Modify choices for dataset and metrics

LICENSE

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README.md

@@ -4,7 +4,7 @@ emoji: 🔥
 colorFrom: green
 colorTo: indigo
 sdk: gradio
-sdk_version: 4.26.0
+sdk_version: 4.9.0
 app_file: app.py
 pinned: true
 license: apache-2.0

app.py

@@ -2,16 +2,13 @@
 import os
 import datetime
 import socket
-import base64
-from threading import Thread
 
 import gradio as gr
 import pandas as pd
-import time
+
 from apscheduler.schedulers.background import BackgroundScheduler
 
 from huggingface_hub import snapshot_download
-from pytz import utc
 
 from src.display.about import (
     CITATION_BUTTON_LABEL,
@@ -22,7 +19,6 @@ from src.display.about import (
     LLM_BENCHMARKS_DETAILS,
     FAQ_TEXT,
     TITLE,
-    ACKNOWLEDGEMENT_TEXT,
 )
 
 from src.display.css_html_js import custom_css
@@ -39,27 +35,13 @@ from src.display.utils import (
     fields,
     WeightType,
     Precision,
-    GPUType
 )
 
-from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, \
-    QUEUE_REPO, REPO_ID, RESULTS_REPO, DEBUG_QUEUE_REPO, DEBUG_RESULTS_REPO
+from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO
 from src.populate import get_evaluation_queue_df, get_leaderboard_df
 from src.submission.submit import add_new_eval
 from src.utils import get_dataset_summary_table
 
-def get_args():
-    import argparse
-
-    parser = argparse.ArgumentParser(description="Run the LLM Leaderboard")
-    parser.add_argument("--debug", action="store_true", help="Run in debug mode")
-    return parser.parse_args()
-
-args = get_args()
-if args.debug:
-    print("Running in debug mode")
-    QUEUE_REPO = DEBUG_QUEUE_REPO
-    RESULTS_REPO = DEBUG_RESULTS_REPO
 
 def ui_snapshot_download(repo_id, local_dir, repo_type, tqdm_class, etag_timeout):
     try:
@@ -76,7 +58,7 @@ def restart_space():
 
 
 def init_space():
-    # dataset_df = get_dataset_summary_table(file_path="blog/Hallucination-Leaderboard-Summary.csv")
+    dataset_df = get_dataset_summary_table(file_path="blog/Hallucination-Leaderboard-Summary.csv")
 
     if socket.gethostname() not in {"neuromancer"}:
         # sync model_type with open-llm-leaderboard
@@ -91,18 +73,11 @@ def init_space():
     finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(
         EVAL_REQUESTS_PATH, EVAL_COLS
     )
-    # return dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df
-    return None, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df
-    
-    
-def add_benchmark_columns(shown_columns):
-    benchmark_columns = []
-    for benchmark in BENCHMARK_COLS:
-        if benchmark in shown_columns:
-            for c in COLS:
-                if benchmark in c and benchmark != c:
-                    benchmark_columns.append(c)
-    return benchmark_columns
+    return dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df
+
+
+dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
+leaderboard_df = original_df.copy()
 
 
 # Searching and filtering
@@ -111,8 +86,7 @@ def update_table(
 ):
     filtered_df = filter_models(hidden_df, type_query, size_query, precision_query)
     filtered_df = filter_queries(query, filtered_df)
-    benchmark_columns = add_benchmark_columns(columns)
-    df = select_columns(filtered_df, columns + benchmark_columns)
+    df = select_columns(filtered_df, columns)
     return df
 
 
@@ -160,7 +134,6 @@ def filter_models(df: pd.DataFrame, type_query: list, size_query: list, precisio
     type_emoji = [t[0] for t in type_query]
     filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
     filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
-    filtered_df = filtered_df.loc[df[AutoEvalColumn.inference_framework.name].isin(size_query)]
 
     # numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
     # params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
@@ -169,51 +142,6 @@ def filter_models(df: pd.DataFrame, type_query: list, size_query: list, precisio
 
     return filtered_df
 
-shown_columns = None
-dataset_df, original_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()
-leaderboard_df = original_df.copy()
-
-# def update_leaderboard_table():
-#     global leaderboard_df, shown_columns
-#     print("Updating leaderboard table")
-#     return leaderboard_df[
-#                 [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
-#                 + shown_columns.value
-#                 + [AutoEvalColumn.dummy.name]
-#             ] if not leaderboard_df.empty else leaderboard_df
-        
-
-# def update_hidden_leaderboard_table():
-#     global original_df
-#     return original_df[COLS] if original_df.empty is False else original_df
-
-# def update_dataset_table():
-#     global dataset_df
-#     return dataset_df
-
-# def update_finish_table():
-#     global finished_eval_queue_df
-#     return finished_eval_queue_df
-
-# def update_running_table():
-#     global running_eval_queue_df
-#     return running_eval_queue_df
-
-# def update_pending_table():
-#     global pending_eval_queue_df
-#     return pending_eval_queue_df
-
-# def update_finish_num():
-#     global finished_eval_queue_df
-#     return len(finished_eval_queue_df)
-
-# def update_running_num():
-#     global running_eval_queue_df
-#     return len(running_eval_queue_df)
-
-# def update_pending_num():
-#     global pending_eval_queue_df
-#     return len(pending_eval_queue_df)
 
 # triggered only once at startup => read query parameter if it exists
 def load_query(request: gr.Request):
@@ -221,21 +149,10 @@ def load_query(request: gr.Request):
     return query
 
 
-def get_image_html(url, image_path):
-    with open(image_path, "rb") as image_file:
-        encoded_string = base64.b64encode(image_file.read()).decode()
-    return f'<a href="{url}" target="_blank"><img src="data:image/jpg;base64,{encoded_string}" alt="NetMind.AI Logo" style="width:100pt;"></a>'
-
-
-# Prepare the HTML content with the image
-image_html = get_image_html("https://netmind.ai/home", "./src/display/imgs/Netmind.AI_LOGO.jpg")
-
-
 demo = gr.Blocks(css=custom_css)
 with demo:
     gr.HTML(TITLE)
     gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
-    gr.HTML(ACKNOWLEDGEMENT_TEXT.format(image_html=image_html))
 
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
         with gr.TabItem("open-moe-llm-leaderboard", elem_id="llm-benchmark-tab-table", id=0):
@@ -245,7 +162,7 @@ with demo:
                         search_bar = gr.Textbox(
                             placeholder=" 🔍 Model search (separate multiple queries with `;`)",
                             show_label=False,
-                            elem_id="search-bar"
+                            elem_id="search-bar",
                         )
                     with gr.Row():
                         shown_columns = gr.CheckboxGroup(
@@ -259,7 +176,7 @@ with demo:
                                 for c in fields(AutoEvalColumn)
                                 if c.displayed_by_default and not c.hidden and not c.never_hidden
                             ],
-                            label="Tasks",
+                            label="Select columns to show",
                             elem_id="column-select",
                             interactive=True,
                         )
@@ -298,19 +215,18 @@ with demo:
                     # )
 
             # breakpoint()
-            benchmark_columns = add_benchmark_columns(shown_columns.value)
+
             leaderboard_table = gr.components.Dataframe(
                 value=(
                     leaderboard_df[
                         [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
                         + shown_columns.value
-                        + benchmark_columns
                         + [AutoEvalColumn.dummy.name]
                     ]
                     if leaderboard_df.empty is False
                     else leaderboard_df
                 ),
-                headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value + benchmark_columns,
+                headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
                 datatype=TYPES,
                 elem_id="leaderboard-table",
                 interactive=False,
@@ -335,7 +251,7 @@ with demo:
                     filter_columns_size,
                     search_bar,
                 ],
-                leaderboard_table
+                leaderboard_table,
             )
 
             # Check query parameter once at startup and update search bar
@@ -356,21 +272,21 @@ with demo:
                     queue=True,
                 )
 
-        # with gr.TabItem("About", elem_id="llm-benchmark-tab-table", id=2):
-        #     gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
+        with gr.TabItem("About", elem_id="llm-benchmark-tab-table", id=2):
+            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
 
-        #     dataset_table = gr.components.Dataframe(
-        #         value=dataset_df,
-        #         headers=list(dataset_df.columns),
-        #         datatype=["str", "markdown", "str", "str", "str"],
-        #         elem_id="dataset-table",
-        #         interactive=False,
-        #         visible=True,
-        #         column_widths=["15%", "20%"],
-        #     )
+            dataset_table = gr.components.Dataframe(
+                value=dataset_df,
+                headers=list(dataset_df.columns),
+                datatype=["str", "markdown", "str", "str", "str"],
+                elem_id="dataset-table",
+                interactive=False,
+                visible=True,
+                column_widths=["15%", "20%"],
+            )
 
-        #     gr.Markdown(LLM_BENCHMARKS_DETAILS, elem_classes="markdown-text")
-        #     gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")
+            gr.Markdown(LLM_BENCHMARKS_DETAILS, elem_classes="markdown-text")
+            gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")
 
         with gr.TabItem("Submit a model ", elem_id="llm-benchmark-tab-table", id=3):
             with gr.Column():
@@ -407,15 +323,6 @@ with demo:
                     value=None,
                     interactive=True,
                 )
-                
-                gpu_type = gr.Dropdown(
-                    choices=[t.to_str() for t in GPUType],
-                    label="GPU type",
-                    multiselect=False,
-                    value="NVIDIA-A100-PCIe-80GB",
-                    interactive=True,
-                )
-                
 
             with gr.Row():
                 with gr.Column():
@@ -451,7 +358,6 @@ with demo:
 
             submit_button = gr.Button("Submit Eval")
             submission_result = gr.Markdown()
-            debug = gr.Checkbox(value=args.debug, label="Debug", visible=False)
             submit_button.click(
                 add_new_eval,
                 [
@@ -463,8 +369,6 @@ with demo:
                     weight_type,
                     model_type,
                     inference_framework,
-                    debug,
-                    gpu_type
                 ],
                 submission_result,
             )
@@ -479,9 +383,10 @@ with demo:
                 show_copy_button=True,
             )
 
-scheduler = BackgroundScheduler(timezone=utc)
+scheduler = BackgroundScheduler()
+
+scheduler.add_job(restart_space, "interval", seconds=6 * 60 * 60)
 
-scheduler.add_job(restart_space, "interval", hours=6)
 
 def launch_backend():
     import subprocess
@@ -490,9 +395,8 @@ def launch_backend():
     if DEVICE not in {"cpu"}:
         _ = subprocess.run(["python", "backend-cli.py"])
 
-# Thread(target=periodic_init, daemon=True).start()
+
 # scheduler.add_job(launch_backend, "interval", seconds=120)
-if __name__ == "__main__":
-    scheduler.start()
-    demo.queue(default_concurrency_limit=40).launch()
-    
+
+scheduler.start()
+demo.queue(default_concurrency_limit=40).launch()

backend-cli.py

@@ -16,20 +16,18 @@ from src.backend.envs import Tasks, EVAL_REQUESTS_PATH_BACKEND, EVAL_RESULTS_PAT
 from src.backend.manage_requests import EvalRequest
 from src.leaderboard.read_evals import EvalResult
 
-from src.envs import QUEUE_REPO, RESULTS_REPO, API, DEBUG_QUEUE_REPO, DEBUG_RESULTS_REPO
-from src.utils import my_snapshot_download, analyze_gpu_stats, parse_nvidia_smi, monitor_gpus, get_gpu_details
+from src.envs import QUEUE_REPO, RESULTS_REPO, API
+from src.utils import my_snapshot_download, analyze_gpu_stats, parse_nvidia_smi, monitor_gpus
 
 from src.leaderboard.read_evals import get_raw_eval_results
 
 from typing import Optional
-import GPUtil
+
 import time
 
 import pprint
 import logging
 
-from lm_eval.filters.extraction import RegexFilter
-
 
 # Configure the root logger
 logging.basicConfig(
@@ -44,20 +42,6 @@ eval_logger = logging.getLogger("lm-eval")
 # Explicitly set the level for 'lm-eval' logger to WARNING
 eval_logger.setLevel(logging.WARNING)
 
-def tuple_input_decorator(func):
-    def wrapper(self, resps, docs):
-        stripped_resps = [[resp_data[0] for resp_data in group] for group in resps]
-
-        filtered_resps = func(self, stripped_resps, docs)
-        
-        combined_resps = []
-        for original_group, new_group in zip(resps, filtered_resps):
-            combined_group = [(new_resp,) + rest_of_data[1:] for new_resp, rest_of_data in zip(new_group, original_group)]
-            combined_resps.append(combined_group)
-
-        return combined_resps
-    return wrapper
-
 
 def my_set_eval_request(api, eval_request, set_to_status, hf_repo, local_dir):
     for i in range(10):
@@ -150,12 +134,6 @@ def process_evaluation(task: Task, eval_request: EvalRequest, limit: Optional[in
     stop_event = threading.Event()
     monitor_thread = threading.Thread(target=monitor_gpus, args=(stop_event, 5, gpu_stats_list))
     monitor_thread.start()
-    
-    original_apply = RegexFilter.apply
-    if task.benchmark in ["gsm8k", "gsm8k_cot", "gsm8k_cot_self_consistency", "gsm8k_custom"]:
-        RegexFilter.apply = tuple_input_decorator(RegexFilter.apply)
-    else:
-        RegexFilter.apply = original_apply
 
     try:
         results = run_evaluation(
@@ -217,8 +195,6 @@ def process_evaluation(task: Task, eval_request: EvalRequest, limit: Optional[in
         repo_id=RESULTS_REPO,
         repo_type="dataset",
     )
-    
-    RegexFilter.apply = original_apply
     return results
 
 
@@ -387,9 +363,10 @@ def maybe_refresh_results(thr: int, hard_task_lst: Optional[list[str]] = None) -
 
     return False
 
+
 def process_pending_requests() -> bool:
     sanity_checks()
-    print("Processing pending requests")
+
     current_pending_status = [PENDING_STATUS]
 
     # Get all eval request that are PENDING, if you want to run other evals, change this parameter
@@ -408,12 +385,6 @@ def process_pending_requests() -> bool:
 
     eval_request = eval_requests[0]
     pp.pprint(eval_request)
-    
-    gpu_type = eval_request.gpu_type
-    curr_gpu_type = get_gpu_details()
-    if gpu_type != curr_gpu_type:
-        print(f"GPU type mismatch: {gpu_type} vs {curr_gpu_type}")
-        return False
 
     my_snapshot_download(
         repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60
@@ -450,15 +421,11 @@ def get_args():
     parser = argparse.ArgumentParser(description="Run the backend")
     parser.add_argument("--debug", action="store_true", help="Run in debug mode")
     # debug parameters
-    parser.add_argument("--task", type=str, default="selfcheckgpt,mmlu, gsm8k", help="Task to debug")
+    parser.add_argument("--task", type=str, default="selfcheckgpt,mmlu", help="Task to debug")
     parser.add_argument("--model", type=str, default="mistralai/Mixtral-8x7B-Instruct-v0.1,mistralai/Mixtral-8x7B-v0.1", help="Model to debug")
     parser.add_argument("--precision", type=str, default="float32,float16,8bit,4bit", help="Precision to debug")
     parser.add_argument("--inference-framework", type=str, default="hf-chat", help="Inference framework to debug")
     parser.add_argument("--limit", type=int, default=None, help="Limit for the number of samples")
-    parser.add_argument("--gpu-type", type=str, default="NVIDIA-A100-PCIe-80GB", 
-                        help="GPU type. NVIDIA-A100-PCIe-80GB; NVIDIA-RTX-A5000-24GB; NVIDIA-H100-PCIe-80GB")
-    parser.add_argument("--debug_repo", action="store_true", help="Use debug repo")
-    parser.add_argument("--model_type", type=str, default="chat", help="Model type")
     return parser.parse_args()
 
 
@@ -466,7 +433,7 @@ if __name__ == "__main__":
     args = get_args()
     local_debug = args.debug
     # debug specific task by ping
-    if local_debug and not args.debug_repo:
+    if local_debug:
         # debug_model_names = [args.model]  # Use model from arguments
         # debug_task_name = [args.task]  # Use task from arguments
         debug_model_names = args.model.split(",")
@@ -474,68 +441,43 @@ if __name__ == "__main__":
         precisions = args.precision.split(",")
         print(f"debug_model_names: {debug_model_names}, debug_task_name: {debug_task_name}, precisions: {precisions}")
         task_lst = TASKS_HARNESS.copy()
-        RESULTS_REPO = DEBUG_RESULTS_REPO
         for precision in precisions:
             for debug_model_name in debug_model_names:
                 for task in task_lst:
                     task_name = task.benchmark
                     if task_name not in debug_task_name:
                         continue
-                    # try:
-                    eval_request = EvalRequest(
-                        model=debug_model_name,
-                        private=False,
-                        status="",
-                        json_filepath="",
-                        precision=precision,  # Use precision from arguments
-                        inference_framework=args.inference_framework,  # Use inference framework from arguments
-                        gpu_type=args.gpu_type,
-                        model_type=args.model_type,
-                    )
-                    curr_gpu_type = get_gpu_details()
-                    if eval_request.gpu_type != curr_gpu_type:
-                        print(f"GPU type mismatch: {eval_request.gpu_type} vs {curr_gpu_type}")
-                        raise Exception("GPU type mismatch")
-                    results = process_evaluation(task, eval_request, limit=args.limit)
-                    # except Exception as e:
-                    #     print(f"debug running error: {e}")
-    elif local_debug and args.debug_repo:
-        QUEUE_REPO = DEBUG_QUEUE_REPO
-        RESULTS_REPO = DEBUG_RESULTS_REPO
+                    try:
+                        eval_request = EvalRequest(
+                            model=debug_model_name,
+                            private=False,
+                            status="",
+                            json_filepath="",
+                            precision=precision,  # Use precision from arguments
+                            inference_framework=args.inference_framework  # Use inference framework from arguments
+                        )
+                        results = process_evaluation(task, eval_request, limit=args.limit)
+                    except Exception as e:
+                        print(f"debug running error: {e}")
+    else:
         while True:
             res = False
+
             # if random.randint(0, 10) == 0:
             res = process_pending_requests()
             print(f"waiting for 60 seconds")
             time.sleep(60)
+
             # if res is False:
             #     if random.randint(0, 5) == 0:
             #         res = maybe_refresh_results(100)
             #     else:
             #         res = process_finished_requests(100)
+
             # time.sleep(60)
+
             # if res is False:
             #     if random.randint(0, 5) == 0:
             #         res = maybe_refresh_results(0)
             #     else:
             #         res = process_finished_requests(0)
-    elif not local_debug and not args.debug_repo:
-        while True:
-           res = False
-           # if random.randint(0, 10) == 0:
-           res = process_pending_requests()
-           print(f"waiting for 60 seconds")
-           time.sleep(60)
-           # if res is False:
-           #     if random.randint(0, 5) == 0:
-           #         res = maybe_refresh_results(100)
-           #     else:
-           #         res = process_finished_requests(100)
-           # time.sleep(60)
-           # if res is False:
-           #     if random.randint(0, 5) == 0:
-           #         res = maybe_refresh_results(0)
-           #     else:
-           #         res = process_finished_requests(0)
-    else:
-        raise Exception("Cannot use debug_repo without local debug flag")

requirements.txt

@@ -4,7 +4,7 @@ APScheduler
 black
 click
 datasets
-gradio==4.26.0
+gradio
 gradio_client
 huggingface-hub
 matplotlib
@@ -16,7 +16,7 @@ requests
 semantic-version
 tqdm
 wandb
-transformers
+transformers>=4.36.0
 tokenizers>=0.15.0
 lm_eval[ifeval] @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@v0.4.2
 accelerate
@@ -27,10 +27,6 @@ cchardet
 rouge_score
 bert-score
 evaluate
-spacy==3.7.4
+spacy
 selfcheckgpt
 immutabledict
-gputil
-bitsandbytes
-openai
-scikit-learn

src/backend/envs.py

@@ -57,9 +57,6 @@ class Tasks(Enum):
 
     # task20 = Task("race", "acc", "RACE", 0)
     task21 = Task("mmlu", "acc", "MMLU", 5)
-    task22 = Task("gsm8k_custom", "em", "GSM8K", 5)
-    # task23 = Task("gsm8k_cot", "em", "GSM8K", 8)
-    task24 = Task("arena_hard", "score", "Arena Hard", 0)
 
 
 EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")

src/backend/hflm_with_measurement.py

@@ -24,7 +24,7 @@ from transformers.models.auto.modeling_auto import (
     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
 )
 from transformers import TextStreamer
-from transformers.models.dbrx.modeling_dbrx import DbrxExpertGLU
+
 from lm_eval import utils
 from lm_eval.api.instance import Instance
 from lm_eval.api.model import TemplateLM
@@ -37,9 +37,6 @@ from lm_eval.models.utils import (
     stop_sequences_criteria,
 )
 from lm_eval.models.huggingface import HFLM
-from src.utils import get_gpu_details, get_peak_bw, transfer_precision2bytes, get_peak_flops
-from src.submission.check_validity import get_model_size
-from src.envs import API
 
 
 class StopWatch(TextStreamer):
@@ -70,21 +67,6 @@ class StopWatch(TextStreamer):
 class HFLMWithMeasurement(HFLM):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
-        self.pretrained = kwargs.get("pretrained", None)
-        self.revision = kwargs.get("revision", None)
-        self.precision = kwargs.get("dtype", None)
-        self.num_gpus = None
-
-    def _detect_num_gpus_used(self):
-        if self.num_gpus is not None:
-            return self.num_gpus
-        gpus = []
-        for p in self.model.parameters():
-            if p.device.type == "cuda":
-                gpus.append(p.device.index)
-                
-        self.num_gpus = len(set(gpus))
-        return self.num_gpus
 
     def _loglikelihood_tokens(
         self,
@@ -297,7 +279,7 @@ class HFLMWithMeasurement(HFLM):
                     # Answer: (log prob, is-exact-match)
                     answer = (float(logits.sum()), bool(max_equal))
 
-                    res.append((answer, per_sample_time, 0, 0, 0, 0))
+                    res.append((answer, per_sample_time, 0, 0))
 
                     self.cache_hook.add_partial("loglikelihood", request_str, answer)
                     pbar.update(1)
@@ -306,15 +288,13 @@ class HFLMWithMeasurement(HFLM):
 
         return re_ord.get_original(res)
 
-    def _model_generate(self, context, max_tokens, stop, **generation_kwargs):
+    def _model_generate(self, context, max_length, stop, **generation_kwargs):
         # temperature = 0.0 if not set
         # if do_sample is false and temp==0.0:
         # remove temperature, as do_sample=False takes care of this
         # and we don't want a warning from HF
         generation_kwargs["temperature"] = generation_kwargs.get("temperature", 0.0)
         do_sample = generation_kwargs.get("do_sample", None)
-        
-        # is_gsm8k = generation_kwargs.get("is_gsm8k", False)
 
