Upload ChatRoom_MaryGPT.ipynb
Browse files- ChatRoom_MaryGPT.ipynb +859 -0
ChatRoom_MaryGPT.ipynb
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| 1 |
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{
|
| 2 |
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"cells": [
|
| 3 |
+
{
|
| 4 |
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"cell_type": "markdown",
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| 5 |
+
"metadata": {
|
| 6 |
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"id": "l7qoJHs1L6WQ"
|
| 7 |
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},
|
| 8 |
+
"source": [
|
| 9 |
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"#01.SETUP"
|
| 10 |
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]
|
| 11 |
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},
|
| 12 |
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{
|
| 13 |
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"cell_type": "code",
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| 14 |
+
"execution_count": 4,
|
| 15 |
+
"metadata": {
|
| 16 |
+
"colab": {
|
| 17 |
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"base_uri": "https://localhost:8080/"
|
| 18 |
+
},
|
| 19 |
+
"id": "Op0GXmC8CCyR",
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| 20 |
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"outputId": "a414537e-71b5-4222-85e3-5cc0bdd3f6a6"
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"text": [
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"Collecting transformers==4.25.1\n",
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"\u001b[?25hInstalling collected packages: tokenizers, transformers\n",
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" Found existing installation: tokenizers 0.19.1\n",
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" Successfully uninstalled tokenizers-0.19.1\n",
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" Found existing installation: transformers 4.42.4\n",
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"\u001b[31mERROR: Could not find a version that satisfies the requirement bitsandbytes-cuda111==0.26.0 (from versions: 0.26.0.post2)\u001b[0m\u001b[31m\n",
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"\u001b[0m\u001b[31mERROR: No matching distribution found for bitsandbytes-cuda111==0.26.0\u001b[0m\u001b[31m\n",
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],
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"source": [
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| 228 |
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"!pip install transformers==4.25.1\n",
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| 229 |
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"!pip install bitsandbytes-cuda111==0.26.0\n",
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"!pip install datasets==1.16.1\n",
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"!pip install bitsandbytes loguru\n",
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"!pip install accelerate\n",
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"!pip install deep_translator\n",
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"!pip install langdetect"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"id": "p0dy1ZFwClcq"
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},
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"outputs": [],
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"source": [
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| 245 |
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"from loguru import logger\n",
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| 246 |
+
"import transformers\n",
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| 247 |
+
"import torch\n",
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| 248 |
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"import torch.nn.functional as F\n",
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| 249 |
+
"from torch import nn\n",
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| 250 |
+
"from torch.cuda.amp import custom_fwd, custom_bwd\n",
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| 251 |
+
"from bitsandbytes.functional import quantize_blockwise, dequantize_blockwise\n",
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| 252 |
+
"from tqdm.auto import tqdm\n",
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| 253 |
+
"from datasets import load_dataset\n",
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| 254 |
+
"from bitsandbytes.optim import Adam8bit\n",
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| 255 |
+
"import time, os\n",
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"\n",
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| 257 |
+
"# ---------------------> Converting the model to 8 bits <------------------- #\n",
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| 258 |
+
"\"\"\"\n",
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| 259 |
+
"We convert EleutherAI's GPT-J-6B model to 8 bits using facebook's [bitsandbytes](https://github.com/facebookresearch/bitsandbytes) library.\n",
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| 260 |
+
"This reduces the model's size from 20Gb down to just 6Gb.\n",
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+
"Note that we don't convert linear layer biases to 8 bit as they take up less that 1% of the model's weight anyway.\n",
