HF_ML_Tasksmith / tasks /tasksmith-749ff8d440e1 /environment /source /method_comparison /processing.py
| # Copyright 2025-present the HuggingFace Inc. team. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Data processing used for analyzing and presenting the results""" | |
| import json | |
| import os | |
| import pandas as pd | |
| def _preprocess_common(row): | |
| """Extract fields common to all tasks from a single result row. | |
| Returns a tuple of metainfo dict and train metrics, or None if the row should be skipped. | |
| """ | |
| run_info = row["run_info"] | |
| train_info = row["train_info"] | |
| meta_info = row["meta_info"] | |
| if run_info["peft_config"]: | |
| peft_type = run_info["peft_config"]["peft_type"] | |
| else: | |
| peft_type = "full-finetuning" | |
| if train_info["status"] != "success": | |
| return None | |
| train_metrics = train_info["metrics"][-1] | |
| dct = { | |
| "experiment_name": run_info["experiment_name"], | |
| "model_id": run_info["train_config"]["model_id"], | |
| "train_config": run_info["train_config"], | |
| "peft_type": peft_type, | |
| "peft_config": run_info["peft_config"], | |
| "accelerator_memory_reserved_avg": train_info["accelerator_memory_reserved_avg"], | |
| "accelerator_memory_max": train_info["accelerator_memory_max"], | |
| "accelerator_memory_reserved_99th": train_info["accelerator_memory_reserved_99th"], | |
| "total_time": run_info["total_time"], | |
| "train_time": train_info["train_time"], | |
| "file_size": train_info["file_size"], | |
| "num_trainable_params": train_info["num_trainable_params"], | |
| "train_loss": train_metrics["train loss"], | |
| "train_samples": train_metrics["train samples"], | |
| "peft_version": meta_info["package_info"]["peft-version"], | |
| "peft_branch": run_info["peft_branch"], | |
| "transformers_version": meta_info["package_info"]["transformers-version"], | |
| "datasets_version": meta_info["package_info"]["datasets-version"], | |
| "torch_version": meta_info["package_info"]["torch-version"], | |
| "package_info": meta_info["package_info"], | |
| "system_info": meta_info["system_info"], | |
| "created_at": run_info["created_at"], | |
| } | |
| return dct, train_metrics | |
| def _preprocess_metamathqa(dct, train_metrics, meta_info): | |
| """Add MetaMathQA-specific fields.""" | |
| dct["test_accuracy"] = train_metrics["test accuracy"] | |
| dct["train_total_tokens"] = train_metrics["train total tokens"] | |
| dct["forgetting*"] = train_metrics.get("forgetting", 123) | |
| dct["bitsandbytes_version"] = meta_info["package_info"]["bitsandbytes-version"] | |
| def _preprocess_image_gen(dct, train_metrics, meta_info): | |
| """Add image-gen-specific fields.""" | |
| dct["test_dino_similarity"] = train_metrics["test dino_similarity"] | |
| dct["drift*"] = train_metrics.get("drift", 123) | |
| dct["diffusers_version"] = meta_info["package_info"]["diffusers-version"] | |
| _TASK_PREPROCESSORS = { | |
| "MetaMathQA": _preprocess_metamathqa, | |
| "image-gen": _preprocess_image_gen, | |
| } | |
| def preprocess(rows, task_name: str, print_fn=print): | |
| task_preprocessor = _TASK_PREPROCESSORS.get(task_name) | |
| if task_preprocessor is None: | |
| raise ValueError(f"Unknown task_name: {task_name!r}. Choose from {list(_TASK_PREPROCESSORS)}") | |
| results = [] | |
| skipped = 0 | |
| for row in rows: | |
| common = _preprocess_common(row) | |
| if common is None: | |
| skipped += 1 | |
| continue | |
| dct, train_metrics = common | |
| dct["task_name"] = task_name | |
| task_preprocessor(dct, train_metrics, row["meta_info"]) | |
| results.append(dct) | |
| if skipped: | |
| print_fn(f"Skipped {skipped} of {len(rows)} entries because the train status != success") | |
| return results | |
| def load_jsons(path): | |
| results = [] | |
| for fn in os.listdir(path): | |
| if fn.endswith(".json"): | |
| with open(os.path.join(path, fn)) as f: | |
| row = json.load(f) | |
| results.append(row) | |
| return results | |
| _COMMON_DTYPES = { | |
| "task_name": "string", | |
| "experiment_name": "string", | |
| "model_id": "string", | |
| "train_config": "string", | |
| "peft_type": "string", | |
| "peft_config": "string", | |
| "accelerator_memory_reserved_avg": int, | |
| "accelerator_memory_max": int, | |
| "accelerator_memory_reserved_99th": int, | |
| "total_time": float, | |
| "train_time": float, | |
| "file_size": int, | |
| "train_loss": float, | |
| "train_samples": int, | |
| "num_trainable_params": int, | |
| "peft_version": "string", | |
| "peft_branch": "string", | |
| "transformers_version": "string", | |
| "datasets_version": "string", | |
| "torch_version": "string", | |
| "package_info": "string", | |
| "system_info": "string", | |
| "created_at": "string", | |
| } | |
| _TASK_DTYPES = { | |
| "MetaMathQA": { | |
| "test_accuracy": float, | |
| "train_total_tokens": int, | |
| "forgetting*": float, | |
| "bitsandbytes_version": "string", | |
| }, | |
| "image-gen": { | |
| "test_dino_similarity": float, | |
| "drift*": float, | |
| "diffusers_version": "string", | |
| }, | |
| } | |
| _TASK_IMPORTANT_COLUMNS = { | |
| "MetaMathQA": [ | |
| "experiment_name", | |
| "peft_type", | |
| "total_time", | |
| "train_time", | |
| "test_accuracy", | |
| "train_loss", | |
| "accelerator_memory_max", | |
| "accelerator_memory_reserved_99th", | |
| "accelerator_memory_reserved_avg", | |
| "num_trainable_params", | |
| "file_size", | |
| "created_at", | |
| "task_name", | |
| "forgetting*", | |
| ], | |
| "image-gen": [ | |
| "experiment_name", | |
| "peft_type", | |
| "total_time", | |
| "train_time", | |
| "test_dino_similarity", | |
| "drift*", | |
| "train_loss", | |
| "accelerator_memory_max", | |
| "accelerator_memory_reserved_99th", | |
| "accelerator_memory_reserved_avg", | |
| "num_trainable_params", | |
| "file_size", | |
| "created_at", | |
| "task_name", | |
| ], | |
| } | |
| def load_df(path, task_name, print_fn=print): | |
| jsons = load_jsons(path) | |
| preprocessed = preprocess(jsons, task_name=task_name, print_fn=print_fn) | |
| dtype_dict = {**_COMMON_DTYPES, **_TASK_DTYPES.get(task_name, {})} | |
| if not preprocessed: | |
| return pd.DataFrame(columns=dtype_dict.keys()) | |
| df = pd.DataFrame(preprocessed) | |
| df = df.astype(dtype_dict) | |
| df["created_at"] = pd.to_datetime(df["created_at"]) | |
| # round training time to nearest second | |
| df["train_time"] = df["train_time"].round().astype(int) | |
| df["total_time"] = df["total_time"].round().astype(int) | |
| # reorder columns for better viewing, pinned_columns arg in Gradio seems not to work correctly | |
| important_columns = _TASK_IMPORTANT_COLUMNS.get(task_name, ["experiment_name", "peft_type"]) | |
| other_columns = [col for col in df if col not in important_columns] | |
| df = df[important_columns + other_columns] | |
| columns = ["experiment_name", "model_id", "peft_type", "created_at"] | |
| # we want to keep only the most recent run for each experiment | |
| df = df.sort_values("created_at").drop_duplicates(columns, keep="last") | |
| return df | |