107 lines
3.6 KiB
Python
107 lines
3.6 KiB
Python
from dataclasses import dataclass
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from typing import List, Tuple
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import torch
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@dataclass
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class FlattenedTensorMetadata:
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"""Metadata for a tensor in a flattened bucket"""
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name: str
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shape: torch.Size
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dtype: torch.dtype
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start_idx: int
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end_idx: int
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numel: int
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class FlattenedTensorBucket:
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"""
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A bucket that flattens multiple tensors into a single tensor for efficient processing
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while preserving all metadata needed for reconstruction.
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"""
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def __init__(
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self,
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named_tensors: List[Tuple[str, torch.Tensor]] = None,
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flattened_tensor: torch.Tensor = None,
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metadata: List[FlattenedTensorMetadata] = None,
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):
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"""
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Initialize a tensor bucket from a list of named tensors OR from pre-flattened data.
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Args:
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named_tensors: List of (name, tensor) tuples (for creating new bucket)
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flattened_tensor: Pre-flattened tensor (for reconstruction)
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metadata: Pre-computed metadata (for reconstruction)
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"""
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if named_tensors is not None:
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# Create bucket from named tensors
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self.metadata: List[FlattenedTensorMetadata] = [None] * len(named_tensors)
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self.flattened_tensor: torch.Tensor = None
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if not named_tensors:
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raise ValueError("Cannot create empty tensor bucket")
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# Collect metadata and flatten tensors
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current_idx = 0
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flattened_tensors: List[torch.Tensor] = [None] * len(named_tensors)
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for i, (name, tensor) in enumerate(named_tensors):
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flattened = tensor.flatten()
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flattened_tensors[i] = flattened
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# Store metadata
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numel = flattened.numel()
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metadata_obj = FlattenedTensorMetadata(
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name=name,
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shape=tensor.shape,
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dtype=tensor.dtype,
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start_idx=current_idx,
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end_idx=current_idx + numel,
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numel=numel,
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)
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self.metadata[i] = metadata_obj
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current_idx += numel
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# Concatenate all flattened tensors
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self.flattened_tensor = torch.cat(flattened_tensors, dim=0)
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else:
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# Initialize from pre-flattened data
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if flattened_tensor is None or metadata is None:
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raise ValueError(
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"Must provide either named_tensors or both flattened_tensor and metadata"
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)
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self.flattened_tensor = flattened_tensor
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self.metadata = metadata
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def get_flattened_tensor(self) -> torch.Tensor:
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"""Get the flattened tensor containing all bucket tensors"""
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return self.flattened_tensor
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def get_metadata(self) -> List[FlattenedTensorMetadata]:
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"""Get metadata for all tensors in the bucket"""
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return self.metadata
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def reconstruct_tensors(self) -> List[Tuple[str, torch.Tensor]]:
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"""
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Reconstruct original tensors from flattened tensor with optimized performance.
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Uses memory-efficient operations to minimize allocations and copies.
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"""
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# preallocate the result list
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reconstructed = [None] * len(self.metadata)
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for i, meta in enumerate(self.metadata):
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tensor = self.flattened_tensor[meta.start_idx : meta.end_idx].reshape(
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meta.shape
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)
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# batch dtype conversion (if needed)
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if tensor.dtype != meta.dtype:
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tensor = tensor.to(meta.dtype)
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reconstructed[i] = (meta.name, tensor)
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return reconstructed
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