Spaces:
Running
Running
File size: 11,530 Bytes
9950308 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 |
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "7d29e9ae",
"metadata": {},
"source": [
"---\n",
"title: \"Accelerate, Three Powerful Sublibraries for PyTorch\" \n",
"author: \"Zachary Mueller\"\n",
"format: \n",
" revealjs: \n",
" theme: moon \n",
" fig-format: png\n",
"categories: [Lesson 6]\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "d2aba289-d771-4be9-a4ec-99ab268c5586",
"metadata": {},
"source": [
"## What is 🤗 Accelerate?"
]
},
{
"cell_type": "markdown",
"id": "329b61de-d7c9-46d2-adff-7a912ba93356",
"metadata": {},
"source": [
"```{mermaid}\n",
"%%| fig-height: 6\n",
"graph LR\n",
" A{\"🤗 Accelerate#32;\"}\n",
" A --> B[\"Launching<br>Interface#32;\"]\n",
" A --> C[\"Training Library#32;\"]\n",
" A --> D[\"Big Model<br>Inference#32;\"]\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "0480b2df-a19c-4b93-b98a-b98da4d0d825",
"metadata": {},
"source": [
"# A Launching Interface\n",
"\n",
"Can't I just use `python do_the_thing.py`?"
]
},
{
"cell_type": "markdown",
"id": "c6d5b3da-aad3-4387-b9f3-65384b521bb9",
"metadata": {},
"source": [
"## A Launching Interface\n",
"\n",
"Launching scripts in different environments is complicated:"
]
},
{
"cell_type": "markdown",
"id": "d2c2079d-ab7d-4e98-94a9-d16093b81ea6",
"metadata": {},
"source": [
"- ```bash \n",
"python script.py\n",
"```\n",
"\n",
"- ```bash \n",
"torchrun --nnodes=1 --nproc_per_node=2 script.py\n",
"```\n",
"\n",
"- ```bash \n",
"deepspeed --num_gpus=2 script.py\n",
"```\n",
"\n",
"And more!"
]
},
{
"cell_type": "markdown",
"id": "77bdbbaa-acaa-4ed3-b809-82e836db93f7",
"metadata": {},
"source": [
"## A Launching Interface\n",
"\n",
"But it doesn't have to be:"
]
},
{
"cell_type": "markdown",
"id": "21456afb-7ae6-4bb6-81ea-e6de6365c13f",
"metadata": {},
"source": [
"```bash\n",
"accelerate launch script.py\n",
"```\n",
"\n",
"A single command to launch with `DeepSpeed`, Fully Sharded Data Parallelism, across single and multi CPUs and GPUs, and to train on TPUs[^1] too! \n",
"\n",
"[^1]: Without needing to modify your code and create a `_mp_fn`"
]
},
{
"cell_type": "markdown",
"id": "d77a2576-5bb4-4a64-bcbf-1be9af3a232b",
"metadata": {},
"source": [
"## A Launching Interface\n",
"\n",
"Generate a device-specific configuration through `accelerate config`\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"id": "05c4fe0c-7b86-49a0-bd83-b9dab39e406f",
"metadata": {},
"source": [
"## A Launching Interface\n",
"\n",
"Or don't. `accelerate config` doesn't *have* to be done!\n",
"\n",
"```bash\n",
"torchrun --nnodes=1 --nproc_per_node=2 script.py\n",
"accelerate launch --multi_gpu --nproc_per_node=2 script.py\n",
"```\n",
"\n",
"A quick default configuration can be made too:\n",
"\n",
"```bash \n",
"accelerate config default\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "c6047a52-3582-41c4-96e4-370ef269be94",
"metadata": {},
"source": [
"## A Launching Interface\n",
"\n",
"With the `notebook_launcher` it's also possible to launch code directly from your Jupyter environment too!"