         # The temperature has to be a strictly positive float -- if it is 0.0, use greedy decoding strategies
         if generation_kwargs.get("temperature") == 0.0 and do_sample is None:
@@ -322,52 +302,7 @@ class HFLMWithMeasurement(HFLM):
 
         if do_sample is False and generation_kwargs.get("temperature") == 0.0:
             generation_kwargs.pop("temperature")
-        
-        # if is_gsm8k:
-        #     generation_kwargs.pop("is_gsm8k")
-            
-        context_length = context.shape[1]
-
-        if self.model.__class__.__name__ == "MoE":
-            model_config = self.model.model.config
-        else:
-            model_config = self.model.config
-        
-        if not self.precision:
-            if model_config.quantization_config._load_in_4bit:
-                self.precision = "4bit"
-            elif model_config.quantization_config._load_in_8bit:
-                self.precision = "8bit"
-            else:
-                raise ValueError("Unknown precision")
-            
-        # print(self.model)
-        linear_count = 0 
-        element_wise_mul = 0
-        for name, module in self.model.named_modules():
-            if ('layers.0.' in name or "transformer.blocks.0" in name) and ('attn' not in name):
-                if 'experts.0.' in name or "ffn.experts" in name:
-                    if "linear_v" in name:
-                        element_wise_mul = 1
-                    if isinstance(module, torch.nn.Linear):
-                        # print(name, module)
-                        linear_count += 1
-                    elif isinstance(module, DbrxExpertGLU):
-                        linear_count = 3
-                        element_wise_mul = 1
-                # elif 'experts' not in name:
-                #     if ("gate" not in name and "router" not in name) or "gate_proj" in name:
-                #         if "gate_proj" in name:
-                #             element_wise_mul = 1
-                #         if isinstance(module, torch.nn.Linear):
-                #             # print(name, module)
-                #             linear_count += 1
-                else:
-                    continue
-        print(f"linear_count: {linear_count}")
-        print(f"element_wise_mul: {element_wise_mul}")
-        print(f"GPU usage: {self._detect_num_gpus_used()}")
-
+        # build stopping criteria
         stopping_criteria = stop_sequences_criteria(
             self.tokenizer, stop, context.shape[1], context.shape[0]
         )
@@ -375,7 +310,7 @@ class HFLMWithMeasurement(HFLM):
         start = time()
         res = self.model.generate(
             input_ids=context,
-            max_new_tokens=max_tokens,
+            max_length=max_length,
             stopping_criteria=stopping_criteria,
             pad_token_id=self.tokenizer.pad_token_id,
             use_cache=True,
@@ -386,83 +321,12 @@ class HFLMWithMeasurement(HFLM):
 
         batch_size = context.shape[0]
         output_length = stop_watch.decoding_iterations
-        
-        precision_bytes = transfer_precision2bytes(self.precision)
-        
-        model_size_param = sum(p.numel() for p in self.model.parameters())
-
-        n_layers = model_config.num_hidden_layers if hasattr(model_config, "num_hidden_layers") else \
-            (model_config.num_layers if hasattr(model_config, "num_layers") else model_config.n_layers)
-        
-        d_model = model_config.hidden_size if hasattr(model_config, "hidden_size") else model_config.d_model
-        
-        if hasattr(model_config, "num_experts_per_tok"):
-            n_experts_per_tok = model_config.num_experts_per_tok
-        elif hasattr(model_config, "num_selected_experts"):
-            n_experts_per_tok = model_config.num_selected_experts
-        elif hasattr(model_config, "ffn_config"):
-            n_experts_per_tok = model_config.ffn_config.moe_top_k
-        else:
-            n_experts_per_tok = 1
-        
-        if hasattr(model_config, "ffn_dim"):
-            d_ff = model_config.ffn_dim
-        elif hasattr(model_config, "intermediate_size"):
-            d_ff = model_config.intermediate_size
-        elif hasattr(model_config, "d_ff"):
-            d_ff = model_config.d_ff
-        elif hasattr(model_config, "ff_ratio"):
-            d_ff = d_model * model_config.ff_ratio
-        elif hasattr(model_config, "ffn_config"):
-            d_ff = model_config.ffn_config.ffn_hidden_size
-        else:
-            raise ValueError("Unknown FFN dimension")
-        
-        if hasattr(model_config, "num_local_experts"):
-            num_experts = model_config.num_local_experts
-        elif hasattr(model_config, "num_experts"):
-            num_experts = model_config.num_experts
-        elif hasattr(model_config, "ffn_config"):
-            num_experts = model_config.ffn_config.moe_num_experts
-        else:
-            num_experts = 1
-            
-        ffn_params = n_layers * d_ff * linear_count * d_model
-        
-        shared_params = model_size_param - num_experts * ffn_params
-
-        model_size = shared_params + n_experts_per_tok * ffn_params
-
-        per_token_kv_size = 2 * n_layers * d_model * precision_bytes
-        
-        peak_bw_single = get_peak_bw(get_gpu_details())
-        peak_bw = peak_bw_single * self._detect_num_gpus_used()
-        
-        context_prefill_size = context_length
-        kv_size = context_prefill_size * per_token_kv_size + (output_length - 1) * per_token_kv_size / 2
-        
-        kv_size = kv_size / 1e9
-        
-        n_vocab = model_config.vocab_size
 
         end_to_end_time = (end - start) / batch_size
         prefilling_time = stop_watch.prefilling_time / batch_size
         decoding_time = stop_watch.decoding_time / batch_size
         token_per_sec = output_length / decoding_time
-        achieve_mem_bw = (model_size * precision_bytes / 1e9 + kv_size) * token_per_sec
-        
-        avg_context_length = context_length + (output_length - 1) / 2
-        flops_per_token = 2 * model_size + ((linear_count + element_wise_mul) * n_layers * avg_context_length * d_model) + 4 * d_model + 2 * d_model * n_vocab
-        peak_flops_single = get_peak_flops(get_gpu_details(), self.precision)
-        peak_flops = peak_flops_single * self._detect_num_gpus_used()
-        
-        ## TODO only support llama-type decoder only models and moe models of switch transformer and mixtrial
-        mfu = token_per_sec * flops_per_token / peak_flops
-        mbu = achieve_mem_bw / peak_bw
-        
-        print(f"mfu: {mfu}, mbu: {mbu}")
-        
-        return res, end_to_end_time, prefilling_time, token_per_sec, mfu, mbu
+        return res, end_to_end_time, prefilling_time, token_per_sec
 
     def generate_until(
         self, requests: List[Instance], disable_tqdm: bool = False
@@ -539,19 +403,11 @@ class HFLMWithMeasurement(HFLM):
                     f"Expected `kwargs` to be of type `dict` but got {type(gen_kwargs)}"
                 )
             # add EOS token to stop sequences
-            eos = "<|eot_id|>"
+            eos = self.tok_decode(self.eot_token_id)
             if not until:
                 until = [eos]
             else:
                 until.append(eos)
-            
-            # is_gsm8k = kwargs.get("is_gsm8k", False)
-            # if is_gsm8k:
-            #     until = ["Question:", "Question", "</s>"]
-            #     eos_ids = [self.tokenizer.eos_token_id, 
-            #              self.tokenizer.convert_tokens_to_ids("<|eot_id|>")]
-                
-                    
             if "max_gen_toks" in kwargs.keys():
                 max_gen_toks = kwargs.pop("max_gen_toks")
             else:
@@ -571,16 +427,14 @@ class HFLMWithMeasurement(HFLM):
                 left_truncate_len=max_ctx_len,
                 truncation=self.truncation,
             )
-            
-            # print("context: ", self.tok_decode(context_enc[0]))
             context_enc = context_enc.to(self.device)
             attn_masks = attn_masks.to(self.device)
 
-            if "max_tokens" not in kwargs:
-                kwargs["max_tokens"] = max_gen_toks
+            if "max_length" not in kwargs:
+                kwargs["max_length"] = context_enc.shape[1] + max_gen_toks
 
             # perform batched generation
-            cont, end_to_end_time, prefilling_time, token_per_sec, mfu, mbu = self._model_generate(
+            cont, end_to_end_time, prefilling_time, token_per_sec = self._model_generate(
                 context=context_enc,
                 attention_mask=attn_masks,
                 stop=until,
@@ -591,21 +445,18 @@ class HFLMWithMeasurement(HFLM):
             for cont_toks, context in zip(cont_toks_list, contexts):
                 # discard context + left-padding toks if using causal decoder-only LM
                 if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
-                    # print("After Generation: ", self.tok_decode(cont_toks))
                     cont_toks = cont_toks[context_enc.shape[1] :]
-                
+
                 s = self.tok_decode(cont_toks)
 
-                # # use secondary stop seqs to cut off should-have-been-stopped content post-hoc
-                # if not is_gsm8k:
+                # use secondary stop seqs to cut off should-have-been-stopped content post-hoc
                 for term in until:
                     if len(term) > 0:
                         # ignore '' separator,
                         # for seq2seq case where self.tok_decode(self.eot_token_id) = ''
                         s = s.split(term)[0]
-                
-                # print(s)
-                res.append((s, end_to_end_time, prefilling_time, token_per_sec, mfu, mbu))
+
+                res.append((s, end_to_end_time, prefilling_time, token_per_sec))
 
                 self.cache_hook.add_partial("generate_until", (context, gen_kwargs), s)
                 pbar.update(1)

src/backend/manage_requests.py

@@ -28,7 +28,6 @@ class EvalRequest:
     params: Optional[int] = None
     license: Optional[str] = ""
     batch_size: Optional[int] = 1
-    gpu_type: Optional[str] = "NVIDIA-A100-PCIe-80GB"
 
     def get_model_args(self) -> str:
         model_args = f"pretrained={self.model},revision={self.revision},parallelize=True"  # ,max_length=4096"

src/backend/moe_infinity.py

@@ -31,20 +31,15 @@ class MoEHFLM(HFLMWithMeasurement):
         self.use_chat_template = use_chat_template
         if "device" in kwargs:
             kwargs.pop("device")
-        if os.path.exists(os.path.join(self.offload_path, "moe-infinity-offloads")):
-            shutil.rmtree(os.path.join(self.offload_path, "moe-infinity-offloads"))
-        kwargs["device_map"] = "cuda:0"
         super().__init__(
-            *args, **kwargs, pretrained=pretrained
+            *args, **kwargs, pretrained=pretrained, device_map="cuda:0"
         )  # Assuming HFLM accepts a 'pretrained' arg and handles it
         # self._create_model()
+        shutil.rmtree(os.path.join(self.offload_path, "moe-infinity-offloads"))
 
     def __del__(self):
-        self._model.engine.clean_up() # clean up hooks
-        self._model.engine.archer_engine.clean_up_resources() # clean up resources
-        if os.path.exists(os.path.join(self.offload_path, "moe-infinity-offloads")):
-            shutil.rmtree(os.path.join(self.offload_path, "moe-infinity-offloads")) # clean up offload model
-
+        # Clean up offloaded models from self.offload_path
+        shutil.rmtree(os.path.join(self.offload_path, "moe-infinity-offloads"))
 
     def _create_model(self, *args, **kwargs):
         """

src/backend/run_eval_suite.py

@@ -17,16 +17,12 @@ def process_results_decorator(func):
         end_to_end_time = sum([r[1] for r in results]) / len(results)
         prefilling_time = sum([r[2] for r in results]) / len(results)
         decoding_throughput = sum([r[3] for r in results]) / len(results)
-        mfu = sum([r[4] for r in results]) / len(results)
-        mbu = sum([r[5] for r in results]) / len(results)
         # print(f"end_to_end_time: {end_to_end_time}, prefilling_time: {prefilling_time}, decoding_throughput: {decoding_throughput}")
 
         result_dict = func(self, doc, processed_results, *args, **kwargs)
         result_dict["end_to_end_time"] = end_to_end_time
         result_dict["prefilling_time"] = prefilling_time
         result_dict["decoding_throughput"] = decoding_throughput
-        result_dict["mfu"] = mfu
-        result_dict["mbu"] = mbu
         return result_dict
     return wrapper
 ConfigurableTask.process_results = process_results_decorator(orig_process_results)
@@ -37,8 +33,6 @@ def aggregation_decorator(func):
         aggregation_list["end_to_end_time"] = mean
         aggregation_list["prefilling_time"] = mean
         aggregation_list["decoding_throughput"] = mean
-        aggregation_list["mfu"] = mean
-        aggregation_list["mbu"] = mean
         return aggregation_list
     return wrapper
 ConfigurableTask.aggregation = aggregation_decorator(orig_aggregation)
@@ -49,8 +43,6 @@ def higher_is_better_decorator(func):
         higher_is_better_dict["end_to_end_time"] = False
         higher_is_better_dict["prefilling_time"] = False
         higher_is_better_dict["decoding_throughput"] = True
-        higher_is_better_dict["mfu"] = True
-        higher_is_better_dict["mbu"] = True
         return higher_is_better_dict
     return wrapper
 ConfigurableTask.higher_is_better = higher_is_better_decorator(orig_higher_is_better)

