previous_chapters.py

import torch
import torch.nn as nn
import tiktoken
def generate_text_simple(model, idx,
                         max_new_tokens, context_size):
    for _ in range(max_new_tokens):
        idx_cond = idx[:, -context_size:] # サポートされているコンテキストサイズを超える場合は現在のコンテキストを切り詰める。最後のトークンが使われる。
        with torch.no_grad():
            logits = model(idx_cond)
    
        logits = logits[:, -1, :] # 最後のタイムステップにのみ着目する。(batch, n_token, vocab_size) -> (batch, vcab_size)

        probas = torch.softmax(logits, dim=-1) # (batch, vocab_size) 今のことろ、logits のときと大小の順序が変わらないので以下の argmax に影響しない冗長な実装
        
        idx_next = torch.argmax(probas, dim=-1, keepdim=True) # 最も確率の高いトークンを返す。(batch, 1)
        
        idx = torch.cat((idx, idx_next), dim=-1) # 実行中のシーケンスに追加。
    
    return idx

def text_to_token_ids(text, tokenizer):
    encoded = tokenizer.encode(text, allowed_special={'<|endoftext|>'})
    encoded_tensor = torch.tensor(encoded).unsqueeze(0) # バッチ次元を追加
    
    return encoded_tensor

def token_ids_to_text(token_ids, tokenizer):
    flat = token_ids.squeeze(0)
    return tokenizer.decode(flat.tolist())

class LayerNorm(nn.Module):
    def __init__(self, emb_dim, eps=1e-5):
        super().__init__()
        self.eps = 1e-5
        self.scale = nn.Parameter(torch.ones(emb_dim))
        self.shift = nn.Parameter(torch.zeros(emb_dim))
    
    def forward(self, x):
        mean = x.mean(dim=-1, keepdim=True)
        var = x.var(dim=-1, keepdim=True, unbiased=False) # 不変ではなく有偏分散。n がデカいのでその差がほぼ無視でき、正規化層との互換性を保つため。
        norm_x = (x - mean) / torch.sqrt(var + self.eps)
        return self.scale * norm_x + self.shift

class GELU(nn.Module):
    def __init__(self):
        super().__init__()
        
    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(
            torch.sqrt(torch.tensor(2.0 / torch.pi)) *
            (x + 0.044715 * torch.pow(x, 3))))
class FeedForward(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.layers = nn. Sequential(
            nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]), # 埋め込み次元を 4 倍に拡張
            GELU(),
            nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]) # 元の埋め込み次元に戻す
        )
    def forward(self, x):
        return self.layers(x)

class MultiHeadAttention(nn.Module):
    def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
        super().__init__()
        assert (d_out % num_heads == 0), "d_out must be divisible by num_heads"
    
        self.d_out = d_out
        self.num_heads = num_heads
        self.head_dim = d_out // num_heads # 出力次数をhead で分割    

        self.d_out = d_out
        self.num_heads = num_heads
        self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
        self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
        self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
        self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
        
        self.out_proj = nn.Linear(d_out, d_out) # Linear 層を使ってヘッドの出力を組み合わせる
        self.dropout = nn.Dropout(dropout)
        self.register_buffer(
            "mask",
            torch.triu(torch.ones(context_length, context_length), diagonal=1)
        )
        
    def forward(self, x):
        b, num_tokens, d_in = x.shape
        keys = self.W_key(x)
        queries = self.W_query(x)
        values = self.W_value(x)
        
        keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
        values = values.view(b, num_tokens, self.num_heads, self.head_dim)
        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
        
        keys = keys.transpose(1, 2) # (b, num_heads, num_tokens, head_dim)
        queries = queries.transpose(1, 2) # (b, num_heads, num_tokens, head_dim)
        values = values.transpose(1, 2) # (b, num_heads, num_tokens, head_dim)
        
        attn_scores = queries @ keys.transpose(2, 3) # 各ヘッドドット積を計算
        
        mask_bool = self.mask.bool()[:num_tokens, :num_tokens] # マスクをトークン数で切り捨て
        attn_scores.masked_fill_(mask_bool, -torch.inf) # Attention スコアを埋めるためにマスクを使う
        
        attn_weights = torch.softmax(
            attn_scores / keys.shape[-1]**0.5, dim=-1
        )
        attn_weights = self.dropout(attn_weights) # ドロップアウトを適用
        
        context_vec = (attn_weights @ values).transpose(1, 2) # (b, num_heads, num_tokens, head_dim)
        context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out) # self.out に基づいてヘッドを結合
        
        context_vec = self.out_proj(context_vec) # 線形射像を追加 (これなにやってんの？)
        
        return context_vec
class TransformerBlock(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.att = MultiHeadAttention(
            d_in = cfg["emb_dim"],
            d_out = cfg["emb_dim"],
            context_length=cfg["context_length"],
            num_heads=cfg["n_heads"],
            dropout=cfg["drop_rate"],
            qkv_bias=cfg["qkv_bias"],
        )
        self.ff = FeedForward(cfg)
        self.norm1 = LayerNorm(cfg["emb_dim"])
        self.norm2 = LayerNorm(cfg["emb_dim"])
        self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
    
    def forward(self, x):
        shortcut = x
        x = self.norm1(x)
        x = self.att(x)
        x = self.drop_shortcut(x)
        x = x + shortcut
        
        shortcut = x
        x = self.norm2(x)
        x = self.ff(x)
        x = self.drop_shortcut(x)
        x = x + shortcut
        return x

class GPTModel(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
        self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
        self.drop_emb = nn.Dropout(cfg["drop_rate"])
        
        self.trf_blocks = nn.Sequential(
            *[TransformerBlock(cfg) for _ in range(cfg["n_layers"])]
        )
        
        self.final_norm = LayerNorm(cfg["emb_dim"])
        self.out_head = nn.Linear(
            cfg["emb_dim"], cfg["vocab_size"], bias=False
        )
    
    def forward(self, in_idx):
        batch_size, seq_len = in_idx.shape
        tok_embeds = self.tok_emb(in_idx)
        
        pos_embeds = self.pos_emb(
            torch.arange(seq_len, device=in_idx.device) # 入力データがGPUにあるかCPUのどちらにあるかに応じて、モデルをどちらかのデバイスで訓練できる。
        )
        x = tok_embeds + pos_embeds
        x = self.drop_emb(x)
        x = self.trf_blocks(x)
        x = self.final_norm(x)
        logits = self.out_head(x)
        return logits # 次に来るトークンの、正規化されていない確率で返す
    

def assign(left, right):
    if left.shape != right.shape:
        raise ValueError(
            f"Shape mismatch. Left: {left.shape}, "
            f"!= {right.shape}"
        )
    else:
        return torch.nn.Parameter(torch.tensor(right))

import numpy as np

def calc_loss_batch(input_batch, target_batch, model, device):
    input_batch = input_batch.to(device)
    target_batch = target_batch.to(device)
    logits = model(input_batch)
    loss = torch.nn.functional.cross_entropy(
        logits.flatten(0, 1), target_batch.flatten()
    )
    return loss


def calc_loss_loader(data_loader, model, defvice, num_batches=None):
    total_loss = 0.
    if len(data_loader) == 0:
        return float("nan")
    elif num_batches is None:
        num_batches = len(data_loader)
    else:
        num_batches = min(num_batches, len(data_loader))
    
    for i, (input_batch, target_batch) in enumerate(data_loader):
        if i < num_batches:
            loss = calc_loss_batch(
                input_batch, target_batch, model, defvice
                )
            total_loss += loss.item()
        else:
            break
        
