&`i ddlZddlmZmZmZerddlZddlZGddeZ ej de ddd e d ee d fd e d df dZ ddd eeeed fffdZdS)N) TYPE_CHECKINGAnyTupleceZdZdZdS)ZeroCopyTensorsWarningzV Warning for unsafe or failed zero-copy tensor serialization/deserialization. N)__name__ __module__ __qualname____doc__{/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/_private/tensor_serialization_utils.pyrr s Dr ronce)categorynp_arrayz np.ndarray dtype_strshape. device_strreturnz torch.Tensorc  ddl}n"#t$r}td|d}~wwxYw |j|}|dd}t ||st d|t ||}|jg|} |j | zdkrt d|j d | d || | |} | | S#t$r8}dd lm} | d |d|d|dt!|jd| |d}~wwxYw)a Reconstructs a torch.Tensor from a zero-copy NumPy byte array. Args: np_array: 1D uint8 NumPy array of the original tensor's raw bytes. dtype_str: Full string representation of the original tensor's dtype (e.g., 'torch.float32'). shape: The original shape of the tensor before serialization. device_str: String representation of the original device (e.g., 'cpu', 'cuda:0'). Returns: Reconstructed torch.Tensor on the specified device if successful; otherwise, returns the input np_array unchanged and issues a warning. Raises: ImportError/DeserializationError: If deserialization fails for any reason (e.g., missing PyTorch dtype mismatch, shape inconsistency, device error, etc.). rNzBZero-copy tensor deserialization failed: PyTorch is not installed..z%Invalid or unsupported dtype string: )dtypezByte array size (z") is not divisible by dtype size (z ) for dtype )device)DeserializationErrorzDFailed to deserialize zero-copy tensor from byte array. Input dtype=z, shape=z , device=z. Underlying error: z: )torch ImportError from_numpysplithasattr ValueErrorgetattrtensor element_sizesizeviewreshapeto Exceptionray._private.serializationrtyper) rrrrre uint8_tensor dtype_nameoriginal_dtype dtype_sizerestored_tensorrs r_zero_copy_tensors_deserializerr2s(  P    'u'11 __S))"- uj)) RPYPPQQ Q  33"U\"N;;;HHJJ =: % * *CHMCC)CC7@CC  '++N;;CCEJJ!!!444 CCCCCC"" 9$ 9 9.3 9 9>H 9 9!%a!1 9 956 9 9    s( &!&C)D E3EEr#c"tjdtdddl}|}|s0tjdtd|}|d}| |j }| }t|t|jt|jt|jffS)uPickle serializer for zero-copy serialization of read-only torch.Tensor. This serializer aims to avoid copying tensor data by using a NumPy uint8 view, which enables pickle5's out-of-band buffer transmission. However, true zero-copy is only possible when the input tensor is already: - On CPU, - Detached from the computation graph (no gradients), - Contiguous in memory. If the input tensor does **not** meet these conditions, this function will: - Call `.detach()` to remove gradient information, - Move the tensor to CPU (copying data if it's on GPU or another device), - Make the tensor contiguous (copying data if it's non-contiguous). These operations may incur one or two full copies of the tensor data, negating zero-copy benefits. A warning is issued in such cases. Args: tensor: The input torch.Tensor to serialize. Can be on any device, with or without gradients, contiguous or not — but zero-copy is only achieved if it is already CPU, detached, and contiguous. Returns: A tuple (deserializer_callable, args_tuple) suitable for pickle. zZero-copy tensor serialization is enabled, but it only works safely for read-only tensors (detached, no gradients, contiguous). Modifiable or non-contiguous tensors may cause data corruption.) stacklevelrNzThe input tensor is non-contiguous. A copy will be made to ensure contiguity. For zero-copy serialization, please ensure the tensor is contiguous before passing it (e.g., by calling `.contiguous()`).r)warningswarnrrdetachcpu is_contiguous contiguousr'r&uint8numpyr2strrtuplerr)r#r cpu_tensor flat_tensor uint8_viewrs rzero_copy_tensors_reducerrCQs8 M p LLL$$&&J  # # % %-  2 #      **,, $$R((K!!%+..J!!H * FL fl FM - r )r6typingrrrr=npr UserWarningrfilterwarningsr>intr2rCr r rrIs,,,,,,,,,,LLL     [   )?@@@@::'*:38c?:PS:::::z@n@sE#s(O?S9T@@@@@@r