Add files using upload-large-folder tool

.gitattributes

@@ -125,3 +125,4 @@ lm-evaluation-harness/wandb/run-20240605_062502-fvj55jrc/run-fvj55jrc.wandb filt
 lm-evaluation-harness/wandb/run-20240608_122925-vmbmpokf/run-vmbmpokf.wandb filter=lfs diff=lfs merge=lfs -text
 lm-evaluation-harness/wandb/run-20240605_110038-lwgnduuo/run-lwgnduuo.wandb filter=lfs diff=lfs merge=lfs -text
 lm-evaluation-harness/wandb/run-20240605_140919-mkdnls2x/run-mkdnls2x.wandb filter=lfs diff=lfs merge=lfs -text
+lm-evaluation-harness/wandb/run-20240605_093020-laxetjfu/run-laxetjfu.wandb filter=lfs diff=lfs merge=lfs -text

lm-evaluation-harness/scripts/build_benchmark.py

@@ -0,0 +1,61 @@
+import argparse
+import os
+
+import yaml
+from promptsource.templates import DatasetTemplates
+from tqdm import tqdm
+
+# from lm_eval.api.registry import ALL_TASKS
+from lm_eval.logger import eval_logger
+
+
+# from lm_eval.tasks import include_task_folder
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--benchmark_name", required=True)
+    parser.add_argument("--benchmark_path", required=True)
+    parser.add_argument("--task_save_path", default="lm_eval/tasks/")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    with open(args.benchmark_path, encoding="utf-8") as file:
+        TASK_LIST = yaml.full_load(file)
+        for task in tqdm(TASK_LIST):
+            eval_logger.info(f"Processing {task}")
+
+            dataset_name = task["dataset_path"]
+            if "dataset_name" in task:
+                subset_name = task["dataset_name"]
+                file_subdir = f"{dataset_name}/{subset_name}"
+            else:
+                subset_name = None
+                file_subdir = f"{dataset_name}"
+
+            file_path = os.path.join(args.task_save_path, file_subdir, "promptsource/")
+
+            os.makedirs(file_path, exist_ok=True)
+
+            if subset_name is None:
+                prompts = DatasetTemplates(dataset_name=dataset_name)
+            else:
+                prompts = DatasetTemplates(
+                    dataset_name=dataset_name, subset_name=subset_name
+                )
+
+            for idx, prompt_name in enumerate(prompts.all_template_names):
+                full_file_name = f"promptsource_{idx}.yaml"
+                config_dict = {
+                    "group": args.benchmark_name,
+                    "include": "promptsource_template.yaml",
+                    "use_prompts": f"promptsource:{prompt_name}",
+                }
+
+                file_save_path = os.path.join(file_path, full_file_name)
+                eval_logger.info(f"Save to {file_save_path}")
+                with open(file_save_path, "w", encoding="utf-8") as yaml_file:
+                    yaml.dump(config_dict, yaml_file)

lm-evaluation-harness/scripts/cost_estimate.py

@@ -0,0 +1,99 @@
+import random
+
+import transformers
+
+from lm_eval import evaluator, tasks
+from lm_eval.api.model import LM
+
+
+class DryrunLM(LM):
+    def __init__(self):
+        self.tokencost = 0
+        self.tokenizer = transformers.GPT2TokenizerFast.from_pretrained("gpt2")
+        self.tokenizer.pad_token = "<|endoftext|>"
+
+    @classmethod
+    def create_from_arg_string(cls, arg_string):
+        return cls()
+
+    def loglikelihood(self, requests):
+        res = []
+
+        for ctx, cont in requests:
+            res.append((-random.random(), False))
+            self.tokencost += len(self.tokenizer.tokenize(ctx + cont))
+
+        return res
+
+    def generate_until(self, requests):
+        res = []
+
+        for ctx, _ in requests:
+            res.append("lol")
+
+            # assume worst case - generates until 256
+            self.tokencost += len(self.tokenizer.tokenize(ctx)) + 256
+
+        return res
+
+    def loglikelihood_rolling(self, requests):
+        res = []
+
+        for (s,) in requests:
+            # assume worst case: extra full context
+            self.tokencost += len(self.tokenizer.tokenize(s)) + 2048
+
+        return res
+
+
+def main():
+    lm = DryrunLM()
+
+    task_list = "arc_challenge,arc_easy,boolq,cola,copa,headqa,hellaswag,lambada,logiqa,mathqa,mc_taco,mrpc,multirc,openbookqa,piqa,prost,pubmedqa,qnli,qqp,race,record,rte,sciq,sst,triviaqa,webqs,wic,wikitext,winogrande,wnli,wsc"
+    values = []
+    for taskname in task_list.split(","):
+        lm.tokencost = 0
+        evaluator.simple_evaluate(
+            lm=lm,
+            task_dict={taskname: tasks.get_task(taskname)()},
+            num_fewshot=0,
+            limit=None,
+            bootstrap_iters=10,
+        )
+
+        print(taskname, lm.tokencost)
+        values.append(
+            [
+                taskname,
+                lm.tokencost,
+                lm.tokencost / 1000 * 0.0008,
+                lm.tokencost / 1000 * 0.0012,
+                lm.tokencost / 1000 * 0.006,
+                lm.tokencost / 1000 * 0.06,
+            ]
+        )
+    from pytablewriter import MarkdownTableWriter
+
+    writer = MarkdownTableWriter()
+    writer.headers = ["Task", "Tokens", "Ada", "Babbage", "Curie", "Davinci"]
+
+    values.sort(key=lambda x: -x[1])
+    totcost = sum([x[1] for x in values])
+    values.append(
+        [
+            "**Total**",
+            totcost,
+            totcost / 1000 * 0.0008,
+            totcost / 1000 * 0.0012,
+            totcost / 1000 * 0.006,
+            totcost / 1000 * 0.06,
+        ]
+    )
+
+    writer.value_matrix = values
+
+    print(writer.dumps())
+
+
+if __name__ == "__main__":
+    main()

lm-evaluation-harness/scripts/get_prompts.py

@@ -0,0 +1,25 @@
+from itertools import islice
+
+from lm_eval import tasks
+
+
+ct = 3
+
+for (
+    tname,
+    Task,
+) in tasks.TASK_REGISTRY.items():  # [('record', tasks.superglue.ReCoRD)]:#
+    task = Task()
+
+    print("#", tname)
+    docs = islice(
+        task.validation_docs() if task.has_validation_docs() else task.test_docs(), ct
+    )
+    print()
+    for i in range(ct):
+        print()
+        doc = next(docs)
+        print("**Context**:", "\n```\n" + task.doc_to_text(doc) + "\n```\n")
+        print()
+        print("**Target**:", "\n```\n" + task.doc_to_target(doc) + "\n```\n")
+        print()

lm-evaluation-harness/scripts/make_table_results.py

@@ -0,0 +1,74 @@
+"""
+Usage:
+   python make_table_tasks.py --output <markdown_filename>
+"""
+import json
+import logging
+import os
+
+from pytablewriter import LatexTableWriter, MarkdownTableWriter
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def make_table(result_dict):
+    """Generate table of results."""
+    md_writer = MarkdownTableWriter()
+    latex_writer = LatexTableWriter()
+    md_writer.headers = ["Task", "Version", "Metric", "Value", "", "Stderr"]
+    latex_writer.headers = ["Task", "Version", "Metric", "Value", "", "Stderr"]
+
+    values = []
+
+    for k, dic in sorted(result_dict["results"].items()):
+        version = result_dict["versions"][k]
+        percent = k == "squad2"
+        for m, v in dic.items():
+            if m.endswith("_stderr"):
+                continue
+
+            if m + "_stderr" in dic:
+                se = dic[m + "_stderr"]
+                if percent or m == "ppl":
+                    values.append([k, version, m, "%.2f" % v, "±", "%.2f" % se])
+                else:
+                    values.append(
+                        [k, version, m, "%.2f" % (v * 100), "±", "%.2f" % (se * 100)]
+                    )
+            else:
+                if percent or m == "ppl":
+                    values.append([k, version, m, "%.2f" % v, "", ""])
+                else:
+                    values.append([k, version, m, "%.2f" % (v * 100), "", ""])
+            k = ""
+            version = ""
+    md_writer.value_matrix = values
+    latex_writer.value_matrix = values
+
+    # todo: make latex table look good
+    # print(latex_writer.dumps())
+
+    return md_writer.dumps()
+
+
+if __name__ == "__main__":
+    # loop dirs and subdirs in results dir
+    # for each dir, load json files
+    for dirpath, dirnames, filenames in os.walk("../results"):
+        # skip dirs without files
+        if not filenames:
+            continue
+        path_readme = os.path.join(dirpath, "README.md")
+        with open(path_readme, "w", encoding="utf-8") as f:
+            # get path name, only last folder
+            path_name = dirpath.split("/")[-1]
+            f.write(f"# {path_name} \n\n")
+        for filename in sorted([f for f in filenames if f.endswith(".json")]):
+            path = os.path.join(dirpath, filename)
+            with open(path, "r", encoding="utf-8") as f:
+                result_dict = json.load(f)
+            with open(path_readme, "a", encoding="utf-8") as f:
+                f.write(f"## {filename} \n")
+                f.write(f"{make_table(result_dict)} \n")