]
},
{
"cell_type": "markdown",
"id": "1097c474-2ec5-4214-9477-ad6bac25317a",
"metadata": {},
"source": [
"```python\n",
"from accelerate import notebook_launcher\n",
"notebook_launcher(\n",
" training_loop_function, \n",
" args, \n",
" num_processes=2\n",
")\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "d8ac070e-bb81-4624-b826-8cb072646ea7",
"metadata": {},
"source": [
"```python\n",
"Launching training on 2 GPUs.\n",
"epoch 0: 88.12\n",
"epoch 1: 91.73\n",
"epoch 2: 92.58\n",
"epoch 3: 93.90\n",
"epoch 4: 94.71\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "debe8ec0-4078-4f85-835e-f38c102ddfaf",
"metadata": {},
"source": [
"# A Training Library\n",
"\n",
"Okay, will `accelerate launch` make `do_the_thing.py` use all my GPUs magically?"
]
},
{
"cell_type": "markdown",
"id": "7c8d4a16-7b57-4eee-8974-e39ff459c5e5",
"metadata": {},
"source": [
"## A Training Library\n",
"\n",
"- Just showed that its possible using `accelerate launch` to *launch* a python script in various distributed environments\n",
"- This does *not* mean that the script will just \"use\" that code and still run on the new compute efficiently.\n",
"- Training on different computes often means *many* lines of code changed for each specific compute.\n",
"- 🤗 `accelerate` solves this by ensuring the same code can be ran on a CPU or GPU, multiples, and on TPUs!"
]
},
{
"cell_type": "markdown",
"id": "d8f7dfdd-5af5-4f6a-831b-d4e0a8f312d3",
"metadata": {},
"source": [
"## A Training Library\n",
"\n",
"\n",
"```{.python}\n",
"for batch in dataloader:\n",
" optimizer.zero_grad()\n",
" inputs, targets = batch\n",
" inputs = inputs.to(device)\n",
" targets = targets.to(device)\n",
" outputs = model(inputs)\n",
" loss = loss_function(outputs, targets)\n",
" loss.backward()\n",
" optimizer.step()\n",
" scheduler.step()\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "2c13ef82-d4c4-4564-8d3d-ef4c4ad3c9d8",
"metadata": {},
"source": [
"## A Training Library {.smaller}"
]
},
{
"cell_type": "markdown",
"id": "992909f7-8f5c-4138-8f31-94f305f564de",
"metadata": {},
"source": [
":::: {.columns}\n",
"::: {.column width=\"43%\"}\n",
"<br><br><br>\n",
"```{.python code-line-numbers=\"5-6,9\"}\n",
"# For alignment purposes\n",
"for batch in dataloader:\n",
" optimizer.zero_grad()\n",
" inputs, targets = batch\n",
" inputs = inputs.to(device)\n",
" targets = targets.to(device)\n",
" outputs = model(inputs)\n",
" loss = loss_function(outputs, targets)\n",
" loss.backward()\n",
" optimizer.step()\n",
" scheduler.step()\n",
"```\n",
":::\n",
"::: {.column width=\"57%\"}\n",
"```{.python code-line-numbers=\"1-7,12-13,16\"}\n",
"from accelerate import Accelerator\n",
"accelerator = Accelerator()\n",
"dataloader, model, optimizer scheduler = (\n",
" accelerator.prepare(\n",
" dataloader, model, optimizer, scheduler\n",
" )\n",
")\n",
"\n",
"for batch in dataloader:\n",
" optimizer.zero_grad()\n",
" inputs, targets = batch\n",
" # inputs = inputs.to(device)\n",
" # targets = targets.to(device)\n",
" outputs = model(inputs)\n",
" loss = loss_function(outputs, targets)\n",
" accelerator.backward(loss) # loss.backward()\n",
" optimizer.step()\n",
" scheduler.step()\n",
"```\n",
":::\n",
"\n",
"::::"
]
},
{
"cell_type": "markdown",
"id": "4028a9a1-5c25-41a3-9c76-f116d6fbb1db",
"metadata": {},
"source": [
"## A Training Library\n",
"\n",
"What all happened in `Accelerator.prepare`?\n",
"\n",
"::: {.incremental}\n",
"1. `Accelerator` looked at the configuration\n",
"2. The `dataloader` was converted into one that can dispatch each batch onto a seperate GPU\n",
"3. The `model` was wrapped with the appropriate DDP wrapper from either `torch.distributed` or `torch_xla`\n",
"4. The `optimizer` and `scheduler` were both converted into an `AcceleratedOptimizer` and `AcceleratedScheduler` which knows how to handle any distributed scenario\n",
":::"
]
},
{
"cell_type": "markdown",
"id": "92e112c3-cdf9-4d84-8076-df33a79da641",
"metadata": {},
"source": [
"## Let's bring in `fastai`\n",
"\n",
"To utilize the `notebook_launcher` and `accelerate` at once it requires a few steps:\n",
"\n",
"1. Migrate the `DataLoaders` creation to inside the `train` function\n",
"2. Use the `distrib_ctx` context manager fastai provides\n",
"3. Train!"
