example_papers/multi_style_adapter/run_4.py

import os
import time
import math
import pickle
import inspect
import json
from contextlib import nullcontext
from dataclasses import dataclass
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
import argparse
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC

# --- BEGIN model.py ---
class LayerNorm(nn.Module):
    """LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False"""

    def __init__(self, ndim, bias):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(ndim))
        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None

    def forward(self, input):
        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)


class CausalSelfAttention(nn.Module):

    def __init__(self, config):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        # key, query, value projections for all heads, but in a batch
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
        # output projection
        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
        # regularization
        self.attn_dropout = nn.Dropout(config.dropout)
        self.resid_dropout = nn.Dropout(config.dropout)
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        self.dropout = config.dropout
        # flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0
        self.flash = hasattr(torch.nn.functional, "scaled_dot_product_attention")
        if not self.flash:
            print(
                "WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0"
            )
            # causal mask to ensure that attention is only applied to the left in the input sequence
            self.register_buffer(
                "bias",
                torch.tril(torch.ones(config.block_size, config.block_size)).view(
                    1, 1, config.block_size, config.block_size
                ),
            )

    def forward(self, x):
        B, T, C = (
            x.size()
        )  # batch size, sequence length, embedding dimensionality (n_embd)

        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
        k = k.view(B, T, self.n_head, C // self.n_head).transpose(
            1, 2
        )  # (B, nh, T, hs)
        q = q.view(B, T, self.n_head, C // self.n_head).transpose(
            1, 2
        )  # (B, nh, T, hs)
        v = v.view(B, T, self.n_head, C // self.n_head).transpose(
            1, 2
        )  # (B, nh, T, hs)

        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
        if self.flash:
            # efficient attention using Flash Attention CUDA kernels
            y = torch.nn.functional.scaled_dot_product_attention(
                q,
                k,
                v,
                attn_mask=None,
                dropout_p=self.dropout if self.training else 0,
                is_causal=True,
            )
        else:
            # manual implementation of attention
            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
            att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float("-inf"))
            att = F.softmax(att, dim=-1)
            att = self.attn_dropout(att)
            y = att @ v  # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
        y = (
            y.transpose(1, 2).contiguous().view(B, T, C)
        )  # re-assemble all head outputs side by side

        # output projection
        y = self.resid_dropout(self.c_proj(y))
        return y


class MLP(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
        self.gelu = nn.GELU()
        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, x):
        x = self.c_fc(x)
        x = self.gelu(x)
        x = self.c_proj(x)
        x = self.dropout(x)
        return x


class StyleAdapter(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.linear = nn.Linear(config.n_embd, config.n_embd)

    def forward(self, x, style_emb):
        return x * self.linear(style_emb).unsqueeze(1)

class Block(nn.Module):

    def __init__(self, config):
        super().__init__()
        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
        self.mlp = MLP(config)

    def forward(self, x):
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


@dataclass
class GPTConfig:
    block_size: int = 1024
    vocab_size: int = (
        50304  # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency
    )
    n_layer: int = 12
    n_head: int = 12
    n_embd: int = 768
    dropout: float = 0.0
    bias: bool = (
        True  # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
    )
    n_styles: int = 4  # number of styles for Multi-Style Adapter
    style_embd_dim: int = 64  # dimension of style embeddings


class GPT(nn.Module):

    def __init__(self, config):
        super().__init__()
        assert config.vocab_size is not None
        assert config.block_size is not None
        self.config = config

        self.transformer = nn.ModuleDict(
            dict(
                wte=nn.Embedding(config.vocab_size, config.n_embd),
                wpe=nn.Embedding(config.block_size, config.n_embd),
                drop=nn.Dropout(config.dropout),
                h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
                ln_f=LayerNorm(config.n_embd, bias=config.bias),
            )
        )
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        # with weight tying when using torch.compile() some warnings get generated:
        # "UserWarning: functional_call was passed multiple values for tied weights.
        # This behavior is deprecated and will be an error in future versions"
        # not 100% sure what this is, so far seems to be harmless. TODO investigate
        self.transformer.wte.weight = (
            self.lm_head.weight
        )  # https://paperswithcode.com/method/weight-tying

