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

* 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 0e756434bd.

* 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>

src/transformers/activations.py

@@ -16,7 +16,6 @@ import math
 from collections import OrderedDict
 
 import torch
-from packaging import version
 from torch import Tensor, nn
 
 from .utils import logging
@@ -34,14 +33,6 @@ class PytorchGELUTanh(nn.Module):
     match due to rounding errors.
     """
 
-    def __init__(self):
-        super().__init__()
-        if version.parse(torch.__version__) < version.parse("1.12.0"):
-            raise ImportError(
-                f"You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use "
-                "PytorchGELUTanh. Please upgrade torch."
-            )
-
     def forward(self, input: Tensor) -> Tensor:
         return nn.functional.gelu(input, approximate="tanh")
 
@@ -145,10 +136,7 @@ class MishActivation(nn.Module):
 
     def __init__(self):
         super().__init__()
-        if version.parse(torch.__version__) < version.parse("1.9.0"):
+        self.act = nn.functional.mish
-            self.act = self._mish_python
-        else:
-            self.act = nn.functional.mish
 
     def _mish_python(self, input: Tensor) -> Tensor:
         return input * torch.tanh(nn.functional.softplus(input))

src/transformers/modeling_utils.py

@@ -1500,7 +1500,6 @@ class ModuleUtilsMixin:
         seq_ids = torch.arange(seq_length, device=device)
         causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
         # in case past_key_values are used we need to add a prefix ones mask to the causal mask
-        # causal and attention masks must have same type with pytorch version < 1.3
         causal_mask = causal_mask.to(attention_mask.dtype)
 
         if causal_mask.shape[1] < attention_mask.shape[1]:

src/transformers/models/blip/modeling_blip_text.py

@@ -633,7 +633,6 @@ class BlipTextModel(BlipTextPreTrainedModel):
                 seq_ids = torch.arange(seq_length, device=device)
                 causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
                 # in case past_key_values are used we need to add a prefix ones mask to the causal mask
-                # causal and attention masks must have same type with pytorch version < 1.3
                 causal_mask = causal_mask.to(attention_mask.dtype)
 
                 if causal_mask.shape[1] < attention_mask.shape[1]:

src/transformers/models/code_llama/tokenization_code_llama_fast.py

@@ -20,11 +20,8 @@ from tokenizers import normalizers, processors
 
 from ...tokenization_utils_fast import PreTrainedTokenizerFast
 from ...utils import is_sentencepiece_available, logging
-from ...utils.versions import require_version
 
 
-require_version("tokenizers>=0.13.3")
-
 if is_sentencepiece_available():
     from .tokenization_code_llama import CodeLlamaTokenizer
 else:

src/transformers/models/cohere/tokenization_cohere_fast.py

@@ -23,11 +23,8 @@ from tokenizers import processors
 from ...tokenization_utils_base import BatchEncoding
 from ...tokenization_utils_fast import PreTrainedTokenizerFast
 from ...utils import logging
-from ...utils.versions import require_version
 
 
-require_version("tokenizers>=0.13.3")
-
 logger = logging.get_logger(__name__)
 VOCAB_FILES_NAMES = {"tokenizer_file": "tokenizer.json"}
 

src/transformers/models/gemma/tokenization_gemma_fast.py

@@ -20,11 +20,8 @@ from tokenizers import processors
 
 from ...tokenization_utils_fast import PreTrainedTokenizerFast
 from ...utils import is_sentencepiece_available, logging
-from ...utils.versions import require_version
 
 
-require_version("tokenizers>=0.13.3")
-
 if is_sentencepiece_available():
     from .tokenization_gemma import GemmaTokenizer
 else:

src/transformers/models/gpt_neo/modeling_gpt_neo.py

@@ -42,7 +42,6 @@ from ...utils import (
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     is_torch_flex_attn_available,
-    is_torch_fx_available,
     logging,
 )
 from .configuration_gpt_neo import GPTNeoConfig
@@ -60,8 +59,7 @@ if is_flash_attn_available():
 
