diff --git a/.gitignore b/.gitignore index 9062184..c712074 100644 --- a/.gitignore +++ b/.gitignore @@ -167,4 +167,5 @@ config.yml hydra_outputs/ .commit0* .agent* -docs/analysis*.md \ No newline at end of file +docs/analysis*.md +wandb/ diff --git a/commit0/cli.py b/commit0/cli.py index 1b67080..5a33f20 100644 --- a/commit0/cli.py +++ b/commit0/cli.py @@ -2,6 +2,7 @@ from pathlib import Path from typing import Union, List from typing_extensions import Annotated +import commit0.harness.batch_run_pytest_ids import commit0.harness.run_pytest_ids import commit0.harness.get_pytest_ids import commit0.harness.build @@ -300,6 +301,49 @@ def test( ) +@commit0_app.command() +def batch_test( + test_ids: str = typer.Argument( + None, + help='All ways pytest supports to run and select tests. Please provide a single string. Example: "test_mod.py", "testing/", "test_mod.py::test_func", "-k \'MyClass and not method\'"', + ), + backend: str = typer.Option("modal", help="Backend to use for testing"), + timeout: int = typer.Option(1800, help="Timeout for tests in seconds"), + num_cpus: int = typer.Option(1, help="Number of CPUs to use"), + reference: Annotated[ + bool, typer.Option("--reference", help="Test the reference commit") + ] = False, + coverage: Annotated[ + bool, typer.Option("--coverage", help="Whether to get coverage information") + ] = False, + rebuild: bool = typer.Option( + False, "--rebuild", help="Whether to rebuild an image" + ), + commit0_config_file: str = typer.Option( + ".commit0.yaml", + help="Path to the commit0 dot file, where the setup config is stored", + ), + verbose: int = typer.Option( + 1, + "--verbose", + "-v", + help="Set this to 2 for more logging information", + count=True, + ), +) -> None: + """Run tests on a Commit0 repository.""" + commit0.harness.batch_run_pytest_ids.main( + test_ids, + reference, + coverage, + backend, + timeout, + num_cpus, + rebuild, + verbose, + ) + + @commit0_app.command() def evaluate( branch: Union[str, None] = typer.Option( diff --git a/commit0/harness/run_pytest_ids.py b/commit0/harness/run_pytest_ids.py index 06857df..f860314 100644 --- a/commit0/harness/run_pytest_ids.py +++ b/commit0/harness/run_pytest_ids.py @@ -68,14 +68,12 @@ def main( or "bigcodebench" in dataset_name or "codecontests" in dataset_name ): - repo_name = example["instance_id"] + repo_name = str(example["instance_id"]) dataset_type = "simple" else: repo_name = example["repo"].split("/")[-1] dataset_type = "commit0" - if repo_name in os.path.basename(repo_or_repo_dir) or repo_or_repo_dir.endswith( - repo_name - ): + if repo_name == os.path.basename(repo_or_repo_dir): spec = make_spec(example, dataset_type) break assert spec is not None, "No spec available" @@ -174,7 +172,7 @@ def main( prompt = example["prompt"] if "prompt" in example.keys() else "" matches = extract_code_blocks(solution) if len(matches) > 0: - solution = "\n\n".join(matches) + solution = matches[0] else: solution = prompt + "\n\n" + solution patch = solution + "\n\n" + example["test"] diff --git a/commit0/harness/utils.py b/commit0/harness/utils.py index 7bef05b..d63747b 100644 --- a/commit0/harness/utils.py +++ b/commit0/harness/utils.py @@ -239,7 +239,7 @@ def extract_code_blocks(text: str) -> List[str]: from the text. """ - pattern = r"```python\n(.*?)```" + pattern = r'(?s)```(?:python|py)?(.*?)```' matches = re.finditer(pattern, text, re.DOTALL) return [match.group(1).strip() for match in matches] diff --git a/examples/star/inference.py b/examples/star/inference.py new file mode 100644 index 0000000..43754a8 --- /dev/null +++ b/examples/star/inference.py @@ -0,0 +1,45 @@ +from typing import Dict, List +from datasets import Dataset +from vllm import LLM, SamplingParams +from examples.star.utils import generate_prompt, cleanup + + +def generate_predictions( + model_name: str, dataset: Dataset, temperature: float = 1.0, n: int = 1 +) -> List[List[str]]: + """Generate predictions for a given dataset using a specified language model and + sampling parameters. The function loads the dataset, constructs prompts from + each example, and obtains generated predictions. The resulting