mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-04-29 03:00:14 -04:00
9b58d4cb37
* cleanup_mops * no expand * nothing * revert that * add comment * add correctness check to disk tensor
50 lines
3.6 KiB
Python
50 lines
3.6 KiB
Python
import torch
|
|
import numpy as np
|
|
from typing import Dict, Callable, Optional
|
|
from tinygrad.ops import BufferOps, UnaryOps, BinaryOps, MovementOps, TernaryOps, Op, Interpreted
|
|
from tinygrad.helpers import getenv, dtypes, prod, DType
|
|
from tinygrad.runtime.ops_cpu import base_fxn_for_op, einsum_mulacc
|
|
from tinygrad.runtime.lib import RawBuffer
|
|
|
|
device = torch.device("cuda:0" if torch.cuda.is_available() else ("mps" if getenv("MPS", 0) else "cpu"))
|
|
type_map = {torch.float64: dtypes.float64, torch.float16: dtypes.float16, torch.float32: dtypes.float32, torch.int8: dtypes.int8, torch.int32: dtypes.int32, torch.int64: dtypes.int64, torch.uint8: dtypes.uint8, torch.bool: dtypes.bool, torch.int16: dtypes.int16}
|
|
inverse_type_map = {v:k for k,v in type_map.items()}
|
|
|
|
def output_type(x, y): return x.dtype if type_map[x.dtype].priority > type_map[y.dtype].priority else y.dtype
|
|
def match_types(x, y, disallow_bool=False):
|
|
up = output_type(x, y)
|
|
if disallow_bool and up == torch.bool: up = torch.float
|
|
return x.type(up), y.type(up)
|
|
|
|
def as_strided(x, arg):
|
|
if any(i < 0 for i in arg[1]):
|
|
return torch.as_strided(x.contiguous(), arg[0], tuple(abs(i) for i in arg[1]),
|
|
arg[2] + sum((s-1)*a if a < 0 else 0 for (s,a) in zip(arg[0], arg[1]))).flip([i for i,a in enumerate(arg[1]) if a < 0])
|
|
return torch.as_strided(x.contiguous(), arg[0], arg[1], arg[2])
|
|
|
|
torch_fxn_for_op: Dict[Op, Callable] = {**base_fxn_for_op, **{
|
|
# TODO: torch.tensor should work here
|
|
#BufferOps.CONST: lambda val, dtype: torch.tensor(val, device=device, dtype=inverse_type_map[dtype]),
|
|
BufferOps.CONST: lambda val, dtype: torch.from_numpy(np.array(val, dtype=dtype.np)).requires_grad_(False).to(device),
|
|
UnaryOps.NOOP: lambda x: x.contiguous(), UnaryOps.SQRT: lambda x: x.sqrt(), UnaryOps.EXP2: lambda x: x.exp2(), UnaryOps.LOG2: lambda x: x.log2(), UnaryOps.SIN: torch.sin,
|
|
UnaryOps.CAST: lambda x,y: (x.view if y[1] else x.type)(next(k for k,v in type_map.items() if v==y[0])), UnaryOps.NEG: lambda x: torch.logical_not(x) if x.dtype is torch.bool else torch.neg(x),
|
|
BinaryOps.MAX: torch.maximum, BinaryOps.CMPLT: lambda x,y: (x<y).type(torch.promote_types(x.dtype, y.dtype)),
|
|
BinaryOps.ADD: lambda x,y: torch.add(*match_types(x, y)).type(output_type(x,y)),
|
|
BinaryOps.SUB: lambda x,y: torch.sub(*match_types(x, y, disallow_bool=True)).type(output_type(x,y)),
|
|
BinaryOps.MUL: lambda x,y: torch.mul(*match_types(x, y)).type(output_type(x,y)),
|
|
BinaryOps.DIV: lambda x,y: torch.div(*match_types(x, y)).type(torch.promote_types(x.dtype, y.dtype)),
|
|
MovementOps.AS_STRIDED: as_strided, MovementOps.EXPAND: lambda x, arg: x.expand(arg),
|
|
MovementOps.PAD: lambda x, padding: torch.nn.functional.pad(x, [item for sublist in padding[::-1] for item in sublist]), # pylint: disable=E1102
|
|
TernaryOps.MULACC: einsum_mulacc(lambda s,a,b: torch.einsum(s, a.float(), b.float()).type(output_type(a,b)), lambda x: x.stride(), lambda x,s: x.expand(s)),
|
|
TernaryOps.WHERE: lambda x, y, z: torch.where(x != 0, y, z),
|
|
}}
|
|
|
|
class RawTorchBuffer(RawBuffer):
|
|
def __init__(self, size:int, dtype:DType, buf:Optional[torch.Tensor]=None, allocator=lambda size, dtype: torch.empty([size], device=device, dtype=inverse_type_map[dtype])): super().__init__(size, dtype, buf, allocator)
|
|
@classmethod
|
|
def fromCPU(cls, x):
|
|
buf = torch.from_numpy(x if all(s>=0 for s in x.strides) else x.copy()).requires_grad_(False).to(device)
|
|
return cls(prod(x.shape), type_map[buf.dtype], buf)
|
|
def toCPU(self): return self._buf.cpu().numpy()
|
|
TorchBuffer = Interpreted(RawTorchBuffer, torch_fxn_for_op, lambda x: RawTorchBuffer(prod(x.shape), type_map[x.dtype], x))
|