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+python-versions = "^3.11" +content-hash = "d01b27a4e3a9cfec1125e811c485eaf75d06b9ba5a2bca63f667ec1b870d1a88" diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..1750ede --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,23 @@ +[tool.poetry] +name = "less" +version = "0.1.0" +description = "" +authors = ["rinapch "] +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.11" +peft = "0.7.1" +transformers = "4.36.2" +traker = "0.1.3" +torch = "2.1.2" +fire = "^0.7.0" +datasets = "^3.1.0" +numpy = "<2" +accelerate = "0.28.0" +wandb = "^0.19.0" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" From 7c00498bfeba49d3bc56f7daedc1ed72968a469f Mon Sep 17 00:00:00 2001 From: rinapch Date: Sat, 7 Dec 2024 19:35:28 +0000 Subject: [PATCH 02/12] add warmup lora script --- README.md | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 1932fdd..50b3bf0 100644 --- a/README.md +++ b/README.md @@ -27,6 +27,8 @@ pip3 install torch==2.1.2 torchvision torchaudio ``` cd LESS pip install -r requirement.txt +or +poetry install ``` **Step 3**: Finally, install the `less` package in editable mode to make it accessible for your development environment: @@ -44,15 +46,10 @@ We follow the [open-instruct](https://github.com/allenai/open-instruct?tab=readm To enhance downstream performance from data selection, it's crucial to start with a warmup training step. This involves selecting a small portion of your entire dataset to train using the LoRA method. Follow these steps for effective warmup training: ```bash -DATA_DIR=../data -MODEL_PATH=meta-llama/Llama-2-7b-hf -PERCENTAGE=0.05 # percentage of the full data to train, you can specify the training file you want to use in the script -DATA_SEED=3 -JOB_NAME=llama2-7b-p${PERCENTAGE}-lora-seed${DATA_SEED} - -./less/scripts/train/warmup_lora_train.sh "$DATA_DIR" "$MODEL_PATH" "$PERCENTAGE" "$DATA_SEED" "$JOB_NAME" +python3 -m less/scripts/train/warmup_lora_train --train_file --model_path ``` - +NB: there are more optional arguments that you can use to specify the training process. Please refer to the script for more details. +The checkpoint will be saved in the `out` directory. ### Step 2: Building the gradient datastore Once the initial warmup training stage is completed, we will collect gradients for the entire training dataset. For each checkpoint, our goal is to obtain the gradients of all the training data that we would like to select from. An example script is shown below. From f09a6cc3b4fbc085262f5797753141aac2f8c5d3 Mon Sep 17 00:00:00 2001 From: rinapch Date: Sat, 7 Dec 2024 19:35:46 +0000 Subject: [PATCH 03/12] add warmup lora script --- less/scripts/train/warmup_lora_train.py | 89 +++++++++++++++++++++++++ less/scripts/train/warmup_lora_train.sh | 2 +- 2 files changed, 90 insertions(+), 1 deletion(-) create mode 100644 less/scripts/train/warmup_lora_train.py diff --git a/less/scripts/train/warmup_lora_train.py b/less/scripts/train/warmup_lora_train.py new file mode 100644 index 0000000..0bceee3 --- /dev/null +++ b/less/scripts/train/warmup_lora_train.py @@ -0,0 +1,89 @@ +import subprocess +from pathlib import Path +import fire +import random + +def run_training( + train_file: str, + model_path: str, + data_dir: str="data/train", + percentage: float=0.05, + data_seed: int=3, +): + job_name = f"{model_path.split('/')[-1]}-p{percentage}-lora-seed{data_seed}" + # Create output directory + output_dir = Path("../out") / job_name + output_dir.mkdir(parents=True, exist_ok=True) + + # Training files + train_file = [str(Path(data_dir) / train_file)] + + # Base training args from base_training_args.sh + base_training_args = [ + "--do_train", "True", + "--max_seq_length", "2048", + "--use_fast_tokenizer", "True", + "--lr_scheduler_type", "linear", + "--warmup_ratio", "0.03", + "--weight_decay", "0.0", + "--evaluation_strategy", "no", + "--logging_steps", "1", + "--save_strategy", "epoch", + "--num_train_epochs", "4", + "--bf16", "True", + "--tf32", "False", + "--fp16", "False", + "--overwrite_output_dir", "True", + "--report_to", "wandb", + "--optim", "adamw_torch", + "--lora", "True", + "--lora_r", "128", + "--lora_alpha", "512", + "--lora_dropout", "0.1", + "--lora_target_modules", "q_proj", "k_proj", "v_proj", "o_proj", + "--learning_rate", "2e-05", + "--per_device_train_batch_size", "1", + "--gradient_accumulation_steps", "32" +] + + # Add FSDP config for large models + if model_path == "meta-llama/Llama-2-13b-hf": + base_training_args.extend([ + "--fsdp", "full_shard auto_wrap", + "--fsdp_config", "llama2_13b_finetune" + ]) + elif model_path == "mistralai/Mistral-7B-v0.1": + base_training_args.extend([ + "--fsdp", "full_shard auto_wrap", + "--fsdp_config", "mistral_7b_finetune" + ]) + + # Combine all training args + training_args = [ + "--model_name_or_path", model_path, + "--output_dir", str(output_dir), + "--percentage", str(percentage), + "--data_seed", str(data_seed), + "--train_files" + ] + train_file + base_training_args + + # Set up logging + log_file = output_dir / "train.log" + with open(log_file, "w") as f: + print(training_args) + # Run training command + process = subprocess.Popen( + ["torchrun", "--nproc_per_node", "1", "--nnodes", "1", "--rdzv-id", str(random.randint(0,65535)), "--rdzv_backend", "c10d", "-m", "less.train.train"] + training_args, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + universal_newlines=True + ) + + # Stream output to console + for line in process.stdout: + print(line, end="") + + process.wait() + +if __name__ == "__main__": + fire.Fire(run_training) diff --git a/less/scripts/train/warmup_lora_train.sh b/less/scripts/train/warmup_lora_train.sh index 4d5e0c5..1ef4ffb 100755 --- a/less/scripts/train/warmup_lora_train.sh +++ b/less/scripts/train/warmup_lora_train.sh @@ -13,7 +13,7 @@ if [[ ! -d $output_dir ]]; then mkdir -p $output_dir fi -train_files=("$data_dir/kstack_500k/train.jsonl") +train_files=("$data_dir/train.jsonl") # use fsdp for large models if [[ $model_path == "meta-llama/Llama-2-13b-hf" ]]; then From 67f687bbcec91885e1fc40256990242c718ccff1 Mon Sep 17 00:00:00 2001 From: rinapch Date: Mon, 9 Dec 2024 14:48:56 +0000 Subject: [PATCH 04/12] rewrite using argparse --- less/scripts/train/warmup_lora_train.py | 16 +++++++++----- poetry.lock | 29 +------------------------ pyproject.toml | 1 - 3 files changed, 12 insertions(+), 34 deletions(-) diff --git a/less/scripts/train/warmup_lora_train.py b/less/scripts/train/warmup_lora_train.py index 0bceee3..f97ee71 100644 --- a/less/scripts/train/warmup_lora_train.py +++ b/less/scripts/train/warmup_lora_train.py @@ -1,6 +1,6 @@ import subprocess from pathlib import Path -import fire +import argparse import random def run_training( @@ -11,11 +11,9 @@ def run_training( data_seed: int=3, ): job_name = f"{model_path.split('/')[-1]}-p{percentage}-lora-seed{data_seed}" - # Create output directory output_dir = Path("../out") / job_name output_dir.mkdir(parents=True, exist_ok=True) - # Training files train_file = [str(Path(data_dir) / train_file)] # Base training args from base_training_args.sh @@ -79,11 +77,19 @@ def run_training( universal_newlines=True ) - # Stream output to console + # Stream output to console and log file for line in process.stdout: print(line, end="") + f.write(line) process.wait() if __name__ == "__main__": - fire.Fire(run_training) + parser = argparse.ArgumentParser() + parser.add_argument("--train_file", type=str, required=True) + parser.add_argument("--model_path", type=str, required=True) + parser.add_argument("--data_dir", type=str, default="data/train") + parser.add_argument("--percentage", type=float, default=0.05) + parser.add_argument("--data_seed", type=int, default=3) + args = parser.parse_args() + run_training(args.train_file, args.model_path, args.data_dir, args.percentage, args.data_seed) \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 6c18cd8..0c0a3a4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -419,19 +419,6 @@ docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2. testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.2)", "pytest (>=8.3.3)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.4)"] typing = ["typing-extensions (>=4.12.2)"] -[[package]] -name = "fire" -version = "0.7.0" -description = "A library for automatically generating command line interfaces." -optional = false -python-versions = "*" -files = [ - {file = "fire-0.7.0.tar.gz", hash = "sha256:961550f07936eaf65ad1dc8360f2b2bf8408fad46abbfa4d2a3794f8d2a95cdf"}, -] - -[package.dependencies] -termcolor = "*" - [[package]] name = "frozenlist" version = "1.5.0" @@ -2111,20 +2098,6 @@ mpmath = ">=1.1.0,<1.4" [package.extras] dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"] -[[package]] -name = "termcolor" -version = "2.5.0" -description = "ANSI color formatting for output in terminal" -optional = false -python-versions = ">=3.9" -files = [ - {file = "termcolor-2.5.0-py3-none-any.whl", hash = "sha256:37b17b5fc1e604945c2642c872a3764b5d547a48009871aea3edd3afa180afb8"}, - {file = "termcolor-2.5.0.tar.gz", hash = "sha256:998d8d27da6d48442e8e1f016119076b690d962507531df4890fcd2db2ef8a6f"}, -] - -[package.extras] -tests = ["pytest", "pytest-cov"] - [[package]] name = "tokenizers" version = "0.15.2" @@ -2755,4 +2728,4 @@ propcache = ">=0.2.0" [metadata] lock-version = "2.0" python-versions = "^3.11" -content-hash = "d01b27a4e3a9cfec1125e811c485eaf75d06b9ba5a2bca63f667ec1b870d1a88" +content-hash = "1411bac941a0c525667755d74067eb6e4fe888bcb8834daabe85ef3f0a69913a" diff --git a/pyproject.toml b/pyproject.toml index 1750ede..cce6214 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,7 +11,6 @@ peft = "0.7.1" transformers = "4.36.2" traker = "0.1.3" torch = "2.1.2" -fire = "^0.7.0" datasets = "^3.1.0" numpy = "<2" accelerate = "0.28.0" From 650701260287fce52b0e8daf3278634d94e7c222 Mon Sep 17 00:00:00 2001 From: rinapch Date: Mon, 9 Dec 2024 15:24:32 +0000 Subject: [PATCH 05/12] rewrite get_train_lora_gards.sh --- README.md | 22 ++++----- .../get_info/grad/get_train_lora_gards.py | 47 +++++++++++++++++++ less/scripts/train/warmup_lora_train.py | 4 -- 3 files changed, 57 insertions(+), 16 deletions(-) create mode 100644 less/scripts/get_info/grad/get_train_lora_gards.py diff --git a/README.md b/README.md index 50b3bf0..f60f1a8 100644 --- a/README.md +++ b/README.md @@ -46,26 +46,24 @@ We follow the [open-instruct](https://github.com/allenai/open-instruct?tab=readm To enhance downstream performance from data selection, it's crucial to start with a warmup training step. This involves selecting a small portion of your entire dataset to train using the LoRA method. Follow these steps for effective warmup training: ```bash -python3 -m less/scripts/train/warmup_lora_train --train_file --model_path +python3 -m less/scripts/train/warmup_lora_train --train_file --model_path ``` -NB: there are more optional arguments that you can use to specify the training process. Please refer to the script for more details. +NB: there are more optional arguments that you can use to alter the training process. Please refer to the script for more details. +You can also set `--percentage` to specify the percentage of data to train on (default is 0.05) and `--data_seed` to specify the seed for data selection (default is 3). The checkpoint will be saved in the `out` directory. + ### Step 2: Building the gradient datastore Once the initial warmup training stage is completed, we will collect gradients for the entire training dataset. For each checkpoint, our goal is to obtain the gradients of all the training data that we would like to select from. An example script is shown below. ```bash -CKPT=105 - -TRAINING_DATA_NAME=dolly -TRAINING_DATA_FILE=../data/train/processed/dolly/dolly_data.jsonl # when changing data name, change the data path accordingly -GRADIENT_TYPE="adam" -MODEL_PATH=../out/llama2-7b-p0.05-lora-seed3/checkpoint-${CKPT} -OUTPUT_PATH=../grads/llama2-7b-p0.05-lora-seed3/${TRAINING_DATA_NAME}-ckpt${CKPT}-${GRADIENT_TYPE} -DIMS="8192" - -./less/scripts/get_info/grad/get_train_lora_grads.sh "$TRAINING_DATA_FILE" "$MODEL_PATH" "$OUTPUT_PATH" "$DIMS" "$GRADIENT_TYPE" +python3 -m less/scripts/get_info/grad/get_train_lora_grads --train_data_name --train_file --model_path --ckpts --dims ``` Ideally, you would aim to create a datastore that encompasses a gradient of all the checkpoints and training data from which you wish to choose. +`train_data_name` is the name of the training data, which will be used to store the gradients, it should be comprehensive for you to easily distignuish between different experiments. +`train_file` is the path to the training file. +`model_path` is the path to the model in the `out` directory, e.g. `llama2-7b-p0.05-lora-seed3`. +`ckpts` is the list of checkpoints to compute gradients for, e.g. `105 211 317 420`. The paper recommends using all four checkpoints. +`dims` is the dimension of projection, default is 8192. ### Step 3: Selecting data for a task To select data for a particular downstream task, it's necessary to first prepare data specific to that task, using the same instruction-tuning prompt format as was employed during training. We have set up data loading modules for three evaluation datasets featured in our work: BBH, TydiQA, and MMLU. If you're interested in data selection for additional tasks, you can expand the [`less/data_selection/get_validation_dataset.py`](less/data_selection/get_validation_dataset.py) script to accommodate those tasks. Similar to obtaining gradients for training data, run the following script. The primary difference is that this process will yield SGD gradients for the validation data, following the formulation of the influence estimation. diff --git a/less/scripts/get_info/grad/get_train_lora_gards.py b/less/scripts/get_info/grad/get_train_lora_gards.py new file mode 100644 index 0000000..d2dca74 --- /dev/null +++ b/less/scripts/get_info/grad/get_train_lora_gards.py @@ -0,0 +1,47 @@ +import os +import argparse +import subprocess + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--train_data_name", type=str, required=True, help="Name of the training data (will be used to store gradients)") + parser.add_argument("--train_file", type=str, required=True, help="Path to training file") + parser.add_argument("--model_path", type=str, required=True, help="Path to model in the `out` directory") + parser.add_argument("--ckpts", type=str, required=True, help="List of checkpoints to compute gradients for") + parser.add_argument("--dims", default=8192, type=int, required=False, help="Dimension of projection") + args = parser.parse_args() + + # Split checkpoint string into list of ints + ckpts = [int(x) for x in args.ckpts.split()] + + # Process each checkpoint + for ckpt in ckpts: + # Construct model path with checkpoint + model = os.path.join("../out", args.model_path, f"/checkpoint-{ckpt}") + + # Construct output path with checkpoint + output_path = os.path.join("../grads", args.model_path, f"{args.train_data_name}-ckpt{ckpt}-adam") + + # Store paths for use in command + args.model = model + args.output_path = output_path + + # Create output directory if it doesn't exist + os.makedirs(output_path, exist_ok=True) + + # Build command + cmd = [ + "python3", "-m", "less.data_selection.get_info", + "--train_file", args.train_file, + "--info_type", "grads", + "--model_path", args.model, + "--output_path", args.output_path, + "--gradient_projection_dimension", args.dims, + "--gradient_type", "adam" + ] + + subprocess.run(cmd) + +if __name__ == "__main__": + main() + diff --git a/less/scripts/train/warmup_lora_train.py b/less/scripts/train/warmup_lora_train.py index f97ee71..1adfba7 100644 --- a/less/scripts/train/warmup_lora_train.py +++ b/less/scripts/train/warmup_lora_train.py @@ -6,7 +6,6 @@ def run_training( train_file: str, model_path: str, - data_dir: str="data/train", percentage: float=0.05, data_seed: int=3, ): @@ -14,8 +13,6 @@ def run_training( output_dir = Path("../out") / job_name output_dir.mkdir(parents=True, exist_ok=True) - train_file = [str(Path(data_dir) / train_file)] - # Base training args from base_training_args.sh base_training_args = [ "--do_train", "True", @@ -88,7 +85,6 @@ def run_training( parser = argparse.ArgumentParser() parser.add_argument("--train_file", type=str, required=True) parser.add_argument("--model_path", type=str, required=True) - parser.add_argument("--data_dir", type=str, default="data/train") parser.add_argument("--percentage", type=float, default=0.05) parser.add_argument("--data_seed", type=int, default=3) args = parser.parse_args() From 03055f66af7b6bdb102a036a725c7ad95c2b2e52 Mon Sep 17 00:00:00 2001 From: rinapch Date: Mon, 9 Dec 2024 15:52:08 +0000 Subject: [PATCH 06/12] rewrite get_eval_lora_grads.sh --- README.md | 21 +++++----- less/data_selection/get_info.py | 3 ++ less/data_selection/get_validation_dataset.py | 9 +--- .../get_info/grad/get_eval_lora_grads.py | 42 +++++++++++++++++++ ..._lora_gards.py => get_train_lora_grads.py} | 12 ++---- 5 files changed, 61 insertions(+), 26 deletions(-) create mode 100644 less/scripts/get_info/grad/get_eval_lora_grads.py rename less/scripts/get_info/grad/{get_train_lora_gards.py => get_train_lora_grads.py} (83%) diff --git a/README.md b/README.md index f60f1a8..fab93dc 100644 --- a/README.md +++ b/README.md @@ -68,20 +68,19 @@ Ideally, you would aim to create a datastore that encompasses a gradient of all ### Step 3: Selecting data for a task To select data for a particular downstream task, it's necessary to first prepare data specific to that task, using the same instruction-tuning prompt format as was employed during training. We have set up data loading modules for three evaluation datasets featured in our work: BBH, TydiQA, and MMLU. If you're interested in data selection for additional tasks, you can expand the [`less/data_selection/get_validation_dataset.py`](less/data_selection/get_validation_dataset.py) script to accommodate those tasks. Similar to obtaining gradients for training data, run the following script. The primary difference is that this process will yield SGD gradients for the validation data, following the formulation of the influence estimation. -NB: for your custom datasets, you can provide a full path to the data or HF repo name in the DATA_DIR. Don't forget to adjust `less/data_selection/get_validation_dataset.py` to add your task name to the appropriate load method. +You should gain the gradients of the validation data for all the checkpoints you used for building the gradient datastore in the previous step. ```bash - -CKPT=105 -TASK=tydiqa -MODEL_PATH=../out/llama2-7b-p0.05-lora-seed3/checkpoint-${CKPT} -OUTPUT_PATH=../grads/llama2-7b-p0.05-lora-seed3/${TASK}-ckpt${CKPT}-sgd # for validation data, we always use sgd -DATA_DIR=../data -DIMS="4096 8192" # We use 8192 as our default projection dimension - -./less/scripts/get_info/grad/get_eval_lora_grads.sh "$TASK" "$DATA_DIR" "$MODEL_PATH" $OUTPUT_PATH "$DIMS" +python3 -m less/scripts/get_info/grad/get_eval_lora_grads --task --data_dir --val_task_load_method --model_path --ckpts --dims ``` -You should gain the gradients of the validation data for all the checkpoints you used for building the gradient datastore in the previous step. After obtaining the gradients for the validation data, we can then select data for the task. The following script will calculate the influence score for each training data point, and select the top-k data points with the highest influence score. +`task` is the name of the task, which will be used to store the gradients. +`data_dir` is the path to the data directory. If you are using one of the predifined datasets ("bbh", "tydiqa", "mmlu"), this should point to the data directory. If you are using your own custom dataset, this should be a full path to a JSONL file or a HF repo name. +`val_task_load_method` is the method to load the validation data, can be `hf`, `local_hf`, `local_json`. You should specify this if you are using your own custom dataset. Default is `None`, then it's assumned that you are using the predifined datasets. +`model_path` is the path to the model in the `out` directory, e.g. `llama2-7b-p0.05-lora-seed3`. +`ckpts` is the list of checkpoints to compute gradients for, e.g. `105 211 317 420`. +`dims` is the dimension of projection, default is 8192. + +After obtaining the gradients for the validation data, we can then select data for the task. The following script will calculate the influence score for each training data point, and select the top-k data points with the highest influence score. ```bash DIM=8192 # decide which dimension to use diff --git a/less/data_selection/get_info.py b/less/data_selection/get_info.py index 5476f6c..c6f725b 100644 --- a/less/data_selection/get_info.py +++ b/less/data_selection/get_info.py @@ -113,6 +113,8 @@ def load_adam_state(model, optimizer_state_path): default="tulu", help="The chat format") parser.add_argument("--use_chat_format", type=bool, default=True, help="Whether to use chat format") +parser.add_argument("--val_task_load_method", type=str, + default=None, help="The method to load the validation data, can be 'hf', 'local_hf', 'local_json'") parser.add_argument("--max_length", type=int, default=2048, help="The maximum length") parser.add_argument("--zh", default=False, action="store_true", @@ -169,6 +171,7 @@ def load_adam_state(model, optimizer_state_path): if args.task is not None: dataset = get_dataset(args.task, data_dir=args.data_dir, + val_task_load_method=args.val_task_load_method, tokenizer=tokenizer, chat_format=args.chat_format, use_chat_format=args.use_chat_format, diff --git a/less/data_selection/get_validation_dataset.py b/less/data_selection/get_validation_dataset.py index 9d1f909..77b51a2 100644 --- a/less/data_selection/get_validation_dataset.py +++ b/less/data_selection/get_validation_dataset.py @@ -456,13 +456,8 @@ def get_dataset(task, **kwargs): return get_tydiqa_dataset(**kwargs) elif task == "mmlu": return get_mmlu_dataset(**kwargs) - - elif task in ["kstack_clean"]: - return get_custom_dataset(**kwargs, load_method="hf") - elif task in ["kstack"]: - return get_custom_dataset(**kwargs, load_method="local_json") - elif task in ["golden_repos", "lca_no_context"]: - return get_custom_dataset(**kwargs, load_method="local_hf") + elif "val_task_load_method" in kwargs and kwargs["val_task_load_method"] is not None: + return get_custom_dataset(**kwargs, load_method=kwargs["val_task_load_method"]) else: raise ValueError("Invalid task name") diff --git a/less/scripts/get_info/grad/get_eval_lora_grads.py b/less/scripts/get_info/grad/get_eval_lora_grads.py new file mode 100644 index 0000000..7c19f36 --- /dev/null +++ b/less/scripts/get_info/grad/get_eval_lora_grads.py @@ -0,0 +1,42 @@ +import os +import argparse +import subprocess + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--task", type=str, required=True, help="Task name (e.g. tydiqa, mmlu), will be used to store the gradients") + parser.add_argument("--data_dir", type=str, required=True, help="Path to data directory, can also be a full path or a HF repo name") + parser.add_argument("--val_task_load_method", default=None,type=str, required=False, help="The method to load the validation data, can be 'hf', 'local_hf', 'local_json'") + parser.add_argument("--model_path", type=str, required=True, help="Path to model in the `out` directory, e.g. 'llama2-7b-p0.05-lora-seed3'") + parser.add_argument("--ckpts", type=str, required=True, help="List of checkpoints to compute gradients for, e.g. '105 211 317 420'") + parser.add_argument("--dims", default=8192, type=int, required=False, help="Dimension of projection") + args = parser.parse_args() + + # Split checkpoint string into list of ints + ckpts = [int(x) for x in args.ckpts.split()] + + # Process each checkpoint + for ckpt in ckpts: + # Create output directory if it doesn't exist + model = os.path.join("../out", args.model_path, f"/checkpoint-{ckpt}") + + # Construct output path with checkpoint + output_path = os.path.join("../grads", args.model_path, f"{args.task}-ckpt{ckpt}-sgd") + + # Build command + cmd = [ + "python3", "-m", "less.data_selection.get_info", + "--task", args.task, + "--info_type", "grads", + "--model_path", model, + "--output_path", output_path, + "--gradient_projection_dimension", args.dims, + "--gradient_type", "sgd", + "--data_dir", args.data_dir, + "--val_task_load_method", args.val_task_load_method + ] + + subprocess.run(cmd) + +if __name__ == "__main__": + main() diff --git a/less/scripts/get_info/grad/get_train_lora_gards.py b/less/scripts/get_info/grad/get_train_lora_grads.py similarity index 83% rename from less/scripts/get_info/grad/get_train_lora_gards.py rename to less/scripts/get_info/grad/get_train_lora_grads.py index d2dca74..9cf7020 100644 --- a/less/scripts/get_info/grad/get_train_lora_gards.py +++ b/less/scripts/get_info/grad/get_train_lora_grads.py @@ -6,8 +6,8 @@ def main(): parser = argparse.ArgumentParser() parser.add_argument("--train_data_name", type=str, required=True, help="Name of the training data (will be used to store gradients)") parser.add_argument("--train_file", type=str, required=True, help="Path to training file") - parser.add_argument("--model_path", type=str, required=True, help="Path to model in the `out` directory") - parser.add_argument("--ckpts", type=str, required=True, help="List of checkpoints to compute gradients for") + parser.add_argument("--model_path", type=str, required=True, help="Path to model in the `out` directory, e.g. 'llama2-7b-p0.05-lora-seed3'") + parser.add_argument("--ckpts", type=str, required=True, help="List of checkpoints to compute gradients for, e.g. '105 211 317 420'") parser.add_argument("--dims", default=8192, type=int, required=False, help="Dimension of projection") args = parser.parse_args() @@ -22,10 +22,6 @@ def main(): # Construct output path with checkpoint output_path = os.path.join("../grads", args.model_path, f"{args.train_data_name}-ckpt{ckpt}-adam") - # Store paths for use in command - args.model = model - args.output_path = output_path - # Create output directory if it doesn't exist os.makedirs(output_path, exist_ok=True) @@ -34,8 +30,8 @@ def main(): "python3", "-m", "less.data_selection.get_info", "--train_file", args.train_file, "--info_type", "grads", - "--model_path", args.model, - "--output_path", args.output_path, + "--model_path", model, + "--output_path", output_path, "--gradient_projection_dimension", args.dims, "--gradient_type", "adam" ] From 4140be05bfbdf78e835c3ed82ceec15fa17fdf55 Mon Sep 17 00:00:00 2001 From: rinapch Date: Mon, 9 Dec 2024 16:21:07 +0000 Subject: [PATCH 07/12] readme formatting --- README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index fab93dc..5086508 100644 --- a/README.md +++ b/README.md @@ -58,11 +58,11 @@ Once the initial warmup training stage is completed, we will collect gradients f ```bash python3 -m less/scripts/get_info/grad/get_train_lora_grads --train_data_name --train_file --model_path --ckpts --dims ``` -Ideally, you would aim to create a datastore that encompasses a gradient of all the checkpoints and training data from which you wish to choose. +Ideally, you would aim to create a datastore that encompasses a gradient of all the checkpoints and training data from which you wish to choose. `train_data_name` is the name of the training data, which will be used to store the gradients, it should be comprehensive for you to easily distignuish between different experiments. -`train_file` is the path to the training file. -`model_path` is the path to the model in the `out` directory, e.g. `llama2-7b-p0.05-lora-seed3`. -`ckpts` is the list of checkpoints to compute gradients for, e.g. `105 211 317 420`. The paper recommends using all four checkpoints. +`train_file` is the path to the training file. +`model_path` is the path to the model in the `out` directory, e.g. `llama2-7b-p0.05-lora-seed3`. +`ckpts` is the list of checkpoints to compute gradients for, e.g. `105 211 317 420`. The paper recommends using all four checkpoints. `dims` is the dimension of projection, default is 8192. ### Step 3: Selecting data for a task @@ -73,11 +73,11 @@ You should gain the gradients of the validation data for all the checkpoints you ```bash python3 -m less/scripts/get_info/grad/get_eval_lora_grads --task --data_dir --val_task_load_method --model_path --ckpts --dims ``` -`task` is the name of the task, which will be used to store the gradients. -`data_dir` is the path to the data directory. If you are using one of the predifined datasets ("bbh", "tydiqa", "mmlu"), this should point to the data directory. If you are using your own custom dataset, this should be a full path to a JSONL file or a HF repo name. -`val_task_load_method` is the method to load the validation data, can be `hf`, `local_hf`, `local_json`. You should specify this if you are using your own custom dataset. Default is `None`, then it's assumned that you are using the predifined datasets. -`model_path` is the path to the model in the `out` directory, e.g. `llama2-7b-p0.05-lora-seed3`. -`ckpts` is the list of checkpoints to compute gradients for, e.g. `105 211 317 420`. +`task` is the name of the task, which will be used to store the gradients. +`data_dir` is the path to the data directory. If you are using one of the predifined datasets ("bbh", "tydiqa", "mmlu"), this should point to the data directory. If you are using your own custom dataset, this should be a full path to a JSONL file or a HF repo name. +`val_task_load_method` is the method to load the validation data, can be `hf`, `local_hf`, `local_json`. You should specify this if you are using your own custom dataset. Default is `None`, then it's assumned that you are using the predifined datasets. +`model_path` is the path to the model in the `out` directory, e.g. `llama2-7b-p0.05-lora-seed3`. +`ckpts` is the list of checkpoints to compute gradients for, e.g. `105 211 317 420`. `dims` is the dimension of projection, default is 8192. After obtaining the gradients for the validation data, we can then select data for the task. The following script will calculate the influence score for each training data point, and select the top-k data points with the highest influence score. @@ -91,7 +91,7 @@ CHECKPOINT_WEIGHTS="1.6877e-05 1.2859e-05 7.7030e-06 2.5616e-06" # average lr of VALIDATION_GRADIENT_PATH=../grads/llama2-7b-p0.05-lora-seed3/{}-ckpt{}-sgd/dim${DIM} TARGET_TASK_NAMES="tydiqa" -TARGET_TASK_FILES="..." +TARGET_TASK_FILES="..." SELECTED_DATA_OUTPUT_PATH="../selected_data" MODEL_PATH=../out/llama2-7b-p0.05-lora-seed3/checkpoint-${CKPT} From 7e3534e5295124b501f568f641e83e5fb4f77309 Mon Sep 17 00:00:00 2001 From: rinapch Date: Mon, 9 Dec 2024 23:55:13 +0000 Subject: [PATCH 08/12] README formatting --- README.md | 49 +++++++++++++++++++++++++++------ less/data_selection/matching.py | 41 +++++++++++---------------- 2 files changed, 57 insertions(+), 33 deletions(-) diff --git a/README.md b/README.md index 5086508..8c9c603 100644 --- a/README.md +++ b/README.md @@ -56,7 +56,12 @@ The checkpoint will be saved in the `out` directory. Once the initial warmup training stage is completed, we will collect gradients for the entire training dataset. For each checkpoint, our goal is to obtain the gradients of all the training data that we would like to select from. An example script is shown below. ```bash -python3 -m less/scripts/get_info/grad/get_train_lora_grads --train_data_name --train_file --model_path --ckpts --dims +python3 -m less/scripts/get_info/grad/get_train_lora_grads \ + --train_data_name \ + --train_file \ + --model_path \ + --ckpts \ + --dims ``` Ideally, you would aim to create a datastore that encompasses a gradient of all the checkpoints and training data from which you wish to choose. `train_data_name` is the name of the training data, which will be used to store the gradients, it should be comprehensive for you to easily distignuish between different experiments. @@ -65,21 +70,31 @@ Ideally, you would aim to create a datastore that encompasses a gradient of all `ckpts` is the list of checkpoints to compute gradients for, e.g. `105 211 317 420`. The paper recommends using all four checkpoints. `dims` is the dimension of projection, default is 8192. +The gradients will be saved in