riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-07-31 02:02:21 +06:00
Code Issues Actions 33 Packages Projects Releases Wiki Activity

Fix some fa2 tests (#35340)

Browse Source 
* remove fa2 test

* remove other failing tests

* style
This commit is contained in:
Arthur 2024-12-19 17:05:25 +01:00 committed by GitHub
parent 667ed5635e
commit 1fa807fa63
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 0 additions and 119 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

29
tests/models/granite/test_modeling_granite.py
View File

@ -14,14 +14,12 @@
# limitations under the License.
"""Testing suite for the PyTorch Granite model."""
import tempfile
import unittest
from parameterized import parameterized
from transformers import GraniteConfig, is_torch_available, set_seed
from transformers.testing_utils import (
require_flash_attn,
require_read_token,
require_torch,
require_torch_gpu,
@ -417,33 +415,6 @@ class GraniteModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
with self.assertRaises(AssertionError):
torch.testing.assert_close(yarn_sin_long, original_sin_long)
@require_flash_attn
@require_torch_gpu
@slow
def test_use_flash_attention_2_true(self):
"""
NOTE: this is the only test testing that the legacy `use_flash_attention=2` argument still works as intended.
"""
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with tempfile.TemporaryDirectory() as tmp_dir:
model = model_class(config)
model.save_pretrained(tmp_dir)
new_model = GraniteForCausalLM.from_pretrained(
tmp_dir, use_flash_attention_2=True, torch_dtype=torch.float16
).to("cuda")
self.assertTrue(new_model.config._attn_implementation == "flash_attention_2")
has_flash = False
for name, submodule in new_model.named_modules():
if "FlashAttention" in submodule.__class__.__name__:
has_flash = True
break
if not has_flash:
raise ValueError("The flash model should have flash attention layers")
@require_torch_gpu
class GraniteIntegrationTest(unittest.TestCase):

29
tests/models/granitemoe/test_modeling_granitemoe.py
View File

@ -14,14 +14,12 @@
# limitations under the License.
"""Testing suite for the PyTorch GraniteMoe model."""
import tempfile
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GraniteMoeConfig, is_torch_available, set_seed
from transformers.testing_utils import (
require_flash_attn,
require_read_token,
require_torch,
require_torch_gpu,
@ -416,33 +414,6 @@ class GraniteMoeModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.Test
with self.assertRaises(AssertionError):
torch.testing.assert_close(yarn_sin_long, original_sin_long)
@require_flash_attn
@require_torch_gpu
@slow
def test_use_flash_attention_2_true(self):
"""
NOTE: this is the only test testing that the legacy `use_flash_attention=2` argument still works as intended.
"""
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with tempfile.TemporaryDirectory() as tmp_dir:
model = model_class(config)
model.save_pretrained(tmp_dir)
new_model = GraniteMoeForCausalLM.from_pretrained(
tmp_dir, use_flash_attention_2=True, torch_dtype=torch.float16
).to("cuda")
self.assertTrue(new_model.config._attn_implementation == "flash_attention_2")
has_flash = False
for name, submodule in new_model.named_modules():
if "FlashAttention" in submodule.__class__.__name__:
has_flash = True
break
if not has_flash:
raise ValueError("The flash model should have flash attention layers")
@require_torch_gpu
class GraniteMoeIntegrationTest(unittest.TestCase):

31
tests/models/llama/test_modeling_llama.py
View File

@ -14,10 +14,8 @@
# limitations under the License.
"""Testing suite for the PyTorch LLaMA model."""
import tempfile
import unittest
import pytest
from packaging import version
from parameterized import parameterized
@ -25,7 +23,6 @@ from transformers import AutoTokenizer, LlamaConfig, StaticCache, is_torch_avail
from transformers.generation.configuration_utils import GenerationConfig
from transformers.testing_utils import (
cleanup,
require_flash_attn,
require_read_token,
require_torch,
require_torch_accelerator,
@ -543,34 +540,6 @@ class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
with self.assertRaises(KeyError):
config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear"}}) # missing "factor"
@require_flash_attn
@require_torch_gpu
@slow
@pytest.mark.flash_attn_test
def test_use_flash_attention_2_true(self):
"""
NOTE: this is the only test testing that the legacy `use_flash_attention=2` argument still works as intended.
"""
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with tempfile.TemporaryDirectory() as tmp_dir:
model = model_class(config)
model.save_pretrained(tmp_dir)
new_model = LlamaForCausalLM.from_pretrained(
tmp_dir, use_flash_attention_2=True, torch_dtype=torch.float16
).to("cuda")
self.assertTrue(new_model.config._attn_implementation == "flash_attention_2")
has_flash = False
for name, submodule in new_model.named_modules():
if "FlashAttention" in submodule.__class__.__name__:
has_flash = True
break
if not has_flash:
raise ValueError("The flash model should have flash attention layers")
@require_torch_gpu
class LlamaIntegrationTest(unittest.TestCase):

30
tests/test_modeling_common.py
View File

@ -2769,8 +2769,6 @@ class ModelTesterMixin:
attributes = tuple([f"{name}_{idx}" for idx in range(len(fx_outputs))])
for fx_output, pt_output, attr in zip(fx_outputs, pt_outputs, attributes):
if isinstance(pt_output, DynamicCache):
pt_output = pt_output.to_legacy_cache()
self.check_pt_flax_outputs(fx_output, pt_output, model_class, tol=tol, name=attr)
elif isinstance(fx_outputs, jnp.ndarray):
@ -3612,34 +3610,6 @@ class ModelTesterMixin:
num_params < 1000000
), f"{model_class} is too big for the common tests ({num_params})! It should have 1M max."
@require_flash_attn
@require_torch_gpu
@mark.flash_attn_test
@slow
def test_flash_attn_2_conversion(self):
if not self.has_attentions:
self.skipTest(reason="Model architecture does not support attentions")
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if not model_class._supports_flash_attn_2:
self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(
tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2"
).to(torch_device)
for _, module in model.named_modules():
if "FlashAttention" in module.__class__.__name__:
return
self.assertTrue(False, "FlashAttention2 modules not found in model")
@require_flash_attn
@require_torch_gpu
@mark.flash_attn_test