    return total_loss / num_batches

def train_model_simple(model, train_loader, val_loader, optimizer, device,
                       num_epochs, eval_freq, eval_iter, start_context,
                       tokenizer):
    train_losses, val_losses, track_tokens_seen = [], [], []
    tokens_seen, global_step = 0, -1
    
    for epoch in range(num_epochs):
        model.train()
        for input_batch, target_batch in train_loader:
            optimizer.zero_grad()
            loss = calc_loss_batch(input_batch, target_batch, model, device)
            loss.backward()
            optimizer.step()
            tokens_seen += input_batch.numel()
            
            global_step += 1
            if global_step % eval_freq == 0:
                train_loss, val_loss = evaluate_model(
                    model, train_loader, val_loader, device, eval_iter
                )
                train_losses.append(train_loss)
                val_losses.append(val_loss)
                track_tokens_seen.append(tokens_seen)
                print(f"Ep {epoch+1} (Step {global_step:06d}) "
                      f"Train loss {train_loss:.3f}, "
                      f"Val loss {val_loss:.3f}")
        generate_and_print_sample(
            model, tokenizer, device, start_context
        )
    return train_losses, val_losses, track_tokens_seen

def evaluate_model(model, train_loader, val_loader, device, eval_iter):
    model.eval()
    with torch.no_grad():
        train_loss = calc_loss_loader(
            train_loader, model, device, num_batches=eval_iter
        )
        val_loss = calc_loss_loader(
            val_loader, model, device, num_batches=eval_iter
        )
        model.train()
    return train_loss, val_loss

def generate_and_print_sample(model, tokenizer, device, start_context):
    model.eval()
    context_size = model.pos_emb.weight.shape[0]
    encoded = text_to_token_ids(start_context, tokenizer).to(device)
    with torch.no_grad():
        token_ids = generate_text_simple(
            model=model,
            idx=encoded,
            max_new_tokens=50,
            context_size=context_size
        )
    decoded_text = token_ids_to_text(token_ids, tokenizer)
    print(decoded_text.replace("\n", " "))
    model.train()

def generate(model, idx, max_new_tokens, context_size, temperature=0.0,
             top_k=None, eos_id=None):
    for _ in range(max_new_tokens):
        idx_cond = idx[:, -context_size:]
        with torch.no_grad():
            logits = model(idx_cond)
        logits = logits[:, -1, :]
        
        if top_k is not None:
            top_logits, _ = torch.topk(logits, top_k)
            min_val = top_logits[:, -1]
            logits = torch.where(
                logits < min_val,
                torch.tensor(float("-inf"), device=logits.device),
                logits
            )
        
        if temperature > 0.0:
            logits = logits / temperature
            probs = torch.softmax(logits, dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
        else:
            idx_next = torch.argmax(logits, dim=-1, keepdim=True)
        
        if idx_next == eos_id:
            break
        
        idx = torch.cat((idx, idx_next), dim=1) # idx_next を idx に追加
    return idx

def load_weights_into_gpt(gpt, params):
    gpt.pos_emb.weight = assign(gpt.pos_emb.weight, params["wpe"])
    gpt.tok_emb.weight = assign(gpt.tok_emb.weight, params["wte"])
    
    for b in range(len(params["blocks"])):
        # Attention の Q, K, W の重み
        q_w, k_w, v_w = np.split(
            (params["blocks"][b]["attn"]["c_attn"])["w"], 3, axis=1)
        gpt.trf_blocks[b].att.W_query.weight = assign(
            gpt.trf_blocks[b].att.W_query.weight, q_w.T)
        gpt.trf_blocks[b].att.W_key.weight = assign(
            gpt.trf_blocks[b].att.W_key.weight, k_w.T
        )
        gpt.trf_blocks[b].att.W_value.weight = assign(
            gpt.trf_blocks[b].att.W_value.weight, v_w.T
        )