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+
"\"\"\"\n",
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+
"\n",
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| 264 |
+
"class FrozenBNBLinear(nn.Module):\n",
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| 265 |
+
" def __init__(self, weight, absmax, code, bias=None):\n",
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| 266 |
+
" assert isinstance(bias, nn.Parameter) or bias is None\n",
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| 267 |
+
" super().__init__()\n",
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| 268 |
+
" self.out_features, self.in_features = weight.shape\n",
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| 269 |
+
" self.register_buffer(\"weight\", weight.requires_grad_(False))\n",
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| 270 |
+
" self.register_buffer(\"absmax\", absmax.requires_grad_(False))\n",
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| 271 |
+
" self.register_buffer(\"code\", code.requires_grad_(False))\n",
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| 272 |
+
" self.adapter = None\n",
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| 273 |
+
" self.bias = bias\n",
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+
"\n",
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| 275 |
+
" # def forward(self, input):\n",
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| 276 |
+
" # output = DequantizeAndLinear.apply(input, self.weight, self.absmax, self.code, self.bias)\n",
|
| 277 |
+
" # if self.adapter:\n",
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| 278 |
+
" # output += self.adapter(input)\n",
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| 279 |
+
" # return output\n",
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| 280 |
+
" def forward(self, input):\n",
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| 281 |
+
" output = DequantizeAndLinear.apply(input, self.weight, self.absmax, self.code, self.bias)\n",
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| 282 |
+
" if self.adapter:\n",
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| 283 |
+
" output_cloned = torch.clone(output + self.adapter(input))\n",
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| 284 |
+
" return output_cloned\n",
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| 285 |
+
" else:\n",
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| 286 |
+
" return output\n",
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+
"\n",
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| 288 |
+
" @classmethod\n",
|
| 289 |
+
" def from_linear(cls, linear: nn.Linear) -> \"FrozenBNBLinear\":\n",
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| 290 |
+
" weights_int8, state = quantize_blockise_lowmemory(linear.weight)\n",
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| 291 |
+
" return cls(weights_int8, *state, linear.bias)\n",
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+
"\n",
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| 293 |
+
" def __repr__(self):\n",
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" return f\"{self.__class__.__name__}({self.in_features}, {self.out_features})\"\n",
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"\n",
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"\n",
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+
"\n",
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| 298 |
+
"class DequantizeAndLinear(torch.autograd.Function):\n",
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| 299 |
+
" @staticmethod\n",
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| 300 |
+
" @custom_fwd\n",
|
| 301 |
+
" def forward(ctx, input: torch.Tensor, weights_quantized: torch.ByteTensor,\n",
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| 302 |
+
" absmax: torch.FloatTensor, code: torch.FloatTensor, bias: torch.FloatTensor):\n",
|
| 303 |
+
" weights_deq = dequantize_blockwise(weights_quantized, absmax=absmax, code=code)\n",
|
| 304 |
+
" ctx.save_for_backward(input, weights_quantized, absmax, code)\n",
|
| 305 |
+
" ctx._has_bias = bias is not None\n",
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| 306 |
+
" return F.linear(input, weights_deq, bias)\n",
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+
"\n",
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| 308 |
+
" @staticmethod\n",
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| 309 |
+
" @custom_bwd\n",
|
| 310 |
+
" def backward(ctx, grad_output: torch.Tensor):\n",
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| 311 |
+
" assert not ctx.needs_input_grad[1] and not ctx.needs_input_grad[2] and not ctx.needs_input_grad[3]\n",
|
| 312 |
+
" input, weights_quantized, absmax, code = ctx.saved_tensors\n",
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| 313 |
+
" # grad_output: [*batch, out_features]\n",
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| 314 |
+
" weights_deq = dequantize_blockwise(weights_quantized, absmax=absmax, code=code)\n",
|
| 315 |
+
" grad_input = grad_output @ weights_deq\n",
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| 316 |
+
" grad_bias = grad_output.flatten(0, -2).sum(dim=0) if ctx._has_bias else None\n",
|
| 317 |
+
" return grad_input, None, None, None, grad_bias\n",
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| 318 |
+
"\n",
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| 319 |
+
"\n",
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| 320 |
+
"class FrozenBNBEmbedding(nn.Module):\n",
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| 321 |
+
" def __init__(self, weight, absmax, code):\n",
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| 322 |
+
" super().__init__()\n",
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| 323 |
+
" self.num_embeddings, self.embedding_dim = weight.shape\n",
|
| 324 |
+
" self.register_buffer(\"weight\", weight.requires_grad_(False))\n",
|
| 325 |
+
" self.register_buffer(\"absmax\", absmax.requires_grad_(False))\n",
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| 326 |
+
" self.register_buffer(\"code\", code.requires_grad_(False))\n",
|
| 327 |
+
" self.adapter = None\n",
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| 328 |
+
"\n",
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| 329 |
+
" def forward(self, input, **kwargs):\n",
|
| 330 |
+
" with torch.no_grad():\n",
|
| 331 |
+
" # note: both quantuized weights and input indices are *not* differentiable\n",
|
| 332 |
+
" weight_deq = dequantize_blockwise(self.weight, absmax=self.absmax, code=self.code)\n",
|
| 333 |
+
" output = F.embedding(input, weight_deq, **kwargs)\n",
|
| 334 |
+
" if self.adapter:\n",
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| 335 |
+
" output += self.adapter(input)\n",
|
| 336 |
+
" return output\n",
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| 337 |
+
"\n",
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| 338 |
+
" @classmethod\n",
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| 339 |
+
" def from_embedding(cls, embedding: nn.Embedding) -> \"FrozenBNBEmbedding\":\n",
|
| 340 |
+
" weights_int8, state = quantize_blockise_lowmemory(embedding.weight)\n",
|
| 341 |
+
" return cls(weights_int8, *state)\n",
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| 342 |
+
"\n",
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| 343 |
+
" def __repr__(self):\n",
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| 344 |
+
" return f\"{self.__class__.__name__}({self.num_embeddings}, {self.embedding_dim})\"\n",
|
| 345 |
+
"\n",
|
| 346 |
+
"def quantize_blockise_lowmemory(matrix: torch.Tensor, chunk_size: int = 2 ** 20):\n",
|
| 347 |
+
" assert chunk_size % 4096 == 0\n",
|
| 348 |
+
" code = None\n",
|
| 349 |
+
" chunks = []\n",
|
| 350 |
+
" absmaxes = []\n",
|
| 351 |
+
" flat_tensor = matrix.view(-1)\n",
|
| 352 |
+
" for i in range((matrix.numel() - 1) // chunk_size + 1):\n",
|
| 353 |
+
" input_chunk = flat_tensor[i * chunk_size: (i + 1) * chunk_size].clone()\n",
|
| 354 |
+
" quantized_chunk, (absmax_chunk, code) = quantize_blockwise(input_chunk, code=code)\n",
|
| 355 |
+
" chunks.append(quantized_chunk)\n",
|
| 356 |
+
" absmaxes.append(absmax_chunk)\n",
|
| 357 |
+
"\n",
|
| 358 |
+
" matrix_i8 = torch.cat(chunks).reshape_as(matrix)\n",
|
| 359 |
+
" absmax = torch.cat(absmaxes)\n",
|
| 360 |
+
" return matrix_i8, (absmax, code)\n",
|
| 361 |
+
"\n",
|
| 362 |
+
"\n",
|
| 363 |
+
"def convert_to_int8(model):\n",
|
| 364 |
+
" \"\"\"Convert linear and embedding modules to 8-bit with optional adapters\"\"\"\n",
|
| 365 |
+
" for module in list(model.modules()):\n",
|
| 366 |
+
" for name, child in module.named_children():\n",
|
| 367 |
+
" if isinstance(child, nn.Linear):\n",
|
| 368 |
+
" print(name, child)\n",
|
| 369 |
+
" setattr(\n",
|
| 370 |
+
" module,\n",
|
| 371 |
+
" name,\n",
|
| 372 |
+
" FrozenBNBLinear(\n",
|
| 373 |
+
" weight=torch.zeros(child.out_features, child.in_features, dtype=torch.uint8),\n",
|
| 374 |
+
" absmax=torch.zeros((child.weight.numel() - 1) // 4096 + 1),\n",
|
| 375 |
+
" code=torch.zeros(256),\n",
|
| 376 |
+
" bias=child.bias,\n",
|
| 377 |
+
" ),\n",
|
| 378 |
+
" )\n",
|
| 379 |
+
" elif isinstance(child, nn.Embedding):\n",
|
| 380 |
+
" setattr(\n",
|
| 381 |
+
" module,\n",
|
| 382 |
+
" name,\n",
|
| 383 |
+
" FrozenBNBEmbedding(\n",
|
| 384 |
+
" weight=torch.zeros(child.num_embeddings, child.embedding_dim, dtype=torch.uint8),\n",
|
| 385 |
+
" absmax=torch.zeros((child.weight.numel() - 1) // 4096 + 1),\n",
|
| 386 |
+
" code=torch.zeros(256),\n",
|
| 387 |
+
" )\n",
|
| 388 |
+
" )\n",
|
| 389 |
+
"\n",
|
| 390 |
+
"class GPTJBlock(transformers.models.gptj.modeling_gptj.GPTJBlock):\n",
|
| 391 |
+
" def __init__(self, config):\n",
|
| 392 |
+
" super().__init__(config)\n",
|
| 393 |
+
"\n",
|
| 394 |
+
" convert_to_int8(self.attn)\n",
|
| 395 |
+
" convert_to_int8(self.mlp)\n",
|
| 396 |
+
"\n",
|
| 397 |
+
"\n",
|
| 398 |
+
"class GPTJModel(transformers.models.gptj.modeling_gptj.GPTJModel):\n",
|
| 399 |
+
" def __init__(self, config):\n",
|
| 400 |
+
" super().__init__(config)\n",
|
| 401 |
+
" convert_to_int8(self)\n",
|
| 402 |
+
"\n",
|
| 403 |
+
"\n",
|
| 404 |
+
"class GPTJForCausalLM(transformers.models.gptj.modeling_gptj.GPTJForCausalLM):\n",
|
| 405 |
+
" def __init__(self, config):\n",
|
| 406 |
+
" super().__init__(config)\n",
|
| 407 |
+
" convert_to_int8(self)"
|
| 408 |
+
]
|
| 409 |
+
},
|
| 410 |
+
{
|
| 411 |
+
"cell_type": "markdown",
|
| 412 |
+
"metadata": {
|
| 413 |
+
"id": "PJg_VgpqMDkY"
|
| 414 |
+
},
|
| 415 |
+
"source": [
|
| 416 |
+
"#02.LOAD MARYGPT"
|
| 417 |
+
]
|
| 418 |
+
},
|
| 419 |
+
{
|
| 420 |
+
"cell_type": "code",
|
| 421 |
+
"execution_count": 7,
|
| 422 |
+
"metadata": {
|
| 423 |
+
"colab": {
|
| 424 |
+
"base_uri": "https://localhost:8080/"
|
| 425 |
+
},
|
| 426 |
+
"id": "6yHWUS-h-Hs8",
|
| 427 |
+
"outputId": "3c7cfa66-a95d-442e-b595-4ccd023d913b"
|
| 428 |
+
},
|
| 429 |
+
"outputs": [
|
| 430 |
+
{
|
| 431 |
+
"output_type": "stream",
|
| 432 |
+
"name": "stdout",
|
| 433 |
+
"text": [
|
| 434 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 435 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 436 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 437 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 438 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 439 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 440 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 441 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 442 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 443 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 444 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 445 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 446 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 447 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 448 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 449 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 450 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 451 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 452 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 453 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 454 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 455 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 456 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 457 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 458 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 459 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 460 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 461 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 462 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 463 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 464 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 465 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 466 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 467 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 468 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 469 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 470 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 471 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 472 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 473 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 474 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 475 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 476 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 477 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 478 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 479 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 480 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 481 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 482 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 483 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 484 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 485 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 486 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 487 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 488 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 489 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 490 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 491 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 492 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 493 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 494 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 495 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 496 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 497 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 498 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 499 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 500 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 501 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 502 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 503 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 504 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 505 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 506 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 507 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 508 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 509 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 510 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 511 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 512 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 513 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 514 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 515 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 516 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 517 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 518 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 519 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 520 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 521 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 522 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 523 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 524 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 