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tests/models/fuyu/test_processing_fuyu.py
465 строк
29 KB
Yih-Dar
Add tiny_model_id support to ProcessorTesterMixin for memory-sensitive tests (#47005)
03 июл 2026, 15:51
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03 июл 2026, 15:51
89917d0
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# Copyright 2023 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. import unittest import numpy as np from transformers import ( FuyuImageProcessor, FuyuProcessor, is_torch_available, ) from transformers.image_utils import load_image from transformers.testing_utils import require_torch, require_vision from ...test_processing_common import ProcessorTesterMixin, url_to_local_path if is_torch_available(): import torch from transformers.models.fuyu.processing_fuyu import construct_full_unpacked_stream, full_unpacked_stream_to_tensor @require_torch @require_vision class FuyuProcessingTest(ProcessorTesterMixin, unittest.TestCase): processor_class = FuyuProcessor model_id = "adept/fuyu-8b" # Fuyu uses a tokenizer with a very large vocabulary (~262K tokens), making tests slow and # memory-intensive. tiny_model_id points to a trimmed tokenizer repo to keep tests lightweight. tiny_model_id = "hf-internal-testing/tiny-processor-fuyu" @classmethod def _setup_test_attributes(cls, processor): cls.text_prompt = "Generate a coco-style caption.\\n" bus_image_url = url_to_local_path( "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/bus.png" ) cls.bus_image_pil = load_image(bus_image_url) @unittest.skip("FuyuProcessor doesn't return typical pixel values for images") def test_image_processor_defaults(self): pass @unittest.skip("FuyuProcessor doesn't return typical pixel values for images") def test_processor_with_multiple_inputs(self): pass def test_get_num_vision_tokens(self): "Tests general functionality of the helper used internally in vLLM" processor = self.get_processor() output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)]) self.assertTrue("num_image_tokens" in output) self.assertEqual(len(output["num_image_tokens"]), 3) self.assertTrue("num_image_patches" in output) self.assertEqual(len(output["num_image_patches"]), 3) def test_fuyu_processing(self): """ Test to ensure that the standard processing on a gold example matches adept's code. """ # fmt: off EXPECTED_IMAGE_PATCH_INPUTS = torch.Tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, -1, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, -1, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, -1, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, -1, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, -1, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, -1, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, -1, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, -1, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, -1, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, -1, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, -1, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, -1, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, -1, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,]]).to(torch.int64) EXPECTED_PADDED_UNPACKED_TOKEN_INPUTS = torch.Tensor([[71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 1, 128340, 71374, 71389, 120412, 71377, 71835, 71374, 73615, 71375, 71399, 71435, 71122,]]).to(torch.int64) processor = self.get_processor(use_tiny_ckpt=False) one_image_bus_model_inputs = processor(text=self.text_prompt, images=self.bus_image_pil) # fmt: on torch.testing.assert_close(one_image_bus_model_inputs["image_patches_indices"], EXPECTED_IMAGE_PATCH_INPUTS) torch.testing.assert_close(one_image_bus_model_inputs["input_ids"], EXPECTED_PADDED_UNPACKED_TOKEN_INPUTS) def test_fuyu_processing_no_image(self): """ Test to check processor works with just text input """ processor_outputs = self.get_processor()(text=self.text_prompt) tokenizer_outputs = self.get_component("tokenizer")(self.text_prompt) self.assertEqual(processor_outputs["input_ids"], tokenizer_outputs["input_ids"]) def test_fuyu_processing_no_text(self): """ Test to check processor works with just image input """ # fmt: off EXPECTED_IMAGE_PATCH_INPUTS = torch.Tensor([ [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, -1, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, -1, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, -1, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, -1, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, -1, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, -1, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, -1, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, -1, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, -1, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, -1, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, -1, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, -1, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, -1, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] ]).to(torch.int64) # fmt: on processor_outputs = self.get_processor()(images=self.bus_image_pil) self.assertTrue((processor_outputs["image_patches_indices"] == EXPECTED_IMAGE_PATCH_INPUTS).all()) def test_fuyu_processing_multiple_image_sample(self): """ Test to check processor works with multiple image inputs for a single text input """ # fmt: off SINGLE_IMAGE_PATCH_INPUTS = torch.Tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, -1, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, -1, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, -1, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, -1, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, -1, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, -1, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, -1, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, -1, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, -1, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, -1, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, -1, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, -1, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, -1, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,]]).to(torch.int64) SINGLE_PADDED_UNPACKED_TOKEN_INPUTS = torch.Tensor([[71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 1, 128340, 71374, 71389, 120412, 71377, 71835, 71374, 73615, 71375, 71399, 71435, 71122,]]).to(torch.int64) SINGLE_RESIZED_IMAGE_PATCH_INPUTS = torch.Tensor([[ 0, 1, 2, -1, 3, 4, 5, -1, 6, 7, 8, -1, 9, 10, 11, -1, 12, 13, 14, -1, 15, 16, 17, -1, 18, 19, 20, -1, 21, 22, 23, -1, 24, 25, 26, -1, 27, 28, 29, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]]) SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS = torch.Tensor([[ 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71019, 1, 128340, 71374, 71389, 120412, 71377, 71835, 71374, 73615, 71375, 71399, 71435, 71122]]) # fmt: on # Load once and reuse across all assertions in this test to avoid repeatedly loading the # full processor (which carries the large 262K-vocab tokenizer). processor = self.get_processor(use_tiny_ckpt=False) # Batch of two images - equally sized images = [self.bus_image_pil, self.bus_image_pil] processor_outputs = processor(text=[self.text_prompt, self.text_prompt], images=images) self.assertTrue( ( processor_outputs["image_patches_indices"] == torch.cat([SINGLE_IMAGE_PATCH_INPUTS, SINGLE_IMAGE_PATCH_INPUTS], dim=0) ).all() ) self.assertTrue( ( processor_outputs["input_ids"] == torch.cat([SINGLE_PADDED_UNPACKED_TOKEN_INPUTS, SINGLE_PADDED_UNPACKED_TOKEN_INPUTS], dim=0) ).all() ) # Processes single images with different sizes as expected images = [self.bus_image_pil] processor_outputs = processor(text=self.text_prompt, images=images) self.assertTrue((processor_outputs["image_patches_indices"] == SINGLE_IMAGE_PATCH_INPUTS).all()) self.assertTrue((processor_outputs["input_ids"] == SINGLE_PADDED_UNPACKED_TOKEN_INPUTS).all()) images = [self.bus_image_pil.resize((64, 300))] processor_outputs = processor(text=self.text_prompt, images=images) self.assertTrue((processor_outputs["image_patches_indices"] == SINGLE_RESIZED_IMAGE_PATCH_INPUTS).all()) self.assertTrue((processor_outputs["input_ids"] == SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS).all()) # Batch of two images - different sizes. Left-pads the smaller image inputs images = [self.bus_image_pil, self.bus_image_pil.resize((64, 300))] processor_outputs = processor(text=[self.text_prompt, self.text_prompt], images=images) padding_len_patch = SINGLE_IMAGE_PATCH_INPUTS.shape[1] - SINGLE_RESIZED_IMAGE_PATCH_INPUTS.shape[1] padded_single_resized_image_patch = torch.cat( [torch.ones([1, padding_len_patch]) * -1, SINGLE_RESIZED_IMAGE_PATCH_INPUTS], dim=1 ) expected_image_patch_inputs = torch.cat([SINGLE_IMAGE_PATCH_INPUTS, padded_single_resized_image_patch], dim=0) padding_len_token = ( SINGLE_PADDED_UNPACKED_TOKEN_INPUTS.shape[1] - SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS.shape[1] ) padded_single_resized_padded_unpacked_token_inputs = torch.cat( [torch.zeros([1, padding_len_token]), SINGLE_RESIZED_PADDED_UNPACKED_TOKEN_INPUTS], dim=1 ) expected_padded_unpacked_token_inputs = torch.cat( [SINGLE_PADDED_UNPACKED_TOKEN_INPUTS, padded_single_resized_padded_unpacked_token_inputs], dim=0 ) self.assertTrue((processor_outputs["image_patches_indices"] == expected_image_patch_inputs).all()) self.assertTrue((processor_outputs["input_ids"] == expected_padded_unpacked_token_inputs).all()) # Rewrite as Fuyu supports tokenizer kwargs only when image is None. @require_vision @require_torch def test_kwargs_overrides_default_tokenizer_kwargs(self): if "image_processor" not in self.processor_class.get_attributes(): self.skipTest(f"image_processor attribute not present in {self.processor_class}") image_processor = self.get_component("image_processor") tokenizer = self.get_component("tokenizer", max_length=117) processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) self.skip_processor_without_typed_kwargs(processor) input_str = self.prepare_text_inputs() # Fuyu uses tokenizer kwargs only when image is None. image_input = None inputs = processor( text=input_str, images=image_input, return_tensors="pt", max_length=112, padding="max_length" ) self.assertEqual(len(inputs["input_ids"][0]), 112) @unittest.skip("Fuyu processor does not support image_processor kwargs") def test_image_processor_defaults_preserved_by_image_kwargs(self): pass @unittest.skip("Fuyu processor does not support image_processor kwargs") def test_kwargs_overrides_default_image_processor_kwargs(self): pass # Rewrite as Fuyu supports tokenizer kwargs only when image is None. @require_vision @require_torch def test_tokenizer_defaults_preserved_by_kwargs(self): if "image_processor" not in self.processor_class.get_attributes(): self.skipTest(f"image_processor attribute not present in {self.processor_class}") image_processor = self.get_component("image_processor") tokenizer = self.get_component("tokenizer", max_length=117, padding="max_length") processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) self.skip_processor_without_typed_kwargs(processor) input_str = self.prepare_text_inputs() # Fuyu uses tokenizer kwargs only when image is None. image_input = None inputs = processor(text=input_str, images=image_input, return_tensors="pt") self.assertEqual(len(inputs["input_ids"][0]), 117) # Rewrite as Fuyu image processor does not return pixel values @require_torch @require_vision def test_structured_kwargs_nested(self): if "image_processor" not in self.processor_class.get_attributes(): self.skipTest(f"image_processor attribute not present in {self.processor_class}") image_processor = self.get_component("image_processor") tokenizer = self.get_component("tokenizer") processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) self.skip_processor_without_typed_kwargs(processor) input_str = self.prepare_text_inputs() # Fuyu uses tokenizer kwargs only when image is None. image_input = None # Define the kwargs for each modality all_kwargs = { "common_kwargs": {"return_tensors": "pt"}, "text_kwargs": {"padding": "max_length", "max_length": 76}, } inputs = processor(text=input_str, images=image_input, **all_kwargs) self.skip_processor_without_typed_kwargs(processor) self.assertEqual(len(inputs["input_ids"][0]), 76) # Rewrite as Fuyu image processor does not return pixel values @require_torch @require_vision def test_structured_kwargs_nested_from_dict(self): if "image_processor" not in self.processor_class.get_attributes(): self.skipTest(f"image_processor attribute not present in {self.processor_class}") image_processor = self.get_component("image_processor") tokenizer = self.get_component("tokenizer") processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) self.skip_processor_without_typed_kwargs(processor) input_str = self.prepare_text_inputs() # Fuyu uses tokenizer kwargs only when image is None. image_input = None # Define the kwargs for each modality all_kwargs = { "common_kwargs": {"return_tensors": "pt"}, "text_kwargs": {"padding": "max_length", "max_length": 76}, } inputs = processor(text=input_str, images=image_input, **all_kwargs) self.assertEqual(len(inputs["input_ids"][0]), 76) # Rewrite as Fuyu supports tokenizer kwargs only when image is None. @require_torch @require_vision def test_unstructured_kwargs(self): if "image_processor" not in self.processor_class.get_attributes(): self.skipTest(f"image_processor attribute not present in {self.processor_class}") image_processor = self.get_component("image_processor") tokenizer = self.get_component("tokenizer") processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) self.skip_processor_without_typed_kwargs(processor) input_str = self.prepare_text_inputs() # Fuyu uses tokenizer kwargs only when image is None. image_input = None inputs = processor( text=input_str, images=image_input, return_tensors="pt", padding="max_length", max_length=76, ) self.assertEqual(len(inputs["input_ids"][0]), 76) # Rewrite as Fuyu supports tokenizer kwargs only when image is None. @require_torch @require_vision def test_unstructured_kwargs_batched(self): if "image_processor" not in self.processor_class.get_attributes(): self.skipTest(f"image_processor attribute not present in {self.processor_class}") image_processor = self.get_component("image_processor", use_tiny_ckpt=False) tokenizer = self.get_component("tokenizer", use_tiny_ckpt=False) processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor) self.skip_processor_without_typed_kwargs(processor) input_str = self.prepare_text_inputs(batch_size=2) # Fuyu uses tokenizer kwargs only when image is None. image_input = None inputs = processor( text=input_str, images=image_input, return_tensors="pt", padding="longest", max_length=76, ) self.assertEqual(len(inputs["input_ids"][0]), 7) def test_processor_text_has_no_visual(self): # Overwritten: Fuyu has a complicated processing so we don't check id values processor = self.get_processor() text = self.prepare_text_inputs(batch_size=3, modalities="image") image_inputs = self.prepare_image_inputs(batch_size=3) processing_kwargs = {"return_tensors": "pt", "padding": True, "multi_page": True} # Call with nested list of vision inputs image_inputs_nested = [[image] if not isinstance(image, list) else image for image in image_inputs] inputs_dict_nested = {"text": text, "images": image_inputs_nested} inputs = processor(**inputs_dict_nested, **processing_kwargs) self.assertTrue(self.text_input_name in inputs) # Call with one of the samples with no associated vision input plain_text = "lower newer" image_inputs_nested[0] = [] text[0] = plain_text inputs_dict_no_vision = {"text": text, "images": image_inputs_nested} inputs_nested = processor(**inputs_dict_no_vision, **processing_kwargs) self.assertTrue(self.text_input_name in inputs_nested) def test_get_num_multimodal_tokens_matches_processor_call(self): "Tests that the helper used internally in vLLM works correctly" # Override -> model siltently ignores multiimage and processes one image per sample processor = self.get_processor() if processor.tokenizer.pad_token_id is None: processor.tokenizer.pad_token_id = processor.tokenizer.eos_token_id image_sizes = [(100, 100), (300, 100), (500, 30), (213, 167)] image_inputs = [] for h, w in image_sizes: image_inputs.append(np.random.randint(255, size=(h, w, 3), dtype=np.uint8)) image_token = getattr(self, "image_token", "") text = [f"This is an image {image_token}"] * len(image_inputs) inputs = processor( text=text, images=image_inputs, padding=True, return_mm_token_type_ids=True, return_tensors="pt" ) num_image_tokens_from_call = inputs.mm_token_type_ids.sum(-1).tolist() num_image_tokens_from_helper = processor._get_num_multimodal_tokens(image_sizes=image_sizes) self.assertListEqual(num_image_tokens_from_call, num_image_tokens_from_helper["num_image_tokens"]) @require_torch class TestImageTextProcessingUtils(unittest.TestCase): def setUp(self): self.batch_size = 2 self.new_seq_len = 8 self.num_sub_sequences = 1 self.all_bi_tokens_to_place = [4, 6] self.full_unpacked_stream = [torch.tensor([1, 2, 3, 4]), torch.tensor([5, 6, 7, 8, 9, 10])] self.fill_value = 0 self.num_real_text_tokens = [[3, 2], [2, 4]] # Here the input stream is padded to avoid inconsistencies (current model release matches) self.input_stream = torch.tensor([[[1, 2, 3], [4, 5, 0]], [[6, 7, 0], [8, 9, 10]]]) self.image_tokens = [ [torch.tensor([1, 2]), torch.tensor([3])], [torch.tensor([4, 5, 6]), torch.tensor([7, 8])], ] def test_full_unpacked_stream_to_tensor(self): result = full_unpacked_stream_to_tensor( self.all_bi_tokens_to_place, self.full_unpacked_stream, self.fill_value, self.batch_size, self.new_seq_len, offset=0, ) EXPECTED_TENSOR = torch.tensor([[1, 2, 3, 4, 0, 0, 0, 0], [5, 6, 7, 8, 9, 10, 0, 0]]) self.assertTrue(torch.equal(result, EXPECTED_TENSOR)) def test_construct_full_unpacked_stream(self): result = construct_full_unpacked_stream( self.num_real_text_tokens, self.input_stream, self.image_tokens, self.batch_size, self.num_sub_sequences ) EXPECTED_UNPACKED_STREAM = [torch.tensor([1, 2, 1, 2, 3]), torch.tensor([4, 5, 6, 6, 7])] for i in range(len(result)): self.assertTrue(torch.equal(result[i], EXPECTED_UNPACKED_STREAM[i])) @require_torch class TestProcessImagesForModelInput(unittest.TestCase): def setUp(self): """ Adding a mix of present and absent images. """ self.image_input = torch.randn([1, 1, 3, 64, 64]) self.image_present = torch.tensor([[1]]) self.image_unpadded_h = torch.tensor([[45]]) # Adjusted for subsequence of 1 self.image_unpadded_w = torch.tensor([[50]]) # Adjusted for subsequence of 1 self.image_patch_dim_h = 16 self.image_patch_dim_w = 16 self.image_placeholder_id = 999 self.image_newline_id = 888 self.variable_sized = True self.image_processor = FuyuImageProcessor( patch_size={"height": self.image_patch_dim_h, "width": self.image_patch_dim_w} ) def test_process_images_for_model_input_fixed_sized(self): self.variable_sized = False result = self.image_processor.preprocess_with_tokenizer_info( image_input=self.image_input, image_present=self.image_present, image_unpadded_h=self.image_unpadded_h, image_unpadded_w=self.image_unpadded_w, image_placeholder_id=self.image_placeholder_id, image_newline_id=self.image_newline_id, variable_sized=self.variable_sized, ) self.assertEqual(result["images"][0][0].shape, torch.Size([3, 64, 64]))