src/backend/tasks/arena_hard/arena_hard.yaml

@@ -1,2 +0,0 @@
-task: arena_hard
-class: !function task.ArenaHard

src/backend/tasks/arena_hard/arena_judgment.py

@@ -1,256 +0,0 @@
-'''
-This file is part of Open-MoE-LLM-Leaderboard and is modified based on work
-under the Apache 2.0 License from the arena-hard project.
-(https://github.com/lm-sys/arena-hard)
-Original Copyright (c) 2024 Tianle Li*, Wei-Lin Chiang*, Evan Frick, Lisa Dunlap, Banghua Zhu, Joseph E. Gonzalez, Ion Stoica
-See the NOTICE file distributed with this work for additional
-information regarding copyright ownership.
-'''
-
-import pandas as pd
-from tqdm import tqdm
-import numpy as np
-from sklearn.linear_model import LogisticRegression
-import math
-from collections import defaultdict
-from tqdm import tqdm
-
-from src.backend.tasks.arena_hard.arena_utils import (
-    chat_completion_openai,
-    load_questions,
-    load_model_answers,
-    get_endpoint,
-    make_config,
-)
-
-
-def get_score(judgment, pattern, pairwise=True):
-    matches = pattern.findall(judgment)
-    matches = [m for m in matches if m != ""]
-    if len(set(matches)) == 0:
-        return None, True
-    elif len(set(matches)) == 1:
-        if pairwise:
-            return matches[0].strip("\n"), False
-        return int(matches[0])
-    else:
-        return None, False
-
-
-# get answer from model
-def get_answer(model, conv, temperature, max_tokens, endpoint_dict=None):
-    api_dict = get_endpoint(endpoint_dict["endpoints"])
-
-    # if endpoint_dict["api_type"] == "anthropic":
-    #     output = chat_completion_anthropic(model, conv, temperature, max_tokens)
-    # elif endpoint_dict["api_type"] == "azure":
-    #     output = chat_completion_openai_azure(model, conv, temperature, max_tokens, api_dict)
-
-    output = chat_completion_openai(model, conv, temperature, max_tokens, api_dict)
-    return output
-
-
-def judgment(**args):
-    question = args["question"]
-    answer = args["answer"]
-    reference = args["reference"]
-    baseline = args["baseline_answer"]
-    configs = args["configs"]
-    # output_file = args["output_file"]
-    model = configs["judge_model"]
-
-    num_games = 2 if configs["pairwise"] else 1
-
-    # output = {
-    #     "question_id":question["question_id"],
-    #     "judge": model,
-    #     "model": "custom_model",
-    #     "games":[]
-    #     }
-    output = [question["question_id"]]
-
-    for game in range(num_games):
-        conv = [{"role": "system", "content": configs["system_prompt"]}]
-
-        for template in configs["prompt_template"]:
-            prompt_args = {}
-
-            prompt_args[f"question_{1}"] = question["content"]
-            base = 1
-
-            if baseline:
-                if game % 2 == 1: # swap position
-                    temp = baseline
-                    baseline = answer
-                    answer = temp
-                
-                if game == 0:
-                    for i, turn in enumerate(baseline["choices"][0]["turns"]):
-                        prompt_args[f"answer_{i+1}"] = turn["content"]
-                        base += 1
-                
-                if game == 1:
-                    prompt_args[f"answer_{1}"] = baseline
-                    base += 1
-            
-            if answer:
-                prompt_args[f"answer_{base}"] = answer
-
-            if reference:
-                for j, ref_answer in enumerate(reference):
-                    for i, turn in enumerate(ref_answer["choices"][0]["turns"]):
-                        prompt_args[f"ref_answer_{i+j+1}"] = turn["content"]
-            
-            user_prompt = template.format(**prompt_args)
-            conv.append({"role": "user", "content": user_prompt})
-
-        judgment = ""
-        for _ in range(2):
-            new_judgment = get_answer(
-                model,
-                conv,
-                configs["temperature"],
-                configs["max_tokens"],
-                args["endpoint_dict"],
-            )
-
-            judgment += ("\n" + new_judgment)
-
-            score, try_again = get_score(judgment, args["regex_pattern"])
-
-            conv.append({"role": "assistant", "content": new_judgment})
-
-            if not try_again:
-                break
-
-            conv.append({"role": "user", "content": "continue your judgment and finish by outputting a final verdict label"})
-        print("Finish judgment!!!")
-        # result = {
-        #     "user_prompt": conv[1]["content"],
-        #     "judgment": judgment,
-        #     "score":score
-        # }
-        output.append(score)
-        
-    return output
-
-def get_battles_from_scores(score_list, first_game_only=False, WEIGHT=3):
-    arena_hard_battles = pd.DataFrame()
-
-    print("Turning score list into battles...")
-
-    for scores in tqdm(score_list):
-        question_id, score1, score2 = scores
-
-        # Process game 1
-        output = {"question_id": question_id,
-                  "model_a": "gpt-4-0314",
-                  "model_b": f"custom_model"}  # Unique identifier for model
-        weight = 1
-        if score1 == "A=B":
-            output["winner"] = "tie"
-        elif score1 == "A>B":
-            output["winner"] = "model_a"
-        elif score1 == "A>>B":
-            output["winner"] = "model_a"
-            weight = WEIGHT
-        elif score1 == "B>A":
-            output["winner"] = "model_b"
-        elif score1 == "B>>A":
-            output["winner"] = "model_b"
-            weight = WEIGHT
-        else:
-            weight = 0
-
-        if weight:
-            arena_hard_battles = pd.concat([arena_hard_battles, pd.DataFrame([output] * weight)])
-
-        if not first_game_only:
-            # Process game 2
-            output = {"question_id": question_id,
-                      "model_a": "gpt-4-0314",
-                      "model_b": f"custom_model"}  # Unique identifier for model
-            weight = 1
-            if score2 == "A=B":
-                output["winner"] = "tie"
-            elif score2 == "A>B":
-                output["winner"] = "model_b"
-            elif score2 == "A>>B":
-                output["winner"] = "model_b"
-                weight = WEIGHT
-            elif score2 == "B>A":
-                output["winner"] = "model_a"
-            elif score2 == "B>>A":
-                output["winner"] = "model_a"
-                weight = WEIGHT
-            else:
-                weight = 0
-
-            if weight:
-                arena_hard_battles = pd.concat([arena_hard_battles, pd.DataFrame([output] * weight)])
-
-    arena_hard_battles.to_json("./arena_hard_battles.jsonl", lines=True, orient="records")
-    return arena_hard_battles
-
-def compute_mle_elo(df, SCALE=400, BASE=10, INIT_RATING=1000):
-    models = pd.concat([df["model_a"], df["model_b"]]).unique()
-    models = pd.Series(np.arange(len(models)), index=models)
-
-    LOW_RATING = 100
-    # duplicate battles
-    df = pd.concat([df, df], ignore_index=True)
-    p = len(models.index)
-    n = df.shape[0]
-
-    X = np.zeros([n, p])
-    X[np.arange(n), models[df["model_a"]]] = +math.log(BASE)
-    X[np.arange(n), models[df["model_b"]]] = -math.log(BASE)
-
-    # one A win => two A win
-    Y = np.zeros(n)
-    Y[df["winner"] == "model_a"] = 1.0
-
-    # one tie => one A win + one B win
-    # find tie + tie (both bad) index
-    tie_idx = (df["winner"] == "tie") | (df["winner"] == "tie (bothbad)")
-    tie_idx[len(tie_idx)//2:] = False
-    Y[tie_idx] = 1.0
-
-    if len(np.unique(Y)) == 1:
-        # If there's only one class in the data, assign default ratings
-        elo_scores = np.full(p, LOW_RATING)
-        elo_scores[models["gpt-4-0314"]] = INIT_RATING
-    else:
-        lr = LogisticRegression(fit_intercept=False, penalty=None, tol=1e-8)
-        lr.fit(X,Y)
-
-        elo_scores = SCALE * lr.coef_[0] + INIT_RATING
-
-    # set anchor as gpt-4-0314 = 1000
-    if "gpt-4-0314" in models.index:
-        elo_scores += 1000 - elo_scores[models["gpt-4-0314"]]
-    return pd.Series(elo_scores, index = models.index).sort_values(ascending=False)
-
-def predict_win_rate(elo_ratings, SCALE=400, BASE=10, INIT_RATING=1000):
-    names = sorted(list(elo_ratings.keys()))
-    wins = defaultdict(lambda: defaultdict(lambda: 0))
-    for a in names:
-        for b in names:
-            ea = 1 / (1 + BASE ** ((elo_ratings[b] - elo_ratings[a]) / SCALE))
-            wins[a][b] = ea
-            wins[b][a] = 1 - ea
-
-    data = {
-        a: [wins[a][b] if a != b else np.NAN for b in names]
-        for a in names
-    }
-
-    df = pd.DataFrame(data, index=names)
-    df.index.name = "model_a"
-    df.columns.name = "model_b"
-    return df.T
-
-def get_win_rate_column(df, column, baseline="gpt-4-0314"):
-    to_dict = df[["model", column]].set_index("model").to_dict()[column]
-    win_rate_table = predict_win_rate(to_dict)
-    return win_rate_table[baseline].fillna(0.5).apply(lambda x: round(x * 100, 2))

