diff --git a/tests/framework/callbacks/test_module_summary.py b/tests/framework/callbacks/test_module_summary.py
index 217705ec57..81417b0fc3 100644
--- a/tests/framework/callbacks/test_module_summary.py
+++ b/tests/framework/callbacks/test_module_summary.py
@@ -19,11 +19,6 @@
 
 from torchtnt.framework.callbacks.module_summary import ModuleSummary
 from torchtnt.framework.state import EntryPoint, PhaseState, State
-from torchtnt.utils.version import is_torch_version_geq_1_13
-
-MODULE_SUMMARY_FLOPS_AVAILABLE = False
-if is_torch_version_geq_1_13():
-    MODULE_SUMMARY_FLOPS_AVAILABLE = True
 
 
 class ModuleSummaryTest(unittest.TestCase):
@@ -85,10 +80,6 @@ def forward(self, x):
         self.assertTrue("b1" in ms.submodule_summaries)
         self.assertTrue("l2" in ms.submodule_summaries)
 
-    @unittest.skipUnless(
-        condition=MODULE_SUMMARY_FLOPS_AVAILABLE,
-        reason="This test needs PyTorch 1.13 or greater to run.",
-    )
     def test_module_summary_retrieve_module_summaries_module_inputs(self) -> None:
         """
         Test ModuleSummary callback in train
diff --git a/tests/framework/test_auto_unit.py b/tests/framework/test_auto_unit.py
index fe2142fe21..c059ef32c4 100644
--- a/tests/framework/test_auto_unit.py
+++ b/tests/framework/test_auto_unit.py
@@ -12,15 +12,6 @@
 from unittest.mock import MagicMock, patch
 
 import torch
-from torchtnt.framework.auto_unit import TrainStepResults
-from torchtnt.utils.test_utils import skip_if_not_distributed
-
-from torchtnt.utils.version import is_torch_version_geq_1_13
-
-COMPILE_AVAIL = False
-if is_torch_version_geq_1_13():
-    COMPILE_AVAIL = True
-    import torch._dynamo
 
 from pyre_extensions import none_throws, ParameterSpecification as ParamSpec
 
@@ -37,6 +28,7 @@
     AutoUnit,
     SWALRParams,
     SWAParams,
+    TrainStepResults,
 )
 from torchtnt.framework.evaluate import evaluate
 from torchtnt.framework.predict import predict
@@ -49,6 +41,7 @@
 from torchtnt.utils.lr_scheduler import TLRScheduler
 from torchtnt.utils.prepare_module import DDPStrategy
 from torchtnt.utils.progress import Progress
+from torchtnt.utils.test_utils import skip_if_not_distributed
 from torchtnt.utils.timer import Timer
 
 TParams = ParamSpec("TParams")
diff --git a/tests/framework/test_auto_unit_gpu.py b/tests/framework/test_auto_unit_gpu.py
index 6f8bdf710b..8223508614 100644
--- a/tests/framework/test_auto_unit_gpu.py
+++ b/tests/framework/test_auto_unit_gpu.py
@@ -8,24 +8,16 @@
 # pyre-strict
 
 import unittest
+
+from copy import deepcopy
 from typing import TypeVar
 from unittest.mock import MagicMock, patch
 
 import torch
-from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
-from torchtnt.utils.test_utils import skip_if_not_distributed, skip_if_not_gpu
-
-from torchtnt.utils.version import is_torch_version_geq_1_13
-
-COMPILE_AVAIL = False
-if is_torch_version_geq_1_13():
-    COMPILE_AVAIL = True
-    import torch._dynamo
-
-from copy import deepcopy
 
 from pyre_extensions import ParameterSpecification as ParamSpec
 from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
 from torchtnt.framework._test_utils import (
     DummyAutoUnit,
     generate_random_dataloader,
@@ -40,6 +32,7 @@
 from torchtnt.utils.distributed import spawn_multi_process
 from torchtnt.utils.env import init_from_env, seed
 from torchtnt.utils.prepare_module import DDPStrategy, FSDPStrategy, TorchCompileParams
+from torchtnt.utils.test_utils import skip_if_not_distributed, skip_if_not_gpu
 
 TParams = ParamSpec("TParams")
 T = TypeVar("T")
@@ -320,10 +313,6 @@ def test_predict_mixed_precision_fp16(self, mock_autocast: MagicMock) -> None:
             device_type="cuda", dtype=torch.float16, enabled=True
         )
 
-    @unittest.skipUnless(
-        condition=COMPILE_AVAIL,
-        reason="This test needs PyTorch 1.13 or greater to run.",
-    )
     @skip_if_not_gpu
     @patch("torch.compile")
     def test_compile_predict(self, mock_dynamo: MagicMock) -> None:
diff --git a/tests/utils/test_memory_snapshot_profiler.py b/tests/utils/test_memory_snapshot_profiler.py
index 00c14d983c..f277cc9334 100644
--- a/tests/utils/test_memory_snapshot_profiler.py
+++ b/tests/utils/test_memory_snapshot_profiler.py
@@ -14,17 +14,9 @@
     MemorySnapshotParams,
     MemorySnapshotProfiler,
 )
-from torchtnt.utils.version import is_torch_version_geq_2_0
 
 
 class MemorySnapshotProfilerTest(unittest.TestCase):
-
-    torch_version_geq_2_0: bool = is_torch_version_geq_2_0()
-
-    @unittest.skipUnless(
-        condition=torch_version_geq_2_0,
-        reason="This test needs changes from PyTorch 2.0 to run.",
-    )
     def test_validation(self) -> None:
         """Test parameter validation."""
         with tempfile.TemporaryDirectory() as temp_dir:
diff --git a/tests/utils/test_memory_snapshot_profiler_gpu.py b/tests/utils/test_memory_snapshot_profiler_gpu.py
index d4060d6b4d..3087a2e826 100644
--- a/tests/utils/test_memory_snapshot_profiler_gpu.py
+++ b/tests/utils/test_memory_snapshot_profiler_gpu.py
@@ -18,18 +18,10 @@
     MemorySnapshotProfiler,
 )
 from torchtnt.utils.test_utils import skip_if_not_gpu
-from torchtnt.utils.version import is_torch_version_geq_2_0
 
 
 class MemorySnapshotProfilerGPUTest(unittest.TestCase):
-
-    torch_version_geq_2_0: bool = is_torch_version_geq_2_0()
-
     @skip_if_not_gpu
-    @unittest.skipUnless(
-        condition=torch_version_geq_2_0,
-        reason="This test needs changes from PyTorch 2.0 to run.",
-    )
     def test_stop_step(self) -> None:
         """Test that a memory snapshot is saved when stop_step is reached."""
         with tempfile.TemporaryDirectory() as temp_dir:
diff --git a/tests/utils/test_oom_gpu.py b/tests/utils/test_oom_gpu.py
index 0ab67ce19d..850199d762 100644
--- a/tests/utils/test_oom_gpu.py
+++ b/tests/utils/test_oom_gpu.py
@@ -16,15 +16,10 @@
 from torchtnt.utils.oom import log_memory_snapshot
 
 from torchtnt.utils.test_utils import skip_if_not_gpu
-from torchtnt.utils.version import is_torch_version_geq_2_0
 
 
 class OomGPUTest(unittest.TestCase):
     @skip_if_not_gpu
-    @unittest.skipUnless(
-        condition=bool(is_torch_version_geq_2_0()),
-        reason="This test needs changes from PyTorch 2.0 to run.",
-    )
     def test_log_memory_snapshot(self) -> None:
         with tempfile.TemporaryDirectory() as temp_dir:
             # Record history
diff --git a/tests/utils/test_prepare_module.py b/tests/utils/test_prepare_module.py
index 2924d82f8e..65543feebf 100644
--- a/tests/utils/test_prepare_module.py
+++ b/tests/utils/test_prepare_module.py
@@ -22,12 +22,7 @@
     TorchCompileParams,
 )
 from torchtnt.utils.test_utils import skip_if_not_distributed
-from torchtnt.utils.version import is_torch_version_geq_1_13, Version
-
-COMPILE_AVAIL = False
-if is_torch_version_geq_1_13():
-    COMPILE_AVAIL = True
-    import torch._dynamo
+from torchtnt.utils.version import Version
 
 
 class PrepareModelTest(unittest.TestCase):
@@ -170,10 +165,6 @@ def _test_prepare_module_ddp_throws_with_compile_params_and_static_graph() -> No
             torch_compile_params=TorchCompileParams(backend="inductor"),
         )
 
