File size: 12,747 Bytes
30df0c3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 |
{
"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
}
|