Datasets:
File size: 1,973 Bytes
691a758 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | from dataclasses import dataclass
from typing import Literal
import numpy as np
Backend = Literal["numpy", "jax", "torch"]
@dataclass
class BackendOps:
backend: Backend
xp: object
def asarray(self, x, atleast_2d: bool = False):
if self.backend == "torch":
if isinstance(x, self.xp.Tensor):
arr = x.to(dtype=self.xp.float64)
else:
arr = self.xp.as_tensor(x, dtype=self.xp.float64)
if atleast_2d and arr.ndim < 2:
if arr.ndim == 1:
arr = arr.unsqueeze(0)
else:
arr = arr.reshape(1, 1)
return arr
arr = self.xp.asarray(x, dtype=self.xp.float64)
if atleast_2d:
arr = self.xp.atleast_2d(arr)
return arr
def maximum(self, x, y):
return self.xp.maximum(x, y)
def minimum(self, x, y):
return self.xp.minimum(x, y)
def clamp(self, x, min=None, max=None):
if self.backend == "torch":
return self.xp.clamp(x, min=min, max=max)
if min is not None:
x = self.xp.maximum(x, min)
if max is not None:
x = self.xp.minimum(x, max)
return x
def clamp_min(self, x, min_value):
return self.maximum(x, min_value)
def clamp_max(self, x, max_value):
return self.minimum(x, max_value)
def exp(self, x):
return self.xp.exp(x)
def stack(self, arrays, axis=-1):
if self.backend == "torch":
return self.xp.stack(arrays, dim=axis)
return self.xp.stack(arrays, axis=axis)
def get_ops(backend: Backend) -> BackendOps:
if backend == "numpy":
xp = np
elif backend == "jax":
import jax.numpy as jnp
xp = jnp
elif backend == "torch":
import torch
xp = torch
else:
raise ValueError(f"Unsupported backend: {backend}")
return BackendOps(backend=backend, xp=xp) |