the `grads` directory. + ### Step 3: Selecting data for a task To select data for a particular downstream task, it's necessary to first prepare data specific to that task, using the same instruction-tuning prompt format as was employed during training. We have set up data loading modules for three evaluation datasets featured in our work: BBH, TydiQA, and MMLU. If you're interested in data selection for additional tasks, you can expand the [`less/data_selection/get_validation_dataset.py`](less/data_selection/get_validation_dataset.py) script to accommodate those tasks. Similar to obtaining gradients for training data, run the following script. The primary difference is that this process will yield SGD gradients for the validation data, following the formulation of the influence estimation. You should gain the gradients of the validation data for all the checkpoints you used for building the gradient datastore in the previous step. ```bash -python3 -m less/scripts/get_info/grad/get_eval_lora_grads --task --data_dir --val_task_load_method --model_path --ckpts --dims +python3 -m less/scripts/get_info/grad/get_eval_lora_grads \ + --task \ + --data_dir \ + --val_task_load_method \ + --model_path \ + --ckpts \ + --dims ``` `task` is the name of the task, which will be used to store the gradients. `data_dir` is the path to the data directory. If you are using one of the predifined datasets ("bbh", "tydiqa", "mmlu"), this should point to the data directory. If you are using your own custom dataset, this should be a full path to a JSONL file or a HF repo name. `val_task_load_method` is the method to load the validation data, can be `hf`, `local_hf`, `local_json`. You should specify this if you are using your own custom dataset. Default is `None`, then it's assumned that you are using the predifined datasets. `model_path` is the path to the model in the `out` directory, e.g. `llama2-7b-p0.05-lora-seed3`. -`ckpts` is the list of checkpoints to compute gradients for, e.g. `105 211 317 420`. +`ckpts` is the list of checkpoints to compute gradients for, e.g. `'105 211 317 420'`. `dims` is the dimension of projection, default is 8192. +The gradients will be saved in the `grads` directory. + After obtaining the gradients for the validation data, we can then select data for the task. The following script will calculate the influence score for each training data point, and select the top-k data points with the highest influence score. ```bash @@ -96,17 +111,35 @@ SELECTED_DATA_OUTPUT_PATH="../selected_data" MODEL_PATH=../out/llama2-7b-p0.05-lora-seed3/checkpoint-${CKPT} ./less/scripts/data_selection/matching.sh "$GRADIENT_PATH" "$TRAIN_FILE_NAMES" "$CKPTS" "$CHECKPOINT_WEIGHTS" "$VALIDATION_GRADIENT_PATH" "$TARGET_TASK_NAMES" "$TARGET_TASK_FILES" "$SELECTED_DATA_OUTPUT_PATH" "$MODEL_PATH" + +python3 -m less.data_selection.matching \ + --train_file_names \ + --ckpts \ + --dims \ + --checkpoint_weights \ + --target_task_names \ + --target_task_files \ + --val_task_load_method \ + --model_path + ``` +`train_file_names` is a list of training data names that you used to store the gradients. +`ckpts` is a list of checkpoints, e.g. `'105 211 317 420'`. +`dims` is the dimension of projection, default is 8192. +`checkpoint_weights` is a list of average lr of the epoch (check in Wandb), e.g. `'1.6877e-05 1.2859e-05 7.7030e-06 2.5616e-06'`. +`target_task_names` is a list of target task names that you used to store the gradients. +`target_task_files` can be a full path or a HF repo name, don't forget to specify the `val_task_load_method` accordingly. +`model_path` is the path to the model in the `out` directory, e.g. `llama2-7b-p0.05-lora-seed3`. The influence score for each training data point will be saved in the `OUTPUT_PATH` directory. You can use the following script to select the top-k data points with the highest influence score. ```bash python3 -m less.data_selection.write_selected_data \ ---target_task_names ${TARGET_TASK_NAMES} \ ---train_file_names ${TRAIN_FILE_NAMES} \ ---train_files ../data/train/processed/dolly/dolly_data.jsonl ../data/train/processed/oasst1/oasst1_data.jsonl \ ---output_path $SELECTED_DATA_OUTPUT_PATH \ ---percentage 0.05 +--target_task_names \ +--train_file_names \ +--train_files \ +--output_path \ +--percentage ``` ### Step 4: Train with your selected data diff --git a/less/data_selection/matching.py b/less/data_selection/matching.py index f39f33c..5ac9a0e 100644 --- a/less/data_selection/matching.py +++ b/less/data_selection/matching.py @@ -7,25 +7,17 @@ argparser = argparse.ArgumentParser( description='Script for selecting the data for training') -argparser.add_argument('--gradient_path', type=str, default="{} ckpt{}", - help='The path to the gradient file') -argparser.add_argument('--train_file_names', type=str, nargs='+', +argparser.add_argument('--train_file_name', type=str, nargs='+', help='The name of the training file') -argparser.add_argument('--ckpts', type=int, nargs='+', - help="Checkpoint numbers.") -argparser.add_argument('--checkpoint_weights', type=float, nargs='+', - help="checkpoint weights") -argparser.add_argument('--target_task_names', type=str, - nargs='+', help="The name of the target tasks") -argparser.add_argument('--target_task_files', type=str, nargs='+', - help='The name of the validation file') -argparser.add_argument('--validation_gradient_path', type=str, - default="{} ckpt{}", help='The path to the validation gradient file') -argparser.add_argument('--output_path', type=str, default="selected_data", - help='The path to the output') -argparser.add_argument('--model_path', type=str, default="Model path (to initialize a tokenizer)", - help='The path to the output') - +argparser.add_argument('--ckpts', type=int, nargs='+', help="Checkpoints, e.g. '105 211 317 420'") +argparser.add_argument('--dims', default=8192, type=int, required=False, help='Dimention used for grads') +argparser.add_argument('--checkpoint_weights', type=float, nargs='+', help="Average lr of the epoch (check in wandb)") +argparser.add_argument('--target_task_name', type=str, + nargs='+', help="The name of the target task") +argparser.add_argument('--target_task_file', type=str, nargs='+', + help='Can be a full path or a HF repo name') +argparser.add_argument('--val_task_load_method', type=str, default=None, help='The method to load the validation data, can be "hf", "local_hf", "local_json"') +argparser.add_argument('--model_path', type=str, required=True, help='Model path, e.g. llama2-7b-p0.05-lora-seed3') args = argparser.parse_args() @@ -52,15 +44,15 @@ def calculate_influence_score(training_info: torch.Tensor, validation_info: torc # calculate the influence score for each validation task for target_task_name, target_task_file in zip(args.target_task_names, args.target_task_files): - val_dataset = get_dataset(task=target_task_name, data_dir=target_task_file, model_path=args.model_path,) + val_dataset = get_dataset(task=target_task_name, data_dir=target_task_file, + model_path=args.model_path, val_task_load_method=args.val_task_load_method) num_val_examples = len(val_dataset) for train_file_name in args.train_file_names: influence_score = 0 for i, ckpt in enumerate(args.ckpts): # validation_path = args.validation_gradient_path.format( # target_task_name, ckpt) - validation_path = args.validation_gradient_path.format( - target_task_name, ckpt) + validation_path = os.path.join("../grads", args.model_path, f"{train_file_name}_ckpt{ckpt}_sgd/dim{args.dims}") if os.path.isdir(validation_path): validation_path = os.path.join(validation_path, "all_orig.pt") validation_info = torch.load(validation_path) @@ -69,7 +61,7 @@ def calculate_influence_score(training_info: torch.Tensor, validation_info: torc validation_info = torch.tensor(validation_info) validation_info = validation_info.to(device).half() # gradient_path = args.gradient_path.format(train_file_name, ckpt) - gradient_path = args.gradient_path.format(train_file_name, ckpt) + gradient_path = os.path.join("../grads", args.model_path, f"{train_file_name}_ckpt{ckpt}_adam/dim{args.dims}") if os.path.isdir(gradient_path): gradient_path = os.path.join(gradient_path, "all_orig.pt") training_info = torch.load(gradient_path) @@ -84,10 +76,9 @@ def calculate_influence_score(training_info: torch.Tensor, validation_info: torc influence_score = influence_score.cpu().reshape( influence_score.shape[0], num_val_examples, -1 ).mean(-1).max(-1)[0] - output_dir = os.path.join(args.output_path, target_task_name) + output_dir = os.path.join("../selected_data", target_task_name) if not os.path.exists(output_dir): os.makedirs(output_dir) - output_file = os.path.join( - args.output_path, target_task_name, f"{train_file_name}_influence_score.pt") + output_file = os.path.join(output_dir, f"{train_file_name}_influence_score.pt") torch.save(influence_score, output_file) print("Saved influence score to {}".format(output_file)) From c572b4691d415e8219630a08f39570becdcd86f8 Mon Sep 17 00:00:00 2001 From: rinapch Date: Tue, 10 Dec 2024 15:58:22 +0000 Subject: [PATCH 09/12] remove unnecessary code in readme --- README.md | 21 +++------------------ 1 file changed, 3 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index 8c9c603..3e4761e 100644 --- a/README.md +++ b/README.md @@ -98,20 +98,6 @@ The gradients will be saved in the `grads` directory. After obtaining the gradients for the validation data, we can then select data for the task. The following script will calculate the influence score for each training data point, and select the top-k data points with the highest influence score. ```bash -DIM=8192 # decide which dimension to use -GRADIENT_PATH=../grads/llama2-7b-p0.05-lora-seed3/{}-ckpt{}-adam/dim${DIM} -TRAIN_FILE_NAMES="flan_v2 cot dolly oasst1" -CKPTS="105 211 317 420" # checkpoing index -CHECKPOINT_WEIGHTS="1.6877e-05 1.2859e-05 7.7030e-06 2.5616e-06" # average lr of the epoch - -VALIDATION_GRADIENT_PATH=../grads/llama2-7b-p0.05-lora-seed3/{}-ckpt{}-sgd/dim${DIM} -TARGET_TASK_NAMES="tydiqa" -TARGET_TASK_FILES="..." -SELECTED_DATA_OUTPUT_PATH="../selected_data" -MODEL_PATH=../out/llama2-7b-p0.05-lora-seed3/checkpoint-${CKPT} - -./less/scripts/data_selection/matching.sh "$GRADIENT_PATH" "$TRAIN_FILE_NAMES" "$CKPTS" "$CHECKPOINT_WEIGHTS" "$VALIDATION_GRADIENT_PATH" "$TARGET_TASK_NAMES" "$TARGET_TASK_FILES" "$SELECTED_DATA_OUTPUT_PATH" "$MODEL_PATH" - python3 -m less.data_selection.matching \ --train_file_names \ --ckpts \ @@ -121,14 +107,13 @@ python3 -m less.data_selection.matching \ --target_task_files \ --val_task_load_method \ --model_path - ``` `train_file_names` is a list of training data names that you used to store the gradients. `ckpts` is a list of checkpoints, e.g. `'105 211 317 420'`. `dims` is the dimension of projection, default is 8192. -`checkpoint_weights` is a list of average lr of the epoch (check in Wandb), e.g. `'1.6877e-05 1.2859e-05 7.7030e-06 2.5616e-06'`. -`target_task_names` is a list of target task names that you used to store the gradients. -`target_task_files` can be a full path or a HF repo name, don't forget to specify the `val_task_load_method` accordingly. +`checkpoint_weights` is a list of average lr of the epoch (check in Wandb), e.g. `'1.6877e-05 1.2859e-05 7.7030e-06 2.5616e-06'`. +`target_task_names` is a list of target task names that you used to store the gradients. +`target_task_files` can be a full path or a HF repo name, don't forget to specify the `val_task_load_method` accordingly. `model_path` is the path to the model in the `out` directory, e.g. `llama2-7b-p0.05-lora-seed3`. The influence score for each training data point will be saved in the `OUTPUT_PATH` directory. You can use the following script to select the top-k data points with the highest influence score. From 61e6477c36c6be7069f1a91b472749363bf096e2 Mon Sep 17 00:00:00 2001 From: rinapch Date: Wed, 11 Dec 2024 02:16:53 +0000 Subject: [PATCH 10/12] fix all bugs found during testing --- README.md | 8 ++--- less/data_selection/get_validation_dataset.py | 2 -- less/data_selection/matching.py | 29 ++++++++++--------- less/data_selection/write_selected_data.py | 2 +- .../get_info/grad/get_eval_lora_grads.py | 4 +-- .../get_info/grad/get_train_lora_grads.py | 6 ++-- less/scripts/train/warmup_lora_train.py | 4 +-- 7 files changed, 28 insertions(+), 27 deletions(-) diff --git a/README.md b/README.md index 3e4761e..7610839 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ We follow the [open-instruct](https://github.com/allenai/open-instruct?tab=readm To enhance downstream performance from data selection, it's crucial to start with a warmup training step. This involves selecting a small portion of your entire dataset to train using the LoRA method. Follow these steps for effective warmup training: ```bash -python3 -m less/scripts/train/warmup_lora_train --train_file --model_path +python3 -m less.scripts.train.warmup_lora_train --train_file --model_path ``` NB: there are more optional arguments that you can use to alter the training process. Please refer to the script for more details. You can also set `--percentage` to specify the percentage of data to train on (default is 0.05) and `--data_seed` to specify the seed for data selection (default is 3). @@ -56,7 +56,7 @@ The checkpoint will be saved in the `out` directory. Once the initial warmup training stage is completed, we will collect gradients for the entire training dataset. For each checkpoint, our goal is to obtain the gradients of all the training data that we would like to select from. An example script is shown below. ```bash -python3 -m less/scripts/get_info/grad/get_train_lora_grads \ +python3 -m less.scripts.get_info.grad.get_train_lora_grads \ --train_data_name \ --train_file \ --model_path \ @@ -78,7 +78,7 @@ To select data for a particular downstream task, it's necessary to first prepare You should gain the gradients of the validation data for all the checkpoints you used for building the gradient datastore in the previous step. ```bash -python3 -m less/scripts/get_info/grad/get_eval_lora_grads \ +python3 -m less.scripts.get_info.grad.get_eval_lora_grads \ --task \ --data_dir \ --val_task_load_method \ @@ -87,7 +87,7 @@ python3 -m less/scripts/get_info/grad/get_eval_lora_grads \ --dims ``` `task` is the name of the task, which will be used to store the gradients. -`data_dir` is the path to the data directory. If you are using one of the predifined datasets ("bbh", "tydiqa", "mmlu"), this should point to the data directory. If you are using your own custom dataset, this should be a full path to a JSONL file or a HF repo name. +`data_dir` is the path to the data directory. If you are using one of the predifined datasets ("bbh", "tydiqa", "mmlu"), this should point to the data directory. If you are using your own custom dataset, this should be a full path to a JSONL file or a HF repo name. We also expect that every custom dataset has a `content` column. If that's not the case, you can change the tokenization function in the `less/data_selection/get_validation_dataset.py` script to encode the data. `val_task_load_method` is the method to load the validation data, can be `hf`, `local_hf`, `local_json`. You should specify this if you are using your own custom dataset. Default is `None`, then it's assumned that you are using the predifined datasets. `model_path` is the path to the model in the `out` directory, e.g. `llama2-7b-p0.05-lora-seed3`. `ckpts` is the list of checkpoints to compute gradients for, e.g. `'105 211 317 420'`. diff --git a/less/data_selection/get_validation_dataset.py b/less/data_selection/get_validation_dataset.py index 77b51a2..d9c1de8 100644 --- a/less/data_selection/get_validation_dataset.py +++ b/less/data_selection/get_validation_dataset.py @@ -447,8 +447,6 @@ def get_dataset(task, **kwargs): if tokenizer.pad_token is None: tokenizer.add_special_tokens({"pad_token": ""}) kwargs["tokenizer"] = tokenizer - else: - raise ValueError("No tokenizer found") if task == "bbh": return get_bbh_dataset(**kwargs) diff --git a/less/data_selection/matching.py b/less/data_selection/matching.py index 5ac9a0e..d2b0b83 100644 --- a/less/data_selection/matching.py +++ b/less/data_selection/matching.py @@ -7,14 +7,14 @@ argparser = argparse.ArgumentParser( description='Script for selecting the data for training') -argparser.add_argument('--train_file_name', type=str, nargs='+', +argparser.add_argument('--train_file_names', type=str, nargs='+', help='The name of the training file') -argparser.add_argument('--ckpts', type=int, nargs='+', help="Checkpoints, e.g. '105 211 317 420'") +argparser.add_argument('--ckpts', type=str, help="Checkpoints, e.g. '105 211 317 420'") argparser.add_argument('--dims', default=8192, type=int, required=False, help='Dimention used for grads') -argparser.add_argument('--checkpoint_weights', type=float, nargs='+', help="Average lr of the epoch (check in wandb)") -argparser.add_argument('--target_task_name', type=str, - nargs='+', help="The name of the target task") -argparser.add_argument('--target_task_file', type=str, nargs='+', +argparser.add_argument('--checkpoint_weights', type=str, help="Average lr of the epoch (check in wandb)") +argparser.add_argument('--target_task_names', type=str, + nargs='+', help="The name of the target task(s)") +argparser.add_argument('--target_task_files', type=str, nargs='+', help='Can be a full path or a HF repo name') argparser.add_argument('--val_task_load_method', type=str, default=None, help='The method to load the validation data, can be "hf", "local_hf", "local_json"') argparser.add_argument('--model_path', type=str, required=True, help='Model path, e.g. llama2-7b-p0.05-lora-seed3') @@ -36,23 +36,26 @@ def calculate_influence_score(training_info: torch.Tensor, validation_info: torc training_info, validation_info.transpose(0, 1)) return influence_scores +checkpoints = [int(i) for i in args.ckpts.split(" ")] +checkpoint_weights = [float(i) for i in args.checkpoint_weights.split(" ")] # renormalize the checkpoint weights -if sum(args.checkpoint_weights) != 1: - s = sum(args.checkpoint_weights) - args.checkpoint_weights = [i/s for i in args.checkpoint_weights] +if sum(checkpoint_weights) != 1: + s = sum(checkpoint_weights) + args.checkpoint_weights = [i/s for i in checkpoint_weights] # calculate the influence score for each validation task for target_task_name, target_task_file in zip(args.target_task_names, args.target_task_files): + model_path = os.path.join("../out", args.model_path, f"checkpoint-{checkpoints[0]}") val_dataset = get_dataset(task=target_task_name, data_dir=target_task_file, - model_path=args.model_path, val_task_load_method=args.val_task_load_method) + model_path=model_path, val_task_load_method=args.val_task_load_method) num_val_examples = len(val_dataset) for train_file_name in args.train_file_names: influence_score = 0 - for i, ckpt in enumerate(args.ckpts): + for i, ckpt in enumerate(checkpoints): # validation_path = args.validation_gradient_path.format( # target_task_name, ckpt) - validation_path = os.path.join("../grads", args.model_path, f"{train_file_name}_ckpt{ckpt}_sgd/dim{args.dims}") + validation_path = os.path.join("../grads", args.model_path, f"{target_task_name}-ckpt{ckpt}-sgd/dim{args.dims}") if os.path.isdir(validation_path): validation_path = os.path.join(validation_path, "all_orig.pt") validation_info = torch.load(validation_path) @@ -61,7 +64,7 @@ def calculate_influence_score(training_info: torch.Tensor, validation_info: torc validation_info = torch.tensor(validation_info) validation_info = validation_info.to(device).half() # gradient_path = args.gradient_path.format(train_file_name, ckpt) - gradient_path = os.path.join("../grads", args.model_path, f"{train_file_name}_ckpt{ckpt}_adam/dim{args.dims}") + gradient_path = os.path.join("../grads", args.model_path, f"{train_file_name}-ckpt{ckpt}-adam/dim{args.dims}") if os.path.isdir(gradient_path): gradient_path = os.path.join(gradient_path, "all_orig.pt") training_info = torch.load(gradient_path) diff --git a/less/data_selection/write_selected_data.py b/less/data_selection/write_selected_data.py index 3112caa..f14f6cb 100644 --- a/less/data_selection/write_selected_data.py +++ b/less/data_selection/write_selected_data.py @@ -14,7 +14,7 @@ def parse_args(): argparser.add_argument('--target_task_names', type=str, nargs='+', help='The name of the target task') argparser.add_argument('--output_path', type=str, - default="selected_data", help='The path to the output') + default="../selected_data", help='The path to the output') argparser.add_argument('--max_samples', type=int, default=None, help='The maximum number of samples') argparser.add_argument('--percentage', type=float, default=None, diff --git a/less/scripts/get_info/grad/get_eval_lora_grads.py b/less/scripts/get_info/grad/get_eval_lora_grads.py index 7c19f36..b38d832 100644 --- a/less/scripts/get_info/grad/get_eval_lora_grads.py +++ b/less/scripts/get_info/grad/get_eval_lora_grads.py @@ -18,7 +18,7 @@ def main(): # Process each checkpoint for ckpt in ckpts: # Create output directory if it doesn't exist - model = os.path.join("../out", args.model_path, f"/checkpoint-{ckpt}") + model = os.path.join("../out", args.model_path, f"checkpoint-{ckpt}") # Construct output path with checkpoint output_path = os.path.join("../grads", args.model_path, f"{args.task}-ckpt{ckpt}-sgd") @@ -30,7 +30,7 @@ def main(): "--info_type", "grads", "--model_path", model, "--output_path", output_path, - "--gradient_projection_dimension", args.dims, + "--gradient_projection_dimension", str(args.dims), "--gradient_type", "sgd", "--data_dir", args.data_dir, "--val_task_load_method", args.val_task_load_method diff --git a/less/scripts/get_info/grad/get_train_lora_grads.py b/less/scripts/get_info/grad/get_train_lora_grads.py index 9cf7020..ca3b5af 100644 --- a/less/scripts/get_info/grad/get_train_lora_grads.py +++ b/less/scripts/get_info/grad/get_train_lora_grads.py @@ -17,7 +17,7 @@ def main(): # Process each checkpoint for ckpt in ckpts: # Construct model path with checkpoint - model = os.path.join("../out", args.model_path, f"/checkpoint-{ckpt}") + model_checkpoint_path = os.path.join("../out", args.model_path, f"checkpoint-{ckpt}") # Construct output path with checkpoint output_path = os.path.join("../grads", args.model_path, f"{args.train_data_name}-ckpt{ckpt}-adam") @@ -30,9 +30,9 @@ def main(): "python3", "-m", "less.data_selection.get_info", "--train_file", args.train_file, "--info_type", "grads", - "--model_path", model, + "--model_path", model_checkpoint_path, "--output_path", output_path, - "--gradient_projection_dimension", args.dims, + "--gradient_projection_dimension", str(args.dims), "--gradient_type", "adam" ] diff --git a/less/scripts/train/warmup_lora_train.py b/less/scripts/train/warmup_lora_train.py index 1adfba7..727f7f7 100644 --- a/less/scripts/train/warmup_lora_train.py +++ b/less/scripts/train/warmup_lora_train.py @@ -60,7 +60,7 @@ def run_training( "--percentage", str(percentage), "--data_seed", str(data_seed), "--train_files" - ] + train_file + base_training_args + ] + [train_file] + base_training_args # Set up logging log_file = output_dir / "train.log" @@ -88,4 +88,4 @@ def run_training( parser.add_argument("--percentage", type=float, default=0.05) parser.add_argument("--data_seed", type=int, default=3) args = parser.parse_args() - run_training(args.train_file, args.model_path, args.data_dir, args.percentage, args.data_seed) \ No newline at end of file + run_training(args.train_file, args.model_path, args.percentage, args.data_seed) \ No newline at end of file From 16fc5f91873644ad31bdbfe4eba1b3c2410486af Mon Sep 17 00:00:00 2001 From: rinapch Date: Wed, 11 Dec 2024 13:25:53 +0000 Subject: [PATCH 11/12] disambiguate variable name --- README.md | 2 +- less/scripts/train/warmup_lora_train.py | 14 +++++++------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 7610839..eb58be3 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ We follow the [open-instruct](https://github.com/allenai/open-instruct?tab=readm To enhance downstream performance from data selection, it's crucial to start with a warmup training step. This involves selecting a small portion of your entire dataset to train using the LoRA method. Follow