predictions are + then added as a new column to the dataset. + + Args: + ---- + model_name (str): Name of the model to use for generation. + dataset (Dataset): The Dataset object. + temperature (float, optional): Temperature setting for the model's + sampling strategy. Default is 1.0. + n (int, optional): Number of sampling runs per prompt. Default is 1. + + Returns: + ------- + predictions (List[List[str]]): Predictions on the dataset. + + """ + sampling_params = SamplingParams(n=n, temperature=temperature, max_tokens=512) + llm = LLM(model=model_name) + + prompts: List[List[Dict]] = [] + for example in dataset: + prompt = example["prompt"] + test = example["test"] + prompt = generate_prompt(prompt, test) + prompts.append([{"role": "user", "content": prompt}]) + + outputs = llm.chat(prompts, sampling_params) + + results: List[List[str]] = [] + for output in outputs: + generated_texts = [one.text for one in output.outputs] + results.append(generated_texts) + cleanup(llm, vllm=True) + return results diff --git a/examples/star/run.sh b/examples/star/run.sh new file mode 100644 index 0000000..e71e7ef --- /dev/null +++ b/examples/star/run.sh @@ -0,0 +1,15 @@ +python examples/star/star.py \ + --model_name_or_path meta-llama/Llama-3.1-8B-Instruct \ + --dataset_name commit0/mbpp \ + -n 100 \ + --output_dir outputs \ + --low_cpu_mem_usage \ + --with_tracking \ + --report_to wandb \ + --iteration 5 \ + --learning_rate 1e-6 \ + --per_device_train_batch_size 1 \ + --gradient_accumulation_steps 8 \ + --max_workers 64 \ + --temperature 1 + diff --git a/examples/star/star.py b/examples/star/star.py new file mode 100644 index 0000000..759594d --- /dev/null +++ b/examples/star/star.py @@ -0,0 +1,96 @@ +"""Main STaR Loop""" + +from copy import deepcopy +from datasets import Dataset, DatasetDict, load_dataset +from examples.star.inference import generate_predictions +from examples.star.train import train +from examples.star.utils import ( + execute_tests, + format_solution, + generate_prompt, + parse_args, +) + + +def main() -> None: + args = parse_args() + ds = load_dataset(args.dataset_name, args.dataset_config_name) + assert "train" in ds + # format the dataset for training and evaluation + for split in ds: + texts = [] + if split == "train": + continue + for example in ds[split]: + canonical_solution = f"```python\n{example['canonical_solution']}\n```" + text = [ + { + "role": "user", + "message": generate_prompt(example["prompt"], example["test"]), + }, + { + "role": "assistant", + "message": format_solution(canonical_solution, example["prompt"]), + }, + ] + texts.append(text) + ds[split] = ds[split].add_column(name="text", column=texts) + + model_name = args.model_name_or_path + output_dir = deepcopy(args.output_dir) + for i in range(args.iteration): + # sample + all_samples = generate_predictions( + model_name, ds["train"], args.temperature, args.n + ) + ds["train"].add_column(name="sample", column=all_samples).to_json( + f"{output_dir}/data/samples-iter{i}.json" + ) + assert len(ds["train"]) == len(all_samples) + + # verify and construct the training set + all_traces, all_execution_results = execute_tests( + ds["train"], all_samples, max_workers=args.max_workers + ) + passed_examples = [] + for example, execution_results, samples in zip( + ds["train"], all_execution_results, all_samples + ): + for execution_result, sample in zip(execution_results, samples): + # pytest exit code: https://docs.pytest.org/en/stable/reference/exit-codes.html + if execution_result == 0: + example["text"] = [ + { + "role": "user", + "message": generate_prompt( + example["prompt"], example["test"] + ), + }, + { + "role": "assistant", + "message": format_solution(sample, example["prompt"]), + }, + ] + passed_examples.append(example) + break + raw_datasets = DatasetDict( + { + "train": Dataset.from_list(passed_examples), + "validation": ds["validation"], + } + ) + raw_datasets["train"].to_json( + f"{output_dir}/data/verified-samples-iter{i}.json" + ) + + # train + args.output_dir = f"{output_dir}/models-iter{i}" + train(raw_datasets, model_name, args) + model_name = args.output_dir + + +if __name__ == "__main__": + main() + + +__all__ = [] diff --git a/examples/star/test.py b/examples/star/test.py new file mode 100644 index 0000000..f4833ab --- /dev/null +++ b/examples/star/test.py @@ -0,0 +1,44 @@ +"""Get test accuracy""" + +from datasets import load_dataset +from examples.star.inference import generate_predictions +from examples.star.utils import ( + execute_tests, + generate_prompt, + parse_args, +) + + +def main() -> None: + args = parse_args() + ds = load_dataset(args.dataset_name, args.dataset_config_name)['test'] + model_name = args.model_name_or_path + + # sample + all_samples = generate_predictions( + model_name, ds, args.temperature, args.n + ) + ds.add_column(name="sample", column=all_samples).to_json( + f"{args.output_dir}/data/{model_name.split('/')[-1]}-test-samples.json" + ) + assert len(ds) == len(all_samples) + + # verify and construct the training set + all_traces, all_execution_results = execute_tests( + ds, all_samples, max_workers=args.max_workers + ) + passed = 0 + for example, execution_results, samples in zip( + ds, all_execution_results, all_samples + ): + for execution_result, sample in zip(execution_results, samples): + # pytest exit code: https://docs.pytest.org/en/stable/reference/exit-codes.html + if execution_result == 0: + passed += 1 + print(f"passed: {passed/len(ds)}") + +if __name__ == "__main__": + main() + + +__all__ = [] diff --git a/examples/star/test.sh b/examples/star/test.sh new file mode 100644 index 0000000..b536db1 --- /dev/null +++ b/examples/star/test.sh @@ -0,0 +1,8 @@ +python examples/star/test.py \ + --model_name_or_path $1 \ + --dataset_name commit0/mbpp \ + -n $2 \ + --output_dir outputs \ + --max_workers 64 \ + --temperature 0 + diff --git a/examples/star/train.py b/examples/star/train.py new file mode 100644 index 0000000..377aa41 --- /dev/null +++ b/examples/star/train.py @@ -0,0 +1,409 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) +on a text file or a dataset without using HuggingFace Trainer. + +Here is the full list of checkpoints on the hub that can be fine-tuned by this script: +https://huggingface.co/models?filter=text-generation +""" +# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments. + +import json +import logging +import math +import os +import random +from pathlib import Path + +import datasets +import torch +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import set_seed +from huggingface_hub import HfApi +from torch.utils.data import DataLoader +from tqdm.auto import tqdm + +import transformers +from transformers import ( + AutoConfig, + AutoModelForCausalLM, + AutoTokenizer, + DataCollatorForSeq2Seq, + get_scheduler, +) + +from examples.star.utils import cleanup + + +logger = get_logger(__name__) + + +def train(raw_datasets, model_name_or_path, args) -> None: + # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. + # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers + # in the environment + accelerator_log_kwargs = {} + + if args.with_tracking: + accelerator_log_kwargs["log_with"] = args.report_to + accelerator_log_kwargs["project_dir"] = args.output_dir + + accelerator = Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + **accelerator_log_kwargs, + ) + + # Make one log on every process with the configuration for debugging. + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, + ) + logger.info(accelerator.state, main_process_only=False) + if accelerator.is_local_main_process: + datasets.utils.logging.set_verbosity_warning() + transformers.utils.logging.set_verbosity_info() + else: + datasets.utils.logging.set_verbosity_error() + transformers.utils.logging.set_verbosity_error() + + # If passed along, set the training seed now. + if args.seed is not None: + set_seed(args.seed) + + # Handle the repository creation + if accelerator.is_main_process: + if args.push_to_hub: + # Retrieve of infer repo_name + repo_name = args.hub_model_id + if repo_name is None: + repo_name = Path(args.output_dir).absolute().name + # Create repo and retrieve repo_id + api = HfApi() + repo_id = api.create_repo( + repo_name, exist_ok=True, token=args.hub_token + ).repo_id + + with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: + if "step_*" not in gitignore: + gitignore.write("step_*\n") + if "epoch_*" not in gitignore: + gitignore.write("epoch_*\n") + elif args.output_dir is not None: + os.makedirs(args.output_dir, exist_ok=True) + accelerator.wait_for_everyone() + + # Load pretrained model and tokenizer + # + # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently + # download model & vocab. + config = AutoConfig.from_pretrained( + model_name_or_path, + trust_remote_code=args.trust_remote_code, + ) + tokenizer = AutoTokenizer.from_pretrained( + model_name_or_path, + use_fast=not args.use_slow_tokenizer, + trust_remote_code=args.trust_remote_code, + ) + tokenizer.padding_side = "right" + + model = AutoModelForCausalLM.from_pretrained( + model_name_or_path, + from_tf=bool(".ckpt" in args.model_name_or_path), + config=config, + low_cpu_mem_usage=args.low_cpu_mem_usage, + trust_remote_code=args.trust_remote_code, + torch_dtype=torch.bfloat16, + device_map="auto", + ) + + # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch + # on a small vocab and want a smaller embedding size, remove this test. + embedding_size = model.get_input_embeddings().weight.shape[0] + if len(tokenizer) > embedding_size: + model.resize_token_embeddings(len(tokenizer)) + + # Preprocessing the datasets. + # First we tokenize all the texts. + column_names = raw_datasets["train"].column_names + text_column_name = "text" if "text" in column_names else column_names[0] + + def tokenize_function(examples): + out = tokenizer.apply_chat_template(examples[text_column_name]) + out = {"input_ids": out} + return out + + with accelerator.main_process_first(): + tokenized_datasets = raw_datasets.map( + tokenize_function, + batched=True, + num_proc=args.preprocessing_num_workers, + remove_columns=column_names, + load_from_cache_file=not args.overwrite_cache, + desc="Running tokenizer on dataset", + ) + + train_dataset = tokenized_datasets["train"] + eval_dataset = tokenized_datasets["validation"] + + # Log a few random samples from the training set: + for index in random.sample(range(len(train_dataset)), 3): + logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") + + tokenizer.pad_token = tokenizer.eos_token + data_collator = DataCollatorForSeq2Seq( + tokenizer, + model=model, + label_pad_token_id=tokenizer.pad_token_id, + ) + + # DataLoaders creation: + train_dataloader = DataLoader( + train_dataset, + shuffle=True, + collate_fn=data_collator, + batch_size=args.per_device_train_batch_size, + ) + eval_dataloader = DataLoader( + eval_dataset, + collate_fn=data_collator, + batch_size=args.per_device_eval_batch_size, + ) + + # Optimizer + # Split weights in two groups, one with weight decay and the other not. + no_decay = ["bias", "layer_norm.weight"] + optimizer_grouped_parameters = [ + { + "params": [ + p + for n, p in model.named_parameters() + if not any(nd in n for nd in no_decay) + ], + "weight_decay": args.weight_decay, + }, + { + "params": [ + p + for n, p in model.named_parameters() + if any(nd in n for nd in no_decay) + ], + "weight_decay": 0.0, + }, + ] + optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil( + len(train_dataloader) / args.gradient_accumulation_steps + ) + if args.max_train_steps is None: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + overrode_max_train_steps = True + + lr_scheduler = get_scheduler( + name=args.lr_scheduler_type, + optimizer=optimizer, + num_warmup_steps=args.num_warmup_steps * accelerator.num_processes, + num_training_steps=args.max_train_steps + if overrode_max_train_steps + else args.max_train_steps * accelerator.num_processes, + ) + + # Prepare everything with our `accelerator`. + model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = ( + accelerator.prepare( + model, optimizer, train_dataloader, eval_dataloader, lr_scheduler + ) + ) + + # We need to recalculate our total training steps as the size of the training dataloader may have changed. + num_update_steps_per_epoch = math.ceil( + len(train_dataloader) / args.gradient_accumulation_steps + ) + if