mirror of https://github.com/Jittor/Jittor
1660 lines
53 KiB
Python
1660 lines
53 KiB
Python
# ***************************************************************
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# Copyright (c) 2021 Jittor. All Rights Reserved.
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# Maintainers:
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# Dun Liang <randonlang@gmail.com>.
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# Wenyang Zhou <576825820@qq.com>
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# Guoye Yang <498731903@qq.com>
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#
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# This file is subject to the terms and conditions defined in
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# file 'LICENSE.txt', which is part of this source code package.
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# ***************************************************************
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import jittor as jt
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import numpy as np
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import math
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from collections.abc import Sequence,Iterable
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def index_add_(x, dim, index, tensor):
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""" Take out each index subscript vector of the dim dimension and add the corresponding tensor variable.
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Example:
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x = jt.ones((5,3))
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tensor = jt.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
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index = jt.array([0,4,2])
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x.index_add_(0, index, tensor)
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print(x)
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>>> jt.Var([[ 2., 3., 4.],
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[ 1., 1., 1.],
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[ 8., 9., 10.],
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[ 1., 1., 1.],
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[ 5., 6., 7.]])
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"""
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assert len(index.shape) == 1
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assert tensor.shape[0] == index.shape[0]
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x[(slice(None,),)*dim+(index,)] += tensor
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jt.Var.index_add_ = index_add_
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def __copy__(x):
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return x.copy().detach()
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jt.Var.__copy__ = __copy__
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def __deepcopy__(x,memo):
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result = x.copy().detach()
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memo[id(x)]=result
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return result
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jt.Var.__deepcopy__ = __deepcopy__
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def __len__(x):
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return x.shape[0]
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jt.Var.__len__ = __len__
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def __iter__(x):
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result = []
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for i in range(x.shape[0]):
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result.append(x[i])
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return result.__iter__()
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jt.Var.__iter__ = __iter__
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def all(x, dim=[]):
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return x.all_(dim).bool()
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jt.Var.all = all
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def any(x,dim):
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return x.any_(dim).bool()
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jt.Var.any = any
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def bernoulli(input):
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return (input>jt.rand_like(input)).cast(input.dtype)
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def repeat(x, *shape):
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r'''
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Repeats this var along the specified dimensions.
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Args:
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x (var): jittor var.
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shape (tuple): int or tuple. The number of times to repeat this var along each dimension.
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Example:
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>>> x = jt.array([1, 2, 3])
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>>> x.repeat(4, 2)
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[[ 1, 2, 3, 1, 2, 3],
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[ 1, 2, 3, 1, 2, 3],
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[ 1, 2, 3, 1, 2, 3],
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[ 1, 2, 3, 1, 2, 3]]
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>>> x.repeat(4, 2, 1).size()
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[4, 2, 3,]
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'''
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if len(shape) == 1 and isinstance(shape[0], Sequence):
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shape = shape[0]
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len_x_shape = len(x.shape)
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len_shape = len(shape)
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x_shape = x.shape
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rep_shape = shape
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if len_x_shape < len_shape:
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x_shape = (len_shape - len_x_shape) * [1] + x.shape
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x = x.broadcast(x_shape)
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elif len_x_shape > len_shape:
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rep_shape = (len_x_shape - len_shape) * [1] + list(shape)
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reshape_shape = []
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broadcast_shape = []
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for x_s,r_s in zip(x_shape,rep_shape):
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if r_s != 1:
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reshape_shape.append(1)
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broadcast_shape.append(r_s)
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reshape_shape.append(x_s)
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broadcast_shape.append(1)
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x = x.reshape(reshape_shape)
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x = x.broadcast(broadcast_shape)
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tar_shape = (np.array(x_shape) * np.array(rep_shape)).tolist()
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x = x.reshape(tar_shape)
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return x
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jt.Var.repeat = repeat
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def repeat_interleave(x,repeats,dim=None):
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# TODO repeats is jt.Var
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assert isinstance(repeats,int)
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if dim == None:
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x = x.reshape(-1)
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dim=0
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if dim<0: dim+=x.ndim
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tar_shape = list(x.shape)
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x_shape = list(x.shape)
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tar_shape[dim] = tar_shape[dim]*repeats
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dims = []
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for i in range(len(tar_shape)):
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if dim==i:
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dims.append(f"i{i}/{repeats}")
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else:
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dims.append(f"i{i}")
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return x.reindex(tar_shape,dims)
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jt.Var.repeat_interleave = repeat_interleave
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def chunk(x, chunks, dim=0):
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r'''
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Splits a var into a specific number of chunks. Each chunk is a view of the input var.
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Last chunk will be smaller if the var size along the given dimension dim is not divisible by chunks.
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Args:
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input (var) – the var to split.
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chunks (int) – number of chunks to return.
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dim (int) – dimension along which to split the var.
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Example:
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>>> x = jt.random((10,3,3))
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>>> res = jt.chunk(x, 2, 0)
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>>> print(res[0].shape, res[1].shape)
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[5,3,3,] [5,3,3,]
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'''
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if dim<0:
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dim += x.ndim
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l = x.shape[dim]
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res = []
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if l <= chunks:
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for i in range(l):
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res.append(x[(slice(None,),)*dim+([i,],)])
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else:
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nums = (l-1) // chunks + 1
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for i in range(chunks-1):
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res.append(x[(slice(None,),)*dim+(slice(i*nums,(i+1)*nums),)])
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if (i+1)*nums < l:
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res.append(x[(slice(None,),)*dim+(slice((i+1)*nums,None),)])
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return res
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jt.Var.chunk = chunk
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def expand(x, *shape):
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''' Expand and broadcast this array, -1 represents this dimension is not changed.
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Example::
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a = jt.zeros((3,1))
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b = a.expand(3, 4)
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assert b.shape == (3,4)
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b = a.expand(-1, 4)
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assert b.shape == (3,4)
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b = a.expand((3, 4))
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assert b.shape == (3,4)
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b = a.expand((-1, 4))
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assert b.shape == (3,4)
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'''
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if len(shape) == 1 and isinstance(shape[0], (tuple,list,jt.NanoVector)):
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shape = shape[0]
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shape = list(shape)
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for i in range(len(shape)):
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if shape[i] == -1:
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shape[i] = x.shape[i]
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return x.broadcast(shape)
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jt.Var.expand = expand
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def t(x):
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pose = [i for i in range(x.ndim)]
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pose[-1], pose[-2] = pose[-2], pose[-1]
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return x.transpose(*pose)
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jt.Var.t = t
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def median(x,dim=None,keepdim=False, keepdims=False):
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keepdim = keepdim or keepdims
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if dim is None:
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x = x.reshape(-1)
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dim=0
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_,x = x.argsort(dim)
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slices = [slice(None) for i in range(dim-1)]
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k = (x.shape[dim]-1)//2
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if keepdim:
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slices.append(slice(k,k+1))
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else:
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slices.append(k)
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return x[tuple(slices)]
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jt.Var.median = median
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def stack(x, dim=0):
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r'''
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Concatenates sequence of vars along a new dimension.
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All vars need to be of the same size.
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Args:
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x (sequence of vars) – sequence of vars to concatenate.
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dim (int) – dimension to insert. Has to be between 0 and the number of dimensions of concatenated vars (inclusive).
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Example:
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>>> a1 = jt.array([[1,2,3]])
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>>> a2 = jt.array([[4,5,6]])
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>>> jt.stack([a1, a2], 0)
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[[[1 2 3]
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[[4 5 6]]]
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'''
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assert isinstance(x, Sequence)
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if len(x) < 2:
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return x[0].unsqueeze(dim)
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res = [x_.unsqueeze(dim) for x_ in x]
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return jt.contrib.concat(res, dim=dim)
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jt.Var.stack = stack
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def flip(x, dim=0):
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r'''
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Reverse the order of a n-D var along given axis in dims.
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Args:
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input (var) – the input var.
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dims (a list or tuple) – axis to flip on.
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Example:
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>>> x = jt.array([[1,2,3,4]])
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>>> x.flip(1)
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[[4 3 2 1]]
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'''
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if isinstance(dim, int):
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dim = [dim]
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for i in range(len(dim)):
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if dim[i]<0:
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dim[i] += x.ndim
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assert dim[i]>=0 and dim[i]<x.ndim
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dim = set(dim)
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tar_dims = []
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for i in range(len(x.shape)):
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if i in dim:
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tar_dims.append(f"xshape{i}-1-i{i}")
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else:
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tar_dims.append(f"i{i}")
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return x.reindex(x.shape, tar_dims)
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jt.Var.flip = flip
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def cross(input, other, dim=-1):
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r'''
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Returns the cross product of vectors in dimension dim of input and other.
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the cross product can be calculated by (a1,a2,a3) x (b1,b2,b3) = (a2b3-a3b2, a3b1-a1b3, a1b2-a2b1)
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input and other must have the same size, and the size of their dim dimension should be 3.
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If dim is not given, it defaults to the first dimension found with the size 3.
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Args:
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input (Tensor) – the input tensor.
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other (Tensor) – the second input tensor
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dim (int, optional) – the dimension to take the cross-product in.
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out (Tensor, optional) – the output tensor.
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Example:
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>>> input = jt.random((6,3))
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>>> other = jt.random((6,3))
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>>> jt.cross(input, other, dim=1)
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[[-0.42732686 0.6827885 -0.49206433]
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[ 0.4651107 0.27036983 -0.5580432 ]
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[-0.31933784 0.10543461 0.09676848]
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[-0.58346975 -0.21417202 0.55176204]
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[-0.40861478 0.01496297 0.38638002]
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[ 0.18393655 -0.04907863 -0.17928357]]
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>>> jt.cross(input, other)
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[[-0.42732686 0.6827885 -0.49206433]
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[ 0.4651107 0.27036983 -0.5580432 ]
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[-0.31933784 0.10543461 0.09676848]
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[-0.58346975 -0.21417202 0.55176204]
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[-0.40861478 0.01496297 0.38638002]
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[ 0.18393655 -0.04907863 -0.17928357]]
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'''
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assert input.shape==other.shape, "input shape and other shape must be same"
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if dim < 0: dim += len(input.shape)
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assert input.shape[dim] == 3, "input dim shape must be 3"
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a1 = input[(slice(None,),)*dim+(1,)]*other[(slice(None,),)*dim+(2,)]-input[(slice(None,),)*dim+(2,)]*other[(slice(None,),)*dim+(1,)]
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a2 = input[(slice(None,),)*dim+(2,)]*other[(slice(None,),)*dim+(0,)]-input[(slice(None,),)*dim+(0,)]*other[(slice(None,),)*dim+(2,)]
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a3 = input[(slice(None,),)*dim+(0,)]*other[(slice(None,),)*dim+(1,)]-input[(slice(None,),)*dim+(1,)]*other[(slice(None,),)*dim+(0,)]
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return jt.contrib.concat([a1.unsqueeze(dim),a2.unsqueeze(dim),a3.unsqueeze(dim)], dim=dim)
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jt.Var.cross = cross
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def normalize(input, p=2, dim=1, eps=1e-30):
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r'''
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Performs L_p normalization of inputs over specified dimension.
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Args:
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input – input array of any shape
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p (float) – the exponent value in the norm formulation. Default: 2
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dim (int) – the dimension to reduce. Default: 1
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eps (float) – small value to avoid division by zero. Default: 1e-12
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Example:
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>>> x = jt.random((6,3))
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[[0.18777736 0.9739261 0.77647036]
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[0.13710196 0.27282116 0.30533272]
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[0.7272278 0.5174613 0.9719775 ]
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[0.02566639 0.37504175 0.32676998]
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[0.0231761 0.5207773 0.70337296]
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[0.58966476 0.49547017 0.36724383]]
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>>> jt.normalize(x)
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[[0.14907198 0.7731768 0.61642134]
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[0.31750825 0.63181424 0.7071063 ]
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[0.5510936 0.39213243 0.736565 ]
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[0.05152962 0.7529597 0.656046 ]
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[0.02647221 0.59484214 0.80340654]
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[0.6910677 0.58067477 0.4303977 ]]
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'''
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return input / input.norm(p, dim, True, eps)
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jt.Var.normalize = normalize
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def unbind(x, dim=0):
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r'''
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Removes a var dimension.
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Returns a tuple of all slices along a given dimension, already without it.
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Args:
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input (var) – the var to unbind
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dim (int) – dimension to remove
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Example:
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a = jt.random((3,3))
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b = jt.unbind(a, 0)
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'''
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if dim < 0: dim += len(x.shape)
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return [x[(slice(None),)*dim+(i,)] for i in range(x.shape[dim])]
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jt.Var.unbind = unbind
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def make_grid(x, nrow=8, padding=2, normalize=False, range=None, scale_each=False, pad_value=0):
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assert isinstance(range, tuple) or range is None
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assert scale_each == False
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if isinstance(x, list): x = jt.stack(x)
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assert isinstance(x, jt.Var)
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if x.ndim < 4: return x
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if x.ndim == 4 and x.shape[0] <= 1: return x
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nrow = min(nrow, x.shape[0])
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if normalize:
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if range is None: x = (x - x.min()) / (x.max() - x.min())
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else: x = (x - range[0]) / (range[1] - range[0])
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b,c,h,w = x.shape
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ncol = math.ceil(b / nrow)
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return x.reindex([c, h*ncol+(ncol+1)*padding, w*nrow+(nrow+1)*padding],
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[f"i1/{padding+h}*{nrow}+i2/{padding+w}", "i0",
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f"i1-i1/{padding+h}*{padding+h}-{padding}", f"i2-i2/{padding+w}*{padding+w}-{padding}"], overflow_value=pad_value)
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def save_image(
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x,
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filepath,
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nrow: int = 8,
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padding: int = 2,
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normalize: bool = False,
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range = None,
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scale_each = False,
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pad_value = 0,
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format = None
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):
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from PIL import Image
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grid = make_grid(x, nrow=nrow, padding=padding, pad_value=pad_value,
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normalize=normalize, range=range, scale_each=scale_each)
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ndarr = (grid*255+0.5).clamp(0, 255).permute(1, 2, 0).uint8().numpy()
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im = Image.fromarray(ndarr)
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im.save(filepath, format=format)
|
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|
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def _ntuple(n):
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def parse(x):
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if isinstance(x, Iterable):
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return x
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||
return tuple([x]*n)
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return parse
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_single = _ntuple(1)
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_pair = _ntuple(2)
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_triple = _ntuple(3)
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_quadruple = _ntuple(4)
|
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def unique(x):
|
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r'''
|
||
Returns the unique elements of the input tensor.
|
||
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||
Args:
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||
|
||
x– the input tensor.
|
||
'''
|
||
x = x.reshape(-1)
|
||
_,x = jt.argsort(x)
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index,= jt.index((x.shape[0],))
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y = x[1:][x[index[1:]] != x[index[:-1]]]
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x = jt.contrib.concat([x[:1],y],dim=0)
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return x
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jt.Var.unique = unique
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def hypot(a,b):
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return jt.sqrt(a.sqr()+b.sqr())
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def rad2deg(x):
|
||
return 180 * x / np.pi
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||
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jt.Var.rad2deg = rad2deg
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||
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def deg2rad(x):
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||
return x * np.pi / 180.
|
||
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jt.Var.deg2rad = deg2rad
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def arctan2(y,x):
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||
angle = jt.zeros(x.shape,dtype=x.dtype)
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||
mask = x!=0.0
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||
if angle[mask].numel()>0:
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angle[mask] = jt.arctan(y[mask]/x[mask])
|
||
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||
mask = (y<0) & (x<0)
|
||
if angle[mask].numel()>0:
|
||
angle[mask] -= np.pi
|
||
|
||
mask = (y>0) &(x<0)
|
||
if angle[mask].numel()>0:
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||
angle[mask] +=np.pi
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||
return angle
|
||
|
||
|
||
|
||
def nonzero(x):
|
||
r'''
|
||
Return the index of the elements of input tensor which are not equal to zero.
|
||
'''
|
||
x = jt.where(x)
|
||
x = [xx.unsqueeze(1) for xx in x]
|
||
if len(x)<2:
|
||
return x[0]
|
||
x = jt.contrib.concat(x,dim=1)
|
||
return x
|
||
|
||
jt.Var.nonzero = nonzero
|
||
|
||
|
||
def arange(start=0, end=None, step=1,dtype=None):
|
||
if end is None:
|
||
end,start = start,0
|
||
l = round((end-start)//step)+1
|
||
if (l-1)*step+start>=end:
|
||
l-=1
|
||
x = jt.index((l,),0)
|
||
x = x*step+start
|
||
if dtype is not None:
|
||
x= x.cast(dtype)
|
||
return x
|
||
|
||
def log2(x):
|
||
return jt.log(x)/math.log(2.0)
|
||
|
||
jt.Var.log2 = log2
|
||
|
||
def meshgrid(*tensors):
|
||
r'''
|
||
Take N tensors, each of which can be 1-dimensional vector, and create N n-dimensional grids,
|
||
where the i th grid is defined by expanding the i th input over dimensions defined by other inputs.
