{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "gS9Rp2w7GjPW" }, "source": [ "# Fine-tune an 8B model on 4 GB of VRAM — run it yourself\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MakazhanAlpamys/Soup/blob/main/notebooks/proof-4gb.ipynb)\n", "\n", "[Soup](https://github.com/MakazhanAlpamys/Soup) trains a model whose weights do not fit\n", "in your GPU. The frozen base stays in host RAM and is streamed to the GPU one decoder\n", "layer at a time, so peak VRAM is bounded by **one layer** instead of by the model.\n", "\n", "This notebook does not ask you to believe that. It caps this process to **4 GB** on\n", "Colab's free T4 and then measures what actually happens.\n", "\n", "| Section | What it proves | Time |\n", "|---|---|---|\n", "| 1–3 | The cap is real, and this GPU has no bf16 | ~2 min |\n", "| 4 | A streamed model and a normal one produce **bit-identical** logits | ~3 min |\n", "| 5 | Llama-3.1-8B trains with a measured peak under 4 GB | ~20 min |\n", "\n", "Sections 1–4 are the argument. Section 5 is the headline and is optional.\n", "\n", "**Runtime → Change runtime type → T4 GPU** before you start.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "EU3gwmuZGjPY" }, "source": [ "## 1. Install\n", "\n", "This notebook is pinned to **soup-cli==0.75.0**, the release this run was verified\n", "against. **v0.74.0** was the first release carrying both the T4 precision fix\n", "([#385](https://github.com/MakazhanAlpamys/Soup/issues/385),\n", "[#387](https://github.com/MakazhanAlpamys/Soup/issues/387)) and the T4 GradScaler fix\n", "([#429](https://github.com/MakazhanAlpamys/Soup/issues/429)) that section 5 needs to\n", "train at all on this card; 0.75.0 is pinned here only because it is the exact release\n", "this run's committed outputs were produced against. Bump the pin only after re-running\n", "this notebook end to end on a real T4 and recording the new outputs.\n", "\n", "The `torchao` line is not incidental either. Colab preinstalls **torchao 0.10.0**, and\n", "`peft` does not merely decline to use a version it considers too old — it *raises*\n", "`ImportError` from `is_torchao_available()`, several frames inside `get_peft_model`.\n", "Nothing here needs torchao, so it is removed rather than upgraded (upgrading risks\n", "pulling a wheel built against a different torch).\n", "\n", "**Colab's default Python can be newer than this project supports.** `soup-cli` caps at `<3.13` on purpose (untested torch/bitsandbytes wheels on newer interpreters fail with an opaque loader crash, not a clean error). Colab's hosted runtime does not let you pick an arbitrary kernel the way local Jupyter does, so if you land on Python 3.13+, the next cell falls back to installing with `--ignore-requires-python` — that trades the guarantee for a best-effort install, on the assumption that the `[train]` extras (torch,\n", "transformers, peft, bitsandbytes, accelerate) have caught up with a 3.13 wheel by the time you are reading this. If anything below fails with an import or ABI error, that assumption did not hold on this Colab image; open an issue with the\n", "traceback rather than treating it as this notebook's bug.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pmPJQk1ZGjPY", "outputId": "e66c8068-1288-4152-f0b7-3bed5c681bec" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Colab Python: 3.13.15 (soup-cli supports <3.13)\n", "Falling back to --ignore-requires-python for the install below;\n", "see the note above this cell for what that trades away.\n" ] } ], "source": [ "import sys\n", "\n", "PY_OK = sys.version_info < (3, 13)\n", "print(f\"Colab Python: {sys.version.split()[0]} (soup-cli supports <3.13)\")\n", "if not PY_OK:\n", " print(\"Falling back to --ignore-requires-python for the install below;\")\n", " print(\"see the note above this cell for what that trades away.\")\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R2ZoGhWwGjPZ", "outputId": "62cb934a-8bf5-4a34-d98b-2efc0fda7768" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m29.