Upload fine-tuning-example.ipynb

fine-tuning-example.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e13eff4e-c134-4dac-9523-07b297164250",
+   "metadata": {},
+   "source": [
+    "# Example of Fine-tuning 176 billion Bloom with 8-bit weights\n",
+    "\n",
+    "This notebook shows an example of how to fine tune Bloom with Low Rank Adapters. Heavily inspired by [Hivemind's work](https://colab.research.google.com/drive/1ft6wQU0BhqG5PRlwgaZJv2VukKKjU4Es)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "699e94eb-3ce1-4788-999b-fb6d593ba7e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install transformers==4.20.1\n",
+    "!pip install bitsandbytes-cuda110\n",
+    "!pip install datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0afea72c-691d-4719-a84a-663f1891af6e",
+   "metadata": {},
+   "source": [
+    "### Load and convert original Bloom structure to 8-bit LoRA\n",
+    "\n",
+    "You can load an already compressed 8-bit version of Bloom from [joaoalvarenga/bloom-8bit](https://huggingface.co/joaoalvarenga/bloom-8bit), but first we need to make some adaptations into original model structure. Some of the following code is an adaptation from [Hivemind's GPT-J 8-bit fine-tuning notebook](https://colab.research.google.com/drive/1ft6wQU0BhqG5PRlwgaZJv2VukKKjU4Es)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa5f4118-d4d9-474f-ac36-acaadb920c1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import transformers\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "from torch import nn\n",
+    "from torch.cuda.amp import custom_fwd, custom_bwd\n",
+    "\n",
+    "from bitsandbytes.functional import quantize_blockwise, dequantize_blockwise\n",
+    "\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc4f262e-70de-4a06-a5a6-52d1cd5223d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FrozenBNBLinear(nn.Module):\n",
+    "    def __init__(self, weight, absmax, code, bias=None):\n",
+    "        assert isinstance(bias, nn.Parameter) or bias is None\n",
+    "        super().__init__()\n",
+    "        self.out_features, self.in_features = weight.shape\n",
+    "        self.register_buffer(\"weight\", weight.requires_grad_(False))\n",
+    "        self.register_buffer(\"absmax\", absmax.requires_grad_(False))\n",
+    "        self.register_buffer(\"code\", code.requires_grad_(False))\n",
+    "        self.adapter = None\n",
+    "        self.bias = bias\n",
+    " \n",
+    "    def forward(self, input):\n",
+    "        output = DequantizeAndLinear.apply(input, self.weight, self.absmax, self.code, self.bias)\n",
+    "        if self.adapter:\n",
+    "            output += self.adapter(input)\n",
+    "        return output\n",
+    " \n",
+    "    @classmethod\n",
+    "    def from_linear(cls, linear: nn.Linear) -> \"FrozenBNBLinear\":\n",
+    "        weights_int8, state = quantize_blockise_lowmemory(linear.weight)\n",
+    "        return cls(weights_int8, *state, linear.bias)\n",
+    " \n",
+    "    def __repr__(self):\n",
+    "        return f\"{self.__class__.__name__}({self.in_features}, {self.out_features})\"\n",
+    " \n",
+    " \n",
+    "class DequantizeAndLinear(torch.autograd.Function): \n",
+    "    @staticmethod\n",
+    "    @custom_fwd\n",
+    "    def forward(ctx, input: torch.Tensor, weights_quantized: torch.ByteTensor,\n",
+    "                absmax: torch.FloatTensor, code: torch.FloatTensor, bias: torch.FloatTensor):\n",
+    "        weights_deq = dequantize_blockwise(weights_quantized, absmax=absmax, code=code)\n",
+    "        ctx.save_for_backward(input, weights_quantized, absmax, code)\n",
+    "        ctx._has_bias = bias is not None\n",
+    "        return F.linear(input, weights_deq, bias)\n",
+    " \n",
+    "    @staticmethod\n",
+    "    @custom_bwd\n",
+    "    def backward(ctx, grad_output: torch.Tensor):\n",
+    "        assert not ctx.needs_input_grad[1] and not ctx.needs_input_grad[2] and not ctx.needs_input_grad[3]\n",
+    "        input, weights_quantized, absmax, code = ctx.saved_tensors\n",
