ones, which mean each entry has the same w/h and thus the same ids
    height, width = patch_embeds.shape[-2:]
    mesh = torch.meshgrid(torch.arange(height), torch.arange(width), indexing="ij")
    h_grid, v_grid = torch.stack(mesh, dim=-1).reshape(-1, 2).chunk(2, -1)
    ids = h_grid * max_width + v_grid
    # expand ids to batch size
    ids = ids.reshape(1, -1).repeat(patch_embeds.shape[0], 1)
    return ids


def create_attention_mask_batch(w, h, image_sizes, patch_size):
    def foo(i, j):
        return ((torch.arange(h).unsqueeze(1) < i) & (torch.arange(w).unsqueeze(0) < j)).float()

    mask = [foo(size[0] // patch_size, size[1] // patch_size) for size in image_sizes]
    return torch.stack(mask, dim=0)


def pixtral_attention_fix_forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
    """Input shape: Batch x Time x Channel"""

    batch_size, patches, _ = hidden_states.size()

    query_states = self.q_proj(hidden_states)
    key_states = self.k_proj(hidden_states)
    value_states = self.v_proj(hidden_states)

    query_states = query_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
    key_states = key_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)
    value_states = value_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2)

    cos, sin = position_embeddings
    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, unsqueeze_dim=1)

    attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale

    if attention_mask is not None:
        attn_weights = attn_weights + attention_mask

    # upcast attention to fp32
    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
    attn_output = torch.matmul(attn_weights, value_states)

    attn_output = attn_output.transpose(1, 2).contiguous()
    attn_output = attn_output.reshape(batch_size, patches, -1)

    attn_output = self.o_proj(attn_output)

    return attn_output, attn_weights


# monkey patch a fix for unsqueeze dim for position embedds (since our input is batched and the old one is not)
PixtralAttention.forward = pixtral_attention_fix_forward


class PixtralVisionModelBatch(PixtralVisionModel):
    config_class = PixtralVisionModelBatchConfig

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

    def forward(
            self,
            pixel_values: torch.Tensor,
            image_sizes: Optional[torch.Tensor] = None,
            attention_mask: Optional[torch.Tensor] = None,
            output_hidden_states: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            return_dict: Optional[bool] = None,
            *args,
            **kwargs,
    ) -> Union[Tuple, BaseModelOutput]:
        """
        Returns:
            pixel_values: tensor of token features for
                all tokens of all images of shape (N_toks, D)
        """
        if attention_mask is None and image_sizes is None:
            raise ValueError("Either `attention_mask` or `image_sizes` must be defined")
        # pass images through initial convolution independently
        patch_embeds = self.patch_conv(pixel_values)
        # build attention mask based on image_sizes if not provided
        if attention_mask is None:
            h, w = patch_embeds.shape[-2:]
            attention_mask = create_attention_mask_batch(w, h, image_sizes, self.patch_size).to(patch_embeds.device)
            attention_mask = attention_mask.flatten(start_dim=-2)

        # positional embeddings
        position_ids = position_ids_in_meshgrid_batch(
            patch_embeds, max_width=self.config.image_size // self.config.patch_size
        )
        position_embeddings = self.patch_positional_embedding(patch_embeds, position_ids)

        # flatten patch_embeds
        # seq_len = (h*w); hidden x seq_len -> seq_len x hidden.
        patch_embeds = patch_embeds.flatten(start_dim=-2).transpose(-1, -2)

        attention_mask = _prepare_4d_attention_mask(attention_mask, torch.float)

        patch_embeds = self.ln_pre(patch_embeds)

        out = self.transformer(
            patch_embeds,
            attention_mask=attention_mask,
            position_embeddings=position_embeddings,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
            return_dict=return_dict,
        )
        return out


class VisionPixtralEncoderDecoder(VisionEncoderDecoderModel):
    config_class = VisionPixtralEncoderDecoderConfig

    def __init__(self, config,
                 encoder: Optional[PixtralVisionModelBatch] = None,
                 decoder: Optional[PreTrainedModel] = None):
        super().__init__(config, encoder, decoder)

    def forward(
            self,
            pixel_values: Optional[torch.Tensor] = None,
            image_sizes: Optional[torch.Tensor] = None,
            encoder_attention_mask: Optional[torch.Tensor] = None,
            decoder_input_ids: Optional[torch.LongTensor] = None,
            decoder_attention_mask: Optional[torch.BoolTensor] = None,
            encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
            decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
            labels: Optional[torch.LongTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
            **kwargs,
    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # num_items_in_batch is only needed for loss computation
        num_items_in_batch = kwargs.pop("num_items_in_batch", None)

        kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}

        kwargs_decoder = {
            argument[len("decoder_"):]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
        }

        if encoder_outputs is None:
            if pixel_values is None:
                raise ValueError("You have to specify pixel_values")
            if encoder_attention_mask is None and image_sizes is None:
                raise ValueError("Either `encoder_attention_mask` or `image_sizes` must be defined")
            if encoder_attention_mask is None:
                h, w = pixel_values.shape[-2:]
                h = h // self.encoder.patch_size  # simulate convolution to get num_patches
                w = w // self.encoder.patch_size  # simulate convolution to get num_patches
                encoder_attention_mask = create_attention_mask_batch(w, h, image_sizes, self.encoder.patch_size)
                encoder_attention_mask = encoder_attention_mask.to(pixel_values.device)
                encoder_attention_mask = encoder_attention_mask.flatten(start_dim=-2)

            encoder_outputs = self.encoder(
                pixel_values=pixel_values,
                image_sizes=image_sizes,
                attention_mask=encoder_attention_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                **kwargs_encoder,
            )
        elif isinstance(encoder_outputs, tuple):
            encoder_outputs = BaseModelOutput(*encoder_outputs)

        encoder_hidden_states = encoder_outputs[0]

        # optionally project encoder_hidden_states
        if (
                self.encoder.config.hidden_size != self.decoder.config.hidden_size
                and self.decoder.config.cross_attention_hidden_size is None
        ):
            encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)

        # else:
        # encoder_attention_mask = None

        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
            decoder_input_ids = shift_tokens_right(
                labels, self.config.pad_token_id, self.config.decoder_start_token_id
            )

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            use_cache=use_cache,
            past_key_values=past_key_values,
            return_dict=return_dict,
            **kwargs_decoder,
        )

        # Compute loss independent from decoder (as some shift the logits inside them)
        loss = None
        if labels is not None:
            logits = decoder_outputs.logits if return_dict else decoder_outputs[0]

            loss = self.loss_function(
                logits=logits,
                labels=labels,
                vocab_size=self.decoder.config.vocab_size,
                num_items_in_batch=num_items_in_batch,
            )

        if not return_dict:
            if loss is not None:
                return (loss,) + decoder_outputs + encoder_outputs
            else:
                return decoder_outputs + encoder_outputs

        return Seq2SeqLMOutput(
            loss=loss,
            logits=decoder_outputs.logits,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )