mirror of
https://github.com/huggingface/transformers.git
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set test_torchscript = False for Blip2 testing (#35972)
* just skip * fix * fix * fix --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
GitHub
parent
0a9923a609
commit
dd16acb8a3
@ -15,7 +15,6 @@
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"""Testing suite for the PyTorch BLIP-2 model."""
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"""Testing suite for the PyTorch BLIP-2 model."""
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import inspect
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import inspect
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import os
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import tempfile
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import tempfile
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import unittest
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import unittest
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@ -36,7 +35,7 @@ from transformers.testing_utils import (
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slow,
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slow,
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torch_device,
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torch_device,
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)
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)
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from transformers.utils import is_torch_available, is_torch_sdpa_available, is_vision_available
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from transformers.utils import is_torch_available, is_vision_available
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from ...generation.test_utils import GenerationTesterMixin
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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_configuration_common import ConfigTester
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@ -477,7 +476,7 @@ class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, GenerationT
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test_pruning = False
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test_pruning = False
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test_resize_embeddings = False
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test_resize_embeddings = False
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test_attention_outputs = False
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test_attention_outputs = False
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test_torchscript = True
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test_torchscript = False
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_is_composite = True
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_is_composite = True
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def setUp(self):
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def setUp(self):
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@ -494,116 +493,6 @@ class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, GenerationT
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_conditional_generation(*config_and_inputs)
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self.model_tester.create_and_check_for_conditional_generation(*config_and_inputs)
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def _create_and_check_torchscript(self, config, inputs_dict):
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# overwrite because BLIP requires ipnut ids and pixel values as input
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if not self.test_torchscript:
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self.skipTest(reason="test_torchscript is set to `False`")
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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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for model_class in self.all_model_classes:
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for attn_implementation in ["eager", "sdpa"]:
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if attn_implementation == "sdpa" and (not model_class._supports_sdpa or not is_torch_sdpa_available()):
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continue
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configs_no_init._attn_implementation = attn_implementation
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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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inputs = self._prepare_for_class(inputs_dict, model_class)
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main_input_name = model_class.main_input_name
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try:
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if model.config.is_encoder_decoder:
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model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
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main_input = inputs[main_input_name]
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input_ids = inputs["input_ids"]
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attention_mask = inputs["attention_mask"]
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decoder_input_ids = inputs["decoder_input_ids"]
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decoder_attention_mask = inputs["decoder_attention_mask"]
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model(main_input, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
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traced_model = torch.jit.trace(
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model, (main_input, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
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)
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else:
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main_input = inputs[main_input_name]
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input_ids = inputs["input_ids"]
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if model.config._attn_implementation == "sdpa":
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trace_input = {main_input_name: main_input, "input_ids": input_ids}
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if "attention_mask" in inputs:
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trace_input["attention_mask"] = inputs["attention_mask"]
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else:
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self.skipTest(reason="testing SDPA without attention_mask is not supported")
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model(main_input, attention_mask=inputs["attention_mask"])
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# example_kwarg_inputs was introduced in torch==2.0, but it is fine here since SDPA has a requirement on torch>=2.1.
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traced_model = torch.jit.trace(model, example_kwarg_inputs=trace_input)
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else:
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model(main_input, input_ids)
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traced_model = torch.jit.trace(model, (main_input, input_ids))
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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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non_persistent_buffers = {}
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for key in loaded_model_state_dict.keys():
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if key not in model_state_dict.keys():
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non_persistent_buffers[key] = loaded_model_state_dict[key]
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loaded_model_state_dict = {
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key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
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}
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self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
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model_buffers = list(model.buffers())
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for non_persistent_buffer in non_persistent_buffers.values():
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found_buffer = False
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for i, model_buffer in enumerate(model_buffers):
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if torch.equal(non_persistent_buffer, model_buffer):
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found_buffer = True
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break
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self.assertTrue(found_buffer)
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model_buffers.pop(i)
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models_equal = True
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for layer_name, p1 in model_state_dict.items():
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if layer_name in loaded_model_state_dict:
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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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# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
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# (Even with this call, there are still memory leak by ~0.04MB)
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self.clear_torch_jit_class_registry()
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@unittest.skip(reason="Hidden_states is tested in individual model tests")
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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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def test_hidden_states_output(self):
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pass
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pass
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@ -1015,7 +904,7 @@ class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, GenerationTesterMixi
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test_pruning = False
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test_pruning = False
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test_resize_embeddings = True
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test_resize_embeddings = True
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test_attention_outputs = False
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test_attention_outputs = False
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test_torchscript = True
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test_torchscript = False
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_is_composite = True
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_is_composite = True
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# TODO: Fix the failed tests
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# TODO: Fix the failed tests
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@ -1049,116 +938,6 @@ class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, GenerationTesterMixi
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_conditional_generation(*config_and_inputs)
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self.model_tester.create_and_check_for_conditional_generation(*config_and_inputs)
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def _create_and_check_torchscript(self, config, inputs_dict):
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# overwrite because BLIP requires ipnut ids and pixel values as input
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if not self.test_torchscript:
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self.skipTest(reason="test_torchscript is set to `False`")
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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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for model_class in self.all_model_classes:
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for attn_implementation in ["eager", "sdpa"]:
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if attn_implementation == "sdpa" and (not model_class._supports_sdpa or not is_torch_sdpa_available()):
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continue
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configs_no_init._attn_implementation = attn_implementation
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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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inputs = self._prepare_for_class(inputs_dict, model_class)
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main_input_name = model_class.main_input_name
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try:
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if model.config.is_encoder_decoder:
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model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
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main_input = inputs[main_input_name]
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input_ids = inputs["input_ids"]
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attention_mask = inputs["attention_mask"]
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decoder_input_ids = inputs["decoder_input_ids"]
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decoder_attention_mask = inputs["decoder_attention_mask"]
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model(main_input, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
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traced_model = torch.jit.trace(
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model, (main_input, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
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)
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else:
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main_input = inputs[main_input_name]
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input_ids = inputs["input_ids"]
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if model.config._attn_implementation == "sdpa":
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trace_input = {main_input_name: main_input, "input_ids": input_ids}
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if "attention_mask" in inputs:
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trace_input["attention_mask"] = inputs["attention_mask"]
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else:
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self.skipTest(reason="testing SDPA without attention_mask is not supported")
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model(main_input, attention_mask=inputs["attention_mask"])
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# example_kwarg_inputs was introduced in torch==2.0, but it is fine here since SDPA has a requirement on torch>=2.1.
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traced_model = torch.jit.trace(model, example_kwarg_inputs=trace_input)
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else:
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model(main_input, input_ids)
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traced_model = torch.jit.trace(model, (main_input, input_ids))
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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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non_persistent_buffers = {}
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for key in loaded_model_state_dict.keys():
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if key not in model_state_dict.keys():
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non_persistent_buffers[key] = loaded_model_state_dict[key]
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loaded_model_state_dict = {
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key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
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}
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self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
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model_buffers = list(model.buffers())
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for non_persistent_buffer in non_persistent_buffers.values():
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found_buffer = False
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for i, model_buffer in enumerate(model_buffers):
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if torch.equal(non_persistent_buffer, model_buffer):
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found_buffer = True
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break
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self.assertTrue(found_buffer)
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model_buffers.pop(i)
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models_equal = True
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for layer_name, p1 in model_state_dict.items():
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if layer_name in loaded_model_state_dict:
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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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# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
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# (Even with this call, there are still memory leak by ~0.04MB)
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self.clear_torch_jit_class_registry()
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@unittest.skip(reason="Hidden_states is tested in individual model tests")
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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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def test_hidden_states_output(self):
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pass
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pass
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