-    @unittest.skipUnless(
-        condition=COMPILE_AVAIL,
-        reason="This test needs PyTorch 1.13 or greater to run.",
-    )
     def test_prepare_module_compile_invalid_backend(self) -> None:
         """
         verify error is thrown on invalid backend
@@ -199,10 +190,6 @@ def test_prepare_module_incompatible_FSDP_torchcompile_params(self) -> None:
                 torch_compile_params=TorchCompileParams(),
             )
 
-    @unittest.skipUnless(
-        condition=COMPILE_AVAIL,
-        reason="This test needs PyTorch 1.13 or greater to run.",
-    )
     def test_prepare_module_compile_module_state_dict(self) -> None:
         device = init_from_env()
         my_module = torch.nn.Linear(2, 2, device=device)
diff --git a/tests/utils/test_prepare_module_gpu.py b/tests/utils/test_prepare_module_gpu.py
index 7668341c2e..d583a7085d 100644
--- a/tests/utils/test_prepare_module_gpu.py
+++ b/tests/utils/test_prepare_module_gpu.py
@@ -7,9 +7,10 @@
 
 # pyre-strict
 import unittest
-from unittest.mock import patch
 
 import torch
+
+from torch.distributed._composable import fully_shard
 from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
 from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
 from torch.nn.parallel import DistributedDataParallel as DDP
@@ -24,15 +25,6 @@
     prepare_module,
 )
 from torchtnt.utils.test_utils import skip_if_not_distributed, skip_if_not_gpu
-from torchtnt.utils.version import is_torch_version_geq_1_13, is_torch_version_geq_2_0
-
-COMPILE_AVAIL = False
-if is_torch_version_geq_1_13():
-    COMPILE_AVAIL = True
-    import torch._dynamo
-
-if is_torch_version_geq_2_0():
-    from torch.distributed._composable import fully_shard
 
 
 class PrepareModelGPUTest(unittest.TestCase):
@@ -81,33 +73,6 @@ def _test_prepare_fsdp() -> None:
         tc = unittest.TestCase()
         tc.assertTrue(isinstance(fsdp_module, FSDP))
 
-    @skip_if_not_distributed
-    @skip_if_not_gpu
-    def test_fsdp_pytorch_version(self) -> None:
-        """
-        Test that a RuntimeError is thrown when using FSDP, and PyTorch < v1.12
-        """
-        spawn_multi_process(
-            2,
-            "nccl",
-            self._test_fsdp_pytorch_version,
-        )
-
-    @staticmethod
-    def _test_fsdp_pytorch_version() -> None:
-        device = init_from_env()
-        module = torch.nn.Linear(2, 2).to(device)
-
-        tc = unittest.TestCase()
-        with patch(
-            "torchtnt.utils.prepare_module.is_torch_version_geq_1_12",
-            return_value=False,
-        ), tc.assertRaisesRegex(
-            RuntimeError,
-            "Please install PyTorch 1.12 or higher to use FSDP: https://pytorch.org/get-started/locally/",
-        ):
-            _ = prepare_fsdp(module, device, FSDPStrategy())
-
     @skip_if_not_distributed
     @unittest.skipUnless(
         condition=bool(torch.cuda.device_count() >= 2),
@@ -128,9 +93,8 @@ def _test_is_fsdp_module() -> None:
         model = FSDP(torch.nn.Linear(1, 1, device=device))
         assert _is_fsdp_module(model)
         model = torch.nn.Linear(1, 1, device=device)
-        if is_torch_version_geq_2_0():
-            fully_shard(model)
-            assert _is_fsdp_module(model)
+        fully_shard(model)
+        assert _is_fsdp_module(model)
 
     @skip_if_not_distributed
     @skip_if_not_gpu
diff --git a/tests/utils/test_version.py b/tests/utils/test_version.py
index 0fda7c916b..c30b6d3070 100644
--- a/tests/utils/test_version.py
+++ b/tests/utils/test_version.py
@@ -48,48 +48,5 @@ def test_get_torch_version(self) -> None:
             self.assertEqual(version.get_torch_version(), Version("1.12.0"))
 
     def test_torch_version_comparators(self) -> None:
-        with patch.object(torch, "__version__", "1.7.0"):
-            self.assertFalse(version.is_torch_version_geq_1_8())
-            self.assertFalse(version.is_torch_version_geq_1_9())
-            self.assertFalse(version.is_torch_version_geq_1_10())
-            self.assertFalse(version.is_torch_version_geq_1_11())
-            self.assertFalse(version.is_torch_version_geq_1_12())
-
-        with patch.object(torch, "__version__", "1.8.0"):
-            self.assertTrue(version.is_torch_version_geq_1_8())
-            self.assertFalse(version.is_torch_version_geq_1_9())
-            self.assertFalse(version.is_torch_version_geq_1_10())
-            self.assertFalse(version.is_torch_version_geq_1_11())
-            self.assertFalse(version.is_torch_version_geq_1_12())
-
-        with patch.object(torch, "__version__", "1.9.0"):
-            self.assertTrue(version.is_torch_version_geq_1_8())
-            self.assertTrue(version.is_torch_version_geq_1_9())
-            self.assertFalse(version.is_torch_version_geq_1_10())
-            self.assertFalse(version.is_torch_version_geq_1_11())
-            self.assertFalse(version.is_torch_version_geq_1_12())
-
-        with patch.object(torch, "__version__", "1.10.0"):
-            self.assertTrue(version.is_torch_version_geq_1_8())
-            self.assertTrue(version.is_torch_version_geq_1_9())
-            self.assertTrue(version.is_torch_version_geq_1_10())
-            self.assertFalse(version.is_torch_version_geq_1_11())
-            self.assertFalse(version.is_torch_version_geq_1_12())
-
-        with patch.object(torch, "__version__", "1.11.0"):
-            self.assertTrue(version.is_torch_version_geq_1_8())
-            self.assertTrue(version.is_torch_version_geq_1_9())
-            self.assertTrue(version.is_torch_version_geq_1_10())
-            self.assertTrue(version.is_torch_version_geq_1_11())
-            self.assertFalse(version.is_torch_version_geq_1_12())
-
-        with patch.object(torch, "__version__", "1.12.0"):
-            self.assertTrue(version.is_torch_version_geq_1_8())
-            self.assertTrue(version.is_torch_version_geq_1_9())
-            self.assertTrue(version.is_torch_version_geq_1_10())
-            self.assertTrue(version.is_torch_version_geq_1_11())
-            self.assertTrue(version.is_torch_version_geq_1_12())
-
         with patch.object(torch, "__version__", "2.0.0a0"):
-            self.assertTrue(version.is_torch_version_ge_1_13_1())
-            self.assertFalse(version.is_torch_version_geq_2_0())
+            self.assertFalse(version.is_torch_version_geq_2_1())
diff --git a/torchtnt/framework/auto_unit.py b/torchtnt/framework/auto_unit.py
index 24c8507677..dd061a01de 100644
--- a/torchtnt/framework/auto_unit.py
+++ b/torchtnt/framework/auto_unit.py
@@ -50,7 +50,6 @@
     TorchCompileParams,
 )
 from torchtnt.utils.swa import AveragedModel
-from torchtnt.utils.version import is_torch_version_ge_1_13_1
 from typing_extensions import Literal
 
 
@@ -166,8 +165,6 @@ def __init__(
         torch_compile_params: Optional[TorchCompileParams] = None,
     ) -> None:
         super().__init__()
-        if torch_compile_params:
-            _validate_torch_compile_available()
 
         self.device: torch.device = device or init_from_env()
         self.precision: Optional[torch.dtype] = (
@@ -879,11 +876,3 @@ def _update_lr_and_swa(self, state: State, number_of_steps_or_epochs: int) -> No
                     state, f"{self.__class__.__name__}.lr_scheduler_step"
                 ):
                     self.step_lr_scheduler()
-
-
-def _validate_torch_compile_available() -> None:
-    if not is_torch_version_ge_1_13_1():
-        raise RuntimeError(
-            "Torch compile support is available only in PyTorch 2.0 or higher. "
-            "Please install PyTorch 2.0 or higher to continue: https://pytorch.org/get-started/locally/"
-        )
diff --git a/torchtnt/utils/__init__.py b/torchtnt/utils/__init__.py
index 00d6f74f5a..c1d13d28ef 100644
--- a/torchtnt/utils/__init__.py
+++ b/torchtnt/utils/__init__.py
@@ -74,15 +74,6 @@
 from .version import (
     get_python_version,
     get_torch_version,
-    is_torch_version_ge_1_13_1,
-    is_torch_version_geq_1_10,
-    is_torch_version_geq_1_11,
-    is_torch_version_geq_1_12,
-    is_torch_version_geq_1_13,
-    is_torch_version_geq_1_14,
-    is_torch_version_geq_1_8,
-    is_torch_version_geq_1_9,
-    is_torch_version_geq_2_0,
     is_torch_version_geq_2_1,
     is_windows,
 )
@@ -153,15 +144,6 @@
     "TLRScheduler",
     "get_python_version",
     "get_torch_version",
-    "is_torch_version_ge_1_13_1",
-    "is_torch_version_geq_1_10",
-    "is_torch_version_geq_1_11",
-    "is_torch_version_geq_1_12",
-    "is_torch_version_geq_1_13",
-    "is_torch_version_geq_1_14",
-    "is_torch_version_geq_1_8",
-    "is_torch_version_geq_1_9",
-    "is_torch_version_geq_2_0",
     "is_torch_version_geq_2_1",
     "is_windows",
     "get_pet_launch_config",
diff --git a/torchtnt/utils/device.py b/torchtnt/utils/device.py
index cc9f16d0ef..5f80cd2989 100644
--- a/torchtnt/utils/device.py
+++ b/torchtnt/utils/device.py
@@ -16,7 +16,6 @@
 from typing import Any, Dict, Mapping, TypeVar
 
 import torch
-from torchtnt.utils.version import is_torch_version_geq_1_12
 from typing_extensions import Protocol, runtime_checkable, TypedDict
 
 logger: logging.Logger = logging.getLogger(__name__)
@@ -42,11 +41,7 @@ def get_device_from_env() -> torch.device:
             )
         device = torch.device(f"cuda:{local_rank}")
         torch.cuda.set_device(device)
-    elif (
-        is_torch_version_geq_1_12()
-        and torch.backends.mps.is_built()
-        and torch.backends.mps.is_available()
-    ):
+    elif torch.backends.mps.is_built() and torch.backends.mps.is_available():
         device = torch.device("mps")
     else:
         device = torch.device("cpu")
@@ -340,10 +335,7 @@ def set_float32_precision(precision: str = "high") -> None:
     Args:
         precision: The setting to determine which datatypes to use for matrix multiplication and convolution operations.
     """
-    if not (
-        torch.cuda.is_available()  # Not relevant for non-CUDA devices
-        and is_torch_version_geq_1_12()  # API exposed from PyTorch 1.12 onward
-    ):
+    if not (torch.cuda.is_available()):  # Not relevant for non-CUDA devices
         return
     # set precision for matrix multiplications
     torch.set_float32_matmul_precision(precision)
diff --git a/torchtnt/utils/memory_snapshot_profiler.py b/torchtnt/utils/memory_snapshot_profiler.py
index 09d2076fd6..a94890c390 100644
--- a/torchtnt/utils/memory_snapshot_profiler.py
+++ b/torchtnt/utils/memory_snapshot_profiler.py
@@ -14,7 +14,6 @@
 
 import torch
 from torchtnt.utils.oom import attach_oom_observer, log_memory_snapshot
-from torchtnt.utils.version import is_torch_version_geq_2_0
 
 logger: logging.Logger = logging.getLogger(__name__)
 
@@ -133,8 +132,6 @@ def __init__(
         self.step_num: int = 0
         self.is_started: bool = False
 
-        if not is_torch_version_geq_2_0():
-            raise RuntimeError("CUDA memory snapshot requires torch>=2.0")
         if self.params.enable_oom_observer:
             attach_oom_observer(
                 output_dir=output_dir, trace_max_entries=self.params.max_entries
diff --git a/torchtnt/utils/module_summary.py b/torchtnt/utils/module_summary.py
index eacdbe8bbb..84d937a694 100644
--- a/torchtnt/utils/module_summary.py
+++ b/torchtnt/utils/module_summary.py
@@ -30,8 +30,8 @@
 from torch.nn.parameter import UninitializedParameter
 from torch.utils._pytree import PyTree, tree_flatten
 from torch.utils.hooks import RemovableHandle
+from torchtnt.utils.flops import FlopTensorDispatchMode
 