525 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 526 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 527 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 528 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 529 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 530 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 531 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 532 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 533 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 534 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 535 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 536 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 537 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 538 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 539 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 540 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 541 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 542 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 543 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 544 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 545 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 546 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 547 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 548 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 549 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 550 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 551 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 552 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 553 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 554 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 555 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 556 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 557 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 558 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 559 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 560 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 561 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 562 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 563 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 564 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 565 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 566 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 567 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 568 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 569 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 570 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 571 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 572 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 573 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 574 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 575 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 576 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 577 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 578 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 579 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 580 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 581 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 582 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 583 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 584 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 585 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 586 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 587 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 588 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 589 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 590 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 591 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 592 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 593 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 594 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 595 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 596 |
+
"k_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 597 |
+
"v_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 598 |
+
"q_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 599 |
+
"out_proj Linear(in_features=4096, out_features=4096, bias=False)\n",
|
| 600 |
+
"fc_in Linear(in_features=4096, out_features=16384, bias=True)\n",
|
| 601 |
+
"fc_out Linear(in_features=16384, out_features=4096, bias=True)\n",
|
| 602 |
+
"lm_head Linear(in_features=4096, out_features=50400, bias=True)\n"
|
| 603 |
+
]
|
| 604 |
+
},
|
| 605 |
+
{
|
| 606 |
+
"output_type": "stream",
|
| 607 |
+
"name": "stderr",
|
| 608 |
+
"text": [
|
| 609 |
+
