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ef89079712
Use bytearray to create a mutable copy of the binary data before passing to np.frombuffer. This ensures the numpy array is writable, avoiding UserWarning from torch.from_numpy on read-only arrays. The warning occurred because base64.b64decode returns immutable bytes, and np.frombuffer on immutable bytes returns a read-only array. When converted to a torch tensor via torch.from_numpy, PyTorch would emit: "UserWarning: The given buffer is not writable..." This fix maintains the efficient numpy-based conversion while ensuring compatibility with all embed_dtype formats (float32, float16, bfloat16, fp8_e4m3, fp8_e5m2) that numpy doesn't natively support. Fixes #26781 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> Signed-off-by: yurekami <yurekami@users.noreply.github.com>
80 lines
2.9 KiB
Python
80 lines
2.9 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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@pytest.mark.parametrize("embed_dtype", EMBED_DTYPE_TO_TORCH_DTYPE.keys())
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@torch.inference_mode()
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def test_binary2tensor_no_warning(embed_dtype: str):
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"""Test that binary2tensor does not emit UserWarning about non-writable buffers.
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This addresses issue #26781 where torch.frombuffer on non-writable bytes
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would emit: "UserWarning: The given buffer is not writable..."
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"""
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import warnings
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tensor = torch.rand(10, 20, device="cpu", dtype=torch.float32)
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binary = tensor2binary(tensor, embed_dtype, "native")
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# Capture warnings during binary2tensor call
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter("always")
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result = binary2tensor(binary, tensor.shape, embed_dtype, "native")
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# Filter for the specific UserWarning about non-writable buffers
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buffer_warnings = [
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warning
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for warning in w
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if issubclass(warning.category, UserWarning)
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and "not writable" in str(warning.message)
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]
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assert len(buffer_warnings) == 0, (
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f"Expected no warnings about non-writable buffers, got: {buffer_warnings}"
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)
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# Verify the result is correct
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result_float = result.to(torch.float32)
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if embed_dtype in ["float32", "float16"]:
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torch.testing.assert_close(tensor, result_float, atol=0.001, rtol=0.001)
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elif embed_dtype == "bfloat16":
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torch.testing.assert_close(tensor, result_float, atol=0.01, rtol=0.01)
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else: # fp8
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torch.testing.assert_close(tensor, result_float, atol=0.1, rtol=0.1)
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