mirror of https://github.com/commaai/tinygrad.git
748 lines
45 KiB
Python
748 lines
45 KiB
Python
import functools, io, math
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from typing import Union, Tuple, Optional, List, Any
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from tinygrad.tensor import Tensor
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from tinygrad.helpers import prod, dtypes, ImageDType, flatten
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from extra.onnx import safe_numpy
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from onnx.helper import tensor_dtype_to_np_dtype
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from onnx import TensorProto
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import numpy as np
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tensor_methods = {"Neg", "Reciprocal", "Sqrt", "Sign", "Abs", "Exp", "Log", "Mish", "Sin", "Cos", "Tan", "Relu", "Sigmoid", "MatMul",
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"Floor", "Ceil", "Softplus", "HardSwish", "Where", "Mul", "Sinh", "Cosh", "Tanh", "Softsign", "Asinh", "Acosh", "Atanh",
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"Elu", "Celu"}
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# **************** Free Ops ****************
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def Identity(x: Tensor): return x
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def Add(x: Tensor, other: Tensor, broadcast=None): return x + other if x.dtype == dtypes.float or isinstance(x.dtype, ImageDType) else (x + other).cast(x.dtype)
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def Sub(x: Union[Tensor, Any], other: Tensor): return x - other # some test has input as int
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def Div(x: Tensor, other: Tensor): return x / other if x.dtype == dtypes.float or isinstance(x.dtype, ImageDType) else x.div(other).floor() # TODO: this has dtype issues
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def Pow(x: Tensor, other: Tensor): return x.float() ** other.float()
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def Less(x:Tensor,y:Tensor): return (x<y).cast(dtypes.bool)
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def LessOrEqual(x:Tensor,y:Tensor): return (x<=y).cast(dtypes.bool)
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def Greater(x:Tensor,y:Tensor): return (x>y).cast(dtypes.bool)
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def GreaterOrEqual(x:Tensor,y:Tensor): return (x>=y).cast(dtypes.bool)
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def Equal(x:Tensor,y:Tensor): return (x==y).cast(dtypes.bool)
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def Max(*data_0): return functools.reduce(Tensor.maximum, data_0)
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def Min(*data_0): return functools.reduce(Tensor.minimum, data_0)
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def Sum(*data_0): return functools.reduce(Tensor.__add__, data_0)
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def Mean(*data_0): return functools.reduce(Tensor.__add__, data_0) / len(data_0)
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def Cast(x: Tensor, to): return x.cast(dtypes.from_np(tensor_dtype_to_np_dtype(to)))
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# **************** Simple Ops ****************
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def Constant(value: Tensor=None, value_float=None, value_floats=None, value_int=None, value_ints=None, value_string=None, value_strings=None):
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if value: return value
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if value_float: return Tensor(value_float, dtype=dtypes.float32, requires_grad=False)
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if value_floats: return Tensor(list(value_floats), dtype=dtypes.float32, requires_grad=False)
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if value_int: return Tensor(value_int, dtype=dtypes.int64, requires_grad=False)
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if value_ints: return Tensor(list(value_ints), dtype=dtypes.int64, requires_grad=False)
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if value_string or value_strings: raise NotImplementedError('value_string or value_strings not implemented for Constant op')
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def HardSigmoid(x: Tensor, alpha=0.2, beta=0.5): return (alpha*x + beta).clip(0, 1)
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def Gelu(x:Tensor, approximate=None): return x.gelu() if approximate == "tanh" else 0.5 * x * (1 + Erf(x/math.sqrt(2)))
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def Selu(X: Tensor, alpha=1.67326319217681884765625, gamma=1.05070102214813232421875): return gamma * (X.relu() - (-alpha*X.exp()+alpha).relu())
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def PRelu(X:Tensor, slope:Tensor):
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slope = slope[0] if slope.shape[-1] != X.shape[-1] else slope # HACK OnnxBackendPyTorchConvertedModelTest HAS WEIRD SLOPE WHERE IT'S [0.25, 0.25, 0.25] FOR ANY X.SHAPE
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return (X > 0).where(X, X * slope)
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def LeakyRelu(X: Tensor, alpha=0.01): return X.leakyrelu(alpha)
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def ThresholdedRelu(X: Tensor, alpha=1.0): return (X > alpha).where(X, 0)
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def Softmax_1(x: Tensor, axis=1): return x.softmax(axis)
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def Softmax_13(x: Tensor, axis=-1): return x.softmax(axis)
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Softmax = {1: Softmax_1, 13: Softmax_13} # Softmax default axis changed
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def LogSoftmax(x: Tensor, axis=-1): return x.log_softmax(axis)
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def Clip(x: Tensor, min=None, max=None): return x.clip(float('-inf') if min is None else min, float('inf') if max is None else max)
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# NOTE ReduceProd would require a new llop
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def _axes(axes, noop_with_empty_axes):
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if axes is not None and not (isinstance(axes, Tensor) and axes.shape == (0,)): return [int(x) for x in safe_numpy(axes)]
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return [] if noop_with_empty_axes else None
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def ReduceMax(data: Tensor, axes=None, keepdims=1, noop_with_empty_axes=0): return data.max(_axes(axes, noop_with_empty_axes), keepdim=keepdims)
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def ReduceMin(data: Tensor, axes=None, keepdims=1, noop_with_empty_axes=0): return data.min(_axes(axes, noop_with_empty_axes), keepdim=keepdims)
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def ReduceSum(data: Tensor, axes=None, keepdims=1, noop_with_empty_axes=0): return data.sum(_axes(axes, noop_with_empty_axes), keepdim=keepdims)
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def ReduceMean(data: Tensor, axes=None, keepdims=1, noop_with_empty_axes=0): return data.mean(_axes(axes, noop_with_empty_axes), keepdim=keepdims)
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def ReduceSumSquare(data: Tensor, axes=None, keepdims=1, noop_with_empty_axes=0): return data.square().sum(_axes(axes, noop_with_empty_axes), keepdim=keepdims)
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def ReduceL1(data: Tensor, axes=None, keepdims=1, noop_with_empty_axes=0): return data.abs().sum(_axes(axes, noop_with_empty_axes), keepdim=keepdims)
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def ReduceL2(data: Tensor, axes=None, keepdims=1, noop_with_empty_axes=0): return data.square().sum(_axes(axes, noop_with_empty_axes), keepdim=keepdims).sqrt()
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def ReduceLogSum(data: Tensor, axes=None, keepdims=1, noop_with_empty_axes=0): return data.sum(_axes(axes, noop_with_empty_axes), keepdim=keepdims).log()
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def ReduceLogSumExp(data: Tensor, axes=None, keepdims=1, noop_with_empty_axes=0): return data.exp().sum(_axes(axes, noop_with_empty_axes), keepdim=keepdims).log()
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def GlobalAveragePool(X: Tensor): return X.mean(axis=tuple(range(2, len(X.shape))), keepdim=True)
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def GlobalMaxPool(X: Tensor): return X.max(axis=tuple(range(2, len(X.shape))), keepdim=True)
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def OptionalHasElement(x: Optional[Tensor]=None): return Tensor(x is not None and x.numel() > 0)
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def OptionalGetElement(x: Optional[Tensor]=None): return x if x is not None else Tensor([])
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def Tile(x: Tensor, repeats): return x.repeat([int(x) for x in safe_numpy(repeats)])
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def Range(start: Tensor, limit, delta): return Tensor.arange(start=safe_numpy(start).item(), stop=safe_numpy(limit).item(), step=safe_numpy(delta).item())
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def Shape(data: Tensor, end=None, start=0): return Tensor(list(data.shape)[start:end], dtype=dtypes.int64)
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def Size(data: Tensor): return prod(data if isinstance(data, list) else data.shape)
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def Flatten(x: Tensor, axis=1): return x.reshape(prod((1,) + x.shape[0:axis]), -1)
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def Reshape(data: Tensor, shape: Tensor, allowzero=0):
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return data.reshape([int(x) if x != 0 else (0 if allowzero else data.shape[i]) for i,x in enumerate(safe_numpy(shape))])
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def Shrink(x: Tensor, bias=0.0, lambd=0.5): return (x < -lambd)*(x+bias) + (x > lambd)*(x-bias)
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def And(x:Tensor, y:Tensor): return (x==y).where(x, 0).cast(dtypes.bool)
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def Or(x:Tensor, y:Tensor): return (x==y).where(x, 1).cast(dtypes.bool)
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def Xor(x:Tensor, y:Tensor): return (x==y).where(0, 1).cast(dtypes.bool)
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def Not(x:Tensor): return (x==1).where(0, 1).cast(dtypes.bool)
