File size: 3,981 Bytes
53e0dae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | # Copyright Lightning AI. Licensed under the Apache License 2.0, see LICENSE file.
from typing import Optional
import pytest
import torch
from litgpt.data.base import SFTDataset, get_sft_collate_fn
from litgpt.prompts import PromptStyle
@pytest.mark.parametrize("mask_prompt", [True, False])
@pytest.mark.parametrize("ignore_index", [-1, -100])
@pytest.mark.parametrize("max_seq_length", [1000, 5, -1])
def test_sft_dataset(max_seq_length, ignore_index, mask_prompt, mock_tokenizer):
class Style(PromptStyle):
def apply(self, prompt: str, *, sys_prompt: Optional[str] = None, **kwargs) -> str:
return f"In: {prompt} Out:"
i = ignore_index
data = [{"instruction": "Foo", "output": "Bar"}, {"instruction": "Boo", "output": "Ahh"}]
dataset = SFTDataset(
data=data,
tokenizer=mock_tokenizer,
prompt_style=Style(),
mask_prompt=mask_prompt,
ignore_index=ignore_index,
max_seq_length=max_seq_length,
)
assert len(dataset) == len(data)
expected_input_ids = torch.tensor([73, 110, 58, 32, 70, 111, 111, 32, 79, 117, 116, 58, 66, 97, 114, 1])
# If prompt is not masked, labels == input_ids
expected_labels = (
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
)
if max_seq_length == -1:
assert torch.equal(dataset[0]["input_ids"], expected_input_ids)
assert torch.equal(dataset[0]["labels"], expected_labels)
else:
assert torch.equal(dataset[0]["input_ids"], expected_input_ids[:max_seq_length])
assert torch.equal(dataset[0]["labels"], expected_labels[:max_seq_length])
@pytest.mark.parametrize("ignore_index", [-1, -100])
@pytest.mark.parametrize("pad_id", [0, 100])
def test_sft_collate_fn_padding(pad_id, ignore_index):
collate = get_sft_collate_fn(pad_id=pad_id, ignore_index=ignore_index)
samples = [
{
"input_ids": torch.tensor([1, 2, 3]),
"labels": torch.tensor([10, 20, 30]),
"token_counts": {"raw": 3, "raw_plus_prompt_template": 25},
},
{
"input_ids": torch.tensor([4, 5, 6, 7, 8]),
"labels": torch.tensor([40, 50, 60, 70, 80]),
"token_counts": {"raw": 5, "raw_plus_prompt_template": 27},
},
]
expected = {
"input_ids": torch.tensor([[1, 2, 3, pad_id, pad_id], [4, 5, 6, 7, 8]]),
"labels": torch.tensor([[10, 20, 30, ignore_index, ignore_index], [40, 50, 60, 70, 80]]),
"token_counts": {"raw": torch.tensor([[3], [5]]), "raw_plus_prompt_template": torch.tensor([[25], [27]])},
}
batch = collate(samples)
assert all(torch.equal(batch[k], expected[k]) for k in ("input_ids", "labels"))
for key in ("raw", "raw_plus_prompt_template"):
assert torch.equal(batch["token_counts"][key], expected["token_counts"][key]), f"Token count mismatch for {key}"
def test_sft_collate_fn_truncation():
collate = get_sft_collate_fn(max_seq_length=2)
samples = [
{
"input_ids": torch.tensor([1, 2, 3]),
"labels": torch.tensor([10, 20, 30]),
"token_counts": {"raw": 3, "raw_plus_prompt_template": 25},
},
{
"input_ids": torch.tensor([4, 5, 6, 7, 8]),
"labels": torch.tensor([40, 50, 60, 70, 80]),
"token_counts": {"raw": 5, "raw_plus_prompt_template": 27},
},
]
expected = {
"input_ids": torch.tensor([[1, 2], [4, 5]]),
"labels": torch.tensor([[10, 20], [40, 50]]),
"token_counts": {"raw": torch.tensor([[3], [5]]), "raw_plus_prompt_template": torch.tensor([[25], [27]])},
}
batch = collate(samples)
assert all(torch.equal(batch[k], expected[k]) for k in ("input_ids", "labels"))
for key in ("raw", "raw_plus_prompt_template"):
assert torch.equal(batch["token_counts"][key], expected["token_counts"][key]), f"Token count mismatch for {key}"
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