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83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
import unittest
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from functools import partial
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from transformers import AutoProcessor
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from pdelfin.train.dataloader import (
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build_finetuning_dataset,
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extract_openai_batch_response,
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load_jsonl_into_ds,
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list_dataset_files
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)
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from pdelfin.train.dataprep import batch_prepare_data_for_qwen2_training
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class TestBatchQueryResponseDataset(unittest.TestCase):
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def testLoadS3(self):
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ds = load_jsonl_into_ds("s3://ai2-oe-data/jakep/openai_batch_data_v2/*.jsonl", first_n_files=3)
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print(f"Loaded {len(ds)} entries")
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print(ds)
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print(ds["train"])
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def testFinetuningDS(self):
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ds = build_finetuning_dataset(
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response_glob_path="s3://ai2-oe-data/jakep/pdfdata/openai_batch_done_v5_1_eval/*.json",
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)
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print(ds)
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
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ds = ds.with_transform(partial(batch_prepare_data_for_qwen2_training, processor=processor, target_longest_image_dim=1024, target_anchor_text_len=6000))
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print(ds[0])
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def testPlotSequenceLengthHistogram(self):
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import plotly.express as px
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ds = build_finetuning_dataset(
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response_glob_path="s3://ai2-oe-data/jakep/pdfdata/openai_batch_done_v5_1_eval/*.json",
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)
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
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ds = ds.with_transform(partial(batch_prepare_data_for_qwen2_training, processor=processor, target_longest_image_dim=1024, target_anchor_text_len=6000))
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
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initial_len = len(ds)
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train_dataloader = DataLoader(ds, batch_size=1, num_workers=30, shuffle=False)
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max_seen_len = 0
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steps = 0
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sequence_lengths = [] # List to store sequence lengths
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for entry in tqdm(train_dataloader):
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num_input_tokens = entry["input_ids"].shape[1]
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max_seen_len = max(max_seen_len, num_input_tokens)
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sequence_lengths.append(num_input_tokens) # Collecting sequence lengths
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if steps % 100 == 0:
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print(f"Max input len {max_seen_len}")
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steps += 1
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# model.forward(**{k: v.to("cuda:0") for (k,v) in entry.items()})
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print(f"Max input len {max_seen_len}")
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print(f"Total elements before filtering: {initial_len}")
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print(f"Total elements after filtering: {steps}")
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# Plotting the histogram using Plotly
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fig = px.histogram(
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sequence_lengths,
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nbins=100,
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title="Distribution of Input Sequence Lengths",
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labels={'value': 'Sequence Length', 'count': 'Frequency'}
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)
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fig.write_image("sequence_lengths_histogram.png")
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