"Some weights of the model checkpoint at obake2ai/MaryGPT were not used when initializing GPTJForCausalLM: ['transformer.h.5.mlp.fc_in.adapter.1.weight', 'transformer.h.15.attn.v_proj.adapter.0.weight', 'transformer.h.18.mlp.fc_out.adapter.0.weight', 'transformer.h.4.mlp.fc_in.adapter.0.weight', 'transformer.h.22.attn.v_proj.adapter.1.weight', 'transformer.h.21.attn.out_proj.adapter.1.weight', 'transformer.h.14.attn.q_proj.adapter.1.weight', 'transformer.h.2.attn.q_proj.adapter.0.weight', 'transformer.h.0.attn.k_proj.adapter.1.weight', 'transformer.h.14.attn.k_proj.adapter.1.weight', 'transformer.h.7.mlp.fc_in.adapter.0.weight', 'transformer.h.26.mlp.fc_out.adapter.1.weight', 'transformer.h.24.attn.q_proj.adapter.1.weight', 'transformer.h.20.mlp.fc_out.adapter.1.weight', 'transformer.h.5.attn.out_proj.adapter.1.weight', 'transformer.h.5.attn.q_proj.adapter.1.weight', 'transformer.h.15.attn.k_proj.adapter.0.weight', 'transformer.h.25.mlp.fc_out.adapter.0.weight', 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'transformer.h.24.attn.v_proj.adapter.1.weight', 'transformer.h.14.mlp.fc_out.adapter.0.weight', 'transformer.h.11.attn.out_proj.adapter.0.weight', 'transformer.h.25.attn.q_proj.adapter.1.weight', 'transformer.h.16.attn.k_proj.adapter.0.weight', 'transformer.h.13.attn.v_proj.adapter.1.weight', 'transformer.h.12.attn.q_proj.adapter.0.weight', 'transformer.h.21.attn.out_proj.adapter.0.weight', 'transformer.h.26.attn.v_proj.adapter.1.weight', 'transformer.h.13.attn.q_proj.adapter.0.weight', 'transformer.h.2.attn.out_proj.adapter.0.weight', 'transformer.h.10.attn.v_proj.adapter.1.weight', 'transformer.h.1.mlp.fc_in.adapter.1.weight', 'transformer.h.21.attn.q_proj.adapter.0.weight', 'lm_head.adapter.0.weight', 'transformer.h.3.attn.v_proj.adapter.0.weight', 'transformer.h.16.attn.q_proj.adapter.0.weight', 'transformer.h.7.attn.out_proj.adapter.1.weight', 'lm_head.adapter.1.weight', 'transformer.h.11.attn.v_proj.adapter.1.weight', 'transformer.h.0.mlp.fc_out.adapter.1.weight', 'transformer.h.4.attn.k_proj.adapter.0.weight', 'transformer.h.13.mlp.fc_in.adapter.0.weight', 'transformer.h.17.mlp.fc_in.adapter.0.weight', 'transformer.h.8.mlp.fc_out.adapter.0.weight', 'transformer.h.15.attn.q_proj.adapter.1.weight', 'transformer.h.27.attn.q_proj.adapter.1.weight', 'transformer.h.12.attn.v_proj.adapter.1.weight', 'transformer.h.3.mlp.fc_out.adapter.1.weight', 'transformer.h.17.attn.k_proj.adapter.1.weight', 'transformer.h.18.mlp.fc_in.adapter.0.weight', 'transformer.h.14.attn.v_proj.adapter.0.weight', 'transformer.h.22.attn.k_proj.adapter.1.weight', 'transformer.h.21.mlp.fc_in.adapter.0.weight', 'transformer.h.24.attn.out_proj.adapter.0.weight', 'transformer.h.5.attn.k_proj.adapter.0.weight', 'transformer.h.12.attn.k_proj.adapter.1.weight', 'transformer.h.6.attn.v_proj.adapter.0.weight', 'transformer.h.17.attn.v_proj.adapter.1.weight', 'transformer.h.1.attn.v_proj.adapter.0.weight', 'transformer.h.18.attn.q_proj.adapter.1.weight', 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'transformer.h.10.mlp.fc_out.adapter.0.weight', 'transformer.h.15.mlp.fc_out.adapter.0.weight', 'transformer.h.27.mlp.fc_out.adapter.0.weight', 'transformer.h.4.attn.out_proj.adapter.0.weight', 'transformer.h.14.attn.k_proj.adapter.0.weight', 'transformer.h.23.mlp.fc_in.adapter.0.weight', 'transformer.h.22.attn.q_proj.adapter.1.weight', 'transformer.wte.adapter.0.weight', 'transformer.h.22.mlp.fc_out.adapter.0.weight', 'transformer.h.25.attn.out_proj.adapter.0.weight', 'transformer.h.6.attn.k_proj.adapter.1.weight', 'transformer.h.22.mlp.fc_in.adapter.0.weight', 'transformer.h.19.attn.k_proj.adapter.1.weight', 'transformer.h.22.mlp.fc_in.adapter.1.weight', 'transformer.h.21.attn.k_proj.adapter.1.weight', 'transformer.h.26.attn.k_proj.adapter.1.weight', 'transformer.h.12.mlp.fc_in.adapter.0.weight', 'transformer.h.0.attn.out_proj.adapter.0.weight', 'transformer.h.7.attn.q_proj.adapter.0.weight', 'transformer.h.10.attn.v_proj.adapter.0.weight', 'transformer.h.6.mlp.fc_out.adapter.1.weight', 'transformer.h.7.mlp.fc_out.adapter.1.weight', 'transformer.h.15.attn.out_proj.adapter.0.weight', 'transformer.h.8.mlp.fc_in.adapter.0.weight', 'transformer.h.4.attn.out_proj.adapter.1.weight', 'transformer.h.10.attn.k_proj.adapter.1.weight', 'transformer.h.11.mlp.fc_in.adapter.0.weight', 'transformer.h.23.attn.out_proj.adapter.0.weight', 'transformer.h.10.attn.q_proj.adapter.0.weight', 'transformer.h.2.mlp.fc_in.adapter.1.weight', 'transformer.h.2.mlp.fc_in.adapter.0.weight', 'transformer.h.13.mlp.fc_out.adapter.1.weight', 'transformer.h.19.mlp.fc_in.adapter.0.weight', 'transformer.h.24.mlp.fc_in.adapter.1.weight', 'transformer.h.24.attn.k_proj.adapter.1.weight', 'transformer.h.4.attn.q_proj.adapter.0.weight', 'transformer.h.5.mlp.fc_out.adapter.0.weight', 'transformer.h.18.attn.k_proj.adapter.1.weight', 'transformer.h.2.attn.q_proj.adapter.1.weight', 'transformer.h.4.attn.v_proj.adapter.1.weight', 'transformer.h.12.attn.q_proj.adapter.1.weight', 'transformer.h.20.mlp.fc_in.adapter.0.weight', 'transformer.h.10.attn.out_proj.adapter.0.weight', 'transformer.h.23.mlp.fc_out.adapter.0.weight', 'transformer.h.3.attn.k_proj.adapter.1.weight', 'transformer.h.27.attn.v_proj.adapter.1.weight', 'transformer.h.2.attn.v_proj.adapter.0.weight', 'transformer.h.11.attn.v_proj.adapter.0.weight', 'transformer.h.12.mlp.fc_out.adapter.0.weight', 'transformer.h.14.mlp.fc_in.adapter.1.weight', 'transformer.h.7.mlp.fc_in.adapter.1.weight', 'transformer.h.14.attn.v_proj.adapter.1.weight', 'transformer.h.15.attn.out_proj.adapter.1.weight', 'transformer.h.23.attn.v_proj.adapter.1.weight', 'transformer.h.0.mlp.fc_in.adapter.1.weight', 'transformer.h.5.mlp.fc_in.adapter.0.weight', 'transformer.h.9.attn.v_proj.adapter.1.weight', 'transformer.h.7.mlp.fc_out.adapter.0.weight', 'transformer.h.17.mlp.fc_in.adapter.1.weight', 'transformer.h.2.attn.k_proj.adapter.0.weight', 'transformer.h.7.attn.v_proj.adapter.0.weight', 'transformer.h.26.attn.q_proj.adapter.1.weight', 'transformer.h.21.mlp.fc_in.adapter.1.weight', 'transformer.h.10.mlp.fc_in.adapter.1.weight', 'transformer.h.1.attn.q_proj.adapter.0.weight', 'transformer.h.16.mlp.fc_in.adapter.0.weight', 'transformer.h.19.attn.out_proj.adapter.0.weight', 'transformer.h.12.attn.v_proj.adapter.0.weight', 'transformer.h.1.attn.out_proj.adapter.0.weight', 'transformer.h.11.attn.q_proj.adapter.0.weight', 'transformer.h.16.attn.k_proj.adapter.1.weight', 'transformer.h.20.attn.out_proj.adapter.0.weight', 'transformer.h.3.mlp.fc_in.adapter.1.weight', 'transformer.h.3.attn.out_proj.adapter.0.weight', 'transformer.h.24.mlp.fc_out.adapter.1.weight', 'transformer.h.24.mlp.fc_out.adapter.0.weight', 'transformer.h.13.attn.out_proj.adapter.1.weight', 'transformer.h.8.mlp.fc_out.adapter.1.weight', 'transformer.h.18.attn.v_proj.adapter.1.weight', 'transformer.h.27.mlp.fc_in.adapter.0.weight', 'transformer.h.3.attn.out_proj.adapter.1.weight']\n",