        # Attention の Q, K, W のバイアス
        q_b, k_b, v_b = np.split(
            (params["blocks"][b]["attn"]["c_attn"])["b"], 3, axis=-1)
        gpt.trf_blocks[b].att.W_query.bias = assign(
            gpt.trf_blocks[b].att.W_query.bias, q_b)
        gpt.trf_blocks[b].att.W_key.bias = assign(
            gpt.trf_blocks[b].att.W_key.bias, k_b)
        gpt.trf_blocks[b].att.W_value.bias = assign(
            gpt.trf_blocks[b].att.W_value.bias, v_b
        )
        
        # Attention のヘッド結合時の線形写像
        gpt.trf_blocks[b].att.out_proj.weight = assign(
            gpt.trf_blocks[b].att.out_proj.weight,
            params["blocks"][b]["attn"]["c_proj"]["w"].T)
        # Attention のヘッド結合時のバイアス
        gpt.trf_blocks[b].att.out_proj.bias = assign(
            gpt.trf_blocks[b].att.out_proj.bias,
            params["blocks"][b]["attn"]["c_proj"]["b"])
        
        # FeedForward の最初の線形写像
        gpt.trf_blocks[b].ff.layers[0].weight = assign(
            gpt.trf_blocks[b].ff.layers[0].weight,
            params["blocks"][b]["mlp"]["c_fc"]["w"].T)
        # FeedForward の最初のバイアス
        gpt.trf_blocks[b].ff.layers[0].bias = assign(
            gpt.trf_blocks[b].ff.layers[0].bias,
            params["blocks"][b]["mlp"]["c_fc"]["b"])

        # FeedForward の最後の線形写像
        gpt.trf_blocks[b].ff.layers[2].weight = assign(
            gpt.trf_blocks[b].ff.layers[2].weight,
            params["blocks"][b]["mlp"]["c_proj"]["w"].T)
        # FeedForward の最後のバイアス
        gpt.trf_blocks[b].ff.layers[2].bias = assign(
            gpt.trf_blocks[b].ff.layers[2].bias,
            params["blocks"][b]["mlp"]["c_proj"]["b"])
        
        # transformer 内 LayerNorm 1 のパラメータを設定
        gpt.trf_blocks[b].norm1.scale = assign(
            gpt.trf_blocks[b].norm1.scale,
            params["blocks"][b]["ln_1"]["g"])
        gpt.trf_blocks[b].norm1.shift = assign(
            gpt.trf_blocks[b].norm1.shift,
            params["blocks"][b]["ln_1"]["b"])
         
        # transformer 内 LayerNorm 2 のパラメータを設定
        gpt.trf_blocks[b].norm2.scale = assign(
            gpt.trf_blocks[b].norm2.scale,
            params["blocks"][b]["ln_2"]["g"])
        gpt.trf_blocks[b].norm2.shift = assign(
            gpt.trf_blocks[b].norm2.shift,
            params["blocks"][b]["ln_2"]["b"])

    # 最後の LayerNorm のパラメータを設定
    gpt.final_norm.scale = assign(gpt.final_norm.scale, params["g"])
    gpt.final_norm.shift = assign(gpt.final_norm.scale, params["b"])
    gpt.out_head.weight = assign(gpt.out_head.weight, params["wte"])


import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator

def plot_losses(epochs_seen, tokens_seen, train_losses, val_losses):
    fig, ax1 = plt.subplots(figsize=(5, 3))
    ax1.plot(epochs_seen, train_losses, label="Training loss")
    ax1.plot(
        epochs_seen, val_losses, linestyle="--", label="Validation loss"
    )
    ax1.set_xlabel("Epochs")
    ax1.set_ylabel("Loss")
    ax1.legend(loc="upper right")
    ax1.xaxis.set_major_locator(MaxNLocator(integer=True))
    
    ax2 = ax1.twiny()
    ax2.plot(tokens_seen, train_losses, alpha=0)
    ax2.set_xlabel("Tokens seen")
    fig.tight_layout()
    plt.show()