File size: 27,730 Bytes
cd6952b |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "e119e71a-f88a-4d5c-90fb-e60b84c4f42c",
"metadata": {},
"outputs": [],
"source": [
"from transformers import (\n",
" AutoModelForCausalLM,\n",
" AutoTokenizer,\n",
" AutoTokenizer,\n",
")\n",
"from peft import PeftModel, PeftConfig\n",
"import torch\n",
"\n",
"d_map = {\"\": torch.cuda.current_device()} if torch.cuda.is_available() else None\n",
"local_model_path = \"outputs/checkpoint-100\" # Path to the combined weights"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "ba591ab9-5029-46e8-b9a9-428de3896e62",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ee8258a57258444baf79b52af6444788",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "OutOfMemoryError",
"evalue": "CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 23.68 GiB of which 79.62 MiB is free. Process 657358 has 23.59 GiB memory in use. Of the allocated memory 23.00 GiB is allocated by PyTorch, and 357.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[23], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Loading the base Model\u001b[39;00m\n\u001b[1;32m 2\u001b[0m config \u001b[38;5;241m=\u001b[39m PeftConfig\u001b[38;5;241m.\u001b[39mfrom_pretrained(local_model_path)\n\u001b[0;32m----> 4\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mAutoModelForCausalLM\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mtorch_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat16\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# load_in_4bit=True, \u001b[39;49;00m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43md_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(config\u001b[38;5;241m.\u001b[39mbase_model_name_or_path)\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:566\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 564\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(config) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m 565\u001b[0m model_class \u001b[38;5;241m=\u001b[39m _get_model_class(config, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping)\n\u001b[0;32m--> 566\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 567\u001b[0m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 568\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 569\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 570\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 571\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(c\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 572\u001b[0m )\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:3850\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 3841\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_orig \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3842\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_default_dtype(dtype_orig)\n\u001b[1;32m 3843\u001b[0m (\n\u001b[1;32m 3844\u001b[0m model,\n\u001b[1;32m 3845\u001b[0m missing_keys,\n\u001b[1;32m 3846\u001b[0m unexpected_keys,\n\u001b[1;32m 3847\u001b[0m mismatched_keys,\n\u001b[1;32m 3848\u001b[0m offload_index,\n\u001b[1;32m 3849\u001b[0m error_msgs,\n\u001b[0;32m-> 3850\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_pretrained_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3851\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3852\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3853\u001b[0m \u001b[43m \u001b[49m\u001b[43mloaded_state_dict_keys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# XXX: rename?\u001b[39;49;00m\n\u001b[1;32m 3854\u001b[0m \u001b[43m \u001b[49m\u001b[43mresolved_archive_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3855\u001b[0m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3856\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_mismatched_sizes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_mismatched_sizes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3857\u001b[0m \u001b[43m \u001b[49m\u001b[43msharded_metadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msharded_metadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3858\u001b[0m \u001b[43m \u001b[49m\u001b[43m_fast_init\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_fast_init\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3859\u001b[0m \u001b[43m \u001b[49m\u001b[43mlow_cpu_mem_usage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlow_cpu_mem_usage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3860\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3861\u001b[0m \u001b[43m \u001b[49m\u001b[43moffload_folder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffload_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3862\u001b[0m \u001b[43m \u001b[49m\u001b[43moffload_state_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffload_state_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3863\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3864\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_quantized\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mquantization_method\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mQuantizationMethod\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBITS_AND_BYTES\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3865\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeep_in_fp32_modules\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_in_fp32_modules\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3866\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3868\u001b[0m model\u001b[38;5;241m.\u001b[39mis_loaded_in_4bit \u001b[38;5;241m=\u001b[39m load_in_4bit\n\u001b[1;32m 3869\u001b[0m model\u001b[38;5;241m.