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Added inference notebook #15

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Added inference notebook #15

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7064238
Update server.py
AlexWortega Jun 30, 2022
bc5fa81
Create godel_faststart.ipynb
AlexWortega Jun 30, 2022
c0ed3e4
Update README.md
AlexWortega Jun 30, 2022
e0511f8
Merge branch 'microsoft:main' into main
AlexWortega Oct 18, 2022
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3 changes: 2 additions & 1 deletion GODEL/server.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
AutoConfig,
AutoModelForSeq2SeqLM,
AutoTokenizer,
AutoModelForCausalLM
)


Expand Down Expand Up @@ -47,7 +48,7 @@ def main():
global model, tokenizer, args

config = AutoConfig.from_pretrained(args.model_name_or_path)
model = AutoModelForSeq2SeqLM.from_pretrained(
model = AutoModelForCausalLM.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
Expand Down
4 changes: 4 additions & 0 deletions README.md
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Expand Up @@ -87,6 +87,10 @@ python generate.py --model_name_or_path ${MODEL_PATH} \

We provide a demo interface to chat with finetuned models. The backend server is based on *flask* and the interface is based on *vue*, *bootstrap-vue*, and *BasicVueChat*.

Simple jupiter notebook:

[Notebook](https://github.com/AlexWortega/GODEL/blob/main/jupiters/godel_faststart.ipynb)

Start the backend server:
```bash
# Please create the backend server refering to e.g., dstc9_server.py
Expand Down
238 changes: 238 additions & 0 deletions jupiters/godel_faststart.ipynb
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@@ -0,0 +1,238 @@
{
"cells": [
{
"cell_type": "markdown",
"source": [
"# download model"
],
"metadata": {
"id": "Rzfk_BRio6AA"
}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"\n",
"!wget https://bapengstorage.blob.core.windows.net/fileshare/GODEL-XLarge.tar.gz\n",
"!tar -zxvf GODEL-XLarge.tar.gz"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2022-06-28 06:44:41-- https://bapengstorage.blob.core.windows.net/fileshare/GODEL-XLarge.tar.gz\n",
"Resolving bapengstorage.blob.core.windows.net (bapengstorage.blob.core.windows.net)... 13.66.176.16\n",
"Connecting to bapengstorage.blob.core.windows.net (bapengstorage.blob.core.windows.net)|13.66.176.16|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 19643503525 (18G) [application/gzip]\n",
"Saving to: ‘GODEL-XLarge.tar.gz’\n",
"\n",
"GODEL-XLarge.tar.gz 100%[===================>] 18.29G 23.6MB/s in 11m 20s \n",
"\n",
"2022-06-28 06:56:01 (27.6 MB/s) - ‘GODEL-XLarge.tar.gz’ saved [19643503525/19643503525]\n",
"\n"
]
}
],
"metadata": {
"id": "7djvb_LH185s",
"outputId": "c56159f6-419a-4e56-948e-ad5127fa4174",
"colab": {
"base_uri": "https://localhost:8080/"
}
}
},
{
"cell_type": "markdown",
"source": [
"# install libs"
],
"metadata": {
"id": "lDf27O9uo7Lh"
}
},
{
"cell_type": "code",
"source": [
"# installs\n",
"!pip install transformers\n",
"!pip install dotmap"
],
"metadata": {
"id": "9koyYG4wo1k7"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# inference"
],
"metadata": {
"id": "2T8Sz48_o9eJ"
}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"import torch\n",
"import numpy as np\n",
"import dotmap\n",
"\n",
"from transformers import (\n",
" AutoConfig,\n",
" AutoModelForSeq2SeqLM,\n",
" AutoTokenizer,\n",
" AutoModelForCausalLM\n",
")\n",
"\n",
"\n",
"def set_seed(args):\n",
" np.random.seed(args.seed)\n",
" torch.manual_seed(args.seed)\n",
" if args.n_gpu > 0:\n",
" torch.cuda.manual_seed_all(args.seed)\n",
"\n",
"\n",
"model = None\n",
"tokenizer = None\n",
"args = dotmap.DotMap()\n",
"args.model_name_or_path = 'GODEL-XLarge'\n",
"args.prompt = ''\n",
"args.padding_text = ''\n",
"args.length = 128\n",
"args.num_samples = 1\n",
"args.temperature = 1\n",
"args.num_beams = 5\n",
"args.repetition_penalty = 1\n",
"args.top_k = 0\n",
"args.top_p = 0.5\n",
"args.no_cuda = False\n",
"args.seed = 2022\n",
"args.stop_token = '<|endoftext|>'\n",
"args.n_gpu = 1\n",
"args.device = 'cuda:5'\n",
"\n",
"set_seed(args)\n",
"\n",
"#config = AutoConfig.from_pretrained(args.model_name_or_path)\n",
"model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path)\n",
"\n",
"model = model.half().to(args.device)\n",
"tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)\n",
"\n",
"\n"
],
"outputs": [],
"metadata": {
"id": "_VXW9Y3p185v"
}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"\n",
"\n",
" \n",
"\n",
"\n",
"def generate(context, knowledge):\n",
" \n",
" \n",
" \n",
" context = ' EOS '.join(context)\n",
"\n",
" input_ids = tokenizer(context + ' <|knowledge|> ' + knowledge +\n",
" ' =>', return_tensors=\"pt\").input_ids.to(args.device)\n",
" gen_kwargs = {\n",
" # 'num_beams': args.num_beams,\n",
" 'max_length': args.length,\n",
" 'min_length': 32,\n",
" 'top_k': 2000,\n",
" 'no_repeat_ngram_size': 4\n",
"\n",
" }\n",
"\n",
" output_sequences = model.generate(input_ids, **gen_kwargs)\n",
" output_sequences = tokenizer.batch_decode(\n",
" output_sequences, skip_special_tokens=True)\n",
"\n",
" return output_sequences[0].split('=>')[1]"
],
"outputs": [],
"metadata": {
"id": "PDlug971185w"
}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"context = ['User: Hi']\n",
"knowledge = 'I am searching for new knoweledge.'\n",
"generate(context,knowledge )"
],
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"' Do you have any interesting things to tell me?'"
]
},
"metadata": {},
"execution_count": 15
}
],
"metadata": {
"id": "tGToZQh9185x",
"outputId": "0f247f66-f0f4-44de-f83e-717024a72005"
}
}
],
"metadata": {
"orig_nbformat": 4,
"language_info": {
"name": "python",
"version": "3.8.10",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3.8.10 64-bit"
},
"interpreter": {
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
},
"colab": {
"name": "GODEL",
"provenance": [],
"machine_shape": "hm",
"collapsed_sections": []
},
"accelerator": "GPU",
"gpuClass": "standard"
},
"nbformat": 4,
"nbformat_minor": 0
}