|
||
'''
|
||
if len(tensors)==1 and isinstance(tensors[0], list):
|
||
tensors = tensors[0]
|
||
size = len(tensors)
|
||
shape = []
|
||
for i in range(size):
|
||
assert isinstance(tensors[i],jt.Var) and tensors[i].ndim==1
|
||
shape.append(tensors[i].shape[0])
|
||
grids = []
|
||
view_shape = [1]*size
|
||
for i in range(size):
|
||
vs = view_shape[:]
|
||
vs[i]=-1
|
||
grids.append(tensors[i].reshape(vs).expand(shape))
|
||
|
||
return grids
|
||
|
||
|
||
def split(d,split_size,dim):
|
||
r'''
|
||
Splits the tensor into chunks. Each chunk is a view of the original tensor.
|
||
|
||
If split_size is an integer type, then tensor will be split into equally sized chunks (if possible). Last chunk will be smaller if the tensor size along the given dimension dim is not divisible by split_size.
|
||
|
||
If split_size is a list, then tensor will be split into len(split_size) chunks with sizes in dim according to split_size_or_sections.
|
||
|
||
Args:
|
||
d (Tensor) – tensor to split.
|
||
|
||
split_size (int) or (list(int)) – size of a single chunk or list of sizes for each chunk
|
||
|
||
dim (int) – dimension along which to split the tensor.
|
||
'''
|
||
if isinstance(split_size,int):
|
||
shape = d.shape[dim]
|
||
if shape % split_size == 0:
|
||
split_size = [split_size]*(shape//split_size)
|
||
else:
|
||
split_size = [split_size]*(shape//split_size)+[shape%split_size]
|
||
if isinstance(split_size, Iterable):
|
||
assert sum(split_size)==d.shape[dim]
|
||
|
||
if dim<0:
|
||
dim+=d.ndim
|
||
|
||
ans = []
|
||
last = 0
|
||
for i in split_size:
|
||
if i==0:
|
||
shape = list(d.shape)
|
||
shape[dim]=0
|
||
new_d = jt.zeros(tuple(shape),dtype=d.dtype)
|
||
ans.append(new_d)
|
||
continue
|
||
|
||
ss = (slice(None),)*dim+(slice(last,last+i),)
|
||
new_d = d[ss]
|
||
last +=i
|
||
ans.append(new_d)
|
||
return tuple(ans)
|
||
|
||
jt.Var.split = split
|
||
|
||
def tolist(x):
|
||
return x.numpy().tolist()
|
||
jt.Var.tolist = tolist
|
||
|
||
def view_as(x,y):
|
||
return x.reshape(y.shape)
|
||
jt.Var.view_as = view_as
|
||
|
||
def diag(x,diagonal=0):
|
||
assert x.ndim==1 or (x.ndim==2 and x.shape[0]==x.shape[1])
|
||
d = diagonal if diagonal>=0 else -diagonal
|
||
d_str = f'+{diagonal}' if diagonal>=0 else f'{diagonal}'
|
||
|
||
if x.ndim==1:
|
||
output_shape = (x.shape[0]+d,)*2
|
||
return x.reindex(output_shape,[f'i1-{d}' if diagonal>=0 else f'i0-{d}'],overflow_conditions=[f'i0{d_str}!=i1'])
|
||
else:
|
||
output_shape = (x.shape[0]-d,)
|
||
return x.reindex(output_shape,[f'i0+{d}' if diagonal<=0 else 'i0',f'i0+{d}' if diagonal>=0 else 'i0'])
|
||
|
||
jt.Var.diag = diag
|
||
|
||
|
||
def topk(input, k, dim=None, largest=True, sorted=True):
|
||
if input.numel()==0:
|
||
return jt.array([],dtype=input.dtype),jt.array([],dtype='int32')
|
||
if dim is None:
|
||
dim = -1
|
||
if dim<0:
|
||
dim+=input.ndim
|
||
|
||
index,values = jt.argsort(input,dim=dim,descending=largest)
|
||
dims = (slice(None),)*dim+(slice(0,k),)
|
||
indices = index[dims]
|
||
values = values[dims]
|
||
return values,indices
|
||
|
||
jt.Var.topk = topk
|
||
|
||
def kthvalue(input, k, dim=None, keepdim=False, keepdims=False):
|
||
keepdim = keepdim or keepdims
|
||
if dim is None:
|
||
dim = -1
|
||
if dim<0:
|
||
dim+=input.ndim
|
||
index,values = jt.argsort(input,dim=dim)
|
||
dims = (slice(None),)*dim+(slice(k-1,k),)
|
||
indices = index[dims]
|
||
values = values[dims]
|
||
if not keepdim and indices.ndim>1:
|
||
indices = indices.squeeze(dim)
|
||
values = values.squeeze(dim)
|
||
return values,indices
|
||
|
||
jt.Var.kthvalue = kthvalue
|
||
|
||
def _prod(x,dim=0):
|
||
x = jt.log(x)
|
||
x = x.sum(dim=dim)
|
||
return jt.exp(x)
|
||
|
||
|
||
def numpy_cumsum(x, dim=None):
|
||
def cumsum_forward(np, data):
|
||
dim = data['inputs'][1].item()
|
||
a = data['inputs'][0]
|
||
b = data['outputs'][0]
|
||
np.cumsum(a, axis=dim, out=b)
|
||
|
||
def cumsum_backward(np, data):
|
||
dim = data['inputs'][1].item()
|
||
dout = data['dout']
|
||
out = data['outputs'][0]
|
||
np.cumsum(dout[..., ::-1], axis=dim, out=out)
|
||
np.copyto(out, out[..., ::-1])
|
||
if (dim == None):
|
||
dim = -1
|
||
assert(dim >= -1 and dim < len(x.shape))
|
||
dim_var = jt.array([dim],dtype=int)
|
||
return jt.numpy_code(x.shape, x.dtype, [x, dim_var.detach()], cumsum_forward, [cumsum_backward])
|
||
|
||
def cub_cumsum(x, dim=None):
|
||
if (dim == None):
|
||
dim = -1
|
||
assert(dim >= -1 and dim < len(x.shape))
|
||
shape = x.shape
|
||
if (dim != -1 and dim != len(shape) - 1):
|
||
order = range(len(shape))
|
||
order[dim], order[-1] = order[-1], order[dim]
|
||
shape[dim], shape[-1] = shape[-1], shape[dim]
|
||
x = x.permute(order)
|
||
if (len(shape) > 2):
|
||
x = x.reshape([-1, shape[-1]])
|
||
x = jt.compile_extern.cub_ops.cub_cumsum(x)
|
||
if (len(shape) > 2):
|
||
x = x.reshape(shape)
|
||
if (dim != -1 and dim != len(shape) - 1):
|
||
x = x.permute(order)
|
||
return x
|
||
|
||
def cumsum(x, dim=None):
|
||
'''
|
||
Parameters:
|
||
-----------
|
||
x: jt.var
|
||
dim: int
|
||
|
||
Returns:
|
||
--------
|
||
the cumulative sum in dim of x
|
||
'''
|
||
if (dim == None):
|
||
dim = -1
|
||
assert(dim >= -1 and dim < len(x.shape))
|
||
if jt.has_cuda:
|
||
return cub_cumsum(x, dim)
|
||
else:
|
||
return numpy_cumsum(x, dim)
|
||
|
||
jt.Var.cumsum = cumsum
|
||
|
||
def cumprod(x,dim=None):
|
||
x = jt.log(x)
|
||
x = cumsum(x,dim=dim)
|
||
return jt.exp(x)
|
||
|
||
jt.Var.cumprod=cumprod
|
||
|
||
def nms(dets,thresh):
|
||
'''
|
||
dets jt.array [x1,y1,x2,y2,score]
|
||
x(:,0)->x1,x(:,1)->y1,x(:,2)->x2,x(:,3)->y2,x(:,4)->score
|
||
'''
|
||
threshold = str(thresh)
|
||
order = jt.argsort(dets[:,4],descending=True)[0]
|
||
dets = dets[order]
|
||
s_1 = '(@x(j,2)-@x(j,0)+1)*(@x(j,3)-@x(j,1)+1)'
|
||
s_2 = '(@x(i,2)-@x(i,0)+1)*(@x(i,3)-@x(i,1)+1)'
|
||
s_inter_w = 'max((Tx)0,min(@x(j,2),@x(i,2))-max(@x(j,0),@x(i,0))+1)'
|
||
s_inter_h = 'max((Tx)0,min(@x(j,3),@x(i,3))-max(@x(j,1),@x(i,1))+1)'
|
||
s_inter = s_inter_h+'*'+s_inter_w
|
||
iou = s_inter + '/(' + s_1 +'+' + s_2 + '-' + s_inter + ')'
|
||
fail_cond = iou+'>'+threshold
|
||
selected = jt.candidate(dets, fail_cond)
|
||
return order[selected]
|
||
|
||
|
||
jt.Var.expand_as = jt.Var.broadcast_var
|
||
|
||
|
||
def index_fill_(x,dim,indexs,val):
|
||
r'''
|
||
Fills the elements of the input tensor with value val by selecting the indices in the order given in index.