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.1/43.1 MB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m475.8/475.8 kB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m531.0/531.0 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.4/47.4 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hsoup 0.75.0\n", "pre-Ampere fix present: True\n", "torchao gone (peft raises on an old one): True\n" ] } ], "source": [ "%pip uninstall -q -y torchao\n", "import sys\n", "\n", "_extra_flag = \"--ignore-requires-python\" if sys.version_info >= (3, 13) else \"\"\n", "%pip install -q \"soup-cli[train]==0.75.0\" {_extra_flag}\n", "\n", "import importlib.util\n", "\n", "import soup_cli\n", "import soup_cli.utils.gpu\n", "\n", "print(\"soup\", soup_cli.__version__)\n", "print(\"pre-Ampere fix present:\", hasattr(soup_cli.utils.gpu, \"bf16_fp16_flags\"))\n", "print(\"torchao gone (peft raises on an old one):\",\n", " importlib.util.find_spec(\"torchao\") is None)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "k9_uWtXUGjPZ", "outputId": "5a953364-68e2-4093-ace8-b1266d337581" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "torch 2.11.0+cu128\n", "transformers 5.16.1\n", "peft 0.20.0\n", "bitsandbytes 0.50.2\n", "soup_cli 0.75.0\n" ] } ], "source": [ "import bitsandbytes\n", "import peft\n", "import torch\n", "import transformers\n", "\n", "print(\"torch \", torch.__version__)\n", "print(\"transformers\", transformers.__version__)\n", "print(\"peft \", peft.__version__)\n", "print(\"bitsandbytes\", bitsandbytes.__version__)\n", "print(\"soup_cli \", soup_cli.__version__)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "1IEi4wOPGjPZ" }, "source": [ "## 2. What card did we get, and does it have bf16?\n", "\n", "Colab's free tier is a **T4** — Turing, sm_75. bf16 hardware arrived with Ampere, so a\n", "T4 has none.\n", "\n", "**Read the two lines below carefully, because they disagree, and the disagreement is\n", "the point.** `torch.cuda.is_bf16_supported()` defaults to `including_emulation=True`:\n", "when the compute-capability check fails it falls through to *constructing* a bf16\n", "tensor, which software emulation satisfies. So a T4 answers **True** to the question\n", "everyone asks, and False only to `is_bf16_supported(including_emulation=False)`.\n", "\n", "Soup asked the permissive question and therefore handed bf16 to a card with no bf16\n", "units. The first version of this fix asked it too, and was a no-op on exactly the\n", "hardware it was written for — caught by running this notebook on a real T4, not before\n", "([#385](https://github.com/MakazhanAlpamys/Soup/issues/385),\n", "[#387](https://github.com/MakazhanAlpamys/Soup/issues/387)).\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ebZmw05TGjPa", "outputId": "a348715e-5852-4369-b219-110cc80ecb06" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU Tesla T4 (sm_75)\n", "VRAM 15.6 GB\n", "bf16, incl. emulation True\n", "bf16 IN HARDWARE False\n", "Soup will train in fp16\n" ] } ], "source": [ "import torch\n", "\n", "from soup_cli.utils.gpu import bf16_fp16_flags\n", "\n", "assert torch.cuda.is_available(), \"No GPU. Runtime -> Change runtime type -> T4 GPU.\"\n", "\n", "name = torch.cuda.get_device_name(0)\n", "major, minor = torch.cuda.get_device_capability(0)\n", "total = torch.cuda.get_device_properties(0).total_memory\n", "\n", "print(f\"GPU {name} (sm_{major}{minor})\")\n", "print(f\"VRAM {total / 1e9:.1f} GB\")\n", "print(f\"bf16, incl. emulation {torch.cuda.is_bf16_supported()}\")\n", "print(f\"bf16 IN HARDWARE \"\n", " f\"{torch.cuda.is_bf16_supported(including_emulation=False)}\")\n", "\n", "bf16, fp16 = bf16_fp16_flags(\"cuda\")\n", "print(f\"Soup will train in {'bf16' if bf16 else 'fp16' if fp16 else 'fp32'}\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "l7M_09spGjPa" }, "source": [ "## 3. Cap this process to 4 GB\n", "\n", "`set_per_process_memory_fraction` caps PyTorch's allocator. Everything after this cell\n", "runs as if the card were a 4 GB laptop GPU — an allocation past the cap raises, exactly\n", "as it would on the real thing.\n", "\n", "**One historical caveat, now closed.