train_gpt2.py

import os
from dataclasses import dataclass
import math
import torch
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
from hellswag import render_example,iterate_examples
import tiktoken
import time
import sys
import inspect
from torch.distributed import init_process_group,destroy_process_group
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as dist


class CausalSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
        self.c_proj.NANOGPT_SCALE_INIT = 1
        self.n_head = config.n_head
        self.n_embd = config.n_embd

    def forward(self, x):
        B, T, C = x.shape
        qkv = self.c_attn(x)
        q, k, v = qkv.split(self.n_embd, dim=2)
        q = q.view(B, T, self.n_head, C//self.n_head).transpose(1,2)
        k = k.view(B, T, self.n_head, C//self.n_head).transpose(1,2)
        v = v.view(B, T, self.n_head, C//self.n_head).transpose(1,2)
        # attn = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
        # attn = attn.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
        # attn = F.softmax(attn, dim=-1)
        # y = attn @ v
        y = F.scaled_dot_product_attention(q, k, v,is_causal=True)
        y = y.transpose(1,2).contiguous().view(B, T, C)
        y = 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)
        self.gelu = nn.GELU()
        self.c_proj = nn.Linear(4*config.n_embd, config.n_embd)
        self.c_proj.NANOGPT_SCALE_INIT = 1

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

class Block(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.ln_1 = nn.LayerNorm(config.n_embd)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = nn.LayerNorm(config.n_embd)
        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:
    n_layer: int = 12
    n_head: int = 12
    n_embd: int = 768
    vocab_size: int = 50257
    block_size: int = 1024
class GPT(nn.Module):

    def __init__(self, config):
        super().__init__()
        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),
            h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
            ln_f = nn.LayerNorm(config.n_embd)
        ))
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size,bias=False)
        self.transformer.wte.weight = self.lm_head.weight
        self.apply(self._init_weights)

    def _init_weights(self,module):
        if isinstance(module,nn.Linear):
            std = 0.02
            if hasattr(module,'NANOGPT_SCALE_INIT'):
                std *= (2*self.config.n_layer)**-0.5
            torch.nn.init.normal_(module.weight,mean=0.0,std=std)
            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):
        B,T = idx.size()
        assert T <= self.config.block_size, "Cannot forward sequence of length %d, block size is only %d" % (T, self.config.block_size)
        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
        pos_emb = self.transformer.wpe(pos)
        tok_emb = self.transformer.wte(idx)
        x = tok_emb + pos_emb
        for block in self.transformer.h:
            x = block(x)
        x = self.transformer.ln_f(x)
        logits = self.lm_head(x)
        loss = None
        if targets is not None:
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
        return logits, loss

    def configure_optimizers(self,weight_decay,learning_rate,device):
        param_dict = {pn:p for pn,p in self.named_parameters()}
        param_dict = {pn:p for pn,p in param_dict.items() if p.requires_grad}

        decay_params = {p for pn,p in param_dict.items() if p.dim()>=2}
        nodecay_params = {p for pn,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 'cuda' in device
        print(f"using fused AdamW: {use_fused}")
        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=(0.9, 0.95), eps=1e-8, fused=use_fused)
        return optimizer


    @classmethod
    def from_pretrained(cls, model_type):
        """Loads pretrained GPT-2 model weights from huggingface"""
        assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
        from transformers import GPT2LMHeadModel
        print("loading weights from pretrained gpt: %s" % model_type)

        # n_layer, n_head and n_embd are determined from model_type
        config_args = {
            'gpt2': dict(n_layer=12, n_head=12, n_embd=768),  # 124M params
            'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024),  # 350M params
            'gpt2-large': dict(n_layer=36, n_head=20, n_embd=1280),  # 774M params
            'gpt2-xl': dict(n_layer=48, n_head=25, n_embd=1600),  # 1558M params
        }[model_type]
        config_args['vocab_size'] = 50257  # always   50257 for GPT model checkpoints
        config_args['block_size'] = 1024  # always 1024 for GPT model checkpoints
        # create a from-scratch initialized minGPT model
        config = GPTConfig(**config_args)
        model = GPT(config)
        sd = model.state_dict()
        sd_keys = sd.keys()
        sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')]  # discard this mask / buffer, not a param

