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2f9f759587
While memory usage correlates with the number of trainable params, having this number directly makes it easier to see that methods are using similar numbers of trainable params and outliers can be inspected easily.
148 lines
5.4 KiB
Python
148 lines
5.4 KiB
Python
# Copyright 2025-present the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Data processing used for analyzing and presenting the results"""
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import json
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import os
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import pandas as pd
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def preprocess(rows, task_name: str, print_fn=print):
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results = []
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skipped = 0
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for row in rows:
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run_info = row["run_info"]
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train_info = row["train_info"]
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meta_info = row["meta_info"]
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if run_info["peft_config"]:
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peft_type = run_info["peft_config"]["peft_type"]
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else:
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peft_type = "full-finetuning"
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if train_info["status"] != "success":
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skipped += 1
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continue
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train_metrics = train_info["metrics"][-1]
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# extract the fields that make most sense
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dct = {
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"task_name": task_name,
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"experiment_name": run_info["experiment_name"],
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"model_id": run_info["train_config"]["model_id"],
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"train_config": run_info["train_config"],
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"peft_type": peft_type,
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"peft_config": run_info["peft_config"],
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"accelerator_memory_reserved_avg": train_info["accelerator_memory_reserved_avg"],
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"accelerator_memory_max": train_info["accelerator_memory_max"],
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"accelerator_memory_reserved_99th": train_info["accelerator_memory_reserved_99th"],
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"total_time": run_info["total_time"],
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"train_time": train_info["train_time"],
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"file_size": train_info["file_size"],
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"num_trainable_params": train_info["num_trainable_params"],
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"test_accuracy": train_metrics["test accuracy"],
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"train_loss": train_metrics["train loss"],
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"train_samples": train_metrics["train samples"],
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"train_total_tokens": train_metrics["train total tokens"],
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"peft_version": meta_info["package_info"]["peft-version"],
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"peft_branch": run_info["peft_branch"],
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"transformers_version": meta_info["package_info"]["transformers-version"],
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"datasets_version": meta_info["package_info"]["datasets-version"],
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"torch_version": meta_info["package_info"]["torch-version"],
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"bitsandbytes_version": meta_info["package_info"]["bitsandbytes-version"],
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"package_info": meta_info["package_info"],
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"system_info": meta_info["system_info"],
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"created_at": run_info["created_at"],
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}
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results.append(dct)
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if skipped:
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print_fn(f"Skipped {skipped} of {len(rows)} entries because the train status != success")
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return results
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def load_jsons(path):
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results = []
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for fn in os.listdir(path):
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if fn.endswith(".json"):
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with open(os.path.join(path, fn)) as f:
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row = json.load(f)
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results.append(row)
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return results
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def load_df(path, task_name, print_fn=print):
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jsons = load_jsons(path)
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preprocessed = preprocess(jsons, task_name=task_name, print_fn=print_fn)
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dtype_dict = {
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"task_name": "string",
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"experiment_name": "string",
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"model_id": "string",
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"train_config": "string",
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"peft_type": "string",
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"peft_config": "string",
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"accelerator_memory_reserved_avg": int,
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"accelerator_memory_max": int,
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"accelerator_memory_reserved_99th": int,
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"total_time": float,
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"train_time": float,
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"file_size": int,
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"test_accuracy": float,
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"train_loss": float,
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"train_samples": int,
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"train_total_tokens": int,
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"num_trainable_params": int,
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"peft_version": "string",
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"peft_branch": "string",
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"transformers_version": "string",
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"datasets_version": "string",
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"torch_version": "string",
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"bitsandbytes_version": "string",
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"package_info": "string",
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"system_info": "string",
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"created_at": "string",
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}
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df = pd.DataFrame(preprocessed)
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df = df.astype(dtype_dict)
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df["created_at"] = pd.to_datetime(df["created_at"])
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# round training time to nearest second
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df["train_time"] = df["train_time"].round().astype(int)
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df["total_time"] = df["total_time"].round().astype(int)
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# reorder columns for better viewing, pinned_columns arg in Gradio seems not to work correctly
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important_columns = [
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"experiment_name",
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"peft_type",
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"total_time",
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"train_time",
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"test_accuracy",
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"train_loss",
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"accelerator_memory_max",
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"accelerator_memory_reserved_99th",
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"accelerator_memory_reserved_avg",
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"num_trainable_params",
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"file_size",
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"created_at",
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"task_name",
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]
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other_columns = [col for col in df if col not in important_columns]
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df = df[important_columns + other_columns]
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columns = ["experiment_name", "model_id", "peft_type", "created_at"]
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# we want to keep only the most recent run for each experiment
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df = df.sort_values("created_at").drop_duplicates(columns, keep="last")
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return df
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