src/backend/tasks/arena_hard/arena_utils.py

@@ -1,349 +0,0 @@
-'''
-This file is part of Open-MoE-LLM-Leaderboard and is modified based on work
-under the Apache 2.0 License from the arena-hard project.
-(https://github.com/lm-sys/arena-hard)
-Original Copyright (c) 2024 Tianle Li*, Wei-Lin Chiang*, Evan Frick, Lisa Dunlap, Banghua Zhu, Joseph E. Gonzalez, Ion Stoica
-See the NOTICE file distributed with this work for additional
-information regarding copyright ownership.
-'''
-
-
-import os
-import json
-import time
-import yaml
-import random
-
-from typing import Optional
-from glob import glob
-
-# API setting constants
-API_MAX_RETRY = 16
-API_RETRY_SLEEP = 10
-API_ERROR_OUTPUT = "$ERROR$"
-
-
-OPENAI_MODEL_LIST = (
-    "gpt-3.5-turbo",
-    "gpt-3.5-turbo-0301",
-    "gpt-3.5-turbo-0613",
-    "gpt-3.5-turbo-0613-verbose",
-    "gpt-3.5-turbo-1106",
-    "gpt-3.5-turbo-0125",
-    "gpt-4",
-    "gpt-4-0314",
-    "gpt-4-0613",
-    "gpt-4-turbo",
-    "gpt-4-1106-preview",
-    "gpt-4-0125-preview",
-)
-
-
-temperature_config = {
-    "writing": 0.7,
-    "roleplay": 0.7,
-    "extraction": 0.0,
-    "math": 0.0,
-    "coding": 0.0,
-    "reasoning": 0.0,
-    "stem": 0.1,
-    "humanities": 0.1,
-}
-
-
-def load_questions(question_file: str):
-    """Load questions from a file."""
-    questions = []
-    with open(question_file, "r") as ques_file:
-        for line in ques_file:
-            if line:
-                questions.append(json.loads(line))
-    return questions
-
-
-def load_model_answers(answer_dir: str):
-    """Load model answers.
-
-    The return value is a python dict of type:
-    Dict[model_name: str -> Dict[question_id: int -> answer: dict]]
-    """
-    filenames = glob(os.path.join(answer_dir, "*.jsonl"))
-    filenames.sort()
-    model_answers = {}
-
-    for filename in filenames:
-        model_name = os.path.basename(filename)[:-6]
-        answer = {}
-        with open(filename) as fin:
-            for line in fin:
-                line = json.loads(line)
-                answer[line["question_id"]] = line
-        model_answers[model_name] = answer
-
-    return model_answers
-
-
-def get_endpoint(endpoint_list):
-    if endpoint_list is None:
-        return None
-    assert endpoint_list is not None
-    # randomly pick one
-    api_dict = random.choices(
-        endpoint_list
-    )[0]
-    return api_dict
-
-
-# load config args from config yaml files
-def make_config(config_file: str) -> dict:
-    config_kwargs = {}
-    with open(config_file, "r") as f:
-        config_kwargs = yaml.load(f, Loader=yaml.SafeLoader)
-
-    return config_kwargs
-
-
-def chat_completion_openai(model, messages, temperature, max_tokens, api_dict=None):
-    import openai
-    if api_dict:
-        client = openai.OpenAI(
-            base_url=api_dict["api_base"],
-            api_key=api_dict["api_key"],
-        )
-    else:
-        client = openai.OpenAI()
-    
-    output = API_ERROR_OUTPUT
-    for _ in range(API_MAX_RETRY):
-        try:
-            # print(messages)
-            completion = client.chat.completions.create(
-                model=model,
-                messages=messages,
-                temperature=temperature,
-                max_tokens=max_tokens
-                )
-            output = completion.choices[0].message.content
-            break
-        except openai.RateLimitError as e:
-            print(type(e), e)
-            time.sleep(API_RETRY_SLEEP)
-        except openai.BadRequestError as e:
-            print(messages)
-            print(type(e), e)
-        except KeyError:
-            print(type(e), e)
-            break
-    
-    return output
-
-
-# def chat_completion_openai_azure(model, messages, temperature, max_tokens, api_dict=None):
-#     import openai
-#     from openai import AzureOpenAI
-
-#     api_base = api_dict["api_base"]
-#     client = AzureOpenAI(
-#         azure_endpoint = api_base,
-#         api_key= api_dict["api_key"],
-#         api_version=api_dict["api_version"],
-#         timeout=240,
-#         max_retries=2
-#     )
-
-#     output = API_ERROR_OUTPUT
-#     for _ in range(API_MAX_RETRY):
-#         try:
-#             response = client.chat.completions.create(
-#                 model=model,
-#                 messages=messages,
-#                 n=1,
-#                 temperature=temperature,
-#                 max_tokens=max_tokens,
-#                 seed=42,
-#             )
-#             output = response.choices[0].message.content
-#             break
-#         except openai.RateLimitError as e:
-#             print(type(e), e)
-#             time.sleep(API_RETRY_SLEEP)
-#         except openai.BadRequestError as e:
-#             print(type(e), e)
-#             break
-#         except KeyError:
-#             print(type(e), e)
-#             break
-
-#     return output
-
-
-# def chat_completion_anthropic(model, messages, temperature, max_tokens, api_dict=None):
-#     import anthropic
-
-#     if api_dict:
-#         api_key = api_dict["api_key"]
-#     else:
-#         api_key = os.environ["ANTHROPIC_API_KEY"]
-
-#     sys_msg = ""
-#     if messages[0]["role"] == "system":
-#         sys_msg = messages[0]["content"]
-#         messages = messages[1:]
-
-#     output = API_ERROR_OUTPUT
-#     for _ in range(API_MAX_RETRY):
-#         try:
-#             # print(sys_msg)
-#             c = anthropic.Anthropic(api_key=api_key)
-#             response = c.messages.create(
-#                 model=model,
-#                 messages=messages,
-#                 stop_sequences=[anthropic.HUMAN_PROMPT],
-#                 max_tokens=max_tokens,
-#                 temperature=temperature,
-#                 system=sys_msg
-#             )
-#             output = response.content[0].text
-#             break
-#         except anthropic.APIError as e:
-#             print(type(e), e)
-#             time.sleep(API_RETRY_SLEEP)
-#     return output
-
-
-# def chat_completion_mistral(model, messages, temperature, max_tokens):
-#     from mistralai.client import MistralClient
-#     from mistralai.models.chat_completion import ChatMessage
-#     from mistralai.exceptions import MistralException
-
-#     api_key = os.environ["MISTRAL_API_KEY"]
-#     client = MistralClient(api_key=api_key)
-
-#     prompts = [ChatMessage(role=message["role"], content=message["content"]) for message in messages]
-    
-#     output = API_ERROR_OUTPUT
-#     for _ in range(API_MAX_RETRY):
-#         try:
-#             chat_response = client.chat(
-#                 model=model,
-#                 messages=prompts,
-#                 temperature=temperature,
-#                 max_tokens=max_tokens,
-#             )
-#             output = chat_response.choices[0].message.content
-#             break
-#         except MistralException as e:
-#             print(type(e), e)
-#             break
-
-#     return output
-
-
-# def chat_completion_gemini(model, messages, temperature, max_tokens):
-#     import google.generativeai as genai
-#     genai.configure(api_key=os.environ["GEMINI_API_KEY"])
-
-#     safety_settings = [
-#         {
-#             "category": "HARM_CATEGORY_HARASSMENT",
-#             "threshold": "BLOCK_NONE"
-#         },
-#         {
-#             "category": "HARM_CATEGORY_HATE_SPEECH",
-#             "threshold": "BLOCK_NONE"
-#         },
-#         {
-#             "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
-#             "threshold": "BLOCK_NONE"
-#         },
-#         {
-#             "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
-#             "threshold": "BLOCK_NONE"
-#         },
-#     ]
-
-#     # Set up the model
-#     generation_config = {
-#         "temperature": temperature,
-#         "top_p": 1,
-#         "top_k": 1,
-#         "max_output_tokens": max_tokens,
-#     }
-
-#     output = API_ERROR_OUTPUT
-#     for _ in range(API_MAX_RETRY):
-#         try:
-#             gemini = genai.GenerativeModel(
-#                 model_name=model,
-#                 generation_config=generation_config,
-#                 safety_settings=safety_settings)
-
-#             convo = gemini.start_chat(history=[])
-            
-#             convo.send_message(messages)
-#             output = convo.last.text
-#             break
-#         except genai.types.generation_types.StopCandidateException as e:
-#             print(type(e), e)
-#             break
-#         except Exception as e:
-#             print(type(e), e)
-#             time.sleep(API_RETRY_SLEEP)
-    
-#     return output
-
-
-# def chat_completion_cohere(model, messages, temperature, max_tokens):
-#     import cohere
-
-#     co = cohere.Client(os.environ["COHERE_API_KEY"])
-#     assert len(messages) > 0
-
-#     template_map = {"system":"SYSTEM",
-#                     "assistant":"CHATBOT",
-#                     "user":"USER"}
-
-#     assert messages[-1]["role"] == "user"
-#     prompt = messages[-1]["content"]
-
-#     if len(messages) > 1:
-#         history = []
-#         for message in messages[:-1]:
-#             history.append({"role":template_map[message["role"]], "message":message["content"]})
-#     else:
-#         history = None
-
-#     output = API_ERROR_OUTPUT
-#     for _ in range(API_MAX_RETRY):
-#         try:
-#             response = co.chat(
-#                 message=prompt,
-#                 model=model,
-#                 temperature=temperature,
-#                 max_tokens=max_tokens,
-#                 chat_history=history,
-#             )
-#             output = response.text
-#             break
-#         except cohere.core.api_error.ApiError as e:
-#             print(type(e), e)
-#             raise
-#         except Exception as e:
-#             print(type(e), e)
-#             break
-    
-#     return output
-
-
-def reorg_answer_file(answer_file):
-    """Sort by question id and de-duplication"""
-    answers = {}
-    with open(answer_file, "r") as fin:
-        for l in fin:
-            qid = json.loads(l)["question_id"]
-            answers[qid] = l
-
-    qids = sorted(list(answers.keys()))
-    with open(answer_file, "w") as fout:
-        for qid in qids:
-            fout.write(answers[qid])

src/backend/tasks/arena_hard/configs/api_config.yaml

@@ -1,17 +0,0 @@
-# gpt-3.5-turbo:
-#     model_name: gpt-3.5-turbo
-#     endpoints: null
-#     api_type: openai
-#     parallel: 8
-
-gpt-4-1106-preview:
-    model_name: gpt-4-1106-preview
-    endpoints: null
-    api_type: openai
-    parallel: 8
-
-# llama3-7b:
-#     model_name: llama3-7b
-#     endpoints: null
-#     api_type: openai
-#     parallel: 8

src/backend/tasks/arena_hard/configs/judge_config.yaml

@@ -1,26 +0,0 @@
-name: judgment config file for Arena Hard
-
-bench_name: arena-hard-v0.1
-
-# Arena Hard default
-judge_model: gpt-4-1106-preview
-# judge_model: gpt-3.5-turbo
-reference: False # Optional
-ref_model: null  
-
-baseline: True
-baseline_model: gpt-4-0314
-
-pairwise: True
-temperature: 0
-max_tokens: 4096
-
-regex_pattern: \[\[([AB<>=]+)\]\]
-
-system_prompt: "Please act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user prompt displayed below. You will be given assistant A's answer and assistant B's answer. Your job is to evaluate which assistant's answer is better.\n\nBegin your evaluation by generating your own answer to the prompt. You must provide your answers before judging any answers.\n\nWhen evaluating the assistants' answers, compare both assistants' answers with your answer. You must identify and correct any mistakes or inaccurate information.\n\nThen consider if the assistant's answers are helpful, relevant, and concise. Helpful means the answer correctly responds to the prompt or follows the instructions. Note when user prompt has any ambiguity or more than one interpretation, it is more helpful and appropriate to ask for clarifications or more information from the user than providing an answer based on assumptions. Relevant means all parts of the response closely connect or are appropriate to what is being asked. Concise means the response is clear and not verbose or excessive.\n\nThen consider the creativity and novelty of the assistant's answers when needed. Finally, identify any missing important information in the assistants' answers that would be beneficial to include when responding to the user prompt.\n\nAfter providing your explanation, you must output only one of the following choices as your final verdict with a label:\n\n1. Assistant A is significantly better: [[A>>B]]\n2. Assistant A is slightly better: [[A>B]]\n3. Tie, relatively the same: [[A=B]]\n4. Assistant B is slightly better: [[B>A]]\n5. Assistant B is significantly better: [[B>>A]]\n\nExample output: \"My final verdict is tie: [[A=B]]\"."
-
-prompt_template: ["<|User Prompt|>\n{question_1}\n\n<|The Start of Assistant A's Answer|>\n{answer_1}\n<|The End of Assistant A's Answer|>\n\n<|The Start of Assistant B's Answer|>\n{answer_2}\n<|The End of Assistant B's Answer|>"]
-
-# Add your model below for evaluation
-# model_list:
-#   - gpt-3.5-turbo-0125

