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transformers/tests/models/janus/test_modeling_janus.py

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# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch Janus model."""
import re
import tempfile
import unittest
from functools import reduce
import numpy as np
import requests
from transformers import (
AutoProcessor,
JanusConfig,
JanusForConditionalGeneration,
JanusModel,
JanusVQVAE,
JanusVQVAEConfig,
is_torch_available,
is_vision_available,
)
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import (
Expectations,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
class JanusVisionText2TextModelTester:
def __init__(
self,
parent,
image_token_index=0,
seq_length=25,
initializer_range=0.02,
text_config={
"model_type": "llama",
"seq_length": 7,
"is_training": True,
"use_input_mask": True,
"use_token_type_ids": False,
"use_labels": True,
"vocab_size": 99,
"hidden_size": 32,
"num_hidden_layers": 2,
"num_attention_heads": 4,
"intermediate_size": 37,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"attention_probs_dropout_prob": 0.1,
"max_position_embeddings": 512,
"type_vocab_size": 16,
"type_sequence_label_size": 2,
"initializer_range": 0.02,
"num_labels": 3,
"num_choices": 4,
"pad_token_id": 1,
},
is_training=True,
vision_config={
"use_labels": True,
"image_size": 20,
"patch_size": 5,
"num_image_tokens": 4,
"num_channels": 3,
"is_training": True,
"hidden_size": 32,
"projection_dim": 32,
"num_key_value_heads": 1,
"num_hidden_layers": 2,
"num_attention_heads": 4,
"mlp_ratio": 2,
"dropout": 0.1,
"attention_dropout": 0.1,
"initializer_range": 0.02,
"vision_feature_select_strategy": "default",
"vision_feature_layer": -1,
},
use_cache=False,
vq_num_embeds=12,
vq_embed_dim=12,
vq_channel_multiplier=[1, 1],
):
self.parent = parent
self.initializer_range = initializer_range
# `image_token_index` is set to 0 to pass "resize_embeddings" test, do not modify
self.image_token_index = image_token_index
self.text_config = text_config
self.vision_config = vision_config
self.seq_length = seq_length
self.pad_token_id = text_config["pad_token_id"]
self.num_hidden_layers = text_config["num_hidden_layers"]
self.vocab_size = text_config["vocab_size"]
self.hidden_size = text_config["hidden_size"]
self.num_attention_heads = text_config["num_attention_heads"]
self.is_training = is_training
self.batch_size = 3
self.num_channels = vision_config["num_channels"]
self.image_size = vision_config["image_size"]
self.num_image_tokens = vision_config["num_image_tokens"]
self.use_cache = use_cache
# vq model params
self.vq_num_embeds = vq_num_embeds
self.vq_embed_dim = vq_embed_dim
self.vq_channel_multiplier = vq_channel_multiplier
def get_vq_config(self):
return {
"embed_dim": self.vq_embed_dim,
"num_embeddings": self.vq_num_embeds,
"latent_channels": self.vq_embed_dim,
"in_channels": 3,
"base_channels": 32, # we have a GroupNorm of 32 groups, so can't do less
"channel_multiplier": self.vq_channel_multiplier,
"initializer_range": self.initializer_range,
"projection_dim": 10,
"image_token_embed_dim": 32, # Same as text model hidden size
}
def get_config(self):
return JanusConfig(
text_config=self.text_config,
vision_config=self.vision_config,
vq_config=self.get_vq_config(),
)
def prepare_config_and_inputs(self):
config = self.get_config()
pixel_values = floats_tensor(
[
self.batch_size,
3,
self.image_size,
self.image_size,
]
)
return config, pixel_values
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1
attention_mask = input_ids.ne(self.pad_token_id).to(torch_device)
# set the 16 first tokens to be image, and ensure that no other tokens are image tokens
# do not change this unless you modified image size or patch size
input_ids[input_ids == self.image_token_index] = self.pad_token_id
input_ids[:, : self.num_image_tokens] = self.image_token_index
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": input_ids,
"generation_mode": "text", # Required to perform text generation instead of image generation.
