commit fe8042867ca499aef1df0d6c2606aabae3124b26
Author: Andrej Karpathy <andrej.karpathy@gmail.com>
Date:   Wed Dec 28 00:58:19 2022 +0000

    first very bad commit

diff --git a/README.md b/README.md
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+++ b/README.md
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+
+# nanoGPT
+
+The cleanest, fastest repository for training/finetuning medium-sized GPTs.
+
+This repo currently requires reading the code, but it's not that bad. work ongoing...
+
+Getting started:
+
+We need a few dependencies:
+
+- [pytorch](https://pytorch.org), of course
+- numpy
+- `pip install datasets` for huggingface datasets
+- `pip install tiktoken` for OpenAI's fast bpe code
+- `pip install wandb` for optional logging
+
+```
+$ cd data/openwebtext
+$ python prepare.py
+```
+
+To download and tokenize the [openwebtext](https://huggingface.co/datasets/openwebtext) dataset. It will create a `train.bin` and `val.bin` which holds the GPT2 BPE token ids in a massive sequence. Then we're ready to kick off training. First open up train.py and read it, make sure the settings look ok. Then:
+
+```
+$ python train.py
+```
+
+Once some checkpoints are written to the output directory `out`, we're ready to sample from the model:
+
+```
+$ python sample.py
+```
+
diff --git a/data/openwebtext/prepare.py b/data/openwebtext/prepare.py
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+++ b/data/openwebtext/prepare.py
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+# saves the openwebtext dataset to a binary file for training. following was helpful:
+# https://github.com/HazyResearch/flash-attention/blob/main/training/src/datamodules/language_modeling_hf.py
+
+import mmap
+import subprocess
+import numpy as np
+import tiktoken
+from datasets import load_dataset # huggingface datasets
+
+# number of workers in .map() calls
+# good number to use is ~order num_cpu_cores()
+num_proc = 16
+
+# takes 54GB in huggingface .cache dir, about 8M documents (8,013,769)
+dataset = load_dataset("openwebtext")
+
+# owt by default only contains the 'train' split, so create a test split
+split_dataset = dataset["train"].train_test_split(test_size=0.0005, seed=2357, shuffle=True)
+split_dataset['val'] = split_dataset.pop('test') # rename the test split to val
+
+# this results in:
+# >>> split_dataset
+# DatasetDict({
+#     train: Dataset({
+#         features: ['text'],
+#         num_rows: 8009762
+#     })
+#     val: Dataset({
+#         features: ['text'],
+#         num_rows: 4007
+#     })
+# })
+
+# we now want to tokenize the dataset. first define the encoding function (gpt2 bpe)
+enc = tiktoken.get_encoding("gpt2")
+def process(example):
+    ids = enc.encode_ordinary(example['text']) # encode_ordinary ignores any special tokens
+    ids.append(enc.eot_token) # add the end of text token, e.g. 50256 for gpt2 bpe
+    out = {'ids': ids, 'len': len(ids)}
+    return out
+
+# tokenize the dataset
+tokenized = split_dataset.map(
+    process,
+    remove_columns=['text'],
+    desc="tokenizing the splits",
+    num_proc=num_proc,
+)
+
+# concatenate all the ids in each dataset into one large file we can use for training
+for split, dset in tokenized.items():
+
+    offset = np.cumsum(dset['len']).tolist()
+    total = offset[-1] # total number of tokens in the dataset
+    dset = dset.add_column('offset', offset)
+
+    # preallocate space in a temporary file to store the concatenated ids
+    filename = f'{split}.bin'
+    dtype = np.uint16 # (can do since enc.max_token_value == 50256 is < 2**16)
+    bytes_per_token = 2 # i.e. np.dtype(dtype).itemsize
+    subprocess.run(['truncate', '-s', str(total * bytes_per_token), filename], check=True)
+
+    # write the ids to the file
+    def write_to_file(example):
+        with open(filename, 'r+b') as f:
+            arr_len = len(example['ids'])
+            start = example['offset'] - arr_len
+            mm = mmap.mmap(f.fileno(), 0)
+            arr = np.ndarray((arr_len,), dtype=dtype, buffer=mm, offset=bytes_per_token * start)
+            arr[:] = example['ids']
+            mm.flush()
+
+    dset.map(
+        write_to_file,
+        desc=f"writing {split} split to file {filename}",
+        num_proc=num_proc,
+    )
+
+# train.bin is ~17GB, val.bin ~8.5MB
+# train has ~9B tokens (9,035,582,198)
+# val has ~4M tokens (4,434,897)
+
+# to read the bin files later, e.g. with numpy:
+# m = np.memmap('train.bin', dtype=np.uint16, mode='r')
diff --git a/data/openwebtext/readme.md b/data/openwebtext/readme.md
new file mode 100644
index 0000000..95eb1bf
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+++ b/data/openwebtext/readme.md
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+
+## openwebtext dataset
+
+after running `prepare.py` (preprocess) we get:
+
+- train.bin is ~17GB, val.bin ~8.5MB
+- train has ~9B tokens (9,035,582,198)
+- val has ~4M tokens (4,434,897)
+
+this came from 8,013,769 documents in total.