|
||
|
||
Args:
|
||
x - the input tensor
|
||
dim - dimension along which to index
|
||
index – indices of input tensor to fill in
|
||
val – the value to fill with
|
||
'''
|
||
overflow_conditions = [f'i{dim}=={i}'for i in indexs]
|
||
indexs = [f'i{i}' for i in range(len(x.shape))]
|
||
return x.reindex(shape = x.shape,indexes = indexs,overflow_conditions=overflow_conditions,overflow_value=val)
|
||
|
||
def triu_(x,diagonal=0):
|
||
r'''
|
||
Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices input, the other elements of the result tensor out are set to 0.
|
||
|
||
The upper triangular part of the matrix is defined as the elements on and above the diagonal.
|
||
|
||
Args:
|
||
x – the input tensor.
|
||
|
||
diagonal – the diagonal to consider,default =0
|
||
'''
|
||
l = len(x.shape)
|
||
assert l>1
|
||
overflow_conditions=[f'i{l-1}<i{l-2}+{diagonal}']
|
||
indexs = [f'i{i}' for i in range(l)]
|
||
return x.reindex(x.shape,indexs,overflow_conditions=overflow_conditions,overflow_value=0)
|
||
|
||
jt.Var.triu_ = triu_
|
||
|
||
def print_tree(now, max_memory_size, prefix1, prefix2, build_by):
|
||
def format_size(s, end='B'):
|
||
if (s < 1024):
|
||
s = str(s)
|
||
return s + ' '+end
|
||
|
||
if (s < 1024*1024):
|
||
s = format(s/1024, '.2f')
|
||
return s + ' K'+end
|
||
|
||
if (s < 1024*1024*1024):
|
||
s = format(s/1024/1024, '.2f')
|
||
return s + ' M'+end
|
||
|
||
s = format(s/1024/1024/1024, '.2f')
|
||
return s + ' G'+end
|
||
|
||
out = ''
|
||
tab = ' '
|
||
out += prefix1+now['name']+'('+now['type']+')\n'
|
||
out += prefix2+'['+format_size(now['size'])+'; '+format(now['size']/max_memory_size*100, '.2f')+'%; cnt:'+format_size(now['cnt'],'') + ']\n'
|
||
if (build_by == 0):
|
||
for p in now['path']:
|
||
out += prefix2+p+'\n'
|
||
else:
|
||
out += prefix2+now['path'] + '\n'
|
||
if (len(now['children']) > 0):
|
||
out += prefix2 + tab + '| ' + '\n'
|
||
else:
|
||
out += prefix2 + '\n'
|
||
for i in range(len(now['children'])):
|
||
c = now['children'][i]
|
||
if i < len(now['children']) - 1:
|
||
prefix1_ = prefix2 + tab + '├─'
|
||
prefix2_ = prefix2 + tab + '| '
|
||
else:
|
||
prefix1_ = prefix2 + tab + '└─'
|
||
prefix2_ = prefix2 + tab + ' '
|
||
out += print_tree(c, max_memory_size, prefix1_, prefix2_, build_by)
|
||
return out
|
||
|
||
def get_max_memory_treemap(build_by=0, do_print=True):
|
||
'''show treemap of max memory consumption
|
||
|
||
Example::
|
||
|
||
net = jt.models.resnet18()
|
||
with jt.flag_scope(trace_py_var=3, profile_memory_enable=1):
|
||
imgs = jt.randn((1,3,224,224))
|
||
net(imgs).sync()
|
||
jt.get_max_memory_treemap()
|
||
|
||
Output::
|
||
|
||
|
|
||
├─./python/jittor/test/test_memory_profiler.py:100(test_sample)
|
||
| [19.03 MB; 29.67%]
|
||
| ./python/jittor/test/test_memory_profiler.py:100
|
||
| |
|
||
| └─./python/jittor/__init__.py:730(__call__)
|
||
| [19.03 MB; 29.67%]
|
||
| ./python/jittor/__init__.py:730
|
||
| |
|
||
| └─./python/jittor/models/resnet.py:152(execute)
|
||
| [19.03 MB; 29.67%]
|
||
| ./python/jittor/models/resnet.py:152
|
||
| |
|
||
| ├─./python/jittor/models/resnet.py:142(_forward_impl)
|
||
| | [6.13 MB; 9.55%]
|
||
| | ./python/jittor/models/resnet.py:142
|
||
| | |
|
||
|
||
|
||
|
||
'''
|
||
div1 = "[!@#div1!@#]"
|
||
div2 = "[!@#div2!@#]"
|
||
div3 = "[!@#div3!@#]"
|
||
info = jt.get_max_memory_info()
|
||
|
||
vars = []
|
||
vars_ = info.split(div1)
|
||
max_memory_size = int(vars_[0])
|
||
vars_ = vars_[1:]
|
||
for v_ in vars_:
|
||
v__ = v_.split(div2)
|
||
var = {'size':int(v__[1]), 'stack':[], 'cnt':1}
|
||
v__ = v__[2:-1]
|
||
for s_ in v__:
|
||
s__ = s_.split(div3)
|
||
s = {'path':s__[0], 'name':s__[1], 'type':s__[2]}
|
||
var['stack'].append(s)
|
||
vars.append(var)
|
||
if (build_by == 0): # build tree by name
|
||
tree = {'name':'root', "children":[], 'size':0, 'cnt':1, 'path':[], 'type':''}
|
||
|
||
def find_child(now, key):
|
||
for c in now['children']:
|
||
if (c['name'] == key):
|
||
return c
|
||
return None
|
||
for v in vars:
|
||
now = tree
|
||
now['size'] += v['size']
|
||
now['cnt'] += v['cnt']
|
||
for s in v['stack']:
|
||
ch = find_child(now, s['name'])
|
||
if (ch is not None):
|
||
if (not s['path'] in ch['path']):
|
||
ch['path'].append(s['path'])
|
||
assert(ch['type']==s['type'])
|
||
now = ch
|
||
now['size'] += v['size']
|
||
now['cnt'] += v['cnt']
|
||
else:
|
||
now_ = {'name':s['name'], "children":[], 'size':v['size'], 'cnt':v['cnt'], 'path':[s['path']], 'type':s['type']}
|
||
now['children'].append(now_)
|
||
now = now_
|
||
elif (build_by == 1): # build tree by path
|
||
tree = {'name':'root', "children":[], 'size':0, 'cnt':0, 'path':'_root_', 'type':''}
|
||
|
||
def find_child(now, key):
|
||
for c in now['children']:
|
||
if (c['path'] == key):
|
||
return c
|
||
return None
|
||
for v in vars:
|
||
now = tree
|
||
now['size'] += v['size']
|
||
now['cnt'] += v['cnt']
|
||
for s in v['stack']:
|
||
ch = find_child(now, s['path'])
|
||
if (ch is not None):
|
||
now = ch
|
||
now['size'] += v['size']
|
||
now['cnt'] += v['cnt']
|
||
else:
|
||
now_ = {'name':s['name'], "children":[], 'size':v['size'], 'cnt':v['cnt'], 'path':s['path'], 'type':s['type']}
|
||
now['children'].append(now_)
|
||
now = now_
|
||
else:
|
||
assert(False)
|
||
|
||
def sort_tree(now):
|
||
def takeSize(elem):
|
||
return elem['size']
|
||
now['children'].sort(key=takeSize, reverse=True)
|
||
for c in now['children']:
|
||
sort_tree(c)
|
||
sort_tree(tree)
|
||
out = print_tree(tree, max_memory_size, '', '', build_by)
|
||
if (do_print):
|
||
print(out)
|
||
return tree, out
|
||
|
||
def python_pass_wrapper(mod_func, args, kw):
|
||
import importlib
|
||
mod, func = mod_func.rsplit(".", 1)
|
||
mod = importlib.import_module(mod)
|
||
func = getattr(mod, func)
|
||
args = args + ("**kw",)
|
||
args = ",".join(args)
|
||
return eval(f"func({args})")
|
||
|
||
def auto_parallel(n, src, **kw):
|
||
"""
|
||
auto parallel(CPU and GPU) n-d for loop function like below:
|
||
|
||
Before:
|
||
|
||
void inner_func(int n0, int i0, int n1, int i1) {
|
||
...
