263 lines
10 KiB
Python
263 lines
10 KiB
Python
# Copyright 2025 the LlamaFactory 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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import random
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import pytest
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from datasets import load_dataset
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from llamafactory.v1.config.data_args import DataArguments
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from llamafactory.v1.core.data_engine import DataEngine
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from llamafactory.v1.plugins.data_plugins.converter import DataConverterPlugin
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@pytest.mark.parametrize("num_samples", [16])
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def test_alpaca_converter(num_samples: int):
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data_args = DataArguments(train_dataset="llamafactory/v1-dataset-info/tiny-supervised-dataset.yaml")
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data_engine = DataEngine(data_args.train_dataset)
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original_data = load_dataset("llamafactory/tiny-supervised-dataset", split="train")
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indexes = random.choices(range(len(data_engine)), k=num_samples)
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for index in indexes:
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print(data_engine[index])
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expected_data = {
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"messages": [
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{
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"role": "user",
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"content": [
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{"type": "text", "value": original_data[index]["instruction"] + original_data[index]["input"]}
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],
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"loss_weight": 0.0,
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},
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{
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"role": "assistant",
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"content": [{"type": "text", "value": original_data[index]["output"]}],
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"loss_weight": 1.0,
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},
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]
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}
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assert data_engine[index] == {"_dataset_name": "tiny_dataset", **expected_data}
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def test_sharegpt_converter():
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example = {
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"conversations": [
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{"from": "system", "value": "System"},
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{"from": "human", "value": "User"},
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{"from": "function_call", "value": "1"},
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{"from": "observation", "value": "Observation"},
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{"from": "gpt", "value": "Assistant"},
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]
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}
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expected_data = {
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"messages": [
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{"role": "system", "content": [{"type": "text", "value": "System"}], "loss_weight": 0.0},
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{"role": "user", "content": [{"type": "text", "value": "User"}], "loss_weight": 0.0},
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{"role": "assistant", "content": [{"type": "tool_call", "value": "1"}], "loss_weight": 1.0},
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{"role": "tool", "content": [{"type": "text", "value": "Observation"}], "loss_weight": 0.0},
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{"role": "assistant", "content": [{"type": "text", "value": "Assistant"}], "loss_weight": 1.0},
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]
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}
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assert DataConverterPlugin("sharegpt")(example) == expected_data
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def test_sharegpt_converter_multimodal():
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example = {
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"conversations": [
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{"from": "human", "value": "What is <image> and what happens in <video>?"},
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{"from": "gpt", "value": "An image and a video."},
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],
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"images": ["/p/a.jpg"],
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"videos": ["/p/v.mp4"],
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}
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expected_data = {
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"messages": [
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{
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"role": "user",
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"content": [
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{"type": "text", "value": "What is "},
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{"type": "image_url", "value": "/p/a.jpg"},
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{"type": "text", "value": " and what happens in "},
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{"type": "video_url", "value": "/p/v.mp4"},
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{"type": "text", "value": "?"},
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],
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"loss_weight": 0.0,
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},
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{"role": "assistant", "content": [{"type": "text", "value": "An image and a video."}], "loss_weight": 1.0},
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]
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}
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assert DataConverterPlugin("sharegpt")(example) == expected_data
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def test_sharegpt_converter_multiple_images_in_order():
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# images are a sample-level list consumed by <image> tags in document order across turns
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example = {
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"conversations": [
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{"from": "human", "value": "<image><image>Compare these."},
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{"from": "gpt", "value": "Done."},
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],
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"images": ["/p/a.jpg", "/p/b.jpg"],
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}
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user = DataConverterPlugin("sharegpt")(example)["messages"][0]
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assert user["content"] == [
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{"type": "image_url", "value": "/p/a.jpg"},
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{"type": "image_url", "value": "/p/b.jpg"},
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{"type": "text", "value": "Compare these."},
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]
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def test_sharegpt_converter_no_media_unchanged():
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# backward compatibility: a scalar (non-list) image column and no tags is normalized; with no
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# media columns at all the output is byte-identical to the text-only path.
