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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "6c066f8c-5b3b-486b-b958-76cc9d380146",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"TORCH_USE_CUDA_DSA\"] = \"1\" # Enable CUDA Dynamic Shared Allocation\n",
"os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c20b0689-1347-47fb-a259-48ab1e1c1420",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"!{sys.executable} -m pip install datasets transformers accelerate==0.30.0 peft flash-attn"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a4b1142a-c51e-4794-94d7-805e70fb308d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.45.5\n"
]
}
],
"source": [
"import bitsandbytes as bab\n",
"print(bab.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b3900693-64f2-4f48-803a-196de8f616f7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NVIDIA L4\n",
"True\n"
]
}
],
"source": [
"import torch\n",
"print(torch.cuda.get_device_name(0))\n",
"print(torch.cuda.is_bf16_supported())"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "de679cdb-4fb6-4bd6-be66-6379c4131312",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8, 9)\n"
]
}
],
"source": [
"print(torch.cuda.get_device_capability())"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b0b4c7df-aa9e-40ed-9827-e5436e33168c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.45.5)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (1.26.4)\n",
"Requirement already satisfied: torch<3,>=2.0 in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (2.6.0)\n",
"Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (10.3.5.147)\n",
"Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (4.12.2)\n",
"Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (0.6.2)\n",
"Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (12.4.127)\n",
"Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (2024.9.0)\n",
"Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (1.13.1)\n",
"Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (3.4.2)\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (12.4.5.8)\n",
"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (12.4.127)\n",
"Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (11.2.1.3)\n",
"Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (12.3.1.170)\n",
"Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (9.1.0.70)\n",
"Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (3.2.0)\n",
"Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (11.6.1.9)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (3.1.5)\n",
"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (12.4.127)\n",
"Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (2.21.5)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (3.17.0)\n",
"Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.10/dist-packages (from torch<3,>=2.0->bitsandbytes) (12.4.127)\n",
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch<3,>=2.0->bitsandbytes) (1.3.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch<3,>=2.0->bitsandbytes) (3.0.2)\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
}
],
"source": [
"import sys\n",
"!{sys.executable} -m pip install -U bitsandbytes "
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "08edce1c-0326-4688-a557-5f80b33cb077",
"metadata": {},
"outputs": [],
"source": [
"device_map = (\n",
" int(os.environ.get(\"LOCAL_RANK\", -1))\n",
" if torch.distributed.is_available() and torch.distributed.is_initialized()\n",
" else \"auto\"\n",
") # {\"\": 0}"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ca502d8f-a0e2-421c-b615-5bd232236fb2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"auto\n"
]
}
],
"source": [
"print(device_map)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6d4c31bf-9087-40e2-8cf1-50d793a50cc4",
"metadata": {},
"outputs": [],
"source": [
"MODEL = \"bigcode/starcoderbase-1b\" # Model checkpoint on the Hugging Face Hub\n",
"DATASET = \"smangrul/hf-stack-v1\" # Dataset on the Hugging Face Hub\n",
"DATA_COLUMN = \"content\" # Column name containing the code content\n",
"\n",
"SEQ_LENGTH = 2048 # Sequence length\n",
"\n",
"MAX_STEPS = 2000 # max_steps\n",
"BATCH_SIZE = 1 # batch_size\n",
"GR_ACC_STEPS = 1 # gradient_accumulation_steps\n",
"LR = 5e-4 # learning_rate\n",
"LR_SCHEDULER_TYPE = \"cosine\" # lr_scheduler_type\n",
"WEIGHT_DECAY = 0.01 # weight_decay\n",
"NUM_WARMUP_STEPS = 30 # num_warmup_steps\n",
"EVAL_FREQ = 100 # eval_freq\n",
"SAVE_FREQ = 100 # save_freq\n",
"LOG_FREQ = 25 # log_freq\n",
"OUTPUT_DIR = \"peft-starcoder-lora-a100\" # output_dir\n",
"BF16 = True # bf16\n",
"FP16 = False # no_fp16\n",
"\n",
"# FIM trasformations arguments\n",
"FIM_RATE = 0.5 # fim_rate\n",
"FIM_SPM_RATE = 0.5 # fim_spm_rate\n",
"\n",
"# LORA\n",
"LORA_R = 8 # lora_r\n",
"LORA_ALPHA = 32 # lora_alpha\n",
"LORA_DROPOUT = 0.0 # lora_dropout\n",
"LORA_TARGET_MODULES = \"c_proj,c_attn,q_attn,c_fc,c_proj\" # lora_target_modules\n",
"\n",
"# bitsandbytes config\n",
"USE_NESTED_QUANT = True # use_nested_quant\n",
"BNB_4BIT_COMPUTE_DTYPE = \"bfloat16\" # bnb_4bit_compute_dtype\n",
"\n",
"SEED = 0"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2e60f3f7-c90f-41ec-91d3-98b6532e9446",
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import login\n",
"from transformers import (\n",
" AutoModelForCausalLM,\n",
" AutoTokenizer,\n",
" Trainer,\n",
" TrainingArguments,\n",
" logging,\n",
" set_seed,\n",
" BitsAndBytesConfig,\n",
")\n",
"\n",
"from datasets import load_dataset\n",
"import torch\n",
"from tqdm import tqdm\n",
"\n",
"#Prepare Data\n",
"dataset = load_dataset(\n",
" DATASET,\n",
" data_dir=\"data\",\n",
" split=\"train\",\n",
" streaming=True,\n",
")\n",
"\n",
"valid_data = dataset.take(4000)\n",
"train_data = dataset.skip(4000)\n",
"train_data = train_data.shuffle(buffer_size=5000, seed=SEED)\n",
"\n",
"set_seed(SEED)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "88201294-50c4-44b0-9209-1873feba0dae",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
" warnings.warn(\n",
"100%|██████████| 400/400 [00:03<00:00, 109.96it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The character to token ratio of the dataset is: 2.43\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)\n",
"\n",
"\n",
"def chars_token_ratio(dataset, tokenizer, data_column, nb_examples=400):\n",
" \"\"\"\n",
" Estimate the average number of characters per token in the dataset.\n",
" \"\"\"\n",
"\n",
" total_characters, total_tokens = 0, 0\n",
" for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):\n",
" total_characters += len(example[data_column])\n",
" total_tokens += len(tokenizer(example[data_column]).tokens())\n",
"\n",
" return total_characters / total_tokens\n",
"\n",
"\n",
"chars_per_token = chars_token_ratio(train_data, tokenizer, DATA_COLUMN)\n",
"print(f\"The character to token ratio of the dataset is: {chars_per_token:.2f}\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "eee2c81e-f0df-435e-b4b9-e2a1d4f8c853",
"metadata": {},
"outputs": [],
"source": [
"import functools\n",
"import numpy as np\n",
"\n",
"\n",
"# Helper function to get token ids of the special tokens for prefix, suffix and middle for FIM transformations.\n",
"@functools.lru_cache(maxsize=None)\n",
"def get_fim_token_ids(tokenizer):\n",
" try:\n",
" FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD = tokenizer.special_tokens_map[\"additional_special_tokens\"][1:5]\n",
" suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = (\n",
" tokenizer.vocab[tok] for tok in [FIM_SUFFIX, FIM_PREFIX, FIM_MIDDLE, FIM_PAD]\n",
" )\n",
" except KeyError:\n",
" suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = None, None, None, None\n",
" return suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id\n",
"\n",
"\n",
"## Adapted from https://github.com/bigcode-project/Megatron-LM/blob/6c4bf908df8fd86b4977f54bf5b8bd4b521003d1/megatron/data/gpt_dataset.py\n",
"def permute(\n",
" sample,\n",
" np_rng,\n",
" suffix_tok_id,\n",
" prefix_tok_id,\n",
" middle_tok_id,\n",
" pad_tok_id,\n",
" fim_rate=0.5,\n",
" fim_spm_rate=0.5,\n",
" truncate_or_pad=False,\n",
"):\n",
" \"\"\"\n",
" Take in a sample (list of tokens) and perform a FIM transformation on it with a probability of fim_rate, using two FIM modes:\n",
" PSM and SPM (with a probability of fim_spm_rate).\n",
" \"\"\"\n",
"\n",
" # The if condition will trigger with the probability of fim_rate\n",
" # This means FIM transformations will apply to samples with a probability of fim_rate\n",
" if np_rng.binomial(1, fim_rate):\n",
"\n",
" # Split the sample into prefix, middle, and suffix, based on randomly generated indices stored in the boundaries list.\n",
" boundaries = list(np_rng.randint(low=0, high=len(sample) + 1, size=2))\n",
" boundaries.sort()\n",
"\n",
" prefix = np.array(sample[: boundaries[0]], dtype=np.int64)\n",
" middle = np.array(sample[boundaries[0] : boundaries[1]], dtype=np.int64)\n",
" suffix = np.array(sample[boundaries[1] :], dtype=np.int64)\n",
"\n",
" if truncate_or_pad:\n",
" # calculate the new total length of the sample, taking into account tokens indicating prefix, middle, and suffix\n",
" new_length = suffix.shape[0] + prefix.shape[0] + middle.shape[0] + 3\n",
" diff = new_length - len(sample)\n",
"\n",
" # trancate or pad if there's a difference in length between the new length and the original\n",
" if diff > 0:\n",
" if suffix.shape[0] <= diff:\n",
" return sample, np_rng\n",
" suffix = suffix[: suffix.shape[0] - diff]\n",
" elif diff < 0:\n",
" suffix = np.concatenate([suffix, np.full((-1 * diff), pad_tok_id)])\n",
"\n",
" # With the probability of fim_spm_rateapply SPM variant of FIM transformations\n",
" # SPM: suffix, prefix, middle\n",
" if np_rng.binomial(1, fim_spm_rate):\n",
" new_sample = np.concatenate(\n",
" [\n",
" [prefix_tok_id, suffix_tok_id],\n",
" suffix,\n",
" [middle_tok_id],\n",
" prefix,\n",
" middle,\n",
" ]\n",
" )\n",
" # Otherwise, apply the PSM variant of FIM transformations\n",
" # PSM: prefix, suffix, middle\n",
" else:\n",
"\n",
" new_sample = np.concatenate(\n",
" [\n",
" [prefix_tok_id],\n",
" prefix,\n",
" [suffix_tok_id],\n",
" suffix,\n",
" [middle_tok_id],\n",
" middle,\n",
" ]\n",
" )\n",
" else:\n",
" # don't apply FIM transformations\n",
" new_sample = sample\n",
"\n",
" return list(new_sample), np_rng"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3a9ebdef-4178-44af-9b35-12c8189c27f7",
"metadata": {},
"outputs": [],
"source": [
"from torch.utils.data import IterableDataset\n",
"from torch.utils.data.dataloader import DataLoader\n",
"import random\n",
"\n",
"# Create an Iterable dataset that returns constant-length chunks of tokens from a stream of text files.\n",
"\n",
"\n",
"class ConstantLengthDataset(IterableDataset):\n",
" \"\"\"\n",
" Iterable dataset that returns constant length chunks of tokens from stream of text files.\n",
" Args:\n",
" tokenizer (Tokenizer): The processor used for proccessing the data.\n",
" dataset (dataset.Dataset): Dataset with text files.\n",
" infinite (bool): If True the iterator is reset after dataset reaches end else stops.\n",
" seq_length (int): Length of token sequences to return.\n",
" num_of_sequences (int): Number of token sequences to keep in buffer.\n",
" chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.\n",
" fim_rate (float): Rate (0.0 to 1.0) that sample will be permuted with FIM.\n",
" fim_spm_rate (float): Rate (0.0 to 1.0) of FIM permuations that will use SPM.\n",
" seed (int): Seed for random number generator.\n",
" \"\"\"\n",
"\n",
" def __init__(\n",
" self,\n",
" tokenizer,\n",
" dataset,\n",
" infinite=False,\n",
" seq_length=1024,\n",
" num_of_sequences=1024,\n",
" chars_per_token=3.6,\n",
" content_field=\"content\",\n",
" fim_rate=0.5,\n",
" fim_spm_rate=0.5,\n",
" seed=0,\n",
" ):\n",
" self.tokenizer = tokenizer\n",
" self.concat_token_id = tokenizer.eos_token_id\n",
" self.dataset = dataset\n",
" self.seq_length = seq_length\n",
" self.infinite = infinite\n",
" self.current_size = 0\n",
" self.max_buffer_size = seq_length * chars_per_token * num_of_sequences\n",
" self.content_field = content_field\n",
" self.fim_rate = fim_rate\n",
" self.fim_spm_rate = fim_spm_rate\n",
" self.seed = seed\n",
"\n",
" (\n",
" self.suffix_tok_id,\n",
" self.prefix_tok_id,\n",
" self.middle_tok_id,\n",
" self.pad_tok_id,\n",
" ) = get_fim_token_ids(self.tokenizer)\n",
" if not self.suffix_tok_id and self.fim_rate > 0:\n",
" print(\"FIM is not supported by tokenizer, disabling FIM\")\n",
" self.fim_rate = 0\n",
"\n",
" def __iter__(self):\n",
" iterator = iter(self.dataset)\n",
" more_examples = True\n",
" np_rng = np.random.RandomState(seed=self.seed)\n",
" while more_examples:\n",
" buffer, buffer_len = [], 0\n",
" while True:\n",
" if buffer_len >= self.max_buffer_size:\n",
" break\n",
" try:\n",
" buffer.append(next(iterator)[self.content_field])\n",
" buffer_len += len(buffer[-1])\n",
" except StopIteration:\n",
" if self.infinite:\n",
" iterator = iter(self.dataset)\n",
" else:\n",
" more_examples = False\n",
" break\n",
" tokenized_inputs = self.tokenizer(buffer, truncation=False)[\"input_ids\"]\n",
" all_token_ids = []\n",
"\n",
" for tokenized_input in tokenized_inputs:\n",
" # optionally do FIM permutations\n",
" if self.fim_rate > 0:\n",
" tokenized_input, np_rng = permute(\n",
" tokenized_input,\n",
" np_rng,\n",
" self.suffix_tok_id,\n",
" self.prefix_tok_id,\n",
" self.middle_tok_id,\n",
" self.pad_tok_id,\n",
" fim_rate=self.fim_rate,\n",
" fim_spm_rate=self.fim_spm_rate,\n",
" truncate_or_pad=False,\n",
" )\n",
"\n",
" all_token_ids.extend(tokenized_input + [self.concat_token_id])\n",
" examples = []\n",
" for i in range(0, len(all_token_ids), self.seq_length):\n",
" input_ids = all_token_ids[i : i + self.seq_length]\n",
" if len(input_ids) == self.seq_length:\n",
" examples.append(input_ids)\n",
" random.shuffle(examples)\n",
" for example in examples:\n",
" self.current_size += 1\n",
" yield {\n",
" \"input_ids\": torch.LongTensor(example),\n",
" \"labels\": torch.LongTensor(example),\n",
" }\n",
"\n",
"\n",
"train_dataset = ConstantLengthDataset(\n",
" tokenizer,\n",
" train_data,\n",
" infinite=True,\n",
" seq_length=SEQ_LENGTH,\n",
" chars_per_token=chars_per_token,\n",
" content_field=DATA_COLUMN,\n",
" fim_rate=FIM_RATE,\n",
" fim_spm_rate=FIM_SPM_RATE,\n",
" seed=SEED,\n",
")\n",
"eval_dataset = ConstantLengthDataset(\n",
" tokenizer,\n",
" valid_data,\n",
" infinite=False,\n",
" seq_length=SEQ_LENGTH,\n",
" chars_per_token=chars_per_token,\n",
" content_field=DATA_COLUMN,\n",
" fim_rate=FIM_RATE,\n",
" fim_spm_rate=FIM_SPM_RATE,\n",
" seed=SEED,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5021e686-2e1c-4608-9477-1f07adf2de35",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e4ea2b63e63448938020858c06143ca5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"model.safetensors: 0%| | 0.00/4.55G [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "ImportError",
"evalue": "FlashAttention2 has been toggled on, but it cannot be used due to the following error: the package flash_attn seems to be not installed. Please refer to the documentation of https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2 to install Flash Attention 2.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[8], line 26\u001b[0m\n\u001b[1;32m 17\u001b[0m bnb_config \u001b[38;5;241m=\u001b[39m BitsAndBytesConfig(\n\u001b[1;32m 18\u001b[0m load_in_4bit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 19\u001b[0m bnb_4bit_quant_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnf4\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 20\u001b[0m bnb_4bit_compute_dtype\u001b[38;5;241m=\u001b[39mcompute_dtype,\n\u001b[1;32m 21\u001b[0m bnb_4bit_use_double_quant\u001b[38;5;241m=\u001b[39mUSE_NESTED_QUANT,\n\u001b[1;32m 22\u001b[0m )\n\u001b[1;32m 24\u001b[0m device_map \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m0\u001b[39m}\n\u001b[0;32m---> 26\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 27\u001b[0m \u001b[43m \u001b[49m\u001b[43mMODEL\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43mquantization_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbnb_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 29\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 30\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# We will be using gradient checkpointing\u001b[39;49;00m\n\u001b[1;32m 31\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrust_remote_code\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 32\u001b[0m \u001b[43m \u001b[49m\u001b[43mattn_implementation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mflash_attention_2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 33\u001b[0m \u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:564\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 562\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 563\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--> 564\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 565\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 566\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 567\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 568\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 569\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 570\u001b[0m )\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:3804\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 3801\u001b[0m init_contexts\u001b[38;5;241m.\u001b[39mappend(init_empty_weights())\n\u001b[1;32m 3803\u001b[0m config \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(config) \u001b[38;5;66;03m# We do not want to modify the config inplace in from_pretrained.\u001b[39;00m\n\u001b[0;32m-> 3804\u001b[0m config \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_autoset_attn_implementation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3805\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_flash_attention_2\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_flash_attention_2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch_dtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice_map\u001b[49m\n\u001b[1;32m 3806\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ContextManagers(init_contexts):\n\u001b[1;32m 3809\u001b[0m \u001b[38;5;66;03m# Let's make sure we don't run the init function of buffer modules\u001b[39;00m\n\u001b[1;32m 3810\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(config, \u001b[38;5;241m*\u001b[39mmodel_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs)\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:1534\u001b[0m, in \u001b[0;36mPreTrainedModel._autoset_attn_implementation\u001b[0;34m(cls, config, use_flash_attention_2, torch_dtype, device_map, check_device_map)\u001b[0m\n\u001b[1;32m 1531\u001b[0m config\u001b[38;5;241m.\u001b[39m_attn_implementation \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflash_attention_2\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1533\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config\u001b[38;5;241m.\u001b[39m_attn_implementation \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflash_attention_2\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1534\u001b[0m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_and_enable_flash_attn_2\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1535\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1536\u001b[0m \u001b[43m \u001b[49m\u001b[43mtorch_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1537\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 1538\u001b[0m \u001b[43m \u001b[49m\u001b[43mhard_check_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 1539\u001b[0m \u001b[43m \u001b[49m\u001b[43mcheck_device_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_device_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1540\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1541\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m requested_attn_implementation \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msdpa\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_xla_available():\n\u001b[1;32m 1542\u001b[0m \u001b[38;5;66;03m# use_flash_attention_2 takes priority over SDPA, hence SDPA treated in this elif.\u001b[39;00m\n\u001b[1;32m 1543\u001b[0m config \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_check_and_enable_sdpa(\n\u001b[1;32m 1544\u001b[0m config,\n\u001b[1;32m 1545\u001b[0m hard_check_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m requested_attn_implementation \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 1546\u001b[0m )\n",
"File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:1636\u001b[0m, in \u001b[0;36mPreTrainedModel._check_and_enable_flash_attn_2\u001b[0;34m(cls, config, torch_dtype, device_map, check_device_map, hard_check_only)\u001b[0m\n\u001b[1;32m 1633\u001b[0m install_message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease refer to the documentation of https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2 to install Flash Attention 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m importlib\u001b[38;5;241m.\u001b[39mutil\u001b[38;5;241m.\u001b[39mfind_spec(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflash_attn\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1636\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpreface\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m the package flash_attn seems to be not installed. \u001b[39m\u001b[38;5;132;01m{\u001b[39;00minstall_message\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1638\u001b[0m flash_attention_version \u001b[38;5;241m=\u001b[39m version\u001b[38;5;241m.\u001b[39mparse(importlib\u001b[38;5;241m.\u001b[39mmetadata\u001b[38;5;241m.\u001b[39mversion(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mflash_attn\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 1639\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mversion\u001b[38;5;241m.\u001b[39mcuda:\n",
"\u001b[0;31mImportError\u001b[0m: FlashAttention2 has been toggled on, but it cannot be used due to the following error: the package flash_attn seems to be not installed. Please refer to the documentation of https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2 to install Flash Attention 2."