        # Multi-Style Adapter components
        self.style_embeddings = nn.Parameter(torch.randn(config.n_styles, config.style_embd_dim))
        self.style_proj = nn.Linear(config.style_embd_dim, config.n_embd)
        self.style_classifier = nn.Sequential(
            nn.Linear(config.n_embd, config.n_embd),
            nn.ReLU(),
            nn.Linear(config.n_embd, config.n_styles)
        )
        self.style_adapters = nn.ModuleList([StyleAdapter(config) for _ in range(config.n_layer)])

        # init all weights
        self.apply(self._init_weights)
        # apply special scaled init to the residual projections, per GPT-2 paper
        for pn, p in self.named_parameters():
            if pn.endswith("c_proj.weight"):
                torch.nn.init.normal_(
                    p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer)
                )

        # report number of parameters
        print("number of parameters: %.2fM" % (self.get_num_params() / 1e6,))

    def get_num_params(self, non_embedding=True):
        """
        Return the number of parameters in the model.
        For non-embedding count (default), the position embeddings get subtracted.
        The token embeddings would too, except due to the parameter sharing these
        params are actually used as weights in the final layer, so we include them.
        """
        n_params = sum(p.numel() for p in self.parameters())
        if non_embedding:
            n_params -= self.transformer.wpe.weight.numel()
        return n_params

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)

    def forward(self, idx, targets=None):
        device = idx.device
        b, t = idx.size()
        assert (
            t <= self.config.block_size
        ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
        pos = torch.arange(0, t, dtype=torch.long, device=device)  # shape (t)

        # forward the GPT model itself
        tok_emb = self.transformer.wte(idx)  # token embeddings of shape (b, t, n_embd)
        pos_emb = self.transformer.wpe(pos)  # position embeddings of shape (t, n_embd)
        x = self.transformer.drop(tok_emb + pos_emb)

        style_logits = None
        for i, block in enumerate(self.transformer.h):
            x = block(x)
            style_logits = self.style_classifier(x[:, -1, :])  # Use last token for classification
            style_probs = F.softmax(style_logits, dim=-1)
            style_emb = (style_probs @ self.style_embeddings)  # Weighted sum of style embeddings
            style_emb = self.style_proj(style_emb)
            x = self.style_adapters[i](x, style_emb)

        x = self.transformer.ln_f(x)

        if targets is not None:
            # if we are given some desired targets also calculate the loss
            logits = self.lm_head(x)
            loss = F.cross_entropy(
                logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1
            )
        else:
            # inference-time mini-optimization: only forward the lm_head on the very last position
            logits = self.lm_head(
                x[:, [-1], :]
            )  # note: using list [-1] to preserve the time dim
            loss = None

        return logits, loss, style_logits

    def crop_block_size(self, block_size):
        # model surgery to decrease the block size if necessary
        # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024)
        # but want to use a smaller block size for some smaller, simpler model
        assert block_size <= self.config.block_size
        self.config.block_size = block_size
        self.transformer.wpe.weight = nn.Parameter(
            self.transformer.wpe.weight[:block_size]
        )
        for block in self.transformer.h:
            if hasattr(block.attn, "bias"):
                block.attn.bias = block.attn.bias[:, :, :block_size, :block_size]

    def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
        # start with all of the candidate parameters
        param_dict = {pn: p for pn, p in self.named_parameters()}
        # filter out those that do not require grad
        param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
        # create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
        # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
        decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
        optim_groups = [
            {"params": decay_params, "weight_decay": weight_decay},
            {"params": nodecay_params, "weight_decay": 0.0},
        ]
        num_decay_params = sum(p.numel() for p in decay_params)
        num_nodecay_params = sum(p.numel() for p in nodecay_params)
        print(
            f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters"
        )
        print(
            f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters"
        )
        # Create AdamW optimizer and use the fused version if it is available
        fused_available = "fused" in inspect.signature(torch.optim.AdamW).parameters
        use_fused = fused_available and device_type == "cuda"
        extra_args = dict(fused=True) if use_fused else dict()
        optimizer = torch.optim.AdamW(
            optim_groups, lr=learning_rate, betas=betas, **extra_args
        )
        print(f"using fused AdamW: {use_fused}")