-from torchtnt.utils.version import is_torch_version_geq_1_13
 from typing_extensions import Literal
 
 _ATTRIB_TO_COL_HEADER = {
@@ -244,35 +244,25 @@ def _get_module_flops_and_activation_sizes(
     module_kwargs = module_kwargs or {}
     flops_forward = None
     flops_backward = None
-    if not is_torch_version_geq_1_13():
-        warnings.warn(
-            "Please install PyTorch 1.13 or higher to compute FLOPs: https://pytorch.org/get-started/locally/"
-        )
-        module(*module_args, **module_kwargs)
+
+    with FlopTensorDispatchMode(module) as ftdm:
+        # Count for forward flops (+ compute activation sizes)
+        res = module(*module_args, **module_kwargs)
+
         # detach activation size hook handles
         for hook_handle in activation_size_handles:
             hook_handle.remove()
-    else:
-        from torchtnt.utils.flops import FlopTensorDispatchMode
-
-        with FlopTensorDispatchMode(module) as ftdm:
-            # Count for forward flops (+ compute activation sizes)
-            res = module(*module_args, **module_kwargs)
-
-            # detach activation size hook handles
-            for hook_handle in activation_size_handles:
-                hook_handle.remove()
-
-            flops_forward = copy.deepcopy(ftdm.flop_counts)
-            if isinstance(res, torch.Tensor):
-                # Count for backward flops
-                ftdm.reset()
-                res.mean().backward()
-                flops_backward = copy.deepcopy(ftdm.flop_counts)
-            else:
-                warnings.warn(
-                    "Backward FLOPs are only computed if module foward returns a tensor."
-                )
+
+        flops_forward = copy.deepcopy(ftdm.flop_counts)
+        if isinstance(res, torch.Tensor):
+            # Count for backward flops
+            ftdm.reset()
+            res.mean().backward()
+            flops_backward = copy.deepcopy(ftdm.flop_counts)
+        else:
+            warnings.warn(
+                "Backward FLOPs are only computed if module foward returns a tensor."
+            )
 
     # remove forward time elapsed handles
     for hook_handle in forward_elapsed_time_handles:
diff --git a/torchtnt/utils/oom.py b/torchtnt/utils/oom.py
index 3a47c61933..9e65a0bed8 100644
--- a/torchtnt/utils/oom.py
+++ b/torchtnt/utils/oom.py
@@ -15,7 +15,6 @@
 import torch
 from torchtnt.utils.distributed import get_global_rank
 from torchtnt.utils.fsspec import get_filesystem
-from torchtnt.utils.version import is_torch_version_geq_2_0
 
 logger: logging.Logger = logging.getLogger(__name__)
 
@@ -90,11 +89,6 @@ def log_memory_snapshot(output_dir: str, file_prefix: Optional[str] = None) -> N
     if not torch.cuda.is_available():
         logger.info("CUDA unavailable. Not logging snapshot")
         return
-    if not is_torch_version_geq_2_0():
-        logger.warning(
-            "CUDA memory snapshot utilities are unavailable. Not logging snapshot"
-        )
-        return
 
     rank = get_global_rank()
     if file_prefix is None:
@@ -134,11 +128,6 @@ def attach_oom_observer(output_dir: str, trace_max_entries: int = 1000000) -> No
     if not torch.cuda.is_available():
         logger.info("CUDA unavailable. Not attaching OOM observer.")
         return
-    if not is_torch_version_geq_2_0():
-        logger.warning(
-            "CUDA memory snapshot utilities are unavailable. Not attaching OOM observer."
-        )
-        return
 
     torch.cuda.memory._record_memory_history(
         enabled="all", max_entries=trace_max_entries
diff --git a/torchtnt/utils/prepare_module.py b/torchtnt/utils/prepare_module.py
index 8f5f1cf529..702bd05e1b 100644
--- a/torchtnt/utils/prepare_module.py
+++ b/torchtnt/utils/prepare_module.py
@@ -13,10 +13,18 @@
 import torch
 import torch.distributed as dist
 from torch.distributed import ProcessGroup
+
+from torch.distributed._composable_state import _get_module_state
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    apply_activation_checkpointing,
+    checkpoint_wrapper,
+    CheckpointImpl,
+)
 from torch.distributed.fsdp import (
     FullyShardedDataParallel as FSDP,
     StateDictType as _StateDictType,
 )
+from torch.distributed.fsdp._common_utils import _FSDPState
 from torch.distributed.fsdp.api import OptimStateDictConfig, StateDictConfig
 from torch.distributed.fsdp.fully_sharded_data_parallel import (
     BackwardPrefetch as _BackwardPrefetch,
@@ -33,20 +41,7 @@
 )
 
