burn_ndarray/
backend.rs

1use crate::element::{FloatNdArrayElement, IntNdArrayElement, QuantElement};
2use crate::{NdArrayQTensor, NdArrayTensor, NdArrayTensorFloat};
3use alloc::string::String;
4use burn_common::stub::Mutex;
5use burn_tensor::backend::{Backend, DeviceId, DeviceOps};
6use burn_tensor::ops::{BoolTensor, FloatTensor, IntTensor, QuantizedTensor};
7use burn_tensor::repr::{HandleKind, ReprBackend, TensorHandle};
8use core::marker::PhantomData;
9use rand::{rngs::StdRng, SeedableRng};
10
11pub(crate) static SEED: Mutex<Option<StdRng>> = Mutex::new(None);
12
13/// The device type for the ndarray backend.
14#[derive(Clone, Copy, Debug, PartialEq, Eq)]
15pub enum NdArrayDevice {
16    /// The CPU device.
17    Cpu,
18}
19
20impl DeviceOps for NdArrayDevice {
21    fn id(&self) -> burn_tensor::backend::DeviceId {
22        match self {
23            NdArrayDevice::Cpu => DeviceId::new(0, 0),
24        }
25    }
26}
27
28impl Default for NdArrayDevice {
29    fn default() -> Self {
30        Self::Cpu
31    }
32}
33
34/// Tensor backend that uses the [ndarray](ndarray) crate for executing tensor operations.
35///
36/// This backend is compatible with CPUs and can be compiled for almost any platform, including
37/// `wasm`, `arm`, and `x86`.
38#[derive(Clone, Copy, Default, Debug)]
39pub struct NdArray<E = f32, I = i64, Q = i8> {
40    _e: PhantomData<E>,
41    _i: PhantomData<I>,
42    _q: PhantomData<Q>,
43}
44
45impl<E: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement> Backend for NdArray<E, I, Q> {
46    type Device = NdArrayDevice;
47
48    type FloatTensorPrimitive = NdArrayTensorFloat;
49    type FloatElem = E;
50
51    type IntTensorPrimitive = NdArrayTensor<I>;
52    type IntElem = I;
53
54    type BoolTensorPrimitive = NdArrayTensor<bool>;
55    type BoolElem = bool;
56
57    type QuantizedTensorPrimitive = NdArrayQTensor<Q>;
58    type QuantizedEncoding = Q;
59
60    fn ad_enabled() -> bool {
61        false
62    }
63
64    fn name() -> String {
65        String::from("ndarray")
66    }
67
68    fn seed(seed: u64) {
69        let rng = StdRng::seed_from_u64(seed);
70        let mut seed = SEED.lock().unwrap();
71        *seed = Some(rng);
72    }
73}
74
75impl<E: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement> ReprBackend
76    for NdArray<E, I, Q>
77{
78    type Handle = HandleKind<Self>;
79
80    fn float_tensor(handle: TensorHandle<Self::Handle>) -> FloatTensor<Self> {
81        match handle.handle {
82            HandleKind::Float(handle) => handle,
83            _ => panic!("Expected float handle, got {}", handle.handle.name()),
84        }
85    }
86
87    fn int_tensor(handle: TensorHandle<Self::Handle>) -> IntTensor<Self> {
88        match handle.handle {
89            HandleKind::Int(handle) => handle,
90            _ => panic!("Expected int handle, got {}", handle.handle.name()),
91        }
92    }
93
94    fn bool_tensor(handle: TensorHandle<Self::Handle>) -> BoolTensor<Self> {
95        match handle.handle {
96            HandleKind::Bool(handle) => handle,
97            _ => panic!("Expected bool handle, got {}", handle.handle.name()),
98        }
99    }
100
101    fn quantized_tensor(handle: TensorHandle<Self::Handle>) -> QuantizedTensor<Self> {
102        match handle.handle {
103            HandleKind::Quantized(handle) => handle,
104            _ => panic!("Expected quantized handle, got {}", handle.handle.name()),
105        }
106    }
107
108    fn float_tensor_handle(tensor: FloatTensor<Self>) -> Self::Handle {
109        HandleKind::Float(tensor)
110    }
111
112    fn int_tensor_handle(tensor: IntTensor<Self>) -> Self::Handle {
113        HandleKind::Int(tensor)
114    }
115
116    fn bool_tensor_handle(tensor: BoolTensor<Self>) -> Self::Handle {
117        HandleKind::Bool(tensor)
118    }
119
120    fn quantized_tensor_handle(tensor: QuantizedTensor<Self>) -> Self::Handle {
121        HandleKind::Quantized(tensor)
122    }
123}
burn_ndarray/backend.rs

burn_ndarray/
backend.rs