        # init a huggingface/transformers model
        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
        sd_hf = model_hf.state_dict()

        # copy while ensuring all of the parameters are aligned and match in names and shapes
        sd_keys_hf = sd_hf.keys()
        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')]  # ignore these, just a buffer
        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')]  # same, just the mask (buffer)
        transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
        # basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
        # this means that we have to transpose these weights when we import them
        assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
        for k in sd_keys_hf:
            if any(k.endswith(w) for w in transposed):
                # special treatment for the Conv1D weights we need to transpose
                assert sd_hf[k].shape[::-1] == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k].t())
            else:
                # vanilla copy over the other parameters
                assert sd_hf[k].shape == sd[k].shape
                with torch.no_grad():
                    sd[k].copy_(sd_hf[k])

        return model

def load_tokens(filename):
    npt = np.load(filename)
    ptt = torch.tensor(npt, dtype=torch.long)
    return ptt

class DataLoaderLite:
    def __init__(self,B,T,process_rank,num_proccesses,split):
        self.B,self.T = B,T
        self.process_rank,self.num_proccesses = process_rank,num_proccesses
        assert split in {'train', 'val'}

        data_root = "edu_fineweb10B"
        shards = os.listdir(data_root)
        shards = [s for s in shards if split in s]
        shards = sorted(shards)
        shards = [os.path.join(data_root,s) for s in shards]
        self.shards = shards
        assert len(self.shards) > 0, "no shards found"
        if master_process:
            print(f"found {len(self.shards)} shards for split: {split}")
        self.reset()


        # with open('input.txt', 'r') as f:
        #     text = f.read()
        # enc = tiktoken.get_encoding("gpt2")
        # self.tokens = torch.tensor(enc.encode(text))
        # print(f"loaded{len(self.tokens)} tokens")
        # print(f"1 epoch = {len(self.tokens)//(B*T)} batches")
        # self.current_position = self.B*T*process_rank

    def reset(self):
        self.current_position = self.B * T * self.process_rank
        self.current_shard = 0
        self.tokens = load_tokens(self.shards[self.current_shard])

    def next_batch(self):
        B,T = self.B,self.T
        buf = self.tokens[self.current_position:self.current_position+B*T+1]
        buf = buf.to(device)
        x = buf[:-1].view(B, T)
        y = buf[1:].view(B, T)
        self.current_position +=  B*T*self.num_proccesses
        if self.current_position + (B*T*self.num_proccesses+1) > len(self.tokens):
            self.current_shard = (self.current_shard + 1) % len(self.shards)
            self.tokens = load_tokens(self.shards[self.current_shard])
            self.current_position = self.B*T*self.process_rank
        return x,y

ddp = int(os.environ.get('RANK', -1)) != -1

if ddp:
    assert torch.cuda.is_available(), "DDP mode requires CUDA"
    init_process_group(backend='nccl')
    ddp_rank = int(os.environ['RANK'])
    ddp_local_rank = int(os.environ['LOCAL_RANK'])
    ddp_world_size = int(os.environ['WORLD_SIZE'])
    device = f'cuda:{ddp_local_rank}'
    torch.cuda.set_device(device)
    master_process = ddp_rank == 0
else:
    ddp_rank = 0
    ddp_local_rank = 0
    ddp_world_size = 1
    master_process = True
    device = 'cpu'
    if torch.backends.mps.is_available():
        device = 'mps'
    elif torch.cuda.is_available():
        device = 'cuda'
    print(f"using device: {device}")

if torch.cuda.is_available():
    device = 'cuda'
else:
    device = 'cpu'

torch.manual_seed(1337)
if torch.cuda.is_available():
    torch.cuda.manual_seed_all(1337)

enc = tiktoken.get_encoding("gpt2")
total_batch_size = 524288
B = 16
T = 1024
assert total_batch_size%(B*T*ddp_world_size)==0, "batch size must be divisible by (B*T*ddp_wrold_size)"
grad_accum_steps = total_batch_size//(B*T*ddp_world_size)
if master_process:
    print(f"total desired batch size: {total_batch_size}")
    print(f"=> grad accum steps: {grad_accum_steps}")