 from torchtnt.utils.rank_zero_log import rank_zero_warn
-from torchtnt.utils.version import (
-    is_torch_version_geq_1_12,
-    is_torch_version_geq_2_0,
-    is_torch_version_geq_2_1,
-)
-
-if is_torch_version_geq_2_0():
-    from torch.distributed._composable_state import _get_module_state
-    from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
-        apply_activation_checkpointing,
-        checkpoint_wrapper,
-        CheckpointImpl,
-    )
-    from torch.distributed.fsdp._common_utils import _FSDPState
+from torchtnt.utils.version import is_torch_version_geq_2_1
 
 
 @dataclass
@@ -237,10 +232,6 @@ def prepare_fsdp(
         device = torch.device("cuda")
         fsdp_module = prepare_fsdp(module, device, strategy)
     """
-    if not is_torch_version_geq_1_12():
-        raise RuntimeError(
-            "Please install PyTorch 1.12 or higher to use FSDP: https://pytorch.org/get-started/locally/"
-        )
     strategy = strategy if strategy is not None else FSDPStrategy()
 
     # we use __dict__ and not asdict() here because asdict() is recursively applied on nested objects
@@ -291,11 +282,10 @@ def _is_fsdp_module(module: torch.nn.Module) -> bool:
     if isinstance(module, FSDP):
         return True
 
-    if is_torch_version_geq_2_0():
-        # Also check for composable FSDP API
-        maybe_composable_state = _get_module_state(module)
-        if maybe_composable_state is not None:
-            return isinstance(maybe_composable_state, _FSDPState)
+    # Also check for composable FSDP API
+    maybe_composable_state = _get_module_state(module)
+    if maybe_composable_state is not None:
+        return isinstance(maybe_composable_state, _FSDPState)
 
     return False
 
@@ -359,8 +349,6 @@ def prepare_module(
         module = module.to(device)
 
     if activation_checkpoint_params:
-        if not is_torch_version_geq_2_0():
-            raise RuntimeError("Activation checkpointing requires torch>=2.0")
         checkpoint_impl = activation_checkpoint_params.checkpoint_impl
         check_fn = activation_checkpoint_params.check_fn
         auto_wrap_policy = activation_checkpoint_params.auto_wrap_policy
diff --git a/torchtnt/utils/version.py b/torchtnt/utils/version.py
index 4e56cb8abc..c140361826 100644
--- a/torchtnt/utils/version.py
+++ b/torchtnt/utils/version.py
@@ -56,41 +56,5 @@ def get_torch_version() -> Version:
     return pkg_version
 
 
-def is_torch_version_geq_1_8() -> bool:
-    return get_torch_version() >= Version("1.8.0")
-
-
-def is_torch_version_geq_1_9() -> bool:
-    return get_torch_version() >= Version("1.9.0")
-
-
-def is_torch_version_geq_1_10() -> bool:
-    return get_torch_version() >= Version("1.10.0")
-
-
-def is_torch_version_geq_1_11() -> bool:
-    return get_torch_version() >= Version("1.11.0")
-
-
-def is_torch_version_geq_1_12() -> bool:
-    return get_torch_version() >= Version("1.12.0")
-
-
-def is_torch_version_geq_1_13() -> bool:
-    return get_torch_version() >= Version("1.13.0")
-
-
-def is_torch_version_ge_1_13_1() -> bool:
-    return get_torch_version() > Version("1.13.1")
-
-
-def is_torch_version_geq_1_14() -> bool:
-    return get_torch_version() >= Version("1.14.0")
-
-
-def is_torch_version_geq_2_0() -> bool:
-    return get_torch_version() >= Version("2.0.0")
-
-
 def is_torch_version_geq_2_1() -> bool:
     return get_torch_version() >= Version("2.1.0")