|
| 610 |
+
"- This IS expected if you are initializing GPTJForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
|
| 611 |
+
"- This IS NOT expected if you are initializing GPTJForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
|
| 612 |
+
]
|
| 613 |
+
},
|
| 614 |
+
{
|
| 615 |
+
"output_type": "execute_result",
|
| 616 |
+
"data": {
|
| 617 |
+
"text/plain": [
|
| 618 |
+
"GPTJForCausalLM(\n",
|
| 619 |
+
" (transformer): GPTJModel(\n",
|
| 620 |
+
" (wte): FrozenBNBEmbedding(50400, 4096)\n",
|
| 621 |
+
" (drop): Dropout(p=0.0, inplace=False)\n",
|
| 622 |
+
" (h): ModuleList(\n",
|
| 623 |
+
" (0-27): 28 x GPTJBlock(\n",
|
| 624 |
+
" (ln_1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n",
|
| 625 |
+
" (attn): GPTJAttention(\n",
|
| 626 |
+
" (attn_dropout): Dropout(p=0.0, inplace=False)\n",
|
| 627 |
+
" (resid_dropout): Dropout(p=0.0, inplace=False)\n",
|
| 628 |
+
" (k_proj): FrozenBNBLinear(4096, 4096)\n",
|
| 629 |
+
" (v_proj): FrozenBNBLinear(4096, 4096)\n",
|
| 630 |
+
" (q_proj): FrozenBNBLinear(4096, 4096)\n",
|
| 631 |
+
" (out_proj): FrozenBNBLinear(4096, 4096)\n",
|
| 632 |
+
" )\n",
|
| 633 |
+
" (mlp): GPTJMLP(\n",
|
| 634 |
+
" (fc_in): FrozenBNBLinear(4096, 16384)\n",
|
| 635 |
+
" (fc_out): FrozenBNBLinear(16384, 4096)\n",
|
| 636 |
+
" (act): NewGELUActivation()\n",
|
| 637 |
+
" (dropout): Dropout(p=0.0, inplace=False)\n",
|
| 638 |
+
" )\n",
|
| 639 |
+
" )\n",
|
| 640 |
+
" )\n",
|
| 641 |
+
" (ln_f): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n",
|
| 642 |
+
" )\n",
|
| 643 |
+
" (lm_head): FrozenBNBLinear(4096, 50400)\n",
|
| 644 |
+
")"
|
| 645 |
+
]
|
| 646 |
+
},
|
| 647 |
+
"metadata": {},
|
| 648 |
+
"execution_count": 7
|
| 649 |
+
}
|
| 650 |
+
],
|
| 651 |
+
"source": [
|
| 652 |
+
"transformers.models.gptj.modeling_gptj.GPTJBlock = GPTJBlock # monkey-patch GPT-J\n",
|
| 653 |
+
"\n",
|
| 654 |
+
"# ---------------------> Loading EleutherAI/gpt-j-6B config and tokenizer <------------------- #\n",
|
| 655 |
+
"# config = transformers.GPTJConfig.from_pretrained(\"EleutherAI/gpt-j-6b\")\n",
|
| 656 |
+
"tokenizer = transformers.AutoTokenizer.from_pretrained(\"EleutherAI/gpt-j-6b\")\n",
|
| 657 |
+
"\n",
|
| 658 |
+
"# ---------------------> Downloading gpt-j-6B-8bit model from huggingface <------------------- #\n",
|
| 659 |
+
"#gpt = GPTJForCausalLM.from_pretrained(\"hivemind/gpt-j-6B-8bit\", low_cpu_mem_usage=True)\n",
|
| 660 |
+
"\n",
|
| 661 |
+
"# ----------------> Saving gpt-j-6B-8bit model to server <-----------------#\n",
|
| 662 |
+
"#save_dir = \"/home/paperspace/project/saved_models_gpt-j-6B-8bit/gpt-j-6B\"\n",
|
| 663 |
+
"#gpt.save_pretrained(save_dir)\n",
|
| 664 |
+
"#logger.info(\"Saved model to {}\".format(save_dir))\n",
|
| 665 |
+
"\n",
|
| 666 |
+
"# ---------------------> Loading saved gpt-j-6B-8bit model <------------------- #\n",
|
| 667 |
+
"#gpt = GPTJForCausalLM.from_pretrained(\"./saved_models_gpt-j-6B-8bit/gpt-j-6B\",low_cpu_mem_usage=True)\n",
|
| 668 |
+
"gpt = GPTJForCausalLM.from_pretrained(\"obake2ai/MaryGPT\", device_map=\"auto\", low_cpu_mem_usage=True)\n",
|
| 669 |
+
"config = transformers.GPTJConfig.from_pretrained(\"obake2ai/MaryGPT\")\n",
|
| 670 |
+
"\n",
|
| 671 |
+
"\n",
|
| 672 |
+
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
|
| 673 |
+
"gpt.to(device)\n",
|
| 674 |
+
"\n",
|
| 675 |
+
"# # ---------------------> Text generation example <------------------- #\n",
|
| 676 |
+
"# prompt = tokenizer(\"A cat sat on a mat\", return_tensors='pt')\n",
|
| 677 |
+
"# prompt = {key: value.to(device) for key, value in prompt.items()}\n",
|
| 678 |
+
"# out = gpt.generate(**prompt, min_length=128, max_length=128, do_sample=True)\n",
|
| 679 |
+
"# logger.info(\"Generated text: {}\".format(tokenizer.decode(out[0])))"
|
| 680 |
+
]
|
| 681 |
+
},
|
| 682 |
+
{
|
| 683 |
+
"cell_type": "markdown",
|
| 684 |
+
"metadata": {
|
| 685 |
+
"id": "EcQsbN1zZsPN"
|
| 686 |
+
},
|
| 687 |
+
"source": [
|
| 688 |
+
"# 03.ASK QUESTIONS"
|
| 689 |
+
]
|
| 690 |
+
},
|
| 691 |
+
{
|
| 692 |
+
"cell_type": "markdown",
|
| 693 |
+
"source": [],
|
| 694 |
+
"metadata": {
|
| 695 |
+
"id": "TXKlpN2qXCCU"
|
| 696 |
+
}
|
| 697 |
+
},
|
| 698 |
+
{
|
| 699 |
+
"cell_type": "code",
|
| 700 |
+
"execution_count": 8,
|
| 701 |
+
"metadata": {
|
| 702 |
+
"colab": {
|
| 703 |
+
"base_uri": "https://localhost:8080/"
|
| 704 |
+
},
|
| 705 |
+
"id": "IxDyyXp_uZ3U",
|
| 706 |
+
"outputId": "0577917c-8717-47c3-f976-72e1391a539c",
|
| 707 |
+
"cellView": "form"
|
| 708 |
+
},
|
| 709 |
+
"outputs": [
|
| 710 |
+
{
|
| 711 |
+
"output_type": "stream",
|
| 712 |
+
"name": "stderr",
|
| 713 |
+
"text": [
|
| 714 |
+
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
|
| 715 |
+
]
|
| 716 |
+
},
|
| 717 |