overrode_max_train_steps: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + # Afterwards we recalculate our number of training epochs + args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + + # Figure out how many steps we should save the Accelerator states + checkpointing_steps = args.checkpointing_steps + if checkpointing_steps is not None and checkpointing_steps.isdigit(): + checkpointing_steps = int(checkpointing_steps) + + # We need to initialize the trackers we use, and also store our configuration. + # The trackers initializes automatically on the main process. + if args.with_tracking: + experiment_config = vars(args) + accelerator.init_trackers("clm_no_trainer", experiment_config) + + # Train! + total_batch_size = ( + args.per_device_train_batch_size + * accelerator.num_processes + * args.gradient_accumulation_steps + ) + + logger.info("***** Running training *****") + logger.info(f" Num examples = {len(train_dataset)}") + logger.info(f" Num Epochs = {args.num_train_epochs}") + logger.info( + f" Instantaneous batch size per device = {args.per_device_train_batch_size}" + ) + logger.info( + f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}" + ) + logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") + logger.info(f" Total optimization steps = {args.max_train_steps}") + # Only show the progress bar once on each machine. + progress_bar = tqdm( + range(args.max_train_steps), disable=not accelerator.is_local_main_process + ) + completed_steps = 0 + starting_epoch = 0 + + for epoch in range(starting_epoch, args.num_train_epochs): + model.train() + if args.with_tracking: + total_loss = 0 + active_dataloader = train_dataloader + for step, batch in enumerate(active_dataloader): + with accelerator.accumulate(model): + batch["labels"] = batch["input_ids"].clone().detach() + indices = (batch["input_ids"] == tokenizer.eos_token_id).cumsum( + dim=1 + ) == 0 + batch["labels"][indices] = -100 + outputs = model(**batch) + loss = outputs.loss + # We keep track of the loss at each epoch + if args.with_tracking: + total_loss += loss.detach().float() + accelerator.backward(loss) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + progress_bar.update(1) + completed_steps += 1 + + if isinstance(checkpointing_steps, int): + if ( + completed_steps % checkpointing_steps == 0 + and accelerator.sync_gradients + ): + output_dir = f"step_{completed_steps}" + if args.output_dir is not None: + output_dir = os.path.join(args.output_dir, output_dir) + accelerator.save_state(output_dir) + if completed_steps >= args.max_train_steps: + break + + model.eval() + losses = [] + for step, batch in enumerate(eval_dataloader): + with torch.no_grad(): + batch["labels"] = batch["input_ids"].clone().detach() + indices = (batch["input_ids"] == tokenizer.eos_token_id).cumsum( + dim=1 + ) == 0 + batch["labels"][indices] = -100 + outputs = model(**batch) + + loss = outputs.loss + losses.append( + accelerator.gather_for_metrics( + loss.repeat(args.per_device_eval_batch_size) + ) + ) + + losses = torch.cat(losses) + try: + eval_loss = torch.mean(losses) + perplexity = math.exp(eval_loss) + except OverflowError: + perplexity = float("inf") + + logger.info(f"epoch {epoch}: perplexity: {perplexity} eval_loss: {eval_loss}") + + if args.with_tracking: + accelerator.log( + { + "perplexity": perplexity, + "eval_loss": eval_loss, + "train_loss": total_loss.item() / len(train_dataloader), + "epoch": epoch, + "step": completed_steps, + }, + step=completed_steps, + ) + + if args.push_to_hub and epoch < args.num_train_epochs - 1: + accelerator.wait_for_everyone() + unwrapped_model = accelerator.unwrap_model(model) + unwrapped_model.save_pretrained( + args.output_dir, + is_main_process=accelerator.is_main_process, + save_function=accelerator.save, + ) + if accelerator.is_main_process: + tokenizer.save_pretrained(args.output_dir) + api.upload_folder( + commit_message=f"Training in progress epoch {epoch}", + folder_path=args.output_dir, + repo_id=repo_id, + repo_type="model", + token=args.hub_token, + ) + + if args.checkpointing_steps == "epoch": + output_dir = f"epoch_{epoch}" + if args.output_dir is not None: + output_dir = os.path.join(args.output_dir, output_dir) + accelerator.save_state(output_dir) + + if args.with_tracking: + accelerator.end_training() + + if args.output_dir is not None: + accelerator.wait_for_everyone() + unwrapped_model = accelerator.unwrap_model(model) + unwrapped_model.save_pretrained( + args.output_dir, + is_main_process=accelerator.is_main_process, + save_function=accelerator.save, + ) + if accelerator.is_main_process: + tokenizer.save_pretrained(args.output_dir) + if args.push_to_hub: + api.upload_folder( + commit_message="End of training", + folder_path=args.output_dir, + repo_id=repo_id, + repo_type="model", + token=args.hub_token, + ) + with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: + json.dump({"perplexity": perplexity}, f) + cleanup(model) + + +__all__ = [] diff --git a/examples/star/utils.py b/examples/star/utils.py new file mode 100644 index 0000000..25a4a46 --- /dev/null +++ b/examples/star/utils.py @@ -0,0 +1,342 @@ +import argparse +import gc +import subprocess +from concurrent.futures import ThreadPoolExecutor, as_completed +from datasets import Dataset +from tqdm import tqdm +from typing import Any, List, Tuple +from transformers import SchedulerType +from commit0.harness.utils import extract_code_blocks + + +def execute_tests( + examples: Dataset, all_samples: List[List[str]], max_workers: int = 100 +) -> Tuple[List[List[str]], List[List[int]]]: + """Run `commit0 test` in parallel for all (example, sample) pairs and collect results. + + This function: + 1. Flattens the iteration over examples and samples into a single list of tasks. + 2. Executes them in parallel with a ThreadPoolExecutor. + 3. Reassembles the results into two lists of lists: + - `all_traces`, containing the stdout for each sample. + - `all_execution_results`, containing the exit code for each sample. + + We assume: + - `ds["train"]` is a list of dictionaries, each representing an example. + Each example contains an "instance_id" key. + - `all_samples` is a list where each element corresponds to an example from `ds["train"]`. + Each element of `all_samples` is a list of strings (samples). + - All elements of `all_samples` are of equal length. + + Args: + ---- + examples (Dataset): A Dataset object. + all_samples (List[List[str]]): A 2D list of strings, where `all_samples[i]` corresponds to the samples associated with `ds[i]`. + max_workers (int): The number of worker threads to use for parallel execution. Default is 100. + + Returns: + ------- + Tuple[List[List[str]], List[List[int]]]: + A tuple of (all_traces, all_execution_results) where: + - all_traces is a 2D list of strings: all_traces[i][j] is the stdout from running `commit0 test` on `ds[i]` with `all_samples[i][j]` as stdin. + - all_execution_results is a 2D list of ints: all_execution_results[i][j] is the exit code from running the command for that example/sample pair. + + """ + M = len(examples) + N = len(all_samples[0]) if M > 0 else 0 + + # Flatten tasks: each task is (example_index, sample_index, instance_id, input_sample) + tasks = [] + for i, example in enumerate(examples): + instance_id = str(example["instance_id"]) + for j, sample in enumerate(all_samples[i]): + tasks.append((i, j, instance_id, sample)) + + all_traces = [[None] * N for _ in range(M)] + all_execution_results = [[None] * N for _ in range(M)] + + # Run all tasks in parallel + with ThreadPoolExecutor(max_workers=max_workers) as executor: + futures = { + executor.submit( + subprocess.run, + ["commit0", "test", instance_id, "--timeout", "30", "--stdin"], + input=sample, + text=True, + capture_output=True, + timeout=30, + ): (i, j) + for (i, j, instance_id, sample) in tasks + } + + for future in tqdm( + as_completed(futures), total=len(tasks), desc="Executing tests" + ): + i, j = futures[future] + try: + result = future.result() + stdout = result.stdout + exit_code = result.returncode + except subprocess.TimeoutExpired: + stdout = "Timeout" + exit_code = -1 + all_traces[i][j] = stdout + all_execution_results[i][j] = exit_code + + return all_traces, all_execution_results + + +def generate_prompt(prompt: str, test: str) -> str: + """Generate a