Add support for Pixtral (#33449)

* initial commit * gloups * updates * work * weights match * nits * nits * updates to support the tokenizer :) * updates * Pixtral processor (#33454) * rough outline * Add in image break and end tokens * Fix * Udo some formatting changes * Set patch_size default * Fix * Fix token expansion * nit in conversion script * Fix image token list creation * done * add expected results * Process list of list of images (#33465) * updates * working image and processor * this is the expected format * some fixes * push current updated * working mult images! * add a small integration test * Uodate configuration docstring * Formatting * Config docstring fix * simplify model test * fixup modeling and etests * Return BatchMixFeature in image processor * fix some copies * update * nits * Update model docstring * Apply suggestions from code review * Fix up * updates * revert modeling changes * update * update * fix load safe * addd liscence * update * use pixel_values as required by the model * skip some tests and refactor * Add pixtral image processing tests (#33476) * Image processing tests * Add processing tests * woops * defaults reflect pixtral image processor * fixup post merge * images -> pixel values * oups sorry Mr docbuilder * isort * fix * fix processor tests * small fixes * nit * update * last nits * oups this was really breaking! * nits * is composition needs to be true --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
2024-09-14 12:28:39 +02:00
parent 7bb1c99800
commit 8bd2b1e8c2
24 changed files with 2707 additions and 2 deletions
--- a/tests/models/pixtral/test_modeling_pixtral.py
+++ b/tests/models/pixtral/test_modeling_pixtral.py
@@ -0,0 +1,292 @@
+# coding=utf-8
+# Copyright 2024 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 Pixtral model."""
+
+import gc
+import unittest
+
+import requests
+
+from transformers import (
+    AutoProcessor,
+    PixtralModel,
+    PixtralVisionConfig,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.testing_utils import (
+    require_bitsandbytes,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor
+
+
+if is_torch_available():
+    import torch
+else:
+    is_torch_greater_or_equal_than_2_0 = False
+
+if is_vision_available():
+    from PIL import Image
+
+
+class PixtralModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        hidden_size=32,
+        projection_dim=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        initializer_range=0.02,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        self.hidden_size = hidden_size
+        self.projection_dim = projection_dim
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
+        num_patches = (image_size // patch_size) ** 2
+        self.seq_length = num_patches + 1
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def get_config(self):
+        return PixtralVisionConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            projection_dim=self.projection_dim,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, pixel_values):
+        model = PixtralModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(pixel_values)
+        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
+        image_size = (self.image_size, self.image_size)
+        patch_size = (self.patch_size, self.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def create_and_check_model_with_projection(self, config, pixel_values):
+        model = PixtralModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(pixel_values)
+        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
+        image_size = (self.image_size, self.image_size)
+        patch_size = (self.patch_size, self.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
+        self.parent.assertEqual(result.image_embeds.shape, (self.batch_size, self.projection_dim))
+
+    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 PixtralModelModelTest(ModelTesterMixin, unittest.TestCase):
+    """
+    Model tester for `PixtralModel`.
+    """
+
+    all_model_classes = (PixtralModel,) if is_torch_available() else ()
+    test_pruning = False
+    test_head_masking = False
+
+    def setUp(self):
+        self.model_tester = PixtralModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=PixtralVisionConfig, has_text_modality=False)
+
+    @unittest.skip("model does not support input embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip("model does not support input embeds")
+    def test_inputs_embeds_matches_input_ids(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="Compile not yet supported because in Pixtral models")
+    def test_sdpa_can_compile_dynamic(self):
+        pass
+
+    @unittest.skip(reason="Compile not yet supported because in Pixtral models")
+    def test_sdpa_can_dispatch_on_flash(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_attention_outputs(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_cpu_offload(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_batching_equivalence(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_disk_offload_bin(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_model_parallelism(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_model_outputs_equivalence(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_save_load(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_resize_tokens_embeddings(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_model_main_input_name(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_initialization(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_gradient_checkpointing_backward_compatibility(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_feed_forward_chunking(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_disk_offload_safetensors(self):
+        pass
+
+    @unittest.skip(reason="Not supported yet")
+    def test_determinism(self):
+        pass
+
+
+@require_torch
+class PixtralModelIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained("hf-internal-testing/pixtral-12b")
+
+    def tearDown(self):
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    @slow
+    @require_bitsandbytes
+    def test_small_model_integration_test(self):
+        # Let' s make sure we test the preprocessing to replace what is used
+        model = PixtralModel.from_pretrained("hf-internal-testing/pixtral-12b", load_in_4bit=True)
+
+        prompt = "<s>[INST][IMG]\nWhat are the things I should be cautious about when I visit this place?[/INST]"
+        image_file = "https://pixtral-vl.github.io/static/images/view.jpg"
+        raw_image = Image.open(requests.get(image_file, stream=True).raw)
+        inputs = self.processor(prompt, raw_image, return_tensors="pt")
+
+        EXPECTED_INPUT_IDS = torch.tensor([[1, 32000, 28705, 13, 11123, 28747, 1824, 460, 272, 1722,315, 1023, 347, 13831, 925, 684, 739, 315, 3251, 456,1633, 28804, 13, 4816, 8048, 12738, 28747]])  # fmt: skip
+        self.assertTrue(torch.equal(inputs["input_ids"], EXPECTED_INPUT_IDS))
+
+        output = model.generate(**inputs, max_new_tokens=20)
+        EXPECTED_DECODED_TEXT = "\nUSER: What are the things I should be cautious about when I visit this place?\nASSISTANT: When visiting this place, there are a few things one should be cautious about. Firstly,"  # fmt: skip
+
+        self.assertEqual(
+            self.processor.decode(output[0], skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )