# 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 import pytest 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, 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, 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": 2, "eos_token_id": 3, "pad_token_id": 4, }, 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 = text_config["bos_token_id"] self.eos_token_id = text_config["eos_token_id"] self.pad_token_id = text_config["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 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() @unittest.skip(reason="Compile not yet supported because in LLava models") @pytest.mark.torch_compile_test 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, 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 @require_deterministic_for_xpu 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 tabby cats lying on a pink surface, which appears to be a cushion or", ("cuda", 8): 'The image features two cats lying on a pink surface, which appears to be a couch or a bed', ("rocm", (9, 4)): "The image features two cats lying on a pink surface, which appears to be a couch or a bed", ("rocm", (9, 5)): "The image features two tabby cats lying on a pink surface, which appears to be a cushion or" } ) # 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/mistral3-test-data/resolve/main/view.jpg", }, {"type": "text", "text": "Write a haiku for this image"}, ], }, ], [ { "role": "user", "content": [ { "type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/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.", ("rocm", (9, 5)): "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): "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/mistral3-test-data/resolve/main/view.jpg", }, {"type": "text", "text": "Write a haiku for this image"}, ], }, ], [ { "role": "user", "content": [ { "type": "image", "url": "https://huggingface.co/ydshieh/mistral3-test-data/resolve/main/Statue-of-Liberty-Island-New-York-Bay.jpg", }, { "type": "image", "url": "https://huggingface.co/ydshieh/mistral3-test-data/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}", )