        return optimizer

    @torch.no_grad()
    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
        """
        Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete
        the sequence max_new_tokens times, feeding the predictions back into the model each time.
        Most likely you'll want to make sure to be in model.eval() mode of operation for this.
        """
        for _ in range(max_new_tokens):
            # if the sequence context is growing too long we must crop it at block_size
            idx_cond = (
                idx
                if idx.size(1) <= self.config.block_size
                else idx[:, -self.config.block_size :]
            )
            # forward the model to get the logits for the index in the sequence
            logits, _, _ = self(idx_cond)  # Ignore loss and style_logits
            # pluck the logits at the final step and scale by desired temperature
            logits = logits[:, -1, :] / temperature
            # optionally crop the logits to only the top k options
            if top_k is not None:
                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits[logits < v[:, [-1]]] = -float("Inf")
            # apply softmax to convert logits to (normalized) probabilities
            probs = F.softmax(logits, dim=-1)
            # sample from the distribution
            idx_next = torch.multinomial(probs, num_samples=1)
            # append sampled index to the running sequence and continue
            idx = torch.cat((idx, idx_next), dim=1)

        return idx


# --- END model.py ---
def train(dataset="shakespeare_char", out_dir="run_0", seed_offset=0):
    # -----------------------------------------------------------------------------
    # default config values designed to train a gpt2 (124M) on OpenWebText
    # data
    gradient_accumulation_steps = 1
    batch_size = 64 if dataset == "shakespeare_char" else 32
    block_size = 256  # context of up to 256 previous characters
    # I/O
    eval_interval = 250 if dataset == "shakespeare_char" else 1000
    log_interval = 10 if dataset == "shakespeare_char" else 100
    eval_iters = 200
    eval_only = False  # if True, script exits right after the first eval
    always_save_checkpoint = (
        False  # we expect to overfit on this small dataset, so only save when val improves
    )
    never_save_checkpoint = True # never save checkpoints
    # model
    n_layer = 6  # baby GPT model :)
    n_head = 6
    n_embd = 384
    dropout = 0.2  # for pretraining 0 is good, for finetuning try 0.1+
    bias = False  # do we use bias inside LayerNorm and Linear layers?
    n_styles = 4  # number of styles for Multi-Style Adapter
    style_embd_dim = 64  # dimension of style embeddings
    # adamw optimizer
    learning_rate = (
        1e-3  if dataset == "shakespeare_char" else 5e-4
    )
    max_iters = 5000 if dataset == "shakespeare_char" else 100000
    weight_decay = 1e-1
    beta1 = 0.9
    beta2 = 0.99  # make a bit bigger because number of tokens per iter is small
    grad_clip = 1.0  # clip gradients at this value, or disable if == 0.0
    # learning rate decay settings
    decay_lr = True  # whether to decay the learning rate
    warmup_iters = 100 if dataset == "shakespeare_char" else 200
    lr_decay_iters = max_iters # make equal to max_iters usually
    min_lr = 1e-4 if dataset == "shakespeare_char" else 5e-5
    # DDP settings
    backend = "nccl"  # 'nccl', 'gloo', etc.
    # system
    device = "cuda"  # Always use CUDA
    dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32', 'bfloat16', or 'float16', the latter will auto implement a GradScaler
    compile = True  # do not torch compile the model on macbooks


    # various inits, derived attributes, I/O setup
    # if not ddp, we are running on a single gpu, and one process
    master_process = True
    tokens_per_iter = gradient_accumulation_steps * batch_size * block_size
    print(f"tokens per iteration will be: {tokens_per_iter:,}")

    if master_process:
        os.makedirs(out_dir, exist_ok=True)
    torch.manual_seed(1337 + seed_offset)
    torch.backends.cuda.matmul.allow_tf32 = True  # allow tf32 on matmul
    torch.backends.cudnn.allow_tf32 = True  # allow tf32 on cudnn
    device_type = "cuda" if "cuda" in device else "cpu"  # for later use in torch.autocast
    # note: float16 data type will automatically use a GradScaler
    ptdtype = {
        "float32": torch.float32,
        "bfloat16": torch.bfloat16,
        "float16": torch.float16,
    }[dtype]
    ctx = (
        nullcontext()
        if device_type == "cpu"
        else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
    )

