seemore.py

import base64
import io
import pandas as pd
from PIL import Image
import torchvision.transforms as transforms
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn import init


batch_size = 16 # how many independent sequences will we process in parallel?
block_size = 32 # what is the maximum context length for predictions?
max_iters = 100
eval_interval = 10
learning_rate = 1e-3
epochs=1
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 40
num_blks= 3
head_size = 16
n_embd = 128
n_head = 8
n_layer = 8
dropout = 0.1
img_size=96
patch_size =16
image_embed_dim = 512
emb_dropout = blk_dropout =0.1

# Ensure every computation happens on the GPU when available
device = 'cuda' if torch.cuda.is_available() else 'cpu'

#To build the encoding and decoding functions we use the tinyshakespear dataset. However for the sake of brevity we do not pretrain the decoder model on it
#the training function should be able to do it without an issue as well as it could take both images and text
text_path = "./input.txt"
with open(text_path, 'r', encoding='utf-8') as f:
    text = f.read()

# here are all the unique characters that occur in this text
chars = sorted(list(set(text)))
# create a mapping from characters to integers
stoi = { ch:i for i,ch in enumerate(chars) }
stoi['<pad>']= 65
itos = { i:ch for i,ch in enumerate(chars) }
itos[65] = '<pad>'
encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers
decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
vocab_size = len(stoi.keys())

class PatchEmbeddings(nn.Module):
    def __init__(self, img_size=96, patch_size=16, hidden_dim=512):
        super().__init__()
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = (img_size // patch_size) ** 2
        # Ensure the convolution outputs a feature map with hidden_dim channels
        self.conv = nn.Conv2d(in_channels=3, out_channels=hidden_dim,
                              kernel_size=patch_size, stride=patch_size)

    def forward(self, X):
        X = self.conv(X)
        X = X.flatten(2)  # Flatten the patch dimensions
        X = X.transpose(1, 2)  # [B, num_patches, hidden_dim]
        return X

#swapping linear for lazy linear for simplicity. Lazylinear can accept any arbitrary input dimension without having it specified

class MLP(nn.Module):
    def __init__(self, n_embd, dropout=0.1, is_decoder=True):
        super().__init__()
        layers = [
            nn.Linear(n_embd, 4 * n_embd),
            nn.ReLU() if is_decoder else nn.GELU(),
            nn.Linear(4 * n_embd, n_embd),
            nn.Dropout(dropout)
        ]
        self.net = nn.Sequential(*layers)

    def forward(self, x):
        return self.net(x)

class Head(nn.Module):
    def __init__(self, n_embd, head_size, dropout=0.1, is_decoder=False):
        super().__init__()
        self.key = nn.Linear(n_embd, head_size, bias=False)
        self.query = nn.Linear(n_embd, head_size, bias=False)
        self.value = nn.Linear(n_embd, head_size, bias=False)
        self.dropout = nn.Dropout(dropout)
        self.is_decoder = is_decoder

    def forward(self, x):
        B, T, C = x.shape
        k = self.key(x)
        q = self.query(x)
        v = self.value(x)

        # Compute attention scores
        wei = q @ k.transpose(-2, -1) * (C**-0.5)
        if self.is_decoder:
            # Ensure the mask is the correct size for the current sequence length
            tril = torch.tril(torch.ones(T, T, dtype=torch.bool, device=device))
            wei = wei.masked_fill(tril == 0, float('-inf'))

        # Apply softmax to get probabilities
        wei = F.softmax(wei, dim=-1)
        wei = self.dropout(wei)

        # Perform weighted aggregation of values
        out = wei @ v
        return out

class MultiHeadAttention(nn.Module):
    def __init__(self, n_embd, num_heads, dropout=0.1, is_decoder=False):
        super().__init__()
        #Using assert statements for this type of checks is a good idea in general in your code
        assert n_embd % num_heads == 0, "n_embd must be divisible by num_heads"
        self.heads = nn.ModuleList([
            Head(n_embd, n_embd // num_heads, dropout, is_decoder)
            for _ in range(num_heads)
        ])
        self.proj = nn.Linear(n_embd, n_embd)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        out = torch.cat([h(x) for h in self.heads], dim=-1)
        out = self.dropout(self.proj(out))
        return out

class Block(nn.Module):
    def __init__(self, n_embd, num_heads, dropout=0.1, is_decoder=False):
        super().__init__()
        self.ln1 = nn.LayerNorm(n_embd)
        self.attn = MultiHeadAttention(n_embd, num_heads, dropout, is_decoder)
        self.ln2 = nn.LayerNorm(n_embd)
        self.ffn = nn.Sequential(
            nn.Linear(n_embd, 4 * n_embd),
            nn.GELU(),
            nn.Linear(4 * n_embd, n_embd),
        )

