from transformers import GPT2LMHeadModel

2025-08-07 18:38:31.441973: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1754591911.604171      36 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1754591911.652419      36 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

model_hf = GPT2LMHeadModel.from_pretrained("gpt2") # 124M [gpt2-xl: 1558M]
sd_hf = model_hf.state_dict()

for k, v in sd_hf.items():
    print(k, v.shape)

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model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]

generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]

transformer.wte.weight torch.Size([50257, 768])
transformer.wpe.weight torch.Size([1024, 768])
transformer.h.0.ln_1.weight torch.Size([768])
transformer.h.0.ln_1.bias torch.Size([768])
transformer.h.0.attn.c_attn.weight torch.Size([768, 2304])
transformer.h.0.attn.c_attn.bias torch.Size([2304])
transformer.h.0.attn.c_proj.weight torch.Size([768, 768])
transformer.h.0.attn.c_proj.bias torch.Size([768])
transformer.h.0.ln_2.weight torch.Size([768])
transformer.h.0.ln_2.bias torch.Size([768])
transformer.h.0.mlp.c_fc.weight torch.Size([768, 3072])
transformer.h.0.mlp.c_fc.bias torch.Size([3072])
transformer.h.0.mlp.c_proj.weight torch.Size([3072, 768])
transformer.h.0.mlp.c_proj.bias torch.Size([768])
transformer.h.1.ln_1.weight torch.Size([768])
transformer.h.1.ln_1.bias torch.Size([768])
transformer.h.1.attn.c_attn.weight torch.Size([768, 2304])
transformer.h.1.attn.c_attn.bias torch.Size([2304])
transformer.h.1.attn.c_proj.weight torch.Size([768, 768])
transformer.h.1.attn.c_proj.bias torch.Size([768])
transformer.h.1.ln_2.weight torch.Size([768])
transformer.h.1.ln_2.bias torch.Size([768])
transformer.h.1.mlp.c_fc.weight torch.Size([768, 3072])
transformer.h.1.mlp.c_fc.bias torch.Size([3072])
transformer.h.1.mlp.c_proj.weight torch.Size([3072, 768])
transformer.h.1.mlp.c_proj.bias torch.Size([768])
transformer.h.2.ln_1.weight torch.Size([768])
transformer.h.2.ln_1.bias torch.Size([768])
transformer.h.2.attn.c_attn.weight torch.Size([768, 2304])
transformer.h.2.attn.c_attn.bias torch.Size([2304])
transformer.h.2.attn.c_proj.weight torch.Size([768, 768])
transformer.h.2.attn.c_proj.bias torch.Size([768])
transformer.h.2.ln_2.weight torch.Size([768])
transformer.h.2.ln_2.bias torch.Size([768])
transformer.h.2.mlp.c_fc.weight torch.Size([768, 3072])
transformer.h.2.mlp.c_fc.bias torch.Size([3072])
transformer.h.2.mlp.c_proj.weight torch.Size([3072, 768])
transformer.h.2.mlp.c_proj.bias torch.Size([768])
transformer.h.3.ln_1.weight torch.Size([768])
transformer.h.3.ln_1.bias torch.Size([768])
transformer.h.3.attn.c_attn.weight torch.Size([768, 2304])
transformer.h.3.attn.c_attn.bias torch.Size([2304])
transformer.h.3.attn.c_proj.weight torch.Size([768, 768])
transformer.h.3.attn.c_proj.bias torch.Size([768])
transformer.h.3.ln_2.weight torch.Size([768])
transformer.h.3.ln_2.bias torch.Size([768])
transformer.h.3.mlp.c_fc.weight torch.Size([768, 3072])
transformer.h.3.mlp.c_fc.bias torch.Size([3072])
transformer.h.3.mlp.c_proj.weight torch.Size([3072, 768])
transformer.h.3.mlp.c_proj.bias torch.Size([768])
transformer.h.4.ln_1.weight torch.Size([768])
transformer.h.4.ln_1.bias torch.Size([768])
transformer.h.4.attn.c_attn.weight torch.Size([768, 2304])
transformer.h.4.attn.c_attn.bias torch.Size([2304])
transformer.h.4.attn.c_proj.weight torch.Size([768, 768])
transformer.h.4.attn.c_proj.bias torch.Size([768])
transformer.h.4.ln_2.weight torch.Size([768])
transformer.h.4.ln_2.bias torch.Size([768])
transformer.h.4.mlp.c_fc.weight torch.Size([768, 3072])
transformer.h.4.mlp.c_fc.bias torch.Size([3072])
transformer.h.4.mlp.c_proj.weight torch.Size([3072, 768])
transformer.h.4.mlp.c_proj.bias torch.Size([768])
transformer.h.5.ln_1.weight torch.Size([768])
transformer.h.5.ln_1.bias torch.Size([768])
transformer.h.5.attn.c_attn.weight torch.Size([768, 2304])
transformer.h.5.attn.c_attn.bias torch.Size([2304])
transformer.h.5.attn.c_proj.weight torch.Size([768, 768])
transformer.h.5.attn.c_proj.bias torch.Size([768])
transformer.h.5.ln_2.weight torch.Size([768])
transformer.h.5.ln_2.bias torch.Size([768])
transformer.h.5.mlp.c_fc.weight torch.Size([768, 3072])
transformer.h.5.mlp.c_fc.bias torch.Size([3072])
transformer.h.5.mlp.c_proj.weight torch.Size([3072, 768])
transformer.h.5.mlp.c_proj.bias torch.Size([768])
transformer.h.6.ln_1.weight torch.Size([768])
transformer.h.6.ln_1.bias torch.Size([768])
transformer.h.6.attn.c_attn.weight torch.Size([768, 2304])
transformer.h.6.attn.c_attn.bias torch.Size([2304])
transformer.h.6.attn.c_proj.weight torch.Size([768, 768])
transformer.h.6.attn.c_proj.bias torch.Size([768])
transformer.h.6.ln_2.weight torch.Size([768])
transformer.h.6.ln_2.bias torch.Size([768])
transformer.h.6.mlp.c_fc.weight torch.Size([768, 3072])
transformer.h.6.mlp.c_fc.bias torch.Size([3072])
transformer.h.6.mlp.c_proj.weight torch.Size([3072, 768])
transformer.h.6.mlp.c_proj.bias torch.Size([768])
transformer.h.7.ln_1.weight torch.Size([768])
transformer.h.7.ln_1.bias torch.Size([768])
transformer.h.7.attn.c_attn.weight torch.Size([768, 2304])
transformer.h.7.attn.c_attn.bias torch.Size([2304])
transformer.h.7.attn.c_proj.weight torch.Size([768, 768])
transformer.h.7.attn.c_proj.bias torch.Size([768])
transformer.h.7.ln_2.weight torch.Size([768])
transformer.h.7.ln_2.bias torch.Size([768])
transformer.h.7.mlp.c_fc.weight torch.Size([768, 3072])
transformer.h.7.mlp.c_fc.bias torch.Size([3072])
transformer.h.7.mlp.c_proj.weight torch.Size([3072, 768])
transformer.h.7.mlp.c_proj.bias torch.Size([768])
transformer.h.8.ln_1.weight torch.Size([768])
transformer.h.8.ln_1.bias torch.Size([768])
transformer.h.8.attn.c_attn.weight torch.Size([768, 2304])
transformer.h.8.attn.c_attn.bias torch.Size([2304])
transformer.h.8.attn.c_proj.weight torch.Size([768, 768])
transformer.h.8.attn.c_proj.bias torch.Size([768])
transformer.h.8.ln_2.weight torch.Size([768])
transformer.h.8.ln_2.bias torch.Size([768])
transformer.h.8.mlp.c_fc.weight torch.Size([768, 3072])
transformer.h.8.mlp.c_fc.bias torch.Size([3072])
transformer.h.8.mlp.c_proj.weight torch.Size([3072, 768])
transformer.h.8.mlp.c_proj.bias torch.Size([768])
transformer.h.9.ln_1.weight torch.Size([768])
transformer.h.9.ln_1.bias torch.Size([768])
transformer.h.9.attn.c_attn.weight torch.Size([768, 2304])
transformer.h.9.attn.c_attn.bias torch.Size([2304])
transformer.h.9.attn.c_proj.weight torch.Size([768, 768])
transformer.h.9.attn.c_proj.bias torch.Size([768])
transformer.h.9.ln_2.weight torch.Size([768])
transformer.h.9.ln_2.bias torch.Size([768])
transformer.h.9.mlp.c_fc.weight torch.Size([768, 3072])
transformer.h.9.mlp.c_fc.bias torch.Size([3072])
transformer.h.9.mlp.c_proj.weight torch.Size([3072, 768])
transformer.h.9.mlp.c_proj.bias torch.Size([768])
transformer.h.10.ln_1.weight torch.Size([768])
transformer.h.10.ln_1.bias torch.Size([768])
transformer.h.10.attn.c_attn.weight torch.Size([768, 2304])
transformer.h.10.attn.c_attn.bias torch.Size([2304])
transformer.h.10.attn.c_proj.weight torch.Size([768, 768])
transformer.h.10.attn.c_proj.bias torch.Size([768])
transformer.h.10.ln_2.weight torch.Size([768])
transformer.h.10.ln_2.bias torch.Size([768])
transformer.h.10.mlp.c_fc.weight torch.Size([768, 3072])
transformer.h.10.mlp.c_fc.bias torch.Size([3072])
transformer.h.10.mlp.c_proj.weight torch.Size([3072, 768])
transformer.h.10.mlp.c_proj.bias torch.Size([768])
transformer.h.11.ln_1.weight torch.Size([768])
transformer.h.11.ln_1.bias torch.Size([768])
transformer.h.11.attn.c_attn.weight torch.Size([768, 2304])
transformer.h.11.attn.c_attn.bias torch.Size([2304])
transformer.h.11.attn.c_proj.weight torch.Size([768, 768])
transformer.h.11.attn.c_proj.bias torch.Size([768])
transformer.h.11.ln_2.weight torch.Size([768])
transformer.h.11.ln_2.bias torch.Size([768])
transformer.h.11.mlp.c_fc.weight torch.Size([768, 3072])
transformer.h.11.mlp.c_fc.bias torch.Size([3072])
transformer.h.11.mlp.c_proj.weight torch.Size([3072, 768])
transformer.h.11.mlp.c_proj.bias torch.Size([768])
transformer.ln_f.weight torch.Size([768])
transformer.ln_f.bias torch.Size([768])
lm_head.weight torch.Size([50257, 768])

sd_hf["transformer.wpe.weight"].view(-1)[:20]

tensor([-0.0188, -0.1974,  0.0040,  0.0113,  0.0638, -0.1050,  0.0369, -0.1680,
        -0.0491, -0.0565, -0.0025,  0.0135, -0.0042,  0.0151,  0.0166, -0.1381,
        -0.0063, -0.0461,  0.0267, -0.2042])

import matplotlib.pyplot as plt
%matplotlib inline

plt.imshow(sd_hf["transformer.wpe.weight"], cmap="gray")

<matplotlib.image.AxesImage at 0x7fce8040ef50>

plt.plot(sd_hf["transformer.wpe.weight"][:, 150])
plt.plot(sd_hf["transformer.wpe.weight"][:, 200])
plt.plot(sd_hf["transformer.wpe.weight"][:, 250])

[<matplotlib.lines.Line2D at 0x7fce80218210>]

# plt.imshow(sd_hf["transformer.h.1.attn.c_attn.weight"][:300,:300], cmap="gray")
# plt.show()

import numpy as np

w = sd_hf["transformer.h.1.attn.c_attn.weight"][:300, :300]
w_np = w.detach().cpu().numpy()

# Optional: normalize to [0,1]
# w_np = (w_np - w_np.min()) / (w_np.max() - w_np.min())

plt.imshow(w_np, cmap="gray")
plt.title("c_attn weight matrix")
plt.colorbar()
plt.show()

from transformers import pipeline

generator = pipeline("text-generation", model="gpt2")
output = generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)

from transformers import pipeline, set_seed
generator = pipeline('text-generation', model='gpt2')
set_seed(42)
generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)

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vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]

merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]

tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]

Device set to use cuda:0
Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.
Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
Both `max_new_tokens` (=256) and `max_length`(=30) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)

[{'generated_text': "Hello, I'm a language model, so I can write things that are easy to understand with a little bit of code. But when you think of words, it's hard to think of words that are as simple as a little word in a sentence.\n\nSo I'm going to use a little bit of code, and I'll talk a little bit about the syntax.\n\nThis is how you write your first line:\n\n$trans = new $trans [1, 2, 3, 4]; $trans -> write ( 'Hello, I'm a language model, so I can write things that are easier to understand with a little bit of code.' );\n\nThis code is pretty simple, but it really doesn't have to be. We want to write this code in a few lines, and we're going to use an expression, which is a shorthand for the literal of the language.\n\nIn this case, we're going to use a variable named trans that's a symbol. We want to write this expression as an expression, where we're going to look for a line that matches the line we want to write.\n\nThe syntax for writing a line like this is very simple:\n\n$trans = new $trans [1, 2, 3, 4]; $"},
 {'generated_text': "Hello, I'm a language model, I've had a lot of good experiences with it, I'm a native speaker. I've been working with it for almost five years.\n\nWe're working on different programming languages. I'm working on several different languages.\n\nDo I feel like I'm in a better position to work on this type of thing?\n\nNo. I don't feel like I'm in a better position to work on this type of thing. I feel like I'm in a better position to work on code that's actually good.\n\nIt's not like I'm the only person that's been able to master a language. Even if you're not a very good programmer, I'd be more inclined to be able to master that language. That's what I'm working on.\n\nDo you have any thoughts on the idea of using some of the other languages that are now out there, especially Clojure, Python, and even the Java language?\n\nNo, I don't think Clojure is a better language. It's just a better language. I don't think I've ever been able to understand it before. Clojure is a very well-developed language.\n\nI think it's fun to be able to work on other languages, and I think it"},
 {'generated_text': 'Hello, I\'m a language model, I\'m an editor," the writer said. "But I don\'t think that\'s a good idea. I mean, what are you doing?"\n\nA few words from the man, who did not return calls.\n\nHe said she was "overworked" and that he had "made a mistake." "I think there\'s not one right answer," he said, adding that he had been told there were "no more questions."\n\nThe writer said the problem stemmed from her job as a writer in the online publication The New Yorker, where she was a part-time writer and editor.\n\nShe said she had been told that while she was happy to work at The New Yorker, her "job at this time was to write fiction and I was not. I thought I could have a full-time job at The New Yorker. I was wrong."\n\nThe writer\'s employer did not respond to a request for comment Friday.\n\nThe writer was a co-founder of G.I. Joe\'s magazine and a co-founder of the online publishing company Ado, which has its own website.\n\nA copy of G.I. Joe\'s website listed her as "a contributing editor and contributing editor to [The New Yorker]."'},
 {'generated_text': 'Hello, I\'m a language model, and it\'s not about me. It\'s about people.\n\n"If you\'re a person and you want to tell people what a language is, you have to be able to tell them what the language is about."\n\nLang, who came to the UK with her mother, has been studying English since she was 8.\n\nShe says she is passionate about how to understand people and how they use language.\n\n"I\'m a language model, and it\'s not about me. It\'s about people. It\'s about people as far as I\'m concerned."\n\nShe says she\'s always been interested in learning English and how to express herself and the world around her.\n\nBut she also says she doesn\'t understand why some people don\'t understand her and her language.\n\n"What do you get when you talk about the world of your language?"\n\n"You get to know people and you know people speak more than you do, but you\'re not allowed to do that.\n\n"So you\'re not allowed to do that."\n\nTheresa May has repeatedly claimed she wants to "unite the world" and is working to create an "open-ended" international language system.\n\nBut the Government has'},
 {'generated_text': 'Hello, I\'m a language model, not a language model. I\'m thinking of the languages in which we have formal semantics. One of my favorite languages is C#, which is the language of the language model. We\'re not talking about the semantics of a language model in a formal sense. We\'re talking about language models in which the language model is the only set of semantics that you can apply to any particular language.\n\nOne of the things I like about this kind of formal semantics is that it\'s a good way to develop a language model without having to go through languages that are not formal models. And I think you can do it with C#, which is not formal models.\n\nA lot of the things you will be interested in coming out of this are examples of non-formal semantics. I would like to talk about the second way that you can say, "I want to write this language model in C#."\n\nThere\'s a lot of things that you will be interested in. First of all, we have the language model. It\'s a language model, it\'s not a syntax model. We have a language model that we can do what we want. It\'s a language model that we can look at. It\'s a language model that you can apply to'}]

# let's instead sample manually
import torch
from torch.nn import functional as F

model_ = GPT2LMHeadModel.from_pretrained("gpt2") # 124M
model_.eval()
model_.to('cuda')
torch.manual_seed(42)
torch.cuda.manual_seed(42)
tokens = [15496, 11, 314, 1101, 257, 3303, 2746, 11] # "Hello, I'm a language model,"
tokens = torch.tensor(tokens, dtype=torch.long) # (8,)
tokens = tokens.unsqueeze(0).repeat(5, 1) # (5, 8)
x = tokens.to('cuda')

# generate!
while x.size(1) < 30: # max_length=30
    # forward the model to get the logits
    with torch.no_grad():
        logits = model_(x)[0] # (B, T, vocab_size)
        # take the logits at the last position
        logits = logits[:, -1, :] # (B, vocab_size)
        # get the probabilities
        probs = F.softmax(logits, dim=-1)
        # do top-k sampling of 50 (huggingface pipeline default)
        # topk_probs here becomes (5, 50), topk_indices is (5, 50)
        topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
        # select a token from the top-k probabilities
        # note: multinomial does not demand the input to sum to 1
        ix = torch.multinomial(topk_probs, 1) # (B, 1)
        # gather the corresponding indices
        xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
        # append to the sequence
        x = torch.cat((x, xcol), dim=1)

# print the generated text
import tiktoken
enc = tiktoken.get_encoding('gpt2')
for i in range(5):
    tokens = x[i, :30].tolist()
    decoded = enc.decode(tokens)
    print(">", decoded)

> Hello, I'm a language model, not a program.

