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839893c86b
transformers/tests/models/mistral3/test_modeling_mistral3.py
Yih-Dar 839893c86b
fix mistral3 tests (#38989) 
* fix

* fix

* fix

* fix

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
2025-06-27 15:44:10 +02:00

508 lines
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Python
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# Copyright 2024 The Qwen team, Alibaba Group and 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 GotOcr2 model."""
import unittest
import accelerate
from transformers import (
AutoProcessor,
Mistral3Config,
is_torch_available,
)
from transformers.testing_utils import (
Expectations,
cleanup,
require_deterministic_for_xpu,
require_read_token,
require_torch,
require_torch_accelerator,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
Mistral3ForConditionalGeneration,
Mistral3Model,
)
class Mistral3VisionText2TextModelTester:
def __init__(
self,
parent,
batch_size=3,
seq_length=7,
image_seq_length=4,
vision_feature_layer=-1,
ignore_index=-100,
bos_token_id=0,
eos_token_id=0,
pad_token_id=0,
image_token_index=1,
num_channels=3,
image_size=30,
model_type="mistral3",
is_training=True,
text_config={
"model_type": "mistral",
"vocab_size": 99,
"attention_dropout": 0.0,
"hidden_act": "silu",
"hidden_size": 32,
"initializer_range": 0.02,
"intermediate_size": 37,
"max_position_embeddings": 512,
"num_attention_heads": 4,
"num_hidden_layers": 2,
"num_key_value_heads": 2,
"rms_norm_eps": 1e-05,
"rope_theta": 1000000000.0,
"sliding_window": None,
"bos_token_id": 0,
"eos_token_id": 0,
"pad_token_id": 0,
},
vision_config={
"model_type": "pixtral",
"hidden_size": 32,
"num_hidden_layers": 2,
"num_attention_heads": 4,
"intermediate_size": 37,
"image_size": 30,
"patch_size": 6,
"num_channels": 3,
"hidden_act": "gelu",
},
):
self.parent = parent
self.ignore_index = ignore_index
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.image_token_index = image_token_index
self.model_type = model_type
self.text_config = text_config
self.vision_config = vision_config
self.batch_size = batch_size
self.vision_feature_layer = vision_feature_layer
self.is_training = is_training
self.image_seq_length = image_seq_length
self.num_channels = num_channels
self.image_size = image_size
self.seq_length = seq_length + self.image_seq_length
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"]
def get_config(self):
return Mistral3Config(
text_config=self.text_config,
vision_config=self.vision_config,
model_type=self.model_type,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
image_token_index=self.image_token_index,
image_seq_length=self.image_seq_length,
vision_feature_layer=self.vision_feature_layer,
)
def prepare_config_and_inputs(self):
config = self.get_config()
pixel_values = floats_tensor([self.batch_size, self.num_channels, 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], self.vocab_size)
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
image_sizes = torch.tensor(
[[self.image_size, self.image_size]] * self.batch_size, dtype=torch.long, device=torch_device
)
# input_ids[:, -1] = self.pad_token_id
input_ids[input_ids == self.image_token_index] = self.pad_token_id
input_ids[:, : self.image_seq_length] = self.image_token_index
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
"image_sizes": image_sizes,
}
return config, inputs_dict
@require_torch
class Mistral3ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
Mistral3Model,
Mistral3ForConditionalGeneration,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (Mistral3ForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"image-text-to-text": Mistral3ForConditionalGeneration,
}
if is_torch_available()
else {}
)
_is_composite = True
test_headmasking = False
test_pruning = False
def setUp(self):
self.model_tester = Mistral3VisionText2TextModelTester(self)
self.config_tester = ConfigTester(self, config_class=Mistral3Config, has_text_modality=False)
def test_config(self):
# overwritten from `tests/test_configuration_common.py::ConfigTester` after #36077
# TODO: avoid overwritten once there is a better fix for #36077
def check_config_can_be_init_without_params():
config = self.config_tester.config_class()
self.config_tester.parent.assertIsNotNone(config)
self.config_tester.check_config_can_be_init_without_params = check_config_can_be_init_without_params
self.config_tester.run_common_tests()
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
# 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"]
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 LVLMs
# while some other models require pixel_values to be present
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"]
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)
@unittest.skip(reason="Compile not yet supported because in LLava models")
def test_sdpa_can_compile_dynamic(self):
pass
@unittest.skip("FlashAttention only support fp16 and bf16 data type")
def test_flash_attn_2_fp32_ln(self):
pass
@unittest.skip("Pixtral does not support attention interfaces.")
def test_eager_matches_fa2_generate(self):
pass
@unittest.skip("Pixtral does not support attention interfaces.")
def test_eager_matches_sdpa_generate(self):
pass
@unittest.skip("Pixtral does not support attention interfaces.")
def test_flash_attn_2_from_config(self):
pass
@unittest.skip("Pixtral does not support attention interfaces.")
def test_flash_attn_2_inference_equivalence(self):
pass
@unittest.skip("Pixtral does not support attention interfaces.")
def test_flash_attn_2_inference_equivalence_right_padding(self):
pass
@unittest.skip("Pixtral does not support attention interfaces.")
def test_sdpa_can_dispatch_on_flash(self):
pass
@unittest.skip("Pixtral does not support attention interfaces.")
def test_flex_attention_with_grads(self):
pass
@slow
@require_torch_accelerator