+
+references:
+
+- OpenAI's WebText dataset is discussed in [GPT-2 paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
+- [OpenWebText](https://skylion007.github.io/OpenWebTextCorpus/) dataset
diff --git a/model.py b/model.py
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+++ b/model.py
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+"""
+Full definition of a GPT Language Model, all of it in this single file.
+References:
+1) the official GPT-2 TensorFlow implementation released by OpenAI:
+https://github.com/openai/gpt-2/blob/master/src/model.py
+2) huggingface/transformers PyTorch implementation:
+https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py
+"""
+
+import math
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+@torch.jit.script
+def fused_gelu(x):
+    """
+    Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT).
+    Reference: Gaussian Error Linear Units (GELU) paper: https://arxiv.org/abs/1606.08415
+    """
+    return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
+
+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)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
+        # regularization
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        # 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))
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+
+    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)
+        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)
+        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = fused_gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(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:
+    block_size: int = 1024
+    vocab_size: int = 50257
+    n_layer: int = 12
+    n_head: int = 12
+    n_embd: int = 768
+    dropout: float = 0.1
+
+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.block_size = config.block_size
+
+        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 = nn.LayerNorm(config.n_embd),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        # report number of parameters (note we don't count the decoder parameters in lm_head)
+        n_params = sum(p.numel() for p in self.transformer.parameters())
+        print("number of parameters: %.2fM" % (n_params/1e6,))
+
+    def forward(self, idx, targets=None):
+        device = idx.device
+        b, t = idx.size()
+        assert t <= self.block_size, f"Cannot forward sequence of length {t}, block size is only {self.block_size}"
+        pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0) # shape (1, 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 (1, t, n_embd)
+        x = self.transformer.drop(tok_emb + pos_emb)
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+        logits = self.lm_head(x)
+
+        # if we are given some desired targets also calculate the loss
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
+
+        return logits, loss
+
+    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.block_size
+        self.block_size = block_size
+        self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size])
+        for block in self.transformer.h:
+            block.attn.bias = block.attn.bias[:,:,:block_size,:block_size]
+
+    @classmethod
+    def from_pretrained(cls, model_type):
+        assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
+        from transformers import GPT2LMHeadModel
+        print("loading weights from pretrained gpt: %s" % model_type)
+
+        layer_config = {
+            '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]
+
+        # create a from-scratch initialized minGPT model
+        config = GPTConfig(block_size=1024, **layer_config)
+        model = GPT(config)
+        sd = model.state_dict()
+
+        # 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
+        keys = [k for k in sd_hf if not k.endswith('attn.masked_bias')] # ignore these
+        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(keys) == len(sd)
+        for k in keys:
+            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 configure_optimizers(self, weight_decay, learning_rate, betas):
+        """
+        This long function is unfortunately doing something very simple and is being very defensive:
+        We are separating out all parameters of the model into two buckets: those that will experience
+        weight decay for regularization and those that won't (biases, and layernorm/embedding weights).
+        We are then returning the PyTorch optimizer object.
+        """
+
+        # separate out all parameters to those that will and won't experience regularizing weight decay
+        decay = set()
+        no_decay = set()
+        whitelist_weight_modules = (torch.nn.Linear, )
+        blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
+        for mn, m in self.named_modules():
+            for pn, p in m.named_parameters():
+                fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
+                # random note: because named_modules and named_parameters are recursive
+                # we will see the same tensors p many many times. but doing it this way
+                # allows us to know which parent module any tensor p belongs to...