    def forward(self, x):
        original_x = x  # Save for residual connection
        x = self.ln1(x)
        attn_output = self.attn(x)
        x = original_x + attn_output
        x = self.ln2(x)
        ffn_output = self.ffn(x)
        x = x + ffn_output
        return x

class ViT(nn.Module):
    def __init__(self, img_size, patch_size, num_hiddens, num_heads, num_blks, emb_dropout, blk_dropout):
        super().__init__()
        self.patch_embedding = PatchEmbeddings(img_size, patch_size, num_hiddens)
        self.cls_token = nn.Parameter(torch.zeros(1, 1, num_hiddens))
        num_patches = (img_size // patch_size) ** 2
        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, num_hiddens))
        self.dropout = nn.Dropout(emb_dropout)
        self.blocks = nn.ModuleList([Block(num_hiddens, num_heads, blk_dropout, is_decoder=False) for _ in range(num_blks)])
        self.layer_norm = nn.LayerNorm(num_hiddens)

    def forward(self, X):
        x = self.patch_embedding(X)
        cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x += self.pos_embedding
        x = self.dropout(x)
        for block in self.blocks:
            x = block(x)
        x = self.layer_norm(x[:, 0])
        return x

class MultiModalProjector(nn.Module):
    def __init__(self, n_embd, image_embed_dim, dropout=0.1):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(image_embed_dim, 4 * image_embed_dim),
            nn.GELU(),
            nn.Linear(4 * image_embed_dim, n_embd),
            nn.Dropout(dropout)
        )

    def forward(self, x):
        x = self.net(x)
        return x

class DecoderLanguageModel(nn.Module):
    def __init__(self, n_embd, image_embed_dim, vocab_size, num_heads, n_layer, use_images=False):
        super().__init__()
        self.use_images = use_images
        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
        self.position_embedding_table = nn.Embedding(1000, n_embd)
        if use_images:
            self.image_projection = MultiModalProjector(n_embd, image_embed_dim)
        self.blocks = nn.Sequential(*[Block(n_embd, num_heads, is_decoder=True) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd)
        self.lm_head = nn.Linear(n_embd, vocab_size)

    def forward(self, idx, image_embeds=None, targets=None):
        tok_emb = self.token_embedding_table(idx)
        if self.use_images and image_embeds is not None:
            img_emb = self.image_projection(image_embeds).unsqueeze(1)
            tok_emb = torch.cat([img_emb, tok_emb], dim=1)
        pos_emb = self.position_embedding_table(torch.arange(tok_emb.size(1), device=device)).unsqueeze(0)
        x = tok_emb + pos_emb
        x = self.blocks(x)
        x = self.ln_f(x)
        logits = self.lm_head(x)
        if targets is not None:
            if self.use_images and image_embeds is not None:
                batch_size = idx.size(0)
                targets = torch.cat([torch.full((batch_size, 1), -100, dtype=torch.long, device=device), targets], dim=1)
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-100)
            return logits, loss
        return logits

    def generate(self, idx, image_embeds, max_new_tokens):
        B, T = idx.shape
        generated = idx

        if self.use_images and image_embeds is not None:
            img_emb = self.image_projection(image_embeds).unsqueeze(1)
            current_output = torch.cat([img_emb, self.token_embedding_table(idx)], dim=1)
        else:
            current_output = self.token_embedding_table(idx)

        for i in range(max_new_tokens):
            T_current = current_output.size(1)
            current_pos_emb = self.position_embedding_table(torch.arange(T_current, device=device)).unsqueeze(0)
            current_output += current_pos_emb

            for block in self.blocks:
                current_output = block(current_output)

            logits = self.lm_head(current_output[:, -1, :])
            probs = F.softmax(logits, dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
            generated = torch.cat((generated, idx_next), dim=1)
            idx_next_emb = self.token_embedding_table(idx_next)
            current_output = torch.cat((current_output, idx_next_emb), dim=1)

        return generated

class VisionLanguageModel(nn.Module):
    def __init__(self, n_embd, image_embed_dim, vocab_size, n_layer, img_size, patch_size, num_heads, num_blks, emb_dropout, blk_dropout):
        super().__init__()
        num_hiddens = image_embed_dim  # Set num_hiddens equal to image_embed_dim
        assert num_hiddens % num_heads == 0, "num_hiddens must be divisible by num_heads"
        self.vision_encoder = ViT(img_size, patch_size, num_hiddens, num_heads, num_blks, emb_dropout, blk_dropout)
        self.decoder = DecoderLanguageModel(n_embd, image_embed_dim, vocab_size, num_heads, n_layer, use_images=True)

    def forward(self, img_array, idx, targets=None):
        image_embeds = self.vision_encoder(img_array)

