Add model loader arg to generate_layers_metrics

aiu_fms_testing_utils/utils/metrics_utils.py

@@ -3,6 +3,44 @@
 import torch.nn as nn
 
 
+def get_model_prefix(
+    model_path,
+    shapes_size,
+    max_new_tokens: None,
+    batch_size: None,
+    seq_length: None,
+    dtype: None,
+    include_shapes: False,
+):
+    """
+    Generate a prefix for a model based on its path and other parameters.
+
+    Args:
+        model_path (str): The path to the model file.
+        shapes_size (int): The size of the shapes array to use in the model.
+        max_new_tokens (int): The maximum number of new tokens to use in the model.
+        batch_size (int): The batch size to use in the model.
+        seq_length (int): The sequence length to use in the model.
+        dtype (str): The data type to use in the model.
+        include_shapes (bool): Include or not the shapes to the prefix.
+    Returns:
+        str: A prefix for the model based on its path and other parameters.
+    """
+    if model_path.count("/") > 1:
+        # this means that the model_path does NOT match to the hf pattern
+        # Eg.: /home/another-dir/another/ibm-granite/granite-3.3-8b-base
+        model_prefix = model_path.split("/")[-2] + "--" + model_path.split("/")[-1]
+    else:
+        # this means that the model_path does match to the hf pattern
+        # Eg.: ibm-granite/granite-3.3-8b-base
+        model_prefix = model_path.replace("/", "--")
+
+    if shapes_size > 1 or include_shapes:
+        model_prefix = f"{model_prefix}_max-new-tokens-{max_new_tokens}_batch-size-{batch_size}_seq-length-{seq_length}_dtype-{dtype}"
+
+    return model_prefix
+
+
 def abs_diff_linalg_norm(res_vector):
     """
     Calculates the Euclidean norm (also known as the L2 norm) of a given array res_vector. This is equivalent to finding the square