lm-evaluation-harness/scripts/make_table_tasks.py

@@ -0,0 +1,54 @@
+"""
+Usage:
+   python make_table_tasks.py --output <markdown_filename>
+"""
+import argparse
+import logging
+
+from pytablewriter import MarkdownTableWriter
+
+from lm_eval import tasks
+
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def check(tf):
+    if tf:
+        return "✓"
+    else:
+        return " "
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--output", type=str, default="task_table.md")
+    args = parser.parse_args()
+
+    writer = MarkdownTableWriter()
+    writer.headers = ["Task Name", "Train", "Val", "Test", "Val/Test Docs", "Metrics"]
+    values = []
+
+    tasks = tasks.TASK_REGISTRY.items()
+    tasks = sorted(tasks, key=lambda x: x[0])
+    for tname, Task in tasks:
+        task = Task()
+        v = [
+            tname,
+            check(task.has_training_docs()),
+            check(task.has_validation_docs()),
+            check(task.has_test_docs()),
+            len(
+                list(
+                    task.test_docs() if task.has_test_docs() else task.validation_docs()
+                )
+            ),
+            ", ".join(task.aggregation().keys()),
+        ]
+        logger.info(v)
+        values.append(v)
+    writer.value_matrix = values
+    table = writer.dumps()
+    with open(args.output, "w", encoding="utf-8") as f:
+        f.write(table)

lm-evaluation-harness/scripts/requests_caching.py

@@ -0,0 +1,92 @@
+"""
+Usage:
+   python requests_caching.py --tasks=comma,separated,list,of,tasks --cache_requests=<true|refresh|delete]>
+"""
+
+import argparse
+import os
+from typing import List
+
+import torch
+from transformers import (
+    pipeline as trans_pipeline,
+)
+
+from lm_eval import simple_evaluate
+from lm_eval.evaluator import request_caching_arg_to_dict
+from lm_eval.utils import eval_logger
+
+
+MODULE_DIR = os.path.dirname(os.path.realpath(__file__))
+
+# Used to specify alternate cache path, useful if run in a docker container
+# NOTE raw datasets will break if you try to transfer the cache from your host to a docker image
+LM_HARNESS_CACHE_PATH = os.getenv("LM_HARNESS_CACHE_PATH")
+
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+MODEL = "EleutherAI/pythia-70m"
+
+TASK = "text-generation"
+
+
+def run_model_for_task_caching(tasks: List[str], cache_requests: str):
+    eval_logger.info(f"Loading HF model: {MODEL}")
+
+    trans_pipe = trans_pipeline(
+        task=TASK, model=MODEL, device=DEVICE, trust_remote_code=True
+    )
+
+    model = trans_pipe.model
+    tokenizer = trans_pipe.tokenizer
+
+    eval_logger.info(
+        f"Running simple_evaluate to cache request objects for tasks: {tasks}"
+    )
+
+    cache_args = request_caching_arg_to_dict(cache_requests=cache_requests)
+
+    eval_logger.info(
+        f"The following operations will be performed on the cache: {cache_requests}"
+    )
+
+    eval_data = simple_evaluate(
+        model="hf-auto",
+        model_args={
+            "pretrained": model,
+            "tokenizer": tokenizer,
+        },
+        limit=1,
+        device=DEVICE,
+        tasks=tasks,
+        write_out=True,
+        **cache_args,
+    )
+
+    return eval_data
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--tasks",
+        "-t",
+        default=None,
+        metavar="task1,task2",
+    )
+    parser.add_argument(
+        "--cache_requests",
+        type=str,
+        default=None,
+        choices=["true", "refresh", "delete"],
+        help="Speed up evaluation by caching the building of dataset requests. `None` if not caching.",
+    )
+
+    args = parser.parse_args()
+
+    tasks = args.tasks.split(",")
+
+    eval_data = run_model_for_task_caching(
+        tasks=tasks, model=MODEL, device=DEVICE, cache_requests=args.cache_requests
+    )

lm-evaluation-harness/scripts/write_out.py

@@ -0,0 +1,92 @@
+import argparse
+import os
+import random
+
+import numpy as np
+
+from lm_eval import tasks
+from lm_eval.tasks import TaskManager
+from lm_eval.utils import eval_logger, join_iters
+
+
+EXAMPLE_DIVIDER = "!!@@##@@!! -- Example {i}\n"
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--output_base_path", "--output_path", required=True)
+    parser.add_argument("--tasks", default="all_tasks")
+    parser.add_argument("--sets", type=str, default="val")  # example: val,test
+    parser.add_argument("--num_fewshot", type=int, default=1)
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--num_examples", type=int, default=1)
+    parser.add_argument(
+        "--include_path",
+        type=str,
+        default=None,
+        help="Additional path to include if there are external tasks to include.",
+    )
+    parser.add_argument(
+        "--verbosity",
+        type=str,
+        default="INFO",
+        help="Log error when tasks are not registered.",
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    np.random.seed(args.seed)
+
+    if args.include_path is not None:
+        eval_logger.info(f"Including path: {args.include_path}")
+
+    task_manager = TaskManager(args.verbosity, include_path=args.include_path)
+
+    if args.tasks == "all_tasks":
+        task_names = task_manager.all_tasks
+    else:
+        task_names = args.tasks.split(",")
+    task_dict = tasks.get_task_dict(task_names, task_manager)
+
+    os.makedirs(args.output_base_path, exist_ok=True)
+    for task_name, task in task_dict.items():
+        if isinstance(task, tuple):
+            _, task = task
+        rnd = random.Random()
+        rnd.seed(args.seed)
+
+        iters = []
+
+        for set in args.sets.split(","):
+            docs = None
+            if set == "train" and task.has_training_docs():
+                docs = task.training_docs()
+            if set == "val" and task.has_validation_docs():
+                docs = task.validation_docs()
+            if set == "test" and task.has_test_docs():
+                docs = task.test_docs()
+            if docs is not None:
+                iters.append(docs)
+
+        docs = join_iters(iters)
+
+        with open(
+            os.path.join(args.output_base_path, task_name), "w", encoding="utf8"
+        ) as f:
+            for i, doc in (
+                zip(range(args.num_examples), docs)
+                if args.num_examples > 0
+                else enumerate(docs)
+            ):
+                f.write(EXAMPLE_DIVIDER.format(i=i))
+                ctx = task.fewshot_context(
+                    doc=doc,
+                    num_fewshot=args.num_fewshot,
+                )
+                f.write(ctx + "\n")
+
+
+if __name__ == "__main__":
+    main()