    # poor man's data loader
    data_dir = os.path.join("../../../data", dataset)


    def get_batch(split):
        # We recreate np.memmap every batch to avoid a memory leak, as per
        # https://stackoverflow.com/questions/45132940/numpy-memmap-memory-usage-want-to-iterate-once/61472122#61472122
        if split == "train":
            data = np.memmap(os.path.join(data_dir, "train.bin"), dtype=np.uint16, mode="r")
        else:
            data = np.memmap(os.path.join(data_dir, "val.bin"), dtype=np.uint16, mode="r")
        ix = torch.randint(len(data) - block_size, (batch_size,))
        x = torch.stack(
            [torch.from_numpy((data[i : i + block_size]).astype(np.int64)) for i in ix]
        )
        y = torch.stack(
            [
                torch.from_numpy((data[i + 1 : i + 1 + block_size]).astype(np.int64))
                for i in ix
            ]
        )
        if device_type == "cuda":
            # pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True)
            x, y = x.pin_memory().to(device, non_blocking=True), y.pin_memory().to(
                device, non_blocking=True
            )
        else:
            x, y = x.to(device), y.to(device)
        return x, y

    iter_num = 0
    best_val_loss = 1e9

    # attempt to derive vocab_size from the dataset
    meta_path = os.path.join(data_dir, "meta.pkl")
    meta_vocab_size = None
    if os.path.exists(meta_path):
        with open(meta_path, "rb") as f:
            meta = pickle.load(f)
        meta_vocab_size = meta["vocab_size"]
        print(f"found vocab_size = {meta_vocab_size} (inside {meta_path})")

    # model init
    model_args = dict(
        n_layer=n_layer,
        n_head=n_head,
        n_embd=n_embd,
        block_size=block_size,
        bias=bias,
        vocab_size=None,
        dropout=dropout,
        n_styles=n_styles,
        style_embd_dim=style_embd_dim,
    )  # start with model_args from command line
    # init a new model from scratch
    print("Initializing a new model from scratch")
    # determine the vocab size we'll use for from-scratch training
    if meta_vocab_size is None:
        print(
            "defaulting to vocab_size of GPT-2 to 50304 (50257 rounded up for efficiency)"
        )
    model_args["vocab_size"] = meta_vocab_size if meta_vocab_size is not None else 50304
    gptconf = GPTConfig(**model_args)
    model = GPT(gptconf)
    # crop down the model block size if desired, using model surgery
    if block_size < model.config.block_size:
        model.crop_block_size(block_size)
        model_args["block_size"] = (
            block_size  # so that the checkpoint will have the right value
        )
    model.to(device)

    # initialize a GradScaler. If enabled=False scaler is a no-op
    scaler = torch.cuda.amp.GradScaler(enabled=(dtype == "float16"))

    # optimizer
    optimizer = model.configure_optimizers(
        weight_decay, learning_rate, (beta1, beta2), device_type
    )
    checkpoint = None  # free up memory

    # compile the model
    if compile:
        print("compiling the model... (takes a ~minute)")
        unoptimized_model = model
        model = torch.compile(model)  # requires PyTorch 2.0


    # helps estimate an arbitrarily accurate loss over either split using many batches
    @torch.no_grad()
    def estimate_loss():
        out = {}
        model.eval()
        for split in ["train", "val"]:
            losses = torch.zeros(eval_iters)
            for k in range(eval_iters):
                X, Y = get_batch(split)
                with ctx:
                    logits, loss, _ = model(X, Y)  # Ignore the style_logits
                losses[k] = loss.item()
            out[split] = losses.mean()
        model.train()
        return out


    # learning rate decay scheduler (cosine with warmup)
    def get_lr(it):
        # 1) linear warmup for warmup_iters steps
        if it < warmup_iters:
            return learning_rate * it / warmup_iters
        # 2) if it > lr_decay_iters, return min learning rate
        if it > lr_decay_iters:
            return min_lr
        # 3) in between, use cosine decay down to min learning rate
        decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
        assert 0 <= decay_ratio <= 1
        coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))  # coeff ranges 0..1
        return min_lr + coeff * (learning_rate - min_lr)


    # logging
    val_log_info = []
    train_log_info = []

    # training loop
    X, Y = get_batch("train")  # fetch the very first batch
    og_t0 = time.time()
    t0 = time.time()
    local_iter_num = 0  # number of iterations in the lifetime of this process
    raw_model = model
    while True:

        # determine and set the learning rate for this iteration
        lr = get_lr(iter_num) if decay_lr else learning_rate
        for param_group in optimizer.param_groups:
            param_group["lr"] = lr