]
}
],
"source": [
"import torch\n",
"from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training\n",
"from peft.tuners.lora import LoraLayer\n",
"\n",
"load_in_8bit = False\n",
"\n",
"# 4-bit quantization\n",
"compute_dtype = getattr(torch, BNB_4BIT_COMPUTE_DTYPE)\n",
"\n",
"bnb_config2 = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_use_double_quant=True,\n",
" bnb_4bit_quant_type=\"nf4\",\n",
" bnb_4bit_compute_dtype=torch.bfloat16\n",
")\n",
"\n",
"bnb_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_quant_type=\"nf4\",\n",
" bnb_4bit_compute_dtype=compute_dtype,\n",
" bnb_4bit_use_double_quant=USE_NESTED_QUANT,\n",
")\n",
"\n",
"device_map = {\"\": 0}\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" MODEL,\n",
" quantization_config=bnb_config,\n",
" device_map=device_map,\n",
" use_cache=False, # We will be using gradient checkpointing\n",
" trust_remote_code=True,\n",
" attn_implementation=\"flash_attention_2\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c0a9fdb-4087-4ec5-aefa-fc5f413252e4",
"metadata": {},
"outputs": [],
"source": [
"model = prepare_model_for_kbit_training(model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b5fd3293-76ef-48eb-8241-478e311ec947",
"metadata": {},
"outputs": [],
"source": [
"# Set up lora\n",
"peft_config = LoraConfig(\n",
" lora_alpha=LORA_ALPHA,\n",
" lora_dropout=LORA_DROPOUT,\n",
" r=LORA_R,\n",
" bias=\"none\",\n",
" task_type=\"CAUSAL_LM\",\n",
" target_modules=LORA_TARGET_MODULES.split(\",\"),\n",
")\n",
"\n",
"model = get_peft_model(model, peft_config)\n",
"model.print_trainable_parameters()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "082c6a7b-db61-4800-8a94-419331b1fd22",
"metadata": {},
"outputs": [],
"source": [
"train_data.start_iteration = 0\n",
"\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=f\"ernyou/{OUTPUT_DIR}\",\n",
" dataloader_drop_last=True,\n",
" eval_strategy=\"steps\",\n",
" save_strategy=\"steps\",\n",
" max_steps=MAX_STEPS,\n",
" eval_steps=EVAL_FREQ,\n",
" save_steps=SAVE_FREQ,\n",
" logging_steps=LOG_FREQ,\n",
" per_device_train_batch_size=BATCH_SIZE,\n",
" per_device_eval_batch_size=BATCH_SIZE,\n",
" learning_rate=LR,\n",
" lr_scheduler_type=LR_SCHEDULER_TYPE,\n",
" warmup_steps=NUM_WARMUP_STEPS,\n",
" gradient_accumulation_steps=GR_ACC_STEPS,\n",
" gradient_checkpointing_kwargs={\"use_reentrant\": True},\n",
" gradient_checkpointing=True,\n",
" fp16=FP16,\n",
" bf16=BF16,\n",
" weight_decay=WEIGHT_DECAY,\n",
" push_to_hub=True,\n",
" include_tokens_per_second=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2c302ded-f017-433c-9622-55ecb45141bd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.3.0\n"
]
}
],
"source": [
"import accelerate as ac\n",
"print(ac.__version__)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5318efa4-83da-41fa-9123-50b505e9a615",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"torch.cuda.empty_cache()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ceddceb0-8e1e-493f-99a1-b77c6b0c40b6",
"metadata": {},
"outputs": [],
"source": [
"trainer = Trainer(model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset)\n",
"\n",
"print(\"Training...\")\n",
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "76fb1530-5e16-4b4b-a30f-b689df3483f3",
"metadata": {},
"outputs": [],
"source": []
}
],
"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
}
|