[FEAT][load_model_torch]

swarms/utils/__init__.py

@@ -6,15 +6,16 @@
 from swarms.utils.pdf_to_text import pdf_to_text
 from swarms.utils.math_eval import math_eval
 from swarms.utils.llm_metrics_decorator import metrics_decorator
-
-# from swarms.utils.phoenix_handler import phoenix_trace_decorator
+from swarms.utils.device_checker_cuda import check_device
+from swarms.utils.load_model_torch import load_model_torch
 
 __all__ = [
     "display_markdown_message",
     "SubprocessCodeInterpreter",
     "extract_code_in_backticks_in_string",
     "pdf_to_text",
-    # "phoenix_trace_decorator",
     "math_eval",
     "metrics_decorator",
+    "check_device",
+    "load_model_torch",
 ]

swarms/utils/load_model_torch.py

@@ -0,0 +1,57 @@
+import torch
+from torch import nn
+
+
+def load_model_torch(
+    model_path: str = None,
+    device: torch.device = None,
+    model: nn.Module = None,
+    strict: bool = True,
+    map_location=None,
+    *args,
+    **kwargs,
+) -> nn.Module:
+    """
+    Load a PyTorch model from a given path and move it to the specified device.
+
+    Args:
+        model_path (str): Path to the saved model file.
+        device (torch.device): Device to move the model to.
+        model (nn.Module): The model architecture, if the model file only contains the state dictionary.
+        strict (bool): Whether to strictly enforce that the keys in the state dictionary match the keys returned by the model's `state_dict()` function.
+        map_location (callable): A function to remap the storage locations of the loaded model.
+        *args: Additional arguments to pass to `torch.load`.
+        **kwargs: Additional keyword arguments to pass to `torch.load`.
+
+    Returns:
+        nn.Module: The loaded model.
+
+    Raises:
+        FileNotFoundError: If the model file is not found.
+        RuntimeError: If there is an error while loading the model.
+    """
+    if device is None:
+        device = torch.device(
+            "cuda" if torch.cuda.is_available() else "cpu"
+        )
+
+    try:
+        if model is None:
+            model = torch.load(
+                model_path, map_location=map_location, *args, **kwargs
+            )
+        else:
+            model.load_state_dict(
+                torch.load(
+                    model_path,
+                    map_location=map_location,
+                    *args,
+                    **kwargs,
+                ),
+                strict=strict,
+            )
+        return model.to(device)
+    except FileNotFoundError:
+        raise FileNotFoundError(f"Model file not found: {model_path}")
+    except RuntimeError as e:
+        raise RuntimeError(f"Error loading model: {str(e)}")

tests/utils/load_models_torch.py

@@ -0,0 +1,56 @@
+import pytest
+import torch
+from unittest.mock import MagicMock
+from swarms.utils.load_model_torch import load_model_torch
+
+
+def test_load_model_torch_no_model_path():
+    with pytest.raises(FileNotFoundError):
+        load_model_torch()
+
+
+def test_load_model_torch_model_not_found(mocker):
+    mocker.patch("torch.load", side_effect=FileNotFoundError)
+    with pytest.raises(FileNotFoundError):
+        load_model_torch("non_existent_model_path")
+
+
+def test_load_model_torch_runtime_error(mocker):
+    mocker.patch("torch.load", side_effect=RuntimeError)
+    with pytest.raises(RuntimeError):
+        load_model_torch("model_path")
+
+
+def test_load_model_torch_no_device_specified(mocker):
+    mock_model = MagicMock(spec=torch.nn.Module)
+    mocker.patch("torch.load", return_value=mock_model)
+    mocker.patch("torch.cuda.is_available", return_value=False)
+    model = load_model_torch("model_path")
+    mock_model.to.assert_called_once_with(torch.device("cpu"))
+
+
+def test_load_model_torch_device_specified(mocker):
+    mock_model = MagicMock(spec=torch.nn.Module)
+    mocker.patch("torch.load", return_value=mock_model)
+    model = load_model_torch(
+        "model_path", device=torch.device("cuda")
+    )
+    mock_model.to.assert_called_once_with(torch.device("cuda"))
+
+
+def test_load_model_torch_model_specified(mocker):
+    mock_model = MagicMock(spec=torch.nn.Module)
+    mocker.patch("torch.load", return_value={"key": "value"})
+    load_model_torch("model_path", model=mock_model)
+    mock_model.load_state_dict.assert_called_once_with(
+        {"key": "value"}, strict=True
+    )
+
+
+def test_load_model_torch_model_specified_strict_false(mocker):
+    mock_model = MagicMock(spec=torch.nn.Module)
+    mocker.patch("torch.load", return_value={"key": "value"})
+    load_model_torch("model_path", model=mock_model, strict=False)
+    mock_model.load_state_dict.assert_called_once_with(
+        {"key": "value"}, strict=False
+    )