these steps for effective warmup training: ```bash -python3 -m less.scripts.train.warmup_lora_train --train_file --model_path +python3 -m less.scripts.train.warmup_lora_train --train_file --model_name_or_path ``` NB: there are more optional arguments that you can use to alter the training process. Please refer to the script for more details. You can also set `--percentage` to specify the percentage of data to train on (default is 0.05) and `--data_seed` to specify the seed for data selection (default is 3). diff --git a/less/scripts/train/warmup_lora_train.py b/less/scripts/train/warmup_lora_train.py index 727f7f7..8e9051f 100644 --- a/less/scripts/train/warmup_lora_train.py +++ b/less/scripts/train/warmup_lora_train.py @@ -5,11 +5,11 @@ def run_training( train_file: str, - model_path: str, + model_name_or_path: str, percentage: float=0.05, data_seed: int=3, ): - job_name = f"{model_path.split('/')[-1]}-p{percentage}-lora-seed{data_seed}" + job_name = f"{model_name_or_path.split('/')[-1]}-p{percentage}-lora-seed{data_seed}" output_dir = Path("../out") / job_name output_dir.mkdir(parents=True, exist_ok=True) @@ -42,12 +42,12 @@ def run_training( ] # Add FSDP config for large models - if model_path == "meta-llama/Llama-2-13b-hf": + if model_name_or_path == "meta-llama/Llama-2-13b-hf": base_training_args.extend([ "--fsdp", "full_shard auto_wrap", "--fsdp_config", "llama2_13b_finetune" ]) - elif model_path == "mistralai/Mistral-7B-v0.1": + elif model_name_or_path == "mistralai/Mistral-7B-v0.1": base_training_args.extend([ "--fsdp", "full_shard auto_wrap", "--fsdp_config", "mistral_7b_finetune" @@ -55,7 +55,7 @@ def run_training( # Combine all training args training_args = [ - "--model_name_or_path", model_path, + "--model_name_or_path", model_name_or_path, "--output_dir", str(output_dir), "--percentage", str(percentage), "--data_seed", str(data_seed), @@ -84,8 +84,8 @@ def run_training( if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--train_file", type=str, required=True) - parser.add_argument("--model_path", type=str, required=True) + parser.add_argument("--model_name_or_path", type=str, required=True) parser.add_argument("--percentage", type=float, default=0.05) parser.add_argument("--data_seed", type=int, default=3) args = parser.parse_args() - run_training(args.train_file, args.model_path, args.percentage, args.data_seed) \ No newline at end of file + run_training(args.train_file, args.model_name_or_path, args.percentage, args.data_seed) \ No newline at end of file From ecb9b955a3f07eef0124072b2cd2a94a5c0518f0 Mon Sep 17 00:00:00 2001 From: rinapch Date: Thu, 12 Dec 2024 14:01:31 +0000 Subject: [PATCH 12/12] make val method not optional --- README.md | 3 ++- less/data_selection/get_info.py | 2 +- less/data_selection/matching.py | 2 +- less/scripts/get_info/grad/get_eval_lora_grads.py | 2 +- 4 files changed, 5 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index eb58be3..ebce162 100644 --- a/README.md +++ b/README.md @@ -88,7 +88,7 @@ python3 -m less.scripts.get_info.grad.get_eval_lora_grads \ ``` `task` is the name of the task, which will be used to store the gradients. `data_dir` is the path to the data directory. If you are using one of the predifined datasets ("bbh", "tydiqa", "mmlu"), this should point to the data directory. If you are using your own custom dataset, this should be a full path to a JSONL file or a HF repo name. We also expect that every custom dataset has a `content` column. If that's not the case, you can change the tokenization function in the `less/data_selection/get_validation_dataset.py` script to encode the data. -`val_task_load_method` is the method to load the validation data, can be `hf`, `local_hf`, `local_json`. You should specify this if you are using your own custom dataset. Default is `None`, then it's assumned that you are using the predifined datasets. +`val_task_load_method` is the method to load the validation data, can be `hf`, `local_hf`, `local_json` or `predefined`. First three methods are self-explanatory, `predefined` is used when you want to use the predefined validation data for a task, e.g. `tydiqa`. `model_path` is the path to the model in the `out` directory, e.g. `llama2-7b-p0.05-lora-seed3`. `ckpts` is the list of checkpoints to compute gradients for, e.g. `'105 211 317 420'`. `dims` is the dimension of projection, default is 8192. @@ -114,6 +114,7 @@ python3 -m less.data_selection.matching \ `checkpoint_weights` is a list of average lr of the epoch (check in Wandb), e.g. `'1.6877e-05 1.2859e-05 7.7030e-06 2.5616e-06'`. `target_task_names` is a list of target task names that you used to store the gradients. `target_task_files` can be a full path or a HF repo name, don't forget to specify the `val_task_load_method` accordingly. +`val_task_load_method` is the method to load the validation data, can be `hf`, `local_hf`, `local_json` or `predefined`. `model_path` is the path to the model in the `out` directory, e.g. `llama2-7b-p0.05-lora-seed3`. The influence score for each training data point will be saved in the `OUTPUT_PATH` directory. You can use the following script to select the top-k data points with the highest influence score. diff --git a/less/data_selection/get_info.py b/less/data_selection/get_info.py index c6f725b..789fb39 100644 --- a/less/data_selection/get_info.py +++ b/less/data_selection/get_info.py @@ -114,7 +114,7 @@ def load_adam_state(model, optimizer_state_path): parser.add_argument("--use_chat_format", type=bool, default=True, help="Whether to use chat format") parser.add_argument("--val_task_load_method", type=str, - default=None, help="The method to load the validation data, can be 'hf', 'local_hf', 'local_json'") + help="The method to load the validation data, can be 'hf', 'local_hf', 'local_json' or 'predefined'") parser.add_argument("--max_length", type=int, default=2048, help="The maximum length") parser.add_argument("--zh", default=False, action="store_true", diff --git a/less/data_selection/matching.py b/less/data_selection/matching.py index d2b0b83..963d004 100644 --- a/less/data_selection/matching.py +++ b/less/data_selection/matching.py @@ -16,7 +16,7 @@ nargs='+', help="The name of the target task(s)") argparser.add_argument('--target_task_files', type=str, nargs='+', help='Can be a full path or a HF repo name') -argparser.add_argument('--val_task_load_method', type=str, default=None, help='The method to load the validation data, can be "hf", "local_hf", "local_json"') +argparser.add_argument('--val_task_load_method', type=str, help='The method to load the validation data, can be "hf", "local_hf", "local_json" or "predefined"') argparser.add_argument('--model_path', type=str, required=True, help='Model path, e.g. llama2-7b-p0.05-lora-seed3') args = argparser.parse_args() diff --git a/less/scripts/get_info/grad/get_eval_lora_grads.py b/less/scripts/get_info/grad/get_eval_lora_grads.py index b38d832..36c8ee1 100644 --- a/less/scripts/get_info/grad/get_eval_lora_grads.py +++ b/less/scripts/get_info/grad/get_eval_lora_grads.py @@ -6,7 +6,7 @@ def main(): parser = argparse.ArgumentParser() parser.add_argument("--task", type=str, required=True, help="Task name (e.g. tydiqa, mmlu), will be used to store the gradients") parser.add_argument("--data_dir", type=str, required=True, help="Path to data directory, can also be a full path or a HF repo name") - parser.add_argument("--val_task_load_method", default=None,type=str, required=False, help="The method to load the validation data, can be 'hf', 'local_hf', 'local_json'") + parser.add_argument("--val_task_load_method", type=str, required=True, help="The method to load the validation data, can be 'hf', 'local_hf', 'local_json'") parser.add_argument("--model_path", type=str, required=True, help="Path to model in the `out` directory, e.g. 'llama2-7b-p0.05-lora-seed3'") parser.add_argument("--ckpts", type=str, required=True, help="List of checkpoints to compute gradients for, e.g. '105 211 317 420'") parser.add_argument("--dims", default=8192, type=int, required=False, help="Dimension of projection")