\u001b[39mis_loaded_in_8bit \u001b[38;5;241m=\u001b[39m load_in_8bit\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:4284\u001b[0m, in \u001b[0;36mPreTrainedModel._load_pretrained_model\u001b[0;34m(cls, model, state_dict, loaded_keys, resolved_archive_file, pretrained_model_name_or_path, ignore_mismatched_sizes, sharded_metadata, _fast_init, low_cpu_mem_usage, device_map, offload_folder, offload_state_dict, dtype, is_quantized, keep_in_fp32_modules)\u001b[0m\n\u001b[1;32m 4280\u001b[0m set_module_quantized_tensor_to_device(\n\u001b[1;32m 4281\u001b[0m model_to_load, key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m, torch\u001b[38;5;241m.\u001b[39mempty(\u001b[38;5;241m*\u001b[39mparam\u001b[38;5;241m.\u001b[39msize(), dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m 4282\u001b[0m )\n\u001b[1;32m 4283\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 4284\u001b[0m new_error_msgs, offload_index, state_dict_index \u001b[38;5;241m=\u001b[39m \u001b[43m_load_state_dict_into_meta_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4285\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel_to_load\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4286\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4287\u001b[0m \u001b[43m \u001b[49m\u001b[43mloaded_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4288\u001b[0m \u001b[43m \u001b[49m\u001b[43mstart_prefix\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4289\u001b[0m \u001b[43m \u001b[49m\u001b[43mexpected_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4290\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4291\u001b[0m \u001b[43m \u001b[49m\u001b[43moffload_folder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffload_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4292\u001b[0m \u001b[43m \u001b[49m\u001b[43moffload_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffload_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4293\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_dict_folder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstate_dict_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4294\u001b[0m \u001b[43m \u001b[49m\u001b[43mstate_dict_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstate_dict_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4295\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4296\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_quantized\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_quantized\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4297\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_safetensors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_safetensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4298\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeep_in_fp32_modules\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_in_fp32_modules\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4299\u001b[0m \u001b[43m \u001b[49m\u001b[43munexpected_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munexpected_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4300\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4301\u001b[0m error_msgs \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m new_error_msgs\n\u001b[1;32m 4302\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:805\u001b[0m, in \u001b[0;36m_load_state_dict_into_meta_model\u001b[0;34m(model, state_dict, loaded_state_dict_keys, start_prefix, expected_keys, device_map, offload_folder, offload_index, state_dict_folder, state_dict_index, dtype, is_quantized, is_safetensors, keep_in_fp32_modules, unexpected_keys)\u001b[0m\n\u001b[1;32m 802\u001b[0m state_dict_index \u001b[38;5;241m=\u001b[39m offload_weight(param, param_name, state_dict_folder, state_dict_index)\n\u001b[1;32m 803\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_quantized:\n\u001b[1;32m 804\u001b[0m \u001b[38;5;66;03m# For backward compatibility with older versions of `accelerate`\u001b[39;00m\n\u001b[0;32m--> 805\u001b[0m \u001b[43mset_module_tensor_to_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparam_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparam_device\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mset_module_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 806\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m param\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01min\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39mint8, torch\u001b[38;5;241m.\u001b[39muint8) \u001b[38;5;129;01mand\u001b[39;00m is_quantized:\n\u001b[1;32m 807\u001b[0m \u001b[38;5;66;03m# handling newly quantized weights and loaded quantized weights\u001b[39;00m\n\u001b[1;32m 808\u001b[0m \u001b[38;5;66;03m# edit the param.dtype restrictions and is_quantized condition when adding new quant methods\u001b[39;00m\n\u001b[1;32m 809\u001b[0m quantized_stats \u001b[38;5;241m=\u001b[39m {}\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/modeling.py:347\u001b[0m, in \u001b[0;36mset_module_tensor_to_device\u001b[0;34m(module, tensor_name, device, value, dtype, fp16_statistics)\u001b[0m\n\u001b[1;32m 345\u001b[0m module\u001b[38;5;241m.\u001b[39m_parameters[tensor_name] \u001b[38;5;241m=\u001b[39m param_cls(new_value, requires_grad\u001b[38;5;241m=\u001b[39mold_value\u001b[38;5;241m.\u001b[39mrequires_grad)\n\u001b[1;32m 346\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[0;32m--> 347\u001b[0m new_value \u001b[38;5;241m=\u001b[39m \u001b[43mvalue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 348\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 349\u001b[0m new_value \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(value, device\u001b[38;5;241m=\u001b[39mdevice)\n",
"\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 112.00 MiB. GPU 0 has a total capacty of 23.68 GiB of which 79.62 MiB is free. Process 657358 has 23.59 GiB memory in use. Of the allocated memory 23.00 GiB is allocated by PyTorch, and 357.02 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF"
]
}
],
"source": [
"# Loading the base Model\n",
"config = PeftConfig.from_pretrained(local_model_path)\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" config.base_model_name_or_path, \n",
" return_dict=True,\n",
