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73a6ed7862
* applying st
* tests pass
* minor cleanups
* torch too
* hack
* contiguous
* move mops
* contig in BN
* tests should pass
* make torch fast
* make zeros and ones contig by default
* no contig there
* fix padding with expanding
* might fix tests
* still doesn't fix bug, but should be there
* Revert "still doesn't fix bug, but should be there"
This reverts commit 8ea92f3e07.
* minor cleanups
52 lines
3.7 KiB
Python
52 lines
3.7 KiB
Python
import numpy as np
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import operator
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from typing import Callable, Dict, Tuple, Optional
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from tinygrad.helpers import dtypes, DType
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from tinygrad.ops import UnaryOps, BinaryOps, MovementOps, ReduceOps, TernaryOps, Op, Interpreted
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from tinygrad.runtime.lib import RawBuffer
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def shape_to_axis(old_shape:Tuple[int, ...], new_shape:Tuple[int, ...]) -> Tuple[int, ...]:
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assert len(old_shape) == len(new_shape), "reduce shapes must have same dimensions"
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return tuple(i for i,(a,b) in enumerate(zip(old_shape, new_shape)) if a != b)
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base_fxn_for_op: Dict[Op, Callable] = {
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UnaryOps.NEG: operator.neg, BinaryOps.ADD: operator.add, BinaryOps.SUB: operator.sub, BinaryOps.MUL: operator.mul, BinaryOps.DIV: operator.truediv,
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ReduceOps.SUM: lambda x, new_shape: x.sum(shape_to_axis(x.shape, new_shape), keepdims=True) if tuple(x.shape) != tuple(new_shape) else x[:],
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ReduceOps.MAX: lambda x, new_shape: (x.amax if hasattr(x, 'amax') else x.max)(shape_to_axis(x.shape, new_shape), keepdims=True) if tuple(x.shape) != tuple(new_shape) else x[:],
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MovementOps.RESHAPE: lambda x, arg: x.reshape(arg), MovementOps.SHRINK: lambda x, arg: x[tuple(slice(p[0], p[1], None) for p in arg)],
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}
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def match_types(x, y):
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up = x.dtype if dtypes.from_np(x.dtype).priority > dtypes.from_np(y.dtype).priority else y.dtype
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return x.astype(up, copy=False), y.astype(up, copy=False)
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def einsum_mulacc(einsum, get_strides, expand):
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def einscripts(x): return ''.join(["abcdefghijklmnopqrstuvwxyz"[i] for i in x])
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def axes_slice(strides): return [i for i,s in enumerate(strides) if s != 0], tuple([slice(None) if s != 0 else 0 for i,s in enumerate(strides)])
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def mulacc(a, b, new_shape):
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(a_axes, a_slices), (b_axes, b_slices) = axes_slice(get_strides(a)), axes_slice(get_strides(b))
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out = [i for i in range(len(new_shape)) if a.shape[i] == new_shape[i] and (i in a_axes or i in b_axes)]
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ret = einsum(f"{einscripts(a_axes)}, {einscripts(b_axes)} -> {einscripts(out)}", a[a_slices], b[b_slices])
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return expand(ret.reshape([(1 if i not in a_axes and i not in b_axes else s) for i,s in enumerate(new_shape)]), new_shape)
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return mulacc
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numpy_fxn_for_op: Dict[Op, Callable] = {**base_fxn_for_op, **{
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UnaryOps.NOOP: lambda x: np.require(x, requirements='C'), UnaryOps.EXP2: np.exp2, UnaryOps.LOG2: np.log2, UnaryOps.SIN: np.sin,
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UnaryOps.CAST: lambda x,y: x.view(y[0].np) if y[1] else x.astype(y[0].np, copy=False),
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BinaryOps.MAX: np.maximum, BinaryOps.CMPLT: lambda x,y: (x<y).astype(np.promote_types(x.dtype,y.dtype)), BinaryOps.ADD: lambda x, y: np.add(*match_types(x, y)),
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BinaryOps.SUB: lambda x, y: np.subtract(*match_types(x, y)), BinaryOps.MUL: lambda x, y: np.multiply(*match_types(x, y)),
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BinaryOps.DIV: lambda x, y: np.divide(*match_types(x, y)), UnaryOps.SQRT: np.sqrt,
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MovementOps.PERMUTE: lambda x, order: x.transpose(order), MovementOps.PAD: np.pad, MovementOps.EXPAND: np.broadcast_to,
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MovementOps.STRIDE: lambda x, arg: x[tuple(slice(None, None, i) for i in arg)],
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MovementOps.AS_STRIDED: lambda x, arg: np.ndarray(arg[0], buffer=np.require(x, requirements='C'), dtype=x.dtype, offset=arg[2]*x.dtype.itemsize, strides=tuple(y*x.dtype.itemsize for y in arg[1])),
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TernaryOps.MULACC: einsum_mulacc(lambda s,a,b: np.einsum(s, *match_types(a.copy(), b.copy()), optimize=True), lambda x: x.strides, np.broadcast_to),
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TernaryOps.WHERE: np.where,
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}}
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class RawNumpyBuffer(RawBuffer):
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def __init__(self, size:int, dtype:DType, buf:Optional[np.ndarray]=None): super().__init__(size, dtype, buf if buf is not None else np.empty([size], dtype.np))
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@classmethod
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def fromCPU(cls, x): return cls(x.size, dtypes.from_np(x.dtype), x)
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def toCPU(self): return self._buf
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CPUBuffer = Interpreted(RawNumpyBuffer, numpy_fxn_for_op, from_underlying=RawNumpyBuffer.fromCPU)
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