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def Asin(x): return Atan(x / Tensor.sqrt(1 - x * x))
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def Acos(x: Tensor):
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negate = (x < 0)
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x = x.abs()
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ret = ((((-0.0187293 * x) + 0.0742610)*x - 0.2121144) * x + 1.5707288) * Tensor.sqrt(1.0 - x)
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ret = ret - 2 * negate * ret
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return negate * 3.14159265358979 + ret
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def Atan(y: Tensor):
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x = Tensor.ones(y.shape)
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t3 = x
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t1 = y.abs()
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t0 = (t3 > t1).where(t3, t1)
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t1 = (t3 < t1).where(t3, t1)
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t3 = t1 / t0
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t4 = t3 * t3
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t0 = ((((-0.013480470 * t4 + 0.057477314) * t4 - 0.121239071) * t4 + 0.195635925) * t4 - 0.332994597) * t4 + 0.999995630
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t3 = t0 * t3
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t3 = (y.abs() > x.abs()).where(1.570796327 - t3, t3)
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return (y < 0).where(-t3, t3)
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def Trilu(x: Tensor, k: Union[Tensor, int]=0, upper=1):
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k = int(k.numpy().item()) if isinstance(k, Tensor) else 0 # onnx passes k as a tensor int64 with one element, default is 0
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return x.triu(k).cast(dtypes.int64) if upper else x.tril(k).cast(dtypes.int64)
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def Squeeze(data: Tensor, axes):
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if isinstance(axes, Tensor): axes = safe_numpy(axes)
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axes = [int(x) + data.ndim if x < 0 else int(x) for x in axes]
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return data.reshape([s for i,s in enumerate(data.shape) if i not in axes])
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def Unsqueeze(data: Tensor, axes):
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axes = [int(x) + data.ndim if x < 0 else int(x) for x in safe_numpy(axes)]
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new_shape = [1] * (data.ndim + len(axes))
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ptr = iter(data.shape)
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for i in range(len(new_shape)):
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if i not in axes:
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new_shape[i] = next(ptr)
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return data.reshape(new_shape)
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def Binarizer(x, threshold=0.0): return (x > threshold).cast(dtypes.float32)
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def ArgMax(x: Tensor, axis=0, keepdims=1, select_last_index=0):
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axis = axis + x.ndim if axis < 0 else axis
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m = x == (x.max(axis=axis, keepdim=keepdims) if keepdims else x.max(axis=axis, keepdim=keepdims).unsqueeze(axis))
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c = Tensor.arange(x.shape[axis]).reshape(*[1]*(axis), x.shape[axis], *[1]*(x.ndim - axis-1)) * m
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return c.max(axis=axis,keepdim=keepdims).cast(dtypes.int64)
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def ArgMin(x, axis=0, keepdims=1, select_last_index=0): return ArgMax(-x, axis=axis, keepdims=keepdims, select_last_index=select_last_index)
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def Concat(*xs: List[Tensor], axis): return xs[0].cat(*xs[1:], dim=axis)
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def Transpose(x: Tensor, perm=None): return x.permute(order=list(range(len(x.shape))[::-1]) if perm is None else perm)
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# NOTE: since we only have one type, this is valid!
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# TODO: fix this with dtypes
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def CastLike(x, target_type):
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assert isinstance(target_type, Tensor), "can only CastLike Tensor"
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return x
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def ConstantOfShape(x, value:Tensor=None):
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if value is None: value=Tensor([0.0])
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shape = [int(x) for x in safe_numpy(x)]
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return Tensor.ones(*shape, dtype=value.dtype) * (value if shape[0]!=0 else 1)
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# TODO: abstract out the broadcast logic in tensor
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def Expand(x: Tensor, shape):
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x_shape, y_shape = x.shape, [int(x) for x in safe_numpy(shape)]
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# copied from _broadcasted
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x_shape, y_shape = [([1]*(max(len(x_shape), len(y_shape))-len(t_shape)) + list(t_shape)) for t_shape in [x_shape, y_shape]]
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shape_ret = tuple(max(sx, sy) for sx,sy in zip(x_shape, y_shape))
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return x.reshape(x_shape).expand(shape_ret)
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# **************** Complex Ops ****************
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def Gemm(A: Tensor, B: Tensor, C: Tensor=None, alpha=1.0, beta=1.0, transA=0, transB=0, broadcast=0):
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ret = alpha * (A.transpose(transA) @ B.transpose(transB))
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if C is not None: ret += beta * (C if broadcast == 0 else C.reshape([-1 if i < len(C.shape) else 1 for i in range(ret.ndim)][::-1]))
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return ret
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# works with Tensors.ndim != 4
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def _batchnorm(self:Tensor, weight:Optional[Tensor], bias:Optional[Tensor], mean:Tensor, invstd:Tensor):
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shape = [1, -1] + [1] * (self.ndim-2)
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x = (self - mean.reshape(shape=shape))
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if weight: x = x * weight.reshape(shape=shape)
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ret = x.mul(invstd.reshape(shape=shape) if len(invstd.shape) == 1 else invstd)
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return (ret + bias.reshape(shape=shape)) if bias else ret
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# TODO: this is copied from tinygrad/nn/__init__.py
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# spatial is from opset 7 and has since been removed
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def BatchNormalization(X: Tensor, scale, B, input_mean, input_var, epsilon=1e-05, momentum=0.9, training_mode=0, spatial=1, is_test=0):
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if training_mode:
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x_detached = X.detach()
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current_mean = x_detached.mean(axis=(0,2,3))
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y = (x_detached - current_mean.reshape(shape=[1, -1, 1, 1]))
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current_var = (y*y).mean(axis=(0,2,3))
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current_invstd = current_var.add(epsilon).rsqrt()
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running_mean = input_mean * momentum + current_mean * (1 - momentum)
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running_var = input_var * momentum + current_var * (1 - momentum)
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return _batchnorm(X, scale, B, current_mean, current_invstd), running_mean, running_var
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invstd = (input_var + epsilon)**-0.5
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return _batchnorm(X, scale, B, input_mean, invstd)
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def InstanceNormalization(x: Tensor, scale: Tensor, bias: Tensor, epsilon=1e-05):
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axis = tuple(range(2, len(x.shape)))
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mean = x.mean(axis=axis, keepdim=True)
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invstd = x.sub(mean).pow(2).mean(axis=axis, keepdim=True).add(epsilon).rsqrt()
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return x.sub(mean).mul(scale.reshape(shape=[-1, 1, 1])).mul(invstd).add(bias.reshape(shape=[-1, 1, 1]))
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def LayerNormalization(x: Tensor, scale, bias, axis=-1, epsilon=1e-05, stash_type=1):
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assert stash_type == 1, "only float32 is supported"
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axis = tuple(i for i in range(axis if axis >= 0 else x.ndim + axis, x.ndim))
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mean = x.mean(axis=axis, keepdim=True)
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return x.layernorm(axis, epsilon).mul(scale).add(bias), mean, (x.sub(mean)).pow(2).mean(axis=axis, keepdim=True).add(epsilon).rsqrt()
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def GroupNormalization(x: Tensor, scale: Tensor, bias: Tensor, num_groups, epsilon=1e-05):
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return x.reshape(x.shape[0], num_groups, -1).layernorm(axis=-1, eps=epsilon).mul(scale.unsqueeze(-1)).add(bias.unsqueeze(-1)).reshape(x.shape)
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# onnx: [x1_begin, x2_begin, ..., x1_end, x2_end, ...]