}
return config, inputs_dict
@require_torch
class JanusVisionText2TextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (JanusModel, JanusForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (JanusForConditionalGeneration,) if is_torch_available() else ()
fx_compatible = False
test_pruning = False
test_head_masking = False
_is_composite = True
def setUp(self):
self.model_tester = JanusVisionText2TextModelTester(self)
self.config_tester = ConfigTester(self, config_class=JanusConfig, has_text_modality=False)
# overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
input_ids = inputs["input_ids"]
del inputs["input_ids"]
del inputs["pixel_values"]
del inputs["generation_mode"]
wte = model.get_input_embeddings()
inputs["inputs_embeds"] = wte(input_ids)
with torch.no_grad():
model(**inputs)
# Overwrite inputs_embeds tests because we need to delete "pixel values" for VLMs.
def test_inputs_embeds_matches_input_ids(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
input_ids = inputs["input_ids"]
del inputs["input_ids"]
del inputs["pixel_values"]
del inputs["generation_mode"]
inputs_embeds = model.get_input_embeddings()(input_ids)
with torch.no_grad():
out_ids = model(input_ids=input_ids, **inputs)[0]
out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0]
torch.testing.assert_close(out_embeds, out_ids)
def test_sdpa_can_dispatch_composite_models(self):
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
# Load the model with SDPA
model_sdpa = model_class.from_pretrained(tmpdirname)
model_sdpa = model_sdpa.eval().to(torch_device)
# Load model with eager attention
model_eager = model_class.from_pretrained(
tmpdirname,
attn_implementation="eager",
)
model_eager = model_eager.eval().to(torch_device)
# SigLip has one shared cls attr for all models, so we assign both submodels heer
vision_attn = language_attn = "sdpa" if model._supports_sdpa else "eager"
if hasattr(model_sdpa, "vision_model") and hasattr(model_sdpa, "language_model"):
self.assertTrue(model_sdpa.vision_model.config._attn_implementation == vision_attn)
self.assertTrue(model_sdpa.language_model.config._attn_implementation == language_attn)
self.assertTrue(model_eager.vision_model.config._attn_implementation == "eager")
self.assertTrue(model_eager.language_model.config._attn_implementation == "eager")
self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
self.assertTrue(model_eager.config._attn_implementation == "eager")
for name, submodule in model_eager.named_modules():
class_name = submodule.__class__.__name__
if any(re.finditer(r"Attention(?!Pool)", class_name)):
self.assertTrue(submodule.config._attn_implementation == "eager")
for name, submodule in model_sdpa.named_modules():
class_name = submodule.__class__.__name__
if any(re.finditer(r"Attention(?!Pool)", class_name)):
self.assertTrue(submodule.config._attn_implementation == "sdpa")
def check_training_gradient_checkpointing(self, gradient_checkpointing_kwargs=None):
if not self.model_tester.is_training:
self.skipTest(reason="ModelTester is not configured to run training tests")
"""
We skip some parameters when checking for gradient checkpointing:
- VQ model, as its training is not supported.
- A few other modules used for image generation.
"""
skip_patterns = ["vqmodel", "generation_embeddings", "generation_aligner", "generation_head"]
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
if (
model_class.__name__
in [
*get_values(MODEL_MAPPING_NAMES),
*get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES),
]
or not model_class.supports_gradient_checkpointing
):
# TODO (ydshieh): use `skipTest` once pytest-dev/pytest-subtests/pull/169 is merged
# self.skipTest(reason=f"`supports_gradient_checkpointing` is False for {model_class.__name__}.")
continue
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.use_cache = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.gradient_checkpointing_enable(gradient_checkpointing_kwargs=gradient_checkpointing_kwargs)
model.train()
# unfreeze additional layers
for p in model.parameters():
p.requires_grad_(True)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
optimizer.step()
if self.test_all_params_have_gradient:
for k, v in model.named_parameters():
if v.requires_grad and not reduce(lambda t, s: t | (s in k), skip_patterns, False):
self.assertTrue(v.grad is not None, f"{k} in {model_class.__name__} has no gradient!")