** `torch.cuda.mem_get_info()` reports the whole\n", "card, so Soup's own pre-flight check used to read that and print a \"free VRAM\" line that\n", "belonged to the host card, not to this capped process — it would have allowed a\n", "configuration that the allocator then refused. That was\n", "[#347](https://github.com/MakazhanAlpamys/Soup/issues/347), closed by\n", "`training.stream_vram_override` in v0.73.1: section 5's config below sets it to this\n", "notebook's own 4 GB cap, so the pre-flight checks against the same number the allocator\n", "enforces.\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WerOAXc-GjPa", "outputId": "9dea490f-a394-4654-f44a-2fafdceb7571" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "capped at 4.00 GB (fraction 0.256 of this card)\n", "refused, as it should be: CUDA out of memory. Tried to allocate 4.29 GiB. GPU 0 has a total capacity of 14.56 GiB of\n" ] } ], "source": [ "BUDGET_BYTES = 4 * 1000**3 # 4 GB, the card this method was developed on\n", "\n", "fraction = BUDGET_BYTES / total\n", "torch.cuda.set_per_process_memory_fraction(fraction)\n", "print(f\"capped at {BUDGET_BYTES / 1e9:.2f} GB (fraction {fraction:.3f} of this card)\")\n", "\n", "# Prove the cap bites: ask for 15% more than the budget and expect a refusal.\n", "try:\n", " _ = torch.empty(int(BUDGET_BYTES * 1.15), dtype=torch.uint8, device=\"cuda\")\n", " print(\"WARNING: the allocation succeeded — the cap is NOT in force\")\n", "except RuntimeError as exc:\n", " print(\"refused, as it should be:\", str(exc).splitlines()[0][:90])\n" ] }, { "cell_type": "markdown", "metadata": { "id": "OJhzXWHkGjPa" }, "source": [ "## 4. The claim that matters: streamed == resident, bit for bit\n", "\n", "A streaming bug is silent. If the base were substituted wrongly, or the autograd path\n", "severed, the loss would still fall — the upper layers keep learning — and you would ship\n", "a damaged model without an error anywhere.\n", "\n", "So the check is not \"does it train\". It is: **the same weights, through the same kernels,\n", "must produce the same numbers.** Below, one model is streamed layer-by-layer and the\n", "other is an ordinary resident model, both carrying identical adapter weights.\n", "`torch.equal` is exact equality, not a tolerance.\n", "\n", "This runs on a small model because the reference has to fit in memory next to the\n", "streamed copy — that is the whole reason the headline size cannot be checked this way.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 182, "referenced_widgets": [ "5ce8709eb08141b49ec54002daaf6a02", "1ade6c23233e4dc8928b74379f919b52", "64dac42bdc2948108be4191c0ceda935", "703835e267844b2081cc77a980f29a5a", "7bcd0e9f66b44345be83558727ea4f73", "ea2e2f0858604f8cbb26396a34486582", "57d7a95684aa4448a094b483607d8a7b", "26c64bed491b40338f08829696b702d2", "bf620d6b3d3247c3b885a9b52ed50e72", "e2482e92256f4dc3abf1da54bcbd0b4e", "611b1dafb25f44bd88803b30c5fb8be2", "1eb41dbea62d4d38a15c1861465a89af", "653f8a5aec8a4f268ac5131721b95939", "909b04d476b64bfeb01e889a31579ec2", "319044ab454c41ecafd14d019bd3dae6", "8afdfbea52874655b291ad460c7a06ca", "c01d30ec7dc943c7b73eee9bab52ff7d", "1c2eff1f10744844b12048ee187cd7d5", "d609772e7c8a4b92a86dc1e4e381b330", "9f1e69eb590248e2b8f3e9f4705d5efa", "f086e453790c472cb3f7302d0a9a9c59", "5a8439b13e404597ad30f6841c88e503", "cf4ec404d5144f7c9d4b333d0a715c2b", "9b2b590e6a4b475b8de2ea0408f0431f", "a18dada832ec421bbaf389daad3d1dd9", "c5dab0b774fa4f32b8837655f217fe87", "fbefa341e54f44669cbd6b008d332388", "9730f83efe164a26a2f929a2d5389e83", "339fabb1f51b435687047788de187438", "bf1f2e0942a248bb856bcce5fde22450", "c874443864e44b4d96fe173cef76774f", "5e7f3e8b868546c8ba1ada9c93690215", "a80e7b254bc84ea1aab5cb84c07d5167" ] }, "id": "v0m9f-XoGjPa", "outputId": "35c5d4e3-cafa-40e3-9121-09c4e539b104" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "5ce8709eb08141b49ec54002daaf6a02" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Reconstructing (incomplete total...): | | 0.00B / 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "1eb41dbea62d4d38a15c1861465a89af" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Fetching 11 files: 0%| | 0/11 [00:00Auto-detected format: chatml\n", "\n" ], "text/plain": [ "\u001b[2mAuto-detected format: chatml\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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              "│ Layer streaming BETA — arch llama, tier ram                                                                     │\n",