 # This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
 # It means that the function will not be traced through and simply appear as a node in the graph.
-if is_torch_fx_available():
+_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
-    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
 
 
 logger = logging.get_logger(__name__)

src/transformers/models/llama/tokenization_llama_fast.py

@@ -20,11 +20,8 @@ from tokenizers import processors
 
 from ...tokenization_utils_fast import PreTrainedTokenizerFast
 from ...utils import is_sentencepiece_available, logging
-from ...utils.versions import require_version
 
 
-require_version("tokenizers>=0.13.3")
-
 if is_sentencepiece_available():
     from .tokenization_llama import LlamaTokenizer
 else:

src/transformers/models/phimoe/modeling_phimoe.py

@@ -42,7 +42,6 @@ from ...utils import (
     replace_return_docstrings,
 )
 from ...utils.deprecation import deprecate_kwarg
-from ...utils.import_utils import is_torch_fx_available
 from .configuration_phimoe import PhimoeConfig
 
 
@@ -51,8 +50,7 @@ if is_flash_attn_available():
 
 # This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
 # It means that the function will not be traced through and simply appear as a node in the graph.
-if is_torch_fx_available():
+_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
-    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
 
 
 logger = logging.get_logger(__name__)

src/transformers/models/vilt/modeling_vilt.py

@@ -171,7 +171,7 @@ class ViltEmbeddings(nn.Module):
         select = torch.cat(select, dim=0)
         x = x[select[:, 0], select[:, 1]].view(batch_size, -1, num_channels)
         x_mask = x_mask[select[:, 0], select[:, 1]].view(batch_size, -1)
-        # `patch_index` should be on the same device as `select` (for torch>=1.13), which is ensured at definition time.
+        # `patch_index` should be on the same device as `select`, which is ensured at definition time.
         patch_index = patch_index[select[:, 0], select[:, 1]].view(batch_size, -1, 2)
         pos_embed = pos_embed[select[:, 0], select[:, 1]].view(batch_size, -1, num_channels)
 

src/transformers/optimization.py

@@ -25,7 +25,6 @@ from torch.optim.lr_scheduler import LambdaLR, ReduceLROnPlateau
 from .trainer_pt_utils import LayerWiseDummyOptimizer, LayerWiseDummyScheduler
 from .trainer_utils import SchedulerType
 from .utils import logging
-from .utils.versions import require_version
 
 
 logger = logging.get_logger(__name__)
@@ -701,7 +700,6 @@ class Adafactor(Optimizer):
         relative_step=True,
         warmup_init=False,
     ):
-        require_version("torch>=1.5.0")  # add_ with alpha
         if lr is not None and relative_step:
             raise ValueError("Cannot combine manual `lr` and `relative_step=True` options")
         if warmup_init and not relative_step:

src/transformers/testing_utils.py

@@ -138,7 +138,6 @@ from .utils import (
     is_tokenizers_available,
     is_torch_available,
     is_torch_bf16_available_on_device,
-    is_torch_bf16_cpu_available,
     is_torch_bf16_gpu_available,
     is_torch_deterministic,
     is_torch_fp16_available_on_device,
@@ -1073,14 +1072,6 @@ def require_torch_bf16_gpu(test_case):
     )(test_case)
 
 
-def require_torch_bf16_cpu(test_case):
-    """Decorator marking a test that requires torch>=1.10, using CPU."""
-    return unittest.skipUnless(
-        is_torch_bf16_cpu_available(),
-        "test requires torch>=1.10, using CPU",
-    )(test_case)
-
-
 def require_deterministic_for_xpu(test_case):
     if is_torch_xpu_available():
         return unittest.skipUnless(is_torch_deterministic(), "test requires torch to use deterministic algorithms")(

src/transformers/trainer.py

@@ -164,7 +164,6 @@ from .utils import (
     is_sagemaker_dp_enabled,
     is_sagemaker_mp_enabled,
     is_schedulefree_available,
-    is_torch_compile_available,
     is_torch_hpu_available,
     is_torch_mlu_available,
     is_torch_mps_available,
@@ -257,7 +256,7 @@ if is_accelerate_available("0.28.0"):
 
 def _is_peft_model(model):
     if is_peft_available():
-        classes_to_check = (PeftModel,) if is_peft_available() else ()
+        classes_to_check = (PeftModel,)
         # Here we also check if the model is an instance of `PeftMixedModel` introduced in peft>=0.7.0: https://github.com/huggingface/transformers/pull/28321
         if version.parse(importlib.metadata.version("peft")) >= version.parse("0.7.0"):
             from peft import PeftMixedModel
@@ -797,10 +796,6 @@ class Trainer:
         # very last
         self._memory_tracker.stop_and_update_metrics()
 
-        # torch.compile
-        if args.torch_compile and not is_torch_compile_available():
-            raise RuntimeError("Using torch.compile requires PyTorch 2.0 or higher.")
-
         self.is_fsdp_xla_v2_enabled = args.fsdp_config.get("xla_fsdp_v2", False)
         if self.is_fsdp_xla_v2_enabled:
             if not IS_XLA_FSDPV2_POST_2_2:
@@ -1987,7 +1982,7 @@ class Trainer:
         if self.accelerator.unwrap_model(model) is not model:
             return model
 
-        # Mixed precision training with apex (torch < 1.6)
+        # Mixed precision training with apex
         if self.use_apex and training:
             model, self.optimizer = amp.initialize(model, self.optimizer, opt_level=self.args.fp16_opt_level)
 
@@ -3739,7 +3734,7 @@ class Trainer:
                 torch.musa.empty_cache()
             elif is_torch_npu_available():
                 torch.npu.empty_cache()
-            elif is_torch_mps_available(min_version="2.0"):
+            elif is_torch_mps_available():
                 torch.mps.empty_cache()
             elif is_torch_hpu_available():
                 logger.warning(

src/transformers/training_args.py

@@ -44,7 +44,6 @@ from .utils import (
     is_sagemaker_dp_enabled,
     is_sagemaker_mp_enabled,
     is_torch_available,
-    is_torch_bf16_cpu_available,
     is_torch_bf16_gpu_available,
     is_torch_hpu_available,
     is_torch_mlu_available,
@@ -1161,7 +1160,6 @@ class TrainingArguments:
             "help": (
                 "Number of batches loaded in advance by each worker. "
                 "2 means there will be a total of 2 * num_workers batches prefetched across all workers. "
-                "Default is 2 for PyTorch < 2.0.0 and otherwise None."
             )
         },
     )
@@ -1681,7 +1679,7 @@ class TrainingArguments:
                 self.half_precision_backend = self.fp16_backend
 
             if self.bf16 or self.bf16_full_eval:
-                if self.use_cpu and not is_torch_bf16_cpu_available() and not is_torch_xla_available():
+                if self.use_cpu and not is_torch_available() and not is_torch_xla_available():
                     # cpu
                     raise ValueError("Your setup doesn't support bf16/(cpu, tpu, neuroncore). You need torch>=1.10")
                 elif not self.use_cpu:

src/transformers/utils/fx.py

@@ -61,10 +61,7 @@ from ..models.auto.modeling_auto import (
 )
 from .import_utils import (
     ENV_VARS_TRUE_VALUES,
-    TORCH_FX_REQUIRED_VERSION,
-    get_torch_version,
     is_peft_available,
-    is_torch_fx_available,
 )
 
 
@@ -891,12 +888,6 @@ class HFTracer(Tracer):
     def __init__(self, autowrap_modules=(math,), autowrap_functions=()):
         super().__init__(autowrap_modules=autowrap_modules, autowrap_functions=autowrap_functions)
 
-        if not is_torch_fx_available():
-            raise ImportError(
-                f"Found an incompatible version of torch. Found version {get_torch_version()}, but only version "
-                f"{TORCH_FX_REQUIRED_VERSION} is supported."
-            )
-
     def _generate_dummy_input(
         self, model: "PreTrainedModel", input_name: str, shape: list[int], input_names: list[str]
     ) -> dict[str, torch.Tensor]:

src/transformers/utils/import_utils.py

@@ -222,6 +222,10 @@ _torch_version = "N/A"
 _torch_available = False
 if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
     _torch_available, _torch_version = _is_package_available("torch", return_version=True)
+    if _torch_available:
+        _torch_available = version.parse(_torch_version) >= version.parse("2.1.0")
+        if not _torch_available:
+            logger.warning(f"Disabling PyTorch because PyTorch >= 2.1 is required but found {_torch_version}")
 else:
     logger.info("Disabling PyTorch because USE_TF is set")
     _torch_available = False
@@ -310,15 +314,6 @@ if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
             _jax_version = _flax_version = "N/A"
 
 
-_torch_fx_available = False
-if _torch_available:
-    torch_version = version.parse(_torch_version)
-    _torch_fx_available = (torch_version.major, torch_version.minor) >= (
-        TORCH_FX_REQUIRED_VERSION.major,
-        TORCH_FX_REQUIRED_VERSION.minor,
-    )
-
-
 _torch_xla_available = False
 if USE_TORCH_XLA in ENV_VARS_TRUE_VALUES:
     _torch_xla_available, _torch_xla_version = _is_package_available("torch_xla", return_version=True)
@@ -526,19 +521,8 @@ def is_torch_bf16_gpu_available():
     return torch.cuda.is_available() and torch.cuda.is_bf16_supported()
 
 
-def is_torch_bf16_cpu_available():
+def is_torch_bf16_cpu_available() -> bool:
-    if not is_torch_available():
+    return is_torch_available()
-        return False
-
-    import torch
-
-    try:
-        # multiple levels of AttributeError depending on the pytorch version so do them all in one check
-        _ = torch.cpu.amp.autocast
-    except AttributeError:
-        return False
-
-    return True
 
 
 def is_torch_bf16_available():
@@ -618,16 +602,11 @@ def is_torch_tf32_available():
         return False
     if torch.cuda.get_device_properties(torch.cuda.current_device()).major < 8:
         return False
-    if int(torch.version.cuda.split(".")[0]) < 11:
-        return False
-    if version.parse(version.parse(torch.__version__).base_version) < version.parse("1.7"):
-        return False
-
     return True
 
 
 def is_torch_fx_available():
-    return _torch_fx_available
+    return is_torch_available()
 
 
 def is_peft_available():
@@ -832,21 +811,11 @@ def is_habana_gaudi1():
 
 
 def is_torchdynamo_available():
-    if not is_torch_available():
+    return is_torch_available()
-        return False
-
-    return True
 
 
 def is_torch_compile_available():
-    if not is_torch_available():
+    return is_torch_available()
-        return False
-
-    import torch
-
-    # We don't do any version check here to support nighlies marked as 1.14. Ultimately needs to check version against
-    # 2.0 but let's do it later.
-    return hasattr(torch, "compile")
 
 
 def is_torchdynamo_compiling():
@@ -979,10 +948,10 @@ def is_torch_xpu_available(check_device=False):
         return False
 
     torch_version = version.parse(_torch_version)
-    if torch_version.major < 2 or (torch_version.major == 2 and torch_version.minor < 6):
+    if torch_version.major == 2 and torch_version.minor < 6:
         if is_ipex_available():
             import intel_extension_for_pytorch  # noqa: F401
-        elif torch_version.major < 2 or (torch_version.major == 2 and torch_version.minor < 4):
+        elif torch_version.major == 2 and torch_version.minor < 4:
             return False
 
     import torch

tests/fsdp/test_fsdp.py

@@ -323,7 +323,6 @@ class TrainerIntegrationFSDP(TestCasePlus, TrainerIntegrationCommon):
 
     @require_torch_multi_accelerator
     @slow
-    @require_fsdp
     @require_fsdp_v2_version
     @require_accelerate_fsdp2
     def test_accelerate_fsdp2_integration(self):

tests/models/bert/test_modeling_bert.py

@@ -510,7 +510,6 @@ class BertModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/models/bert_generation/test_modeling_bert_generation.py

@@ -273,7 +273,6 @@ class BertGenerationEncoderTest(ModelTesterMixin, GenerationTesterMixin, Pipelin
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/models/big_bird/test_modeling_big_bird.py

@@ -506,7 +506,6 @@ class BigBirdModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/models/chinese_clip/test_modeling_chinese_clip.py

@@ -354,7 +354,6 @@ class ChineseCLIPTextModelTest(ModelTesterMixin, unittest.TestCase):
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/models/data2vec/test_modeling_data2vec_text.py

@@ -409,7 +409,6 @@ class Data2VecTextModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTes
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/models/ernie/test_modeling_ernie.py

@@ -492,7 +492,6 @@ class ErnieModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/models/gpt_neox/test_modeling_gpt_neox.py