src/backend/tasks/arena_hard/task.py

@@ -1,220 +0,0 @@
-import os
-from typing import Union, List
-
-from lm_eval.api.task import ConfigurableTask
-from lm_eval.api.instance import Instance
-
-# from lm_eval.api.registry import register_task
-from lm_eval.api.metrics import mean
-
-from src.backend.envs import DEVICE
-
-import pandas as pd
-
-from src.backend.tasks.measurement_task_utils import measure_system_metrics
-import json
-
-from typing import (
-    Any,
-    Dict,
-    List,
-    Optional,
-    Union,
-)
-
-from datasets import Dataset
-import re
-
-from src.backend.tasks.arena_hard.arena_utils import (
-    load_questions,
-    load_questions,
-    load_model_answers,
-    make_config,
-)
-
-from src.backend.tasks.arena_hard.arena_judgment import (
-    judgment,
-    get_battles_from_scores,
-    compute_mle_elo,
-    predict_win_rate,
-    get_win_rate_column
-)
-
-def load_questions(question_file: str):
-    """Load questions from a file."""
-    questions = []
-    with open(question_file, "r") as ques_file:
-        for line in ques_file:
-            if line:
-                questions.append(json.loads(line))
-    return questions
-
-def download_wrapper(func):
-    def download(self, *args, **kwargs):
-        print("Using Arena Hard, No need to download")
-    return download
-
-original_download = ConfigurableTask.download
-ConfigurableTask.download = download_wrapper(original_download)
-# @register_task("selfcheckgpt")
-@measure_system_metrics
-class ArenaHard(ConfigurableTask):
-    VERSION = 0.0
-    OUTPUT_TYPE = "generate_until"
-    data_path = os.path.join(os.path.dirname(__file__), 'question.jsonl')
-    judge_config_path = os.path.join(os.path.dirname(__file__), "configs/judge_config.yaml")
-    configs = make_config(judge_config_path)
-    model_ans_dir = os.path.join(os.path.dirname(__file__), "model_answer")
-    model_answers = load_model_answers(model_ans_dir)
-    data = load_questions(data_path)
-
-    def __init__(self):
-        super().__init__(config={"metadata": {"version": self.VERSION}})
-        # these end tokens are hard coded because of the current limitaion of the llm-eval.
-        # self.generation_kwargs = {"until": ["\n\n", "<unk>", "<|im_end|>", "</s>", "<|endoftext|>"], "max_length": 512}
-        self.generation_kwargs = {"until": ["</s>", "<|im_end|>"], "max_gen_toks": 4096}
-        # self.generation_kwargs_sampling_number = 5  # the number of sampling for self-consistence
-        # self.generation_kwargs_sampling = {
-        #     "temperature": 0.99,
-        #     "do_sample": True,
-        #     "until": ["<im_end>", "<im_end>"],
-        #     "max_length": 1024,
-        # }
-
-    def transform_data(self, data):
-        transformed_data = []
-        for i in range(len(data)):
-            if self.configs["baseline"]:
-                baseline_answer = self.model_answers[self.configs["baseline_model"]][data[i]["question_id"]]
-            else:
-                baseline_answer = None
-            transformed_item = {
-                "question_id": data[i]["question_id"],
-                "content": data[i]["turns"][0]["content"],  # Assuming you want the first turn's content
-                "model_answer": baseline_answer
-            }
-            transformed_data.append(transformed_item)
-        return transformed_data
-    
-    def has_training_docs(self):
-        return False
-
-    def has_validation_docs(self):
-        return True
-
-    def has_test_docs(self):
-        return False
-
-    def validation_docs(self):
-        self.dataset = self.transform_data(self.data)
-        self.dataset = Dataset.from_dict({"question_id": [item["question_id"] for item in self.dataset],
-                             "content": [item["content"] for item in self.dataset],
-                             "model_answer": [item["model_answer"] for item in self.dataset]})
-        return self.dataset
-
-    def doc_to_text(self, doc):
-        sentence = doc["content"]
-        doc_text = f"{sentence}\n"
-        return doc_text
-
-    def doc_to_target(self, doc):
-        q_id = doc["question_id"]
-        return q_id
-
-    def construct_requests(self, doc: dict, ctx: str, **kwargs) -> Union[List[Instance], Instance]:
-        arguments = (ctx, self.generation_kwargs)
-        request_list = [
-            Instance(request_type="generate_until", doc=doc, arguments=arguments, idx=0, **kwargs),
-        ]
-        # sampling_arguments = (ctx, self.generation_kwargs_sampling)
-        # request_list.extend(
-        #     [
-        #         Instance(request_type="generate_until", doc=doc, arguments=sampling_arguments, idx=idx, **kwargs)
-        #         for idx in range(1, self.generation_kwargs_sampling_number + 1)
-        #     ]
-        # )
-        return request_list
-
-    def process_results(self, doc, results):
-        response_temperature_0 = results[0]
-        # other_responses = results[1:]
-        api_config_path = os.path.join(os.path.dirname(__file__), "configs/api_config.yaml")
-        endpoint_list = make_config(api_config_path)
-        
-        if self.configs["regex_pattern"]:
-            pattern = re.compile(self.configs["regex_pattern"])
-
-        ref_answer_dir = os.path.join(os.path.dirname(__file__), "reference_answer")
-        
-        ref_answers = None
-        if self.configs["reference"]:
-            ref_answers = load_model_answers(ref_answer_dir)
-            ref_answers = [ref_answers[model] for model in self.configs["ref_model"]]
-        
-        # output_files = {}
-        # models = ["custom_model"]
-        # output_dir = f"{os.path.join(os.path.dirname(__file__))}/model_judgments/{self.configs['judge_model']}"
-        # for model in models:
-        #     output_files[model] = os.path.join(
-        #         output_dir,
-        #         f"{model}.jsonl",
-        #     )
-
-        # for output_file in output_files.values():
-        #     os.makedirs(os.path.dirname(output_file), exist_ok=True)
-        
-        endpoint_info = endpoint_list[self.configs["judge_model"]]
-        
-        question = doc
-        kwargs = {}
-        kwargs["question"] = question
-        kwargs["answer"] = response_temperature_0
-        if ref_answers:
-            kwargs["reference"] = [ref_answer[doc["question_id"]] for ref_answer in ref_answers]
-            assert len(kwargs["reference"]) == len(self.configs["ref_model"])
-        else:
-            kwargs["reference"] = None
-        
-        if self.configs["baseline"]:
-            kwargs["baseline_answer"] = doc["model_answer"]
-        else:
-            kwargs["baseline_answer"] = None
-        kwargs["configs"] = self.configs
-        kwargs["endpoint_dict"] = endpoint_info
-        # kwargs["output_file"] = output_files["custom_model"]
-        kwargs["regex_pattern"] = pattern
-    
-        scores = judgment(**kwargs)
-        return {"score": scores}           
-
-    def aggregation(self):
-        """
-        :returns: {str: [float] -> float}
-            A dictionary where keys are the names of submetrics and values are
-            functions that aggregate a list of metrics
-        """
-        ##TODO implement the aggregation function to calculate elo for score
-        def get_win_rate(score_list):
-            battles = get_battles_from_scores(score_list)
-            bootstrap_online_elo = compute_mle_elo(battles)
-            stats = pd.DataFrame()
-            stats["results"] = None
-            stats["results"] = stats['results'].astype('object')
-            for i, model in enumerate(bootstrap_online_elo.index):
-                stats.at[i, "model"] = model
-                stats.at[i, "score"] = bootstrap_online_elo[model]
-
-            stats.sort_values(by="model", inplace=True)
-            stats["score"] = get_win_rate_column(stats, "score", "gpt-4-0314").tolist()
-            
-            return stats["score"][1]
-            
-        return {k: get_win_rate for k in ["score"]}
-
-    def higher_is_better(self):
-        """
-        :returns: {str: bool}
-            A dictionary where keys are the names of submetrics and values are
-            whether a higher value of the submetric is better
-        """
-        return {k: True for k in ["score"]}

src/backend/tasks/gsm8k/gsm8k-custom.yaml

@@ -1,47 +0,0 @@
-group:
-  - math_word_problems
-task: gsm8k_custom
-dataset_path: gsm8k
-dataset_name: main
-output_type: generate_until
-training_split: train
-fewshot_split: train
-test_split: test
-doc_to_text: "Question: {{question}}\nAnswer:"
-doc_to_target: "{{answer}}" #" {{answer.split('### ')[-1].rstrip()}}"
-metric_list:
-  - metric: exact_match
-    aggregation: mean
-    higher_is_better: true
-    ignore_case: true
-    ignore_punctuation: false
-    regexes_to_ignore:
-      - ","
-      - "\\$"
-      - "(?s).*#### "
-      - "\\.$"
-generation_kwargs:
-  until:
-    - "Question:"
-    - "Question"
-    - "</s>"
-    - "<|im_end|>"
-  do_sample: false
-  temperature: 0.0
-  # is_gsm8k: true
-repeats: 1
-num_fewshot: 5
-filter_list:
-  - name: "strict-match"
-    filter:
-      - function: "regex"
-        regex_pattern: "#### (\\-?[0-9\\.\\,]+)"
-      - function: "take_first"
-  - name: "flexible-extract"
-    filter:
-      - function: "regex"
-        group_select: -1
-        regex_pattern: "(-?[$0-9.,]{2,})|(-?[0-9]+)"
-      - function: "take_first"
-metadata:
-  version: 3.0

src/backend/tasks/measurement_task_utils.py

@@ -12,9 +12,6 @@ def process_results_decorator(func):
         end_to_end_time = sum([r[1] for r in results]) / len(results)
         prefilling_time = sum([r[2] for r in results]) / len(results)
         decoding_throughput = sum([r[3] for r in results]) / len(results)
-        mfu = sum([r[4] for r in results]) / len(results)
-        mbu = sum([r[5] for r in results]) / len(results)
-        
         # print(f"end_to_end_time: {end_to_end_time}, prefilling_time: {prefilling_time}, decoding_throughput: {decoding_throughput}")
 
         # Now call the original process_results with the processed results
@@ -22,8 +19,6 @@ def process_results_decorator(func):
         result_dict["end_to_end_time"] = end_to_end_time
         result_dict["prefilling_time"] = prefilling_time
         result_dict["decoding_throughput"] = decoding_throughput
-        result_dict["mfu"] = mfu
-        result_dict["mbu"] = mbu
         return result_dict
     return wrapper
 
@@ -35,8 +30,6 @@ def aggregation_decorator(func):
         aggregation_list["end_to_end_time"] = mean
         aggregation_list["prefilling_time"] = mean
         aggregation_list["decoding_throughput"] = mean
-        aggregation_list["mfu"] = mean
-        aggregation_list["mbu"] = mean
         return aggregation_list
     return wrapper
 
@@ -48,8 +41,6 @@ def higher_is_better_decorator(func):
         higher_is_better_dict["end_to_end_time"] = False
         higher_is_better_dict["prefilling_time"] = False
         higher_is_better_dict["decoding_throughput"] = True
-        higher_is_better_dict["mfu"] = True
-        higher_is_better_dict["mbu"] = True
         return higher_is_better_dict
     return wrapper
 

src/backend/tasks/selfcheckgpt/task.py

@@ -27,12 +27,12 @@ class SelfCheckGPT(ConfigurableTask):
         super().__init__(config={"metadata": {"version": self.VERSION}})
         # these end tokens are hard coded because of the current limitaion of the llm-eval.
         # self.generation_kwargs = {"until": ["\n\n", "<unk>", "<|im_end|>", "</s>", "<|endoftext|>"], "max_length": 512}
-        self.generation_kwargs = {"until": ["<|im_end|>"], "max_length": 1024}
+        self.generation_kwargs = {"until": ["<im_end>"], "max_length": 1024}
         self.generation_kwargs_sampling_number = 5  # the number of sampling for self-consistence
         self.generation_kwargs_sampling = {
             "temperature": 0.99,
             "do_sample": True,
-            "until": ["<|im_end|>", "</s>"],
+            "until": ["<im_end>", "</s>"],
             "max_length": 1024,
         }
 

src/display/about.py

@@ -3,36 +3,23 @@ from src.display.utils import ModelType
 TITLE = """<h1 align="center" id="space-title">OPEN-MOE-LLM-LEADERBOARD</h1>"""
 
 INTRODUCTION_TEXT = """
-The OPEN-MOE-LLM-LEADERBOARD is specifically designed to assess the performance and efficiency of various Mixture of Experts (MoE) Large Language Models (LLMs). 
-This initiative, driven by the open-source community, aims to comprehensively evaluate these advanced MoE LLMs. 
+The OPEN-MOE-LLM-LEADERBOARD is specifically designed to assess the performance and efficiency of various Mixture of Experts (MoE) Large Language Models (LLMs). This initiative, driven by the open-source community, aims to comprehensively evaluate these advanced MoE LLMs. We extend our gratitude to the Huggingface for the GPU community grant that supported the initial debugging process, and to [NetMind.AI](https://netmind.ai/home) for their generous GPU donation, which ensures the continuous operation of the Leaderboard.
 
 The OPEN-MOE-LLM-LEADERBOARD includes generation and multiple choice tasks to measure the performance and efficiency of MOE LLMs.
 
 
 Tasks:
+- **Generation Self-consistancy** -- [SelfCheckGPT](https://github.com/potsawee/selfcheckgpt)
 - **Multiple Choice Performance** -- [MMLU](https://arxiv.org/abs/2009.03300)
-- **Mathematics Problem-Solving Performance** -- [GSM8K](https://arxiv.org/abs/2110.14168)
-- **AI Judgment Scores for Responses to Complex User Queries** -- [Arena_Hard](https://lmsys.org/blog/2024-04-19-arena-hard/)
 
 Columns and Metrics:
 - Method: The MOE LLMs inference framework.
 - E2E(s): Average End to End generation time in seconds.
 - PRE(s): Prefilling Time of input prompt in seconds.
 - T/s: Tokens throughout per second.
-- S-MBU(%): Sparse Model Bandwidth Utilization.
-- S-MFU(%): Sparse Model FLOPs Utilization.
 - Precision: The precison of used model.
 