" torch_dtype=torch.float16,\n",
" # load_in_4bit=True, \n",
" device_map=d_map,\n",
")\n",
"tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c1d36c14-0bfc-4215-8576-bb390a3a6114",
"metadata": {},
"outputs": [],
"source": [
"# load the base model with the Lora model\n",
"model = PeftModel.from_pretrained(model, local_model_path)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "72cdeaa8-4b0c-45d0-a585-2f86626e280b",
"metadata": {},
"outputs": [],
"source": [
"# model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "09fa4575-0dec-4e62-a43f-77e57f68c4a9",
"metadata": {},
"outputs": [],
"source": [
"def inferance(query: str, model, tokenizer, temp = 1.0, limit = 200) -> str:\n",
" device = d_map\n",
"\n",
" prompt_template = \"\"\"\n",
" Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
" ### Question:\n",
" {query}\n",
"\n",
" ### Answer:\n",
" \"\"\"\n",
" prompt = prompt_template.format(query=query)\n",
"\n",
" encodeds = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=True)\n",
"\n",
" model_inputs = encodeds.to(device)\n",
"\n",
" generated_ids = model.generate(**model_inputs, max_new_tokens=int(limit), temperature=temp, do_sample=True, pad_token_id=tokenizer.eos_token_id)\n",
" decoded = tokenizer.batch_decode(generated_ids)\n",
" return (decoded[0])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "ba47700b-0787-4677-a5a1-c1a1b4063fe2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7862\n",
"Running on public URL: https://f4d39c90e01dcf849c.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://f4d39c90e01dcf849c.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Attempting to cast a BatchEncoding to type {'': 0}. This is not supported.\n",
"/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1413: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
" warnings.warn(\n",
"Traceback (most recent call last):\n",
" File \"/usr/local/lib/python3.10/dist-packages/gradio/queueing.py\", line 495, in call_prediction\n",
" output = await route_utils.call_process_api(\n",
" File \"/usr/local/lib/python3.10/dist-packages/gradio/route_utils.py\", line 230, in call_process_api\n",
" output = await app.get_blocks().process_api(\n",
" File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 1590, in process_api\n",
" result = await self.call_function(\n",
" File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 1176, in call_function\n",
" prediction = await anyio.to_thread.run_sync(\n",
" File \"/usr/local/lib/python3.10/dist-packages/anyio/to_thread.py\", line 33, in run_sync\n",
" return await get_async_backend().run_sync_in_worker_thread(\n",
" File \"/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py\", line 2106, in run_sync_in_worker_thread\n",
" return await future\n",
" File \"/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py\", line 833, in run\n",
" result = context.run(func, *args)\n",
" File \"/usr/local/lib/python3.10/dist-packages/gradio/utils.py\", line 678, in wrapper\n",
" response = f(*args, **kwargs)\n",
" File \"/tmp/ipykernel_812/3792119410.py\", line 5, in predict\n",
" out = inferance(prompt, model, tokenizer, temp = 1.0, limit = 200)\n",
" File \"/tmp/ipykernel_812/3078938966.py\", line 17, in inferance\n",
" generated_ids = model.generate(**model_inputs, max_new_tokens=int(limit), temperature=temp, do_sample=True, pad_token_id=tokenizer.eos_token_id)\n",
" File \"/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\", line 1140, in generate\n",
" outputs = self.base_model.generate(*args, **kwargs)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\n",
" return func(*args, **kwargs)\n",
" File \"/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py\", line 1525, in generate\n",
" return self.sample(\n",
" File \"/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py\", line 2622, in sample\n",
" outputs = self(\n",
" File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n",
" return self._call_impl(*args, **kwargs)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n",
" return forward_call(*args, **kwargs)\n",
" File \"/usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py\", line 1154, in forward\n",
" outputs = self.model(\n",
" File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n",
" return self._call_impl(*args, **kwargs)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n",
" return forward_call(*args, **kwargs)\n",
" File \"/usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py\", line 984, in forward\n",
" inputs_embeds = self.embed_tokens(input_ids)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1518, in _wrapped_call_impl\n",
" return self._call_impl(*args, **kwargs)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1527, in _call_impl\n",
" return forward_call(*args, **kwargs)\n",
" File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/sparse.py\", line 162, in forward\n",
" return F.embedding(\n",
" File \"/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py\", line 2233, in embedding\n",
" return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)\n",
"RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument index in method wrapper_CUDA__index_select)\n"
]
}
],
"source": [
"import gradio as gr\n",
"\n",
"def predict(temp, limit, text):\n",
" prompt = text\n",
" out = inferance(prompt, model, tokenizer, temp = 1.0, limit = 200)\n",
" return out\n",
"\n",
"pred = gr.Interface(\n",
" predict,\n",
" inputs=[\n",
" gr.Slider(0.001, 10, value=0.1, label=\"Temperature\"),\n",
" gr.Slider(1, 1024, value=128, label=\"Token Limit\"),\n",
" gr.Textbox(\n",
" label=\"Input\",\n",
" lines=1,\n",
" value=\"#### Human: What's the capital of Australia?#### Assistant: \",\n",
" ),\n",
" ],\n",
" outputs='text',\n",
")\n",
"\n",
"pred.launch(share=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62622184-9d02-4bd0-8c4e-d6775ce20f75",
"metadata": {},
"outputs": [],
"source": [
"###some factors to try\n",
"from_pt=True"
]
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|