]
},
{
"cell_type": "markdown",
"id": "ba04e9b9-4589-4a08-adc3-cb2b4ec6ad43",
"metadata": {},
"source": [
"## Let's bring `fastai`\n",
"\n",
"Here it is in code, based on the [distributed app examples](https://docs.fast.ai/examples/distributed_app_examples.html)\n",
"\n",
"```{.python}\n",
"from fastai.vision.all import *\n",
"from fastai.distributed import *\n",
"\n",
"path = untar_data(URLs.PETS)/'images'\n",
"\n",
"def train():\n",
" dls = ImageDataLoaders.from_name_func(\n",
" path, get_image_files(path), valid_pct=0.2,\n",
" label_func=lambda x: x[0].isupper(), item_tfms=Resize(224))\n",
" learn = vision_learner(dls, resnet34, metrics=error_rate).to_fp16()\n",
" with learn.distrib_ctx(in_notebook=True, sync_bn=False):\n",
" learn.fine_tune(1)\n",
"\n",
"notebook_launcher(train, num_processes=2)\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "95c138db-6ef1-4c20-ba76-5040deca83e1",
"metadata": {},
"source": [
"## Let's bring `fastai`\n",
"\n",
"Here it is in code, based on the [distributed app examples](https://docs.fast.ai/examples/distributed_app_examples.html)\n",
"\n",
"```{.python code-line-numbers=\"1,5,10,13\"}\n",
"from fastai.vision.all import *\n",
"from fastai.distributed import *\n",
"\n",
"path = untar_data(URLs.PETS)/'images'\n",
"\n",
"def train():\n",
" dls = ImageDataLoaders.from_name_func(\n",
" path, get_image_files(path), valid_pct=0.2,\n",
" label_func=lambda x: x[0].isupper(), item_tfms=Resize(224))\n",
" learn = vision_learner(dls, resnet34, metrics=error_rate).to_fp16()\n",
" with learn.distrib_ctx(in_notebook=True, sync_bn=False):\n",
" learn.fine_tune(1)\n",
"\n",
"notebook_launcher(train, num_processes=2)\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "d4e4a37d-8044-4b0b-91a9-ca8ec8d54895",
"metadata": {},
"source": [
"## Let's bring `fastai`\n",
"\n",
"The key important parts to remember are:\n",
"\n",
"- **No** code should *touch* the GPU before calling `notebook_launcher`\n",
"- Generally it's recommended to let fastai handle gradient accumulation and mixed precision in this case, so use their in-house Callbacks\n",
"- Use the `notebook_launcher` to run the training function after everything is complete."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.10 (default, Nov 14 2022, 12:59:47) \n[GCC 9.4.0]"
},
"vscode": {
"interpreter": {
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|