So this morning I started studying for the interview in the lab. This was not
> Hello, I'm a language model, and one of the main things that bothers me when they create languages is how easy it becomes to create something that
> Hello, I'm a language model, and I wrote it off on the grounds that a language model would make me more fluent. But I'm not
> Hello, I'm a language model, I really like languages. I like languages because like, they're good. And the way we talk about languages
> Hello, I'm a language model, a language model I'm using for data modelling. All I did was test the results and then I wrote some

class GPT2(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
        self.wpe = nn.Embedding(config.block_size, config.n_embd)
        self.h = nn.ModuleList([Block(config) for _ in range(config.n_layer)])
        self.ln_f = nn.LayerNorm(config.n_embd)
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

import math
from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F
import tiktoken
# -----------------------------------------------------------------------------
class DataLoaderLite:
    def __init__(self, B, T):
        self.B = B
        self.T = T

        # at init load tokens from disk and store them in memory
        with open('input.txt', 'r') as f:
            text = f.read()
        enc = tiktoken.get_encoding('gpt2')
        tokens = enc.encode(text)
        self.tokens = torch.tensor(tokens)
        print(f"loaded {len(self.tokens)} tokens")
        print(f"1 epoch = {len(self.tokens) // (B * T)} batches")

        # state
        self.current_position = 0

    def next_batch(self):
        B, T = self.B, self.T
        buf = self.tokens[self.current_position : self.current_position+B*T+1]
        x = (buf[:-1]).view(B, T) # inputs
        y = (buf[1:]).view(B, T) # targets
        # advance the position in the tensor
        self.current_position += B * T
        # if loading the next batch would be out of bounds, reset
        if self.current_position + (B * T + 1) > len(self.tokens):
            self.current_position = 0
        return x, y
# -----------------------------------------------------------------------------
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)
        self.c_proj.NANOGPT_SCALE_INIT = 1

        # regularization
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        # not really a 'bias', more of a mask, but following the OpenAI/HF naming though
        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
        # nh is "number of heads", hs is "head size", and C (number of channels) = nh * hs
        # e.g. in GPT-2 (124M), n_head=12, hs=64, so nh*hs=C=768 channels in the Transformer
        qkv = self.c_attn(x)
        q, k, v = qkv.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)
        # attention (materializes the large (T,T) matrix for all the queries and keys)
        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)
        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.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(approximate='tanh')
        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:
    block_size: int = 1024 # max sequence length
    vocab_size: int = 50257 # number of tokens: 50,000 BPE merges + 256 bytes tokens + 1 <|endoftext|> token
    n_layer: int = 12 # number of layers
    n_head: int = 12 # number of heads
    n_embd: int = 768 # embedding dimension

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)

    def forward(self, idx):
        # idx is of shape (B, T)
        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}"
        # forward the token and posisition embeddings
        pos = torch.arange(0, T, dtype=torch.long, device=idx.device) # shape (T)
        pos_emb = self.transformer.wpe(pos) # position embeddings of shape (T, n_embd)
        tok_emb = self.transformer.wte(idx) # token embeddings of shape (B, T, n_embd)
        x = tok_emb + pos_emb
        # forward the blocks of the transformer
        for block in self.transformer.h:
            x = block(x)
        # forward the final layernorm and the classifier
        x = self.transformer.ln_f(x)
        logits = self.lm_head(x) # (B, T, vocab_size)
        return logits

    @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
# -----------------------------------------------------------------------------

import tiktoken
enc = tiktoken.get_encoding("gpt2")
tokens = enc.encode("Hello, I'm a language model,")

num_return_sequences = 5
max_length = 30

model = GPT.from_pretrained('gpt2')
model.eval()
model.to('cuda')

# prefix tokens
import tiktoken
enc = tiktoken.get_encoding('gpt2')
tokens = enc.encode("Hello, I'm a language model,")
tokens = torch.tensor(tokens, dtype=torch.long) # (8,)
tokens = tokens.unsqueeze(0).repeat(num_return_sequences, 1) # (5, 8)
x = tokens.to('cuda')


# generate! right now x is (B, T) where B = 5, T = 8
# set the seed to 42
torch.manual_seed(42)
torch.cuda.manual_seed(42)
while x.size(1) < max_length:
    # forward the model to get the logits
    with torch.no_grad():
        logits = model(x) # (B, T, vocab_size)
        # take the logits at the last position
        logits = logits[:, -1, :] # (B, vocab_size)
        # get the probabilities
        probs = F.softmax(logits, dim=-1)
        # do top-k sampling of 50 (huggingface pipeline default)
        # topk_probs here becomes (5, 50), topk_indices is (5, 50)
        topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
        # select a token from the top-k probabilities
        # note: multinomial does not demand the input to sum to 1
        ix = torch.multinomial(topk_probs, 1) # (B, 1)
        # gather the corresponding indices
        xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
        # append to the sequence
        x = torch.cat((x, xcol), dim=1)

# print the generated text
for i in range(num_return_sequences):
    tokens = x[i, :max_length].tolist()
    decoded = enc.decode(tokens)
    print(">", decoded)

loading weights from pretrained gpt: gpt2
> Hello, I'm a language model, not a program.

So this morning I started studying for the interview in the lab. This was not
> Hello, I'm a language model, and one of the main things that bothers me when they create languages is how easy it becomes to create something that
> Hello, I'm a language model, and I wrote it off on the grounds that a language model would make me more fluent. But I'm not
> Hello, I'm a language model, I really like languages. I like languages because like, they're good. And the way we talk about languages
> Hello, I'm a language model, a language model I'm using for data modelling. All I did was test the results and then I wrote some

device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = model.to(device)
x = tokens.to(device)

# attempt to autodetect the device
device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

num_return_sequences = 5
max_length = 30

model = GPT(GPTConfig())
model.eval()
model.to(device)

# prefix tokens
import tiktoken
enc = tiktoken.get_encoding('gpt2')
tokens = enc.encode("Hello, I'm a language model,")
tokens = torch.tensor(tokens, dtype=torch.long) # (8,)
tokens = tokens.unsqueeze(0).repeat(num_return_sequences, 1) # (5, 8)
x = tokens.to(device)


# generate! right now x is (B, T) where B = 5, T = 8
# set the seed to 42
torch.manual_seed(42)
torch.cuda.manual_seed(42)
while x.size(1) < max_length:
    # forward the model to get the logits
    with torch.no_grad():
        logits = model(x) # (B, T, vocab_size)
        # take the logits at the last position
        logits = logits[:, -1, :] # (B, vocab_size)
        # get the probabilities
        probs = F.softmax(logits, dim=-1)
        # do top-k sampling of 50 (huggingface pipeline default)
        # topk_probs here becomes (5, 50), topk_indices is (5, 50)
        topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
        # select a token from the top-k probabilities
        # note: multinomial does not demand the input to sum to 1
        ix = torch.multinomial(topk_probs, 1) # (B, 1)
        # gather the corresponding indices
        xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
        # append to the sequence
        x = torch.cat((x, xcol), dim=1)

# print the generated text
for i in range(num_return_sequences):
    tokens = x[i, :max_length].tolist()
    decoded = enc.decode(tokens)
    print(">", decoded)

using device: cuda
> Hello, I'm a language model, electronics sped Links Alternatively aerobic baptism Its know des cautiously exerciseBasically Simpson Patrol qual arbitration PIDTown decksDamn You Pegasus
> Hello, I'm a language model, artist sou losMHz Gadget textedoidal Ezekielminus 141 Lifhari domain Annie Kushicit populism wealth alliances archaic calib rich
> Hello, I'm a language model, sonicedomost declared-$21Mrswild PlainsIron fut jung cannon sorcererFour practical Grac worstannot bothered Containerstadt
> Hello, I'm a language model, tranquiloneliness Policyicking congregation gunned FL stressesFactor restraining Rusty fermented Missileanguard viewing adjusting reopenWilliamsrowdWarrenattack hen
> Hello, I'm a language model,alpha 520 Follow designate Main zincoraVOLOver855 procession equippediem dean Turtles vocyah================================================================ressoririn situations RIS

loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))

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)
        
        # weight sharing scheme
        self.transformer.wte.weight = self.lm_head.weight
        
        # init params
        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):
        # idx is of shape (B, T)
        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}"
        # forward the token and posisition embeddings
        pos = torch.arange(0, T, dtype=torch.long, device=idx.device) # shape (T)
        pos_emb = self.transformer.wpe(pos) # position embeddings of shape (T, n_embd)
        tok_emb = self.transformer.wte(idx) # token embeddings of shape (B, T, n_embd)
        x = tok_emb + pos_emb
        # forward the blocks of the transformer
        for block in self.transformer.h:
            x = block(x)
        # forward the final layernorm and the classifier
        x = self.transformer.ln_f(x)
        logits = self.lm_head(x) # (B, T, vocab_size)
        loss = None
        if targets is not None:
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
        return logits, loss

    @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
# -----------------------------------------------------------------------------

# tiny shakespeare dataset
!wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
with open('input.txt', 'r') as f:
    text = f.read()
data = text[:1000] # first 1,000 characters
print(data[:100])

--2025-08-07 18:39:01--  https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1115394 (1.1M) [text/plain]
Saving to: ‘input.txt’

input.txt           100%[===================>]   1.06M  --.-KB/s    in 0.05s   

2025-08-07 18:39:01 (20.8 MB/s) - ‘input.txt’ saved [1115394/1115394]

huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)

First Citizen:
Before we proceed any further, hear me speak.

All:
Speak, speak.

First Citizen:
You

!wc input.txt   # 40000 lines ~202K words, ~1.1million bytes

  40000  202651 1115394 input.txt

huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)

enc = tiktoken.get_encoding('gpt2')
tokens = enc.encode(data)
print(tokens[:24])

[5962, 22307, 25, 198, 8421, 356, 5120, 597, 2252, 11, 3285, 502, 2740, 13, 198, 198, 3237, 25, 198, 5248, 461, 11, 2740, 13]

import torch
buf = torch.tensor(tokens[:24 + 1])
x = buf[:-1].view(4, 6)
y = buf[1:].view(4, 6)
print(x)
print(y)

tensor([[ 5962, 22307,    25,   198,  8421,   356],
        [ 5120,   597,  2252,    11,  3285,   502],
        [ 2740,    13,   198,   198,  3237,    25],
        [  198,  5248,   461,    11,  2740,    13]])
tensor([[22307,    25,   198,  8421,   356,  5120],
        [  597,  2252,    11,  3285,   502,  2740],
        [   13,   198,   198,  3237,    25,   198],
        [ 5248,   461,    11,  2740,    13,   198]])

# attempt to autodetect the device
device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")
device = "cpu"  #override

# get a data batch
import tiktoken
enc = tiktoken.get_encoding('gpt2')
with open('input.txt', 'r') as f:
    text = f.read()
text = text[:1000]
tokens = enc.encode(text)
B, T = 4, 32
buf = torch.tensor(tokens[:B*T + 1])
x = buf[:-1].view(B, T)
y = buf[1:].view(B, T)

# get logits
model = GPT(GPTConfig())
model.to(device)
logits, loss = model(x, y)

print(loss)
# import sys; sys.exit(0)

# # prefix tokens
# import tiktoken
# enc = tiktoken.get_encoding('gpt2')
# model.eval()
# num_return_sequences = 5
# max_length = 30
# tokens = enc.encode("Hello, I'm a language model,")
# tokens = torch.tensor(tokens, dtype=torch.long) # (8,)
# tokens = tokens.unsqueeze(0).repeat(num_return_sequences, 1) # (5, 8)
# x = tokens.to(device)


# # generate! right now x is (B, T) where B = 5, T = 8
# # set the seed to 42
# torch.manual_seed(42)
# torch.cuda.manual_seed(42)
# while x.size(1) < max_length:
#     # forward the model to get the logits
#     with torch.no_grad():
#         logits = model(x) # (B, T, vocab_size)
#         # take the logits at the last position
#         logits = logits[:, -1, :] # (B, vocab_size)
#         # get the probabilities
#         probs = F.softmax(logits, dim=-1)
#         # do top-k sampling of 50 (huggingface pipeline default)
#         # topk_probs here becomes (5, 50), topk_indices is (5, 50)
#         topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
#         # select a token from the top-k probabilities
#         # note: multinomial does not demand the input to sum to 1
#         ix = torch.multinomial(topk_probs, 1) # (B, 1)
#         # gather the corresponding indices
#         xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
#         # append to the sequence
#         x = torch.cat((x, xcol), dim=1)

# # print the generated text
# for i in range(num_return_sequences):
#     tokens = x[i, :max_length].tolist()
#     decoded = enc.decode(tokens)
#     print(">", decoded)

using device: cuda
tensor(11.0088, grad_fn=<NllLossBackward0>)

# optimize!
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
for step in range(max_iters):
    optimizer.zero_grad()
    logits, loss = model(x, y)
    loss.backward()
    optimizer.step()
    print(f"step {i}, loss: {loss.item()}")

# attempt to autodetect the device
device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

# get a data batch
import tiktoken
enc = tiktoken.get_encoding('gpt2')
with open('input.txt', 'r') as f:
    text = f.read()
text = text[:1000]
tokens = enc.encode(text)
B, T = 4, 32
buf = torch.tensor(tokens[:B*T + 1])
buf = buf.to(device)
x = buf[:-1].view(B, T)
y = buf[1:].view(B, T)

# get logits
model = GPT(GPTConfig())
model.to(device)
# logits, loss = model(x, y)
# print(loss)

# optimize!
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
for i in range(50):
    optimizer.zero_grad()
    logits, loss = model(x, y)
    loss.backward()
    optimizer.step()
    print(f"step {i}, loss: {loss.item()}")

using device: cuda
step 0, loss: 10.769306182861328
step 1, loss: 8.232959747314453
step 2, loss: 7.903288841247559
step 3, loss: 7.477176666259766
step 4, loss: 7.222799777984619
step 5, loss: 6.974076747894287
step 6, loss: 6.690158367156982
step 7, loss: 6.36968469619751
step 8, loss: 6.096214294433594
step 9, loss: 5.825082778930664
step 10, loss: 5.520833492279053
step 11, loss: 5.2743821144104
step 12, loss: 4.86961555480957
step 13, loss: 7.977803707122803
step 14, loss: 4.721469402313232
step 15, loss: 4.726749420166016
step 16, loss: 4.6562018394470215
step 17, loss: 4.5155439376831055
step 18, loss: 4.344297409057617
step 19, loss: 4.195880889892578
step 20, loss: 4.066141605377197
step 21, loss: 3.942348003387451
step 22, loss: 3.8445816040039062
step 23, loss: 3.771299123764038
step 24, loss: 3.695563316345215
step 25, loss: 3.6219353675842285
step 26, loss: 3.586193084716797
step 27, loss: 3.499006986618042
step 28, loss: 3.433870315551758
step 29, loss: 3.3526525497436523
step 30, loss: 3.2975974082946777
step 31, loss: 3.2394254207611084
step 32, loss: 3.1776838302612305
step 33, loss: 3.130887269973755
step 34, loss: 3.067812442779541
step 35, loss: 2.990708827972412
step 36, loss: 2.9151458740234375
step 37, loss: 2.8503098487854004
step 38, loss: 2.8130929470062256
step 39, loss: 2.8470816612243652
step 40, loss: 3.2798235416412354
step 41, loss: 3.1968610286712646
step 42, loss: 2.7217764854431152
step 43, loss: 2.9068920612335205
step 44, loss: 2.5936026573181152
step 45, loss: 2.48004150390625
step 46, loss: 2.546128988265991
step 47, loss: 2.523922920227051
step 48, loss: 2.3874573707580566
step 49, loss: 2.2789361476898193

