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from types import SimpleNamespace
import torch
from torch import nn
from model.llava.prima_arch import LlavaMetaForCausalLM
from tools.utils import IGNORE_INDEX, IMAGE_TOKEN_INDEX
class TinyMultimodalModel(nn.Module, LlavaMetaForCausalLM):
def __init__(self, hidden_size=4, image_tokens=3):
super().__init__()
self.config = SimpleNamespace(with_region=False, mm_use_im_start_end=False,
tune_mm_mlp_adapter=False)
self.model = nn.Module()
self.model.embed_tokens = nn.Embedding(32, hidden_size)
self.model.mm_projector = nn.Linear(hidden_size, hidden_size)
self.model.vision_tower = nn.Identity()
self.model.get_vision_tower = lambda: self.model.vision_tower
self.model.use_qformer = True
self.model.Qformer = SimpleNamespace(config=SimpleNamespace(hidden_size=hidden_size))
self.model.device = torch.device('cpu')
self.model.dtype = torch.float32
self.image_features = nn.Parameter(torch.arange(image_tokens * hidden_size).reshape(
image_tokens, hidden_size).float() / 10)
self.encoder_calls = 0
@property
def device(self):
return self.model.embed_tokens.weight.device
def get_model(self):
return self.model
def encode_images_qformer(self, images, *args):
self.encoder_calls += 1
values = images.flatten(1).mean(1)[:, None, None]
return self.image_features.unsqueeze(0) + values, images
def prepare(model, ids, *, mask=None, labels=None, images=None, past=None):
offsets = torch.arange(len(ids) + 1)
return model.prepare_inputs_labels_for_multimodal(
ids, mask, past, labels, images, offsets, offsets, None, None)
class MultimodalTests(unittest.TestCase):
def test_image_features_replace_only_the_image_token(self):
model = TinyMultimodalModel()
ids = torch.tensor([[1, IMAGE_TOKEN_INDEX, 2, 3]])
labels = ids.clone()
result = prepare(model, ids, mask=torch.ones_like(ids, dtype=torch.bool),
labels=labels, images=torch.zeros(1, 3, 2, 2))
self.assertEqual(result[3].shape, (1, 6, 4))
self.assertTrue(torch.equal(result[3][0, 1:4], model.image_features))
self.assertTrue(torch.equal(result[3][0, [0, 4, 5]], model.model.embed_tokens(ids[0, [0, 2, 3]])))
self.assertEqual(result[4].tolist(), [[1, IGNORE_INDEX, IGNORE_INDEX, IGNORE_INDEX, 2, 3]])
self.assertEqual(result[5], [[slice(1, 4)]])
self.assertTrue(result[1].all())
result[3].sum().backward()
self.assertTrue(torch.equal(model.image_features.grad, torch.ones_like(model.image_features)))
def test_ragged_batch_preserves_left_padding_and_masks_new_right_padding(self):
model = TinyMultimodalModel()
ids = torch.tensor([[0, 1, IMAGE_TOKEN_INDEX, 2], [0, 0, 3, 4]])
mask = ids.ne(0)
labels = ids.masked_fill(~mask, IGNORE_INDEX)
for include_labels in (True, False):
with self.subTest(labels=include_labels):
result = prepare(model, ids, mask=mask,
labels=labels if include_labels else None,
images=torch.zeros(1, 3, 2, 2))
self.assertEqual(result[1].tolist(), [[False, True, True, True, True, True],
[False, False, True, True, False, False]])
self.assertTrue(torch.equal(result[3][1, -2:], torch.zeros(2, 4)))
if include_labels:
self.assertEqual(result[4][1, -2:].tolist(), [IGNORE_INDEX, IGNORE_INDEX])
else:
self.assertIsNone(result[4])
def test_cached_generation_extends_attention_without_encoding_images(self):
model = TinyMultimodalModel()
ids = torch.tensor([[5]])
past = ((torch.zeros(1, 2, 7, 4), torch.zeros(1, 2, 7, 4)),)
result = prepare(model, ids, mask=torch.ones(1, 7, dtype=torch.bool),
images=torch.zeros(1, 3, 2, 2), past=past)
self.assertIs(result[0], ids)
self.assertIs(result[2], past)
self.assertIsNone(result[3])
self.assertEqual(result[1].shape, (1, 8))
self.assertTrue(result[1].all())
self.assertEqual(model.encoder_calls, 0)
def test_ragged_embeddings_preserve_dtype_promotion(self):
model = TinyMultimodalModel()
model.model.embed_tokens.to(dtype=torch.bfloat16)
ids = torch.tensor([[1, 2, 3, 4], [1, IMAGE_TOKEN_INDEX, 2, 3]])
result = prepare(model, ids, mask=torch.ones_like(ids, dtype=torch.bool),
images=torch.zeros(1, 3, 2, 2))
self.assertEqual(result[3].dtype, torch.float32)
self.assertTrue(torch.equal(result[3][1, 1:4], model.image_features))
def test_grouped_images_preserve_feature_order(self):
model = TinyMultimodalModel()
ids = torch.tensor([[1, IMAGE_TOKEN_INDEX, 2], [3, IMAGE_TOKEN_INDEX, 4]])
groups = [torch.stack((torch.zeros(3, 2, 2), torch.ones(3, 2, 2))),
torch.full((1, 3, 2, 2), 2.0)]
result = prepare(model, ids, mask=torch.ones_like(ids, dtype=torch.bool), images=groups)
self.assertEqual(result[3].shape, (2, 8, 4))
self.assertTrue(torch.equal(result[3][0, 1:4], model.image_features))
self.assertTrue(torch.equal(result[3][0, 4:7], model.image_features + 1))
self.assertTrue(torch.equal(result[3][1, 1:4], model.image_features + 2))
if __name__ == '__main__':
unittest.main()
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