else:
pass
@unittest.skip("There are recompilations in Janus") # TODO (joao, raushan): fix me
def test_generate_compile_model_forward(self):
pass
class JanusVQModelTester:
def __init__(
self,
parent,
batch_size=5,
is_training=False,
initializer_range=0.02,
image_size=30,
num_embeds=12,
base_channels=32, # we have a GroupNorm of 32 groups, so can't do less
embed_dim=12,
channel_multiplier=[1, 2],
patch_size=2,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.is_training = is_training
self.initializer_range = initializer_range
self.image_size = image_size
self.base_channels = base_channels
self.num_embeds = num_embeds
self.embed_dim = embed_dim
self.channel_multiplier = channel_multiplier
self.num_patches = image_size // patch_size
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, 3, self.image_size, self.image_size])
config = self.get_config()
return config, pixel_values
def get_config(self):
return JanusVQVAEConfig(
embed_dim=self.embed_dim,
num_embeddings=self.num_embeds,
latent_channels=self.embed_dim,
in_channels=3,
base_channels=self.base_channels,
channel_multiplier=self.channel_multiplier,
initializer_range=self.initializer_range,
resolution=self.image_size,
num_patches=self.num_patches,
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class JanusVQModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (JanusVQVAE,) if is_torch_available() else ()
test_head_masking = False
test_pruning = False
fx_compatible = False
has_attentions = False
test_resize_embeddings = False
def setUp(self):
self.model_tester = JanusVQModelTester(self)
self.config_tester = ConfigTester(
self,
config_class=JanusVQVAEConfig,
has_text_modality=False,
common_properties=["embed_dim", "num_embeddings"],
)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip("Janus VQ module cannot offload due to using `self.weight` directly")
def test_cpu_offload(self):
pass
@unittest.skip("Janus VQ module cannot offload due to using `self.weight` directly")
def test_disk_offload_bin(self):
pass
@unittest.skip("Janus VQ module cannot offload due to using `self.weight` directly")
def test_disk_offload_safetensors(self):
pass
@unittest.skip("Janus VQ module has no hidden states")
def test_hidden_states_output(self):
pass
@unittest.skip("Janus VQ module has no hidden states")
def test_model_outputs_equivalence(self):
pass
@unittest.skip("Janus VQ module has no get/set embeddings method")
def test_model_get_set_embeddings(self):
pass
@unittest.skip("Janus VQ module has no hidden states")
def test_retain_grad_hidden_states_attentions(self):
pass
class JanusIntegrationTest(unittest.TestCase):
def setUp(self):
self.model_id = "deepseek-community/Janus-Pro-1B"
@slow
def test_model_text_generation(self):
model = JanusForConditionalGeneration.from_pretrained(self.model_id, device_map="auto")
model.eval()
processor = AutoProcessor.from_pretrained(self.model_id)
image = Image.open(
requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw
)
prompt = "<image_placeholder>\nDescribe what do you see here and tell me about the history behind it?"
inputs = processor(images=image, text=prompt, generation_mode="text", return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=20, generation_mode="text", do_sample=False)
EXPECTED_DECODED_TEXT = 'You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.\n\n\nDescribe what do you see here and tell me about the history behind it?\n\nThe image depicts the constellation of Leo, which is often referred to as the "Lion"' # fmt: skip
text = processor.decode(output[0], skip_special_tokens=True)
self.assertEqual(
text,
EXPECTED_DECODED_TEXT,
)
@slow
def test_model_text_generation_batched(self):
model = JanusForConditionalGeneration.from_pretrained(self.model_id, device_map="auto")
processor = AutoProcessor.from_pretrained(self.model_id)
image_1 = Image.open(
requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw
)
image_2 = Image.open(
requests.get("https://www.kxan.com/wp-content/uploads/sites/40/2020/10/ORION.jpg", stream=True).raw
)
prompts = [
"<image_placeholder>\nDescribe what do you see here and tell me about the history behind it?",
"What constellation is this image showing?<image_placeholder>\n",
]
inputs = processor(
images=[image_1, image_2], text=prompts, generation_mode="text", padding=True, return_tensors="pt"
).to(model.device, torch.float16)
EXPECTED_TEXT_COMPLETION = [
'You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.\n\n\nDescribe what do you see here and tell me about the history behind it?\n\nThe image depicts the constellation of Leo, which is often referred to as the "Lion"',
"You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.\n\nWhat constellation is this image showing?\n\nThe image shows a constellation that is shaped like a stylized figure with a long tail. This",
]
generated_ids = model.generate(**inputs, max_new_tokens=20, generation_mode="text", do_sample=False)
text = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
@slow
def test_model_text_generation_with_multi_image(self):
model = JanusForConditionalGeneration.from_pretrained(self.model_id, device_map="auto")
processor = AutoProcessor.from_pretrained(self.model_id)
image_1 = Image.open(
requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw
)
image_2 = Image.open(
requests.get("https://www.kxan.com/wp-content/uploads/sites/40/2020/10/ORION.jpg", stream=True).raw
)
prompt = "What do these two images <image_placeholder> and <image_placeholder> have in common?"
