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