# attempt to autodetect the device
device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

# get a data batch 
train_loader = DataLoaderLite(B=4, T=32)

# get logits
model = GPT(GPTConfig())
model.to(device)
# logits, loss = model(x, y)
# print(loss)

# optimize!
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
for i in range(50):
    x, y = train_loader.next_batch()
    x, y = x.to(device), y.to(device)
    optimizer.zero_grad()
    logits, loss = model(x, y)
    loss.backward()
    optimizer.step()
    print(f"step {i}, loss: {loss.item()}")

using device: cuda
loaded 338025 tokens
1 epoch = 2640 batches
step 0, loss: 11.054346084594727
step 1, loss: 9.784205436706543
step 2, loss: 9.536847114562988
step 3, loss: 9.280383110046387
step 4, loss: 8.653818130493164
step 5, loss: 8.457844734191895
step 6, loss: 9.097246170043945
step 7, loss: 8.77798080444336
step 8, loss: 8.161457061767578
step 9, loss: 8.045823097229004
step 10, loss: 8.33431625366211
step 11, loss: 7.487844944000244
step 12, loss: 7.906373500823975
step 13, loss: 7.508420467376709
step 14, loss: 7.557555675506592
step 15, loss: 7.272642612457275
step 16, loss: 7.357736587524414
step 17, loss: 8.298192977905273
step 18, loss: 7.260444641113281
step 19, loss: 7.876038074493408
step 20, loss: 7.639876842498779
step 21, loss: 7.8305745124816895
step 22, loss: 6.4622602462768555
step 23, loss: 6.927628993988037
step 24, loss: 6.882352352142334
step 25, loss: 6.784662246704102
step 26, loss: 6.8236894607543945
step 27, loss: 7.606111526489258
step 28, loss: 7.201181411743164
step 29, loss: 6.976284503936768
step 30, loss: 7.04829740524292
step 31, loss: 7.329046249389648
step 32, loss: 7.218647480010986
step 33, loss: 7.086764335632324
step 34, loss: 7.988156318664551
step 35, loss: 7.906185626983643
step 36, loss: 7.785394191741943
step 37, loss: 7.733238697052002
step 38, loss: 8.147987365722656
step 39, loss: 7.602220058441162
step 40, loss: 7.475622653961182
step 41, loss: 7.066772937774658
step 42, loss: 7.190559387207031
step 43, loss: 7.182857990264893
step 44, loss: 7.161768436431885
step 45, loss: 7.199093818664551
step 46, loss: 6.208998680114746
step 47, loss: 6.304914951324463
step 48, loss: 6.973421096801758
step 49, loss: 6.742049694061279

# weight sharing scheme
self.transformer.wte.weight = self.lm_head.weight

print(sd_hf["lm_head.weight"].shape)
print(sd_hf["transformer.wte.weight"].shape)

torch.Size([50257, 768])
torch.Size([50257, 768])

(sd_hf["lm_head.weight"] == sd_hf["transformer.wte.weight"]).all()

tensor(True)

print(sd_hf["lm_head.weight"].data_ptr())
print(sd_hf["transformer.wte.weight"].data_ptr())

140522065219539
140522065219539

nn.init.normal_(param, mean=0.0, std=0.02)

# standard deviation grows inside the residual stream
x = torch.zeros(768)
n = 100 # e.g. 100 layers
for i in range(n):
    x += torch.randn(768)

print(x.std())

tensor(9.8334)

# standard deviation grows inside the residual stream
x = torch.zeros(768)
n = 100 # e.g. 100 layers
for i in range(n):
    x += n**-0.5 * torch.randn(768)

print(x.std())

tensor(0.9681)

# attempt to autodetect the device
device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

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

# get a data batch 
train_loader = DataLoaderLite(B=4, T=32)

# get logits
model = GPT(GPTConfig())
model.to(device)
# logits, loss = model(x, y)
# print(loss)

# optimize!
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
for i in range(50):
    x, y = train_loader.next_batch()
    x, y = x.to(device), y.to(device)
    optimizer.zero_grad()
    logits, loss = model(x, y)
    loss.backward()
    optimizer.step()
    print(f"step {i}, loss: {loss.item()}")

using device: cuda
loaded 338025 tokens
1 epoch = 2640 batches
step 0, loss: 10.960028648376465
step 1, loss: 9.687705993652344
step 2, loss: 9.082895278930664
step 3, loss: 9.145988464355469
step 4, loss: 8.626201629638672
step 5, loss: 8.331700325012207
step 6, loss: 8.89795207977295
step 7, loss: 8.837981224060059
step 8, loss: 8.116044044494629
step 9, loss: 8.042159080505371
step 10, loss: 8.38084888458252
step 11, loss: 7.435604572296143
step 12, loss: 7.8245649337768555
step 13, loss: 7.458939552307129
step 14, loss: 7.5318756103515625
step 15, loss: 7.366677761077881
step 16, loss: 7.436798095703125
step 17, loss: 8.293567657470703
step 18, loss: 7.202799320220947
step 19, loss: 7.887030601501465
step 20, loss: 7.505932807922363
step 21, loss: 7.82287073135376
step 22, loss: 6.425383567810059
step 23, loss: 6.877799034118652
step 24, loss: 6.827328205108643
step 25, loss: 6.701854228973389
step 26, loss: 6.814748764038086
step 27, loss: 7.621225833892822
step 28, loss: 7.173999309539795
step 29, loss: 6.947432041168213
step 30, loss: 6.990048885345459
step 31, loss: 7.249020576477051
step 32, loss: 7.1423749923706055
step 33, loss: 7.010761737823486
step 34, loss: 7.922441482543945
step 35, loss: 7.815272808074951
step 36, loss: 7.735034942626953
step 37, loss: 7.712531566619873
step 38, loss: 8.020227432250977
step 39, loss: 7.527308464050293
step 40, loss: 7.416410446166992
step 41, loss: 6.918368339538574
step 42, loss: 7.015596866607666
step 43, loss: 7.060007095336914
step 44, loss: 6.982024669647217
step 45, loss: 7.039826393127441
step 46, loss: 6.0357255935668945
step 47, loss: 6.309432506561279
step 48, loss: 6.953232765197754
step 49, loss: 6.799220085144043

!nvidia-smi

Thu Aug  7 18:39:23 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.03              Driver Version: 560.35.03      CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  Tesla T4                       Off |   00000000:00:04.0 Off |                    0 |
| N/A   50C    P0             33W /   70W |    4013MiB /  15360MiB |     96%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   1  Tesla T4                       Off |   00000000:00:05.0 Off |                    0 |
| N/A   45C    P8              9W /   70W |       3MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
                                                                                         
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
+-----------------------------------------------------------------------------------------+

huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)

scaler = torch.cuda.amp.GradScaler()

for step in range(max_iters):
    with torch.cuda.amp.autocast():
        logits = model(x)
        loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()
    optimizer.zero_grad()

torch.cuda.get_device_name(0)

'Tesla T4'

!nvidia-smi

Thu Aug  7 18:39:24 2025       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.03              Driver Version: 560.35.03      CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  Tesla T4                       Off |   00000000:00:04.0 Off |                    0 |
| N/A   49C    P0             35W /   70W |    4013MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   1  Tesla T4                       Off |   00000000:00:05.0 Off |                    0 |
| N/A   45C    P8              9W /   70W |       3MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
                                                                                         
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
+-----------------------------------------------------------------------------------------+

huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)

# attempt to autodetect the device
import time

device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

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

# get a data batch 
train_loader = DataLoaderLite(B=4, T=1024) # reduced batch size from 16 to ensure GPU fit (avoid out-of-memory error)

# get logits
model = GPT(GPTConfig())
model.to(device)

# optimize!
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
for i in range(50):
    t0 = time.time()
    x, y = train_loader.next_batch()
    x, y = x.to(device), y.to(device)
    optimizer.zero_grad()
    logits, loss = model(x, y)
    loss.backward()
    optimizer.step()
    torch.cuda.synchronize()  # wait for GPU to finish all scheduled work above
    t1 = time.time()
    dt = (t1 - t0) * 1000  # time difference in milliseconds
    tokens_per_sec = (train_loader.B * train_loader.T) / (t1 - t0)
    print(f"step {i}, loss: {loss.item()}, dt: {dt:.2f}ms, tok/sec: {tokens_per_sec:.2f}")

using device: cuda
loaded 338025 tokens
1 epoch = 82 batches
step 0, loss: 10.928552627563477, dt: 1219.75ms, tok/sec: 3358.08
step 1, loss: 9.525257110595703, dt: 1146.88ms, tok/sec: 3571.42
step 2, loss: 8.98608684539795, dt: 1153.07ms, tok/sec: 3552.26
step 3, loss: 8.6998929977417, dt: 1152.42ms, tok/sec: 3554.25
step 4, loss: 8.393763542175293, dt: 1154.75ms, tok/sec: 3547.08
step 5, loss: 8.022571563720703, dt: 1155.55ms, tok/sec: 3544.63
step 6, loss: 7.91090726852417, dt: 1157.10ms, tok/sec: 3539.90
step 7, loss: 7.710432052612305, dt: 1161.94ms, tok/sec: 3525.13
step 8, loss: 7.629523277282715, dt: 1159.35ms, tok/sec: 3533.02
step 9, loss: 7.342772006988525, dt: 1159.44ms, tok/sec: 3532.75
step 10, loss: 7.3585638999938965, dt: 1142.60ms, tok/sec: 3584.80
step 11, loss: 7.35311222076416, dt: 1160.71ms, tok/sec: 3528.88
step 12, loss: 7.409618854522705, dt: 1165.44ms, tok/sec: 3514.55
step 13, loss: 7.307405471801758, dt: 1166.81ms, tok/sec: 3510.44
step 14, loss: 6.9155988693237305, dt: 1174.30ms, tok/sec: 3488.04
step 15, loss: 6.930902481079102, dt: 1175.51ms, tok/sec: 3484.45
step 16, loss: 6.717788219451904, dt: 1179.52ms, tok/sec: 3472.59
step 17, loss: 6.539855480194092, dt: 1178.49ms, tok/sec: 3475.62
step 18, loss: 6.674693584442139, dt: 1183.97ms, tok/sec: 3459.56
step 19, loss: 6.670464515686035, dt: 1184.05ms, tok/sec: 3459.31
step 20, loss: 6.871865749359131, dt: 1190.47ms, tok/sec: 3440.67
step 21, loss: 6.718591690063477, dt: 1190.53ms, tok/sec: 3440.48
step 22, loss: 6.653460502624512, dt: 1191.97ms, tok/sec: 3436.33
step 23, loss: 6.747048854827881, dt: 1179.44ms, tok/sec: 3472.84
step 24, loss: 6.787980556488037, dt: 1184.74ms, tok/sec: 3457.31
step 25, loss: 6.768372058868408, dt: 1194.10ms, tok/sec: 3430.21
step 26, loss: 6.593522548675537, dt: 1191.67ms, tok/sec: 3437.18
step 27, loss: 6.649495601654053, dt: 1198.53ms, tok/sec: 3417.53
step 28, loss: 6.6570963859558105, dt: 1198.16ms, tok/sec: 3418.57
step 29, loss: 6.5045037269592285, dt: 1200.70ms, tok/sec: 3411.35
step 30, loss: 6.420934677124023, dt: 1206.80ms, tok/sec: 3394.11
step 31, loss: 6.367345809936523, dt: 1207.12ms, tok/sec: 3393.21
step 32, loss: 6.426124572753906, dt: 1216.39ms, tok/sec: 3367.35
step 33, loss: 6.559837341308594, dt: 1217.51ms, tok/sec: 3364.25
step 34, loss: 6.557112693786621, dt: 1218.27ms, tok/sec: 3362.14
step 35, loss: 6.5374274253845215, dt: 1221.94ms, tok/sec: 3352.05
step 36, loss: 6.357591152191162, dt: 1227.32ms, tok/sec: 3337.36
step 37, loss: 6.514996528625488, dt: 1234.14ms, tok/sec: 3318.91
step 38, loss: 6.320087432861328, dt: 1236.36ms, tok/sec: 3312.96
step 39, loss: 6.1555914878845215, dt: 1233.63ms, tok/sec: 3320.27
step 40, loss: 6.273041248321533, dt: 1243.08ms, tok/sec: 3295.05
step 41, loss: 6.372799396514893, dt: 1242.23ms, tok/sec: 3297.29
step 42, loss: 6.222573280334473, dt: 1244.52ms, tok/sec: 3291.22
step 43, loss: 6.214957237243652, dt: 1236.55ms, tok/sec: 3312.45
step 44, loss: 6.36059045791626, dt: 1257.36ms, tok/sec: 3257.63
step 45, loss: 6.262412071228027, dt: 1256.21ms, tok/sec: 3260.60
step 46, loss: 6.1164021492004395, dt: 1264.38ms, tok/sec: 3239.54
step 47, loss: 6.135281085968018, dt: 1262.08ms, tok/sec: 3245.42
step 48, loss: 6.152599334716797, dt: 1255.24ms, tok/sec: 3263.12
step 49, loss: 6.059423923492432, dt: 1273.51ms, tok/sec: 3216.30

torch.set_float32_matmul_precision('high')

# attempt to autodetect the device
import time

device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

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

# get a data batch 
train_loader = DataLoaderLite(B=4, T=1024) # reduced batch size from 16 to ensure GPU fit (avoid out-of-memory error)

torch.set_float32_matmul_precision('high')

# get logits
model = GPT(GPTConfig())
model.to(device)

# optimize!
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
for i in range(50):
    t0 = time.time()
    x, y = train_loader.next_batch()
    x, y = x.to(device), y.to(device)
    optimizer.zero_grad()
    logits, loss = model(x, y)
    loss.backward()
    optimizer.step()
    torch.cuda.synchronize()  # wait for GPU to finish all scheduled work above
    t1 = time.time()
    dt = (t1 - t0) * 1000  # time difference in milliseconds
    tokens_per_sec = (train_loader.B * train_loader.T) / (t1 - t0)
    print(f"step {i}, loss: {loss.item()}, dt: {dt:.2f}ms, tok/sec: {tokens_per_sec:.2f}")

using device: cuda
loaded 338025 tokens
1 epoch = 82 batches
step 0, loss: 10.928552627563477, dt: 1308.47ms, tok/sec: 3130.37
step 1, loss: 9.525257110595703, dt: 1252.21ms, tok/sec: 3271.02
step 2, loss: 8.98608684539795, dt: 1272.92ms, tok/sec: 3217.81
step 3, loss: 8.6998929977417, dt: 1274.88ms, tok/sec: 3212.85
step 4, loss: 8.393763542175293, dt: 1276.10ms, tok/sec: 3209.79
step 5, loss: 8.022571563720703, dt: 1278.22ms, tok/sec: 3204.46
step 6, loss: 7.91090726852417, dt: 1283.23ms, tok/sec: 3191.95
step 7, loss: 7.710432052612305, dt: 1282.97ms, tok/sec: 3192.59
step 8, loss: 7.629523277282715, dt: 1274.62ms, tok/sec: 3213.52
step 9, loss: 7.342772006988525, dt: 1265.65ms, tok/sec: 3236.28
step 10, loss: 7.3585638999938965, dt: 1276.01ms, tok/sec: 3210.02
step 11, loss: 7.35311222076416, dt: 1274.67ms, tok/sec: 3213.37
step 12, loss: 7.409618854522705, dt: 1287.36ms, tok/sec: 3181.71
step 13, loss: 7.307405471801758, dt: 1284.59ms, tok/sec: 3188.56
step 14, loss: 6.9155988693237305, dt: 1292.52ms, tok/sec: 3168.99
step 15, loss: 6.930902481079102, dt: 1290.90ms, tok/sec: 3172.97
step 16, loss: 6.717788219451904, dt: 1299.62ms, tok/sec: 3151.68
step 17, loss: 6.539855480194092, dt: 1317.01ms, tok/sec: 3110.08
step 18, loss: 6.674693584442139, dt: 1313.17ms, tok/sec: 3119.17
step 19, loss: 6.670464515686035, dt: 1311.58ms, tok/sec: 3122.96
step 20, loss: 6.871865749359131, dt: 1318.41ms, tok/sec: 3106.77
step 21, loss: 6.718591690063477, dt: 1302.18ms, tok/sec: 3145.50
step 22, loss: 6.653460502624512, dt: 1315.44ms, tok/sec: 3113.78
step 23, loss: 6.747048854827881, dt: 1324.81ms, tok/sec: 3091.76
step 24, loss: 6.787980556488037, dt: 1304.13ms, tok/sec: 3140.79
step 25, loss: 6.768372058868408, dt: 1316.43ms, tok/sec: 3111.45
step 26, loss: 6.593522548675537, dt: 1307.40ms, tok/sec: 3132.94
step 27, loss: 6.649495601654053, dt: 1329.37ms, tok/sec: 3081.17
step 28, loss: 6.6570963859558105, dt: 1336.06ms, tok/sec: 3065.74
step 29, loss: 6.5045037269592285, dt: 1327.29ms, tok/sec: 3085.98
step 30, loss: 6.420934677124023, dt: 1341.72ms, tok/sec: 3052.80
step 31, loss: 6.367345809936523, dt: 1331.89ms, tok/sec: 3075.32
step 32, loss: 6.426124572753906, dt: 1340.98ms, tok/sec: 3054.49
step 33, loss: 6.559837341308594, dt: 1331.49ms, tok/sec: 3076.26
step 34, loss: 6.557112693786621, dt: 1342.57ms, tok/sec: 3050.86
step 35, loss: 6.5374274253845215, dt: 1351.95ms, tok/sec: 3029.69
step 36, loss: 6.357591152191162, dt: 1347.96ms, tok/sec: 3038.67
step 37, loss: 6.514996528625488, dt: 1348.77ms, tok/sec: 3036.84
step 38, loss: 6.320087432861328, dt: 1358.32ms, tok/sec: 3015.50
step 39, loss: 6.1555914878845215, dt: 1361.24ms, tok/sec: 3009.02
step 40, loss: 6.273041248321533, dt: 1366.77ms, tok/sec: 2996.84
step 41, loss: 6.372799396514893, dt: 1373.65ms, tok/sec: 2981.84
step 42, loss: 6.222573280334473, dt: 1380.76ms, tok/sec: 2966.48
step 43, loss: 6.214957237243652, dt: 1374.58ms, tok/sec: 2979.81
step 44, loss: 6.36059045791626, dt: 1384.19ms, tok/sec: 2959.13
step 45, loss: 6.262412071228027, dt: 1387.79ms, tok/sec: 2951.45
step 46, loss: 6.1164021492004395, dt: 1392.50ms, tok/sec: 2941.47
step 47, loss: 6.135281085968018, dt: 1400.51ms, tok/sec: 2924.66
step 48, loss: 6.152599334716797, dt: 1403.23ms, tok/sec: 2918.99
step 49, loss: 6.059423923492432, dt: 1400.47ms, tok/sec: 2924.72

with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
    logits = model(x)
    loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))

