Add Mistral3 (#36790)
* initial start * style and dummies * Create convert_mistral3_weights_to_hf.py * update * typo * typo * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * up * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * update * update * Update image_processing_mistral3.py * Update convert_mistral3_weights_to_hf.py * fix patch merger * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * up * update modular to fit * style * Update convert_mistral3_weights_to_hf.py * typo * Update modular_mistral3.py * simplify a lot all shape shenanigans * simplify * add working test processor * Add partially working common modeling tests * All tests working and remove mistral3 image processors * add docs and fixup * fix inference with image size >1540 * 🚨fix test image proc pixtral * Remove vision_feature_select_strategy * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * Update convert_mistral3_weights_to_hf.py * clean * fix test checkpoints * Update test_modeling_mistral3.py * Update test_modeling_mistral3.py * style * Use Pixtral processor * up * finish cleaning processor to use pixtral directly * Update __init__.py * Update processing_pixtral.py * doc * Update __init__.py * Update mistral3.md * Update _toctree.yml --------- Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co> Co-authored-by: yonigozlan <yoni.gozlan10@gmail.com>
This commit is contained in:
Cyril Vallez
0
tests/models/mistral3/__init__.py
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0
tests/models/mistral3/__init__.py
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482
tests/models/mistral3/test_modeling_mistral3.py
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tests/models/mistral3/test_modeling_mistral3.py
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# coding=utf-8
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# Copyright 2024 The Qwen team, Alibaba Group and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch GotOcr2 model."""
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import unittest
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from transformers import (
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AutoProcessor,
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Mistral3Config,
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is_bitsandbytes_available,
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is_torch_available,
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)
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from transformers.testing_utils import (
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cleanup,
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require_bitsandbytes,
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require_torch,
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require_torch_gpu,
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slow,
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torch_device,
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)
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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Mistral3ForConditionalGeneration,
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)
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if is_bitsandbytes_available():
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from transformers import BitsAndBytesConfig
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class Mistral3VisionText2TextModelTester:
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def __init__(
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self,
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parent,
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batch_size=3,
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seq_length=7,
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image_seq_length=4,
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vision_feature_layer=-1,
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ignore_index=-100,
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bos_token_id=0,
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eos_token_id=0,
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pad_token_id=0,
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image_token_index=1,
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num_channels=3,
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image_size=30,
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model_type="mistral3",
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is_training=True,
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text_config={
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"model_type": "mistral",
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"vocab_size": 99,
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"attention_dropout": 0.0,
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"hidden_act": "silu",
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"hidden_size": 32,
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"initializer_range": 0.02,
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"intermediate_size": 37,
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"max_position_embeddings": 512,
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"num_attention_heads": 4,
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"num_hidden_layers": 2,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000000.0,
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"sliding_window": None,
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"bos_token_id": 0,
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"eos_token_id": 0,
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"pad_token_id": 0,
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},
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vision_config={
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"model_type": "pixtral",
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"hidden_size": 32,
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"num_hidden_layers": 2,
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"num_attention_heads": 4,
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"intermediate_size": 37,
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"image_size": 30,
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"patch_size": 6,
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"num_channels": 3,
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"hidden_act": "gelu",
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},
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):
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self.parent = parent
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self.ignore_index = ignore_index
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.image_token_index = image_token_index
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self.model_type = model_type
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self.text_config = text_config
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self.vision_config = vision_config
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self.batch_size = batch_size
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self.vision_feature_layer = vision_feature_layer
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self.is_training = is_training
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self.image_seq_length = image_seq_length
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self.num_channels = num_channels
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self.image_size = image_size
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self.seq_length = seq_length + self.image_seq_length
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self.num_hidden_layers = text_config["num_hidden_layers"]
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self.vocab_size = text_config["vocab_size"]
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self.hidden_size = text_config["hidden_size"]
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self.num_attention_heads = text_config["num_attention_heads"]
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def get_config(self):
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return Mistral3Config(
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text_config=self.text_config,
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vision_config=self.vision_config,
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model_type=self.model_type,
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bos_token_id=self.bos_token_id,
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eos_token_id=self.eos_token_id,
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pad_token_id=self.pad_token_id,
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image_token_index=self.image_token_index,
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image_seq_length=self.image_seq_length,
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vision_feature_layer=self.vision_feature_layer,
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)
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def prepare_config_and_inputs(self):
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config = self.get_config()
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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return config, pixel_values
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values = config_and_inputs
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
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image_sizes = torch.tensor(
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[[self.image_size, self.image_size]] * self.batch_size, dtype=torch.long, device=torch_device
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)
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# input_ids[:, -1] = self.pad_token_id
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input_ids[input_ids == self.image_token_index] = self.pad_token_id
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input_ids[:, : self.image_seq_length] = self.image_token_index
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inputs_dict = {
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"pixel_values": pixel_values,
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"image_sizes": image_sizes,
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}
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return config, inputs_dict
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def create_and_check_model_fp16_forward(self, config, input_ids, pixel_values, attention_mask):
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model = Mistral3ForConditionalGeneration(config=config)
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model.to(torch_device)
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model.half()
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model.eval()
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logits = model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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pixel_values=pixel_values.to(torch.bfloat16),
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return_dict=True,
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)["logits"]
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self.parent.assertFalse(torch.isnan(logits).any().item())
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def create_and_check_model_fp16_autocast_forward(self, config, input_ids, pixel_values, attention_mask):
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config.torch_dtype = torch.float16
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model = Mistral3ForConditionalGeneration(config=config)
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model.to(torch_device)
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model.eval()
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with torch.autocast(device_type="cuda", dtype=torch.float16):
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logits = model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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pixel_values=pixel_values.to(torch.bfloat16),
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return_dict=True,
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)["logits"]
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self.parent.assertFalse(torch.isnan(logits).any().item())
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@require_torch
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class Mistral3ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (Mistral3ForConditionalGeneration,) if is_torch_available() else ()
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all_generative_model_classes = (Mistral3ForConditionalGeneration,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"image-text-to-text": Mistral3ForConditionalGeneration,
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}
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if is_torch_available()
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else {}
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)
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_is_composite = True
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test_headmasking = False
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test_pruning = False
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def setUp(self):
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self.model_tester = Mistral3VisionText2TextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Mistral3Config, has_text_modality=False)
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def test_config(self):
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# overwritten from `tests/test_configuration_common.py::ConfigTester` after #36077
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# TODO: avoid overwritten once there is a better fix for #36077
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def check_config_can_be_init_without_params():
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config = self.config_tester.config_class()
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self.config_tester.parent.assertIsNotNone(config)
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self.config_tester.check_config_can_be_init_without_params = check_config_can_be_init_without_params
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self.config_tester.run_common_tests()
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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if param.requires_grad:
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self.assertIn(
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((param.data.mean() * 1e9).round() / 1e9).item(),
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[0.0, 1.0],
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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# overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
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def test_inputs_embeds(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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inputs = self._prepare_for_class(inputs_dict, model_class)
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input_ids = inputs["input_ids"]
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del inputs["input_ids"]
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del inputs["pixel_values"]
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wte = model.get_input_embeddings()
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inputs["inputs_embeds"] = wte(input_ids)
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with torch.no_grad():
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model(**inputs)
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# overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
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# while some other models require pixel_values to be present
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def test_inputs_embeds_matches_input_ids(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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inputs = self._prepare_for_class(inputs_dict, model_class)
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input_ids = inputs["input_ids"]
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del inputs["input_ids"]
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del inputs["pixel_values"]
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inputs_embeds = model.get_input_embeddings()(input_ids)
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with torch.no_grad():
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out_ids = model(input_ids=input_ids, **inputs)[0]
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out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0]
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torch.testing.assert_close(out_embeds, out_ids)
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@unittest.skip(reason="Compile not yet supported because in LLava models")
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def test_sdpa_can_compile_dynamic(self):
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pass
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@unittest.skip("FlashAttention only support fp16 and bf16 data type")
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def test_flash_attn_2_fp32_ln(self):
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pass
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@unittest.skip("Pixtral does not support attention interfaces.")
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def test_eager_matches_fa2_generate(self):
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pass
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@unittest.skip("Pixtral does not support attention interfaces.")
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def test_eager_matches_sdpa_generate(self):
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pass
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@unittest.skip("Pixtral does not support attention interfaces.")
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def test_flash_attn_2_from_config(self):
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pass
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@unittest.skip("Pixtral does not support attention interfaces.")
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def test_flash_attn_2_inference_equivalence(self):
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pass
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@unittest.skip("Pixtral does not support attention interfaces.")
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def test_flash_attn_2_inference_equivalence_right_padding(self):
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pass
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@unittest.skip("Pixtral does not support attention interfaces.")
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def test_sdpa_can_dispatch_on_flash(self):
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pass
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@slow
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@require_torch_gpu
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class Mistral3IntegrationTest(unittest.TestCase):
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def setUp(self):
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self.model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
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def tearDown(self):
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cleanup(torch_device, gc_collect=True)
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def test_mistral3_integration_generate_text_only(self):
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processor = AutoProcessor.from_pretrained(self.model_checkpoint)
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model = Mistral3ForConditionalGeneration.from_pretrained(
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self.model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16
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)
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Write a haiku"},
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],
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}
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]
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inputs = processor.apply_chat_template(
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messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
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).to(torch_device, dtype=torch.bfloat16)
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with torch.no_grad():
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generate_ids = model.generate(**inputs, max_new_tokens=200, do_sample=False)
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decoded_output = processor.decode(
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generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
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)
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expected_output = "Sure, here's a haiku for you:\n\nWhispers of the breeze,\nCherry blossoms softly fall,\nSpring's gentle embrace."
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self.assertEqual(decoded_output, expected_output)
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def test_mistral3_integration_generate(self):
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processor = AutoProcessor.from_pretrained(self.model_checkpoint)
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model = Mistral3ForConditionalGeneration.from_pretrained(
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self.model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16
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)
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
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{"type": "text", "text": "Describe this image"},
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],
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}
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]
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inputs = processor.apply_chat_template(
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messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
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).to(torch_device, dtype=torch.bfloat16)
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with torch.no_grad():
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generate_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
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decoded_output = processor.decode(
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generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
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)
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expected_output = "The image depicts two cats lying on a pink blanket. The larger cat, which appears to be an"
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self.assertEqual(decoded_output, expected_output)
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def test_mistral3_integration_batched_generate(self):
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processor = AutoProcessor.from_pretrained(self.model_checkpoint)
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model = Mistral3ForConditionalGeneration.from_pretrained(
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self.model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16
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)
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messages = [
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[
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{
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"role": "user",
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"content": [
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{"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
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{"type": "text", "text": "Write a haiku for this image"},
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],
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},
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],
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[
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{
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"role": "user",
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"content": [
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{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
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{"type": "text", "text": "Describe this image"},
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],
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},
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],
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]
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inputs = processor.apply_chat_template(
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messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
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).to(model.device, dtype=torch.bfloat16)
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output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
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# Check first output
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decoded_output = processor.decode(output[0], skip_special_tokens=True)
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expected_output = "Write a haiku for this imageSure, here is a haiku inspired by the image:\n\nCalm lake's mirror gleams,\nWhispering pines"
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self.assertEqual(
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decoded_output,
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expected_output,
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f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
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)
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# Check second output
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decoded_output = processor.decode(output[1], skip_special_tokens=True)
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||||
expected_output = "Describe this imageThe image depicts a vibrant street scene in what appears to be a Chinatown district. The focal point is a traditional Chinese"
|
||||
self.assertEqual(
|
||||
decoded_output,
|
||||
expected_output,
|
||||
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
|
||||
)
|
||||
|
||||
@require_bitsandbytes
|
||||
def test_mistral3_integration_batched_generate_multi_image(self):
|
||||
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
|
||||
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
|
||||
model = Mistral3ForConditionalGeneration.from_pretrained(
|
||||
self.model_checkpoint, quantization_config=quantization_config
|
||||
)
|
||||
|
||||
# Prepare inputs
|
||||
messages = [
|
||||
[
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
|
||||
{"type": "text", "text": "Write a haiku for this image"},
|
||||
],
|
||||
},
|
||||
],
|
||||
[
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "image",
|
||||
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
|
||||
},
|
||||
{
|
||||
"type": "image",
|
||||
"url": "https://thumbs.dreamstime.com/b/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(model.device, dtype=torch.float16)
|
||||
|
||||
output = model.generate(**inputs, do_sample=False, max_new_tokens=25)
|
||||
|
||||
# Check first output
|
||||
decoded_output = processor.decode(output[0], skip_special_tokens=True)
|
||||
expected_output = "Write a haiku for this imageSure, here is a haiku inspired by the image:\n\nCalm lake's wooden path\nSilent forest stands guard\n"
|
||||
self.assertEqual(
|
||||
decoded_output,
|
||||
expected_output,
|
||||
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
|
||||
)
|
||||
|
||||
# Check second output
|
||||
decoded_output = processor.decode(output[1], skip_special_tokens=True)
|
||||
expected_output = "These images depict two different landmarks. Can you identify them?Certainly! The images depict two iconic landmarks:\n\n1. The first image shows the Statue of Liberty in New York City."
|
||||
self.assertEqual(
|
||||
decoded_output,
|
||||
expected_output,
|
||||
f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
|
||||
)
|
||||
293
tests/models/mistral3/test_processor_mistral3.py
Normal file
293
tests/models/mistral3/test_processor_mistral3.py
Normal file
@@ -0,0 +1,293 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2025 The HuggingFace 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.
|
||||
|
||||
import shutil
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import requests
|
||||
|
||||
from transformers import PixtralProcessor
|
||||
from transformers.testing_utils import require_vision
|
||||
from transformers.utils import is_torch_available, is_vision_available
|
||||
|
||||
from ...test_processing_common import ProcessorTesterMixin
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
|
||||
@require_vision
|
||||
class Mistral3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
||||
"""This tests Pixtral processor with the new `spatial_merge_size` argument in Mistral3."""
|
||||
|
||||
processor_class = PixtralProcessor
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.url_0 = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
||||
cls.image_0 = Image.open(requests.get(cls.url_0, stream=True).raw)
|
||||
cls.url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
||||
cls.image_1 = Image.open(requests.get(cls.url_1, stream=True).raw)
|
||||
cls.url_2 = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.jpg"
|
||||
cls.image_2 = Image.open(requests.get(cls.url_2, stream=True).raw)
|
||||
|
||||
def setUp(self):
|
||||
self.tmpdirname = tempfile.mkdtemp()
|
||||
processor = PixtralProcessor.from_pretrained("mistralai/Mistral-Small-3.1-24B-Instruct-2503")
|
||||
processor.save_pretrained(self.tmpdirname)
|
||||
|
||||
def tearDown(self):
|
||||
shutil.rmtree(self.tmpdirname)
|
||||
|
||||
def test_chat_template(self):
|
||||
processor = self.processor_class.from_pretrained(self.tmpdirname)
|
||||
expected_prompt = "<s>[INST][IMG]What is shown in this image?[/INST]"
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image"},
|
||||
{"type": "text", "text": "What is shown in this image?"},
|
||||
],
|
||||
},
|
||||
]
|
||||
formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
|
||||
self.assertEqual(expected_prompt, formatted_prompt)
|
||||
|
||||
def test_image_token_filling(self):
|
||||
processor = self.processor_class.from_pretrained(self.tmpdirname)
|
||||
# Important to check with non square image
|
||||
image = torch.randint(0, 2, (3, 500, 316))
|
||||
expected_image_tokens = 198
|
||||
image_token_index = 10
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image"},
|
||||
{"type": "text", "text": "What is shown in this image?"},
|
||||
],
|
||||
},
|
||||
]
|
||||
inputs = processor(
|
||||
text=[processor.apply_chat_template(messages)],
|
||||
images=[image],
|
||||
return_tensors="pt",
|
||||
)
|
||||
image_tokens = (inputs["input_ids"] == image_token_index).sum().item()
|
||||
self.assertEqual(expected_image_tokens, image_tokens)
|
||||
|
||||
def test_processor_with_single_image(self):
|
||||
processor = self.processor_class.from_pretrained(self.tmpdirname)
|
||||
prompt_string = "USER: [IMG]\nWhat's the content of the image? ASSISTANT:"
|
||||
|
||||
# Make small for checking image token expansion
|
||||
processor.image_processor.size = {"longest_edge": 30}
|
||||
processor.patch_size = 6
|
||||
|
||||
# Test passing in an image
|
||||
inputs_image = processor(text=prompt_string, images=self.image_0, return_tensors="pt")
|
||||
self.assertIn("input_ids", inputs_image)
|
||||
self.assertTrue(len(inputs_image["input_ids"]) == 1)
|
||||
self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 30]))
|
||||
|
||||
# fmt: off
|
||||
input_ids = inputs_image["input_ids"]
|
||||
self.assertEqual(
|
||||
input_ids[0].tolist(),
|
||||
# Equivalent to "USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the content of the image? ASSISTANT:"
|
||||
[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
# Test passing in a url
|
||||
inputs_url = processor(text=prompt_string, images=self.url_0, return_tensors="pt")
|
||||
self.assertIn("input_ids", inputs_url)
|
||||
self.assertTrue(len(inputs_url["input_ids"]) == 1)
|
||||
self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 30]))
|
||||
|
||||
# fmt: off
|
||||
input_ids = inputs_url["input_ids"]
|
||||
self.assertEqual(
|
||||
input_ids[0].tolist(),
|
||||
# Equivalent to "USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the content of the image? ASSISTANT:"
|
||||
[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
# Test passing inputs as a single list
|
||||
inputs_image = processor(text=prompt_string, images=[self.image_0], return_tensors="pt")
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 30]))
|
||||
|
||||
# fmt: off
|
||||
self.assertEqual(
|
||||
inputs_image["input_ids"][0].tolist(),
|
||||
[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
# Test as nested single list
|
||||
inputs_image = processor(text=prompt_string, images=[[self.image_0]], return_tensors="pt")
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([1, 3, 24, 30]))
|
||||
|
||||
# fmt: off
|
||||
self.assertEqual(
|
||||
inputs_image["input_ids"][0].tolist(),
|
||||
[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 4701, 1307, 1278, 3937, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
def test_processor_with_multiple_images_single_list(self):
|
||||
processor = self.processor_class.from_pretrained(self.tmpdirname)
|
||||
prompt_string = "USER: [IMG][IMG]\nWhat's the difference between these two images? ASSISTANT:"
|
||||
|
||||
# Make small for checking image token expansion
|
||||
processor.image_processor.size = {"longest_edge": 30}
|
||||
processor.patch_size = 6
|
||||
|
||||
# Test passing in an image
|
||||
inputs_image = processor(text=prompt_string, images=[self.image_0, self.image_1], return_tensors="pt")
|
||||
self.assertIn("input_ids", inputs_image)
|
||||
self.assertTrue(len(inputs_image["input_ids"]) == 1)
|
||||
self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 24, 30]))
|
||||
|
||||
# fmt: off
|
||||
input_ids = inputs_image["input_ids"]
|
||||
self.assertEqual(
|
||||
input_ids[0].tolist(),
|
||||
# Equivalent to ["USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the difference between these two images? ASSISTANT:"]
|
||||
[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
# Test passing in a url
|
||||
inputs_url = processor(text=prompt_string, images=[self.url_0, self.url_1], return_tensors="pt")
|
||||
self.assertIn("input_ids", inputs_url)
|
||||
self.assertTrue(len(inputs_url["input_ids"]) == 1)
|
||||
self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 24, 30]))
|
||||
|
||||
# fmt: off
|
||||
input_ids = inputs_url["input_ids"]
|
||||
self.assertEqual(
|
||||
input_ids[0].tolist(),
|
||||
# Equivalent to ["USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the difference between these two images? ASSISTANT:"]
|
||||
[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
# Test passing in as a nested list
|
||||
inputs_url = processor(text=prompt_string, images=[[self.image_0, self.image_1]], return_tensors="pt")
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([2, 3, 24, 30]))
|
||||
|
||||
# fmt: off
|
||||
self.assertEqual(
|
||||
inputs_url["input_ids"][0].tolist(),
|
||||
[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
def test_processor_with_multiple_images_multiple_lists(self):
|
||||
processor = self.processor_class.from_pretrained(self.tmpdirname)
|
||||
prompt_string = [
|
||||
"USER: [IMG][IMG]\nWhat's the difference between these two images? ASSISTANT:",
|
||||
"USER: [IMG]\nWhat's the content of the image? ASSISTANT:",
|
||||
]
|
||||
processor.tokenizer.pad_token = "</s>"
|
||||
image_inputs = [[self.image_0, self.image_1], [self.image_2]]
|
||||
|
||||
# Make small for checking image token expansion
|
||||
processor.image_processor.size = {"longest_edge": 30}
|
||||
processor.patch_size = 6
|
||||
|
||||
# Test passing in an image
|
||||
inputs_image = processor(text=prompt_string, images=image_inputs, return_tensors="pt", padding=True)
|
||||
self.assertIn("input_ids", inputs_image)
|
||||
self.assertTrue(len(inputs_image["input_ids"]) == 2)
|
||||
self.assertIsInstance(inputs_image["input_ids"], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 30, 30]))
|
||||
|
||||
# fmt: off
|
||||
input_ids = inputs_image["input_ids"]
|
||||
self.assertEqual(
|
||||
input_ids[0].tolist(),
|
||||
# Equivalent to ["USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the difference between these two images? ASSISTANT:"]
|
||||
[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
# Test passing in a url
|
||||
inputs_url = processor(text=prompt_string, images=image_inputs, return_tensors="pt", padding=True)
|
||||
self.assertIn("input_ids", inputs_url)
|
||||
self.assertTrue(len(inputs_url["input_ids"]) == 2)
|
||||
self.assertIsInstance(inputs_url["input_ids"], torch.Tensor)
|
||||
self.assertIsInstance(inputs_image["pixel_values"], torch.Tensor)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 30, 30]))
|
||||
|
||||
# fmt: off
|
||||
input_ids = inputs_url["input_ids"]
|
||||
self.assertEqual(
|
||||
input_ids[0].tolist(),
|
||||
# Equivalent to ["USER: [IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END][IMG][IMG][IMG_BREAK][IMG][IMG][IMG_END]\nWhat's the difference between these two images? ASSISTANT:"]
|
||||
[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
# Test passing as a single flat list
|
||||
inputs_image = processor(
|
||||
text=prompt_string, images=[self.image_0, self.image_1, self.image_2], return_tensors="pt", padding=True
|
||||
)
|
||||
self.assertTrue(inputs_image["pixel_values"].shape == torch.Size([3, 3, 30, 30]))
|
||||
|
||||
# fmt: off
|
||||
self.assertEqual(
|
||||
inputs_image["input_ids"][0].tolist(),
|
||||
[1, 21510, 1058, 1032, 10, 10, 12, 10, 10, 13, 10, 10, 12, 10, 10, 13, 1010, 7493, 1681, 1278, 6592, 2396, 2576, 2295, 8061, 1063, 1349, 4290, 16002, 41150, 1058]
|
||||
)
|
||||
# fmt: on
|
||||
|
||||
def test_processor_returns_full_length_batches(self):
|
||||
# to avoid https://github.com/huggingface/transformers/issues/34204
|
||||
processor = self.processor_class.from_pretrained(self.tmpdirname)
|
||||
prompt_string = [
|
||||
"USER: [IMG]\nWhat's the content of the image? ASSISTANT:",
|
||||
] * 5
|
||||
processor.tokenizer.pad_token = "</s>"
|
||||
image_inputs = [[self.image_0]] * 5
|
||||
|
||||
# Make small for checking image token expansion
|
||||
processor.image_processor.size = {"longest_edge": 30}
|
||||
processor.patch_size = 6
|
||||
|
||||
# Test passing in an image
|
||||
inputs_image = processor(text=prompt_string, images=image_inputs, return_tensors="pt", padding=True)
|
||||
self.assertIn("input_ids", inputs_image)
|
||||
self.assertTrue(len(inputs_image["input_ids"]) == 5)
|
||||
@@ -109,8 +109,8 @@ class PixtralImageProcessingTester:
|
||||
|
||||
ratio = max(height / max_height, width / max_width)
|
||||
if ratio > 1:
|
||||
height = int(np.ceil(height / ratio))
|
||||
width = int(np.ceil(width / ratio))
|
||||
height = int(np.floor(height / ratio))
|
||||
width = int(np.floor(width / ratio))
|
||||
|
||||
patch_height, patch_width = self.patch_size["height"], self.patch_size["width"]
|
||||
num_height_tokens = (height - 1) // patch_height + 1
|
||||
|
||||
Reference in New Issue
Block a user