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# numpy.pad: ((x1_begin, x1_end), (x2_begin, x2_end), ...)
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def _format_padding(onnx_pads, ndims=None, axes=None):
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if ndims and len(onnx_pads)//2 != ndims: onnx_pads = onnx_pads * ndims # for OnnxBackendPyTorchConvertedModelTest the len(onnx_pads) == 2
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if ndims is None: ndims = len(onnx_pads) // 2
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if axes is None: axes = list(range(ndims))
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num_axes = len(axes)
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np_pads = [(0,0)] * ndims
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for i in range(num_axes):
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np_pads[axes[i]] = (onnx_pads[i], onnx_pads[i + num_axes])
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return np_pads
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def _padding(X: Tensor, pads=None, auto_pad="NOTSET", axes=None, constant_value=0., strides=None, kernel_shape=None, dilations=None, ceil_mode=0):
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if auto_pad != "NOTSET": pads = _auto_pad(X, auto_pad, strides, kernel_shape, dilations)
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elif ceil_mode and auto_pad=="NOTSET": # stupid ceil_mode case
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if strides is not None: strides = [strides]*len(kernel_shape) if isinstance(strides, int) else strides if strides else [1]*len(kernel_shape)
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if dilations is not None: dilations = [1]*len(kernel_shape) if dilations == 1 else dilations
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out_spatial_shape = [math.ceil((sh - dil * (ker-1)-1)/st + 1) if ceil_mode else math.floor((sh - dil * (ker-1)-1)/st + 1) for sh, st, ker, dil in zip(X.shape[-len(kernel_shape):], strides, kernel_shape, dilations)]
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pad_shape = [(osh-1)*st+((ks-1)*dil+1)-ish for osh, st, ks, dil, ish in zip(out_spatial_shape, strides, kernel_shape, dilations, X.shape[-len(kernel_shape):])]
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pad_shape = flatten([[sh//2, sh-sh//2] for sh in pad_shape])
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pads = pad_shape[::2] + pad_shape[1::2]
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if pads is None: return X
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pads = _format_padding(pads, ndims=len(X.shape), axes=axes)
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return X.pad(tuple(pads), value=constant_value)
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def _auto_pad(X: Tensor, auto_pad, strides, kernel_shape, dilations):
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strides = [strides]*len(kernel_shape) if isinstance(strides, int) else strides if strides else [1]*len(kernel_shape)
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dilations = [1]*len(kernel_shape) if dilations == 1 else dilations
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if auto_pad == "SAME_UPPER" or auto_pad == "SAME_LOWER":
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pad_shape = [(math.ceil(sh/st)-1)*st+((ks-1)*di+1)-sh for sh, st, ks, di in zip(X.shape[-len(kernel_shape):], strides, kernel_shape, dilations)]
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pad_shape = flatten([[sh//2, sh-sh//2] for sh in pad_shape])
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return pad_shape[::2] + pad_shape[1::2] if auto_pad == "SAME_UPPER" else pad_shape[1::2] + pad_shape[::2]
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raise NotImplementedError(f"auto_pad={auto_pad} not implemented")
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def Pad(x: Tensor, pads: Union[Tensor, Tuple[int, ...]], constant_value: Tensor=None, axes: Tensor=None, mode="constant", value: float=0.):
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constant_value = value if constant_value is None else float(safe_numpy(constant_value))
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seq_pads = list(pads) if isinstance(pads, tuple) else safe_numpy(pads)
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seq_pads = [math.ceil(i) for i in seq_pads]
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seq_axes = safe_numpy(axes).astype(np.int32).tolist() if axes is not None else None
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base_shape = x.shape
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pads = _format_padding(seq_pads, ndims=len(x.shape), axes=seq_axes)
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if mode == "wrap":
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repeat_args = [math.ceil(dim[0]/sh) + math.ceil(dim[1]/sh) + 1 for dim, sh in zip(pads, base_shape)]
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new_shape = [s*r for s,r in zip(base_shape, repeat_args)]
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shrink_args = [(sh-dim[0]%sh if dim[0]%sh != 0 else 0, nsh-(sh-dim[1]%sh) if dim[1]%sh != 0 else nsh) for dim, sh, nsh in zip(pads, base_shape, new_shape)]
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return x.repeat(tuple(repeat_args)).shrink(tuple(shrink_args))
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if mode == "reflect":
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for i,s in enumerate(x.shape):
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if pads[i] == (0,0): continue
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elif pads[i][0] and not pads[i][1]:
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x = x.flip(i).shrink(tuple([(0,s_) if i_ != i else (s-pads[i][0]-1, s_-1) for i_,s_ in enumerate(x.shape)])).pad(tuple([(0,0) if i_ != i else (0,s) for i_ in range(x.ndim)])) + \
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x.pad(tuple([(0,0) if i_ != i else pads[i] for i_ in range(x.ndim)]))
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elif not pads[i][0] and pads[i][1]:
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x = x.flip(i).shrink(tuple([(0,s_) if i_ != i else (1, pads[i][1]+1) for i_,s_ in enumerate(x.shape)])).pad(tuple([(0,0) if i_ != i else (s,0) for i_ in range(x.ndim)])) + \
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x.pad(tuple([(0,0) if i_ != i else pads[i] for i_ in range(x.ndim)]))
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else:
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x = x.flip(i).shrink(tuple([(0,s_) if i_ != i else (s-pads[i][0]-1, s_-1) for i_,s_ in enumerate(x.shape)])).pad(tuple([(0,0) if i_ != i else (0,s+pads[i][1]) for i_ in range(x.ndim)])) + \
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x.flip(i).shrink(tuple([(0,s_) if i_ != i else (1, pads[i][1]+1) for i_,s_ in enumerate(x.shape)])).pad(tuple([(0,0) if i_ != i else (s+pads[i][0],0) for i_ in range(x.ndim)])) + \
|
|
x.pad(tuple([(0,0) if i_ != i else pads[i] for i_ in range(x.ndim)]))
|
|
return x
|
|
if mode == "edge":
|
|
for i,s in enumerate(x.shape):
|
|
if pads[i] == (0,0): continue
|
|
elif pads[i][0] and not pads[i][1]:
|
|
x = x.shrink(tuple([(0,s_) if i_ != i else (0,1) for i_,s_ in enumerate(x.shape)])).expand([pads[i][0] if i_ == i else s_ for i_,s_ in enumerate(x.shape)]).pad(tuple([(0,0) if i_ != i else (0,s) for i_ in range(x.ndim)])) + \
|
|
x.pad(tuple([(0,0) if i_ != i else pads[i] for i_ in range(x.ndim)]))
|
|
elif not pads[i][0] and pads[i][1]:
|
|
x = x.shrink(tuple([(0,s_) if i_ != i else (s_-1, s_) for i_,s_ in enumerate(x.shape)])).expand([pads[i][0] if i_ == i else s_ for i_,s_ in enumerate(x.shape)]).pad(tuple([(0,0) if i_ != i else (s+pads[i][0],0) for i_ in range(x.ndim)])) + \
|
|
x.pad(tuple([(0,0) if i_ != i else pads[i] for i_ in range(x.ndim)]))
|
|
else:
|
|
x = x.shrink(tuple([(0,s_) if i_ != i else (0,1) for i_,s_ in enumerate(x.shape)])).expand([pads[i][0] if i_ == i else s_ for i_,s_ in enumerate(x.shape)]).pad(tuple([(0,0) if i_ != i else (0,s+pads[i][1]) for i_ in range(x.ndim)])) + \
|
|
x.shrink(tuple([(0,s_) if i_ != i else (s_-1, s_) for i_,s_ in enumerate(x.shape)])).expand([pads[i][1] if i_ == i else s_ for i_,s_ in enumerate(x.shape)]).pad(tuple([(0,0) if i_ != i else (s+pads[i][0],0) for i_ in range(x.ndim)])) + \
|
|
x.pad(tuple([(0,0) if i_ != i else pads[i] for i_ in range(x.ndim)]))
|
|
return x
|
|
if mode == "constant":
|
|
return _padding(x, seq_pads, axes=seq_axes, constant_value=constant_value)
|
|
|
|
def AveragePool(X: Tensor, kernel_shape, auto_pad="NOTSET", ceil_mode=0, count_include_pad=0, dilations=1, pads=None, strides=1):
|
|
pixel_axes = tuple(range(len(X.shape)))[2:]
|
|
ret = _padding(X, pads, auto_pad, axes=pixel_axes, strides=strides, kernel_shape=kernel_shape, dilations=dilations, ceil_mode=ceil_mode).avg_pool2d(kernel_shape, stride=strides, dilation=dilations)
|
|
if count_include_pad:
|
|
return ret
|
|
div = _padding(Tensor.ones(*X.shape), pads, auto_pad, axes=pixel_axes, strides=strides, kernel_shape=kernel_shape, dilations=dilations, ceil_mode=ceil_mode).avg_pool2d(kernel_shape, stride=strides, dilation=dilations)
|
|
return ret / div
|
|
|
|
def MaxPool(X: Tensor, kernel_shape, auto_pad="NOTSET", ceil_mode=0, dilations=1, pads=None, storage_order=0, strides=1):
|
|
ret = _padding(X, pads, auto_pad, constant_value=float("-inf"), axes=tuple(range(len(X.shape)))[2:], strides=strides, kernel_shape=kernel_shape, dilations=dilations, ceil_mode=ceil_mode)
|
|
ret = ret.max_pool2d(kernel_shape, stride=strides, dilation=dilations)
|
|
ret_len, X_len = ret.numel(), X.numel()
|
|
indices = ((ret.flatten().unsqueeze(1).expand(ret_len, X_len) == X.flatten().reshape(1, X_len).expand(ret_len, X_len)) * Tensor.arange(X_len).reshape(1, X_len).expand(ret_len, X_len)).sum(1).reshape(ret.shape).cast(dtypes.int64)
|
|
if storage_order: indices = indices.transpose(indices.ndim-2, indices.ndim-1)
|
|
return ret, indices
|
|
|
|
def MaxUnpool(xT: Tensor, xI: Tensor, outshape: Tensor=None, kernel_shape=None, pads=None, strides=None):
|
|
out_sh = [(ks//2)*2 + st * inps for inps, st, ks in zip(xI.shape, strides, kernel_shape)]
|
|
outlength = prod(out_sh)
|
|
xI = xI.flatten().unsqueeze(1).expand(prod(xT.shape), outlength)
|
|
arange = Tensor.arange(outlength, requires_grad=False).reshape(1, outlength).expand(xI.shape)
|
|
xT = xT.flatten().unsqueeze(1).expand(prod(xT.shape), outlength)
|
|
ret = ((xI == arange) * xT).sum(0).reshape([1, 1] + out_sh)
|
|
if outshape is not None:
|
|
outshape = safe_numpy(outshape).tolist()
|
|
if outshape != ret.shape:
|
|
diff = [outshape[2] - ret.shape[2], outshape[3] - ret.shape[3]]
|
|
pad_args = [diff[0]//2, diff[1]//2, diff[0]-diff[0]//2, diff[1]-diff[1]//2]
|
|
ret = ret.pad2d((pad_args[1], pad_args[3], pad_args[0], pad_args[2]))
|
|
return ret
|
|
|
|
def Conv(X: Tensor, W: Tensor, B=None, auto_pad="NOTSET", dilations=1, group=1, kernel_shape=None, pads=None, strides=1):
|
|
if auto_pad != "NOTSET": padding = _auto_pad(X, auto_pad, strides, kernel_shape, dilations)
|
|
else: padding = [p for ps in zip(pads[:len(pads)//2][::-1], pads[len(pads)//2:][::-1]) for p in ps] if pads is not None else 0 # reorder padding
|
|
return X.conv2d(W, B, stride=strides, groups=group, dilation=dilations, padding=padding)
|
|
|
|
def ConvTranspose(X: Tensor, W: Tensor, B=None, auto_pad="NOTSET", dilations=1, group=1, kernel_shape=None, pads=None, output_shape=None, output_padding=0, strides=1):
|
|
if kernel_shape is None: kernel_shape = W.shape[2:]
|
|
if isinstance(strides, int): strides = [strides]*(W.ndim-2)
|
|
if isinstance(dilations, int): dilations = [dilations]*(W.ndim-2)
|
|
if isinstance(output_padding, int): output_padding = [output_padding]*(W.ndim-2)
|
|
out_sh = [st*(xs-1) + (ks-1)*di+1 if n < 2 else st*(xs-1) + (ks-1)*di+1 - pads[n-2] - pads[n-1] for n, (st, xs, ks, di) in enumerate(zip(strides, X.shape[2:], kernel_shape, dilations))] if output_shape is not None or auto_pad != "NOTSET" else []
|
|
if pads is None:
|
|
if output_shape is None: output_shape = [xs*st for xs, st in zip(X.shape[2:], strides)]
|
|
if auto_pad == "NOTSET": pads = [0,0] * (X.ndim - 2)
|
|
else:
|
|
total_padding = [st*(ish-1) + pad + ((ks-1)*dil+1)-osh for st, ish, pad, ks, dil, osh in zip(strides, X.shape[2:], output_padding, kernel_shape, dilations, output_shape)]
|
|
pad_shape = flatten([[sh//2, sh-sh//2] for sh in total_padding])
|
|
pads = pad_shape[::2] + pad_shape[1::2] if auto_pad == "SAME_UPPER" else pad_shape[1::2] + pad_shape[::2]
|
|
else:
|
|
if output_shape is None: output_shape = [st*(xs-1) + (ks-1)*di+1 if n < 2 else st*(xs-1) + (ks-1)*di+1 - pads[n-2] - pads[n-1] for n, (st, xs, ks, di) in enumerate(zip(strides, X.shape[2:], kernel_shape, dilations))]
|
|
if out_sh: output_padding = [os - rs for os, rs in zip(output_shape, out_sh)]
|
|
return X.conv_transpose2d(W, B, stride=strides, groups=group, dilation=dilations, padding=pads if pads is not None else 0, output_padding=output_padding)
|
|
|
|
# Reimplemented here because you need legacy RNG for passing ONNX tests.
|
|
def Dropout(data: Tensor, ratio=0.5, training_mode=False, seed=None):
|
|
if isinstance(ratio, Tensor) and not ratio.shape: ratio = safe_numpy(ratio) # ratio and tensor is passed in as Tensor with shape: ()
|
|
if isinstance(training_mode, Tensor) and not training_mode.shape: training_mode = safe_numpy(training_mode)
|
|
if not training_mode: return data, Tensor.ones(*data.shape, dtype=dtypes.bool) # if mask is requested as output it will contain all True's.
|
|
rng = np.random.RandomState(seed)
|
|
ratio = ratio.lazydata.realize().toCPU()[0] if isinstance(ratio, Tensor) else ratio
|
|
mask = Tensor((rng.random(data.shape) >= ratio), requires_grad=False, device=data.device)
|
|
return data * mask * (1/(1.0 - ratio)), mask
|
|
|
|
def LRN(x: Tensor, size, alpha=1e-4, beta=0.75, bias=1.0):
|
|
bs, c, iy, ix = x.shape
|
|
return x / x.mul(x).reshape(bs,1,c,iy*ix).pad2d((0,0,(size-1)//2, size//2)).avg_pool2d((size, 1), 1).reshape(bs,c,iy,ix).mul(alpha).add(bias).pow(beta)
|
|
|
|
def MeanVarianceNormalization(x: Tensor, axis=(0, 2, 3)):
|
|
data_mean = x.mean(axis=axis, keepdim=True)
|
|
std = ((x**2).mean(axis=axis, keepdim=True) - data_mean**2).sqrt()
|
|
return (x - data_mean) / (std + 1e-9)
|
|
|
|
def NegativeLogLikelihoodLoss(x: Tensor, target: Tensor, weight=None, ignore_index=None, reduction="mean"):
|
|
target = target.cast(dtypes.float32)
|
|
N, C, i_shape = x.shape[0], x.shape[1], x.shape
|
|
t_shape = target.shape
|
|
if len(x.shape) != 3:
|
|
x = x.reshape((N, C, -1))
|
|
target = target.reshape((N, -1))
|
|
if weight is not None:
|
|
mask = target.unsqueeze(-1) == Tensor.arange(C).repeat((N, 1, 1))
|
|
weight = (mask * weight).sum(axis=-1)
|
|
if ignore_index is not None:
|
|
cond = target == ignore_index
|
|
weight = cond.where(0, weight) if weight is not None else cond.where(Tensor.zeros(*target.shape), 1)
|
|
mask = target[:, None, :] == Tensor.arange(C).reshape([1, C] + [1]*(len(x.shape) -2))
|
|
loss = -(mask * x).sum(axis=1) * (1 if weight is None else weight)
|
|
if reduction == "mean": return loss.mean() if weight is None else loss.sum() / weight.sum()
|
|
if reduction == "sum": return loss.sum()
|
|
return loss.reshape(t_shape) if len(i_shape) != 3 else loss
|
|
|
|
def SoftmaxCrossEntropyLoss(scores: Tensor, labels: Tensor, weights=None, ignore_index=None, reduction="mean"):
|
|
_N, C, *s_dimensions = scores.shape
|
|
if ignore_index is not None: labels = (labels == ignore_index).where(C+1, labels).cast(dtypes.int32)
|
|
mask = labels.unsqueeze(1) == Tensor.arange(C).reshape(1, C, *[1]*len(s_dimensions))
|
|
y = scores.log_softmax(axis=1)
|
|
if weights is not None: weights = weights.__getitem__(tuple([labels, *[slice(None)]*(weights.ndim-1)]))
|
|
loss = (mask * -y).sum(1) if weights is None else (mask * -y).sum(1) * weights
|
|
if reduction == "mean": loss = loss.sum() / (loss == 0).where(0, 1).sum() if weights is None else loss.sum() / weights.sum()
|
|
elif reduction == "sum": loss = loss.sum()
|
|
return loss, y
|
|
|
|
def ArrayFeatureExtractor(x: Tensor, indices: Tensor):
|
|
return x.__getitem__(tuple([slice(None) if i != (x.ndim-1) else indices for i in range(x.ndim)]))
|
|
def Gather(x: Tensor, indices: Tensor, axis=0):
|
|
if indices.numel() < 9: # NOTE lessor kernels for smaller indices but kernel number increases depending on size of indices
|
|
x_sh = list(x.shape)
|
|
ret_shape = x_sh[:axis] + list(indices.shape) + x_sh[axis+1:]
|
|
if indices.ndim > 1: indices = indices.flatten()
|
|
indices = [int(safe_numpy(indices))] if indices.shape == () else [x_sh[axis]+int(x) if x<0 else int(x) for x in safe_numpy(indices)]
|
|
args = [[(0,x) if j != axis else (i,i+1) for j, x in enumerate(x_sh)] for i in indices]
|
|
return x.shrink(arg=tuple(args[0])).cat(*[x.shrink(arg=tuple(arg)) for arg in args[1:]], dim=axis).reshape(ret_shape)
|
|
# NOTE faster gather, fixed number of kernels, but exceeds limited kernels for openpilot
|
|
return x.__getitem__(tuple([slice(None) if i != axis else indices for i in range(x.ndim)]))
|
|
|
|
def GatherElements(x: Tensor, indices: Tensor, axis):
|
|
indices = (indices < 0).where(x.shape[axis], 0) + indices
|
|
return x.gather(indices, axis)
|
|
|
|
def _round(x:Tensor, n:float, equidistant_case = "round_down") -> Tensor:
|
|
assert n <= 1, f"n:{n} shouldn't be larger than 1"
|
|
b = x.cast(dtypes.int32).contiguous().cast(x.dtype)
|
|
b = (b >= 0).where(b+n, b-n)
|
|
if equidistant_case == "round_down": return (x > b).where(b+1-n, b-n)
|
|
if equidistant_case == "round_up": return (x >= b).where(b+1-n, b-n)
|
|
if equidistant_case == "round_to_even":
|
|
def _and(cond1, cond2): return ((cond1.cast(dtypes.int) + cond2.cast(dtypes.int)) == 2).where(1, 0)
|
|
x_ceil_fraction = x.ceil()/2
|
|
cond_ceil_even = x_ceil_fraction.ceil() == x_ceil_fraction
|
|
x = (_and(x == b, cond_ceil_even)).where(x+1-n, x)
|
|
x = (x > b).where(b+1-n, b-n)
|
|
return x
|
|
|
|
def Round(X:Tensor): return _round(X, 0.5, "round_to_even")
|
|
|
|
# TODO clean this up, it's taking the longest in CI
|
|
def Resize(X:Tensor, roi=None, scales=None, sizes=None, antialias=0, axes=None, coordinate_transformation_mode='half_pixel',
|
|
cubic_coeff_a=-0.75, exclude_outside=0, extrapolation_value=0.0, keep_aspect_ratio_policy='stretch',
|
|
mode='nearest', nearest_mode='round_prefer_floor'):
|
|
def _nearest_gather(X: Tensor, x_out, y_out): return X[:,:,y_out,:][:,:,:,x_out]
|
|
def _nearest_mode(x_resized: Tensor, nearest_mode: str, x_len):
|
|
if nearest_mode == "round_prefer_floor": ret = _round(x_resized, 0.5, "round_down")
|
|
elif nearest_mode == "round_prefer_ceil": ret = _round(x_resized, 0.5, "round_up")
|
|
elif nearest_mode == "floor": ret = x_resized.floor()
|
|
elif nearest_mode == "ceil": ret = x_resized.ceil()
|
|
return ret.cast(dtypes.int32).clip(0, x_len-1)
|
|
def _coordinate_transformation(x_out, y_out, output_shape, scales_, roi=None):
|
|
if coordinate_transformation_mode == "half_pixel":
|
|
x_out = (x_out + 0.5)/Tensor(scales_[-1]) - 0.5
|
|
y_out = (y_out + 0.5)/Tensor(scales_[-2]) - 0.5
|
|
elif coordinate_transformation_mode == "align_corners":
|
|
x_out = x_out * (X.shape[-1] - 1) / (output_shape[-1] - 1)
|
|
y_out = y_out * (X.shape[-2] - 1) / (output_shape[-2] - 1)
|
|
elif coordinate_transformation_mode == "asymmetric":
|
|
x_out = x_out/scales_[-1]
|
|
y_out = y_out/scales_[-2]
|
|
elif coordinate_transformation_mode == "half_pixel_symmetric":
|
|
x_out = X.shape[-1] / 2 * (1 - int(output_shape[-1]) / output_shape[-1]) + (x_out + 0.5) / scales_[-1] - 0.5
|
|
y_out = X.shape[-2] / 2 * (1 - int(output_shape[-2]) / output_shape[-2]) + (y_out + 0.5) / scales_[-2] - 0.5
|
|
elif coordinate_transformation_mode == "pytorch_half_pixel":
|
|
x_out = (x_out + 0.5)/scales_[-1] - 0.5 if output_shape[-1] > 1 else Tensor([0])
|
|
y_out = (y_out + 0.5)/scales_[-2] - 0.5 if output_shape[-2] > 1 else Tensor([0])
|
|
elif coordinate_transformation_mode == "tf_crop_and_resize":
|
|
x_out = roi[-1][0] * (X.shape[-1] - 1) + x_out * ((roi[-1][1] - roi[-1][0]) * (X.shape[-1] - 1) / (output_shape[-1] - 1)) if output_shape[-1] > 1 else Tensor([0.5 * (roi[-1][0] + roi[-1][1]) * (X.shape[-1] - 1)])
|
|
y_out = roi[-2][0] * (X.shape[-2] - 1) + y_out * ((roi[-2][1] - roi[-2][0]) * (X.shape[-2] - 1) / (output_shape[-2] - 1)) if output_shape[-2] > 1 else Tensor([0.5 * (roi[-2][0] + roi[-2][1]) * (X.shape[-2] - 1)])
|
|
return x_out.clip(0, X.shape[-1]-1), y_out.clip(0, X.shape[-2]-1)
|
|
if roi is not None:
|
|
roi = safe_numpy(roi)
|
|
roi = [(st,ed) for st, ed in zip(roi[:len(roi)//2], roi[len(roi)//2:])]
|
|
roi_ = [(1,1)] * 4
|
|
if axes is not None:
|
|
for a,r in zip(axes, roi):
|
|
roi_[a] = r
|
|
roi = roi_
|
|
if scales is not None:
|
|
scales = safe_numpy(scales).tolist()
|
|
if axes is not None:
|
|
scales_ = [1]*X.ndim
|
|
for a,s in zip(axes, scales):
|
|
scales_[a] = s
|
|
scales = scales_
|
|
elif sizes is not None:
|
|
sizes = [int(i) for i in safe_numpy(sizes)]
|
|
scales = []
|
|
if axes is not None:
|
|
sizes_ = [1]*X.ndim
|
|
for a,s in zip(axes, sizes):
|
|
sizes_[a] = s
|
|
scales.append(s/X.shape[a])
|
|
sizes = sizes_
|
|
else: scales = [si/xs for xs, si in zip(X.shape, sizes)]
|
|
if keep_aspect_ratio_policy == "not_larger":
|
|
scale = min(scales)
|
|
sizes = _round(Tensor(list(X.shape[-2:]))*scale, 0.5, "round_up")
|
|
sizes = list(X.shape[:-2]) + [int(i) for i in safe_numpy(sizes)]
|
|
elif keep_aspect_ratio_policy == "not_smaller":
|
|
scale = max(scales)
|
|
sizes = _round(Tensor(list(X.shape[-2:]))*scale, 0.5, "round_up")
|
|
sizes = list(X.shape[:-2]) + [int(i) for i in safe_numpy(sizes)]
|
|
output_shape = sizes if sizes else [math.floor(x*s) for x,s in zip(X.shape, scales)]
|
|
output_shape_ = sizes if sizes else [x*s for x,s in zip(X.shape, scales)]
|
|
scales_ = [os/xs for xs, os in zip(X.shape, output_shape)]
|
|
x_out = Tensor.arange(output_shape[-1]).cast(dtypes.default_float)
|
|
y_out = Tensor.arange(output_shape[-2]).cast(dtypes.default_float)
|
|
if mode == "nearest":
|
|
x_out, y_out = _coordinate_transformation(x_out, y_out, output_shape, scales_, roi)
|
|
x_out = _nearest_mode(x_out, nearest_mode, X.shape[-1])
|
|
y_out = _nearest_mode(y_out, nearest_mode, X.shape[-1])
|
|
return _nearest_gather(X, x_out, y_out)
|
|
if mode == "linear":
|
|
x_out, y_out = _coordinate_transformation(x_out, y_out, output_shape_, scales, roi)
|
|
ret = []
|
|
for y in safe_numpy(y_out):
|
|
for x in safe_numpy(x_out):
|
|
x_floor, y_floor = int(x), int(y)
|
|
y_shrink = (0, X.shape[2]) if X.shape[2] == 1 else (y_floor, y_floor+2) if y != y_floor else (y_floor, y_floor+1)
|
|
x_shrink = (x_floor, x_floor+2) if x != x_floor else (x_floor, x_floor+1)
|
|
shrink_args = ((0, X.shape[0]), (0, X.shape[1]), y_shrink, x_shrink)
|
|
corners = safe_numpy(X.shrink(shrink_args))
|
|
x1, x2, y1, y2 = x_floor, x_floor+1, y_floor, y_floor+1
|
|
if x == x_floor and y == y_floor: # TODO https://en.wikipedia.org/wiki/Bilinear_interpolation#Weighted_mean maybe do weighted mean?
|
|
ret.append(corners[0,0,0,0])
|
|
elif x == x_floor:
|
|
ret.append((corners[0,0,0,0] * (y2 - y) + corners[0,0,1,0] * (y - y1)) / (y2 - y1))
|
|
elif y == y_floor:
|
|
ret.append((corners[0,0,0,0] * (x2 - x) + corners[0,0,0,1] * (x - x1)) / (x2 - x1))
|
|
else:
|
|
ret.append((corners[0,0,0,0] * (x2 - x) * (y2 - y) + corners[0,0,0,1] * (x - x1) * (y2 - y) + corners[0,0,1,0] * (x2 - x) * (y - y1) + corners[0,0,1,1] * (x - x1) * (y - y1)) / ((x2 - x1) * (y2 - y1)))
|
|
return Tensor(ret).reshape(output_shape)
|
|
if mode == "cubic":
|
|
raise NotImplementedError("cubic interpolation is not implemented")
|
|
|
|
def CenterCropPad(t: Tensor, shape: Tensor, axes=None):
|
|
if not axes: axes = list(range(t.ndim))
|
|
shrink_arg = [(0,i) for i in t.shape]
|
|
pad_arg = [(0,0)] * t.ndim
|
|
shape = safe_numpy(shape).tolist()
|
|
for s, x in zip(shape, axes):
|
|
if s < t.shape[x]: shrink_arg[x] = (t.shape[x]//2 - s//2, t.shape[x]//2 + s//2) if s%2 == 0 else (t.shape[x]//2 - s//2 - 1, t.shape[x]//2 + s//2)
|
|
elif s > t.shape[x]: pad_arg[x] = ((s - t.shape[x])//2, (s - t.shape[x])//2) if (s - t.shape[x])% 2 == 0 else ((s - t.shape[x])//2, (s - t.shape[x])//2 + 1)
|
|
return t.shrink(tuple(shrink_arg)).pad(tuple(pad_arg))
|
|
|
|
def OneHot(indices: Tensor, depth: Tensor, values: Tensor, axis=-1):
|
|
depth = int(safe_numpy(depth).item())
|
|
indices, rank = (indices < 0).where(indices+depth, indices), indices.ndim
|
|
if axis < 0: axis += rank + 1
|
|
ls, rs = indices.shape[0:axis], indices.shape[axis: rank]
|
|
cond = indices[:,None] == Tensor.arange(depth).reshape((1,) * len(ls) + (depth,) + (1,) * len(rs))
|
|
return cond.where(values[1], values[0]).cast(values.dtype)
|
|
|
|
def Erf(x: Tensor):
|
|
t = 1.0 / (1.0 + 0.3275911 * x.abs())
|
|
term1 = 0.254829592 * t
|
|
term2 = -0.284496736 * t ** 2
|
|
term3 = 1.421413741 * t ** 3
|
|
term4 = -1.453152027 * t ** 4
|
|
term5 = 1.061405429 * t ** 5
|
|
y = (term1 + term2 + term3 + term4 + term5)
|
|
z = 1.0 - y * Tensor.exp(-x * x)
|
|
return (x > 0).where(z, -z)
|
|
|
|
def Compress(inp: Tensor, condition: Tensor, axis=None):
|
|
if axis is None:
|
|
inp = inp.flatten()
|
|
axis = 0
|
|
|
|
axis = axis + inp.ndim if axis < 0 else axis
|
|
|
|
con_np = safe_numpy(condition)
|
|
con = Tensor(np.arange(condition.shape[0])[con_np]) # no boolean indexing in Tensor
|
|
return inp.__getitem__(tuple([slice(None) if i != axis else con for i in range(inp.ndim)]))
|
|
|
|
type_map = {TensorProto.DOUBLE: dtypes.double, TensorProto.FLOAT: dtypes.float32}
|
|
def EyeLike(x: Tensor, dtype=None, k=0):
|
|
if dtype is None: dtype = x.dtype
|
|
else: dtype = type_map[dtype]
|
|
shape = x.shape
|
|
dim = min(x.shape)
|
|
if shape[0] == shape[1]:
|
|
return Tensor.eye(dim=dim, dtype=dtype)
|
|
diff = (shape[0]-dim, shape[1]-dim)
|
|
padarg = tuple([(d, d) if d == 0 else (k, d-k) for d in diff])
|
|
return Tensor.eye(dim=dim, dtype=dtype).pad(padarg)
|
|
|
|
def Upsample(X, scales, mode): return Resize(X=X, scales=scales, mode=mode)
|
|
|
|
# Needs work
|
|
def IsInf(x: Tensor, detect_negative=1, detect_positive=1):
|
|
ret = (x == float("inf"))*detect_positive + (x == float("-inf"))*detect_negative + Tensor.zeros(*x.shape)
|
|
return ret.cast(dtypes.bool)
|
|
|
|
def DequantizeLinear(x: Tensor, x_scale: Tensor, x_zero_point: Union[Tensor, int] = 0, axis=1):
|
|
axis = axis + x.ndim if axis < 0 else axis
|
|
x = x.cast(dtypes.float)
|
|
if isinstance(x_zero_point, Tensor): x_zero_point.cast(dtypes.float)
|
|
x_sc = x_scale.reshape(*[1]*axis, *x_scale.shape, *[1]*(x.ndim - axis - x_scale.ndim))
|
|
x_zer = x_zero_point.reshape(*[1]*axis, *x_scale.shape, *[1]*(x.ndim - axis - x_scale.ndim)) if isinstance(x_zero_point, Tensor) else x_zero_point
|
|
return ((x - x_zer) * x_sc).cast(x_scale.dtype)
|
|
|
|
# Needs work
|
|
def IsNaN(x: Tensor):
|
|
return (x < float("-inf")).cast(dtypes.bool)
|
|
|
|
# copied from https://github.com/onnx/onnx/blob/main/onnx/reference/ops/op_image_decoder.py
|
|
# without importing PIL we'll have to manually decode a bunch of image formats like PNG, JPEG, WebP, etc
|
|
def ImageDecoder(encoded_stream: Tensor, pixel_format="RGB"):
|
|
try:
|
|
import PIL.Image
|
|
except ImportError as e:
|
|
raise ImportError("Pillow must be installed to use the reference implementation of the ImageDecoder operator") from e
|
|
img = PIL.Image.open(io.BytesIO(safe_numpy(encoded_stream).tobytes()))
|
|
if pixel_format == "BGR":
|
|
return Tensor(np.array(img))[:, :, ::-1]
|
|
if pixel_format == "RGB":
|
|
return Tensor(np.array(img))
|
|
if pixel_format == "Grayscale":
|
|
img = img.convert("L")
|
|
decoded = Tensor(np.array(img))
|
|
return decoded.unsqueeze(-1) # (H, W) to (H, W, 1)
|
|
raise ValueError(f"pixel_format={pixel_format!r} is not supported.")
|
|
|
|
def AffineGrid(theta: Tensor, size: Tensor, align_corners=0):
|
|
_, _, *data_sz = safe_numpy(size).tolist()
|
|
size_zeros, original_grid = Tensor.zeros(data_sz), Tensor.ones(data_sz)
|
|
stackable = [original_grid]
|
|
for dim, dim_sz in enumerate(data_sz):
|
|
a = Tensor.arange(-1, 1.0001, 2/(dim_sz-1)) if align_corners == 1 else Tensor.arange(-1+1/dim_sz, 1, 2/dim_sz)
|
|
if dim == 0: stackable = [a.reshape(dim_sz, *[1]*(len(data_sz)-1)) + size_zeros, *stackable]
|
|
elif dim == 1: stackable = [a.reshape(1, dim_sz, *[1]*(len(data_sz)-2)) + size_zeros, *stackable]
|
|
else: stackable = [a.reshape(1, dim_sz) + size_zeros, *stackable]
|
|
original_grid = Tensor.stack(stackable, dim=len(data_sz))
|
|
if original_grid.ndim == 3:
|
|
N, dim_2d, dim_homo = theta.shape
|
|
assert dim_2d == 2 and dim_homo == 3
|
|
H, W, dim_homo = original_grid.shape
|
|
assert dim_homo == 3
|
|
original_grid = original_grid.reshape(H*W, dim_homo).transpose()
|
|
return theta.matmul(original_grid).permute(0,2,1).reshape(N, H, W, dim_2d)
|
|
assert original_grid.ndim == 4
|
|
N, dim_3d, dim_homo = theta.shape
|
|
assert dim_3d == 3 and dim_homo == 4
|
|
D, H, W, dim_homo = original_grid.shape
|
|
assert dim_homo == 4
|
|
original_grid = original_grid.reshape(D*H*W, dim_homo).transpose()
|
|
return theta.matmul(original_grid).permute(0,2,1).reshape(N, D, H, W, dim_3d)
|
|
|
|
# **************** com.microsoft Ops ****************
|
|
|
|
def SkipLayerNormalization(x:Tensor, skip:Tensor, gamma, beta:Optional[Tensor]=None, bias:Optional[Tensor]=None, epsilon=None):
|
|
if epsilon is None: epsilon=1e-12
|
|
x = x + skip + bias
|
|
return x.layernorm(eps=epsilon) * gamma + beta, None, None, x
|
|
|
|
def FastGelu(x:Tensor, bias:Optional[Tensor]=None):
|
|
x = x + bias
|
|
return 0.5 * x * (1 + (x * 0.797885 + 0.035677 * x ** 3).tanh())
|
|
|
|
def EmbedLayerNormalization(input_ids: Tensor, segment_ids:Optional[Tensor]=None, word_embedding:Tensor=None, position_embedding:Tensor=None, segment_embedding:Optional[Tensor]=None, gamma=None, beta=None, mask:Optional[Tensor]=None, position_ids:Optional[Tensor]=None, epsilon=None, mask_index_type=None):
|
|
# https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.EmbedLayerNormalization
|
|
assert (segment_ids is None) is (segment_embedding is None)
|
|
assert (mask is None) is (mask_index_type is None)
|
|
assert mask is None, "functionality not supported yet" # TODO
|
|
input_shape = input_ids.shape
|
|
_bsz, seq_length = input_shape[0], input_shape[1]
|
|
compute_seg_emb = (segment_embedding is not None and segment_ids is not None)
|
|
vocab_size, max_position_embeddings, type_vocab_size = word_embedding.shape[0], position_embedding.shape[0], (segment_embedding.shape[0] if compute_seg_emb else None)
|
|
|
|
def embedding(x:Tensor, vocab_size, weight:Tensor)->Tensor: # TODO from nn.Embedding. Could probably upstream this to Tensor
|
|