        if image_embeds.nelement() == 0 or image_embeds.shape[1] == 0:
            raise ValueError("somethign is messed up with the ViT model. It's returning an empty tensor or the embedding dimension is empty")

        if targets is not None:
            logits, loss = self.decoder(idx, image_embeds, targets)
            return logits, loss
        else:
            logits = self.decoder(idx, image_embeds)
            return logits

    def generate(self, img_array, idx, max_new_tokens):
      image_embeds = self.vision_encoder(img_array)

      if image_embeds.nelement() == 0 or image_embeds.shape[1] ==0:
        raise ValueError("somethign is messed up with the ViT model. It's returning an empty tensor or the embedding dimension is empty")

      generated_tokens = self.decoder.generate(idx, image_embeds, max_new_tokens)
      return generated_tokens

def base64_to_tensor(base64_str, img_size=96):
    image = Image.open(io.BytesIO(base64.b64decode(base64_str)))
    if image.mode != 'RGB':
        image = image.convert('RGB')
    transform = transforms.Compose([
        transforms.Resize((img_size, img_size)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
    return transform(image).unsqueeze(0)  # Add batch dimension

#Adjusting the data loader from makemore for multimodal data
def get_batch(df, batch_size, split='train', img_size=96, val_batch_size=8):
    # Split data into training and validation sets
    n = int(0.9 * len(df))  # first 90% will be train, rest val
    df_train = df.iloc[:n]
    df_val = df.iloc[n:]
    data = df_train if split == 'train' else df_val
    batch_size = batch_size if split == 'train' else val_batch_size
    replace = False if split == 'train' else True
    batch = data.sample(n=batch_size, replace=replace)

    images = torch.cat([base64_to_tensor(img, img_size) for img in batch['b64string_images']], dim=0).to(device)
    text_indices = [torch.tensor(encode(desc), dtype=torch.long) for desc in batch['caption']]
    max_length = max(len(t) for t in text_indices)

    padded_text = torch.full((batch_size, max_length), fill_value=stoi['<pad>'], dtype=torch.long).to(device)
    for i, text in enumerate(text_indices):
        padded_text[i, :len(text)] = text

    targets = torch.cat([padded_text[:, 1:], torch.full((batch_size, 1), fill_value=stoi['<pad>'], dtype=torch.long, device=device)], dim=1)

    # Truncate or pad targets to match the length of padded_text
    if targets.size(1) > padded_text.size(1):
        targets = targets[:, :padded_text.size(1)]
    elif targets.size(1) < padded_text.size(1):
        targets = torch.cat([targets, torch.full((batch_size, padded_text.size(1) - targets.size(1)), fill_value=stoi['<pad>'], dtype=torch.long, device=device)], dim=1)

    return images, padded_text, targets

#Adjusting the training loop from makemore for multimodal data
def train_model(model, df, epochs, vocab_size, img_size=96):
    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
    model.to(device)
    for epoch in range(epochs):
        model.train()
        for _ in range(max_iters):
            images, idx, targets = get_batch(df, batch_size, 'train', img_size)
            optimizer.zero_grad()
            logits, loss = model(images, idx, targets)
            loss.backward()
            optimizer.step()
            if _ % eval_interval == 0:
                print(f"Loss at iteration {_}: {loss.item()}")
        val_loss = estimate_loss(model, df, 'val', img_size, val_batch_size=8)
        print(f"Validation Loss after epoch {epoch}: {val_loss}")

def estimate_loss(model, df, split, img_size=96, val_batch_size=8):
    losses = []
    model.eval()
    for _ in range(eval_iters):
        images, idx, targets = get_batch(df, batch_size, split, img_size, val_batch_size=val_batch_size)
        _, loss = model(images, idx, targets)
        losses.append(loss.item())
    return sum(losses) / len(losses)

def main():
    # Load the dataset
    df = pd.read_csv("./inputs.csv")
    #Expanding dataframe so that there's enough data to test. This is just duplicating data. A real dataset would have more rows
    df = pd.concat([df] * 30)[['b64string_images', 'caption']]

    # Initialize the model
    model = VisionLanguageModel(n_embd, image_embed_dim, vocab_size, n_layer, img_size, patch_size, n_head, num_blks, emb_dropout, blk_dropout)
    model.to(device)

    # Dummy data to initialize lazy modules
    dummy_img = torch.randn(1, 3, img_size, img_size).to(device)
    dummy_idx = torch.randint(0, vocab_size, (1, block_size)).to(device)
    model(dummy_img, dummy_idx)  # Forward pass to initialize all parameters

    # Train the model
    train_model(model, df, epochs, vocab_size, img_size)

if __name__ == "__main__":
    main()