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add pairrm
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@jdf-prog
jdf-prog committed Jan 5, 2024
1 parent ec5a900 commit 5d38ca9
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2 changes: 0 additions & 2 deletions llm_blender/pair_ranker/pairrm.py
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from regex import P
import torch
import torch.nn as nn
import torch.nn.functional as F

from transformers.models.deberta_v2.modeling_deberta_v2 import (
DebertaV2PreTrainedModel,
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224 changes: 65 additions & 159 deletions pairrm_to_hf.ipynb
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Expand Up @@ -99,6 +99,13 @@
"trainer.save_model(\"./hf_PairRM/final_checkpoint\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Verifying Correctness"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand All @@ -108,77 +115,78 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-05 15:46:09.979198: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2024-01-05 15:46:10.826106: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/local/cuda-11.8//lib64\n",
"2024-01-05 15:46:10.826187: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/local/cuda-11.8//lib64\n",
"2024-01-05 15:46:10.826193: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
"You're using a DebertaV2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b40fb528bf804a00be8ce15a5747a7c7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading config.json: 0%| | 0.00/967 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "705223617e6346f2b2b97d730dbc9481",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading model.safetensors: 0%| | 0.00/1.74G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
"name": "stdout",
"output_type": "stream",
"text": [
"[1.9003021717071533, -1.2547134160995483]\n",
"tensor([ True, False], device='cuda:0')\n"
]
}
],
"source": [
"import os\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
"from llm_blender.pair_ranker.pairrm import DebertaV2PairRM\n",
"pairrm = DebertaV2PairRM.from_pretrained(\"llm-blender/PairRM-hf\", device_map=\"cuda:0\")"
"from transformers import AutoTokenizer\n",
"from typing import List\n",
"pairrm = DebertaV2PairRM.from_pretrained(\"llm-blender/PairRM-hf\", device_map=\"cuda:0\")\n",
"tokenizer = AutoTokenizer.from_pretrained('llm-blender/PairRM-hf')\n",
"source_prefix = \"<|source|>\"\n",
"cand1_prefix = \"<|candidate1|>\"\n",
"cand2_prefix = \"<|candidate2|>\"\n",
"inputs = [\"hello!\", \"I love you!\"]\n",
"candidates_A = [\"hi!\", \"I hate you!\"]\n",
"candidates_B = [\"f**k off!\", \"I love you, too!\"]\n",
"def tokenize_pair(sources:List[str], candidate1s:List[str], candidate2s:List[str]):\n",
" ids = []\n",
" assert len(sources) == len(candidate1s) == len(candidate2s)\n",
" for i in range(len(sources)):\n",
" source_ids = tokenizer.encode(source_prefix + sources[i])\n",
" candidate1_ids = tokenizer.encode(cand1_prefix + candidate1s[i])\n",
" candidate2_ids = tokenizer.encode(cand2_prefix + candidate2s[i])\n",
" ids.append(source_ids + candidate1_ids + candidate2_ids)\n",
" encodings = tokenizer.pad({\"input_ids\": ids}, return_tensors=\"pt\")\n",
" return encodings\n",
"\n",
"encodings = tokenize_pair(inputs, candidates_A, candidates_B)\n",
"encodings = {k:v.to(pairrm.device) for k,v in encodings.items()}\n",
"outputs = pairrm(**encodings)\n",
"logits = outputs.logits.tolist()\n",
"comparison_results = outputs.logits > 0\n",
"print(logits)\n",
"# [1.9003021717071533, -1.2547134160995483]\n",
"print(comparison_results)\n",
"# tensor([ True, False], device='cuda:0'), which means whether candidate A is better than candidate B for each input"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Verifying Correctness"
"### load from llm-blender wrapper"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-05 15:11:00.611021: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2024-01-05 15:11:01.483983: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/local/cuda-11.8//lib64\n",
"2024-01-05 15:11:01.484081: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/local/cuda-11.8//lib64\n",