        # evaluate the loss on train/val sets and write checkpoints
        if iter_num % eval_interval == 0 and master_process:
            losses = estimate_loss()
            print(
                f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}"
            )
            val_log_info.append(
                {
                    "iter": iter_num,
                    "train/loss": losses["train"].item(),
                    "val/loss": losses["val"].item(),
                    "lr": lr,
                }
            )
            if losses["val"] < best_val_loss or always_save_checkpoint:
                best_val_loss = losses["val"]
                if iter_num > 0 and not never_save_checkpoint:
                    checkpoint = {
                        "model": raw_model.state_dict(),
                        "optimizer": optimizer.state_dict(),
                        "model_args": model_args,
                        "iter_num": iter_num,
                        "best_val_loss": best_val_loss,
                    }
                    print(f"saving checkpoint to {out_dir}")
                    torch.save(checkpoint, os.path.join(out_dir, "ckpt.pt"))
        if iter_num == 0 and eval_only:
            break

        # forward backward update, with optional gradient accumulation to simulate larger batch size
        # and using the GradScaler if data type is float16
        for micro_step in range(gradient_accumulation_steps):
            with ctx:
                logits, loss, style_logits = model(X, Y)
                # Add style classification loss (assuming uniform distribution of styles)
                style_loss = F.cross_entropy(style_logits, torch.randint(0, n_styles, (X.size(0),), device=device))
                style_loss_weight = 0.1  # Adjust this weight to balance style adaptation and language modeling
                total_loss = loss + style_loss_weight * style_loss
                total_loss = total_loss / gradient_accumulation_steps  # scale the loss to account for gradient accumulation
            # immediately async prefetch next batch while model is doing the forward pass on the GPU
            X, Y = get_batch("train")
            # backward pass, with gradient scaling if training in fp16
            scaler.scale(total_loss).backward()
        # clip the gradient
        if grad_clip != 0.0:
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
        # step the optimizer and scaler if training in fp16
        scaler.step(optimizer)
        scaler.update()
        # flush the gradients as soon as we can, no need for this memory anymore
        optimizer.zero_grad(set_to_none=True)

        # timing and logging
        t1 = time.time()
        dt = t1 - t0
        t0 = t1
        if iter_num % log_interval == 0 and master_process:
            # get loss as float. note: this is a CPU-GPU sync point
            # scale up to undo the division above, approximating the true total loss (exact would have been a sum)
            lossf = total_loss.item() * gradient_accumulation_steps
            print(
                f"iter {iter_num}: loss {lossf:.4f}, time {dt*1000:.2f}ms"
            )
            train_log_info.append(
                {
                    "iter": iter_num,
                    "loss": lossf,
                    "time": dt*1000,
                }
            )
        iter_num += 1
        local_iter_num += 1

        # termination conditions
        if iter_num > max_iters:
            break

    print("training done")
    print(f"Best validation loss: {best_val_loss}")
    print(f"Total train time: {(time.time() - og_t0) / 60:.2f} mins")

    final_info = {
        "final_train_loss": lossf,
        "best_val_loss": best_val_loss.item(),
        "total_train_time": time.time() - og_t0,
    }

    # === SAMPLING SCRIPT ===

    # New parameters for generation
    start = " " 
    num_samples = 10  # number of samples to draw
    max_new_tokens = 500  # number of tokens generated in each sample
    temperature = 0.8  # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions
    top_k = 200  # retain only the top_k most likely tokens, clamp others to have 0 probability

    # Encoding setup
    assert os.path.exists(meta_path), "meta.pkl not found, please run training script first"
    print(f"Loading meta from {meta_path}...")
    with open(meta_path, 'rb') as f:
        meta = pickle.load(f)
    stoi, itos = meta['stoi'], meta['itos']
    encode = lambda s: [stoi[c] for c in s]
    decode = lambda l: ''.join([itos[i] for i in l])

    # Encode the beginning of the prompt
    if start.startswith('FILE:'):
        with open(start[5:], 'r', encoding='utf-8') as f:
            start = f.read()
    start_ids = encode(start)
    x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])

    # Run generation
    model.eval()
    results = []
    with torch.no_grad():
        with ctx:
            for k in range(num_samples):
                start_time = time.time()
                y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
                end_time = time.time()
                
                generated_text = decode(y[0].tolist())
                inference_time = end_time - start_time
                tokens_per_second = max_new_tokens / inference_time
                
                print(f"Sample {k+1}:")
                print(generated_text)
                print(f"Inference time: {inference_time:.2f} seconds")
                print(f"Tokens per second: {tokens_per_second:.2f}")
                print('---------------')
                
                results.append({
                    "sample_id": k+1,
                    "generated_text": generated_text,
                    "inference_time": inference_time,
                    "tokens_per_second": tokens_per_second
                })

