set test_torchscript = False for Blip2 testing (#35972)

* just skip

* fix

* fix

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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Yih-Dar 2025-02-14 17:43:32 +01:00 committed by GitHub
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@ -15,7 +15,6 @@
"""Testing suite for the PyTorch BLIP-2 model."""
import inspect
import os
import tempfile
import unittest
@ -36,7 +35,7 @@ from transformers.testing_utils import (
slow,
torch_device,
)
from transformers.utils import is_torch_available, is_torch_sdpa_available, is_vision_available
from transformers.utils import is_torch_available, is_vision_available
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
@ -477,7 +476,7 @@ class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, GenerationT
test_pruning = False
test_resize_embeddings = False
test_attention_outputs = False
test_torchscript = True
test_torchscript = False
_is_composite = True
def setUp(self):
@ -494,116 +493,6 @@ class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, GenerationT
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_conditional_generation(*config_and_inputs)
def _create_and_check_torchscript(self, config, inputs_dict):
# overwrite because BLIP requires ipnut ids and pixel values as input
if not self.test_torchscript:
self.skipTest(reason="test_torchscript is set to `False`")
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
for model_class in self.all_model_classes:
for attn_implementation in ["eager", "sdpa"]:
if attn_implementation == "sdpa" and (not model_class._supports_sdpa or not is_torch_sdpa_available()):
continue
configs_no_init._attn_implementation = attn_implementation
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
main_input_name = model_class.main_input_name
try:
if model.config.is_encoder_decoder:
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
main_input = inputs[main_input_name]
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
decoder_input_ids = inputs["decoder_input_ids"]
decoder_attention_mask = inputs["decoder_attention_mask"]
model(main_input, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
traced_model = torch.jit.trace(
model, (main_input, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
)
else:
main_input = inputs[main_input_name]
input_ids = inputs["input_ids"]
if model.config._attn_implementation == "sdpa":
trace_input = {main_input_name: main_input, "input_ids": input_ids}
if "attention_mask" in inputs:
trace_input["attention_mask"] = inputs["attention_mask"]
else:
self.skipTest(reason="testing SDPA without attention_mask is not supported")
model(main_input, attention_mask=inputs["attention_mask"])
# example_kwarg_inputs was introduced in torch==2.0, but it is fine here since SDPA has a requirement on torch>=2.1.
traced_model = torch.jit.trace(model, example_kwarg_inputs=trace_input)
else:
model(main_input, input_ids)
traced_model = torch.jit.trace(model, (main_input, input_ids))
except RuntimeError:
self.fail("Couldn't trace module.")
with tempfile.TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
model.to(torch_device)
model.eval()
loaded_model.to(torch_device)
loaded_model.eval()
model_state_dict = model.state_dict()
loaded_model_state_dict = loaded_model.state_dict()
non_persistent_buffers = {}
for key in loaded_model_state_dict.keys():
if key not in model_state_dict.keys():
non_persistent_buffers[key] = loaded_model_state_dict[key]
loaded_model_state_dict = {
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
}
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
model_buffers = list(model.buffers())
for non_persistent_buffer in non_persistent_buffers.values():
found_buffer = False
for i, model_buffer in enumerate(model_buffers):
if torch.equal(non_persistent_buffer, model_buffer):
found_buffer = True
break
self.assertTrue(found_buffer)
model_buffers.pop(i)
models_equal = True
for layer_name, p1 in model_state_dict.items():
if layer_name in loaded_model_state_dict:
p2 = loaded_model_state_dict[layer_name]
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
# (Even with this call, there are still memory leak by ~0.04MB)
self.clear_torch_jit_class_registry()
@unittest.skip(reason="Hidden_states is tested in individual model tests")
def test_hidden_states_output(self):
pass
@ -1015,7 +904,7 @@ class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, GenerationTesterMixi
test_pruning = False
test_resize_embeddings = True
test_attention_outputs = False
test_torchscript = True
test_torchscript = False
_is_composite = True
# TODO: Fix the failed tests
@ -1049,116 +938,6 @@ class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, GenerationTesterMixi
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_conditional_generation(*config_and_inputs)
def _create_and_check_torchscript(self, config, inputs_dict):
# overwrite because BLIP requires ipnut ids and pixel values as input
if not self.test_torchscript:
self.skipTest(reason="test_torchscript is set to `False`")
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
for model_class in self.all_model_classes:
for attn_implementation in ["eager", "sdpa"]:
if attn_implementation == "sdpa" and (not model_class._supports_sdpa or not is_torch_sdpa_available()):
continue
configs_no_init._attn_implementation = attn_implementation
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
main_input_name = model_class.main_input_name
try:
if model.config.is_encoder_decoder:
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
main_input = inputs[main_input_name]
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
decoder_input_ids = inputs["decoder_input_ids"]
decoder_attention_mask = inputs["decoder_attention_mask"]
model(main_input, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
traced_model = torch.jit.trace(
model, (main_input, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
)
else:
main_input = inputs[main_input_name]
input_ids = inputs["input_ids"]
if model.config._attn_implementation == "sdpa":
trace_input = {main_input_name: main_input, "input_ids": input_ids}
if "attention_mask" in inputs:
trace_input["attention_mask"] = inputs["attention_mask"]
else:
self.skipTest(reason="testing SDPA without attention_mask is not supported")
model(main_input, attention_mask=inputs["attention_mask"])
# example_kwarg_inputs was introduced in torch==2.0, but it is fine here since SDPA has a requirement on torch>=2.1.
traced_model = torch.jit.trace(model, example_kwarg_inputs=trace_input)
else:
model(main_input, input_ids)
traced_model = torch.jit.trace(model, (main_input, input_ids))
except RuntimeError:
self.fail("Couldn't trace module.")
with tempfile.TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
model.to(torch_device)
model.eval()
loaded_model.to(torch_device)
loaded_model.eval()
model_state_dict = model.state_dict()
loaded_model_state_dict = loaded_model.state_dict()
non_persistent_buffers = {}
for key in loaded_model_state_dict.keys():
if key not in model_state_dict.keys():
non_persistent_buffers[key] = loaded_model_state_dict[key]
loaded_model_state_dict = {
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
}
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
model_buffers = list(model.buffers())
for non_persistent_buffer in non_persistent_buffers.values():
found_buffer = False
for i, model_buffer in enumerate(model_buffers):
if torch.equal(non_persistent_buffer, model_buffer):
found_buffer = True
break
self.assertTrue(found_buffer)
model_buffers.pop(i)
models_equal = True
for layer_name, p1 in model_state_dict.items():
if layer_name in loaded_model_state_dict:
p2 = loaded_model_state_dict[layer_name]
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
# (Even with this call, there are still memory leak by ~0.04MB)
self.clear_torch_jit_class_registry()
@unittest.skip(reason="Hidden_states is tested in individual model tests")
def test_hidden_states_output(self):
pass