GitHub - Entrpi/microgpt-denovo: A tiny GPT from scratch: GPTception

User:
Hello. In this directory our project is to implement the core algorithmic elements of training and inferencing a GPT Transformer model in a terse but pedagogically motivated manner. That means we want to distill just the essential elements of the algorithms into as few well-documented lines of code as possible. It should be a single file with the complete algorithm.

We will train against a list of names: https://raw.githubusercontent.com/karpathy/makemore/988aa59/names.txt

Tokenize each unique char in dataset.
We will use Autograd, a tiny model (1 layer, 16 wide embedding, blocks of 16, 4 heads.
Follow GPT-2 with minor differenes: layernorm -> rmsnorm, no biases, GeLU -> ReLU
Use Adam optimizer.
Train 1000 steps.
Run inference at 0.5 temperature

It's possible to do this in real code, including comment lines and a generous header block in between 80 and 200 lines depending on the language used. Try with pseudocode first.

Assistant:
I added a single-file pseudocode draft at `microgpt_pseudocode.py`. It’s 158 lines and includes the full path end to end: download/tokenize the names corpus, initialize a 1-layer 16-wide 4-head decoder-only GPT, train with Adam for 1000 steps, and sample at temperature `0.5`.

The main modeling choice is to train on the raw newline-delimited character stream, so `'\n'` becomes a real token and doubles as the boundary token for sampling. That keeps the code short and avoids introducing extra BOS/PAD machinery while still matching the dataset cleanly.

I did not run it as code; it is intentionally Python-shaped pseudocode, not yet a verified executable Autograd implementation. If you want, I can turn this directly into the real single-file Autograd version next.

User:
Now instead of Autograd what about pure matrix math? Don't constrain pseudocode to what Python can do. Perhaps the abstractions of Julia or other more mathematically oriented languages would give a cleaner pseudocode basis. It should be possible to express in under 100 clear (pedagogical, not minified) lines.

Assistant:
Yes. A Julia-like matrix-calculus pseudocode basis is cleaner for this teaching goal because it lets the file talk in terms of tensor primitives instead of Python/autograd mechanics.

I added a 90-line version at `microgpt_matrix_pseudocode.jl`. It keeps the whole story in one place: char-stream tokenization over the raw names corpus, tied embeddings, 1-block causal self-attention, RMSNorm, ReLU MLP, explicit backward passes for RMSNorm/attention/block/output, Adam, and temperature-0.5 sampling from `'\n'` to `'\n'`. The remaining abstractions like `bmm`, `embed`, `embed_scatter`, `pick`, and `softmax_last` are intentionally mathematical primitives rather than language-specific code.

I left the earlier Python-shaped draft in `microgpt_pseudocode.py` for comparison. If you want, I can next collapse the matrix version a bit further, or turn this exact pseudocode into a real executable single-file implementation in Julia or pure NumPy with manual backprop.

User:
Every line should have a comment block in a second column explaining what's happening there and why, succinctly and clearly

Assistant:
Updated `microgpt_matrix_pseudocode.jl` so every line now has a right-hand comment explaining both what it does and why it is there. The file stays in the same Julia-like matrix-calculus style, with explicit forward/backward passes and a small set of assumed tensor primitives such as `bmm`, `embed`, `embed_scatter`, and `softmax_last`.

The per-line annotation pushes it to 112 lines, so it no longer fits the earlier sub-100 target without sacrificing clarity. If you want, I can do a second pass to compress it back toward 100 while preserving the two-column comment format.

User:
For the matrix pseudocode, use more lines for optimal clarity. Don't densely pack.

Assistant:
Expanded `microgpt_matrix_pseudocode.jl` into a much less dense version. It now uses one small step per line, keeps the right-hand explanatory comment on every line, and spreads the backward pass and gradient assembly out so the control flow is easier to follow.

It’s now 205 lines, which is the tradeoff for clarity. I did not run anything, since this file is still intentionally language-like pseudocode rather than executable Julia. If you want, I can next do one of two things: turn this exact expanded pseudocode into a real single-file implementation, or tighten just the helper layer while keeping the rest in this clearer style.

User:
Have we made everything which should be derived so?

Assistant:
Not fully before; mostly yes now.

I updated `microgpt_matrix_pseudocode.jl` so the main duplicated facts are now derived instead of restated:
- `S = '\n'` introduces a single named boundary token.
- `D = C ÷ H` derives head width instead of hard-coding it.
- `F = 4 * C` derives MLP width instead of hard-coding it.
- `MAX_NEW = T` derives the sampling cap from context length.
- Sampling now uses `S` instead of repeating raw newline literals.

What remains as literals is mostly intentional config rather than something that should be derived:
- `T`, `C`, `H` are core architectural choices.
- `B`, `STEPS`, `LR`, `B1`, `B2`, `EPS` are optimizer/training hyperparameters.
- `0.5` temperature, `20` samples, and `100` logging cadence are presentation/runtime choices.

If you want maximum consistency, the next cleanup would be to name those last three as constants too, even though they are not really derived quantities.

User:
The comments read more as technical notes on your editing choices than a story told to a student trying to understand GPT algorithms.

Assistant:
Rewrote the right-hand comments in `microgpt_matrix_pseudocode.jl` so they explain the algorithm as a student would read it: what embeddings mean, what queries/keys/values are doing, why residuals exist, how cross-entropy gives `probs - onehot`, and how Adam turns gradients into updates.

The code path is essentially unchanged; the shift is in voice and emphasis. I did not run anything, since this remains pseudocode. If you want, I can do one more pass specifically to make the comments read like a continuous lecture narrative rather than line-by-line local explanations.

User:
Now let's use uv and python 3.14 to validate and rectify any issues with the Python code

Assistant:
Validated `microgpt_pseudocode.py` under `uv` on CPython 3.14.1 with `autograd`, fixed the actual runtime break, and confirmed the full default training run works.

The real code issue was in attention: `autograd`’s traced arrays do not support method-style variadic `.transpose(...)`. I replaced those calls with explicit `np.transpose(..., axes)`, and I updated the header text so it no longer claims to be only pseudocode.

Validation results:
- `uv run --python 3.14 --with autograd python -m py_compile ...` passed.
- A short smoke run passed.
- The full default `1000`-step run passed.
- Loss printed from `2.6077` at step 100 down to `2.1879` at step 1000.

User:
Now rename to not include pseudocode and make the same pass with Julia

Assistant:
Renamed the sources to `microgpt.py` and `microgpt_matrix.jl`, and updated `README.md` so the current artifact names and status match reality while leaving the transcript historical.

The Julia side is no longer pseudocode. I replaced the placeholder tensor primitives with real code: data loading via `Downloads`, a concrete `Params` container, embedding lookup/scatter, batched matmul helpers, explicit RMSNorm/attention/block forward and backward passes, Adam, and sampling. It runs cleanly under Julia 1.12.5 with no external Julia packages.

Validation:
- `microgpt.py` passes `uv run --python 3.14 --with autograd python -m py_compile ...` and the full 1000-step training run.
- `microgpt_matrix.jl` passes a smoke test and the full 1000-step training run.

User:
Now a couple issues remain:
First, the Julia implementation is better documented than the Python implementation.
Second, both aren't really structured to delineate the different major sections or stages of the algorithms. It should be clearer at a glance where responsibilities break.

Assistant:
Updated both implementations to make the algorithm stages easier to scan and to bring the Python file closer to the Julia file’s teaching quality.

In `microgpt.py`, I added explicit stage boundaries for setup, data/parameters, math primitives, model, loss/gradients, batching/optimization/sampling, and the script entrypoint. I also expanded the inline comments around attention, the forward pass, cross-entropy, Adam, and sampling so the file explains the model’s flow instead of just naming operations.

In `microgpt_matrix.jl`, I added the same kind of section headers so the major responsibilities break cleanly at a glance: setup, parameter container, data/init, tensor helpers, model components, loss/gradients, batching/optimization, and train/sample.

User:
Write this as a pedagogical script, not as a normal source file.

Organize the file by the learner’s conceptual journey through the algorithm:
1. setup
2. data and tokenization
3. parameter initialization
4. forward pass
5. loss
6. backward pass or learning signal
7. optimizer
8. training loop
9. inference

Use explicit visual section markers to divide the top level ideas (setup, training, inference, etc.).

Prefer a few large algorithm-shaped blocks over many small helpers.
Only extract a helper if it corresponds to a real conceptual primitive like RMSNorm or softmax.
Do not structure the file like a library, utility module, or maintainable package.

Optimize for “glanceability”:
a student should be able to scan the file and immediately see where forward, loss, backward, optimizer, and sampling live.

Comments should tell the story of what the model is doing and why, from the learner’s perspective.
Do not comment on coding style, refactoring choices, abstraction choices, or implementation hygiene.
Avoid comments that sound like editor notes.

Prefer top-down narrative sequencing over engineering-normal decomposition.
It is acceptable to use globals, in-place mutation, and denser coupling if that makes the algorithm easier to read as one coherent artifact.

Make the file feel like a lecture demonstration or mathematical performance.
The code should read like:
“here is the data, now here is the forward pass, now here is how it learns, now here is how it speaks.”

Assistant:
Restructured both `microgpt.py` and `microgpt_matrix.jl` so the file reads more like a lecture script than a module.

The top-level section markers now follow that learner journey much more directly: setup, data and tokenization, parameter initialization, forward pass, loss, backward pass or learning signal, optimizer, training loop, and inference.

On the Python side, I also strengthened the local comments so the forward path, learning signal, optimizer step, and sampling loop explain what the model is doing, not just what each line computes. On the Julia side, I aligned the section names to the same conceptual sequence and made the training and inference boundaries explicit instead of bundling them together.

User:
For Python:
Even if autograd handles backward, preserve a visible “learning” act.
Make it obvious where the loss is formed, where gradients are requested, and where Adam updates the parameters.
Do not let autograd erase the narrative structure of training.

Assistant:
Updated `microgpt.py` so the learning act stays visible even with `autograd`.

The training loop now first forms the batch loss, then requests gradients through a named `backward` function, then lets Adam update the parameters. I also made the loss formation itself explicit inside `loss(...)` and the gradient request explicit inside `learning_signal(...)`.

User:
For both:
Value narrative coherence over modular purity.
If forced to choose, prefer fewer bigger sections that mirror the canonical Transformer algorithm.

Assistant:
Updated both implementations to favor bigger algorithm-shaped sections over module-like decomposition.

The top level is now just four large sections: setup/data/parameters, Transformer forward pass, learning, and training/inference. In the Julia file I also removed a few obvious utility-style helpers by inlining them into the main algorithm where that improved coherence, for example the true-probability gathering in the loss and categorical sampling in inference.

User:
Now also for each major section include a brief header paragraph explaining each section.

Assistant:
Added brief header paragraphs under each top-level section marker in both `microgpt.py` and `microgpt_matrix.jl`. Each section now opens with a short orientation block that explains, in lecture style, what that stage of the algorithm is about before the code begins.

User:
Let's implement proper autograd from scratch in the py version. We shouldn't be hiding key algorithms.
- Standard library only. No numpy
- Write a scalar Value class from scratch for autograd.
- All tensors must be represented as Python lists / nested lists of Value objects.
- Use explicit Python loops for linear layers, attention, softmax, RMSNorm, MLP, backward, and Adam.
- Prefer minimal code and conceptual atomicity over batching, speed, reuse, or tensor abstraction.
- It should feel like the most reduced executable statement of GPT possible.

Assistant:
Replaced the Python implementation with a standard-library-only scalar autograd version. The file now uses a hand-built `Value` class, nested Python lists of `Value` objects for tensors, explicit loops for the model blocks, and Adam over the same list-shaped parameter tree.

User:
The whole autograd engine should fit cleanly into 45 lines or less if implemented succinctly (but not minified, still pedagogical)

Assistant:
Compressed the scalar `Value` engine down to 44 lines and validated that the shorter backward walk still works.

User:
Can we do similar brevity refactors across the rest of the file? Remember, don't compromise explanatory power, but fine to move description to a comment if needed.

Assistant:
Tightened the rest of the Python file in the same spirit by trimming tree-utility boilerplate and moving a little more explanation into the section comments.

User:
The Python file is hugely undercommented relative to Julia, and the autograd should be more clearly marked.

Assistant:
Added more narrative comments around the Python forward pass, loss, Adam step, and sampling path, and made the scalar `Value` engine read more explicitly as the autograd core.

User:
Let's fully refactor our autograd to be shorter and clearer.

Important:
Choose the representation that makes backward propagation look like `child.grad += local_grad * node.grad`.
Do not optimize for abstraction reuse inside the autograd engine; optimize for the student’s mental model of the chain rule.

Assistant:
Refactored the Python autograd engine around explicit edges of the form `(child, local_grad)`.

The old `_binary(...)`, `_unary(...)`, and `_backward` closure machinery is gone. Each operator now states its own local derivative directly, and the backward pass literally loops over stored edges and applies the chain rule as `child.grad += local_grad * node.grad`. That made the autograd block both shorter and easier to read as mathematics.

User:
Let's review the whitespace and comments to make sure they flow neatly. Avoid adding any more newlines for comments. Most comments should be along a second column that runs across the right of the document.

Assistant:
Cleaned up the Python file so the comments read more consistently as a right-hand second column.

User:
Our model has no ability to add layers.

Assistant:
Added real layer support to both implementations.

In `microgpt.py`, `LAYERS` is now a real architecture setting, the parameter tree stores a `blocks` list, and the forward pass loops across those blocks instead of hardcoding one attention-plus-MLP block. In `microgpt_matrix.jl`, the same idea now appears as a `BlockParams` vector inside `Params`, with forward pass, backward pass, Adam, and zero-filled optimizer buffers all iterating across the layer stack. Both files were then smoke-tested with `2` layers to confirm the new depth path actually runs.

User:
Can we address this issue with MLP?
With linear(vec, mat) interpreting mat as [out_dim][in_dim], fc and proj are reversed. Your comments say C -> F -> C, but the code does not implement that.
Change to fc = init_matrix(F, C) and proj = init_matrix(C, F).

Assistant:
Fixed the MLP shape bug in `microgpt.py`.

`fc` now initializes as `init_matrix(F, C)` so it really expands `C -> F`, and `proj` now initializes as `init_matrix(C, F)` so it contracts `F -> C` back into the residual stream. I also aligned `init_matrix(...)` with the same `[out_dim][in_dim]` convention by treating `cols` as the true fan-in. The Julia implementation was left unchanged because its `Y = XW` matrix convention was already consistent.