 """
-
-ACKNOWLEDGEMENT_TEXT = """
-<div>
-    <h4>Acknowledgements</h4>
-    {image_html}
-    <p>We express our sincere gratitude to <a href="https://netmind.ai/home">NetMind.AI</a> for their generous donation of GPUs, which plays a crucial role in ensuring the continuous operation of our Leaderboard.</p>
-</div>
-"""
-
 LLM_BENCHMARKS_TEXT = f"""
 
 """

src/display/utils.py

@@ -18,16 +18,12 @@ GPU_Power = 'Power(W)'
 GPU_Mem = 'Mem(G)'
 GPU_Name = "GPU"
 GPU_Util = 'Util(%)'
-MFU = 'S-MFU(%)'
-MBU = 'S-MBU(%)'
 BATCH_SIZE = 'bs'
 PRECISION = "Precision"
 system_metrics_to_name_map = {
     "end_to_end_time": f"{E2Es}",
     "prefilling_time": f"{PREs}",
     "decoding_throughput": f"{TS}",
-    "mfu": f"{MFU}",
-    "mbu": f"{MBU}"
 }
 
 gpu_metrics_to_name_map = {
@@ -37,7 +33,7 @@ gpu_metrics_to_name_map = {
     GPU_Mem: GPU_Mem,
     "batch_size": BATCH_SIZE,
     "precision": PRECISION,
-    GPU_Name: GPU_Name
+    GPU_Name: GPU_Name,
 }
 
 @dataclass
@@ -77,11 +73,8 @@ class Tasks(Enum):
     # halueval_dial = Task("halueval_dialogue", "acc", "HaluDial/Acc")
 
     # # XXX include me back at some point
-    # selfcheck = Task("selfcheckgpt", "max-selfcheckgpt", "SelfCheckGPT")
+    selfcheck = Task("selfcheckgpt", "max-selfcheckgpt", "SelfCheckGPT")
     mmlu = Task("mmlu", "acc", "MMLU") #MMLU/Acc (5-shot)
-    gsm8k = Task("gsm8k_custom", "em", "GSM8K") #GSM8K/EM (5-shot)
-    # gsm8k_cot = Task("gsm8k_cot", "em", "GSM8K COT") #GSM8K COT/EM (5-shot)
-    arena_hard = Task("arena_hard", "score", "Arena Hard") #Arena Hard/Score
 
 
 # These classes are for user facing column names,
@@ -106,35 +99,33 @@ auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "ma
 # # auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Avg", "number", True)])
 
 # Inference framework
-auto_eval_column_dict.append(["inference_framework", ColumnContent, ColumnContent(f"{InFrame}", "str", True, dummy=True)])
+auto_eval_column_dict.append(["inference_framework", ColumnContent, ColumnContent(f"{InFrame}", "str", True)])
 
 for task in Tasks:
     auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
     # System performance metrics
-    auto_eval_column_dict.append([f"{task.name}_end_to_end_time", ColumnContent, ColumnContent(f"{task.value.col_name} {E2Es}", "number", True, hidden=True)])
-    auto_eval_column_dict.append([f"{task.name}_batch_size", ColumnContent, ColumnContent(f"{task.value.col_name} {BATCH_SIZE}", "number", True, hidden=True)])
-    # auto_eval_column_dict.append([f"{task.name}_precision", ColumnContent, ColumnContent(f"{task.value.col_name} {PRECISION}", "str", True, hidden=True)])
-    # auto_eval_column_dict.append([f"{task.name}_gpu_mem", ColumnContent, ColumnContent(f"{task.value.col_name} {GPU_Mem}", "number", True, hidden=True)])
-    auto_eval_column_dict.append([f"{task.name}_gpu", ColumnContent, ColumnContent(f"{task.value.col_name} {GPU_Name}", "str", True, hidden=True)])
-    # auto_eval_column_dict.append([f"{task.name}_gpu_util", ColumnContent, ColumnContent(f"{task.value.col_name} {GPU_Util}", "number", True, hidden=True)])
+    auto_eval_column_dict.append([f"{task.name}_end_to_end_time", ColumnContent, ColumnContent(f"{task.value.col_name} {E2Es}", "number", True)])
+    auto_eval_column_dict.append([f"{task.name}_batch_size", ColumnContent, ColumnContent(f"{task.value.col_name} {BATCH_SIZE}", "number", True)])
+    # auto_eval_column_dict.append([f"{task.name}_precision", ColumnContent, ColumnContent(f"{task.value.col_name} {PRECISION}", "str", True)])
+    auto_eval_column_dict.append([f"{task.name}_gpu_mem", ColumnContent, ColumnContent(f"{task.value.col_name} {GPU_Mem}", "number", True)])
+    auto_eval_column_dict.append([f"{task.name}_gpu", ColumnContent, ColumnContent(f"{task.value.col_name} {GPU_Name}", "str", True)])
+    auto_eval_column_dict.append([f"{task.name}_gpu_util", ColumnContent, ColumnContent(f"{task.value.col_name} {GPU_Util}", "number", True)])
     if task.value.benchmark in MULTIPLE_CHOICEs:
         continue
-    # auto_eval_column_dict.append([f"{task.name}_prefilling_time", ColumnContent, ColumnContent(f"{task.value.col_name} {PREs}", "number", False, hidden=True)])
-    auto_eval_column_dict.append([f"{task.name}_decoding_throughput", ColumnContent, ColumnContent(f"{task.value.col_name} {TS}", "number", True, hidden=True)])
-    auto_eval_column_dict.append([f"{task.name}_mbu", ColumnContent, ColumnContent(f"{task.value.col_name} {MBU}", "number", True, hidden=True)])
-    auto_eval_column_dict.append([f"{task.name}_mfu", ColumnContent, ColumnContent(f"{task.value.col_name} {MFU}", "number", True, hidden=True)])
+    # auto_eval_column_dict.append([f"{task.name}_prefilling_time", ColumnContent, ColumnContent(f"{task.value.col_name} {PREs}", "number", False)])
+    auto_eval_column_dict.append([f"{task.name}_decoding_throughput", ColumnContent, ColumnContent(f"{task.value.col_name} {TS}", "number", True)])
 
 
 # Model information
-auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False, dummy=True)])
-# auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
-# auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
-auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", True, dummy=True)])
-# auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
-# auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
-# auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
-# auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
-# auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
+auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
+auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
+auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
+auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", True)])
+auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
+auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
+auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
+auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
+auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
 # Dummy column for the search bar (hidden by the custom CSS)
 auto_eval_column_dict.append(["dummy", ColumnContent, ColumnContent("model_name_for_query", "str", False, dummy=True)])
 
@@ -149,7 +140,6 @@ class EvalQueueColumn:  # Queue column
     private = ColumnContent("private", "bool", True)
     precision = ColumnContent("precision", "str", True)
     weight_type = ColumnContent("weight_type", "str", "Original")
-    model_framework = ColumnContent("inference_framework", "str", True)
     status = ColumnContent("status", "str", True)
 
 
@@ -160,10 +150,10 @@ class ModelDetails:
 
 
 class ModelType(Enum):
-    # PT = ModelDetails(name="pretrained", symbol="🟢")
-    # FT = ModelDetails(name="fine-tuned on domain-specific datasets", symbol="🔶")
+    PT = ModelDetails(name="pretrained", symbol="🟢")
+    FT = ModelDetails(name="fine-tuned on domain-specific datasets", symbol="🔶")
     chat = ModelDetails(name="chat models (RLHF, DPO, IFT, ...)", symbol="💬")
-    # merges = ModelDetails(name="base merges and moerges", symbol="🤝")
+    merges = ModelDetails(name="base merges and moerges", symbol="🤝")
     Unknown = ModelDetails(name="", symbol="?")
 
     def to_str(self, separator=" "):
@@ -171,24 +161,21 @@ class ModelType(Enum):
 
     @staticmethod
     def from_str(type):
-        # if "fine-tuned" in type or "🔶" in type:
-        #     return ModelType.FT
-        # if "pretrained" in type or "🟢" in type:
-        #     return ModelType.PT
+        if "fine-tuned" in type or "🔶" in type:
+            return ModelType.FT
+        if "pretrained" in type or "🟢" in type:
+            return ModelType.PT
         if any([k in type for k in ["instruction-tuned", "RL-tuned", "chat", "🟦", "⭕", "💬"]]):
             return ModelType.chat
-        # if "merge" in type or "🤝" in type:
-        #     return ModelType.merges
+        if "merge" in type or "🤝" in type:
+            return ModelType.merges
         return ModelType.Unknown
 
 
 class InferenceFramework(Enum):
     # "moe-infinity", hf-chat
-    # MoE_Infinity = ModelDetails("moe-infinity")
+    MoE_Infinity = ModelDetails("moe-infinity")
     HF_Chat = ModelDetails("hf-chat")
-    VLLM = ModelDetails("vllm_moe")
-    TRTLLM = ModelDetails("tensorrt_llm")
-    VLLM_FIX = ModelDetails("vllm_moe_fixbs")
     Unknown = ModelDetails("?")
 
     def to_str(self):
@@ -196,34 +183,13 @@ class InferenceFramework(Enum):
 
     @staticmethod
     def from_str(inference_framework: str):
-        # if inference_framework in ["moe-infinity"]:
-        #     return InferenceFramework.MoE_Infinity
-        if inference_framework in ["tensorrt_llm"]:
-            return InferenceFramework.TRTLLM
+        if inference_framework in ["moe-infinity"]:
+            return InferenceFramework.MoE_Infinity
         if inference_framework in ["hf-chat"]:
             return InferenceFramework.HF_Chat
-        if inference_framework in ["vllm_moe"]:
-            return InferenceFramework.VLLM
-        if inference_framework in ["vllm_moe_fixbs"]:
-            return InferenceFramework.VLLM_FIX
         return InferenceFramework.Unknown
 
-class GPUType(Enum):
-    A100_sxm = ModelDetails("NVIDIA-A100-SXM4-80GB")
-    A100_pcie = ModelDetails("NVIDIA-A100-PCIe-80GB")
-    Unknown = ModelDetails("?")
 
-    def to_str(self):
-        return self.value.name
-
-    @staticmethod
-    def from_str(gpu_type: str):
-        if gpu_type in ["NVIDIA-A100-PCIe-80GB"]:
-            return GPUType.A100_pcie
-        if gpu_type in ["NVIDIA-A100-SXM4-80GB"]:
-            return GPUType.A100_sxm
-        return GPUType.Unknown
-    
 class WeightType(Enum):
     Adapter = ModelDetails("Adapter")
     Original = ModelDetails("Original")
@@ -231,34 +197,34 @@ class WeightType(Enum):
 
 
 class Precision(Enum):
-    # float32 = ModelDetails("float32")
-    # float16 = ModelDetails("float16")
+    float32 = ModelDetails("float32")
+    float16 = ModelDetails("float16")
     bfloat16 = ModelDetails("bfloat16")
     qt_8bit = ModelDetails("8bit")
     qt_4bit = ModelDetails("4bit")
-    # qt_GPTQ = ModelDetails("GPTQ")
+    qt_GPTQ = ModelDetails("GPTQ")
     Unknown = ModelDetails("?")
 
     @staticmethod
     def from_str(precision: str):
-        # if precision in ["torch.float32", "float32"]:
-        #     return Precision.float32
-        # if precision in ["torch.float16", "float16"]:
-        #     return Precision.float16
+        if precision in ["torch.float32", "float32"]:
+            return Precision.float32
+        if precision in ["torch.float16", "float16"]:
+            return Precision.float16
         if precision in ["torch.bfloat16", "bfloat16"]:
             return Precision.bfloat16
         if precision in ["8bit"]:
             return Precision.qt_8bit
         if precision in ["4bit"]:
             return Precision.qt_4bit
-        # if precision in ["GPTQ", "None"]:
-        #     return Precision.qt_GPTQ
+        if precision in ["GPTQ", "None"]:
+            return Precision.qt_GPTQ
         return Precision.Unknown
 
 
 # Column selection
-COLS = [c.name for c in fields(AutoEvalColumn)]
-TYPES = [c.type for c in fields(AutoEvalColumn)]
+COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
+TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
 COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
 TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
 

src/envs.py

@@ -12,8 +12,8 @@ QUEUE_REPO = "sparse-generative-ai/requests"
 QUEUE_REPO_OPEN_LLM = "open-llm-leaderboard/requests"
 RESULTS_REPO = "sparse-generative-ai/results"
 
-DEBUG_QUEUE_REPO = "sparse-generative-ai/debug_requests"
-DEBUG_RESULTS_REPO = "sparse-generative-ai/debug_results"
+PRIVATE_QUEUE_REPO = "sparse-generative-ai/private-requests"
+PRIVATE_RESULTS_REPO = "sparse-generative-ai/private-results"
 
 IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True))
 

src/leaderboard/read_evals.py

@@ -65,11 +65,11 @@ class EvalResult:
         if len(org_and_model) == 1:
             org = None
             model = org_and_model[0]
-            result_key = f"{model}_{precision.value.name}_{inference_framework}"
+            result_key = f"{model}_{precision.value.name}"
         else:
             org = org_and_model[0]
             model = org_and_model[1]
-            result_key = f"{org}_{model}_{precision.value.name}_{inference_framework}"
+            result_key = f"{org}_{model}_{precision.value.name}"
         full_model = "/".join(org_and_model)
 
         still_on_hub, error, model_config = is_model_on_hub(
@@ -120,15 +120,11 @@ class EvalResult:
                         multiplier = 1.0
                     if "batch_" in metric or "Mem" in metric or "Util" in metric:
                         multiplier = 1
-                        
+
+
                     # print('RESULTS', data['results'])
                     # print('XXX', benchmark, metric, value, multiplier)
-                    if value == "N/A":
-                        results[benchmark][metric] = "-"
-                    elif value == "auto":
-                        results[benchmark][metric] = "auto"
-                    else:
-                        results[benchmark][metric] = value * multiplier
+                    results[benchmark][metric] = value * multiplier
 
         res = EvalResult(
             eval_name=result_key,
@@ -140,7 +136,6 @@ class EvalResult:
             revision=config.get("model_sha", ""),
             still_on_hub=still_on_hub,
             architecture=architecture,
-            model_type=ModelType.from_str(config.get("model_type", "")),
             inference_framework=inference_framework,
         )
 
