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Remove old code for PyTorch, Accelerator and tokenizers (#37234)

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* Remove unneeded library version checks

Signed-off-by: cyy <cyyever@outlook.com>

* Remove PyTorch condition

Signed-off-by: cyy <cyyever@outlook.com>

* Remove PyTorch condition

Signed-off-by: cyy <cyyever@outlook.com>

* Fix ROCm get_device_capability

Signed-off-by: cyy <cyyever@outlook.com>

* Revert "Fix ROCm get_device_capability"

This reverts commit 0e756434bd7e74ffd73de5500476072b096570a6.

* Remove unnecessary check

Signed-off-by: cyy <cyyever@outlook.com>

* Revert changes

Signed-off-by: cyy <cyyever@outlook.com>

---------

Signed-off-by: cyy <cyyever@outlook.com>
This commit is contained in:
cyyever
2025-04-11 02:54:21 +08:00
committed by GitHub
parent 7ff896c0f2
commit 371c44d0ef
42 changed files with 53 additions and 178 deletions
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11
tests/test_modeling_common.py
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@@ -101,7 +101,6 @@ from transformers.utils import (
is_accelerate_available,
is_torch_bf16_available_on_device,
is_torch_fp16_available_on_device,
is_torch_fx_available,
is_torch_sdpa_available,
)
from transformers.utils.generic import ContextManagers
@@ -125,8 +124,8 @@ if is_torch_available():
from transformers.modeling_utils import load_state_dict, no_init_weights
from transformers.pytorch_utils import id_tensor_storage
if is_torch_fx_available():
from transformers.utils.fx import _FX_SUPPORTED_MODELS_WITH_KV_CACHE, symbolic_trace
from transformers.utils.fx import _FX_SUPPORTED_MODELS_WITH_KV_CACHE, symbolic_trace
if is_deepspeed_available():
import deepspeed
@@ -1190,10 +1189,8 @@ class ModelTesterMixin:
self._create_and_check_torch_fx_tracing(config, inputs_dict, output_loss=True)
def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False):
if not is_torch_fx_available() or not self.fx_compatible:
self.skipTest(
f"Either torch.fx is not available, or the model type {config.model_type} is not compatible with torch.fx"
)
if not self.fx_compatible:
self.skipTest(f"The model type {config.model_type} is not compatible with torch.fx")
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.return_dict = False