    # Calculate and print average inference speed
    avg_tokens_per_second = sum(r['tokens_per_second'] for r in results) / len(results)
    print(f"Average tokens per second: {avg_tokens_per_second:.2f}")

    final_info["avg_inference_tokens_per_second"] = avg_tokens_per_second

    # Analyze style consistency
    style_consistency_scores = analyze_style_consistency(results)
    final_info["style_consistency_scores"] = style_consistency_scores

    with open(os.path.join(out_dir, f"final_info_{dataset}_{seed_offset}.json"), "w") as f:
        json.dump(final_info, f)
    return final_info, train_log_info, val_log_info

def train_style_classifier(texts, labels):
    X_train, X_test, y_train, y_test = train_test_split(texts, labels, test_size=0.2, random_state=42)
    vectorizer = TfidfVectorizer(max_features=5000)
    X_train_vec = vectorizer.fit_transform(X_train)
    X_test_vec = vectorizer.transform(X_test)
    
    classifier = SVC(kernel='linear', C=1.0, random_state=42)
    classifier.fit(X_train_vec, y_train)
    
    return vectorizer, classifier

def analyze_style_consistency(results):
    # Assume we have a pre-defined set of style labels
    style_labels = ["formal", "informal", "poetic", "technical"]
    
    # Generate synthetic data for training the style classifier
    synthetic_texts = []
    synthetic_labels = []
    for style in style_labels:
        synthetic_texts.extend([f"This is a {style} text" for _ in range(100)])
        synthetic_labels.extend([style for _ in range(100)])
    
    vectorizer, classifier = train_style_classifier(synthetic_texts, synthetic_labels)
    
    style_consistency_scores = []
    for sample in results:
        generated_text = sample['generated_text']
        chunks = [generated_text[i:i+100] for i in range(0, len(generated_text), 100)]
        chunk_vectors = vectorizer.transform(chunks)
        chunk_predictions = classifier.predict(chunk_vectors)
        
        # Calculate consistency as the proportion of chunks with the same style
        unique, counts = np.unique(chunk_predictions, return_counts=True)
        most_common_style = unique[np.argmax(counts)]
        consistency_score = np.max(counts) / len(chunk_predictions)
        style_consistency_scores.append(consistency_score)
    
    return {
        "mean_consistency": np.mean(style_consistency_scores),
        "std_consistency": np.std(style_consistency_scores)
    }

parser = argparse.ArgumentParser(description='Run experiment')
parser.add_argument('--out_dir', type=str, default='run_0', help='Output directory')
args = parser.parse_args()

if __name__ == "__main__":
    num_seeds = {
        "shakespeare_char": 3,
        "enwik8": 1,
        "text8": 1,
    }

    out_dir = args.out_dir
    all_results = {}
    final_infos = {}
    for dataset in ["shakespeare_char", "enwik8", "text8"]:
        final_info_list = []
        for seed_offset in range(num_seeds[dataset]):
            final_info, train_info, val_info = train(dataset, out_dir, seed_offset)
            all_results[f"{dataset}_{seed_offset}_final_info"] = final_info
            all_results[f"{dataset}_{seed_offset}_train_info"] = train_info
            all_results[f"{dataset}_{seed_offset}_val_info"] = val_info
            final_info_list.append(final_info)
        final_info_dict = {k: [d[k] for d in final_info_list] for k in final_info_list[0].keys()}
        means = {}
        stderrs = {}
        for k, v in final_info_dict.items():
            if isinstance(v[0], dict):  # If the value is a nested dictionary
                means[k] = {inner_k: np.mean([d[inner_k] for d in v]) for inner_k in v[0].keys()}
                stderrs[k] = {inner_k: np.std([d[inner_k] for d in v]) / len(v) for inner_k in v[0].keys()}
            else:
                means[f"{k}_mean"] = np.mean(v)
                stderrs[f"{k}_stderr"] = np.std(v) / len(v)
        final_infos[dataset] = {
            "means": means,
            "stderrs": stderrs,
            "final_info_dict": final_info_dict,
        }

    with open(os.path.join(out_dir, "final_info.json"), "w") as f:
        json.dump(final_infos, f)

    with open(os.path.join(out_dir, "all_results.npy"), "wb") as f:
        np.save(f, all_results)