Python code request prompt string.""" + return f"""Write a Python function implementation for the following prompt: + +{prompt} + +Your code should satisfy these tests: + +{test} + +Return only the implementation code, no tests or explanations. Be sure to include the relevant import statements: +```python +code +``` +""" + + +def format_solution(text: str, prompt: str) -> str: + """Extracts a code block from the given text and formats it as a Python code snippet. + + Args: + ---- + text (str): The input text which may contain code blocks. + prompt (str): A string that will be returned if no code block is found. + + Returns: + ------- + str: A formatted code snippet if a code block exists, otherwise the prompt and text. + + """ + matches = extract_code_blocks(text) + if len(matches) > 0: + solution = matches[0] + solution = f"```python\n{solution}\n```" + else: + solution = prompt + "\n\n" + text + return solution + + +def parse_args() -> argparse.Namespace: + """Parse command-line arguments. + + Returns + ------- + argparse.Namespace: The parsed command-line arguments. + + """ + parser = argparse.ArgumentParser( + description="Finetune a transformers model on a causal language modeling task" + ) + parser.add_argument("--temperature", type=float, default=1) + parser.add_argument("-n", type=int, default=1) + parser.add_argument("--iteration", type=int, default=1) + parser.add_argument("--max_workers", type=int, default=100) + parser.add_argument( + "--dataset_name", + type=str, + default=None, + help="The name of the dataset to use (via the datasets library).", + ) + parser.add_argument( + "--dataset_config_name", + type=str, + default=None, + help="The configuration name of the dataset to use (via the datasets library).", + ) + parser.add_argument( + "--model_name_or_path", + type=str, + help="Path to pretrained model or model identifier from huggingface.co/models.", + required=False, + ) + parser.add_argument( + "--use_slow_tokenizer", + action="store_true", + help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", + ) + parser.add_argument( + "--per_device_train_batch_size", + type=int, + default=8, + help="Batch size (per device) for the training dataloader.", + ) + parser.add_argument( + "--per_device_eval_batch_size", + type=int, + default=8, + help="Batch size (per device) for the evaluation dataloader.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=5e-5, + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--weight_decay", type=float, default=0.0, help="Weight decay to use." + ) + parser.add_argument( + "--num_train_epochs", + type=int, + default=3, + help="Total number of training epochs to perform.", + ) + parser.add_argument( + "--max_train_steps", + type=int, + default=None, + help="Total number of training steps to perform. If provided, overrides num_train_epochs.", + ) + parser.add_argument( + "--gradient_accumulation_steps", + type=int, + default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.", + ) + parser.add_argument( + "--lr_scheduler_type", + type=SchedulerType, + default="linear", + help="The scheduler type to use.", + choices=[ + "linear", + "cosine", + "cosine_with_restarts", + "polynomial", + "constant", + "constant_with_warmup", + ], + ) + parser.add_argument( + "--num_warmup_steps", + type=int, + default=0, + help="Number of steps for the warmup in the lr scheduler.", + ) + parser.add_argument( + "--output_dir", type=str, default=None, help="Where to store the final model." + ) + parser.add_argument( + "--seed", type=int, default=None, help="A seed for reproducible training." + ) + parser.add_argument( + "--preprocessing_num_workers", + type=int, + default=None, + help="The number of processes to use for the preprocessing.", + ) + parser.add_argument( + "--overwrite_cache", + action="store_true", + help="Overwrite the cached training and evaluation sets", + ) + parser.add_argument( + "--push_to_hub", + action="store_true", + help="Whether or not to push the model to the Hub.", + ) + parser.add_argument( + "--hub_model_id", + type=str, + help="The name of the repository to keep in sync with the local `output_dir`.", + ) + parser.add_argument( + "--hub_token", type=str, help="The token to use to push to the Model Hub." + ) + parser.add_argument( + "--trust_remote_code", + action="store_true", + help=( + "Whether to trust the execution of code from datasets/models defined on the Hub." + " This option should only be set to `True` for repositories you trust and in which you have read the" + " code, as it will execute code present on the Hub on your local machine." + ), + ) + parser.add_argument( + "--checkpointing_steps", + type=str, + default=None, + help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", + ) + parser.add_argument( + "--with_tracking", + action="store_true", + help="Whether to enable experiment trackers for logging.", + ) + parser.add_argument( + "--report_to", + type=str, + default="all", + help=( + 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' + ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. ' + "Only applicable when `--with_tracking` is passed." + ), + ) + parser.add_argument( + "--low_cpu_mem_usage", + action="store_true", + help=( + "It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. " + "If passed, LLM loading time and RAM consumption will be benefited." + ), + ) + args = parser.parse_args() + + if args.push_to_hub: + if args.output_dir is None: + raise ValueError( + "Need an `output_dir` to create a repo when `--push_to_hub` is passed." + ) + + return args + + +def cleanup(model: Any, vllm: bool = False) -> None: + """Clean up resources associated with the given model. + + Parameters + ---------- + model : Any + The model object whose resources are to be cleaned up. + vllm : Boolean + The model object whose resources are to be cleaned up. + + Returns + ------- + None + + """ + try: + import torch + import contextlib + + if torch.cuda.is_available(): + if vllm: + from vllm.distributed.parallel_state import ( + destroy_model_parallel, + destroy_distributed_environment, + ) + + destroy_model_parallel() + destroy_distributed_environment() + del model.llm_engine.model_executor + if not vllm: + model = model.cpu() + del model + with contextlib.suppress(AssertionError): + torch.distributed.destroy_process_group() + gc.collect() + torch.cuda.empty_cache() + torch.cuda.synchronize() + except ImportError: + del model diff --git a/pyproject.toml b/pyproject.toml index 33bdac4..ad5ebef 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,7 +36,15 @@ packages = ["commit0", "agent"] agent = [ "import-deps>=0.3.0", "PyMuPDF>=1.24.5", - "aider-chat", + #"aider-chat>=0.69.1", +] +example = [ + "vllm==0.6.5", + "transformers>=4.47.1", + "setuptools", + "accelerate", + "wandb>=0.19.0", + "openai>=1.58.1", ] [project.urls] diff --git a/uv.lock b/uv.lock index 3b9ed42..116294e 100644 --- a/uv.lock +++ b/uv.lock @@ -1,100 +1,48 @@ version = 1 requires-python = ">=3.10" resolution-markers = [ - "python_full_version < '3.11'", - "python_full_version == '3.11.*'", - "python_full_version == '3.12.*'", - "python_full_version >= '3.13'", -] - -[[package]] -name = "aider-chat" -version = "0.1.dev7624+g1e4fb74" -source = { git = "https://github.com/wenting-zhao/aider.git#1e4fb74e9cc207f3d3f8ac6106e31401f1cfbc2f" } + "python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform != 'linux'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'linux') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')", + "python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform == 'linux'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'", + "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'linux') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux')", + "python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform != 'linux'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'linux'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'linux') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')", + "python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform == 'linux'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'linux') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform 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