|
||
}
|
||
|
||
for (int i0=0; i0<n0; i0++)
|
||
for (int i1=0; i1<n1; i1++)
|
||
inner_func(n0, i0, n1, i1, ...);
|
||
|
||
After:
|
||
|
||
@python.jittor.auto_parallel(2)
|
||
void inner_func(int n0, int i0, int n1, int i1) {
|
||
...
|
||
}
|
||
|
||
inner_func(n0, 0, n1, 0, ...);
|
||
|
||
|
||
"""
|
||
# src = prev_func func_name(args)code
|
||
a, b = src.split('(', 1)
|
||
prev_func, func_name = a.rsplit(None, 1)
|
||
args, code = b.split(')', 1)
|
||
args = args.split(',')
|
||
assert len(args) >= n*2, (args, n)
|
||
oargs = args[n*2:]
|
||
pargs = args[:n*2]
|
||
piargs = pargs[1::2]
|
||
pnargs = pargs[0::2]
|
||
pnargs2 = [ a.split()[-1] for a in pnargs ]
|
||
oargs2 = [ a.split()[-1] for a in oargs ]
|
||
entry_func_args_def = ",".join(["int tn"+str(i) for i in range(n)]
|
||
+ pnargs + oargs)
|
||
entry_func_args = ",".join(["tn"+str(i) for i in range(n)]
|
||
+ pnargs2 + oargs2)
|
||
tid_def = ""
|
||
tid_loop = ""
|
||
call_args = []
|
||
for i in reversed(range(n)):
|
||
tid_def += f"\nauto tid{i} = tid & ((1<<tn{i})-1);"
|
||
tid_def += f"\nauto tnum{i} = 1<<tn{i};"
|
||
tid_def += f"\ntid = tid>>tn{i};"
|
||
for i in range(n):
|
||
tid_loop += f"\nfor (int i{i}=tid{i}; i{i}<{pnargs2[i]}; i{i}+=tnum{i})"
|
||
call_args.append(pnargs2[i])
|
||
call_args.append(f"i{i}")
|
||
call_args += oargs2
|
||
call_args = ",".join(call_args)
|
||
xn = '\n'
|
||
new_src = f"""
|
||
#ifdef JIT_cuda
|
||
__device__
|
||
#endif
|
||
{src.replace(func_name, func_name+"_inner", 1)}
|
||
|
||
#ifdef JIT_cuda
|
||
__global__ static void {func_name}_entry({entry_func_args_def}) {{
|
||
int tid = threadIdx.x + blockIdx.x * blockDim.x;
|
||
{tid_def}
|
||
{tid_loop}
|
||
{func_name}_inner({call_args});
|
||
}}
|
||
#endif
|
||
|
||
inline static void {func_name}({",".join(pargs+oargs)}) {{
|
||
#ifdef JIT_cuda
|
||
int thread_num = 256*1024;
|
||
{xn.join([f"int tn{i} = NanoVector::get_nbits(std::min(thread_num, {pnargs2[i]})) - 2;thread_num >>= tn{i};" for i in reversed(range(n))])}
|
||
thread_num = 1<<({"+".join([f"tn{i}" for i in range(n)])});
|
||
int p1 = std::max(thread_num/1024, 1);
|
||
int p2 = std::min(thread_num, 1024);
|
||
{func_name}_entry<<<p1,p2>>>({entry_func_args});
|
||
#else
|
||
{xn.join([f"for (int i{i}=0; i{i}<{pnargs2[i]}; i{i}++)" for i in range(n)])}
|
||
{func_name}_inner({call_args});
|
||
#endif
|
||
}}
|
||
"""
|
||
return new_src
|
||
|
||
|
||
def cumprod(a, dim):
|
||
class CumprodFunc(jt.Function):
|
||
def forward_code(self, np, data):
|
||
a = data["inputs"][0]
|
||
b = data["outputs"][0]
|
||
out = np.cumprod(a, self.dim)
|
||
np.copyto(b, out)
|
||
|
||
def backward_code(self, np, data):
|
||
a, b, dout = data["inputs"]
|
||
out = data["outputs"][0]
|
||
|
||
sdim = a.shape[self.dim]
|
||
dim = (len(a.shape)+1)*[1]
|
||
dim[self.dim+1] = sdim
|
||
res = np.tile(np.expand_dims(b, self.dim+1), dim)
|
||
dout = np.tile(np.expand_dims(dout, self.dim+1), dim)
|
||
|
||
dim[self.dim]=sdim
|
||
dim[self.dim+1]=1
|
||
a = np.tile(np.expand_dims(a, self.dim), dim)
|
||
res = res/a
|
||
|
||
mask = np.tril(np.ones((sdim, sdim)))
|
||
for i in range(self.dim):
|
||
mask = np.expand_dims(mask, 0)
|
||
for i in range(len(a.shape)-self.dim-2):
|
||
mask = np.expand_dims(mask, -1)
|
||
res = np.sum(mask*res*dout, self.dim)
|
||
|
||
np.copyto(out, res)
|
||
|
||
def execute(self, a, dim):
|
||
self.save_vars = a
|
||
self.dim = dim
|
||
self.res = jt.numpy_code(
|
||
a.shape,
|
||
a.dtype,
|
||
[a],
|
||
self.forward_code,
|
||
)
|
||
return self.res
|
||
|
||
def grad(self, grad_a):
|
||
a = self.save_vars
|
||
b = self.res
|
||
return jt.numpy_code(
|
||
a.shape,
|
||
a.dtype,
|
||
[a, b, grad_a],
|
||
self.backward_code,
|
||
)
|
||
|
||
func = CumprodFunc()
|
||
if dim<0:
|
||
dim+=len(a.shape)
|
||
return func(a, dim)
|
||
|
||
def linspace(start, end, steps):
|
||
res = jt.index((steps,))[0]
|
||
res = res*(end-start)/float(steps-1)+start
|
||
return res
|
||
|
||
def randperm(n, dtype="int32"):
|
||
key = jt.random((n,))
|
||
index, _ = jt.argsort(key)
|
||
return index.cast(dtype)
|
||
|
||
def set_global_seed(seed, different_seed_for_mpi=True):
|
||
''' Sets the seeds of the random number generators of Python, numpy and jittor,
|
||
simultaneously.
|
||
|
||
.. note::
|
||
Jittor also gurantees each worker of jittor.dataset.Dataset to hold a different seed,
|
||
also gurantees each process hold a different seed which using mpi,
|
||
which is (global_seed ^ (worker_id*1167)) ^ 1234 + jt.rank * 2591
|
||
'''
|
||
if (different_seed_for_mpi):
|
||
seed = seed + jt.rank * 2591
|
||
import random
|
||
random.seed(seed)
|
||
jt.set_seed(seed)
|
||
np.random.seed(seed)
|
||
try:
|
||
import cupy
|
||
cupy.random.seed(seed)
|
||
except:
|
||
pass
|
||
|
||
import time
|
||
set_global_seed(int(time.time() * 1000000) % 100000007)
|
||
|
||
def searchsorted(sorted, values, right=False):
|
||
"""
|
||
Find the indices from the innermost dimension of `sorted` for each `values`.
