Add support for JSON encoding torch.tensor to keep it consistent with sagemaker-inference-toolkit

Sagemaker base toolkit: https://github.com/aws/sagemaker-inference-toolkit/blob/e602335fd9a4db08216d1f58ded2861cccb64f7d/src/sagemaker_inference/encoder.py#L25_L44
HF inference toolkit: https://github.com/aws/sagemaker-huggingface-inference-toolkit/blob/27275f40a2bbff85bb507646e6a3ef866d0599af/src/sagemaker_huggingface_inference_toolkit/decoder_encoder.py#L80_L114

Author: maaquib

src/sagemaker_huggingface_inference_toolkit/decoder_encoder.py

@@ -87,7 +87,7 @@ def default(self, obj):
             return int(obj)
         elif isinstance(obj, np.floating):
             return float(obj)
-        elif isinstance(obj, np.ndarray):
+        elif isinstance(obj, np.ndarray) or hasattr(obj, "tolist"):
             return obj.tolist()
         elif isinstance(obj, datetime.datetime):
             return obj.__str__()

tests/unit/test_decoder_encoder.py

@@ -19,6 +19,7 @@
 from mms.service import PredictionException
 from PIL import Image
 from sagemaker_huggingface_inference_toolkit import decoder_encoder
+from transformers.testing_utils import require_torch
 
 
 ENCODE_JSON_INPUT = {"upper": [1425], "lower": [576], "level": [2], "datetime": ["2012-08-08 15:30"]}
@@ -83,6 +84,12 @@ def test_encode_json():
     encoded_data = decoder_encoder.encode_json(ENCODE_JSON_INPUT)
     assert json.loads(encoded_data) == ENCODE_JSON_INPUT
 
+@require_torch
+def test_encode_json_torch():
+    import torch
+    DATA=[1, 0.5, 5.0]
+    encoded_data = decoder_encoder.encode_json({"data": torch.tensor(DATA)})
+    assert json.loads(encoded_data) == {"data": DATA}
 
 def test_encode_csv():
     decoded_data = decoder_encoder.encode_csv(ENCODE_CSV_INPUT)