Update notebooks to use full BLOOM-176B (#104)

Co-authored-by: Alexander Borzunov <borzunov.alexander@gmail.com>

examples/prompt-tuning-personachat.ipynb

@@ -15,7 +15,10 @@
     "\n",
     "We will adapt the BLOOM model for the chatbot task using the [Personachat](https://huggingface.co/datasets/bavard/personachat_truecased) dataset. For a given dialogue context, the model has to provide a relevant answer.\n",
     "\n",
-    "To open this notebook in colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-personachat.ipynb)"
+    "To use this notebook in Colab:\n",
+    "\n",
+    "1. Follow this link: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-personachat.ipynb)\n",
+    "2. Go to **Runtime** -> **Change runtime type** and select the GPU accelerator."
    ]
   },
   {
@@ -33,18 +36,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import subprocess\n",
-    "import sys\n",
-    "\n",
     "!pip install git+https://github.com/bigscience-workshop/petals\n",
-    "!pip install datasets wandb\n",
-    "\n",
-    "IN_COLAB = 'google.colab' in sys.modules\n",
-    "if IN_COLAB:  # Remove CUDA binaries on CPU-only colabs to not confuse bitsandbytes\n",
-    "    try:\n",
-    "        subprocess.check_output([\"nvidia-smi\", \"-L\"])\n",
-    "    except subprocess.CalledProcessError as e:\n",
-    "        subprocess.run(\"rm -r /usr/local/cuda/lib64\", shell=True)"
+    "!pip install datasets wandb"
    ]
   },
   {
@@ -84,11 +77,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "MODEL_NAME = ... # select model you like\n",
-    "INITIAL_PEERS = [...] # add your peers adresses here, like \"/ip4/192.168.1.2/tcp/31000/p2p/Qma....\"\n",
+    "MODEL_NAME = \"bigscience/bloom-petals\" # select model you like\n",
     "NUM_PREFIX_TOKENS = 16\n",
     "DEVICE = 'cpu'\n",
-    "BATCH_SIZE = 4\n",
+    "BATCH_SIZE = 8\n",
     "LR = 1e-2\n",
     "WEIGHT_DECAY = 0.0\n",
     "NUM_SAMPLES = 1000\n",
@@ -116,8 +108,7 @@
     "tokenizer.padding_side = 'right'\n",
     "tokenizer.model_max_length = MODEL_MAX_LENGTH\n",
     "model = DistributedBloomForCausalLM.from_pretrained(\n",
-    "    MODEL_NAME, \n",
-    "    initial_peers=INITIAL_PEERS, \n",
+    "    MODEL_NAME,\n",
     "    pre_seq_len=NUM_PREFIX_TOKENS, \n",
     "    tuning_mode=TUNING_MODE\n",
     ").to(DEVICE)"
@@ -306,7 +297,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.6.9 64-bit",
+   "display_name": "Python 3.8.12 ('bloom-demo')",
    "language": "python",
    "name": "python3"
   },
@@ -320,11 +311,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.8.12"
   },
   "vscode": {
    "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+    "hash": "175c31e15dd38a7dfc9eb4117a9e428ffb6063af97d545b6bfba4d874ecc4bb8"
    }
   }
  },

examples/prompt-tuning-sst2.ipynb

@@ -15,7 +15,10 @@
     "\n",
     "We will adapt the BLOOM model for the classification task using the [SST-2 dataset](https://nlp.stanford.edu/sentiment/). This dataset is a binary classification task, where the goal is to predict whether a sentence is positive or negative. The SST-2 dataset is a subset of the Stanford Sentiment Treebank, and it is available in the [Hugging Face Datasets](https://huggingface.co/datasets) library.\n",
     "\n",
-    "To open this notebook in colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-sst2.ipynb)"
+    "To use this notebook in Colab:\n",
+    "\n",
+    "1. Follow this link: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-sst2.ipynb)\n",
+    "2. Go to **Runtime** -> **Change runtime type** and select the GPU accelerator."
    ]
   },
   {
@@ -33,18 +36,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import subprocess\n",
-    "import sys\n",
-    "\n",
     "!pip install git+https://github.com/bigscience-workshop/petals\n",
-    "!pip install datasets wandb\n",
-    "\n",
-    "IN_COLAB = 'google.colab' in sys.modules\n",
-    "if IN_COLAB:  # Remove CUDA binaries on CPU-only colabs to not confuse bitsandbytes\n",
-    "    try:\n",
-    "        subprocess.check_output([\"nvidia-smi\", \"-L\"])\n",
-    "    except subprocess.CalledProcessError as e:\n",
-    "        subprocess.run(\"rm -r /usr/local/cuda/lib64\", shell=True)"
+    "!pip install datasets wandb"
    ]
   },
   {
@@ -84,14 +77,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "MODEL_NAME = ... # select model you like\n",
-    "INITIAL_PEERS = [...] # add your peers adresses here, like \"/ip4/192.168.1.2/tcp/31000/p2p/Qma....\"\n",
+    "MODEL_NAME = \"bigscience/bloom-petals\" # select model you like\n",
     "NUM_PREFIX_TOKENS = 16\n",
     "DEVICE = 'cpu'\n",
-    "BATCH_SIZE = 4\n",
+    "BATCH_SIZE = 16\n",
     "LR = 1e-2\n",
     "WEIGHT_DECAY = 0.0\n",
-    "NUM_SAMPLES = 1000\n",
     "NUM_EPOCHS = 3\n",
     "SEED = 42\n",
     "MODEL_MAX_LENGTH = 64\n",
@@ -117,9 +108,8 @@
     "tokenizer.padding_side = 'right'\n",
     "tokenizer.model_max_length = MODEL_MAX_LENGTH\n",
     "model = DistributedBloomForSequenceClassification.from_pretrained(\n",
-    "    MODEL_NAME, \n",
-    "    initial_peers=INITIAL_PEERS, \n",
-    "    pre_seq_len=NUM_PREFIX_TOKENS, \n",
+    "    MODEL_NAME,\n",
+    "    pre_seq_len=NUM_PREFIX_TOKENS,\n",
     "    tuning_mode=TUNING_MODE\n",
     ").to(DEVICE)"
    ]
@@ -251,7 +241,6 @@
     "    project=\"bloom-sst-2\",\n",
     "    config={\n",
     "        \"num_epochs\": NUM_EPOCHS,\n",
-    "        \"num_samples\": NUM_SAMPLES,\n",
     "        \"batch_size\": BATCH_SIZE,\n",
     "        \"learning_rate\": LR,\n",
     "        \"weight_decay\": WEIGHT_DECAY,\n",
@@ -291,7 +280,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.10 64-bit",
+   "display_name": "Python 3.8.12 ('bloom-demo')",
    "language": "python",
    "name": "python3"
   },
@@ -305,11 +294,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.8.12"
   },
   "vscode": {
    "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+    "hash": "175c31e15dd38a7dfc9eb4117a9e428ffb6063af97d545b6bfba4d874ecc4bb8"
    }
   }
  },