@@ -306,7 +306,6 @@ class GPTNeoXModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
         self.model_tester.create_and_check_model_as_decoder(config, input_ids, input_mask)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs_for_decoder()
 
         input_mask = None

tests/models/gpt_neox_japanese/test_modeling_gpt_neox_japanese.py

@@ -223,7 +223,6 @@ class GPTNeoXModelJapaneseTest(ModelTesterMixin, GenerationTesterMixin, Pipeline
         self.model_tester.create_and_check_model_as_decoder(config, input_ids, input_mask)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs_for_decoder()
 
         input_mask = None

tests/models/hubert/test_modeling_hubert.py

@@ -23,7 +23,6 @@ import pytest
 
 from transformers import HubertConfig, is_torch_available
 from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device
-from transformers.utils import is_torch_fx_available
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import (
@@ -48,8 +47,7 @@ if is_torch_available():
     )
     from transformers.models.hubert.modeling_hubert import _compute_mask_indices
 
-if is_torch_fx_available():
+from transformers.utils.fx import symbolic_trace
-    from transformers.utils.fx import symbolic_trace
 
 
 class HubertModelTester:
@@ -438,8 +436,8 @@ class HubertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         # TODO: fix it
         self.skipTest(reason="torch 2.1 breaks torch fx tests for wav2vec2/hubert.")
 
-        if not is_torch_fx_available() or not self.fx_compatible:
+        if not self.fx_compatible:
-            self.skipTest(reason="torch fx is not available or not compatible with this model")
+            self.skipTest(reason="torch fx is not compatible with this model")
 
         configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
         configs_no_init.return_dict = False

tests/models/mt5/test_modeling_mt5.py

@@ -27,7 +27,7 @@ from transformers.testing_utils import (
     slow,
     torch_device,
 )
-from transformers.utils import is_torch_fx_available
+from transformers.utils.fx import symbolic_trace
 
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
@@ -35,9 +35,6 @@ from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_ten
 from ...test_pipeline_mixin import PipelineTesterMixin
 
 
-if is_torch_fx_available():
-    from transformers.utils.fx import symbolic_trace
-
 if is_torch_available():
     import torch
     import torch.nn.functional as F
@@ -598,8 +595,8 @@ class MT5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
         return False
 
     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:
+        if not self.fx_compatible:
-            self.skipTest(reason="torch.fx is not available or not compatible with this model")
+            self.skipTest(reason="torch.fx is not compatible with this model")
 
         configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
         configs_no_init.return_dict = False

tests/models/rembert/test_modeling_rembert.py

@@ -416,7 +416,6 @@ class RemBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/models/roberta/test_modeling_roberta.py

@@ -417,7 +417,6 @@ class RobertaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/models/roberta_prelayernorm/test_modeling_roberta_prelayernorm.py

@@ -421,7 +421,6 @@ class RobertaPreLayerNormModelTest(ModelTesterMixin, GenerationTesterMixin, Pipe
 
     # Copied from tests.models.roberta.test_modeling_roberta.RobertaModelTest.test_model_as_decoder_with_default_input_mask
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/models/roc_bert/test_modeling_roc_bert.py

@@ -664,7 +664,6 @@ class RoCBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/models/roformer/test_modeling_roformer.py

@@ -433,7 +433,6 @@ class RoFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/models/t5/test_modeling_t5.py

@@ -32,7 +32,8 @@ from transformers.testing_utils import (
     slow,
     torch_device,
 )
-from transformers.utils import cached_property, is_torch_fx_available
+from transformers.utils import cached_property
+from transformers.utils.fx import symbolic_trace
 
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
@@ -40,10 +41,6 @@ from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_ten
 from ...test_pipeline_mixin import PipelineTesterMixin
 
 
-if is_torch_fx_available():
-    from transformers.utils.fx import symbolic_trace
-
-
 if is_torch_available():
     import torch
     import torch.nn.functional as F
@@ -603,8 +600,8 @@ class T5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin,
         return False
 
