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[misc] fix packing and eval plot (#7623)

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hoshi-hiyouga
2025-04-07 18:20:57 +08:00
committed by GitHub
parent 5115dc8c7f
commit c3c0efbaa0
70 changed files with 288 additions and 194 deletions
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29
tests/data/test_mm_plugin.py
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@@ -20,7 +20,6 @@ import torch
from PIL import Image
from llamafactory.data.mm_plugin import get_mm_plugin
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.hparams import get_infer_args
from llamafactory.model import load_tokenizer
@@ -35,7 +34,8 @@ if TYPE_CHECKING:
HF_TOKEN = os.getenv("HF_TOKEN")
TINY_LLAMA = os.getenv("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TINY_LLAMA4 = os.getenv("TINY_LLAMA4", "llamafactory/tiny-random-Llama-4")
MM_MESSAGES = [
{"role": "user", "content": "<image>What is in this image?"},
@@ -130,13 +130,13 @@ def _check_plugin(
def test_base_plugin():
tokenizer_module = _load_tokenizer_module(model_name_or_path=TINY_LLAMA)
tokenizer_module = _load_tokenizer_module(model_name_or_path=TINY_LLAMA3)
base_plugin = get_mm_plugin(name="base")
check_inputs = {"plugin": base_plugin, **tokenizer_module}
_check_plugin(**check_inputs)
@pytest.mark.skipif(not HF_TOKEN or not is_transformers_version_greater_than("4.50.0"), reason="Gated model.")
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
def test_gemma3_plugin():
image_seqlen = 256
tokenizer_module = _load_tokenizer_module(model_name_or_path="google/gemma-3-4b-it")
@@ -157,6 +157,27 @@ def test_gemma3_plugin():
_check_plugin(**check_inputs)
@pytest.mark.xfail(reason="Unknown error.")
def test_llama4_plugin():
tokenizer_module = _load_tokenizer_module(model_name_or_path=TINY_LLAMA4)
processor = tokenizer_module["processor"]
llama4_plugin = get_mm_plugin(name="llama4", image_token="<|image|>")
check_inputs = {"plugin": llama4_plugin, **tokenizer_module}
mm_inputs = _get_mm_inputs(tokenizer_module["processor"])
image_height, image_width = mm_inputs["pixel_values"][0].shape[-2:]
num_patches_per_chunk = int(
(image_height // processor.patch_size) * (image_width // processor.patch_size) // processor.downsample_ratio
)
aspect_ratios = mm_inputs.pop("aspect_ratios")
tokens_for_this_image = processor._prompt_split_image(aspect_ratios[0], num_patches_per_chunk)
check_inputs["expected_mm_messages"] = [
{key: value.replace("<image>", tokens_for_this_image) for key, value in message.items()}
for message in MM_MESSAGES
]
check_inputs["expected_mm_inputs"] = mm_inputs
_check_plugin(**check_inputs)
def test_llava_plugin():
image_seqlen = 576
tokenizer_module = _load_tokenizer_module(model_name_or_path="llava-hf/llava-1.5-7b-hf")