inputs = processor(images=[image_1, image_2], text=prompt, generation_mode="text", return_tensors="pt").to(
model.device, torch.float16
)
EXPECTED_TEXT_COMPLETION = ['You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.\n\nWhat do these two images and have in common?\n\nThe two images you provided are of the same constellation. The first image shows the constellation of Leo, and the second image shows the constellation of Ursa Major. Both constellations are part of'] # fmt: skip
generated_ids = model.generate(**inputs, max_new_tokens=40, do_sample=False)
text = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
@slow
def test_model_generate_images(self):
model = JanusForConditionalGeneration.from_pretrained(self.model_id, device_map="auto")
processor = AutoProcessor.from_pretrained(self.model_id)
inputs = processor(
text=["A portrait of young girl. masterpiece, film grained, best quality."],
padding=True,
generation_mode="image",
return_tensors="pt",
).to(model.device)
self.assertTrue(inputs.input_ids.shape[1] == 17)
out = model.generate(
**inputs,
generation_mode="image",
do_sample=False,
)
# It should run for num_image_tokens in this case 576.
self.assertTrue(out.shape[1] == 576)
# fmt: off
expected_tokens = Expectations(
{
("rocm", None): [
10367, 1380, 4841, 15155, 1224, 16361, 15834, 13722, 15258, 8321, 10496, 14532, 8770, 12353, 5481,
11484, 2585, 8587, 3201, 14292, 3356, 2037, 3077, 6107, 3758, 2572, 9376, 13219, 6007, 14292, 12696,
10666, 10046, 13483, 8282, 9101, 5208, 4260, 13886, 13335, 6135, 2316, 15423, 311, 5460, 12218,
14172, 8583, 14577, 3648
],
("rocm", (9, 5)): [
4484, 4015, 15750, 506, 3758, 11651, 8597, 5739, 4861, 971, 14985, 14834, 15438, 7548, 1820, 1465,
13529, 12761, 10503, 12761, 14303, 6155, 4015, 11766, 705, 15736, 14146, 10417, 1951, 7713, 14305,
15617, 6169, 2706, 8006, 14893, 3855, 10188, 15652, 6297, 1097, 12108, 15038, 311, 14998, 15165,
897, 4044, 1762, 4676
],
("cuda", None): [
4484, 4015, 15750, 506, 3758, 11651, 8597, 5739, 4861, 971, 14985, 14834, 15438, 7548, 1820, 1465,
13529, 12761, 10503, 12761, 14303, 6155, 4015, 11766, 705, 15736, 14146, 10417, 1951, 7713, 14305,
15617, 6169, 2706, 8006, 14893, 3855, 10188, 15652, 6297, 1097, 12108, 15038, 311, 14998, 15165,
897, 4044, 1762, 4676
],
}
)
expected_tokens = torch.tensor(expected_tokens.get_expectation()).to(model.device)
# fmt: on
# Compare the first 50 generated tokens.
self.assertTrue(torch.allclose(expected_tokens, out[0][:50]))
# Decode generated tokens to pixel values and postprocess them.
decoded_pixel_values = model.decode_image_tokens(out)
images = processor.postprocess(list(decoded_pixel_values.float()), return_tensors="np")
self.assertTrue(images["pixel_values"].shape == (1, 384, 384, 3))
self.assertTrue(isinstance(images["pixel_values"], np.ndarray))
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