+    "        # grad_output: [*batch, out_features]\n",
+    "        weights_deq = dequantize_blockwise(weights_quantized, absmax=absmax, code=code)\n",
+    "        grad_input = grad_output @ weights_deq\n",
+    "        grad_bias = grad_output.flatten(0, -2).sum(dim=0) if ctx._has_bias else None\n",
+    "        return grad_input, None, None, None, grad_bias\n",
+    " \n",
+    " \n",
+    "class FrozenBNBEmbedding(nn.Module):\n",
+    "    def __init__(self, weight, absmax, code):\n",
+    "        super().__init__()\n",
+    "        self.num_embeddings, self.embedding_dim = weight.shape\n",
+    "        self.register_buffer(\"weight\", weight.requires_grad_(False))\n",
+    "        self.register_buffer(\"absmax\", absmax.requires_grad_(False))\n",
+    "        self.register_buffer(\"code\", code.requires_grad_(False))\n",
+    "        self.adapter = None\n",
+    " \n",
+    "    def forward(self, input, **kwargs):\n",
+    "        with torch.no_grad():\n",
+    "            # note: both quantuized weights and input indices are *not* differentiable\n",
+    "            weight_deq = dequantize_blockwise(self.weight, absmax=self.absmax, code=self.code)\n",
+    "            output = F.embedding(input, weight_deq, **kwargs)\n",
+    "        if self.adapter:\n",
+    "            output += self.adapter(input)\n",
+    "        return output \n",
+    " \n",
+    "    @classmethod\n",
+    "    def from_embedding(cls, embedding: nn.Embedding) -> \"FrozenBNBEmbedding\":\n",
+    "        weights_int8, state = quantize_blockise_lowmemory(embedding.weight)\n",
+    "        return cls(weights_int8, *state)\n",
+    " \n",
+    "    def __repr__(self):\n",
+    "        return f\"{self.__class__.__name__}({self.num_embeddings}, {self.embedding_dim})\"\n",
+    " \n",
+    " \n",
+    "def quantize_blockise_lowmemory(matrix: torch.Tensor, chunk_size: int = 2 ** 20):\n",
+    "    assert chunk_size % 4096 == 0\n",
+    "    code = None\n",
+    "    chunks = []\n",
+    "    absmaxes = []\n",
+    "    flat_tensor = matrix.view(-1)\n",
+    "    for i in range((matrix.numel() - 1) // chunk_size + 1):\n",
+    "        input_chunk = flat_tensor[i * chunk_size: (i + 1) * chunk_size].clone()\n",
+    "        quantized_chunk, (absmax_chunk, code) = quantize_blockwise(input_chunk, code=code)\n",
+    "        chunks.append(quantized_chunk)\n",
+    "        absmaxes.append(absmax_chunk)\n",
+    " \n",
+    "    matrix_i8 = torch.cat(chunks).reshape_as(matrix)\n",
+    "    absmax = torch.cat(absmaxes)\n",
+    "    return matrix_i8, (absmax, code)\n",
+    "\n",
+    "\n",
+    "def convert_to_int8(model):\n",
+    "    \"\"\"Convert linear and embedding modules to 8-bit with optional adapters\"\"\"\n",
+    "    for module in list(model.modules()):\n",
+    "        for name, child in module.named_children():\n",
+    "            if isinstance(child, nn.Linear):\n",
+    "                print(name, child)\n",
+    "                setattr( \n",
+    "                    module,\n",
+    "                    name,\n",
+    "                    FrozenBNBLinear(\n",
+    "                        weight=torch.zeros(child.out_features, child.in_features, dtype=torch.uint8),\n",
+    "                        absmax=torch.zeros((child.weight.numel() - 1) // 4096 + 1),\n",
+    "                        code=torch.zeros(256),\n",
+    "                        bias=child.bias,\n",
+    "                    ),\n",
+    "                )\n",
+    "            elif isinstance(child, nn.Embedding):\n",
+    "                setattr(\n",
+    "                    module,\n",
+    "                    name,\n",
+    "                    FrozenBNBEmbedding(\n",
+    "                        weight=torch.zeros(child.num_embeddings, child.embedding_dim, dtype=torch.uint8),\n",
+    "                        absmax=torch.zeros((child.weight.numel() - 1) // 4096 + 1),\n",
+    "                        code=torch.zeros(256),\n",
+    "                    )\n",
+    "                )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4673d4c-0f4e-482e-ac04-b7389397af6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BloomBlock(transformers.models.bloom.modeling_bloom.BloomBlock):\n",
+    "    def __init__(self, config, layer_number=None):\n",
+    "        super().__init__(config, layer_number)\n",
+    "\n",
+    "        convert_to_int8(self.self_attention)\n",
+    "        convert_to_int8(self.mlp)\n",
+    "\n",
+    "\n",
+    "class BloomModel(transformers.models.bloom.modeling_bloom.BloomModel):\n",
+    "    def __init__(self, config):\n",
+    "        super().__init__(config)\n",
+    "        convert_to_int8(self)\n",
+    "        \n",
+    "\n",
+    "class BloomForCausalLM(transformers.models.bloom.modeling_bloom.BloomForCausalLM):\n",
+    "    def __init__(self, config):\n",
+    "        super().__init__(config)\n",
+    "        convert_to_int8(self)\n",
+    "        \n",
+    "transformers.models.bloom.modeling_bloom.BloomBlock = BloomBlock"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eca11b11-9b0b-4958-89f4-401f7a2cac0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import BloomForCausalLM \n",
+    "tokenizer = transformers.AutoTokenizer.from_pretrained('joaoalvarenga/bloom-8bit')\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "model = BloomForCausalLM.from_pretrained('joaoalvarenga/bloom-8bit', low_cpu_mem_usage=True)\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "model.to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82ea942b-7fcf-4bbc-adb9-be0bbd98b9f8",
+   "metadata": {},
+   "source": [
+    "### Fine-tune and save model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26cacf36-56f7-4f9c-b975-33dd34b1ff9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_adapters(model, adapter_dim=16):\n",
+    "    assert adapter_dim > 0\n",
+    "\n",
+    "    for module in model.modules():\n",
+    "        if isinstance(module, FrozenBNBLinear):\n",
+    "            module.adapter = nn.Sequential(\n",
+    "                nn.Linear(module.in_features, adapter_dim, bias=False),\n",
+    "                nn.Linear(adapter_dim, module.out_features, bias=False),\n",
+    "            )\n",
+    "            nn.init.zeros_(module.adapter[1].weight)\n",
+    "        elif isinstance(module, FrozenBNBEmbedding):\n",
+    "            module.adapter = nn.Sequential(\n",
+    "                nn.Embedding(module.num_embeddings, adapter_dim),\n",
+    "                nn.Linear(adapter_dim, module.embedding_dim, bias=False),\n",
+    "            )\n",
+    "            nn.init.zeros_(module.adapter[1].weight)\n",
+    "\n",
+    "add_adapters(model)\n",
+    "model.to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e293eb3-979a-46d7-97b8-cde296f45da8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from bitsandbytes.optim import Adam8bit\n",
+    "\n",
+    "model.gradient_checkpointing_enable()\n",
+    "\n",
+    "wikisql = load_dataset(\"wikisql\", streaming=True)\n",
+    "optimizer = Adam8bit(model.parameters(), lr=1e-5)\n",
+    "\n",
+    "with torch.cuda.amp.autocast():\n",
+    "    for row in tqdm(wikisql['train']):\n",
+    "\n",
+    "        batch = tokenizer(row['question'] + row['sql']['human_readable'], truncation=True, max_length=128, return_tensors='pt')\n",
+    "        batch = {k: v.cuda() for k, v in batch.items()}\n",
+    "\n",
+    "        out = gpt.forward(**batch,)\n",
+    "\n",
+    "        loss = F.cross_entropy(out.logits[:, :-1, :].flatten(0, -2), batch['input_ids'][:, 1:].flatten(),\n",
+    "                               reduction='mean')\n",
+    "        print(loss)\n",
+    "        loss.backward()\n",
+    "\n",
+    "        optimizer.step()\n",
+    "        optimizer.zero_grad()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e2251f6-1a5c-4193-b971-0840d6d59c32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save_pretrained('bloom-8bit-fine-tuned')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}