Struct TensorBlock 

pub struct TensorBlock { /* private fields */ }

A content-addressed tensor block

Combines a regular Block with tensor-specific metadata like shape and dtype. This allows storing neural network weights, activations, and gradients in a content-addressed manner.

Implementations

impl TensorBlock

pub fn new(data: Bytes, shape: TensorShape, dtype: TensorDtype) -> Result<Self>

Create a new tensor block

Arguments

• data - Raw tensor data (should be in native endian format)
• shape - Tensor shape
• dtype - Data type

Errors

Returns an error if:

• Data size doesn’t match shape * dtype size
• Block creation fails

Example

use ipfrs_core::tensor::{TensorBlock, TensorDtype, TensorShape};
use bytes::Bytes;

let shape = TensorShape::new(vec![2, 2]);
let data = Bytes::from(vec![1.0f32, 2.0, 3.0, 4.0]
    .iter()
    .flat_map(|f| f.to_le_bytes())
    .collect::<Vec<u8>>());

let tensor = TensorBlock::new(data, shape, TensorDtype::F32).unwrap();
assert_eq!(tensor.element_count(), 4);

pub fn with_metadata(data: Bytes, metadata: TensorMetadata) -> Result<Self>

Create a tensor block with metadata

pub fn block(&self) -> &Block

Get the underlying block

pub fn metadata(&self) -> &TensorMetadata

Get tensor metadata

pub fn shape(&self) -> &TensorShape

Get the tensor shape

pub fn dtype(&self) -> TensorDtype

Get the data type

pub fn element_count(&self) -> usize

Get the number of elements

pub fn cid(&self) -> &Cid

Get the CID of this tensor

pub fn data(&self) -> &Bytes

Get the raw tensor data

pub fn into_parts(self) -> (Block, TensorMetadata)

Consume and return the underlying block and metadata

pub fn verify(&self) -> Result<bool>

Verify the tensor block integrity

pub fn reshape(&self, new_shape: TensorShape) -> Result<Self>

Reshape the tensor to a new shape (must have same element count)

pub fn size_bytes(&self) -> usize

Get the size in bytes

pub fn is_scalar(&self) -> bool

Check if this is a scalar tensor (0-dimensional)

pub fn is_vector(&self) -> bool

Check if this is a vector (1-dimensional)

pub fn is_matrix(&self) -> bool

Check if this is a matrix (2-dimensional)

impl TensorBlock

Utility functions for creating tensors from typed data

pub fn from_f32_slice(data: &[f32], shape: TensorShape) -> Result<Self>

Create a tensor from a slice of f32 values

pub fn from_f64_slice(data: &[f64], shape: TensorShape) -> Result<Self>

Create a tensor from a slice of f64 values

pub fn from_i32_slice(data: &[i32], shape: TensorShape) -> Result<Self>

Create a tensor from a slice of i32 values

pub fn from_i64_slice(data: &[i64], shape: TensorShape) -> Result<Self>

Create a tensor from a slice of i64 values

pub fn from_u8_slice(data: &[u8], shape: TensorShape) -> Result<Self>

Create a tensor from a slice of u8 values

pub fn to_f32_vec(&self) -> Result<Vec<f32>>

Convert tensor data to a Vec of f32 values (if dtype is F32)

pub fn to_f64_vec(&self) -> Result<Vec<f64>>

Convert tensor data to a Vec of f64 values (if dtype is F64)

pub fn to_i32_vec(&self) -> Result<Vec<i32>>

Convert tensor data to a Vec of i32 values (if dtype is I32)

Trait Implementations

impl Clone for TensorBlock

fn clone(&self) -> TensorBlock

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for TensorBlock

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl TensorBlockArrowExt for TensorBlock

fn to_arrow_array(&self) -> Result<ArrayRef>

Convert to an Arrow array (zero-copy when possible)

fn to_arrow_field(&self, name: &str) -> Field

Convert to an Arrow Field (for schema)

fn to_arrow_schema(&self, field_name: &str) -> Schema

Convert to an Arrow Schema