"2024-01-05 15:11:01.484088: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
"WARNING:root:No ranker config provided, no ranker loaded, please load ranker first through load_ranker()\n",
"WARNING:root:No fuser config provided, no fuser loaded, please load fuser first through load_fuser()\n",
"/home/dongfu/miniconda3/envs/llm-blender/lib/python3.9/site-packages/dataclasses_json/core.py:187: RuntimeWarning: 'NoneType' object value of non-optional type load_checkpoint detected when decoding RankerConfig.\n",
Expand All @@ -193,149 +201,47 @@
"text": [
"Successfully loaded ranker from /home/dongfu/data/.cache/huggingface/hub/llm-blender/PairRM\n"
]
}
],
"source": [
"import os\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
"import llm_blender\n",
"blender = llm_blender.Blender()\n",
"# Load Ranker\n",
"blender.loadranker(\"llm-blender/PairRM\") # load ranker checkpoint"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Ranking candidates: 0%| | 0/1 [00:00<?, ?it/s]"
"Ranking candidates: 100%|██████████| 1/1 [00:00<00:00, 18.13it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 1, 128001, 12018, 300, 2, 1, 128002, 8595, 300,\n",
" 2, 1, 128003, 2994, 1225, 1225, 1165, 442, 300,\n",
" 2, 0, 0, 0, 0],\n",
" [ 1, 128001, 273, 472, 274, 300, 2, 1, 128002,\n",
" 273, 3254, 274, 300, 2, 1, 128003, 273, 472,\n",
" 274, 261, 461, 300, 2]], device='cuda:0')\n"
"[ 1.9 -1.255]\n",
"[ True False]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Ranking candidates: 100%|██████████| 1/1 [00:00<00:00, 1.69it/s]\n"
"\n"
]
},
{
"data": {
"text/plain": [
"array([ 1.9 , -1.255], dtype=float16)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
"import llm_blender\n",
"blender = llm_blender.Blender()\n",
"# Load Ranker\n",
"blender.loadranker(\"llm-blender/PairRM\") # load ranker checkpoint\n",
"inputs = [\"hello!\", \"I love you!\"]\n",
"candidates_A = [\"hi!\", \"I hate you!\"]\n",
"candidates_B = [\"f**k off!\", \"I love you, too!\"]\n",
"comparison_results = blender.compare(inputs, candidates_A, candidates_B, return_logits=True, mode=\"[A,B]\")\n",
"comparison_results"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'[CLS]<|source|> hello![SEP][CLS]<|candidate1|> hi![SEP][CLS]<|candidate2|> f**k off![SEP][PAD][PAD][PAD][PAD]'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"blender.ranker.tokenizer.decode([ 1, 128001, 12018, 300, 2, 1, 128002, 8595, 300,\n",
" 2, 1, 128003, 2994, 1225, 1225, 1165, 442, 300,\n",
" 2, 0, 0, 0, 0], skip_special_tokens=False)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SequenceClassifierOutput(loss=None, logits=tensor([1.9003], device='cuda:0', grad_fn=<MeanBackward1>), hidden_states=None, attentions=None)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch\n",
"input_ids = torch.tensor([ 1, 128001, 12018, 300, 2, 1, 128002, 8595, 300,\n",
" 2, 1, 128003, 2994, 1225, 1225, 1165, 442, 300,\n",
" 2, 0, 0, 0, 0], dtype=torch.long, device='cuda:0')\n",
"attention_masks = input_ids.ne(0).float()\n",
"input_ids = input_ids.unsqueeze(0)\n",
"attention_masks = attention_masks.unsqueeze(0)\n",
"pairrm(input_ids=input_ids, attention_mask=attention_masks)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SequenceClassifierOutput(loss=None, logits=tensor([-1.2547], device='cuda:0', grad_fn=<MeanBackward1>), hidden_states=None, attentions=None)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch\n",
"input_ids = torch.tensor([ 1, 128001, 273, 472, 274, 300, 2, 1, 128002,\n",
" 273, 3254, 274, 300, 2, 1, 128003, 273, 472,\n",
" 274, 261, 461, 300, 2], dtype=torch.long, device='cuda:0')\n",
"attention_masks = input_ids.ne(0).float()\n",
"input_ids = input_ids.unsqueeze(0)\n",
"attention_masks = attention_masks.unsqueeze(0)\n",
"pairrm(input_ids=input_ids, attention_mask=attention_masks)"
"logits = blender.compare(inputs, candidates_A, candidates_B, return_logits=True, mode=\"[A,B]\")\n",
"comparison_results = logits > 0\n",
"print(logits)\n",
"# [1.9003021717071533, -1.2547134160995483]\n",
"print(comparison_results)\n",
"# tensor([ True, False], device='cuda:0'), which means whether candidate A is better than candidate B for each input"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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