#### attempt to autodetect the device
import time

device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

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

# get a data batch 
train_loader = DataLoaderLite(B=4, T=1024) # reduced batch size from 16 to ensure GPU fit (avoid out-of-memory error)

# get logits
model = GPT(GPTConfig())
model.to(device)

# optimize!
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
for i in range(50):
    t0 = time.time()
    x, y = train_loader.next_batch()
    x, y = x.to(device), y.to(device)
    optimizer.zero_grad()
    with torch.autocast(device_type=device, dtype=torch.float16):
        logits, loss = model(x, y)
    loss.backward()
    optimizer.step()
    torch.cuda.synchronize()  # wait for GPU to finish all scheduled work above
    t1 = time.time()
    dt = (t1 - t0) * 1000  # time difference in milliseconds
    tokens_per_sec = (train_loader.B * train_loader.T) / (t1 - t0)
    print(f"step {i}, loss: {loss.item()}, dt: {dt:.2f}ms, tok/sec: {tokens_per_sec:.2f}")

using device: cuda
loaded 338025 tokens
1 epoch = 82 batches
step 0, loss: 10.928529739379883, dt: 612.69ms, tok/sec: 6685.30
step 1, loss: 9.534974098205566, dt: 486.41ms, tok/sec: 8420.91
step 2, loss: 9.092107772827148, dt: 484.30ms, tok/sec: 8457.49
step 3, loss: 8.886457443237305, dt: 482.64ms, tok/sec: 8486.60
step 4, loss: 8.674713134765625, dt: 486.24ms, tok/sec: 8423.90
step 5, loss: 8.411386489868164, dt: 481.39ms, tok/sec: 8508.75
step 6, loss: 8.428609848022461, dt: 486.60ms, tok/sec: 8417.61
step 7, loss: 8.310781478881836, dt: 483.23ms, tok/sec: 8476.30
step 8, loss: 8.323281288146973, dt: 483.97ms, tok/sec: 8463.42
step 9, loss: 8.138256072998047, dt: 485.74ms, tok/sec: 8432.43
step 10, loss: 8.202449798583984, dt: 486.28ms, tok/sec: 8423.21
step 11, loss: 8.155570983886719, dt: 485.96ms, tok/sec: 8428.69
step 12, loss: 8.141721725463867, dt: 484.69ms, tok/sec: 8450.75
step 13, loss: 8.055846214294434, dt: 486.67ms, tok/sec: 8416.40
step 14, loss: 7.730942249298096, dt: 490.07ms, tok/sec: 8358.03
step 15, loss: 7.783799648284912, dt: 486.41ms, tok/sec: 8420.96
step 16, loss: 7.575163841247559, dt: 485.70ms, tok/sec: 8433.23
step 17, loss: 7.472563743591309, dt: 482.05ms, tok/sec: 8496.98
step 18, loss: 7.550014019012451, dt: 484.76ms, tok/sec: 8449.49
step 19, loss: 7.47854471206665, dt: 485.22ms, tok/sec: 8441.45
step 20, loss: 7.597340106964111, dt: 483.03ms, tok/sec: 8479.87
step 21, loss: 7.352836608886719, dt: 485.81ms, tok/sec: 8431.34
step 22, loss: 7.313559532165527, dt: 487.27ms, tok/sec: 8406.04
step 23, loss: 7.299529075622559, dt: 488.63ms, tok/sec: 8382.58
step 24, loss: 7.272910118103027, dt: 486.31ms, tok/sec: 8422.59
step 25, loss: 7.22429084777832, dt: 484.51ms, tok/sec: 8453.83
step 26, loss: 6.992632865905762, dt: 487.60ms, tok/sec: 8400.33
step 27, loss: 7.070698261260986, dt: 488.40ms, tok/sec: 8386.49
step 28, loss: 7.04710578918457, dt: 486.21ms, tok/sec: 8424.32
step 29, loss: 6.867212295532227, dt: 491.32ms, tok/sec: 8336.67
step 30, loss: 6.778204441070557, dt: 486.54ms, tok/sec: 8418.61
step 31, loss: 6.722084999084473, dt: 486.53ms, tok/sec: 8418.74
step 32, loss: 6.660738945007324, dt: 486.92ms, tok/sec: 8412.09
step 33, loss: 6.7278361320495605, dt: 486.03ms, tok/sec: 8427.47
step 34, loss: 6.72897481918335, dt: 485.48ms, tok/sec: 8437.00
step 35, loss: 6.67876672744751, dt: 486.09ms, tok/sec: 8426.40
step 36, loss: 6.545211315155029, dt: 488.27ms, tok/sec: 8388.85
step 37, loss: 6.685978412628174, dt: 489.78ms, tok/sec: 8362.92
step 38, loss: 6.511342525482178, dt: 488.49ms, tok/sec: 8385.09
step 39, loss: 6.385077476501465, dt: 491.59ms, tok/sec: 8332.08
step 40, loss: 6.447443962097168, dt: 487.67ms, tok/sec: 8399.06
step 41, loss: 6.547685146331787, dt: 490.38ms, tok/sec: 8352.72
step 42, loss: 6.433568000793457, dt: 495.98ms, tok/sec: 8258.33
step 43, loss: 6.4360809326171875, dt: 490.64ms, tok/sec: 8348.27
step 44, loss: 6.554992198944092, dt: 486.70ms, tok/sec: 8415.79
step 45, loss: 6.481368064880371, dt: 487.03ms, tok/sec: 8410.08
step 46, loss: 6.330848217010498, dt: 485.60ms, tok/sec: 8434.85
step 47, loss: 6.4358720779418945, dt: 488.24ms, tok/sec: 8389.34
step 48, loss: 6.420417785644531, dt: 483.90ms, tok/sec: 8464.47
step 49, loss: 6.325671672821045, dt: 483.18ms, tok/sec: 8477.14

# attempt to autodetect the device
import time

device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

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

# get a data batch 
train_loader = DataLoaderLite(B=4, T=1024) # reduced batch size from 16 to ensure GPU fit (avoid out-of-memory error)

# get logits
model = GPT(GPTConfig())
model.to(device)

from torch.amp import GradScaler, autocast

scaler = GradScaler()

for i in range(50):
    t0 = time.time()
    x, y = train_loader.next_batch()
    x, y = x.to(device), y.to(device)

    optimizer.zero_grad()
    with autocast(device_type=device):
        logits, loss = model(x, y)

    # AMP might not register anything to scale — check and step safely
    scaled_loss = scaler.scale(loss)
    scaled_loss.backward()
    try:
        scaler.step(optimizer)
        scaler.update()
    except AssertionError:
        pass #print(f"⚠️ AMP scaler skipped step {i}: No inf checks were recorded.")

    torch.cuda.synchronize()
    t1 = time.time()
    dt = (t1 - t0) * 1000
    tokens_per_sec = (train_loader.B * train_loader.T) / (t1 - t0)
    print(f"step {i}, loss: {loss.item():.4f}, dt: {dt:.2f}ms, tok/sec: {tokens_per_sec:.2f}")

using device: cuda
loaded 338025 tokens
1 epoch = 82 batches
step 0, loss: 10.9285, dt: 496.94ms, tok/sec: 8242.38
step 1, loss: 10.9400, dt: 479.21ms, tok/sec: 8547.36
step 2, loss: 10.9361, dt: 483.38ms, tok/sec: 8473.61
step 3, loss: 10.9372, dt: 480.38ms, tok/sec: 8526.52
step 4, loss: 10.9186, dt: 475.24ms, tok/sec: 8618.88
step 5, loss: 10.8968, dt: 483.92ms, tok/sec: 8464.25
step 6, loss: 10.9044, dt: 477.91ms, tok/sec: 8570.58
step 7, loss: 10.9373, dt: 472.20ms, tok/sec: 8674.31
step 8, loss: 10.9304, dt: 486.43ms, tok/sec: 8420.47
step 9, loss: 10.9200, dt: 489.16ms, tok/sec: 8373.47
step 10, loss: 10.9405, dt: 481.27ms, tok/sec: 8510.86
step 11, loss: 10.9636, dt: 486.25ms, tok/sec: 8423.71
step 12, loss: 10.9331, dt: 485.97ms, tok/sec: 8428.49
step 13, loss: 10.9157, dt: 484.44ms, tok/sec: 8455.05
step 14, loss: 10.9456, dt: 489.20ms, tok/sec: 8372.92
step 15, loss: 10.9481, dt: 481.43ms, tok/sec: 8508.06
step 16, loss: 10.9394, dt: 486.32ms, tok/sec: 8422.50
step 17, loss: 10.9290, dt: 486.35ms, tok/sec: 8421.90
step 18, loss: 10.9520, dt: 484.03ms, tok/sec: 8462.34
step 19, loss: 10.9301, dt: 482.35ms, tok/sec: 8491.72
step 20, loss: 10.9580, dt: 485.24ms, tok/sec: 8441.25
step 21, loss: 10.9100, dt: 490.75ms, tok/sec: 8346.44
step 22, loss: 10.9565, dt: 482.57ms, tok/sec: 8487.83
step 23, loss: 10.9416, dt: 484.36ms, tok/sec: 8456.47
step 24, loss: 10.9740, dt: 489.54ms, tok/sec: 8367.06
step 25, loss: 10.9383, dt: 481.66ms, tok/sec: 8503.90
step 26, loss: 10.9399, dt: 485.61ms, tok/sec: 8434.73
step 27, loss: 10.9542, dt: 486.19ms, tok/sec: 8424.68
step 28, loss: 10.9569, dt: 484.43ms, tok/sec: 8455.31
step 29, loss: 10.9231, dt: 486.64ms, tok/sec: 8416.87
step 30, loss: 10.9350, dt: 486.17ms, tok/sec: 8424.96
step 31, loss: 10.9451, dt: 482.78ms, tok/sec: 8484.19
step 32, loss: 10.9261, dt: 481.21ms, tok/sec: 8511.88
step 33, loss: 10.9466, dt: 473.94ms, tok/sec: 8642.42
step 34, loss: 10.9561, dt: 484.38ms, tok/sec: 8456.19
step 35, loss: 10.9516, dt: 486.32ms, tok/sec: 8422.50
step 36, loss: 10.9460, dt: 484.04ms, tok/sec: 8462.04
step 37, loss: 10.9400, dt: 487.85ms, tok/sec: 8396.03
step 38, loss: 10.9711, dt: 482.75ms, tok/sec: 8484.79
step 39, loss: 10.9370, dt: 482.34ms, tok/sec: 8491.92
step 40, loss: 10.9422, dt: 490.27ms, tok/sec: 8354.50
step 41, loss: 10.9505, dt: 480.56ms, tok/sec: 8523.47
step 42, loss: 10.9312, dt: 485.12ms, tok/sec: 8443.29
step 43, loss: 10.9290, dt: 485.43ms, tok/sec: 8437.88
step 44, loss: 10.9504, dt: 482.05ms, tok/sec: 8497.03
step 45, loss: 10.9248, dt: 483.68ms, tok/sec: 8468.36
step 46, loss: 10.9147, dt: 487.73ms, tok/sec: 8398.03
step 47, loss: 10.9154, dt: 485.38ms, tok/sec: 8438.70
step 48, loss: 10.9417, dt: 485.19ms, tok/sec: 8442.10
step 49, loss: 10.9241, dt: 483.63ms, tok/sec: 8469.20

# attempt to autodetect the device
import time

device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

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

# get a data batch 
train_loader = DataLoaderLite(B=4, T=1024) # reduced batch size from 16 to ensure GPU fit (avoid out-of-memory error)

# get logits
model = GPT(GPTConfig())
model.to(device)

from torch.amp import GradScaler, autocast

scaler = GradScaler()

for i in range(50):
    t0 = time.time()
    x, y = train_loader.next_batch()
    x, y = x.to(device), y.to(device)

    optimizer.zero_grad()
    with autocast(device_type=device, dtype=torch.float16):
        logits, loss = model(x, y)

    # AMP might not register anything to scale — check and step safely
    scaled_loss = scaler.scale(loss)
    scaled_loss.backward()
    try:
        scaler.step(optimizer)
        scaler.update()
    except AssertionError:
        pass #print(f"⚠️ AMP scaler skipped step {i}: No inf checks were recorded.")

    torch.cuda.synchronize()
    t1 = time.time()
    dt = (t1 - t0) * 1000
    tokens_per_sec = (train_loader.B * train_loader.T) / (t1 - t0)
    print(f"step {i}, loss: {loss.item():.4f}, dt: {dt:.2f}ms, tok/sec: {tokens_per_sec:.2f}")

using device: cuda
loaded 338025 tokens
1 epoch = 82 batches
step 0, loss: 10.9285, dt: 487.95ms, tok/sec: 8394.35
step 1, loss: 10.9400, dt: 471.51ms, tok/sec: 8686.92
step 2, loss: 10.9361, dt: 464.66ms, tok/sec: 8814.99
step 3, loss: 10.9372, dt: 485.09ms, tok/sec: 8443.84
step 4, loss: 10.9186, dt: 480.91ms, tok/sec: 8517.13
step 5, loss: 10.8968, dt: 471.83ms, tok/sec: 8681.06
step 6, loss: 10.9044, dt: 479.62ms, tok/sec: 8540.04
step 7, loss: 10.9373, dt: 479.83ms, tok/sec: 8536.39
step 8, loss: 10.9304, dt: 472.24ms, tok/sec: 8673.60
step 9, loss: 10.9200, dt: 477.25ms, tok/sec: 8582.54
step 10, loss: 10.9405, dt: 474.22ms, tok/sec: 8637.30
step 11, loss: 10.9636, dt: 479.65ms, tok/sec: 8539.55
step 12, loss: 10.9331, dt: 471.65ms, tok/sec: 8684.43
step 13, loss: 10.9157, dt: 484.55ms, tok/sec: 8453.17
step 14, loss: 10.9456, dt: 482.34ms, tok/sec: 8491.94
step 15, loss: 10.9481, dt: 480.53ms, tok/sec: 8523.86
step 16, loss: 10.9394, dt: 472.75ms, tok/sec: 8664.16
step 17, loss: 10.9290, dt: 484.65ms, tok/sec: 8451.49
step 18, loss: 10.9520, dt: 485.18ms, tok/sec: 8442.18
step 19, loss: 10.9301, dt: 482.95ms, tok/sec: 8481.15
step 20, loss: 10.9580, dt: 486.04ms, tok/sec: 8427.28
step 21, loss: 10.9100, dt: 486.41ms, tok/sec: 8420.79
step 22, loss: 10.9565, dt: 485.07ms, tok/sec: 8444.21
step 23, loss: 10.9416, dt: 484.04ms, tok/sec: 8462.13
step 24, loss: 10.9740, dt: 485.36ms, tok/sec: 8439.16
step 25, loss: 10.9383, dt: 486.09ms, tok/sec: 8426.36
step 26, loss: 10.9399, dt: 485.09ms, tok/sec: 8443.76
step 27, loss: 10.9542, dt: 482.53ms, tok/sec: 8488.58
step 28, loss: 10.9569, dt: 485.21ms, tok/sec: 8441.67
step 29, loss: 10.9231, dt: 486.49ms, tok/sec: 8419.41
step 30, loss: 10.9350, dt: 486.43ms, tok/sec: 8420.54
step 31, loss: 10.9451, dt: 484.42ms, tok/sec: 8455.46
step 32, loss: 10.9261, dt: 480.97ms, tok/sec: 8516.12
step 33, loss: 10.9466, dt: 488.21ms, tok/sec: 8389.83
step 34, loss: 10.9561, dt: 486.94ms, tok/sec: 8411.78
step 35, loss: 10.9516, dt: 484.49ms, tok/sec: 8454.24
step 36, loss: 10.9460, dt: 484.09ms, tok/sec: 8461.28
step 37, loss: 10.9400, dt: 487.35ms, tok/sec: 8404.68
step 38, loss: 10.9711, dt: 482.90ms, tok/sec: 8482.03
step 39, loss: 10.9370, dt: 484.03ms, tok/sec: 8462.24
step 40, loss: 10.9422, dt: 482.93ms, tok/sec: 8481.47
step 41, loss: 10.9505, dt: 487.78ms, tok/sec: 8397.15
step 42, loss: 10.9312, dt: 483.61ms, tok/sec: 8469.56
step 43, loss: 10.9290, dt: 486.56ms, tok/sec: 8418.20
step 44, loss: 10.9504, dt: 483.47ms, tok/sec: 8472.00
step 45, loss: 10.9248, dt: 484.40ms, tok/sec: 8455.78
step 46, loss: 10.9147, dt: 486.75ms, tok/sec: 8414.93
step 47, loss: 10.9154, dt: 483.56ms, tok/sec: 8470.48
step 48, loss: 10.9417, dt: 484.56ms, tok/sec: 8453.05
step 49, loss: 10.9241, dt: 484.25ms, tok/sec: 8458.44

model = torch.compile(model)

# attempt to autodetect the device
import time

device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

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

# get a data batch 
train_loader = DataLoaderLite(B=4, T=1024) # reduced batch size from 16 to ensure GPU fit (avoid out-of-memory error)

# get logits
model = GPT(GPTConfig())
model.to(device)
model = torch.compile(model)


# optimize!
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
for i in range(50):
    t0 = time.time()
    x, y = train_loader.next_batch()
    x, y = x.to(device), y.to(device)
    optimizer.zero_grad()
    with torch.autocast(device_type=device, dtype=torch.float16):
        logits, loss = model(x, y)
    loss.backward()
    optimizer.step()
    torch.cuda.synchronize()  # wait for GPU to finish all scheduled work above
    t1 = time.time()
    dt = (t1 - t0) * 1000  # time difference in milliseconds
    tokens_per_sec = (train_loader.B * train_loader.T) / (t1 - t0)
    print(f"step {i}, loss: {loss.item()}, dt: {dt:.2f}ms, tok/sec: {tokens_per_sec:.2f}")

using device: cuda
loaded 338025 tokens
1 epoch = 82 batches

W0807 18:43:17.748000 36 torch/_inductor/utils.py:1137] [0/0] Not enough SMs to use max_autotune_gemm mode

step 0, loss: 10.928505897521973, dt: 42662.93ms, tok/sec: 96.01
step 1, loss: 9.534719467163086, dt: 317.62ms, tok/sec: 12895.83
step 2, loss: 9.092105865478516, dt: 312.27ms, tok/sec: 13116.87
step 3, loss: 8.886580467224121, dt: 319.62ms, tok/sec: 12815.34
step 4, loss: 8.674884796142578, dt: 323.52ms, tok/sec: 12660.87
step 5, loss: 8.411476135253906, dt: 320.35ms, tok/sec: 12786.12
step 6, loss: 8.428608894348145, dt: 321.65ms, tok/sec: 12734.15
step 7, loss: 8.310811996459961, dt: 313.78ms, tok/sec: 13053.74
step 8, loss: 8.323326110839844, dt: 316.52ms, tok/sec: 12940.56
step 9, loss: 8.138195037841797, dt: 320.64ms, tok/sec: 12774.62
step 10, loss: 8.202305793762207, dt: 314.25ms, tok/sec: 13034.24
step 11, loss: 8.155123710632324, dt: 318.49ms, tok/sec: 12860.55
step 12, loss: 8.140466690063477, dt: 321.95ms, tok/sec: 12722.46
step 13, loss: 8.054699897766113, dt: 321.87ms, tok/sec: 12725.51
step 14, loss: 7.73115348815918, dt: 324.70ms, tok/sec: 12614.57
step 15, loss: 7.770930290222168, dt: 326.75ms, tok/sec: 12535.70
step 16, loss: 7.571919918060303, dt: 321.86ms, tok/sec: 12725.87
step 17, loss: 7.46449089050293, dt: 319.46ms, tok/sec: 12821.58
step 18, loss: 7.544422149658203, dt: 324.45ms, tok/sec: 12624.28
step 19, loss: 7.458747863769531, dt: 325.53ms, tok/sec: 12582.52
step 20, loss: 7.5706024169921875, dt: 324.65ms, tok/sec: 12616.84
step 21, loss: 7.339932441711426, dt: 325.36ms, tok/sec: 12589.32
step 22, loss: 7.280610084533691, dt: 323.15ms, tok/sec: 12675.36
step 23, loss: 7.272912979125977, dt: 324.96ms, tok/sec: 12604.76
step 24, loss: 7.273245334625244, dt: 322.65ms, tok/sec: 12694.94
step 25, loss: 7.207701683044434, dt: 324.37ms, tok/sec: 12627.70
step 26, loss: 6.994104385375977, dt: 334.32ms, tok/sec: 12251.85
step 27, loss: 7.06322717666626, dt: 329.96ms, tok/sec: 12413.81
step 28, loss: 7.023367881774902, dt: 343.84ms, tok/sec: 11912.65
step 29, loss: 6.878029823303223, dt: 328.32ms, tok/sec: 12475.62
step 30, loss: 6.770960807800293, dt: 326.33ms, tok/sec: 12551.78
step 31, loss: 6.72967004776001, dt: 331.60ms, tok/sec: 12352.24
step 32, loss: 6.677548408508301, dt: 330.64ms, tok/sec: 12387.98
step 33, loss: 6.732865333557129, dt: 332.18ms, tok/sec: 12330.62
step 34, loss: 6.726591110229492, dt: 331.79ms, tok/sec: 12344.98
step 35, loss: 6.697101593017578, dt: 327.72ms, tok/sec: 12498.32
step 36, loss: 6.526123046875, dt: 331.66ms, tok/sec: 12349.89
step 37, loss: 6.690964698791504, dt: 334.10ms, tok/sec: 12259.98
step 38, loss: 6.5023016929626465, dt: 330.63ms, tok/sec: 12388.32
step 39, loss: 6.340418815612793, dt: 333.82ms, tok/sec: 12270.03
step 40, loss: 6.452215194702148, dt: 337.15ms, tok/sec: 12148.87
step 41, loss: 6.529232025146484, dt: 332.64ms, tok/sec: 12313.55
step 42, loss: 6.416116237640381, dt: 335.95ms, tok/sec: 12192.14
step 43, loss: 6.428497314453125, dt: 331.08ms, tok/sec: 12371.54
step 44, loss: 6.54385232925415, dt: 335.90ms, tok/sec: 12194.15
step 45, loss: 6.461559772491455, dt: 332.85ms, tok/sec: 12305.88
step 46, loss: 6.321363925933838, dt: 335.83ms, tok/sec: 12196.59
step 47, loss: 6.428633689880371, dt: 341.24ms, tok/sec: 12003.24
step 48, loss: 6.402406692504883, dt: 331.52ms, tok/sec: 12355.24
step 49, loss: 6.310271263122559, dt: 338.30ms, tok/sec: 12107.42

from torch.nn.functional import scaled_dot_product_attention

attn_output = scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=True)

import time
import torch
import torch.nn.functional as F

bz = 32
seq_len = 2048
dims = 64
n_heads = 8
q = torch.randn(bz, n_heads, seq_len, dims, dtype=torch.float16).cuda()
k = torch.randn(bz, n_heads, seq_len, dims, dtype=torch.float16).cuda()
v = torch.randn(bz, n_heads, seq_len, dims, dtype=torch.float16).cuda()

dropout_rate = 0
num_trials = 10

# Standard attention
torch.cuda.synchronize()
start = time.time()
for i in range(num_trials):
    attn = q @ k.transpose(-2, -1)
    attn = attn.softmax(dim=-1)
    attn = F.dropout(attn, p=dropout_rate, training=True)
    x = (attn @ v).transpose(1, 2)  # .reshape(bz, seq_len, n_heads*dims)
torch.cuda.synchronize()
standard_time = time.time() - start
print('Standard attention took {} seconds for {} trials'.format(standard_time, num_trials))

# Optimized attention - let PyTorch choose the best kernel for T4
torch.cuda.synchronize()
start = time.time()
for i in range(num_trials):
    out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
torch.cuda.synchronize()
optimized_time = time.time() - start
print('Flash attention took {} seconds for {} trials'.format(optimized_time, num_trials))

# Calculate and display speedup
speedup = standard_time / optimized_time
time_saved = standard_time - optimized_time
print('\n=== Performance Summary ===')
print('Speedup: {:.2f}x faster'.format(speedup))
print('Time saved: {:.4f} seconds ({:.1f}ms per trial)'.format(time_saved, time_saved * 1000 / num_trials))

Standard attention took 1.0624969005584717 seconds for 10 trials
Flash attention took 0.13919758796691895 seconds for 10 trials

=== Performance Summary ===
Speedup: 7.63x faster
Time saved: 0.9233 seconds (92.3ms per trial)

import math
import inspect
from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F
import tiktoken
# -----------------------------------------------------------------------------
class DataLoaderLite:
    def __init__(self, B, T):
        self.B = B
        self.T = T

        # at init load tokens from disk and store them in memory
        with open('input.txt', 'r') as f:
            text = f.read()
        enc = tiktoken.get_encoding('gpt2')
        tokens = enc.encode(text)
        self.tokens = torch.tensor(tokens)
        print(f"loaded {len(self.tokens)} tokens")
        print(f"1 epoch = {len(self.tokens) // (B * T)} batches")

        # state
        self.current_position = 0

    def next_batch(self):
        B, T = self.B, self.T
        buf = self.tokens[self.current_position : self.current_position+B*T+1]
        x = (buf[:-1]).view(B, T) # inputs
        y = (buf[1:]).view(B, T) # targets
        # advance the position in the tensor
        self.current_position += B * T
        # if loading the next batch would be out of bounds, reset
        if self.current_position + (B * T + 1) > len(self.tokens):
            self.current_position = 0
        return x, y
# -----------------------------------------------------------------------------
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)
        self.c_proj.NANOGPT_SCALE_INIT = 1

        # regularization
        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
        # nh is "number of heads", hs is "head size", and C (number of channels) = nh * hs
        # e.g. in GPT-2 (124M), n_head=12, hs=64, so nh*hs=C=768 channels in the Transformer
        qkv = self.c_attn(x)
        q, k, v = qkv.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)
        # FlashAttention (materializes the large (T,T) matrix for all the queries and keys)
        y = F.scaled_dot_product_attention(q, k, v, is_causal=True) # flash attention
        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
        # output projection
        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(approximate='tanh')
        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:
    block_size: int = 1024 # max sequence length
    vocab_size: int = 50257 # number of tokens: 50,000 BPE merges + 256 bytes tokens + 1 <|endoftext|> token
    n_layer: int = 12 # number of layers
    n_head: int = 12 # number of heads
    n_embd: int = 768 # embedding dimension

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)
        
        # weight sharing scheme
        self.transformer.wte.weight = self.lm_head.weight
        
        # init params
        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):
        # idx is of shape (B, T)
        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}"
        # forward the token and posisition embeddings
        pos = torch.arange(0, T, dtype=torch.long, device=idx.device) # shape (T)
        pos_emb = self.transformer.wpe(pos) # position embeddings of shape (T, n_embd)
        tok_emb = self.transformer.wte(idx) # token embeddings of shape (B, T, n_embd)
        x = tok_emb + pos_emb
        # forward the blocks of the transformer
        for block in self.transformer.h:
            x = block(x)
        # forward the final layernorm and the classifier
        x = self.transformer.ln_f(x)
        logits = self.lm_head(x) # (B, T, vocab_size)
        loss = None
        if targets is not None:
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
        return logits, loss

    @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 configure_optimizers(self, weight_decay, learning_rate, device):
        # start with all of the candidate parameters (that require grad)
        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}
        # 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 '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
# -----------------------------------------------------------------------------

# attempt to autodetect the device
import time

device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

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

# get a data batch 
train_loader = DataLoaderLite(B=4, T=1024) # reduced batch size from 16 to ensure GPU fit (avoid out-of-memory error)

# get logits
model = GPT(GPTConfig())
model.to(device)
model = torch.compile(model)


# optimize!
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
for i in range(50):
    t0 = time.time()
    x, y = train_loader.next_batch()
    x, y = x.to(device), y.to(device)
    optimizer.zero_grad()
    with torch.autocast(device_type=device, dtype=torch.float16):
        logits, loss = model(x, y)
    loss.backward()
    optimizer.step()
    torch.cuda.synchronize()  # wait for GPU to finish all scheduled work above
    t1 = time.time()
    dt = (t1 - t0) * 1000  # time difference in milliseconds
    tokens_per_sec = (train_loader.B * train_loader.T) / (t1 - t0)
    print(f"step {i}, loss: {loss.item()}, dt: {dt:.2f}ms, tok/sec: {tokens_per_sec:.2f}")

using device: cuda
loaded 338025 tokens
1 epoch = 82 batches
step 0, loss: 10.92850399017334, dt: 23254.65ms, tok/sec: 176.14
step 1, loss: 9.534674644470215, dt: 269.74ms, tok/sec: 15184.99
step 2, loss: 9.092467308044434, dt: 263.96ms, tok/sec: 15517.76
step 3, loss: 8.886982917785645, dt: 269.15ms, tok/sec: 15218.14
step 4, loss: 8.675227165222168, dt: 268.57ms, tok/sec: 15251.42
step 5, loss: 8.411211013793945, dt: 268.28ms, tok/sec: 15267.66
step 6, loss: 8.428413391113281, dt: 268.10ms, tok/sec: 15277.64
step 7, loss: 8.311079978942871, dt: 264.13ms, tok/sec: 15507.65
step 8, loss: 8.32304573059082, dt: 268.39ms, tok/sec: 15261.39
step 9, loss: 8.138297080993652, dt: 264.24ms, tok/sec: 15500.99
step 10, loss: 8.20202922821045, dt: 261.50ms, tok/sec: 15663.20
step 11, loss: 8.155302047729492, dt: 269.97ms, tok/sec: 15172.07
step 12, loss: 8.140573501586914, dt: 265.81ms, tok/sec: 15409.63
step 13, loss: 8.054917335510254, dt: 267.24ms, tok/sec: 15326.92
step 14, loss: 7.73063850402832, dt: 273.26ms, tok/sec: 14989.37
step 15, loss: 7.751882553100586, dt: 270.38ms, tok/sec: 15149.10
step 16, loss: 7.568158149719238, dt: 270.23ms, tok/sec: 15157.55
step 17, loss: 7.452803611755371, dt: 271.96ms, tok/sec: 15061.09
step 18, loss: 7.534860610961914, dt: 271.03ms, tok/sec: 15112.68
step 19, loss: 7.409269332885742, dt: 267.61ms, tok/sec: 15306.08
step 20, loss: 7.541338920593262, dt: 274.58ms, tok/sec: 14917.30
step 21, loss: 7.312079429626465, dt: 268.81ms, tok/sec: 15237.39
step 22, loss: 7.221592903137207, dt: 271.08ms, tok/sec: 15110.21
step 23, loss: 7.283299922943115, dt: 276.16ms, tok/sec: 14832.15
step 24, loss: 7.25299072265625, dt: 273.90ms, tok/sec: 14954.14
step 25, loss: 7.196824073791504, dt: 275.67ms, tok/sec: 14858.30
step 26, loss: 6.990842819213867, dt: 277.79ms, tok/sec: 14744.82
step 27, loss: 7.032729148864746, dt: 271.38ms, tok/sec: 15092.98
step 28, loss: 7.019345283508301, dt: 276.16ms, tok/sec: 14831.79
step 29, loss: 6.865221977233887, dt: 274.83ms, tok/sec: 14904.02
step 30, loss: 6.774977207183838, dt: 274.79ms, tok/sec: 14905.83
step 31, loss: 6.733926773071289, dt: 279.40ms, tok/sec: 14660.03
step 32, loss: 6.671619415283203, dt: 274.70ms, tok/sec: 14910.87
step 33, loss: 6.726042747497559, dt: 276.90ms, tok/sec: 14792.24
step 34, loss: 6.706255912780762, dt: 279.77ms, tok/sec: 14640.73
step 35, loss: 6.680185794830322, dt: 275.94ms, tok/sec: 14843.64
step 36, loss: 6.521563529968262, dt: 274.51ms, tok/sec: 14920.97
step 37, loss: 6.676796913146973, dt: 280.50ms, tok/sec: 14602.72
step 38, loss: 6.489032745361328, dt: 284.45ms, tok/sec: 14399.92
step 39, loss: 6.331541538238525, dt: 280.78ms, tok/sec: 14587.82
step 40, loss: 6.434853553771973, dt: 282.42ms, tok/sec: 14502.99
step 41, loss: 6.514583587646484, dt: 279.92ms, tok/sec: 14632.51
step 42, loss: 6.4055705070495605, dt: 281.40ms, tok/sec: 14555.56
step 43, loss: 6.4159650802612305, dt: 280.58ms, tok/sec: 14598.53
step 44, loss: 6.530893325805664, dt: 279.98ms, tok/sec: 14629.67
step 45, loss: 6.450060844421387, dt: 280.54ms, tok/sec: 14600.54
step 46, loss: 6.302554130554199, dt: 277.70ms, tok/sec: 14749.71
step 47, loss: 6.415002822875977, dt: 283.93ms, tok/sec: 14426.17
step 48, loss: 6.387300491333008, dt: 284.18ms, tok/sec: 14413.45
step 49, loss: 6.295866966247559, dt: 283.45ms, tok/sec: 14450.50

vocab_size = 50304  # instead of 50257
self.wte = nn.Embedding(vocab_size, n_embd)
self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)

# attempt to autodetect the device
import time

device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

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

# get a data batch 
train_loader = DataLoaderLite(B=4, T=1024) # reduced batch size from 16 to ensure GPU fit (avoid out-of-memory error)

# get logits
model = GPT(GPTConfig(vocab_size=50304))
model.to(device)
model = torch.compile(model)


# optimize!
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)
for i in range(50):
    t0 = time.time()
    x, y = train_loader.next_batch()
    x, y = x.to(device), y.to(device)
    optimizer.zero_grad()
    with torch.autocast(device_type=device, dtype=torch.float16):
        logits, loss = model(x, y)
    loss.backward()
    optimizer.step()
    torch.cuda.synchronize()  # wait for GPU to finish all scheduled work above
    t1 = time.time()
    dt = (t1 - t0) * 1000  # time difference in milliseconds
    tokens_per_sec = (train_loader.B * train_loader.T) / (t1 - t0)
    print(f"step {i}, loss: {loss.item()}, dt: {dt:.2f}ms, tok/sec: {tokens_per_sec:.2f}")

using device: cuda
loaded 338025 tokens
1 epoch = 82 batches
step 0, loss: 10.92870807647705, dt: 23339.69ms, tok/sec: 175.50
step 1, loss: 9.523971557617188, dt: 255.97ms, tok/sec: 16002.05
step 2, loss: 9.139336585998535, dt: 254.99ms, tok/sec: 16063.34
step 3, loss: 9.019887924194336, dt: 251.74ms, tok/sec: 16270.91
step 4, loss: 8.72701644897461, dt: 251.02ms, tok/sec: 16317.24
step 5, loss: 8.430542945861816, dt: 249.65ms, tok/sec: 16406.86
step 6, loss: 8.483152389526367, dt: 250.95ms, tok/sec: 16321.92
step 7, loss: 8.33853530883789, dt: 254.12ms, tok/sec: 16118.24
step 8, loss: 8.346128463745117, dt: 252.09ms, tok/sec: 16247.94
step 9, loss: 8.18669319152832, dt: 254.51ms, tok/sec: 16093.81
step 10, loss: 8.274518966674805, dt: 250.46ms, tok/sec: 16354.04
step 11, loss: 8.213085174560547, dt: 256.16ms, tok/sec: 15990.09
step 12, loss: 8.234271049499512, dt: 252.85ms, tok/sec: 16199.20
step 13, loss: 8.178937911987305, dt: 255.09ms, tok/sec: 16057.00
step 14, loss: 7.878395080566406, dt: 256.44ms, tok/sec: 15972.42
step 15, loss: 7.8749871253967285, dt: 253.62ms, tok/sec: 16150.05
step 16, loss: 7.641887187957764, dt: 252.72ms, tok/sec: 16207.48
step 17, loss: 7.528213024139404, dt: 252.92ms, tok/sec: 16194.53
step 18, loss: 7.5749192237854, dt: 257.69ms, tok/sec: 15895.17
step 19, loss: 7.467623710632324, dt: 257.52ms, tok/sec: 15905.77
step 20, loss: 7.612698554992676, dt: 255.94ms, tok/sec: 16003.80
step 21, loss: 7.365101337432861, dt: 258.79ms, tok/sec: 15827.52
step 22, loss: 7.2620038986206055, dt: 261.03ms, tok/sec: 15691.63
step 23, loss: 7.291790008544922, dt: 259.57ms, tok/sec: 15779.82
step 24, loss: 7.269532680511475, dt: 261.46ms, tok/sec: 15665.69
step 25, loss: 7.201272010803223, dt: 259.90ms, tok/sec: 15759.70
step 26, loss: 6.986702919006348, dt: 258.42ms, tok/sec: 15849.98
step 27, loss: 7.050078392028809, dt: 258.32ms, tok/sec: 15856.00
step 28, loss: 7.017569541931152, dt: 260.47ms, tok/sec: 15725.48
step 29, loss: 6.835757255554199, dt: 260.80ms, tok/sec: 15705.43
step 30, loss: 6.769442081451416, dt: 263.53ms, tok/sec: 15542.82
step 31, loss: 6.720017910003662, dt: 257.66ms, tok/sec: 15896.79
step 32, loss: 6.647528648376465, dt: 263.57ms, tok/sec: 15540.29
step 33, loss: 6.721739292144775, dt: 259.91ms, tok/sec: 15759.02
step 34, loss: 6.716627597808838, dt: 265.21ms, tok/sec: 15444.09
step 35, loss: 6.69631290435791, dt: 263.90ms, tok/sec: 15520.93
step 36, loss: 6.532194137573242, dt: 263.63ms, tok/sec: 15536.86
step 37, loss: 6.695162773132324, dt: 260.54ms, tok/sec: 15721.42
step 38, loss: 6.5311431884765625, dt: 229.50ms, tok/sec: 17847.14
step 39, loss: 6.360293388366699, dt: 266.15ms, tok/sec: 15390.01
step 40, loss: 6.488943099975586, dt: 265.86ms, tok/sec: 15406.55
step 41, loss: 6.53761625289917, dt: 262.00ms, tok/sec: 15633.40
step 42, loss: 6.4041008949279785, dt: 263.86ms, tok/sec: 15523.61
step 43, loss: 6.423272132873535, dt: 266.69ms, tok/sec: 15358.79
step 44, loss: 6.542690753936768, dt: 268.98ms, tok/sec: 15227.73
step 45, loss: 6.4558916091918945, dt: 266.02ms, tok/sec: 15397.38
step 46, loss: 6.314949989318848, dt: 269.04ms, tok/sec: 15224.43
step 47, loss: 6.412504196166992, dt: 264.69ms, tok/sec: 15474.81
step 48, loss: 6.396947383880615, dt: 254.02ms, tok/sec: 16124.84
step 49, loss: 6.307344913482666, dt: 270.40ms, tok/sec: 15148.17

print(torch.cuda.memory_summary())

|===========================================================================|
|                  PyTorch CUDA memory summary, device ID 0                 |
|---------------------------------------------------------------------------|
|            CUDA OOMs: 0            |        cudaMalloc retries: 1         |
|===========================================================================|
|        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  |
|---------------------------------------------------------------------------|
| Allocated memory      |   6563 MiB |  10933 MiB |   8149 GiB |   8143 GiB |
|       from large pool |   6534 MiB |  10891 MiB |   8081 GiB |   8075 GiB |
|       from small pool |     28 MiB |     67 MiB |     68 GiB |     68 GiB |
|---------------------------------------------------------------------------|
| Active memory         |   6563 MiB |  10933 MiB |   8149 GiB |   8143 GiB |
|       from large pool |   6534 MiB |  10891 MiB |   8081 GiB |   8075 GiB |
|       from small pool |     28 MiB |     67 MiB |     68 GiB |     68 GiB |
|---------------------------------------------------------------------------|
| Requested memory      |   6536 MiB |  10895 MiB |   8134 GiB |   8127 GiB |
|       from large pool |   6507 MiB |  10853 MiB |   8066 GiB |   8059 GiB |
|       from small pool |     28 MiB |     67 MiB |     68 GiB |     68 GiB |
|---------------------------------------------------------------------------|
| GPU reserved memory   |  11008 MiB |  14480 MiB |  16528 MiB |   5520 MiB |
|       from large pool |  10972 MiB |  14408 MiB |  16456 MiB |   5484 MiB |
|       from small pool |     36 MiB |     72 MiB |     72 MiB |     36 MiB |
|---------------------------------------------------------------------------|
| Non-releasable memory |   4420 MiB |   4894 MiB |   4954 GiB |   4950 GiB |
|       from large pool |   4417 MiB |   4890 MiB |   4880 GiB |   4876 GiB |
|       from small pool |      3 MiB |     13 MiB |     73 GiB |     73 GiB |
|---------------------------------------------------------------------------|
| Allocations           |    1252    |    1571    |  626517    |  625265    |
|       from large pool |     408    |     616    |  259169    |  258761    |
|       from small pool |     844    |     955    |  367348    |  366504    |
|---------------------------------------------------------------------------|
| Active allocs         |    1252    |    1571    |  626517    |  625265    |
|       from large pool |     408    |     616    |  259169    |  258761    |
|       from small pool |     844    |     955    |  367348    |  366504    |
|---------------------------------------------------------------------------|
| GPU reserved segments |     163    |     200    |     201    |      38    |
|       from large pool |     145    |     164    |     165    |      20    |
|       from small pool |      18    |      36    |      36    |      18    |
|---------------------------------------------------------------------------|
| Non-releasable allocs |     214    |     261    |  275228    |  275014    |
|       from large pool |     159    |     174    |  128770    |  128611    |
|       from small pool |      55    |     102    |  146458    |  146403    |
|---------------------------------------------------------------------------|
| Oversize allocations  |       0    |       0    |       0    |       0    |
|---------------------------------------------------------------------------|
| Oversize GPU segments |       0    |       0    |       0    |       0    |
|===========================================================================|

# GPT-3 paper specifications
optimizer = torch.optim.AdamW(
    model.parameters(),
    lr=3e-4,
    betas=(0.9, 0.95),  # Changed from default (0.9, 0.999)
    eps=1e-8,           # Default value
    weight_decay=0.1    # Will be implemented later
)

# attempt to autodetect the device
import time

device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

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

# get a data batch 
train_loader = DataLoaderLite(B=4, T=1024) # reduced batch size from 16 to ensure GPU fit (avoid out-of-memory error)

# get logits
model = GPT(GPTConfig(vocab_size=50304))
model.to(device)
model = torch.compile(model)


# learning rate scheduler
max_lr = 6e-4
min_lr = max_lr * 0.1
warmup_steps = 10
max_steps = 50
def get_lr(it):
    # 1) linear warmup for warmup_iters steps
    if it < warmup_steps:
        return max_lr * (it+1) / warmup_steps
    # 2) if it > lr_decay_iters, return min learning rate
    if it > max_steps:
        return min_lr
    # 3) in between, use cosine decay down to min learning rate
    decay_ratio = (it - warmup_steps) / (max_steps - warmup_steps)
    assert 0 <= decay_ratio <= 1
    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff starts at 1 and goes to 0
    return min_lr + coeff * (max_lr - min_lr)


# optimize!
# optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4, betas=(0.9, 0.95), eps=1e-8)
optimizer = model.configure_optimizers(weight_decay=0.1, learning_rate=6e-4, device=device)
for step in range(max_steps):
    t0 = time.time()
    x, y = train_loader.next_batch()
    x, y = x.to(device), y.to(device)
    optimizer.zero_grad()
    with torch.autocast(device_type=device, dtype=torch.float16):
        logits, loss = model(x, y)
    loss.backward()
    norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
    # determine and set the learning rate for this iteration
    lr = get_lr(step)
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr
    optimizer.step()
    torch.cuda.synchronize()  # wait for GPU to finish all scheduled work above
    t1 = time.time()
    dt = t1 - t0 # time difference in seconds
    tokens_processed = train_loader.B * train_loader.T
    tokens_per_sec = tokens_processed / dt
    print(f"step {step:4d} | loss: {loss.item():.6f} | lr {lr:.4e} | norm: {norm:.4f} | dt: {dt*1000:.2f}ms | tok/sec: {tokens_per_sec:.2f}")

using device: cuda
loaded 338025 tokens
1 epoch = 82 batches
num decayed parameter tensors: 50, with 124,354,560 parameters
num non-decayed parameter tensors: 98, with 121,344 parameters
using fused AdamW: True
step    0 | loss: 10.928708 | lr 6.0000e-05 | norm: 30.3805 | dt: 315.04ms | tok/sec: 13001.34
step    1 | loss: 9.621357 | lr 1.2000e-04 | norm: 10.5006 | dt: 266.28ms | tok/sec: 15382.32
step    2 | loss: 9.334407 | lr 1.8000e-04 | norm: 8.0636 | dt: 270.11ms | tok/sec: 15164.40
step    3 | loss: 9.757507 | lr 2.4000e-04 | norm: 6.7143 | dt: 264.74ms | tok/sec: 15471.83
step    4 | loss: 9.174562 | lr 3.0000e-04 | norm: 4.5429 | dt: 261.38ms | tok/sec: 15670.69
step    5 | loss: 8.665694 | lr 3.6000e-04 | norm: 4.2873 | dt: 261.63ms | tok/sec: 15655.88
step    6 | loss: 8.572289 | lr 4.2000e-04 | norm: 2.3125 | dt: 262.90ms | tok/sec: 15579.93
step    7 | loss: 8.460865 | lr 4.8000e-04 | norm: 2.9871 | dt: 261.53ms | tok/sec: 15661.57
step    8 | loss: 8.378544 | lr 5.4000e-04 | norm: 1.9208 | dt: 255.95ms | tok/sec: 16003.14
step    9 | loss: 8.142771 | lr 6.0000e-04 | norm: 2.5803 | dt: 260.22ms | tok/sec: 15740.32
step   10 | loss: 8.124127 | lr 6.0000e-04 | norm: 2.4176 | dt: 256.36ms | tok/sec: 15977.59
step   11 | loss: 7.963734 | lr 5.9917e-04 | norm: 2.2088 | dt: 261.15ms | tok/sec: 15684.28
step   12 | loss: 7.930590 | lr 5.9668e-04 | norm: 1.8306 | dt: 258.71ms | tok/sec: 15832.49
step   13 | loss: 7.777389 | lr 5.9254e-04 | norm: 1.6518 | dt: 266.43ms | tok/sec: 15373.90
step   14 | loss: 7.338685 | lr 5.8679e-04 | norm: 1.5975 | dt: 263.64ms | tok/sec: 15536.12
step   15 | loss: 7.261800 | lr 5.7945e-04 | norm: 1.4975 | dt: 262.35ms | tok/sec: 15613.00
step   16 | loss: 7.020026 | lr 5.7057e-04 | norm: 1.3547 | dt: 263.39ms | tok/sec: 15551.16
step   17 | loss: 6.793896 | lr 5.6021e-04 | norm: 1.2177 | dt: 261.38ms | tok/sec: 15670.46
step   18 | loss: 6.860034 | lr 5.4843e-04 | norm: 1.4144 | dt: 259.27ms | tok/sec: 15798.05
step   19 | loss: 6.808226 | lr 5.3531e-04 | norm: 1.5166 | dt: 264.07ms | tok/sec: 15510.94
step   20 | loss: 6.982955 | lr 5.2092e-04 | norm: 1.0750 | dt: 266.98ms | tok/sec: 15342.24
step   21 | loss: 6.799408 | lr 5.0535e-04 | norm: 2.4346 | dt: 261.28ms | tok/sec: 15676.54
step   22 | loss: 6.722897 | lr 4.8870e-04 | norm: 1.1909 | dt: 262.80ms | tok/sec: 15585.87
step   23 | loss: 6.806604 | lr 4.7107e-04 | norm: 1.2415 | dt: 266.77ms | tok/sec: 15354.03
step   24 | loss: 6.841603 | lr 4.5258e-04 | norm: 0.9648 | dt: 259.98ms | tok/sec: 15755.28
step   25 | loss: 6.831161 | lr 4.3332e-04 | norm: 1.0975 | dt: 257.30ms | tok/sec: 15919.39
step   26 | loss: 6.645350 | lr 4.1343e-04 | norm: 0.8377 | dt: 258.68ms | tok/sec: 15834.54
step   27 | loss: 6.726126 | lr 3.9303e-04 | norm: 0.8305 | dt: 255.86ms | tok/sec: 16008.94
step   28 | loss: 6.716146 | lr 3.7224e-04 | norm: 1.0130 | dt: 261.29ms | tok/sec: 15676.31
step   29 | loss: 6.568641 | lr 3.5118e-04 | norm: 0.9982 | dt: 262.05ms | tok/sec: 15630.81
step   30 | loss: 6.504869 | lr 3.3000e-04 | norm: 0.8102 | dt: 256.06ms | tok/sec: 15996.30
step   31 | loss: 6.462898 | lr 3.0882e-04 | norm: 1.2365 | dt: 259.88ms | tok/sec: 15761.10
step   32 | loss: 6.464743 | lr 2.8776e-04 | norm: 0.9802 | dt: 255.36ms | tok/sec: 16040.26
step   33 | loss: 6.597322 | lr 2.6697e-04 | norm: 0.8144 | dt: 260.00ms | tok/sec: 15753.86
step   34 | loss: 6.597669 | lr 2.4657e-04 | norm: 0.9436 | dt: 254.13ms | tok/sec: 16117.88
step   35 | loss: 6.588067 | lr 2.2668e-04 | norm: 0.9390 | dt: 256.63ms | tok/sec: 15960.92
step   36 | loss: 6.431494 | lr 2.0742e-04 | norm: 0.9586 | dt: 258.01ms | tok/sec: 15875.62
step   37 | loss: 6.603404 | lr 1.8893e-04 | norm: 0.9390 | dt: 261.09ms | tok/sec: 15688.03
step   38 | loss: 6.415227 | lr 1.7130e-04 | norm: 0.8344 | dt: 252.12ms | tok/sec: 16245.98
step   39 | loss: 6.290661 | lr 1.5465e-04 | norm: 0.9135 | dt: 254.82ms | tok/sec: 16073.92
step   40 | loss: 6.396004 | lr 1.3908e-04 | norm: 0.9086 | dt: 257.17ms | tok/sec: 15927.40
step   41 | loss: 6.493940 | lr 1.2469e-04 | norm: 1.0122 | dt: 255.37ms | tok/sec: 16039.76
step   42 | loss: 6.335922 | lr 1.1157e-04 | norm: 1.0972 | dt: 257.64ms | tok/sec: 15897.89
step   43 | loss: 6.329462 | lr 9.9787e-05 | norm: 0.9595 | dt: 252.17ms | tok/sec: 16243.24
step   44 | loss: 6.455802 | lr 8.9428e-05 | norm: 0.9016 | dt: 245.93ms | tok/sec: 16655.05
step   45 | loss: 6.396205 | lr 8.0553e-05 | norm: 0.8157 | dt: 251.41ms | tok/sec: 16292.36
step   46 | loss: 6.273858 | lr 7.3215e-05 | norm: 0.7970 | dt: 254.13ms | tok/sec: 16117.79
step   47 | loss: 6.353633 | lr 6.7460e-05 | norm: 1.0438 | dt: 250.24ms | tok/sec: 16367.98
step   48 | loss: 6.364339 | lr 6.3324e-05 | norm: 0.8662 | dt: 254.17ms | tok/sec: 16115.50
step   49 | loss: 6.275089 | lr 6.0832e-05 | norm: 0.8255 | dt: 254.50ms | tok/sec: 16094.32

# Configuration
total_batch_size = 2**19  # ~524K tokens (close to 0.5M)
micro_batch_size = 16     # What fits in GPU memory
sequence_length = 1024    # T

# Calculate gradient accumulation steps
assert total_batch_size % (micro_batch_size * sequence_length) == 0
grad_accum_steps = total_batch_size // (micro_batch_size * sequence_length)

print(f"total batch size: {total_batch_size}")
print(f"grad accum steps: {grad_accum_steps}")

# Training loop with gradient accumulation
for step in range(max_steps):
    optimizer.zero_grad()
    loss_accum = 0.0
    
    # Accumulate gradients over multiple micro-batches
    for micro_step in range(grad_accum_steps):
        # Load new micro-batch
        x, y = train_loader.next_batch()
        x, y = x.to(device), y.to(device)
        
        # Forward pass
        logits, loss = model(x, y)
        
        # Scale loss by accumulation steps (critical!)
        loss = loss / grad_accum_steps
        
        # Backward pass (gradients accumulate via +=)
        loss.backward()
        
        # Track loss for logging
        loss_accum += loss.detach()
    
    # Clip gradients and step
    grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
    optimizer.step()
    
    print(f"step {step} | loss {loss_accum:.6f} | grad_norm {grad_norm:.4f}")

# attempt to autodetect the device
import time

device = "cpu"
if torch.cuda.is_available():
    device = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = "mps"
print(f"using device: {device}")

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

# get a data batch 

total_batch_size = 524288 # 2**19, ~0.5M, in number of tokens
B = 4 #16 # micro batch size  # reduced batch size from 16 to ensure GPU fit (avoid out-of-memory error)
T = 1024 # sequence length
assert total_batch_size % (B * T) == 0, "make sure total_batch_size is divisible by B * T"
grad_accum_steps = total_batch_size // (B * T)
print(f"total desired batch size: {total_batch_size}")
print(f"=> calculated gradient accumulation steps: {grad_accum_steps}")

train_loader = DataLoaderLite(B=B, T=T)

# get logits
model = GPT(GPTConfig(vocab_size=50304))
model.to(device)
model = torch.compile(model)


# learning rate scheduler
max_lr = 6e-4
min_lr = max_lr * 0.1
warmup_steps = 10
max_steps = 50
def get_lr(it):
    # 1) linear warmup for warmup_iters steps
    if it < warmup_steps:
        return max_lr * (it+1) / warmup_steps
    # 2) if it > lr_decay_iters, return min learning rate
    if it > max_steps:
        return min_lr
    # 3) in between, use cosine decay down to min learning rate
    decay_ratio = (it - warmup_steps) / (max_steps - warmup_steps)
    assert 0 <= decay_ratio <= 1
    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff starts at 1 and goes to 0
    return min_lr + coeff * (max_lr - min_lr)


# optimize!
optimizer = model.configure_optimizers(weight_decay=0.1, learning_rate=6e-4, device=device)
for step in range(max_steps):
    t0 = time.time()
    optimizer.zero_grad()
    loss_accum = 0.0
    for micro_step in range(grad_accum_steps):
        x, y = train_loader.next_batch()
        x, y = x.to(device), y.to(device)
        with torch.autocast(device_type=device, dtype=torch.float16):
            logits, loss = model(x, y)
        # we have to scale the loss to account for gradient accumulation,
        # because the gradients just add on each successive backward().
        # addition of gradients corresponds to a SUM in the objective, but
        # instead of a SUM we want MEAN. Scale the loss here so it comes out right
        loss = loss / grad_accum_steps
        loss_accum += loss.detach()
        loss.backward()
    norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
    # determine and set the learning rate for this iteration
    lr = get_lr(step)
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr
    optimizer.step()
    torch.cuda.synchronize()  # wait for GPU to finish all scheduled work above
    t1 = time.time()
    dt = t1 - t0 # time difference in seconds
    tokens_processed = train_loader.B * train_loader.T * grad_accum_steps
    tokens_per_sec = tokens_processed / dt
    print(f"step {step:4d} | loss: {loss_accum.item():.6f} | lr {lr:.4e} | norm: {norm:.4f} | dt: {dt*1000:.2f}ms | tok/sec: {tokens_per_sec:.2f}")

using device: cuda
total desired batch size: 524288
=> calculated gradient accumulation steps: 128
loaded 338025 tokens
1 epoch = 82 batches
num decayed parameter tensors: 50, with 124,354,560 parameters
num non-decayed parameter tensors: 98, with 121,344 parameters
using fused AdamW: True
step    0 | loss: 10.939034 | lr 6.0000e-05 | norm: 15.3891 | dt: 28463.70ms | tok/sec: 18419.53
step    1 | loss: 9.678656 | lr 1.2000e-04 | norm: 9.9116 | dt: 27808.47ms | tok/sec: 18853.54
step    2 | loss: 9.476817 | lr 1.8000e-04 | norm: 6.8401 | dt: 27828.38ms | tok/sec: 18840.04
step    3 | loss: 9.468809 | lr 2.4000e-04 | norm: 5.8757 | dt: 27608.60ms | tok/sec: 18990.03
step    4 | loss: 9.280705 | lr 3.0000e-04 | norm: 4.2289 | dt: 27146.60ms | tok/sec: 19313.21
step    5 | loss: 9.128562 | lr 3.6000e-04 | norm: 3.3617 | dt: 26967.04ms | tok/sec: 19441.81
step    6 | loss: 9.004288 | lr 4.2000e-04 | norm: 3.3322 | dt: 26620.76ms | tok/sec: 19694.71
step    7 | loss: 8.867443 | lr 4.8000e-04 | norm: 3.3412 | dt: 26571.10ms | tok/sec: 19731.51
step    8 | loss: 8.715653 | lr 5.4000e-04 | norm: 3.3537 | dt: 26559.31ms | tok/sec: 19740.27
step    9 | loss: 8.577296 | lr 6.0000e-04 | norm: 3.3346 | dt: 26481.02ms | tok/sec: 19798.63
step   10 | loss: 8.419370 | lr 6.0000e-04 | norm: 3.3738 | dt: 26434.04ms | tok/sec: 19833.82
step   11 | loss: 8.323701 | lr 5.9917e-04 | norm: 2.7750 | dt: 26488.24ms | tok/sec: 19793.23
step   12 | loss: 8.210462 | lr 5.9668e-04 | norm: 2.8166 | dt: 26473.06ms | tok/sec: 19804.58
step   13 | loss: 8.157615 | lr 5.9254e-04 | norm: 2.7844 | dt: 26456.32ms | tok/sec: 19817.11
step   14 | loss: 8.096796 | lr 5.8679e-04 | norm: 2.8048 | dt: 26474.67ms | tok/sec: 19803.38
step   15 | loss: 8.053272 | lr 5.7945e-04 | norm: 2.8174 | dt: 26495.00ms | tok/sec: 19788.19
step   16 | loss: 8.030201 | lr 5.7057e-04 | norm: 2.8083 | dt: 26470.55ms | tok/sec: 19806.47
step   17 | loss: 7.995072 | lr 5.6021e-04 | norm: 2.8344 | dt: 26454.56ms | tok/sec: 19818.44
step   18 | loss: 7.996810 | lr 5.4843e-04 | norm: 2.8054 | dt: 26476.82ms | tok/sec: 19801.77
step   19 | loss: 7.958810 | lr 5.3531e-04 | norm: 2.8536 | dt: 26457.99ms | tok/sec: 19815.87
step   20 | loss: 7.971348 | lr 5.2092e-04 | norm: 2.8146 | dt: 26446.49ms | tok/sec: 19824.49
step   21 | loss: 7.945467 | lr 5.0535e-04 | norm: 2.8477 | dt: 26443.88ms | tok/sec: 19826.44
step   22 | loss: 7.944326 | lr 4.8870e-04 | norm: 2.8367 | dt: 26422.96ms | tok/sec: 19842.13
step   23 | loss: 7.934910 | lr 4.7107e-04 | norm: 2.8486 | dt: 26431.90ms | tok/sec: 19835.43
step   24 | loss: 7.922026 | lr 4.5258e-04 | norm: 2.8638 | dt: 26403.92ms | tok/sec: 19856.45
step   25 | loss: 7.926643 | lr 4.3332e-04 | norm: 2.8481 | dt: 26379.11ms | tok/sec: 19875.12
step   26 | loss: 7.903821 | lr 4.1343e-04 | norm: 2.8828 | dt: 26448.28ms | tok/sec: 19823.14
step   27 | loss: 7.920915 | lr 3.9303e-04 | norm: 2.8492 | dt: 26394.53ms | tok/sec: 19863.51
step   28 | loss: 7.891791 | lr 3.7224e-04 | norm: 2.8969 | dt: 26451.18ms | tok/sec: 19820.97
step   29 | loss: 7.907578 | lr 3.5118e-04 | norm: 2.8594 | dt: 26426.02ms | tok/sec: 19839.84
step   30 | loss: 7.895090 | lr 3.3000e-04 | norm: 2.8797 | dt: 26415.27ms | tok/sec: 19847.92
step   31 | loss: 7.887503 | lr 3.0882e-04 | norm: 2.8912 | dt: 26465.75ms | tok/sec: 19810.06
step   32 | loss: 7.889046 | lr 2.8776e-04 | norm: 2.8849 | dt: 26451.86ms | tok/sec: 19820.46
step   33 | loss: 7.875173 | lr 2.6697e-04 | norm: 2.9056 | dt: 26391.66ms | tok/sec: 19865.67
step   34 | loss: 7.888106 | lr 2.4657e-04 | norm: 2.8811 | dt: 26389.01ms | tok/sec: 19867.66
step   35 | loss: 7.864651 | lr 2.2668e-04 | norm: 2.9191 | dt: 26385.00ms | tok/sec: 19870.68
step   36 | loss: 7.884865 | lr 2.0742e-04 | norm: 2.8829 | dt: 26434.87ms | tok/sec: 19833.20
step   37 | loss: 7.862608 | lr 1.8893e-04 | norm: 2.9201 | dt: 26425.50ms | tok/sec: 19840.23
step   38 | loss: 7.872652 | lr 1.7130e-04 | norm: 2.8949 | dt: 26447.46ms | tok/sec: 19823.76
step   39 | loss: 7.866786 | lr 1.5465e-04 | norm: 2.9089 | dt: 26433.26ms | tok/sec: 19834.40
step   40 | loss: 7.859489 | lr 1.3908e-04 | norm: 2.9201 | dt: 26404.07ms | tok/sec: 19856.33
step   41 | loss: 7.868372 | lr 1.2469e-04 | norm: 2.9028 | dt: 26437.92ms | tok/sec: 19830.91
step   42 | loss: 7.849229 | lr 1.1157e-04 | norm: 2.9345 | dt: 26427.20ms | tok/sec: 19838.95
step   43 | loss: 7.870172 | lr 9.9787e-05 | norm: 2.8978 | dt: 26414.55ms | tok/sec: 19848.45
step   44 | loss: 7.845533 | lr 8.9428e-05 | norm: 2.9413 | dt: 26348.93ms | tok/sec: 19897.88
step   45 | loss: 7.866387 | lr 8.0553e-05 | norm: 2.9001 | dt: 26302.76ms | tok/sec: 19932.81
step   46 | loss: 7.854386 | lr 7.3215e-05 | norm: 2.9212 | dt: 26290.83ms | tok/sec: 19941.86
step   47 | loss: 7.855478 | lr 6.7460e-05 | norm: 2.9178 | dt: 26228.15ms | tok/sec: 19989.52
step   48 | loss: 7.855094 | lr 6.3324e-05 | norm: 2.9215 | dt: 26217.79ms | tok/sec: 19997.42
step   49 | loss: 7.848067 | lr 6.0832e-05 | norm: 2.9312 | dt: 26242.52ms | tok/sec: 19978.57

for step in range(max_steps):
    t0 = time.time()
    ...
        # instead of a SUM we want MEAN. Scale the loss here so it comes out right
    x, y = train_loader.next_batch()
    x, y = x.to(device), y.to(device)
    optimizer.zero_grad()
        if ddp:
            model.require_backward_grad_sync = (micro_step == grad_accum_steps - 1)
        with torch.autocast(device_type=device, dtype=torch.float16):
            logits, loss = model(x, y)
        loss = loss / grad_accum_steps
        loss_accum += loss.detach()
        loss.backward()
    if ddp:
        dist.all_reduce(loss_accum, op=dist.ReduceOp.AVG)
    ...

# # -----------------------------------------------------------------------------
# class DataLoaderLite:
#     def __init__(self, B, T, process_rank, num_processes):
#         self.B = B
#         self.T = T
#         self.process_rank = process_rank
#         self.num_processes = num_processes

#         # at init load tokens from disk and store them in memory
#         with open('input.txt', 'r') as f:
#             text = f.read()
#         enc = tiktoken.get_encoding('gpt2')
#         tokens = enc.encode(text)
#         self.tokens = torch.tensor(tokens)
#         if master_process:
#             print(f"loaded {len(self.tokens)} tokens")

#         # state
#         self.current_position = self.B * self.T * self.process_rank

#     def next_batch(self):
#         B, T = self.B, self.T
#         buf = self.tokens[self.current_position : self.current_position+B*T+1]
#         x = (buf[:-1]).view(B, T) # inputs
#         y = (buf[1:]).view(B, T) # targets
#         # advance the position in the tensor
#         self.current_position += B * T * self.num_processes
#         # if loading the next batch would be out of bounds, reset
#         if self.current_position + (B * T * self.num_processes + 1) > len(self.tokens):
#             self.current_position = self.B * self.T * self.process_rank
#         return x, y
        
# # -----------------------------------------------------------------------------
# @dataclass
# class GPTConfig:
#     block_size: int = 1024 # max sequence length
#     vocab_size: int = 50257 # number of tokens: 50,000 BPE merges + 256 bytes tokens + 1 <|endoftext|> token
#     n_layer: int = 12 # number of layers
#     n_head: int = 12 # number of heads
#     n_embd: int = 768 # embedding dimension

# 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)
        
#         # weight sharing scheme
#         self.transformer.wte.weight = self.lm_head.weight
        
#         # init params
#         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):
#         # idx is of shape (B, T)
#         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}"
#         # forward the token and posisition embeddings
#         pos = torch.arange(0, T, dtype=torch.long, device=idx.device) # shape (T)
#         pos_emb = self.transformer.wpe(pos) # position embeddings of shape (T, n_embd)
#         tok_emb = self.transformer.wte(idx) # token embeddings of shape (B, T, n_embd)
#         x = tok_emb + pos_emb
#         # forward the blocks of the transformer
#         for block in self.transformer.h:
#             x = block(x)
#         # forward the final layernorm and the classifier
#         x = self.transformer.ln_f(x)
#         logits = self.lm_head(x) # (B, T, vocab_size)
#         loss = None
#         if targets is not None:
#             loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
#         return logits, loss

#     @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 configure_optimizers(self, weight_decay, learning_rate, device_type):
#         # start with all of the candidate parameters (that require grad)
#         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}
#         # 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)
#         if master_process:
#             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"
#         if master_process:
#             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

# # attempt to autodetect the device
# import time
# import os

# # simple launch:
# # python train_gpt2.py
# # DDP launch for e.g. 8 GPUs:
# # torchrun --standalone --nproc_per_node=8 train_gpt2.py

# # run the training loop
# from torch.distributed import init_process_group, destroy_process_group
# from torch.nn.parallel import DistributedDataParallel as DDP
# import torch.distributed as dist

# # set up DDP (distributed data parallel).
# # torchrun command sets the env variables RANK, LOCAL_RANK, and WORLD_SIZE
# ddp = int(os.environ.get('RANK', -1)) != -1 # is this a ddp run?
# if ddp:
#     # use of DDP atm demands CUDA, we set the device appropriately according to rank
#     assert torch.cuda.is_available(), "for now i think we need CUDA for DDP"
#     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 # this process will do logging, checkpointing etc.
# else:
#     # vanilla, non-DDP run
#     ddp_rank = 0
#     ddp_local_rank = 0
#     ddp_world_size = 1
#     master_process = True
#     # attempt to autodetect device
#     device = "cpu"
#     if torch.cuda.is_available():
#         device = "cuda"
#     elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
#         device = "mps"
#     print(f"using device: {device}")

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

# # get a data batch 

# total_batch_size = 524288 # 2**19, ~0.5M, in number of tokens
# B = 4 #16 # micro batch size  # reduced batch size from 16 to ensure GPU fit (avoid out-of-memory error)
# T = 1024 # sequence length
# assert total_batch_size % (B * T * ddp_world_size) == 0, "make sure total_batch_size is divisible by B * T * ddp_world_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"=> calculated gradient accumulation steps: {grad_accum_steps}")

# train_loader = DataLoaderLite(B=B, T=T, process_rank=ddp_rank, num_processes=ddp_world_size)

# # create model
# 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 # always contains the "raw" unwrapped model

# # learning rate scheduler
# max_lr = 6e-4
# min_lr = max_lr * 0.1
# warmup_steps = 10
# max_steps = 50
# def get_lr(it):
#     # 1) linear warmup for warmup_iters steps
#     if it < warmup_steps:
#         return max_lr * (it+1) / warmup_steps
#     # 2) if it > lr_decay_iters, return min learning rate
#     if it > max_steps:
#         return min_lr
#     # 3) in between, use cosine decay down to min learning rate
#     decay_ratio = (it - warmup_steps) / (max_steps - warmup_steps)
#     assert 0 <= decay_ratio <= 1
#     coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff starts at 1 and goes to 0
#     return min_lr + coeff * (max_lr - min_lr)


# # optimize!
# optimizer = raw_model.configure_optimizers(weight_decay=0.1, learning_rate=6e-4, device_type=device)
# for step in range(max_steps):
#     t0 = time.time()
#     optimizer.zero_grad()
#     loss_accum = 0.0
#     for micro_step in range(grad_accum_steps):
#         x, y = train_loader.next_batch()
#         x, y = x.to(device), y.to(device)
#         with torch.autocast(device_type=device, dtype=torch.float16):
#             logits, loss = model(x, y)
#         # we have to scale the loss to account for gradient accumulation,
#         # because the gradients just add on each successive backward().
#         # addition of gradients corresponds to a SUM in the objective, but
#         # instead of a SUM we want MEAN. Scale the loss here so it comes out right
#         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 = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
#     # determine and set the learning rate for this iteration
#     lr = get_lr(step)
#     for param_group in optimizer.param_groups:
#         param_group['lr'] = lr
#     optimizer.step()
#     torch.cuda.synchronize()  # wait for GPU to finish all scheduled work above
#     t1 = time.time()
#     dt = t1 - t0 # time difference in seconds
#     tokens_processed = train_loader.B * train_loader.T * grad_accum_steps * ddp_world_size
#     tokens_per_sec = tokens_processed / dt
#     if master_process:
#         print(f"step {step:4d} | loss: {loss_accum.item():.6f} | lr {lr:.4e} | norm: {norm:.4f} | dt: {dt*1000:.2f}ms | tok/sec: {tokens_per_sec:.2f}")

# if ddp:
#     destroy_process_group()

# Using Hugging Face datasets
from datasets import load_dataset
import os

def download_fineweb_edu(local_dir="fineweb_edu", num_proc=8):
    """Download FineWeb-Edu dataset"""
    
    # Create local directory
    os.makedirs(local_dir, exist_ok=True)
    
    # Load dataset (this will download automatically)
    dataset = load_dataset(
        "HuggingFaceFW/fineweb-edu", 
        name="sample-10BT",  # 10 billion token sample, or use "default" for full dataset
        cache_dir=local_dir,
        num_proc=num_proc
    )
    
    print(f"Dataset downloaded to: {local_dir}")
    print(f"Dataset info: {dataset}")
    return dataset

# Usage
dataset = download_fineweb_edu()

prompt = "In a future where AI has transformed the world,"
encoded = tokenizer.encode(prompt, return_tensors='pt').to(device)
out = model.generate(encoded, max_length=100)
print(tokenizer.decode(out[0]))

reproduction_results = {
    "metric": ["Val Loss", "HellaSwag", "Training Loss"],
    "karpathy_reproduction": [3.31, 0.292, 3.28],
    "original_gpt2": [3.28, 0.297, 3.25],
    "difference": [0.03, -0.005, 0.03]
}

print("=== GPT-2 124M Reproduction Results ===")
for i, metric in enumerate(reproduction_results["metric"]):
    karpathy = reproduction_results["karpathy_reproduction"][i]
    original = reproduction_results["original_gpt2"][i]
    diff = reproduction_results["difference"][i]
    print(f"{metric:12} | Karpathy: {karpathy:.3f} | Original: {original:.3f} | Diff: {diff:+.3f}")

=== GPT-2 124M Reproduction Results ===
Val Loss     | Karpathy: 3.310 | Original: 3.280 | Diff: +0.030
HellaSwag    | Karpathy: 0.292 | Original: 0.297 | Diff: -0.005
Training Loss | Karpathy: 3.280 | Original: 3.250 | Diff: +0.030

# Speed comparison (tokens/second)
framework_comparison = {
    "Framework": ["PyTorch (this tutorial)", "llm.c", "Speedup"],
    "Single GPU": ["~150K", "~300K", "2x"],
    "8x GPU": ["~1.2M", "~2.5M", "2.1x"], 
    "Memory Usage": ["High", "Lower", "~30% less"]
}

print("=== Performance Comparison ===")
for i, framework in enumerate(framework_comparison["Framework"]):
    single = framework_comparison["Single GPU"][i]
    multi = framework_comparison["8x GPU"][i] 
    memory = framework_comparison["Memory Usage"][i]
    print(f"{framework:20} | Single: {single:8} | Multi: {multi:8} | Memory: {memory}")

### What We've Accomplished
# Complete pipeline implemented
pipeline_components = [
    "✅ GPT-2 Architecture (Transformer blocks, attention, MLP)",
    "✅ Training Loop (forward, backward, optimization)",
    "✅ Data Pipeline (tokenization, data loading, batching)",
    "✅ Speedup (tensor cores, TF32, float16, torch.compile, flash attention)",
    "✅ Optimization (AdamW, learning rate scheduling, gradient clipping)",
    "✅ Scaling (gradient accumulation, distributed training)",
    "✅ Evaluation (validation loss, HellaSwag benchmark)",
    "✅ Modern Dataset (FineWeb-Edu instead of private WebText)",
    "✅ Production Results (successful GPT-2 reproduction)"
]

print("=== NanoGPT Implementation Complete ===")
for component in pipeline_components:
    print(component)

#-----------------------------------------------
### Key Features of Final Implementation
class NanoGPTFeatures:
    """Summary of implemented features"""
    
    architecture = {
        "transformer_blocks": 12,
        "attention_heads": 12, 
        "embedding_dim": 768,
        "context_length": 1024,
        "parameters": "124M (GPT-2 small)"
    }
    
    training = {
        "optimizer": "AdamW with weight decay",
        "learning_rate": "Cosine decay with warmup", 
        "batch_size": "524K tokens (via gradient accumulation)",
        "gradient_clipping": "Global norm clipping at 1.0",
        "regularization": "Weight decay on 2D parameters only"
    }
    
    scaling = {
        "gradient_accumulation": "Simulate large batches",
        "distributed_training": "Multi-GPU with DDP",
        "fused_optimizer": "FusedAdamW for speed",
        "mixed_precision": "Ready for fp16 training"
    }
    
    evaluation = {
        "validation_split": "Proper train/val separation",
        "hellaswag_benchmark": "Standard reasoning evaluation", 
        "text_generation": "Configurable sampling strategies",
        "loss_monitoring": "Training and validation loss tracking"
    }

# # Print final statistics
# print("=== Final Training Statistics ===")
# print(f"Total parameters: {124_000_000:,}")
# print(f"Training tokens: {10_000_000_000:,}")
# print(f"Training time: ~8 hours on 8x A100")
# print(f"Final validation loss: 3.31")
# print(f"HellaSwag accuracy: 29.2%")
# print(f"Reproduction quality: 99%+ match to original GPT-2")

#-----------------------------------------------
### Next Steps & Extensions
extensions = [
    "🔬 Experiment with different architectures (RoPE, RMSNorm, etc.)",
    "📊 Add more evaluation benchmarks (MMLU, GSM8K, etc.)", 
    "⚡ Implement mixed precision training (fp16/bf16)",
    "🎯 Add instruction tuning and RLHF pipeline",
    "🔧 Optimize with custom CUDA kernels (like llm.c)",
    "📈 Scale to larger models (350M, 760M, 1.3B parameters)",
    "🌐 Add multi-modal capabilities (vision + text)",
    "🛠️ Production deployment pipeline"
]

print("\n=== Potential Extensions ===")
for ext in extensions:
    print(ext)

=== NanoGPT Implementation Complete ===
✅ GPT-2 Architecture (Transformer blocks, attention, MLP)
✅ Training Loop (forward, backward, optimization)
✅ Data Pipeline (tokenization, data loading, batching)
✅ Speedup (tensor cores, TF32, float16, torch.compile, flash attention)
✅ Optimization (AdamW, learning rate scheduling, gradient clipping)
✅ Scaling (gradient accumulation, distributed training)
✅ Evaluation (validation loss, HellaSwag benchmark)
✅ Modern Dataset (FineWeb-Edu instead of private WebText)
✅ Production Results (successful GPT-2 reproduction)

=== Potential Extensions ===
🔬 Experiment with different architectures (RoPE, RMSNorm, etc.)
📊 Add more evaluation benchmarks (MMLU, GSM8K, etc.)
⚡ Implement mixed precision training (fp16/bf16)
🎯 Add instruction tuning and RLHF pipeline
🔧 Optimize with custom CUDA kernels (like llm.c)
📈 Scale to larger models (350M, 760M, 1.3B parameters)
🌐 Add multi-modal capabilities (vision + text)
🛠️ Production deployment pipeline

Architecture	TF32	FP32	FP16	BF16	Example GPUs	Memory Size Range
Pascal	❌	✅	⚠️	❌	Tesla P100, GTX 1080 Ti	8–16 GB (up to 24 GB on P40)
Volta	❌	✅	✅	❌	Tesla V100	16–32 GB HBM2
Turing	❌	✅	✅	❌	RTX 2080 Ti, T4, Quadro RTX 6000	8–48 GB (Quadro)
Ampere	✅	✅	✅	✅	A100, RTX 3090, RTX A6000	16–80 GB (A100 up to 80 GB)
Ada (Lovelace)	✅	✅	✅	✅	RTX 4090, RTX 4080, RTX 6000 Ada	16–48 GB
Hopper	✅	✅	✅	✅	H100	80–96 GB HBM3

Dataset	Size (Tokens)	Availability	Quality
Tiny Shakespeare	~1M	Public	Educational
WebText (GPT-2)	~8B	Private	High
GPT-3 Mix	~500B	Private	Mixed
FineWeb-Edu	~1.3T	Public	Educational

Use Case	PyTorch (This Tutorial)	llm.c
Learning	✅ Better for understanding concepts	✅ Better for understanding GPU programming
Research	✅ Fast prototyping and experimentation	❌ Slower to modify
Production	✅ Mature ecosystem, debugging tools	✅ Maximum performance
Education	✅ High-level understanding	✅ Low-level understanding

Section	Key Focus Runtime	mine (ms)	karpathy (ms)	Techniques Used
2.1	GPUs, Mixed Precision	~1200	~1000	GPU utilization, float16/float32
2.2	Tensor Cores, `TF32`		~333	Tensor Cores, TF32 precision
2.3	`float16`, `bfloat16`	~460	~300	Gradient scalers, bfloat16
2.4	`torch.compile`	~300	~130	Kernel fusion, reduced Python overhead
2.5	Flash Attention	~260	~96	Optimized attention mechanism
2.6	Nice Numbers	~250	~93	Vocabulary size adjustment (50,257 → 50,304)
3.3	`FusedAdamW`	~250	~90	Weight decay, fusedAdamW optimizer
3.4	Gradient Accumulation	~195 (128 micro batches)	~89 (32 micro batches)	Large batch --> Smaller "micro-batches", accumulate gradients

GPT-2¶

Table of Contents¶

Appendix¶

Figures¶

Tables¶

0. Introduction¶

NVIDIA GPU Architectures: Precision Support + Memory Range + Kaggle Notebook Limitations¶

NVIDIA GPU Architecture Precision Support Table¶

Notes:¶

🔎 Quick Legend¶

Model Overview¶

0.1. GPT-2 (124M) OpenAI Checkpoint¶

Scaling Laws¶

Model Details and Training Targets¶

Visualization: Positional Embeddings¶

Observations¶

Sampling From the Model¶

Use HuggingFace Pipeline¶

Manual Sampling Process (From Scratch)¶

1. GPT-2 nn.Module¶

Goal¶

Differences Between GPT-2 and Original Transformer¶

Model Skeleton (Architecture Overview)¶

Transformer Block Details¶

Attention = Communication Layer¶

MLP = Per-token Processing¶

1.1. Loading the huggingface/GPT-2 Parameters¶

Loading and Matching GPT-2 Weights¶

Step 1: Load Pretrained Model Using HuggingFace¶

Step 2: Explore Model Weights¶

Visualization: Positional Embeddings¶

1.2. Forward Pass: Get Logits¶

1.3. sampling init, prefix tokens, Tokenization¶

1.4. Sampling Loop¶

Generation Loop (Manual Sampling)¶

1.5. Sample, Auto-detect the Device¶

1.6. Model Training: Data Batches (B,T) --> Logits (B,T,C)¶

Training Setup¶

Training loop:¶

1.7. Cross Entropy Loss¶

Expected loss at initialization based on uniform probability:¶

1.8. Optimization Loop: Overfit a Single Branch¶

1.9. Data Loader Lite¶

1.10. Parameter Sharing: wte & lm_head¶

1.11. Model Initialization: std 0.02, residual init¶

2. Let's Make it Fast.¶

2.1. GPUs, mixed precision, 1000ms¶

2.2. Tensor Cores, Timing the Code, TF32 precision, 333ms¶

2.3. float16, Gradient Scalers, bfloat16, 300ms¶

> float16¶

> Gradient Scalers¶

> Gradient Scalers + float16¶

2.4. torch.compile, Python Overhead, Kernel Fusion, 130ms¶

2.5. FlashAttention, 96ms¶

Kernel Fusion:¶

Update CausalSelfAttention class with FlashAttention and add configure_optimizers function in GPT class to handle weight decay & fusedAdamW for the decay parameters¶

2.6. Nice/Ugly Numbers. vocab size: 50257 --> 50304, 93ms¶

3. Model Optimization¶

3.1. Hyperparameters, AdamW, gradient clipping¶

Overview¶

Key Differences: GPT-2 vs GPT-3¶

AdamW Hyperparameters from GPT-3¶

Global Gradient Norm Clipping¶

3.2. Learning Rate Scheduler: Warmup + Cosine Decay¶

Schedule Overview¶

Learning Rate by Model Size¶

Implementation¶

Why This Schedule?¶

3.3. Batch Size Schedule, Weight Decay: FusedAdamW, 90ms¶

Batch Size Schedule (Skipped)¶

Weight Decay Implementation¶

Weight Decay Benefits¶

FusedAdamW Performance¶

3.4. Gradient Accumulation¶

Problem Statement¶

Solution: Simulate Large Batches¶

Implementation¶

Critical Detail: Loss Scaling¶

Performance Impact¶

3.5. Distributed Data Parallel (DDP)¶

0.1. `GPT-2` (124M) OpenAI Checkpoint¶

1. GPT-2 `nn.Module`¶

1.1. Loading the `huggingface`/`GPT-2` Parameters¶

1.3. `sampling init`, `prefix tokens`, Tokenization¶

1.10. Parameter Sharing: `wte` & `lm_head`¶

1.11. Model Initialization: `std 0.02`, `residual init`¶

2.1. `GPUs`, `mixed precision`, `1000ms`¶

2.2. Tensor Cores, Timing the Code, `TF32 precision`, `333ms`¶

2.3. `float16`, Gradient Scalers, `bfloat16`, `300ms`¶

> `float16`¶

> Gradient Scalers + `float16`¶

2.4. `torch.compile`, Python Overhead, Kernel Fusion, `130ms`¶

2.5. FlashAttention, `96ms`¶

Update `CausalSelfAttention` class with `FlashAttention` and add `configure_optimizers` function in `GPT` class to handle weight decay & fusedAdamW for the decay parameters¶

2.6. Nice/Ugly Numbers. `vocab size: 50257 --> 50304`, `93ms`¶

3.1. Hyperparameters, `AdamW`, `gradient clipping`¶

Key Differences: `GPT-2` vs `GPT-3`¶

AdamW Hyperparameters from `GPT-3`¶

3.2. Learning Rate Scheduler: `Warmup + Cosine Decay`¶

3.3. Batch Size Schedule, Weight Decay: `FusedAdamW`, `90ms`¶

3.6. Datasets used in `GPT-2`, `GPT-3`, `FineWeb` (EDU)¶

Modern Solution: FineWeb-Edu ¶

3.8. Evaluation: `HellaSwag`, Starting the Run¶

4.1. `GPT-2`, `GPT-3` Reproduction¶

4.2. Shoutout to `llm.c`, Equivalent but Faster Code in Raw `C/CUDA`¶

4.3. Summary, `build-nanogpt` GitHub Repo¶