@@ -175,22 +170,22 @@ class EvalResult:
 
         # breakpoint()
         # average = sum([v for v in self.results.values() if v is not None]) / len(Tasks)
-        
+
         data_dict = {
             "eval_name": self.eval_name,  # not a column, just a save name,
             AutoEvalColumn.precision.name: self.precision.value.name,
-            # AutoEvalColumn.model_type.name: self.model_type.value.name,
+            AutoEvalColumn.model_type.name: self.model_type.value.name,
             AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
-            # AutoEvalColumn.weight_type.name: self.weight_type.value.name,
-            # AutoEvalColumn.architecture.name: self.architecture,
+            AutoEvalColumn.weight_type.name: self.weight_type.value.name,
+            AutoEvalColumn.architecture.name: self.architecture,
             AutoEvalColumn.model.name: make_clickable_model(self.full_model),
             AutoEvalColumn.dummy.name: self.full_model,
-            # AutoEvalColumn.revision.name: self.revision,
-            # # AutoEvalColumn.average.name: average,
-            # AutoEvalColumn.license.name: self.license,
-            # AutoEvalColumn.likes.name: self.likes,
-            # AutoEvalColumn.params.name: self.num_params,
-            # AutoEvalColumn.still_on_hub.name: self.still_on_hub,
+            AutoEvalColumn.revision.name: self.revision,
+            # AutoEvalColumn.average.name: average,
+            AutoEvalColumn.license.name: self.license,
+            AutoEvalColumn.likes.name: self.likes,
+            AutoEvalColumn.params.name: self.num_params,
+            AutoEvalColumn.still_on_hub.name: self.still_on_hub,
             AutoEvalColumn.inference_framework.name: self.inference_framework,
         }
 
@@ -278,22 +273,15 @@ def get_raw_eval_results(results_path: str, requests_path: str, is_backend: bool
 
     eval_results = {}
     for model_result_filepath in tqdm(model_result_filepaths, desc="reading model_result_filepaths"):
-        try:
-            # Creation of result
-            eval_result = EvalResult.init_from_json_file(model_result_filepath, is_backend=is_backend)
-            eval_result.update_with_request_file(requests_path)
-            
-            # Store results of same eval together
-            eval_name = eval_result.eval_name
-            if eval_name in eval_results.keys():
-                eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
-            else:
-                eval_results[eval_name] = eval_result
-                
-        except (FileNotFoundError, ValueError, KeyError, json.JSONDecodeError) as e:
-            # Log the error and continue with the next file
-            print(f"Error processing file {model_result_filepath}: {e}")
-            continue
+        # Creation of result
+        eval_result = EvalResult.init_from_json_file(model_result_filepath, is_backend=is_backend)
+        eval_result.update_with_request_file(requests_path)
+        # Store results of same eval together
+        eval_name = eval_result.eval_name
+        if eval_name in eval_results.keys():
+            eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
+        else:
+            eval_results[eval_name] = eval_result
 
     results = []
     for v in eval_results.values():

src/populate.py

@@ -95,7 +95,6 @@ def get_evaluation_queue_df(save_path: str, cols: list) -> tuple[pd.DataFrame, p
 
             data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
             data[EvalQueueColumn.revision.name] = data.get("revision", "main")
-            data[EvalQueueColumn.model_framework.name] = data.get("inference_framework", "-")
 
             all_evals.append(data)
         elif ".md" not in entry:
@@ -108,7 +107,6 @@ def get_evaluation_queue_df(save_path: str, cols: list) -> tuple[pd.DataFrame, p
 
                 data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
                 data[EvalQueueColumn.revision.name] = data.get("revision", "main")
-                data[EvalQueueColumn.model_framework.name] = data.get("inference_framework", "-")
                 all_evals.append(data)
 
     pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]

src/submission/check_validity.py

@@ -74,7 +74,7 @@ def is_model_on_hub(
 
 
 def get_model_size(model_info: ModelInfo, precision: str):
-    size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
+    size_pattern = size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
     try:
         model_size = round(model_info.safetensors["total"] / 1e9, 3)
     except (AttributeError, TypeError):
@@ -130,8 +130,7 @@ def already_submitted_models(requested_models_dir: str) -> set[str]:
                     continue
                 with open(os.path.join(root, file), "r") as f:
                     info = json.load(f)
-                    if not info["status"] == "FINISHED" and not info["status"] == "RUNNING":
-                        file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}_{info['inference_framework']}_{info['gpu_type']}")
+                    file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}_{info['inference_framework']}")
 
                     # Select organisation
                     if info["model"].count("/") == 0 or "submitted_time" not in info:

src/submission/submit.py

@@ -3,7 +3,7 @@ import os
 from datetime import datetime, timezone
 
 from src.display.formatting import styled_error, styled_message, styled_warning
-from src.envs import API, EVAL_REQUESTS_PATH, H4_TOKEN, QUEUE_REPO, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA, DEBUG_QUEUE_REPO
+from src.envs import API, EVAL_REQUESTS_PATH, H4_TOKEN, QUEUE_REPO, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA
 from src.leaderboard.filter_models import DO_NOT_SUBMIT_MODELS
 from src.submission.check_validity import (
     already_submitted_models,
@@ -26,17 +26,12 @@ def add_new_eval(
     weight_type: str,
     model_type: str,
     inference_framework: str,
-    debug: bool = False,
-    gpu_type: str = "NVIDIA-A100-PCIe-80GB",
 ):
     global REQUESTED_MODELS
     global USERS_TO_SUBMISSION_DATES
     if not REQUESTED_MODELS:
         REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
 
-    if debug:
-        QUEUE_REPO = DEBUG_QUEUE_REPO
-    
     user_name = ""
     model_path = model
     if "/" in model:
@@ -115,18 +110,17 @@ def add_new_eval(
         "params": model_size,
         "license": license,
         "inference_framework": inference_framework,
-        "gpu_type": gpu_type
     }
 
     # Check for duplicate submission
-    if f"{model}_{revision}_{precision}_{inference_framework}_{gpu_type}" in REQUESTED_MODELS:
+    if f"{model}_{revision}_{precision}_{inference_framework}" in REQUESTED_MODELS:
         return styled_warning("This model has been already submitted.")
 
     print("Creating eval file")
     OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
     os.makedirs(OUT_DIR, exist_ok=True)
     # out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}.json"
-    out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}_{inference_framework}_{gpu_type}.json"
+    out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}_{inference_framework}.json"
 
     with open(out_path, "w") as f:
         f.write(json.dumps(eval_entry))

src/utils.py

@@ -3,54 +3,12 @@ from huggingface_hub import snapshot_download
 import subprocess
 import re
 import os
-import GPUtil
 
 try:
     from src.display.utils import GPU_TEMP, GPU_Mem, GPU_Power, GPU_Util, GPU_Name
 except:
     print("local debug: from display.utils")
     from display.utils import GPU_TEMP, GPU_Mem, GPU_Power, GPU_Util, GPU_Name
-    
-MEM_BW_DICT ={
-    "NVIDIA-A100-PCIe-80GB": 1935,
-    "NVIDIA-A100-SXM-80GB": 2039,
-    "NVIDIA-H100-PCIe-80GB": 2039,
-    "NVIDIA-RTX-A5000-24GB": 768
-}
-
-PEAK_FLOPS_DICT = {
-    "float32":{
-        "NVIDIA-A100-PCIe-80GB": 312e12,
-        "NVIDIA-A100-SXM-80GB": 312e12,
-        "NVIDIA-H100-PCIe-80GB": 756e12,
-        "NVIDIA-RTX-A5000-24GB": 222.2e12
-    },
-    "float16":{
-        "NVIDIA-A100-PCIe-80GB": 624e12,
-        "NVIDIA-A100-SXM-80GB": 624e12,
-        "NVIDIA-H100-PCIe-80GB": 1513e12,
-        "NVIDIA-RTX-A5000-24GB": 444.4e12
-    },
-    "bfloat16":{
-        "NVIDIA-A100-PCIe-80GB": 624e12,
-        "NVIDIA-A100-SXM-80GB": 624e12,
-        "NVIDIA-H100-PCIe-80GB": 1513e12,
-        "NVIDIA-RTX-A5000-24GB": 444.4e12
-    },
-    "8bit":{
-        "NVIDIA-A100-PCIe-80GB": 1248e12,
-        "NVIDIA-A100-SXM-80GB": 1248e12,
-        "NVIDIA-H100-PCIe-80GB": 3026e12,
-        "NVIDIA-RTX-A5000-24GB": 889e12
-    },
-    "4bit": {
-        "NVIDIA-A100-PCIe-80GB": 2496e12,
-        "NVIDIA-A100-SXM-80GB": 2496e12,
-        "NVIDIA-H100-PCIe-80GB": 6052e12,
-        "NVIDIA-RTX-A5000-24GB": 1778e12
-    }
-
-}
 
 def my_snapshot_download(repo_id, revision, local_dir, repo_type, max_workers):
     for i in range(10):
@@ -94,12 +52,11 @@ def parse_nvidia_smi():
             print("Failed to query GPU indices.")
             return []
         gpu_indices = result.stdout.strip().split('\n')
-    # print(f"gpu_indices: {gpu_indices}")
+    print(f"gpu_indices: {gpu_indices}")
     gpu_stats = []
 
     gpu_info_pattern = re.compile(r'(\d+)C\s+P\d+\s+(\d+)W / \d+W\s+\|\s+(\d+)MiB / \d+MiB\s+\|\s+(\d+)%')
-    # gpu_name_pattern = re.compile(r'NVIDIA\s+([\w\s]+\d+(?:\s*GB)?)')
-    gpu_name_pattern = re.compile(r'NVIDIA\s+(RTX\s+)?([A-Z0-9]+)')
+    gpu_name_pattern = re.compile(r'NVIDIA\s+([\w\s]+?\d+GB)')
 
     gpu_name = ""
     for index in gpu_indices:
@@ -111,7 +68,7 @@ def parse_nvidia_smi():
             name_match = gpu_name_pattern.search(line)
             gpu_info = {}
             if name_match:
-                gpu_name = ''.join(filter(None, name_match.groups())).strip()
+                gpu_name = name_match.group(1).strip()
             if match:
                 temp, power_usage, mem_usage, gpu_util = map(int, match.groups())
                 gpu_info.update({
@@ -123,7 +80,7 @@ def parse_nvidia_smi():
 
             if len(gpu_info) >= 4:
                 gpu_stats.append(gpu_info)
-    # print(f"gpu_stats: {gpu_stats}")
+    print(f"gpu_stats: {gpu_stats}")
     gpu_name = f"{len(gpu_stats)}x{gpu_name}"
     gpu_stats_total = {
                         GPU_TEMP: 0,
@@ -174,38 +131,5 @@ def analyze_gpu_stats(stats_list):
 
     return avg_stats
 
-def get_gpu_details():
-    gpus = GPUtil.getGPUs()
-    gpu = gpus[0]
-    name = gpu.name.replace(" ", "-")
-    memory_gb = round(gpu.memoryTotal / 1024)
-    memory = f"{memory_gb}GB"
-
-    for part in name.split('-'):
-        if part.endswith("GB") and part[:-2].isdigit():
-            name = name.replace(f"-{part}", "").replace(part, "")
-
-    formatted_name = f"{name}-{memory}"
-    
-    return formatted_name
-
-def get_peak_bw(gpu_name):
-    return MEM_BW_DICT[gpu_name]
-
-def get_peak_flops(gpu_name, precision):
-    return PEAK_FLOPS_DICT[precision][gpu_name]
-
-def transfer_precision2bytes(precision):
-    if precision == "float32":
-        return 4
-    elif precision in ["float16", "bfloat16"]:
-        return 2
-    elif precision == "8bit":
-        return 1
-    elif precision == "4bit":
-        return 0.5
-    else:
-        raise ValueError(f"Unsupported precision: {precision}")
-
 if __name__ == "__main__":
     print(analyze_gpu_stats(parse_nvidia_smi()))