train_loader = DataLoaderLite(B=B,T=T,process_rank= ddp_rank,num_proccesses=ddp_world_size,split='train')
val_loader = DataLoaderLite(B=B,T=T,process_rank= ddp_rank,num_proccesses=ddp_world_size,split='val')
torch.set_float32_matmul_precision('high')
model = GPT(GPTConfig(vocab_size=50304))
model.to(device)
model = torch.compile(model)
if ddp:
    model = DDP(model,device_ids=[ddp_local_rank])
raw_model = model.module if ddp else model


max_lr = 6e-4
min_lr = max_lr*0.1
warmup_steps = 715
max_steps = 19073
def get_lr(it):
    if it < warmup_steps:
        return max_lr*(it+1)/warmup_steps
    if it > max_steps:
        return min_lr
    decay_ratio = (it-warmup_steps)/(warmup_steps-max_steps)
    assert 0 <= decay_ratio <= 1
    coeff = 0.5*(1.0+math.cos(math.pi*decay_ratio))
    return min_lr + coeff*(max_lr-min_lr)


# optimizer = torch.optim.AdamW(model.parameters(), lr=4e-4,betas=(0.9,0.95),eps=1e-8)
optimizer = raw_model.configure_optimizers(weight_decay=0.1,learning_rate = 6e-4,device = device)
for step in range(max_steps):
    t0 = time.time()
    if step%100==0:
        model.eval()
        val_loader.reset()
        with torch.no_grad():
            val_loss_accum = 0
            val_loss_steps = 20
            for _ in range(val_loss_steps):
                x,y = val_loader.next_batch()
                with torch.autocast(device_type=device, dtype=torch.bfloat16):
                    logits, loss = model(x, targets=y)
                loss = loss / val_loss_steps
                val_loss_accum += loss.detach()
                if ddp:
                    dist.all_reduce(val_loss_accum,op=dist.ReduceOp.AVG)
                if master_process:
                    print(f"val loss: {val_loss_accum.item():.4f}")

    if step > 0 and step % 100 == 0 and False:
        model.eval()
        num_return_sequences = 4
        max_length = 32
        tokens = enc.encode("Hello, I'm a language model,")
        tokens = torch.tensor(tokens, dtype=torch.long)
        tokens = tokens.unsqueeze(0).repeat(num_return_sequences, 1)
        xgen = tokens.to(device)
        sample_rng = torch.Generator(device=device)
        sample_rng.manual_seed(42 + ddp_rank)
        while xgen.size(1) < max_length:
            with torch.no_grad():
                logits, loss = model(xgen)
                logits = logits[:, -1, :]
                probs = F.softmax(logits, dim=-1)
                topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
                ix = torch.multinomial(topk_probs, 1, generator=sample_rng) # (B, 1)
                xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
                xgen = torch.cat((xgen, xcol), dim=1)
        # print the generated text
        for i in range(num_return_sequences):
            tokens = xgen[i, :max_length].tolist()
            decoded = enc.decode(tokens)
            print(f"rank {ddp_rank} sample {i}: {decoded}")
    optimizer.zero_grad()
    loss_accum = 0.0
    for micro_step in range(grad_accum_steps):
        x, y = train_loader.next_batch()
        with torch.autocast(device_type=device,dtype = torch.bfloat16):
            logits,loss = model(x,targets = y)
        loss = loss / grad_accum_steps
        loss_accum += loss.detach()
        if ddp:
            model.require_backward_grad_sync = (micro_step == grad_accum_steps-1)
        loss.backward()
        if ddp:
            dist.all_reduce(loss_accum,op=dist.ReduceOp.AVG)
    norm = nn.utils.clip_grad_norm_(model.parameters(), 1)
    lr_rate = get_lr(step)
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr_rate 
    optimizer.step()
    # torch.cuda.synchronize()
    loss_item = loss.item()
    t1 = time.time()
    dt = (t1-t0)*1000
    tokens_processed = train_loader.B*train_loader.T*grad_accum_steps
    token_per_sec = (train_loader.B*train_loader.T)/(dt/1000)
    if master_process:
        print(f"step {step:5d} | loss: {loss_accum.item():.6f} | lr {lr_rate:.4e} | norm: {norm:.4f} | dt: {dt*1000:.2f}ms | tok/sec: {token_per_sec:.2f}")

if ddp:
    destroy_process_group()

sys.exit(0)