|
||
|
||
Example::
|
||
|
||
sorted = jt.array([[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]])
|
||
values = jt.array([[3, 6, 9], [3, 6, 9]])
|
||
ret = jt.searchsorted(sorted, values)
|
||
assert (ret == [[1, 3, 4], [1, 2, 4]]).all(), ret
|
||
|
||
ret = jt.searchsorted(sorted, values, right=True)
|
||
assert (ret == [[2, 3, 5], [1, 3, 4]]).all(), ret
|
||
|
||
sorted_1d = jt.array([1, 3, 5, 7, 9])
|
||
ret = jt.searchsorted(sorted_1d, values)
|
||
assert (ret == [[1, 3, 4], [1, 3, 4]]).all(), ret
|
||
|
||
|
||
"""
|
||
_searchsorted_header = f"""
|
||
namespace jittor {{
|
||
|
||
@python.jittor.auto_parallel(2)
|
||
inline static void searchsorted(
|
||
int batch_num, int batch_id, int value_num, int value_id,
|
||
int sorted_num, int batch_stride,
|
||
{sorted.dtype}* __restrict__ sort_p, {values.dtype}* __restrict__ value_p,
|
||
int32* __restrict__ index_p) {{
|
||
int32 l = batch_id * batch_stride;
|
||
int32 r = l + sorted_num;
|
||
auto v = value_p[batch_id * value_num + value_id];
|
||
while (l<r) {{
|
||
int32 m = (l+r)/2;
|
||
if (sort_p[m] {"<=" if right else "<"} v)
|
||
l = m+1;
|
||
else
|
||
r = m;
|
||
}}
|
||
index_p[batch_id * value_num + value_id] = l - batch_id * batch_stride;
|
||
}}
|
||
|
||
}}
|
||
"""
|
||
_searchsorted_src = """
|
||
int value_num = in1->shape[in1->shape.size()-1];
|
||
int sorted_num = in0->shape[in0->shape.size()-1];
|
||
int32 batch_num = in0->num / sorted_num;
|
||
int32 batch_num2 = in1->num / value_num;
|
||
int32 batch_stride = batch_num == 1 ? 0 : sorted_num;
|
||
CHECK(batch_num == batch_num2 || batch_num == 1);
|
||
|
||
searchsorted(batch_num2, 0, value_num, 0, sorted_num, batch_stride, in0_p, in1_p, out0_p);
|
||
"""
|
||
return jt.code(values.shape, "int32", [sorted, values],
|
||
cpu_header=_searchsorted_header,
|
||
cpu_src=_searchsorted_src,
|
||
cuda_header=_searchsorted_header,
|
||
cuda_src=_searchsorted_src)
|
||
|
||
|
||
def scatter(x:jt.Var, dim:int, index:jt.Var, src:jt.Var, reduce='void'):
|
||
''' if x is a 3-D array, rewrite x like:
|
||
|
||
self[index[i][j][k]][j][k] = src[i][j][k] # if dim == 0
|
||
self[i][index[i][j][k]][k] = src[i][j][k] # if dim == 1
|
||
self[i][j][index[i][j][k]] = src[i][j][k] # if dim == 2
|
||
|
||
Parameters::
|
||
|
||
* x (jt.Var) – input array
|
||
* dim (int) – the axis along which to index
|
||
* index (jt.Var) – the indices of elements to scatter, can be either empty or of the same dimensionality as src. When empty, the operation returns self unchanged.
|
||
* src (jt.Var) – the source element(s) to scatter.
|
||
* reduce (str, optional) – reduction operation to apply, can be either 'add' or 'multiply'.
|
||
|
||
Example::
|
||
|
||
src = jt.arange(1, 11).reshape((2, 5))
|
||
index = jt.array([[0, 1, 2, 0]])
|
||
x = jt.zeros((3, 5), dtype=src.dtype).scatter_(0, index, src)
|
||
assert (x.data ==
|
||
[[1, 0, 0, 4, 0],
|
||
[0, 2, 0, 0, 0],
|
||
[0, 0, 3, 0, 0]]).all()
|
||
index = jt.array([[0, 1, 2], [0, 1, 4]])
|
||
x = jt.zeros((3, 5), dtype=src.dtype).scatter_(1, index, src)
|
||
assert (x.data ==
|
||
[[1, 2, 3, 0, 0],
|
||
[6, 7, 0, 0, 8],
|
||
[0, 0, 0, 0, 0]]).all()
|
||
x = jt.full((2, 4), 2.).scatter_(1, jt.array([[2], [3]]),
|
||
jt.array(1.23), reduce='multiply')
|
||
assert np.allclose(x.data,
|
||
[[2.0000, 2.0000, 2.4600, 2.0000],
|
||
[2.0000, 2.0000, 2.0000, 2.4600]]), x
|
||
x = jt.full((2, 4), 2.).scatter_(1, jt.array([[2], [3]]),
|
||
jt.array(1.23), reduce='add')
|
||
assert np.allclose(x.data,
|
||
[[2.0000, 2.0000, 3.2300, 2.0000],
|
||
[2.0000, 2.0000, 2.0000, 3.2300]])
|
||
|
||
'''
|
||
shape = index.shape
|
||
if src.shape != shape and src.numel() != 1:
|
||
src = src[tuple( slice(None,s) for s in shape )]
|
||
indexes = [ f'i{i}' for i in range(len(shape)) ]
|
||
indexes[dim] = index
|
||
return x.setitem(tuple(indexes), src, reduce)
|
||
|
||
def scatter_(x, dim, index, src, reduce='void'):
|
||
return x.assign(x.scatter(dim, index, src, reduce))
|
||
|
||
jt.Var.scatter = scatter
|
||
jt.Var.scatter_ = scatter_
|
||
|
||
def gather(x, dim, index):
|
||
''' if x is a 3-D array, reindex x like:
|
||
|
||
out[i][j][k] = input[index[i][j][k]][j][k] # if dim == 0
|
||
out[i][j][k] = input[i][index[i][j][k]][k] # if dim == 1
|
||
out[i][j][k] = input[i][j][index[i][j][k]] # if dim == 2
|
||
|
||
|
||
Parameters::
|
||
|
||
* x (jt.Var) – the source array
|
||
* dim (int) – the axis along which to index
|
||
* index (jt.Var) – the indices of elements to gather
|
||
|
||
Example::
|
||
|
||
t = jt.array([[1, 2], [3, 4]])
|
||
data = t.gather(1, jt.array([[0, 0], [1, 0]]))
|
||
assert (data.data == [[ 1, 1], [ 4, 3]]).all()
|
||
data = t.gather(0, jt.array([[0, 0], [1, 0]]))
|
||
assert (data.data == [[ 1, 2], [ 3, 2]]).all()
|
||
|
||
'''
|
||
shape = index.shape
|
||
indexes = [ f'i{i}' for i in range(len(shape)) ]
|
||
indexes[dim] = index
|
||
return x.getitem(tuple(indexes))
|
||
|
||
jt.Var.gather = gather
|
||
|
||
def roll(x, shifts, dims=None):
|
||
'''Roll the tensor along the given dimension(s).
|
||
|
||
Parameters::
|
||
|
||
* x (jt.Var) – the source array
|
||
* shifts (int or tuple) – shift offset of dims
|
||
* dims (int or tuple) – shift dims
|
||
|
||
Examples::
|
||
|
||
x = jt.array([1, 2, 3, 4, 5, 6, 7, 8]).view(4, 2)
|
||
y = x.roll(1, 0)
|
||
assert (y.numpy() == [[7,8],[1,2],[3,4],[5,6]]).all()
|
||
y = x.roll(-1, 0)
|
||
assert (y.numpy() == [[3,4],[5,6],[7,8],[1,2]]).all()
|
||
y = x.roll(shifts=(2, 1), dims=(0, 1))
|
||
assert (y.numpy() == [[6,5],[8,7],[2,1],[4,3]]).all()
|
||
|
||
'''
|
||
if isinstance(shifts, int):
|
||
shifts = (shifts,)
|
||
if dims is None:
|
||
dims = tuple(range(len(shifts)))
|
||
elif isinstance(dims, int):
|
||
dims = (dims,)
|
||
assert len(dims) == len(shifts)
|
||
ids = [ f'i{i}' for i in range(x.ndim) ]
|
||
for i in range(len(dims)):
|
||
shift = shifts[i]
|
||
d = dims[i]
|
||
size = x.shape[d]
|
||
shift = shift % size
|
||
if shift<0: shift += size
|
||
ids[d] = f'(i{d}<{shift}?i{d}+{size-shift}:(i{d}-{shift}))'
|
||
return x.reindex(x.shape, ids)
|
||
|
||
jt.Var.roll = roll
|
||
|
||
def safe_log(x):
|
||
return jt.safe_clip(x, 1e-30, 1e30).log()
|
||
jt.Var.safe_log = safe_log
|
||
|
||
class _CTCLossFunction(jt.Function):
|
||
def execute(self, log_probs, targets, input_lengths, target_lengths, blank=0, zero_infinity=False):
|
||
self.blank = blank
|
||
T, N, C = log_probs.shape
|
||
_N, S = targets.shape
|
||
assert _N == N
|
||
log_alpha = jt.full([T,N,S*2+1], -1e30)
|
||
result = jt.empty((N,))
|
||
jt.code([log_probs, targets, input_lengths, target_lengths], [log_alpha, result], cpu_src=f"""
|
||
constexpr int blank = {blank};
|
||
for (int i=0; i<in0_shape1; i++) {{
|
||
int input_len = @in2(i);
|
||
int target_len = @in3(i);
|
||
@out0(0,i,0) = @in0(0,i,blank);
|
||
if (target_len)
|
||
@out0(0,i,1) = @in0(0,i,@in1(i,0));
|
||
for (int j=1; j<input_len; j++)
|
||
for (int k=0; k<target_len*2+1; k++) {{
|
||
int target = k%2 ? @in1(i,k/2) : blank;
|
||
int target_2 = target;
|
||
if (k>1 && k%2) target_2 = @in1(i,k/2-1);
|
||
out_type l1 = @out0(j-1,i,k);
|
||
out_type l2 = -1e30;
|
||
if (k>0) l2 = @out0(j-1,i,k-1);
|
||
out_type l3 = -1e30;
|
||
if (k>1 && target_2 != target)
|
||
l3 = @out0(j-1,i,k-2);
|
||
out_type m = std::max(l1, std::max(l2, l3));
|
||
@out0(j,i,k) = std::log(
|
||
std::exp(l1-m) +
|
||
std::exp(l2-m) +
|
||
std::exp(l3-m)
|
||
) + m + @in0(j,i,target);
|
||
}}
|
||
if (input_len==0)
|
||
@out1(i) = @out0(0,i,0);
|
||
else {{
|
||
out_type l1 = @out0(input_len-1, i, target_len*2);
|
||
out_type l2 = -1e30;
|
||
if (target_len)
|
||
l2 = @out0(input_len-1, i, target_len*2-1);
|
||
out_type m = std::max(l1, l2);
|
||
out_type log_likelihood = std::log(std::exp(l1-m)+std::exp(l2-m))+m;
|
||
@out1(i) = -log_likelihood;
|
||
}}
|
||
}}
|
||
""", cuda_src=f"""
|
||
__global__ void kernel(@ARGS_DEF) {{
|
||
@PRECALC;
|
||
constexpr int blank = {blank};
|
||
for (int i=blockIdx.x; i<in0_shape1; i+=gridDim.x) {{
|
||
int input_len = @in2(i);
|
||
int target_len = @in3(i);
|
||
@out0(0,i,0) = @in0(0,i,blank);
|
||
if (target_len)
|
||
@out0(0,i,1) = @in0(0,i,@in1(i,0));
|
||
for (int j=1; j<input_len; j++)
|
||
for (int k=threadIdx.x; k-threadIdx.x<target_len*2+1; k+=blockDim.x) {{
|
||
__syncthreads();
|
||
if (k>=target_len*2+1)
|
||
continue;
|
||
int target = k%2 ? @in1(i,k/2) : blank;
|
||
int target_2 = target;
|
||
if (k>1 && k%2) target_2 = @in1(i,k/2-1);
|
||
out_type l1 = @out0(j-1,i,k);
|
||
out_type l2 = -1e30;
|
||
if (k>0) l2 = @out0(j-1,i,k-1);
|
||
out_type l3 = -1e30;
|
||
if (k>1 && target_2 != target)
|
||
l3 = @out0(j-1,i,k-2);
|
||
out_type m = ::max(l1, ::max(l2, l3));
|
||
@out0(j,i,k) = ::log(
|
||
::exp(l1-m) +
|
||
::exp(l2-m) +
|
||
::exp(l3-m)
|
||
) + m + @in0(j,i,target);
|
||
}}
|
||
__syncthreads();
|
||
if (input_len==0)
|
||
@out1(i) = @out0(0,i,0);
|
||
else {{
|
||
out_type l1 = @out0(input_len-1, i, target_len*2);
|
||
out_type l2 = -1e30;
|
||
if (target_len)
|
||
l2 = @out0(input_len-1, i, target_len*2-1);
|
||
out_type m = ::max(l1, l2);
|
||
out_type log_likelihood = ::log(::exp(l1-m)+::exp(l2-m))+m;
|
||
@out1(i) = -log_likelihood;
|
||
}}
|
||
}}
|
||
}}
|
||
kernel<<<std::min(in0_shape1, 1024), std::min(in1_shape1*2+1, 1024)>>>(@ARGS);
|
||
""")
|
||
self.saved_var = [log_probs, targets, input_lengths, target_lengths, log_alpha, result]
|
||
return result
|
||
|
||
def grad(self, dout):
|
||
blank = self.blank
|
||
inputs = self.saved_var + [dout]
|
||
dlog_probs = jt.zeros_like(inputs[0])
|
||
dlog_alpha = jt.zeros_like(inputs[4])
|
||
jt.code(inputs, [dlog_probs, dlog_alpha], cpu_src=f"""
|
||
constexpr int blank = {blank};
|
||
for (int i=0; i<in0_shape1; i++) {{
|
||
int input_len = @in2(i);
|
||
int target_len = @in3(i);
|
||
if (input_len==0)
|
||
// write out1 --> read in6
|
||
// out1(i) = out0(0,i,0);
|
||
@out1(0,i,0) = @in6(i);
|
||
else {{
|
||
out_type l1 = @in4(input_len-1, i, target_len*2);
|
||
out_type l2 = -1e30;
|
||
if (target_len)
|
||
l2 = @in4(input_len-1, i, target_len*2-1);
|
||
out_type m = std::max(l1, l2);
|
||
// out_type log_likelihood = std::log(std::exp(l1-m)+std::exp(l2-m))+m;
|
||
// out1(i) = -log_likelihood;
|
||
out_type l1_exp = std::exp(l1-m);
|
||
out_type l2_exp = std::exp(l2-m);
|
||
out_type sumexp = l1_exp + l2_exp;
|
||
|
||
out_type dlog_likelihood = -@in6(i);
|
||
out_type dl1 = dlog_likelihood * l1_exp / sumexp;
|
||
out_type dl2 = dlog_likelihood * l2_exp / sumexp;
|
||
|
||
@out1(input_len-1, i, target_len*2) = dl1;
|
||
if (target_len)
|
||
@out1(input_len-1, i, target_len*2-1) = dl2;
|
||
}}
|
||
for (int j=input_len-1; j>0; j--)
|
||
for (int k=0; k<target_len*2+1; k++) {{
|
||
int target = k%2 ? @in1(i,k/2) : blank;
|
||
int target_2 = target;
|
||
if (k>1 && k%2) target_2 = @in1(i,k/2-1);
|
||
out_type l1 = @in4(j-1,i,k);
|
||
out_type l2 = -1e30;
|
||
if (k>0) l2 = @in4(j-1,i,k-1);
|
||
out_type l3 = -1e30;
|
||
if (k>1 && target_2 != target)
|
||
l3 = @in4(j-1,i,k-2);
|
||
out_type m = std::max(l1, std::max(l2, l3));
|
||
out_type l1_exp = std::exp(l1-m);
|
||
out_type l2_exp = std::exp(l2-m);
|
||
out_type l3_exp = std::exp(l3-m);
|
||
out_type sumexp = l1_exp + l2_exp + l3_exp;
|
||
out_type dalpha = @out1(j,i,k);
|
||
|
||
@out0(j,i,target) += dalpha;
|
||
|
||
@out1(j-1,i,k) += dalpha * l1_exp / sumexp;
|
||
if (k>0)
|
||
@out1(j-1,i,k-1) += dalpha * l2_exp / sumexp;
|
||
if (k>1 && target_2 != target)
|
||
@out1(j-1,i,k-2) += dalpha * l3_exp / sumexp;
|
||
}}
|
||
// read in0 -> white out0
|
||
// write out0 ->read out1
|
||
// out0(0,i,0) = in0(0,i,blank);
|
||
@out0(0,i,blank) += @out1(0,i,0);
|
||
if (target_len)
|
||
@out0(0,i,@in1(i,0)) += @out1(0,i,1);
|
||
}}
|
||
""", cuda_src=f"""
|
||
__global__ void kernel(@ARGS_DEF) {{
|
