Bump min torch version (#515)

* Bump min torch version * comment * add torch min version
huggingface · Jan 15, 2024 · 133aa7d · 133aa7d
1 parent 3c196c3
commit 133aa7d
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Showing 3 changed files with 3 additions and 4 deletions.
diff --git a/optimum/intel/neural_compressor/trainer.py b/optimum/intel/neural_compressor/trainer.py
@@ -45,7 +45,6 @@
 from transformers.debug_utils import DebugOption, DebugUnderflowOverflow
 from transformers.modeling_utils import PreTrainedModel, get_parameter_dtype, unwrap_model
 from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
-from transformers.pytorch_utils import is_torch_less_than_1_11
 from transformers.tokenization_utils_base import PreTrainedTokenizerBase
 from transformers.trainer import TRAINER_STATE_NAME
 from transformers.trainer_callback import TrainerCallback, TrainerState
@@ -436,7 +435,7 @@ def _inner_training_loop(
                 if version.parse(accelerate_version) > version.parse("0.23.0"):
                     sampler_kinds.append(SeedableRandomSampler)
                 is_random_sampler = isinstance(sampler, tuple(sampler_kinds))
-                if is_torch_less_than_1_11 or not is_random_sampler:
+                if not is_random_sampler:
                     # We just need to begin an iteration to create the randomization of the sampler.
                     for _ in train_dataloader:
                         break

diff --git a/optimum/intel/openvino/trainer.py b/optimum/intel/openvino/trainer.py
@@ -63,7 +63,6 @@
 from transformers.data.data_collator import DataCollator
 from transformers.debug_utils import DebugOption, DebugUnderflowOverflow
 from transformers.modeling_utils import PreTrainedModel, unwrap_model
-from transformers.pytorch_utils import is_torch_less_than_1_11
 from transformers.tokenization_utils_base import PreTrainedTokenizerBase
 from transformers.trainer import TRAINER_STATE_NAME, TRAINING_ARGS_NAME
 from transformers.trainer_callback import TrainerCallback, TrainerState
@@ -521,7 +520,7 @@ def _inner_training_loop(
                 if version.parse(accelerate_version) > version.parse("0.23.0"):
                     sampler_kinds.append(SeedableRandomSampler)
                 is_random_sampler = isinstance(sampler, tuple(sampler_kinds))
-                if is_torch_less_than_1_11 or not is_random_sampler:
+                if not is_random_sampler:
                     # We just need to begin an iteration to create the randomization of the sampler.
                     for _ in train_dataloader:
                         break

diff --git a/setup.py b/setup.py
@@ -12,6 +12,7 @@
     assert False, "Error: Could not open '%s' due %s\n" % (filepath, error)
 
 INSTALL_REQUIRE = [
+    "torch>=1.11",
     "optimum>=1.14.0",
     "transformers>=4.20.0",
     "datasets>=1.4.0",