class Mistral3IntegrationTest(unittest.TestCase):
@require_read_token
def setUp(self):
cleanup(torch_device, gc_collect=True)
self.model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
self.model = Mistral3ForConditionalGeneration.from_pretrained(
self.model_checkpoint, torch_dtype=torch.bfloat16
)
accelerate.cpu_offload(self.model, execution_device=torch_device)
def tearDown(self):
cleanup(torch_device, gc_collect=True)
@require_read_token
def test_mistral3_integration_generate_text_only(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
processor.chat_template = processor.chat_template.replace('strftime_now("%Y-%m-%d")', '"2025-06-20"')
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": "Write a haiku"},
],
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(torch_device, dtype=torch.bfloat16)
with torch.no_grad():
generate_ids = self.model.generate(**inputs, max_new_tokens=200, do_sample=False)
decoded_output = processor.decode(
generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_outputs = Expectations(
{
("xpu", 3): "Sure, here is a haiku for you:\n\nWhispers of the breeze,\nCherry blossoms softly fall,\nSpring's gentle embrace.",
("cuda", 8): "Sure, here is a haiku for you:\n\nWhispers of the breeze,\nCherry blossoms softly fall,\nSpring's gentle embrace.",
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(decoded_output, expected_output)
@require_read_token
def test_mistral3_integration_generate(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
processor.chat_template = processor.chat_template.replace('strftime_now("%Y-%m-%d")', '"2025-06-20"')
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
{"type": "text", "text": "Describe this image"},
],
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(torch_device, dtype=torch.bfloat16)
with torch.no_grad():
generate_ids = self.model.generate(**inputs, max_new_tokens=20, do_sample=False)
decoded_output = processor.decode(
generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_outputs = Expectations(
{
("xpu", 3): "The image features two cats resting on a pink blanket. The cat on the left is a kitten",
("cuda", 8): 'The image features two cats lying on a pink surface, which appears to be a couch or a bed',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(decoded_output, expected_output)
@require_read_token
@require_deterministic_for_xpu
def test_mistral3_integration_batched_generate(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
processor.chat_template = processor.chat_template.replace('strftime_now("%Y-%m-%d")', '"2025-06-20"')
messages = [
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/ydshieh/kosmos-2.5/resolve/main/view.jpg"},
{"type": "text", "text": "Write a haiku for this image"},
],
},
],
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
{"type": "text", "text": "Describe this image"},
],
},
],
]
inputs = processor.apply_chat_template(
messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(torch_device, dtype=torch.bfloat16)
output = self.model.generate(**inputs, do_sample=False, max_new_tokens=25)
gen_tokens = output[:, inputs["input_ids"].shape[1] :]
# Check first output
decoded_output = processor.decode(gen_tokens[0], skip_special_tokens=True)
expected_outputs = Expectations(
{
("xpu", 3): "Calm lake's mirror gleams,\nWhispering pines stand in silence,\nPath to peace begins.",
("cuda", 8): "Wooden path to calm,\nReflections whisper secrets,\nNature's peace unfolds.",
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
)
# Check second output
decoded_output = processor.decode(gen_tokens[1], skip_special_tokens=True)
expected_outputs = Expectations(
{
("xpu", 3): "The image depicts a vibrant urban scene in what appears to be Chinatown. The focal point is a traditional Chinese archway",
("cuda", 8): 'The image depicts a street scene in what appears to be a Chinatown district. The focal point is a traditional Chinese arch',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
)
@require_read_token
@require_deterministic_for_xpu
def test_mistral3_integration_batched_generate_multi_image(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
processor.chat_template = processor.chat_template.replace('strftime_now("%Y-%m-%d")', '"2025-06-20"')
# Prepare inputs
messages = [
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/ydshieh/kosmos-2.5/resolve/main/view.jpg"},
{"type": "text", "text": "Write a haiku for this image"},
],
},
],
[
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://huggingface.co/ydshieh/kosmos-2.5/resolve/main/Statue-of-Liberty-Island-New-York-Bay.jpg",
},
{
"type": "image",
"url": "https://huggingface.co/ydshieh/kosmos-2.5/resolve/main/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
},
{
"type": "text",
"text": "These images depict two different landmarks. Can you identify them?",
},
],
},
],
]
inputs = processor.apply_chat_template(
messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(torch_device, dtype=torch.bfloat16)
output = self.model.generate(**inputs, do_sample=False, max_new_tokens=25)
gen_tokens = output[:, inputs["input_ids"].shape[1] :]
# Check first output
decoded_output = processor.decode(gen_tokens[0], skip_special_tokens=True)
expected_outputs = Expectations(
{
("cuda", 8): 'Calm waters reflect\nWooden path to distant shore\nSilence in the scene',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
)
# Check second output
decoded_output = processor.decode(gen_tokens[1], skip_special_tokens=True)
expected_outputs = Expectations(
{
("xpu", 3): "Certainly! The images depict two iconic landmarks:\n\n1. The first image shows the Statue of Liberty in New York City.",
("cuda", 8): 'Certainly! The images depict two famous landmarks in the United States:\n\n1. The first image shows the Statue of Liberty,',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
)
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