blip support for training (#21021)
* `blip` support for training * remove labels creation * remove unneeded `decoder_input_ids` creation * final changes - add colab link to documentation - reduction = mean for loss * fix nits * update link * clearer error message
This commit is contained in:
Younes Belkada
@@ -521,7 +521,7 @@ class BlipModelTest(ModelTesterMixin, unittest.TestCase):
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self.assertIsNotNone(model)
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class BlipTextImageModelsModelTester:
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class BlipTextRetrievalModelTester:
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def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
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if text_kwargs is None:
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@@ -569,13 +569,319 @@ class BlipTextImageModelsModelTester:
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return config, inputs_dict
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class BlipTextImageModelsModelTester:
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def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
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if text_kwargs is None:
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text_kwargs = {}
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if vision_kwargs is None:
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vision_kwargs = {}
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self.parent = parent
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self.text_model_tester = BlipTextModelTester(parent, **text_kwargs)
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self.vision_model_tester = BlipVisionModelTester(parent, **vision_kwargs)
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self.is_training = is_training
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def prepare_config_and_inputs(self):
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text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
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vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
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config = self.get_config()
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return config, input_ids, attention_mask, pixel_values
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def get_config(self):
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return BlipConfig.from_text_vision_configs(
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self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
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)
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def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
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model = BlipModel(config).to(torch_device).eval()
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with torch.no_grad():
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result = model(input_ids, pixel_values, attention_mask)
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self.parent.assertEqual(
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result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
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)
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self.parent.assertEqual(
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result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
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)
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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, input_ids, attention_mask, pixel_values = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"labels": input_ids,
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"attention_mask": attention_mask,
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"pixel_values": pixel_values,
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}
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return config, inputs_dict
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@require_torch
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@require_vision
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class BlipVQAModelTest(unittest.TestCase):
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all_model_classes = (BlipForQuestionAnswering,) if is_torch_available() else ()
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def setUp(self):
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self.model_tester = BlipModelTester(self)
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def _prepare_inputs_for_vqa(self):
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_, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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inputs_dict["labels"] = inputs_dict["input_ids"]
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inputs_dict.pop("return_loss")
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return inputs_dict
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def test_class_name_consistency(self):
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"""
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Tests that all VQA models have a class name that ends with "ForQuestionAnswering"
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"""
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for model_class in self.all_model_classes:
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model = model_class(self.model_tester.get_config())
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self.assertTrue(
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model.__class__.__name__.endswith("ForQuestionAnswering"),
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f"Class name should end with 'ForVisualQuestionAnswering' got {model.__class__.__name__}",
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)
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def test_training(self):
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"""
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Tests that all VQA models can be trained on a single batch
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"""
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for model_class in self.all_model_classes:
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model = model_class(self.model_tester.get_config()).to(torch_device)
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model.train()
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loss = model(**self._prepare_inputs_for_vqa()).loss
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loss.backward()
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# verify the gradients are not None
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for name, param in model.named_parameters():
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self.assertIsNotNone(param.grad, f"Gradients should not be None - got {param.grad} for {name}")
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def test_forward_signature(self):
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"""
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Test if the forward function has the expected arguments.
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"""
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for model_class in self.all_model_classes:
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model = model_class(self.model_tester.get_config())
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so args are the first n entries
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args = list(signature.parameters.keys())
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expected_args = [
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"input_ids",
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"attention_mask",
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"labels",
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"decoder_input_ids",
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"decoder_attention_mask",
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]
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for arg in expected_args:
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self.assertTrue(
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arg in args,
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f"Argument {arg} of forward function signature should include {arg}. Found {args}.",
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)
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@require_torch
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class BlipTextRetrievalModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (BlipForImageTextRetrieval,) if is_torch_available() else ()
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fx_compatible = False
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test_head_masking = False
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test_pruning = False
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test_resize_embeddings = False
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test_attention_outputs = False
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test_torchscript = False
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def setUp(self):
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self.model_tester = BlipTextRetrievalModelTester(self)
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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@unittest.skip(reason="Hidden_states is tested in individual model tests")
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def test_hidden_states_output(self):
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pass
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@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="Retain_grad is tested in individual model tests")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip(reason="BlipModel does not have input/output embeddings")
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def test_model_common_attributes(self):
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pass
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def test_forward_signature(self):
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config, _ = 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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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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if model.config.is_encoder_decoder:
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expected_arg_names = [
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"input_ids",
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"attention_mask",
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"decoder_input_ids",
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"decoder_attention_mask",
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]
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expected_arg_names.extend(
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["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
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if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
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else ["encoder_outputs"]
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)
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self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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else:
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expected_arg_names = ["input_ids"] if model_class != BlipForConditionalGeneration else ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_training(self):
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if not self.model_tester.is_training:
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return
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for model_class in self.all_model_classes[:-1]:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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# hardcode labels to be the same as input_ids
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inputs["labels"] = inputs["input_ids"]
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loss = model(**inputs).loss
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loss.backward()
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def test_training_gradient_checkpointing(self):
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if not self.model_tester.is_training:
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return
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for model_class in self.all_model_classes[:-1]:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.use_cache = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.gradient_checkpointing_enable()
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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# hardcode labels to be the same as input_ids
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inputs["labels"] = inputs["input_ids"]
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loss = model(**inputs).loss
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loss.backward()
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# override as the `logit_scale` parameter initilization is different for Blip
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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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# check if `logit_scale` is initilized as per the original implementation
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if name == "logit_scale":
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self.assertAlmostEqual(
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param.data.item(),
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np.log(1 / 0.07),
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delta=1e-3,
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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else:
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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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def _create_and_check_torchscript(self, config, inputs_dict):
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if not self.test_torchscript:
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return
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configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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configs_no_init.torchscript = True
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configs_no_init.return_dict = False
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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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model.to(torch_device)
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model.eval()
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try:
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input_ids = inputs_dict["input_ids"]
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pixel_values = inputs_dict["pixel_values"] # Blip needs pixel_values
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traced_model = torch.jit.trace(model, (input_ids, pixel_values))
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except RuntimeError:
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self.fail("Couldn't trace module.")
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
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try:
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torch.jit.save(traced_model, pt_file_name)
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except Exception:
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self.fail("Couldn't save module.")
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try:
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loaded_model = torch.jit.load(pt_file_name)
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except Exception:
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self.fail("Couldn't load module.")
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model.to(torch_device)
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model.eval()
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loaded_model.to(torch_device)
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loaded_model.eval()
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model_state_dict = model.state_dict()
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loaded_model_state_dict = loaded_model.state_dict()
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self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
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models_equal = True
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for layer_name, p1 in model_state_dict.items():
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p2 = loaded_model_state_dict[layer_name]
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if p1.data.ne(p2.data).sum() > 0:
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models_equal = False
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self.assertTrue(models_equal)
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def test_load_vision_text_config(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# Save BlipConfig and check if we can load BlipVisionConfig from it
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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config.save_pretrained(tmp_dir_name)
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vision_config = BlipVisionConfig.from_pretrained(tmp_dir_name)
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self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
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# Save BlipConfig and check if we can load BlipTextConfig from it
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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config.save_pretrained(tmp_dir_name)
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text_config = BlipTextConfig.from_pretrained(tmp_dir_name)
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self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
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@slow
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def test_model_from_pretrained(self):
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for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = BlipModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@require_torch
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class BlipTextImageModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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BlipForConditionalGeneration,
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BlipForQuestionAnswering,
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BlipForImageTextRetrieval,
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)
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if is_torch_available()
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else ()
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@@ -648,6 +954,10 @@ class BlipTextImageModelTest(ModelTesterMixin, unittest.TestCase):
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model.to(torch_device)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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# hardcode labels to be the same as input_ids
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inputs["labels"] = inputs["input_ids"]
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loss = model(**inputs).loss
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loss.backward()
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@@ -665,6 +975,10 @@ class BlipTextImageModelTest(ModelTesterMixin, unittest.TestCase):
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model.gradient_checkpointing_enable()
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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# hardcode labels to be the same as input_ids
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inputs["labels"] = inputs["input_ids"]
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loss = model(**inputs).loss
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loss.backward()
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