+                if pn.endswith('bias'):
+                    # all biases will not be decayed
+                    no_decay.add(fpn)
+                elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
+                    # weights of whitelist modules will be weight decayed
+                    decay.add(fpn)
+                elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
+                    # weights of blacklist modules will NOT be weight decayed
+                    no_decay.add(fpn)
+
+        # validate that we considered every parameter
+        param_dict = {pn: p for pn, p in self.named_parameters()}
+        inter_params = decay & no_decay
+        union_params = decay | no_decay
+        assert len(inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params), )
+        assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \
+                                                    % (str(param_dict.keys() - union_params), )
+
+        # create the pytorch optimizer object
+        optim_groups = [
+            {"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": weight_decay},
+            {"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0},
+        ]
+        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas)
+        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.block_size else idx[:, -self.block_size:]
+            # forward the model to get the logits for the index in the sequence
+            logits, _ = self(idx_cond)
+            # 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, top_k)
+                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
diff --git a/sample.py b/sample.py
new file mode 100644
index 0000000..7dc9df1
--- /dev/null
+++ b/sample.py
@@ -0,0 +1,37 @@
+"""
+Sample from a trained model
+"""
+import os
+import torch
+import tiktoken
+from model import GPTConfig, GPT
+
+device = 'cuda:2'
+torch.manual_seed(1337)
+torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
+torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
+
+out_dir = 'out'
+ckpt_path = os.path.join(out_dir, 'ckpt.pt')
+checkpoint = torch.load(ckpt_path, map_location=device)
+
+# model
+gptconf = GPTConfig(**checkpoint['model_args'])
+model = GPT(gptconf)
+model.load_state_dict(checkpoint['model'])
+model.eval()
+model.to(device)
+
+enc = tiktoken.get_encoding("gpt2")
+#start = enc.encode("\n")
+start = [enc.eot_token]
+x = (torch.tensor(start, dtype=torch.long, device=device)[None, ...])
+
+for k in range(1):
+
+    with torch.no_grad():
+        with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
+            y = model.generate(x, 300, temperature=0.8, top_k=200)
+
+    print(enc.decode(y[0].tolist()))
+    print('---------------')
diff --git a/train.py b/train.py
new file mode 100644
index 0000000..9931852
--- /dev/null
+++ b/train.py
@@ -0,0 +1,191 @@
+"""
+Train a GPT model on a dataset of text. One GPU version.
+The text is assumed to pre-tokenized and inside files train.pt and val.pt
+"""
+
+import os
+import time
+import math
+
+import numpy as np
+import torch
+import wandb
+
+from model import GPTConfig, GPT
+# -----------------------------------------------------------------------------
+# settings, todo argparse or something
+# I/O
+out_dir = 'out'
+eval_interval = 500
+log_interval = 1
+# wandb logging
+wandb_log = False
+wandb_entity = 'karpathy'
+wandb_project = 'owt'
+wandb_run_name = 'owt1' # 'run' + str(time.time())
+# data
+dataset = 'openwebtext'
+batch_size = 32
+block_size = 512
+# model
+device = 'cuda:0'
+init_from = 'scratch' # 'scratch' or 'resume' or 'gpt2*'
+dropout = 0.1
+n_layer = 12
+n_head = 12
+n_embd = 768
+# adamw optimizer
+learning_rate = 2.5e-4 # max learning rate
+max_iters = 500000 # total number of training iterations
+weight_decay = 1e-2
+betas = (0.9, 0.95)
+# learning rate decay settings
+decay_lr = True # whether to decay the learning rate
+warmup_iters = 2000 # how many steps to warm up for
+lr_decay_iters = 320000 # how many steps to decay the learning rate for
+min_lr = 1e-5 # minimum learning rate
+# -----------------------------------------------------------------------------
+
+os.makedirs(out_dir, exist_ok=True)
+torch.manual_seed(1337)
+torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
+torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
+
+# poor man's data loader, TODO use real DataLoader...
+data_dir = os.path.join('data', dataset)
+train_data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
+val_data = np.memmap(os.path.join(data_dir, 'val.bin'), dtype=np.uint16, mode='r')
+def get_batch(split):
+    data = train_data if split == 'train' else val_data
+    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])
+    x, y = x.to(device), y.to(device)
+    return x, y
+
+# model init
+# TODO I don't love this whole part/API yet
+model_args = dict(n_layer = n_layer, n_head = n_head, n_embd = n_embd, block_size = block_size, dropout = dropout)
+if init_from == 'scratch':
+    # init a new model from scratch
+    gptconf = GPTConfig(**model_args)
+    model = GPT(gptconf)
+elif init_from == 'resume':
+    # resume training from a checkpoint. TODO: do we resume iter_num etc too? (yes...)
+    ckpt_path = os.path.join(out_dir, 'ckpt.pt')
+    checkpoint = torch.load(ckpt_path)
+    checkpoint_model_args = checkpoint['model_args']
+    for k, v in model_args.items():
+        assert checkpoint_model_args[k] == v, "for now"
+    gptconf = GPTConfig(**model_args)
+    model = GPT(gptconf)
+    model.load_state_dict(checkpoint['model'])
+elif init_from.startswith('gpt2'):
+    # initialize from OpenAI GPT-2 weights
+    model = GPT.from_pretrained(init_from)
+    if block_size < model.block_size:
+        model.crop_block_size(block_size)
+model.to(device)
+
+@torch.no_grad()
+def estimate_loss(eval_iters=50):
+    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 torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
+                logits, loss = model(X, Y)
+            losses[k] = loss.item()
+        out[split] = losses.mean()
+    model.train()
+    return out
+
+# optimizer
+optimizer = model.configure_optimizers(weight_decay, learning_rate, betas)
+if init_from == 'resume':
+    optimizer.load_state_dict(checkpoint['optimizer'])
+
+# learning rate decay scheduler (cosine with warmup)
+def get_lr(iter):
+    # 1) linear warmup for warmup_iters steps
+    if iter < warmup_iters:
+        return learning_rate * iter / warmup_iters
+    # 2) if iter > lr_decay_iters, return min learning rate
+    if iter > lr_decay_iters:
+        return min_lr
+    # 3) in between, use cosine decay down to min learning rate
+    decay_ratio = (iter - warmup_iters) / (lr_decay_iters - warmup_iters)
+    assert 0 <= decay_ratio <= 1
+    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # ranges 0..1
+    return min_lr + coeff * (learning_rate - min_lr)
+
+# logging
+if wandb_log:
+    wandb.init(project=wandb_project, entity=wandb_entity, name=wandb_run_name)
+    wandb.config = {
+        "batch_size": batch_size,
+        "block_size": block_size,
+        "learning_rate": learning_rate, # TODO log everything else too
+    }
+
+# training loop
+iter_num = 0
+num_tokens = 0
+best_val_loss = 1e9
+t0 = time.time()
+while True:
+
+    # determine the learning rate for this iteration
+    if decay_lr:
+        lr = get_lr(iter_num)
+        for param_group in optimizer.param_groups:
+            param_group['lr'] = lr
+    else:
+        lr = learning_rate
+
+    if iter_num % eval_interval == 0:
+        losses = estimate_loss()
+        print(f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
+        if wandb_log:
+            wandb.log({
+                "iter": iter_num,
+                "num_tokens": num_tokens,
+                "train/loss": losses['train'],
+                "val/loss": losses['val'],
+                "lr": lr,
+            })
+        if losses['val'] < best_val_loss:
+            best_val_loss = losses['val']
+            if iter_num > 0: # don't save checkpoints on very first iteration...
+                checkpoint = {
+                    'model': model.state_dict(),
+                    'optimizer': optimizer.state_dict(),
+                    'model_args': model_args,
+                    'iter_num': iter_num,
+                }
+                torch.save(checkpoint, os.path.join(out_dir, 'ckpt.pt'))
+
+    X, Y = get_batch('train')
+    with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
+        logits, loss = model(X, Y)
+
+    optimizer.zero_grad(set_to_none=True)
+    loss.backward()
+    # TODO: gradient clipping
+    optimizer.step()
+
+    t1 = time.time()
+    dt = t1 - t0
+    t0 = t1
+    if iter_num % log_interval == 0:
+        lossf = loss.item() # loss as float. TODO CPU-GPU sync: profile, make sure not slow af
+        print(f"iter {iter_num}: loss {lossf:.4f}, time {dt*1000:.2f}ms")
+    iter_num += 1
+    num_tokens += X.numel()
+
+    # termination conditions
+    if iter_num >= max_iters:
+        break
+