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              "│   resident     0 MB extras + adapters                                                                           │\n",
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StepTraining Loss
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33.946751
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74.029544

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "steps: 7 loss: 4.529 -> 4.030\n" ] } ], "source": [ "from soup_cli.config.loader import load_config_from_string\n", "from soup_cli.data.loader import load_dataset\n", "from soup_cli.trainer.sft import SFTTrainerWrapper\n", "\n", "cfg = load_config_from_string(config)\n", "dataset = load_dataset(cfg.data)\n", "\n", "torch.cuda.empty_cache()\n", "torch.cuda.reset_peak_memory_stats()\n", "\n", "wrapper = SFTTrainerWrapper(cfg)\n", "wrapper.setup(dataset) # downloads, shards to NF4, builds the streamed model\n", "result = wrapper.train()\n", "\n", "print(f\"\\nsteps: {result['total_steps']} loss: {result['initial_loss']:.3f}\"\n", " f\" -> {result['final_loss']:.3f}\")\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4YmrBTWkGjPb", "outputId": "735ebe34-4229-40dd-89b7-247069af1c30" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "peak VRAM allocated by this process: 1.83 GB\n", "budget this process was capped to: 4.00 GB\n", "model weights in NF4, for scale: ~4.5 GB\n", "\n", "adapter written: True\n", "adapter tensors: 128, non-zero: 128\n" ] } ], "source": [ "peak = torch.cuda.max_memory_allocated()\n", "print(f\"peak VRAM allocated by this process: {peak / 1e9:.2f} GB\")\n", "print(f\"budget this process was capped to: {BUDGET_BYTES / 1e9:.2f} GB\")\n", "print(\"model weights in NF4, for scale: ~4.5 GB\")\n", "\n", "adapter = Path(\"out-8b/adapter_model.safetensors\")\n", "print(f\"\\nadapter written: {adapter.exists()}\")\n", "if adapter.exists():\n", " from safetensors.torch import load_file\n", "\n", " tensors = load_file(str(adapter))\n", " live = sum(1 for v in tensors.values() if v.abs().max().item() > 0)\n", " print(f\"adapter tensors: {len(tensors)}, non-zero: {live}\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "72bL5REbGjPb" }, "source": [ "## What this proved, and what it did not\n", "\n", "**Proved, on your hardware:**\n", "\n", "- A streamed model returns **bit-identical** logits to an ordinary one (§4).\n", "- An 8B model trained with a measured peak below a cap smaller than its own weights (§5).\n", "- Both on a GPU with **no bf16** — the case that was broken until recently.\n", "\n", "**Not proved:**\n", "\n", "- *Backward* exactness at this size. §4 compares the forward. Gradient exactness is\n", " verified up to 14B against resident references on hardware that can hold them, and\n", " a defect **above** that size was found, named upstream and repaired — see\n", " [`benchmarks/`](https://github.com/MakazhanAlpamys/Soup/tree/main/benchmarks).\n", "- Speed. A T4 under an artificial cap is not a throughput benchmark, and this notebook\n", " deliberately does not quote tok/s.\n", "\n", "Layer streaming is **BETA** and opt-in (`stream_layers: true`).\n", "\n", "**If any assertion above failed, that is worth an issue** — with the cell output. A\n", "reproduction on hardware we do not own is more useful to this project than a star.\n", "\n", "The method, the correctness protocol and every measurement:\n", "[10.5281/zenodo.21771064](https://doi.org/10.5281/zenodo.21771064) ·\n", "[measurement records](https://github.com/MakazhanAlpamys/Soup/tree/main/benchmarks)\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "version_major": 2, "version_minor": 0, "state": { "5ce8709eb08141b49ec54002daaf6a02": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", 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