diff --git a/src/sparseml/compression/helpers.py b/src/sparseml/compression/helpers.py
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+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import List, Optional
+
+import torch
+from tqdm import tqdm
+
+from sparseml.pytorch.utils import get_linear_layers
+
+
+__ALL__ = [
+    "tensor_follows_mask_structure",
+    "infer_sparsity_structure_from_stage_modifiers",
+    "infer_sparsity_structure_from_model",
+]
+
+
+def tensor_follows_mask_structure(tensor, mask: str = "2:4") -> bool:
+    """
+    :param tensor: tensor to check
+    :param mask: mask structure to check for, in the format "n:m"
+    :return: True if the tensor follows the mask structure, False otherwise.
+        Note, some weights can incidentally be zero, so we check for
+        atleast n zeros in each chunk of size m
+    """
+
+    n, m = tuple(map(int, mask.split(":")))
+    # Reshape the tensor into chunks of size m
+    tensor = tensor.view(-1, m)
+
+    # Count the number of zeros in each chunk
+    zero_counts = (tensor == 0).sum(dim=1)
+
+    # Check if the number of zeros in each chunk atleast n
+    # Greater than sign is needed as some weights can incidentally
+    # be zero
+    return torch.all(zero_counts >= n).item()
+
+
+def infer_sparsity_structure_from_stage_modifiers(
+    stage_modifiers: List["StageModifier"],  # noqa E501
+) -> Optional[str]:
+    """
+    Determines the sparsity structure, if any exists, given the
+    list of stage modifiers
+
+    :param stage_modifiers: non-empty list of stage modifiers
+    :return: sparsity structure as a string or None
+    """
+    for stage in stage_modifiers:
+        if stage.applied:
+            for modifier in stage.modifiers:
+                if hasattr(modifier, "mask_structure"):
+                    sparsity_structure = modifier.mask_structure
+                    return sparsity_structure
+    return None
+
+
+def infer_sparsity_structure_from_model(model: torch.nn.Module) -> Optional[str]:
+    """
+    Determines the sparsity structure, if any exists, given the model
+
+    :param model: model to check for sparsity structure
+    :return: sparsity structure as a string or None
+    """
+
+    # check for the common sparsity structures
+    structures = {"2:4"}
+    for sparsity_structure in structures:
+        linear_modules = get_linear_layers(model)
+        linear_modules_with_sparsity_structure = [
+            tensor_follows_mask_structure(layer.weight)
+            for layer in tqdm(
+                linear_modules.values(),
+                desc="Checking whether model follows "
+                f"{sparsity_structure} sparsity structure",
+            )
+        ]
+        # if the majority of the linear modules follow the sparsity structure
+        # we can assume that the model follows the sparsity structure
+        # (taking into consideration the fact that some Linear layers like the
+        # embedding layer might not be sparse)
+        if (
+            sum(linear_modules_with_sparsity_structure)
+            > len(linear_modules_with_sparsity_structure) * 0.8
+        ):
+            return sparsity_structure
+
+    return None