     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:
+        if not self.fx_compatible:
-            self.skipTest(reason="torch.fx is not available or not compatible with this model")
+            self.skipTest(reason="torch.fx is not compatible with this model")
 
         configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
         configs_no_init.return_dict = False

tests/models/umt5/test_modeling_umt5.py

@@ -27,7 +27,7 @@ from transformers.testing_utils import (
     slow,
     torch_device,
 )
-from transformers.utils import is_torch_fx_available
+from transformers.utils.fx import symbolic_trace
 
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
@@ -35,10 +35,6 @@ from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_ten
 from ...test_pipeline_mixin import PipelineTesterMixin
 
 
-if is_torch_fx_available():
-    from transformers.utils.fx import symbolic_trace
-
-
 if is_torch_available():
     import torch
     import torch.nn.functional as F
@@ -300,8 +296,8 @@ class UMT5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
         return False
 
     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:
+        if not self.fx_compatible:
-            self.skipTest(reason="torch fx is not available or not compatible with this model")
+            self.skipTest(reason="torch fx is not compatible with this model")
 
         configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
         configs_no_init.return_dict = False

tests/models/wav2vec2/test_modeling_wav2vec2.py

@@ -42,7 +42,6 @@ from transformers.testing_utils import (
     slow,
     torch_device,
 )
-from transformers.utils import is_torch_fx_available
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import (
@@ -90,8 +89,7 @@ if is_pyctcdecode_available():
     from transformers.models.wav2vec2_with_lm import processing_wav2vec2_with_lm
 
 
-if is_torch_fx_available():
+from transformers.utils.fx import symbolic_trace
-    from transformers.utils.fx import symbolic_trace
 
 
 def _test_wav2vec2_with_lm_invalid_pool(in_queue, out_queue, timeout):
@@ -716,8 +714,8 @@ class Wav2Vec2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
         # TODO: fix it
         self.skipTest(reason="torch 2.1 breaks torch fx tests for wav2vec2/hubert.")
 
-        if not is_torch_fx_available() or not self.fx_compatible:
+        if not self.fx_compatible:
-            self.skipTest(reason="torch fx not available or not compatible with this model")
+            self.skipTest(reason="torch fx is not compatible with this model")
 
         configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
         configs_no_init.return_dict = False

tests/models/xlm_roberta_xl/test_modeling_xlm_roberta_xl.py

@@ -425,7 +425,6 @@ class XLMRobertaXLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTes
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/models/xmod/test_modeling_xmod.py

@@ -420,7 +420,6 @@ class XmodModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
 
     def test_model_as_decoder_with_default_input_mask(self):
-        # This regression test was failing with PyTorch < 1.3
         (
             config,
             input_ids,

tests/test_modeling_common.py

@@ -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:
+        if not self.fx_compatible:
-            self.skipTest(
+            self.skipTest(f"The model type {config.model_type} is not compatible with torch.fx")
-                f"Either torch.fx is not available, or 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

tests/trainer/test_trainer.py

@@ -99,7 +99,6 @@ from transformers.testing_utils import (
     require_torch_tensorrt_fx,
     require_torch_tf32,
     require_torch_up_to_2_accelerators,
-    require_torchdynamo,
     require_vision,
     require_wandb,
     run_first,
@@ -3994,10 +3993,9 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
 
     @require_non_xpu
     @require_torch_non_multi_gpu
-    @require_torchdynamo
     @require_torch_tensorrt_fx
     def test_torchdynamo_full_eval(self):
-        import torchdynamo
+        from torch import _dynamo as torchdynamo
 
         # torchdynamo at the moment doesn't support DP/DDP, therefore require a single gpu
         n_gpus = get_gpu_count()
@@ -4017,30 +4015,35 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
             del trainer
 
             # 2. TorchDynamo eager
-            trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, torchdynamo="eager", output_dir=tmp_dir)
+            trainer = get_regression_trainer(
+                a=a, b=b, eval_len=eval_len, torch_compile_backend="eager", output_dir=tmp_dir
+            )
             metrics = trainer.evaluate()
             self.assertAlmostEqual(metrics["eval_loss"], original_eval_loss)
             del trainer
             torchdynamo.reset()
 