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example = {"conversations": [{"from": "human", "value": "hi"}, {"from": "gpt", "value": "yo"}]}
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assert DataConverterPlugin("sharegpt")(example) == {
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"messages": [
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{"role": "user", "content": [{"type": "text", "value": "hi"}], "loss_weight": 0.0},
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{"role": "assistant", "content": [{"type": "text", "value": "yo"}], "loss_weight": 1.0},
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]
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}
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def test_alpaca_converter_multimodal():
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example = {"instruction": "Describe <image>", "input": "", "output": "ok", "images": ["/p/a.jpg"]}
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user = DataConverterPlugin("alpaca")(example)["messages"][0]
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assert user["content"] == [
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{"type": "text", "value": "Describe "},
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{"type": "image_url", "value": "/p/a.jpg"},
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]
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def test_pair_converter_multimodal_shared_media():
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# chosen and rejected each reference the same sample-level image
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example = {
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"chosen": [
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{"role": "user", "content": "Look at <image>"},
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{"role": "assistant", "content": "good"},
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],
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"rejected": [
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{"role": "user", "content": "Look at <image>"},
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{"role": "assistant", "content": "bad"},
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],
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"images": ["/p/a.jpg"],
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}
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out = DataConverterPlugin("pair")(example)
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for side in ("chosen_messages", "rejected_messages"):
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assert out[side][0]["content"] == [
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{"type": "text", "value": "Look at "},
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{"type": "image_url", "value": "/p/a.jpg"},
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]
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def test_converter_media_count_mismatch():
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# more tags than media files
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with pytest.raises(ValueError, match="More <image> tags"):
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DataConverterPlugin("sharegpt")(
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{
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"conversations": [{"from": "human", "value": "<image><image>"}, {"from": "gpt", "value": "x"}],
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"images": ["/p/a.jpg"],
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}
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)
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# fewer tags than media files
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with pytest.raises(ValueError, match="Fewer <image> tags"):
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DataConverterPlugin("sharegpt")(
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{
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"conversations": [{"from": "human", "value": "<image>"}, {"from": "gpt", "value": "x"}],
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"images": ["/p/a.jpg", "/p/b.jpg"],
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}
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)
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def test_converter_audio_column_and_tag():
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# an <audio> tag consumes the next path from the audios column, lifted into an audio_url block
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example = {
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"conversations": [
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{"from": "human", "value": "hear <audio>What is this?"},
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{"from": "gpt", "value": "A bell."},
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],
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"audios": ["/p/a.wav"],
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}
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user = DataConverterPlugin("sharegpt")(example)["messages"][0]
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assert user["content"] == [
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{"type": "text", "value": "hear "},
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{"type": "audio_url", "value": "/p/a.wav"},
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{"type": "text", "value": "What is this?"},
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]
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def test_converter_audio_count_mismatch():
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# more audio tags than files
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with pytest.raises(ValueError, match="More <audio> tags"):
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DataConverterPlugin("sharegpt")(
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{
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"conversations": [{"from": "human", "value": "<audio><audio>"}, {"from": "gpt", "value": "x"}],
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"audios": ["/p/a.wav"],
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}
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)
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# fewer audio tags than files
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with pytest.raises(ValueError, match="Fewer <audio> tags"):
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DataConverterPlugin("sharegpt")(
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{
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"conversations": [{"from": "human", "value": "<audio>"}, {"from": "gpt", "value": "x"}],
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"audios": ["/p/a.wav", "/p/b.wav"],
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}
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)
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@pytest.mark.parametrize("num_samples", [16])
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def test_pair_converter(num_samples: int):
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data_args = DataArguments(train_dataset="llamafactory/v1-dataset-info/orca-dpo-pairs.yaml")
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data_engine = DataEngine(data_args.train_dataset)
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original_data = load_dataset("HuggingFaceH4/orca_dpo_pairs", split="train_prefs")
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indexes = random.choices(range(len(data_engine)), k=num_samples)
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for index in indexes:
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print(data_engine[index])
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print(original_data[index])
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expected_data = {
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"chosen_messages": [
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{
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"role": "system",
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"content": [{"type": "text", "value": original_data[index]["chosen"][0]["content"]}],
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"loss_weight": 0.0,
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},
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{
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"role": "user",
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"content": [{"type": "text", "value": original_data[index]["chosen"][1]["content"]}],
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"loss_weight": 0.0,
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},
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{
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"role": "assistant",
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"content": [{"type": "text", "value": original_data[index]["chosen"][2]["content"]}],
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"loss_weight": 1.0,
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},
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],
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"rejected_messages": [
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{
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"role": "system",
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"content": [{"type": "text", "value": original_data[index]["rejected"][0]["content"]}],
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"loss_weight": 0.0,
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},
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{
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"role": "user",
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"content": [{"type": "text", "value": original_data[index]["rejected"][1]["content"]}],
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"loss_weight": 0.0,
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},
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{
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"role": "assistant",
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"content": [{"type": "text", "value": original_data[index]["rejected"][2]["content"]}],
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"loss_weight": 1.0,
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},
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],
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}
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assert data_engine[index] == {"_dataset_name": "tiny_dataset", **expected_data}
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