+
{
|
| 718 |
+
"output_type": "stream",
|
| 719 |
+
"name": "stdout",
|
| 720 |
+
"text": [
|
| 721 |
+
"\n",
|
| 722 |
+
" thinking...\n",
|
| 723 |
+
"最近、展覧会のコンセプトの性質と、それがアーティストの展覧会の実践とより広い意味でどのように関係しているかについての記事が多くあります。この質問は、かなり明白に聞こえると感じるかもしれませんが、そうではありません。展覧会のコンセプトについてどう思いますか? それを見たときに、何が見えますか?\n"
|
| 724 |
+
]
|
| 725 |
+
}
|
| 726 |
+
],
|
| 727 |
+
"source": [
|
| 728 |
+
"import os\n",
|
| 729 |
+
"import time\n",
|
| 730 |
+
"import datetime\n",
|
| 731 |
+
"import requests\n",
|
| 732 |
+
"import pytz\n",
|
| 733 |
+
"import random\n",
|
| 734 |
+
"from deep_translator import GoogleTranslator\n",
|
| 735 |
+
"from langdetect import detect\n",
|
| 736 |
+
"import re\n",
|
| 737 |
+
"import shutil\n",
|
| 738 |
+
"\n",
|
| 739 |
+
"path_save_dir = \"./log\"\n",
|
| 740 |
+
"\n",
|
| 741 |
+
"def modify_text(text):\n",
|
| 742 |
+
" sentences = re.findall(r'.+?[.!?]', text)\n",
|
| 743 |
+
" if sentences:\n",
|
| 744 |
+
" modified_text = ' '.join(sentences)\n",
|
| 745 |
+
" else:\n",
|
| 746 |
+
" modified_text = text\n",
|
| 747 |
+
"\n",
|
| 748 |
+
" modified_text = re.sub(r'\\n{2,}', '\\n', modified_text)\n",
|
| 749 |
+
" modified_text = remove_header(modified_text)\n",
|
| 750 |
+
"\n",
|
| 751 |
+
" return modified_text\n",
|
| 752 |
+
"\n",
|
| 753 |
+
"\n",
|
| 754 |
+
"def translate_to_japanese(text):\n",
|
| 755 |
+
" return GoogleTranslator(source='en', target='ja').translate(text).replace(\"岸優馬\", \"岸裕真\")\n",
|
| 756 |
+
"\n",
|
| 757 |
+
"def translate_to_english(text):\n",
|
| 758 |
+
" return GoogleTranslator(source='ja', target='en').translate(text).replace(\"岸優馬\", \"岸裕真\")\n",
|
| 759 |
+
"\n",
|
| 760 |
+
"def is_english(text):\n",
|
| 761 |
+
" try:\n",
|
| 762 |
+
" return detect(text) == 'en'\n",
|
| 763 |
+
" except:\n",
|
| 764 |
+
" return False\n",
|
| 765 |
+
"import random\n",
|
| 766 |
+
"\n",
|
| 767 |
+
"def is_japanese(text):\n",
|
| 768 |
+
" try:\n",
|
| 769 |
+
" return detect(text) == 'ja'\n",
|
| 770 |
+
" except:\n",
|
| 771 |
+
" return False\n",
|
| 772 |
+
"import random\n",
|
| 773 |
+
"\n",
|
| 774 |
+
"def remove_header(text):\n",
|
| 775 |
+
" return text.replace(question_header, \"\")\n",
|
| 776 |
+
"\n",
|
| 777 |
+
"question = \"展示のコンセプトを考えて\" #@param {type:\"string\"}\n",
|
| 778 |
+
"min_words = 60 #@param {type:\"number\"}\n",
|
| 779 |
+
"max_words = 120 #@param {type:\"number\"}\n",
|
| 780 |
+
"\n",
|
| 781 |
+
"question_header = \"\"\"\n",
|
| 782 |
+
"You are MaryGPT, an open-source LLM model fine-tuned on the Gothic novel Frankenstein; or, The Modern Prometheus by Mary Shelley, and an excellent art curator.\n",
|
| 783 |
+
"\"\"\"\n",
|
| 784 |
+
"\n",
|
| 785 |
+
"print_jp = False\n",
|
| 786 |
+
"if is_japanese(question):\n",
|
| 787 |
+
" question = translate_to_english(question)\n",
|
| 788 |
+
" print_jp = True\n",
|
| 789 |
+
"\n",
|
| 790 |
+
"question_format = f\"\"\"\n",
|
| 791 |
+
"{question_header}\n",
|
| 792 |
+
"\n",
|
| 793 |
+
"Question: {question}\n",
|
| 794 |
+
"Answer:\n",
|
| 795 |
+
"\"\"\"\n",
|
| 796 |
+
"\n",
|
| 797 |
+
"def get_mary_response():\n",
|
| 798 |
+
" text_here = question_format\n",
|
| 799 |
+
" prompt = tokenizer(text_here, return_tensors='pt')\n",
|
| 800 |
+
" prompt = {key: value.to(device) for key, value in prompt.items()}\n",
|
| 801 |
+
" out = gpt.generate(**prompt, min_length=min_words, max_length=max_words, do_sample=True)\n",
|
| 802 |
+
" text = tokenizer.decode(out[0])[len(question_format):]\n",
|
| 803 |
+
" return modify_text(text)\n",
|
| 804 |
+
"\n",
|
| 805 |
+
"def create_mary_log():\n",
|
| 806 |
+
" tz_tokyo = pytz.timezone('Asia/Tokyo')\n",
|
| 807 |
+
" current_time = datetime.datetime.now(tz_tokyo)\n",
|
| 808 |
+
" formatted_time = current_time.strftime('%Y/%m/%d %H:%M')\n",
|
| 809 |
+
"\n",
|
| 810 |
+
" filename = f\"log_{current_time.strftime('%Y%m%d_%H%M%S')}.txt\"\n",
|
| 811 |
+
" with open(os.path.join(path_save_dir, filename), 'w') as file:\n",
|
| 812 |
+
"\n",
|
| 813 |
+
" mary_text = get_mary_response()\n",
|
| 814 |
+
" if is_english(mary_text):\n",
|
| 815 |
+
" translated_text = translate_to_japanese(mary_text)\n",
|
| 816 |
+
" file.write(f\"\\n{translated_text}\\n\\n\")\n",
|
| 817 |
+
"\n",
|
| 818 |
+
" file.write(f\"{mary_text}\\n\")\n",
|
| 819 |
+
" print(f\"{mary_text}\\n\")\n",
|
| 820 |
+
" #file.write(f\"***generated: {formatted_time}***\\n\")\n",
|
| 821 |
+
"\n",
|
| 822 |
+
"if not os.path.exists(path_save_dir):\n",
|
| 823 |
+
" os.makedirs(path_save_dir)\n",
|
| 824 |
+
"\n",
|
| 825 |
+
"mary_text_en = get_mary_response()\n",
|
| 826 |
+
"mary_text_jp = translate_to_japanese(mary_text_en)\n",
|
| 827 |
+
"\n",
|
| 828 |
+
"print(\"\\n thinking...\")\n",
|
| 829 |
+
"\n",
|
| 830 |
+
"if print_jp:\n",
|
| 831 |
+
" print(mary_text_jp)\n",
|
| 832 |
+
"else:\n",
|
| 833 |
+
" print(mary_text_en)\n",
|
| 834 |
+
"\n",
|
| 835 |
+
"# create_mary_log()"
|
| 836 |
+
]
|
| 837 |
+
}
|
| 838 |
+
],
|
| 839 |
+
"metadata": {
|
| 840 |
+
"accelerator": "GPU",
|
| 841 |
+
"colab": {
|
| 842 |
+
"collapsed_sections": [
|
| 843 |
+
"l7qoJHs1L6WQ",
|
| 844 |
+
"PJg_VgpqMDkY"
|
| 845 |
+
],
|
| 846 |
+
"machine_shape": "hm",
|
| 847 |
+
"provenance": []
|
| 848 |
+
},
|
| 849 |
+
"kernelspec": {
|
| 850 |
+
"display_name": "Python 3",
|
| 851 |
+
"name": "python3"
|
| 852 |
+
},
|
| 853 |
+
"language_info": {
|
| 854 |
+
"name": "python"
|
| 855 |
+
}
|
| 856 |
+
},
|
| 857 |
+
"nbformat": 4,
|
| 858 |
+
"nbformat_minor": 0
|
| 859 |
+
}
|