             # 3. TorchDynamo nvfuser
-            trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, torchdynamo="nvfuser", output_dir=tmp_dir)
+            trainer = get_regression_trainer(
+                a=a, b=b, eval_len=eval_len, torch_compile_backend="nvfuser", output_dir=tmp_dir
+            )
             metrics = trainer.evaluate()
             self.assertAlmostEqual(metrics["eval_loss"], original_eval_loss)
             torchdynamo.reset()
 
             # 4. TorchDynamo fx2trt
-            trainer = get_regression_trainer(a=a, b=b, eval_len=eval_len, torchdynamo="fx2trt", output_dir=tmp_dir)
+            trainer = get_regression_trainer(
+                a=a, b=b, eval_len=eval_len, torch_compile_backend="fx2trt", output_dir=tmp_dir
+            )
             metrics = trainer.evaluate()
             self.assertAlmostEqual(metrics["eval_loss"], original_eval_loss)
             torchdynamo.reset()
 
-    @unittest.skip(reason="torch 2.0.0 gives `ModuleNotFoundError: No module named 'torchdynamo'`.")
     @require_torch_non_multi_gpu
-    @require_torchdynamo
+    @require_torch_gpu
     def test_torchdynamo_memory(self):
         # torchdynamo at the moment doesn't support DP/DDP, therefore require a single gpu
-        import torchdynamo
+        from torch import _dynamo as torchdynamo
 
         class CustomTrainer(Trainer):
             def compute_loss(self, model, inputs, return_outputs=False):
@@ -4085,7 +4088,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
         with tempfile.TemporaryDirectory() as tmp_dir:
             a = torch.ones(1024, 1024, device="cuda", requires_grad=True)
             a.grad = None
-            args = TrainingArguments(output_dir=tmp_dir, torchdynamo="nvfuser")
+            args = TrainingArguments(output_dir=tmp_dir, torch_compile_backend="nvfuser")
             trainer = CustomTrainer(model=mod, args=args)
             # warmup
             for _ in range(10):

tests/trainer/test_trainer_fsdp.py

@@ -21,7 +21,6 @@ from transformers.testing_utils import (
     get_torch_dist_unique_port,
     require_accelerate,
     require_fp8,
-    require_fsdp,
     require_torch_multi_accelerator,
     run_first,
     torch_device,
@@ -68,7 +67,6 @@ if is_torch_available():
 class TestFSDPTrainer(TestCasePlus):
     @require_torch_multi_accelerator
     @require_accelerate
-    @require_fsdp
     @run_first
     def test_trainer(self):
         output_dir = self.get_auto_remove_tmp_dir()
@@ -95,7 +93,6 @@ class TestFSDPTrainer(TestCasePlus):
 class TestFSDPTrainerFP8(TestCasePlus):
     @require_torch_multi_accelerator
     @require_accelerate
-    @require_fsdp
     @require_fp8
     @run_first
     def test_trainer(self):
@@ -125,7 +122,6 @@ class TestFSDPTrainerFP8(TestCasePlus):
 class TestFSDPTrainerWrap(TestCasePlus):
     @require_torch_multi_accelerator
     @require_accelerate
-    @require_fsdp
     @run_first
     def test_trainer(self):
         output_dir = self.get_auto_remove_tmp_dir()

tests/utils/test_modeling_utils.py

@@ -81,7 +81,6 @@ from transformers.utils.import_utils import (
     is_tf_available,
     is_torch_npu_available,
     is_torch_sdpa_available,
-    is_torchdynamo_available,
 )
 
 
@@ -1483,8 +1482,6 @@ class ModelUtilsTest(TestCasePlus):
                     model.warn_if_padding_and_no_attention_mask(input_ids, attention_mask=None)
             self.assertIn("You may ignore this warning if your `pad_token_id`", cl.out)
 
-        if not is_torchdynamo_available():
-            self.skipTest(reason="torchdynamo is not available")
         with self.subTest("Ensure that the warning code is skipped when compiling with torchdynamo."):
             logger.warning_once.cache_clear()
             from torch._dynamo import config, testing

tests/utils/test_versions_utils.py

@@ -86,7 +86,7 @@ class DependencyVersionCheckTest(TestCasePlus):
 
     def test_python(self):
         # matching requirement
-        require_version("python>=3.6.0")
+        require_version("python>=3.9.0")
 
         # not matching requirements
         for req in ["python>9.9.9", "python<3.0.0"]: