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from typing import Optional |
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import pytest |
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import torch |
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from litgpt.data.base import SFTDataset, get_sft_collate_fn |
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from litgpt.prompts import PromptStyle |
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@pytest.mark.parametrize("mask_prompt", [True, False]) |
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@pytest.mark.parametrize("ignore_index", [-1, -100]) |
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@pytest.mark.parametrize("max_seq_length", [1000, 5, -1]) |
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def test_sft_dataset(max_seq_length, ignore_index, mask_prompt, mock_tokenizer): |
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class Style(PromptStyle): |
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def apply(self, prompt: str, *, sys_prompt: Optional[str] = None, **kwargs) -> str: |
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return f"In: {prompt} Out:" |
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i = ignore_index |
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data = [{"instruction": "Foo", "output": "Bar"}, {"instruction": "Boo", "output": "Ahh"}] |
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dataset = SFTDataset( |
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data=data, |
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tokenizer=mock_tokenizer, |
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prompt_style=Style(), |
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mask_prompt=mask_prompt, |
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ignore_index=ignore_index, |
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max_seq_length=max_seq_length, |
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) |
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assert len(dataset) == len(data) |
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expected_input_ids = torch.tensor([73, 110, 58, 32, 70, 111, 111, 32, 79, 117, 116, 58, 66, 97, 114, 1]) |
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expected_labels = ( |
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torch.tensor([i, i, i, i, i, i, i, i, i, i, i, i, 66, 97, 114, 1]) if mask_prompt else expected_input_ids |
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) |
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if max_seq_length == -1: |
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assert torch.equal(dataset[0]["input_ids"], expected_input_ids) |
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assert torch.equal(dataset[0]["labels"], expected_labels) |
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else: |
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assert torch.equal(dataset[0]["input_ids"], expected_input_ids[:max_seq_length]) |
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assert torch.equal(dataset[0]["labels"], expected_labels[:max_seq_length]) |
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@pytest.mark.parametrize("ignore_index", [-1, -100]) |
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@pytest.mark.parametrize("pad_id", [0, 100]) |
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def test_sft_collate_fn_padding(pad_id, ignore_index): |
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collate = get_sft_collate_fn(pad_id=pad_id, ignore_index=ignore_index) |
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samples = [ |
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{ |
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"input_ids": torch.tensor([1, 2, 3]), |
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"labels": torch.tensor([10, 20, 30]), |
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"token_counts": {"raw": 3, "raw_plus_prompt_template": 25}, |
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}, |
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{ |
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"input_ids": torch.tensor([4, 5, 6, 7, 8]), |
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"labels": torch.tensor([40, 50, 60, 70, 80]), |
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"token_counts": {"raw": 5, "raw_plus_prompt_template": 27}, |
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}, |
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] |
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expected = { |
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"input_ids": torch.tensor([[1, 2, 3, pad_id, pad_id], [4, 5, 6, 7, 8]]), |
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"labels": torch.tensor([[10, 20, 30, ignore_index, ignore_index], [40, 50, 60, 70, 80]]), |
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"token_counts": {"raw": torch.tensor([[3], [5]]), "raw_plus_prompt_template": torch.tensor([[25], [27]])}, |
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} |
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batch = collate(samples) |
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assert all(torch.equal(batch[k], expected[k]) for k in ("input_ids", "labels")) |
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for key in ("raw", "raw_plus_prompt_template"): |
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assert torch.equal(batch["token_counts"][key], expected["token_counts"][key]), f"Token count mismatch for {key}" |
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def test_sft_collate_fn_truncation(): |
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collate = get_sft_collate_fn(max_seq_length=2) |
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samples = [ |
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{ |
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"input_ids": torch.tensor([1, 2, 3]), |
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"labels": torch.tensor([10, 20, 30]), |
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"token_counts": {"raw": 3, "raw_plus_prompt_template": 25}, |
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}, |
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{ |
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"input_ids": torch.tensor([4, 5, 6, 7, 8]), |
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"labels": torch.tensor([40, 50, 60, 70, 80]), |
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"token_counts": {"raw": 5, "raw_plus_prompt_template": 27}, |
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}, |
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] |
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expected = { |
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"input_ids": torch.tensor([[1, 2], [4, 5]]), |
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"labels": torch.tensor([[10, 20], [40, 50]]), |
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"token_counts": {"raw": torch.tensor([[3], [5]]), "raw_plus_prompt_template": torch.tensor([[25], [27]])}, |
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} |
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batch = collate(samples) |
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assert all(torch.equal(batch[k], expected[k]) for k in ("input_ids", "labels")) |
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for key in ("raw", "raw_plus_prompt_template"): |
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assert torch.equal(batch["token_counts"][key], expected["token_counts"][key]), f"Token count mismatch for {key}" |
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