lm-evaluation-harness/scripts/zeno_visualize.py

@@ -0,0 +1,219 @@
+import argparse
+import json
+import os
+import re
+from pathlib import Path
+
+import pandas as pd
+from zeno_client import ZenoClient, ZenoMetric
+
+from lm_eval.utils import eval_logger
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Upload your data to the Zeno AI evaluation platform to visualize results. This requires a ZENO_API_KEY in your environment variables. The eleuther harness must be run with log_samples=True and an output_path set for data to be written to disk."
+    )
+    parser.add_argument(
+        "--data_path",
+        required=True,
+        help="Where to find the results of the benchmarks that have been run. Uses the name of each subfolder as the model name.",
+    )
+    parser.add_argument(
+        "--project_name",
+        required=True,
+        help="The name of the generated Zeno project.",
+    )
+    return parser.parse_args()
+
+
+def main():
+    """Upload the results of your benchmark tasks to the Zeno AI evaluation platform.
+
+    This scripts expects your results to live in a data folder where subfolders contain results of individual models.
+    """
+    args = parse_args()
+
+    client = ZenoClient(os.environ["ZENO_API_KEY"])
+
+    # Get all model subfolders from the parent data folder.
+    models = [
+        os.path.basename(os.path.normpath(f))
+        for f in os.scandir(Path(args.data_path))
+        if f.is_dir()
+    ]
+
+    assert len(models) > 0, "No model directories found in the data_path."
+
+    tasks = set(tasks_for_model(models[0], args.data_path))
+
+    for model in models:  # Make sure that all models have the same tasks.
+        old_tasks = tasks.copy()
+        task_count = len(tasks)
+
+        model_tasks = tasks_for_model(model, args.data_path)
+        tasks.intersection(set(model_tasks))
+
+        if task_count != len(tasks):
+            eval_logger.warning(
+                f"All models must have the same tasks. {model} has tasks: {model_tasks} but have already recorded tasks: {old_tasks}. Taking intersection {tasks}"
+            )
+
+    assert (
+        len(tasks) > 0
+    ), "Must provide at least one task in common amongst models to compare."
+
+    for task in tasks:
+        # Upload data for all models
+        for model_index, model in enumerate(models):
+            model_args = re.sub(
+                r"[\"<>:/\|\\?\*\[\]]+",
+                "__",
+                json.load(
+                    open(Path(args.data_path, model, "results.json"), encoding="utf-8")
+                )["config"]["model_args"],
+            )
+            with open(
+                Path(args.data_path, model, f"{model_args}_{task}.jsonl"),
+                "r",
+                encoding="utf-8",
+            ) as file:
+                data = json.loads(file.read())
+
+            configs = json.load(
+                open(Path(args.data_path, model, "results.json"), encoding="utf-8")
+            )["configs"]
+            config = configs[task]
+
+            if model_index == 0:  # Only need to assemble data for the first model
+                metrics = []
+                for metric in config["metric_list"]:
+                    metrics.append(
+                        ZenoMetric(
+                            name=metric["metric"],
+                            type="mean",
+                            columns=[metric["metric"]],
+                        )
+                    )
+                project = client.create_project(
+                    name=args.project_name + (f"_{task}" if len(tasks) > 1 else ""),
+                    view="text-classification",
+                    metrics=metrics,
+                )
+                project.upload_dataset(
+                    generate_dataset(data, config),
+                    id_column="id",
+                    data_column="data",
+                    label_column="labels",
+                )
+
+            project.upload_system(
+                generate_system_df(data, config),
+                name=model,
+                id_column="id",
+                output_column="output",
+            )
+
+
+def tasks_for_model(model: str, data_path: str):
+    """Get the tasks for a specific model.
+
+    Args:
+        model (str): The name of the model.
+        data_path (str): The path to the data.
+
+    Returns:
+        list: A list of tasks for the model.
+    """
+    dir_path = Path(data_path, model)
+    config = (
+        json.load(open(Path(dir_path, "results.json"), encoding="utf-8"))["configs"],
+    )
+    return list(config[0].keys())
+
+
+def generate_dataset(
+    data,
+    config,
+):
+    """Generate a Zeno dataset from evaluation data.
+
+    Args:
+        data: The data to generate a dataset for.
+        config: The configuration of the task.
+
+    Returns:
+        pd.Dataframe: A dataframe that is ready to be uploaded to Zeno.
+    """
+    ids = [x["doc_id"] for x in data]
+    labels = [x["target"] for x in data]
+    instance = [""] * len(ids)
+
+    if config["output_type"] == "loglikelihood":
+        instance = [x["arguments"][0][0] for x in data]
+        labels = [x["arguments"][0][1] for x in data]
+    elif config["output_type"] == "multiple_choice":
+        instance = [
+            x["arguments"][0][0]
+            + "\n\n"
+            + "\n".join([f"- {y[1]}" for y in x["arguments"]])
+            for x in data
+        ]
+    elif config["output_type"] == "loglikelihood_rolling":
+        instance = [x["arguments"][0][0] for x in data]
+    elif config["output_type"] == "generate_until":
+        instance = [x["arguments"][0][0] for x in data]
+
+    return pd.DataFrame(
+        {
+            "id": ids,
+            "data": instance,
+            "input_len": [len(x) for x in instance],
+            "labels": labels,
+            "output_type": config["output_type"],
+        }
+    )
+
+
+def generate_system_df(data, config):
+    """Generate a dataframe for a specific system to be uploaded to Zeno.
+
+    Args:
+        data: The data to generate a dataframe from.
+        config: The configuration of the task.
+
+    Returns:
+        pd.Dataframe: A dataframe that is ready to be uploaded to Zeno as a system.
+    """
+    ids = [x["doc_id"] for x in data]
+    system_dict = {"id": ids}
+    system_dict["output"] = [""] * len(ids)
+
+    if config["output_type"] == "loglikelihood":
+        system_dict["output"] = [
+            "correct" if x["filtered_resps"][0][1] is True else "incorrect"
+            for x in data
+        ]
+    elif config["output_type"] == "multiple_choice":
+        system_dict["output"] = [
+            ", ".join([str(y[0]) for y in x["filtered_resps"]]) for x in data
+        ]
+        system_dict["num_answers"] = [len(x["filtered_resps"]) for x in data]
+    elif config["output_type"] == "loglikelihood_rolling":
+        system_dict["output"] = [str(x["filtered_resps"][0]) for x in data]
+    elif config["output_type"] == "generate_until":
+        system_dict["output"] = [str(x["filtered_resps"][0]) for x in data]
+        system_dict["output_length"] = [len(str(x["filtered_resps"][0])) for x in data]
+
+    metrics = {}
+    for metric in config["metric_list"]:
+        if "aggregation" in metric and metric["aggregation"] == "mean":
+            metrics[metric["metric"]] = [x[metric["metric"]] for x in data]
+
+    system_dict.update(metrics)
+    system_df = pd.DataFrame(system_dict)
+    return system_df
+
+
+if __name__ == "__main__":
+    main()

lm-evaluation-harness/wandb/run-20240514_103054-b5pto704/files/config.yaml

@@ -0,0 +1,43 @@
+wandb_version: 1
+
+_wandb:
+  desc: null
+  value:
+    python_version: 3.10.12
+    cli_version: 0.17.0
+    framework: huggingface
+    huggingface_version: 4.40.2
+    is_jupyter_run: false
+    is_kaggle_kernel: false
+    start_time: 1715682654
+    t:
+      1:
+      - 1
+      - 5
+      - 11
+      - 49
+      - 51
+      - 53
+      - 55
+      - 71
+      - 98
+      - 100
+      2:
+      - 1
+      - 5
+      - 11
+      - 49
+      - 51
+      - 53
+      - 55
+      - 71
+      - 98
+      - 100
+      3:
+      - 23
+      4: 3.10.12
+      5: 0.17.0
+      6: 4.40.2
+      8:
+      - 5
+      13: linux-x86_64

lm-evaluation-harness/wandb/run-20240514_103054-b5pto704/files/output.log

@@ -0,0 +1,42 @@
+
+2024-05-14:10:30:54,690 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-14:10:30:59,106 INFO     [__main__.py:335] Selected Tasks: ['indiccopa-hi']
+2024-05-14:10:30:59,108 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-14:10:30:59,108 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/data/cronscript/ckpts//hf_ckpt//global_step120'}
+Traceback (most recent call last):
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 398, in cached_file
+    resolved_file = hf_hub_download(
+  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
+    validate_repo_id(arg_value)
+  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
+    raise HFValidationError(
+huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/data/cronscript/ckpts//hf_ckpt//global_step120'. Use `repo_type` argument if needed.
+The above exception was the direct cause of the following exception:
+Traceback (most recent call last):
+  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in <module>
+    cli_evaluate()
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate
+    results = evaluator.simple_evaluate(
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper
+    return fn(*args, **kwargs)
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate
+    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string
+    return cls(**args, **args2)
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__
+    self._get_config(
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config
+    self._config = transformers.AutoConfig.from_pretrained(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 928, in from_pretrained
+    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 631, in get_config_dict
+    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 686, in _get_config_dict
+    resolved_config_file = cached_file(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 462, in cached_file
+    raise EnvironmentError(
+OSError: Incorrect path_or_model_id: '/data/cronscript/ckpts//hf_ckpt//global_step120'. Please provide either the path to a local folder or the repo_id of a model on the Hub.

lm-evaluation-harness/wandb/run-20240514_103054-b5pto704/files/wandb-metadata.json

@@ -0,0 +1,810 @@
+{
+    "os": "Linux-5.15.0-92-generic-x86_64-with-glibc2.35",
+    "python": "3.10.12",
+    "heartbeatAt": "2024-05-14T10:30:54.558191",
+    "startedAt": "2024-05-14T10:30:54.141204",
+    "docker": null,
+    "cuda": null,
+    "args": [
+        "--model",
+        "hf",
+        "--model_args",
+        "pretrained=/data/cronscript/ckpts//hf_ckpt//global_step120",
+        "--tasks",
+        "indiccopa-hi",
+        "--batch_size",
+        "auto",
+        "--wandb_args",
+        "project=bharatgpt"
+    ],
+    "state": "running",
+    "program": "-m lm_eval.__main__",
+    "codePathLocal": null,
+    "git": {
+        "remote": "https://github.com/EleutherAI/lm-evaluation-harness",
+        "commit": null
+    },
+    "email": null,
+    "root": "/data/cronscript/lm-evaluation-harness",
+    "host": "vizzhy-150-3",
+    "username": "root",
+    "executable": "/usr/bin/python3",
+    "cpu_count": 76,
+    "cpu_count_logical": 152,
+    "cpu_freq": {
+        "current": 3395.207467105263,
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lm-evaluation-harness/wandb/run-20240514_103117-od8j0s5f/files/output.log

@@ -0,0 +1,42 @@
+
+2024-05-14:10:31:17,623 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-14:10:31:22,022 INFO     [__main__.py:335] Selected Tasks: ['indiccopa-hi']
+2024-05-14:10:31:22,025 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-14:10:31:22,025 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/data/cronscript/ckpts//hf_ckpt//global_step20'}
+Traceback (most recent call last):
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 398, in cached_file
+    resolved_file = hf_hub_download(
+  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
+    validate_repo_id(arg_value)
+  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
+    raise HFValidationError(
+huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/data/cronscript/ckpts//hf_ckpt//global_step20'. Use `repo_type` argument if needed.
+The above exception was the direct cause of the following exception:
+Traceback (most recent call last):
+  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in <module>
+    cli_evaluate()
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate
+    results = evaluator.simple_evaluate(
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper
+    return fn(*args, **kwargs)
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate
+    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string
+    return cls(**args, **args2)
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__
+    self._get_config(
+  File "/data/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config
+    self._config = transformers.AutoConfig.from_pretrained(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 928, in from_pretrained
+    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 631, in get_config_dict
+    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 686, in _get_config_dict
+    resolved_config_file = cached_file(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 462, in cached_file
+    raise EnvironmentError(
+OSError: Incorrect path_or_model_id: '/data/cronscript/ckpts//hf_ckpt//global_step20'. Please provide either the path to a local folder or the repo_id of a model on the Hub.

lm-evaluation-harness/wandb/run-20240514_103117-od8j0s5f/files/wandb-summary.json

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+2024-05-14 10:31:17,491 ERROR   HandlerThread:10007 [gitlib.py:root():92] git root error: Cmd('git') failed due to: exit code(128)
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lm-evaluation-harness/wandb/run-20240514_114140-9mbhv4sb/files/output.log

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+
+2024-05-14:11:41:40,940 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-14:11:41:46,726 INFO     [__main__.py:335] Selected Tasks: ['indiccopa-hi']
+2024-05-14:11:41:46,728 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-14:11:41:46,728 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/data/cronscript/ckpts//hf_ckpt//global_step100'}
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/core/register.py:145: UserWarning: "hpu:X" notation is not supported by Gaudi PyTorch intergration bridge. Please change to "hpu" without index (Triggered internally at /npu-stack/pytorch-integration/pytorch_helpers/lazy_to_backend.cpp:53.)
+  return func(*args, **kwargs)
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
+  warnings.warn(
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/__init__.py:158: UserWarning: torch.hpu.setDeterministic is deprecated and will be removed in next release. Please use torch.use_deterministic_algorithms instead.
+  warnings.warn(
+You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
+[2024-05-14 11:41:58,454] [INFO] [real_accelerator.py:178:get_accelerator] Setting ds_accelerator to hpu (auto detect)
+2024-05-14:11:41:58,839 WARNING  [task.py:763] [Task: indiccopa-hi] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-14:11:41:58,839 WARNING  [task.py:775] [Task: indiccopa-hi] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for ai4bharat/IndicCOPA contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/ai4bharat/IndicCOPA
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+2024-05-14:11:42:00,045 WARNING  [task.py:322] [Task: indiccopa-hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2024-05-14:11:42:00,045 WARNING  [task.py:322] [Task: indiccopa-hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2024-05-14:11:42:00,064 INFO     [task.py:395] Building contexts for indiccopa-hi on rank 1...
+100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 56/56 [00:00<00:00, 108892.45it/s]
+Passed argument batch_size = auto:1. Detecting largest batch size
+2024-05-14:11:42:01,858 INFO     [evaluator.py:379] Running loglikelihood requests
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
+  warnings.warn(
+Determined largest batch size: 64

lm-evaluation-harness/wandb/run-20240514_114140-9mbhv4sb/files/requirements.txt

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lm-evaluation-harness/wandb/run-20240514_164351-tzl0yppn/files/output.log

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+
+2024-05-14:16:43:51,998 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-14:16:43:56,657 INFO     [__main__.py:335] Selected Tasks: ['indiccopa-hi']
+2024-05-14:16:43:56,661 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-14:16:43:56,661 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/data/cronscript/ckpts//hf_ckpt//global_step100'}
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/core/register.py:145: UserWarning: "hpu:X" notation is not supported by Gaudi PyTorch intergration bridge. Please change to "hpu" without index (Triggered internally at /npu-stack/pytorch-integration/pytorch_helpers/lazy_to_backend.cpp:53.)
+  return func(*args, **kwargs)
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
+  warnings.warn(
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/hpu/__init__.py:158: UserWarning: torch.hpu.setDeterministic is deprecated and will be removed in next release. Please use torch.use_deterministic_algorithms instead.
+  warnings.warn(
+You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
+2024-05-14:16:44:05,369 WARNING  [task.py:763] [Task: indiccopa-hi] metric acc is defined, but aggregation is not. using default aggregation=mean
+2024-05-14:16:44:05,370 WARNING  [task.py:775] [Task: indiccopa-hi] metric acc is defined, but higher_is_better is not. using default higher_is_better=True
+[2024-05-14 16:44:04,912] [INFO] [real_accelerator.py:178:get_accelerator] Setting ds_accelerator to hpu (auto detect)
+/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for ai4bharat/IndicCOPA contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/ai4bharat/IndicCOPA
+You can avoid this message in future by passing the argument `trust_remote_code=True`.
+Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
+  warnings.warn(
+2024-05-14:16:44:07,146 WARNING  [task.py:322] [Task: indiccopa-hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2024-05-14:16:44:07,147 WARNING  [task.py:322] [Task: indiccopa-hi] has_training_docs and has_validation_docs are False, using test_docs as fewshot_docs but this is not recommended.
+2024-05-14:16:44:07,166 INFO     [task.py:395] Building contexts for indiccopa-hi on rank 3...
+100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 56/56 [00:00<00:00, 106136.93it/s]
+2024-05-14:16:44:08,634 INFO     [evaluator.py:379] Running loglikelihood requests
+/usr/local/lib/python3.10/dist-packages/habana_frameworks/torch/gpu_migration/torch/cuda/memory.py:36: UserWarning: No need to call empty_cache on HPU. It manages the memory internally in an effcient way.
+  warnings.warn(
+Passed argument batch_size = auto:1. Detecting largest batch size
+Determined largest batch size: 64

lm-evaluation-harness/wandb/run-20240514_164351-tzl0yppn/files/requirements.txt

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lm-evaluation-harness/wandb/run-20240522_164449-z16tyxyv/files/config.yaml

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+_wandb:
+  desc: null
+  value:
+    python_version: 3.10.12
+    cli_version: 0.17.0
+    framework: huggingface
+    huggingface_version: 4.41.0
+    is_jupyter_run: false
+    is_kaggle_kernel: false
+    start_time: 1716396289
+    t:
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lm-evaluation-harness/wandb/run-20240522_164449-z16tyxyv/files/output.log

@@ -0,0 +1,43 @@
+
+2024-05-22:16:44:49,734 INFO     [__main__.py:251] Verbosity set to INFO
+2024-05-22:16:44:58,265 INFO     [__main__.py:335] Selected Tasks: ['arc_easy', 'hellaswag', 'mrpc', 'openbookqa', 'sst2', 'winogrande']
+2024-05-22:16:44:58,266 INFO     [evaluator.py:131] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+2024-05-22:16:44:58,267 INFO     [evaluator.py:177] Initializing hf model, with arguments: {'pretrained': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step10000'}
+2024-05-22:16:45:00,569 INFO     [huggingface.py:164] Using device 'cuda'
+Traceback (most recent call last):
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 399, in cached_file
+    resolved_file = hf_hub_download(
+  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
+    validate_repo_id(arg_value)
+  File "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
+    raise HFValidationError(
+huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step10000'. Use `repo_type` argument if needed.
+The above exception was the direct cause of the following exception:
+Traceback (most recent call last):
+  File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
+    return _run_code(code, main_globals, None,
+  File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
+    exec(code, run_globals)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 417, in <module>
+    cli_evaluate()
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/__main__.py", line 341, in cli_evaluate
+    results = evaluator.simple_evaluate(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/utils.py", line 288, in _wrapper
+    return fn(*args, **kwargs)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/evaluator.py", line 180, in simple_evaluate
+    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/api/model.py", line 134, in create_from_arg_string
+    return cls(**args, **args2)
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 190, in __init__
+    self._get_config(
+  File "/mnt/weka/peacock/idc/cronscript/lm-evaluation-harness/lm_eval/models/huggingface.py", line 471, in _get_config
+    self._config = transformers.AutoConfig.from_pretrained(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 934, in from_pretrained
+    config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 632, in get_config_dict
+    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
+  File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 689, in _get_config_dict
+    resolved_config_file = cached_file(
+  File "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py", line 463, in cached_file
+    raise EnvironmentError(
+OSError: Incorrect path_or_model_id: '/mnt/weka/peacock/experiments/llama/checkpoint/llamav2-3b//hf_ckpt//global_step10000'. Please provide either the path to a local folder or the repo_id of a model on the Hub.

lm-evaluation-harness/wandb/run-20240522_164449-z16tyxyv/files/requirements.txt

@@ -0,0 +1,155 @@
+DataProperty==1.0.1
+GitPython==3.1.43
+Jinja2==3.1.4
+Markdown==3.6
+MarkupSafe==2.1.5
+Pillow-SIMD==7.0.0.post3
+PyYAML==6.0
+Werkzeug==3.0.3
+absl-py==2.1.0
+accelerate==0.30.1
+aiohttp==3.9.5
+aiosignal==1.3.1
+async-timeout==4.0.3
+attrs==23.2.0
+av==9.2.0
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