||
@PRECALC;
|
||
constexpr int blank = {blank};
|
||
for (int i=blockIdx.x; i<in0_shape1; i+=gridDim.x) {{
|
||
int input_len = @in2(i);
|
||
int target_len = @in3(i);
|
||
if (input_len==0)
|
||
// write out1 --> read in6
|
||
// out1(i) = out0(0,i,0);
|
||
@out1(0,i,0) = @in6(i);
|
||
else {{
|
||
out_type l1 = @in4(input_len-1, i, target_len*2);
|
||
out_type l2 = -1e30;
|
||
if (target_len)
|
||
l2 = @in4(input_len-1, i, target_len*2-1);
|
||
out_type m = ::max(l1, l2);
|
||
// out_type log_likelihood = ::log(::exp(l1-m)+::exp(l2-m))+m;
|
||
// out1(i) = -log_likelihood;
|
||
out_type l1_exp = ::exp(l1-m);
|
||
out_type l2_exp = ::exp(l2-m);
|
||
out_type sumexp = l1_exp + l2_exp;
|
||
|
||
out_type dlog_likelihood = -@in6(i);
|
||
out_type dl1 = dlog_likelihood * l1_exp / sumexp;
|
||
out_type dl2 = dlog_likelihood * l2_exp / sumexp;
|
||
|
||
@out1(input_len-1, i, target_len*2) = dl1;
|
||
if (target_len)
|
||
@out1(input_len-1, i, target_len*2-1) = dl2;
|
||
}}
|
||
for (int j=input_len-1; j>0; j--)
|
||
for (int k=threadIdx.x; k-threadIdx.x<target_len*2+1; k+=blockDim.x) {{
|
||
__syncthreads();
|
||
if (k>=target_len*2+1)
|
||
continue;
|
||
int target = k%2 ? @in1(i,k/2) : blank;
|
||
int target_2 = target;
|
||
if (k>1 && k%2) target_2 = @in1(i,k/2-1);
|
||
out_type l1 = @in4(j-1,i,k);
|
||
out_type l2 = -1e30;
|
||
if (k>0) l2 = @in4(j-1,i,k-1);
|
||
out_type l3 = -1e30;
|
||
if (k>1 && target_2 != target)
|
||
l3 = @in4(j-1,i,k-2);
|
||
out_type m = ::max(l1, ::max(l2, l3));
|
||
out_type l1_exp = ::exp(l1-m);
|
||
out_type l2_exp = ::exp(l2-m);
|
||
out_type l3_exp = ::exp(l3-m);
|
||
out_type sumexp = l1_exp + l2_exp + l3_exp;
|
||
out_type dalpha = @out1(j,i,k);
|
||
|
||
atomicAdd(&@out0(j,i,target), dalpha);
|
||
|
||
atomicAdd(&@out1(j-1,i,k), dalpha * l1_exp / sumexp);
|
||
if (k>0)
|
||
atomicAdd(&@out1(j-1,i,k-1), dalpha * l2_exp / sumexp);
|
||
if (k>1 && target_2 != target)
|
||
atomicAdd(&@out1(j-1,i,k-2), dalpha * l3_exp / sumexp);
|
||
}}
|
||
// read in0 -> white out0
|
||
// write out0 ->read out1
|
||
// out0(0,i,0) = in0(0,i,blank);
|
||
__syncthreads();
|
||
if (threadIdx.x==0) {{
|
||
@out0(0,i,blank) += @out1(0,i,0);
|
||
if (target_len)
|
||
@out0(0,i,@in1(i,0)) += @out1(0,i,1);
|
||
}}
|
||
}}
|
||
}}
|
||
kernel<<<std::min(in0_shape1, 1024), std::min(in1_shape1*2+1, 1024)>>>(@ARGS);
|
||
""")
|
||
return (dlog_probs,)
|
||
|
||
|
||
def ctc_loss(log_probs, targets, input_lengths, target_lengths, blank=0, reduction='mean', zero_infinity=False):
|
||
'''The Connectionist Temporal Classification loss.
|
||
|
||
|
||
Reference:
|
||
A. Graves et al.: Connectionist Temporal Classification:
|
||
Labelling Unsegmented Sequence Data with Recurrent Neural Networks:
|
||
https://www.cs.toronto.edu/~graves/icml_2006.pdf
|
||
|
||
Input:
|
||
|
||
log_probs: shape is [T, N, C], T is the sequence length, N is the batch size, C is the class number.
|
||
targets: shape is [N, S], N is the batch size, S is the target sequence length, element should between [0,C).
|
||
input_lengths: shape is [N], which represents the length of input, element should between [0,T].
|
||
target_lengths: shape is N, which represents the length of target, element should between [0,S].
|
||
blank (int, default 0): blank label index
|
||
reduction (string): reduce batch loss,
|
||
if reduction is none, it will return (N,) array,
|
||
if reduction is mean or sum, it will return one scalar
|
||
zero_infinity (bool, default False):
|
||
zero_infinity for grad
|
||
|
||
Example:
|
||
|
||
import jittor as jt
|
||
T = 50 # Input sequence length
|
||
C = 20 # Number of classes (including blank)
|
||
N = 16 # Batch size
|
||
S = 30 # Target sequence length of longest target in batch (padding length)
|
||
S_min = 10 # Minimum target length, for demonstration purposes
|
||
|
||
input = jt.randn(T, N, C).log_softmax(2)
|
||
# Initialize random batch of targets (0 = blank, 1:C = classes)
|
||
target = jt.randint(low=1, high=C, shape=(N, S), dtype=jt.int)
|
||
|
||
input_lengths = jt.full((N,), T, dtype=jt.int)
|
||
target_lengths = jt.randint(low=S_min, high=S+1, shape=(N,), dtype=jt.int)
|
||
loss = jt.ctc_loss(input, target, input_lengths, target_lengths)
|
||
|
||
dinput = jt.grad(loss, input)
|
||
|
||
'''
|
||
result = _CTCLossFunction.apply(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity)
|
||
if reduction=="mean":
|
||
return result.mean()
|
||
elif reduction=="sum":
|
||
return result.sum()
|
||
assert reduction=="none"
|
||
return result
|
||
|
||
|
||
class CTCLoss(jt.Module):
|
||
'''The Connectionist Temporal Classification loss.
|
||
|
||
|
||
Reference:
|
||
A. Graves et al.: Connectionist Temporal Classification:
|
||
Labelling Unsegmented Sequence Data with Recurrent Neural Networks:
|
||
https://www.cs.toronto.edu/~graves/icml_2006.pdf
|
||
|
||
|
||
Args:
|
||
|
||
blank (int, default 0): blank label index
|
||
reduction (string): reduce batch loss,
|
||
if reduction is none, it will return (N,) array,
|
||
if reduction is mean or sum, it will return one scalar
|
||
zero_infinity (bool, default False):
|
||
zero_infinity for grad
|
||
|
||
Input:
|
||
|
||
log_probs: shape is [T, N, C], T is the sequence length, N is the batch size, C is the class number.
|
||
targets: shape is [N, S], N is the batch size, S is the target sequence length, element should between [0,C).
|
||
input_lengths: shape is [N], which represents the length of input, element should between [0,T].
|
||
target_lengths: shape is N, which represents the length of target, element should between [0,S].
|
||
|
||
Example:
|
||
|
||
import jittor as jt
|
||
T = 50 # Input sequence length
|
||
C = 20 # Number of classes (including blank)
|
||
N = 16 # Batch size
|
||
S = 30 # Target sequence length of longest target in batch (padding length)
|
||
S_min = 10 # Minimum target length, for demonstration purposes
|
||
|
||
input = jt.randn(T, N, C).log_softmax(2)
|
||
# Initialize random batch of targets (0 = blank, 1:C = classes)
|
||
target = jt.randint(low=1, high=C, shape=(N, S), dtype=jt.int)
|
||
|
||
input_lengths = jt.full((N,), T, dtype=jt.int)
|
||
target_lengths = jt.randint(low=S_min, high=S+1, shape=(N,), dtype=jt.int)
|
||
ctc_loss = jt.CTCLoss()
|
||
loss = ctc_loss(input, target, input_lengths, target_lengths)
|
||
|
||
dinput = jt.grad(loss, input)
|
||
|
||
'''
|
||
def __init__(self, blank=0, reduction='mean', zero_infinity=False):
|
||
self.blank = blank
|
||
self.reduction = reduction
|
||
self.zero_infinity = zero_infinity
|
||
|
||
def execute(self, log_probs, targets, input_lengths, target_lengths):
|
||
return ctc_loss(log_probs, targets, input_lengths, target_lengths, self.blank, self.reduction, self.zero_infinity) |