vocab_counter = Tensor.arange(vocab_size, dtype=x.dtype, requires_grad=False).reshape(1, 1, vocab_size).expand(*x.shape, vocab_size)
|
|
return (vocab_counter == x.unsqueeze(2).expand(*x.shape, vocab_size)) @ weight
|
|
|
|
# bert embedding layer
|
|
if epsilon is None: epsilon = 1e-12
|
|
if position_ids is None: position_ids = Tensor.arange(seq_length, requires_grad=False).unsqueeze(0).expand(*input_shape)
|
|
wrd_embedding_res = embedding(input_ids, vocab_size, word_embedding)
|
|
pos_embedding_res = embedding(position_ids, max_position_embeddings, position_embedding)
|
|
seg_embedding_res = embedding(segment_ids, type_vocab_size, segment_embedding) if compute_seg_emb else None
|
|
|
|
embedding_sum = wrd_embedding_res + pos_embedding_res + seg_embedding_res
|
|
out = embedding_sum.layernorm(eps=epsilon) * gamma + beta
|
|
return out, None, embedding_sum
|
|
|
|
def Attention(x:Tensor, weights, bias:Optional[Tensor]=None, mask_index:Optional[Tensor]=None, past:Optional[Tensor]=None, relative_position_bias:Optional[Tensor]=None, past_sequence_length:Optional[Tensor]=None, do_rotary=None, mask_filter_value=None, num_heads=None, past_present_share_buffer=None, qkv_hidden_sizes=None, scale=None, unidirectional=None):
|
|
# https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.Attention
|
|
assert num_heads is not None # required
|
|
assert (qkv_hidden_sizes is None and past is not None) or (qkv_hidden_sizes is not None)
|
|
assert relative_position_bias==do_rotary==past_sequence_length==mask_filter_value==past_present_share_buffer==scale==None, "functionality not supported yet" # TODO strange params
|
|
hidden_size, v_hidden_size = qkv_hidden_sizes[1:] if qkv_hidden_sizes is not None else 2*(weights.shape[1] // 3,)
|
|
|
|
if unidirectional: # gpt-style
|
|
assert hidden_size == v_hidden_size
|
|
xqkv = x.linear(weights, bias)
|
|
xq, xk, xv = [xqkv.slice([None, None, (i*hidden_size, (i+1)*hidden_size)]) for i in range(3)]
|
|
else: # bert-style
|
|
wq, wk, wv = weights[:,:hidden_size], weights[:,hidden_size:hidden_size+v_hidden_size], weights[:,hidden_size+v_hidden_size:]
|
|
bq, bk, bv = (bias[:hidden_size], bias[hidden_size:hidden_size+v_hidden_size], bias[hidden_size+v_hidden_size]) if bias is not None else None
|
|
xq, xk, xv = [x.linear(w, b) for w, b in zip((wq, wk, wv), (bq, bk, bv))]
|
|
xq, xk, xv = [x.reshape(x.shape[0], x.shape[1], num_heads, -1).transpose(1, 2) for x in (xq, xk, xv)]
|
|
|
|
if past is not None:
|
|
xk, xv = Tensor.cat(past[0], xk, dim=-2), Tensor.cat(past[1], xv, dim=-2)
|
|
present = Tensor.cat(xk.unsqueeze(0), xv.unsqueeze(0))
|
|
|
|
def attn(query, key, value, attn_mask):
|
|
query_length, key_length = query.shape[-2], key.shape[-2]
|
|
cdim = max(query_length, key_length) + 1
|
|
attn_weights = query @ key.transpose(-1, -2) / math.sqrt(value.shape[-1])
|
|
# This is where Tensor.scaled_dot_product_attention differs:
|
|
causal_mask = Tensor.ones((cdim, cdim), requires_grad=False, dtype=dtypes.bool).tril(0)[key_length - query_length : key_length, :key_length]
|
|
return (Tensor.where(causal_mask, attn_weights, -float("inf")) + attn_mask).softmax(-1) @ value
|
|
|
|
bsz, _, seq_len, _ = xq.shape
|
|
out = attn(xq, xk, xv, mask_index).transpose(1, 2).reshape(bsz, seq_len, -1)
|
|
return out, present
|
|
|
|
# **************** ai.onnx.preview.training Ops ****************
|
|
|
|
# TODO not entirely sure these optimizers are correct
|
|
def Adagrad(R, T, *inputs, decay_factor=0.0, epsilon=0.0, norm_coefficient=0.0):
|
|
groups = len(inputs) // 3
|
|
grouped_inputs = [inputs[i::groups] for i in range(groups)]
|
|
T, R = safe_numpy(T), safe_numpy(R)
|
|
r = R / (1 + T * decay_factor)
|
|
ret = []
|
|
for X, G, H in grouped_inputs:
|
|
X.grad = norm_coefficient * X + G
|
|
X.grad.requires_grad, H.requires_grad = False, False # TODO manually turning off requires_grad, see TODO under (domain == "ai.onnx.preview.training") in onnx.py
|
|
H.assign(H.detach() + X.grad * X.grad).realize()
|
|
H_adaptive = H.sqrt() + epsilon
|
|
X.assign(X.detach() - r * X.grad / H_adaptive)
|
|
ret.extend([X, H])
|
|
ret = ret[::2] + ret[1::2]
|
|
return tuple(ret)
|
|
|
|
def Momentum(R, T, *inputs, alpha, beta, mode, norm_coefficient):
|
|
groups = len(inputs) // 3
|
|
grouped_inputs = [inputs[i::groups] for i in range(groups)]
|
|
T, R = safe_numpy(T), safe_numpy(R)
|
|
beta_adjusted = beta if T > 0 else 1
|
|
ret = []
|
|
for X, G, V in grouped_inputs:
|
|
X.grad = (norm_coefficient * X + G).realize()
|
|
X.grad.requires_grad, V.requires_grad = False, False
|
|
V.assign(alpha * V + beta_adjusted * X.grad).realize()
|
|
if mode == "standard": X.assign(X.detach() - R * V).realize()
|
|
elif mode == "nesterov": X.assign(X.detach() - R * (X.grad + alpha + V)).realize()
|
|
ret.extend([X, V])
|
|
ret = ret[::2] + ret[1::2]
|
|
return tuple(ret)
|
|
|
|
# copied from tinygrad/nn/optim.py: LAMB with some edits
|
|
def Adam(R, T, *inputs, alpha=0.9, beta=0.999, epsilon=0.0, norm_coefficient=0.0, norm_coefficient_post=0.0):
|
|
groups = len(inputs) // 4
|
|
grouped_inputs = [inputs[i::groups] for i in range(groups)]
|
|
T, R = safe_numpy(T), safe_numpy(R)
|
|
ret = []
|
|
for X, G, V, H in grouped_inputs:
|
|
X.grad = (norm_coefficient * X + G).realize()
|
|
V.requires_grad, H.requires_grad, X.grad.requires_grad = False, False, False
|
|
V.assign(alpha * V + (1.0 - alpha) * X.grad).realize()
|
|
H.assign(beta * H + (1.0 - beta) * (X.grad * X.grad)).realize()
|
|
up = (V / (1.0 - alpha**T)) / ((H / (1.0 - beta**T)).sqrt() + epsilon) if T > 0 else V / (H.sqrt() + epsilon)
|
|
X.assign(X.detach() - R * up).realize()
|
|
X = (1 - norm_coefficient_post) * X
|
|
ret.extend([X, V, H])
|
|
ret = ret[::3] + ret[1::3] + ret[2::3]
|
|
return tuple(ret)
|