Sync from v0.13

tests/utils_/test_serial_utils.py

@@ -0,0 +1,40 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+
+from tests.models.utils import check_embeddings_close
+from vllm.utils.serial_utils import (
+    EMBED_DTYPE_TO_TORCH_DTYPE,
+    ENDIANNESS,
+    binary2tensor,
+    tensor2binary,
+)
+
+
+@pytest.mark.parametrize("endianness", ENDIANNESS)
+@pytest.mark.parametrize("embed_dtype", EMBED_DTYPE_TO_TORCH_DTYPE.keys())
+@torch.inference_mode()
+def test_encode_and_decode(embed_dtype: str, endianness: str):
+    for i in range(10):
+        tensor = torch.rand(2, 3, 5, 7, 11, 13, device="cpu", dtype=torch.float32)
+        shape = tensor.shape
+        binary = tensor2binary(tensor, embed_dtype, endianness)
+        new_tensor = binary2tensor(binary, shape, embed_dtype, endianness).to(
+            torch.float32
+        )
+
+        if embed_dtype in ["float32", "float16"]:
+            torch.testing.assert_close(tensor, new_tensor, atol=0.001, rtol=0.001)
+        elif embed_dtype == "bfloat16":
+            torch.testing.assert_close(tensor, new_tensor, atol=0.01, rtol=0.01)
+        else:  # for fp8
+            torch.testing.assert_close(tensor, new_tensor, atol=0.1, rtol=0.1)
+
+        check_embeddings_close(
+            embeddings_0_lst=tensor.view(1, -1),
+            embeddings_1_lst=new_tensor.view(1, -1),
+            name_0="gt",
+            name_1="new",
+            tol=1e-2,
+        )