mirror of https://github.com/Jittor/Jittor
719 lines
27 KiB
Python
719 lines
27 KiB
Python
# ***************************************************************
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# Copyright (c) 2021 Jittor. All Rights Reserved.
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# Maintainers:
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# Wenyang Zhou <576825820@qq.com>
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# Dun Liang <randonlang@gmail.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 ast, astunparse
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import numpy as np
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pjmap = {
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# ***************************************************************
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# Module
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# ***************************************************************
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'Conv2d': {
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'pytorch': {
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'args': "in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros'"
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},
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'jittor': {
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'module': 'nn',
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'name': 'Conv',
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'args': 'in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True'
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},
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'links': {},
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'extras': {},
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},
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'ConvTranspose2d': {
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'pytorch': {
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'args': "in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1, padding_mode='zeros'"
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},
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'jittor': {
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'module': 'nn',
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'name': 'ConvTranspose',
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'args': 'in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, groups=1, bias=True, dilation=1'
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},
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'links': {},
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'extras': {},
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},
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'MaxPool2d': {
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'pytorch': {
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'args': 'kernel_size, stride=None, padding=0, dilation=1, return_indices=False',
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},
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'jittor': {
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'module': 'nn',
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'name': 'Pool',
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'args': 'kernel_size, stride=None, padding=0, dilation=None, return_indices=None, ceil_mode=False, op="maximum"'
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},
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'links': {},
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'extras': {
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"op": "'maximum'",
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},
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},
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'AvgPool2d': {
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'pytorch': {
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'args': 'kernel_size, stride=None, padding=0, dilation=1, return_indices=False',
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},
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'jittor': {
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'module': 'nn',
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'name': 'Pool',
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'args': 'kernel_size, stride=None, padding=0, dilation=None, return_indices=None, ceil_mode=False, op="maximum"'
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},
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'links': {},
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'extras': {
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"op": "'mean'",
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},
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},
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'ReLU': {
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'pytorch': {
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'args': 'inplace=False',
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},
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'jittor': {
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'module': 'nn',
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'name': 'ReLU',
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'args': ''
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},
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'links': {},
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'extras': {},
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'delete': ['inplace'],
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},
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'relu': {
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'pytorch': {
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'args': 'input',
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},
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'jittor': {
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'module': 'nn',
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'name': 'relu',
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'args': 'x'
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},
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'links': {'input': 'x'},
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'extras': {},
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'delete': [],
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},
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'binary_cross_entropy_with_logits': {
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'pytorch': {
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'args': 'input, target, weight, size_average=True',
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},
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'jittor': {
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'module': 'nn',
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'name': 'binary_cross_entropy_with_logits',
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'args': 'input, target, weight, size_average=True'
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},
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'links': {},
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'extras': {},
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'delete': [],
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},
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'ReLU6': {
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'pytorch': {
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'args': 'inplace=False',
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},
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'jittor': {
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'module': 'nn',
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'name': 'ReLU6',
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'args': ''
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},
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'links': {},
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'extras': {},
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'delete': ['inplace'],
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},
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'PReLU': {
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'pytorch': {
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'args': 'num_parameters=1, init=0.25',
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},
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'jittor': {
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'module': 'nn',
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'name': 'PReLU',
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'args': 'num_parameters=1, init_=0.25'
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},
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'links': {'init': 'init_'},
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'extras': {},
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},
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'LeakyReLU': {
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'pytorch': {
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'args': 'negative_slope=0.01, inplace=False',
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},
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'jittor': {
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'module': 'nn',
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'name': 'LeakyReLU',
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'args': 'scale=0.01'
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},
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'links': {'negative_slope': 'scale'},
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'extras': {},
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'delete': ['inplace'],
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},
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'BatchNorm2d': {
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'pytorch': {
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'args': 'num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True',
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},
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'jittor': {
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'module': 'nn',
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'name': 'BatchNorm',
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'args': 'num_features, eps=1e-5, momentum=0.1, affine=None, is_train=True'
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},
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'links': {},
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'extras': {},
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},
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'BatchNorm1d': {
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'pytorch': {
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'args': "num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True"
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},
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'jittor': {
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'module': 'nn',
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'name': 'BatchNorm1d',
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'args': 'num_features, eps=1e-5, momentum=0.1, affine=None, is_train=True, sync=True',
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},
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'links': {},
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'extras': {'affine': 'None'},
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'delete': ['track_running_stats'],
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},
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'GroupNorm': {
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'pytorch': {
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'args': "num_groups, num_channels, eps=1e-05, momentum=0.1, affine=True"
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},
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'jittor': {
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'module': 'nn',
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'name': 'GroupNorm',
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'args': 'num_groups, num_channels, eps=1e-05, affine=None, is_train=True',
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},
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'links': {},
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'extras': {'affine': 'None'},
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},
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'Parameter':{
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'pytorch': {
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'args': "data,require_grad=True"
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},
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'jittor': {
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'module': 'jt',
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'name': 'array',
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'args': 'data,dtype=None',
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},
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'links': {},
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'extras': {},
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},
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'Dropout2d': {
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'pytorch': {
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'args': 'p=0.5, inplace=False',
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},
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'jittor': {
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'module': 'nn',
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'name': 'Dropout',
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'args': 'p=0.5, is_train=False'
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},
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'links': {},
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'extras': {},
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'delete': ['inplace'],
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},
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'Upsample': {
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'pytorch': {
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'args': "size=None, scale_factor=None, mode='nearest', align_corners=None",
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},
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'jittor': {
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'module': 'nn',
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'name': 'Upsample',
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'args': "scale_factor=None, mode='nearest'"
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},
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'links': {},
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'extras': {},
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},
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'constant_': {
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'pytorch': {
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'args': "tensor, val",
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},
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'jittor': {
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'module': 'init',
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'name': 'constant_',
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'args': 'var, value=0.0'
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},
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'links': {'tensor': 'var', 'val': 'value'},
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'extras': {},
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},
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'normal_': {
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'pytorch': {
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'args': "tensor, mean=0.0, std=1.0",
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},
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'jittor': {
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'module': 'init',
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'name': 'gauss_',
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'args': 'var, mean=0.0, std=1.0'
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},
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'links': {'tensor': 'var'},
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'extras': {},
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},
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'uniform_': {
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'pytorch': {
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'args': "tensor, a=0.0, b=1.0",
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},
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'jittor': {
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'module': 'init',
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'name': 'uniform_',
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'args': 'var, low, high'
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},
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'links': {'tensor': 'var', 'a': 'low', 'b': 'high'},
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'extras': {},
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},
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'cat': {
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'pytorch': {
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'args': "tensors, dim=0, out=None",
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},
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'jittor': {
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'module': 'jt.contrib',
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'name': 'concat',
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'args': 'vars, dim=0'
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},
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'links': {'tensors': 'vars'},
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'extras': {},
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},
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# ***************************************************************
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# Convert format for function which can be writen as either torch.Tensor.xxx(...) or torch.xxx(torch.Tensor, ...)
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# Example: x.reshape([2,3]) and torch.reshape(x, [2,3])
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# ***************************************************************
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'flatten': {
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'pytorch': {
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'prefix': ['torch'],
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'args_prefix': 'input, start_dim=0, end_dim=-1',
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'args': 'start_dim=0, end_dim=-1',
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},
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'jittor': {
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'prefix': 'jt',
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'module': '',
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'name': 'flatten',
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'args_prefix': 'input, start_dim=0, end_dim=-1',
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'args': 'start_dim=0, end_dim=-1'
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},
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'links': {},
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'extras': {},
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},
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'reshape': {
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'pytorch': {
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'prefix': ['torch'],
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'args_prefix': 'input, shape',
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'args': 'shape',
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},
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'jittor': {
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'prefix': 'jt',
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'module': '',
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'name': 'reshape',
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'args_prefix': 'input, shape',
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'args': 'shape'
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},
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'links': {},
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'extras': {},
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},
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'clamp': {
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'pytorch': {
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'prefix': ['torch'],
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'args_prefix': 'input, min, max, out=None',
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'args': 'min, max, out=None',
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},
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'jittor': {
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'prefix': 'jt',
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'module': '',
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'name': 'clamp',
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'args_prefix': 'x, min_v, max_v',
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'args': 'min_v, max_v'
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},
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'links': {'min': 'min_v', 'max': 'max_v'},
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'extras': {},
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'delete': ['out'],
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},
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'permute': {
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'pytorch': {
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'prefix': [],
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'args_prefix': '',
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'args': '*dim',
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},
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'jittor': {
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'prefix': '',
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'module': '',
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'name': 'permute',
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'args_prefix': '',
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'args': '*dim'
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},
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'links': {},
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'extras': {},
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},
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'view': {
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'pytorch': {
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'prefix': [],
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'args_prefix': '',
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'args': '*shape',
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},
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'jittor': {
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'prefix': '',
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'module': '',
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'name': 'view',
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'args_prefix': '',
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'args': '*shape'
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},
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'links': {},
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'extras': {},
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}
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}
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unsupport_ops = [
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# ***************************************************************
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# torch.nn
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# ***************************************************************
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'ModuleDict', 'ParameterList', 'ParameterDict',
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'Conv1d', 'Conv3d', 'ConvTranspose1d', 'ConvTranspose3d', 'Unfold', 'Fold',
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'MaxPool1d', 'MaxUnpool1d', 'MaxUnpool2d', 'AvgPool1d',
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'FractionalMaxPool2d', 'LPPool1d', 'LPPool2d', 'AdaptiveMaxPool1d',
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'AdaptiveAvgPool1d',
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'ReflectionPad1d', 'ReplicationPad1d', 'ReplicationPad3d', 'ConstantPad1d', 'ConstantPad3d',
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'ELU', 'Hardshrink', 'Hardtanh', 'LogSigmoid', 'MultiheadAttention',
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'RReLU', 'SELU', 'CELU', 'GELU', 'Softshrink', 'Softsign', 'Tanhshrink',
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'Threshold', 'Softmin', 'Softmax2d', 'LogSoftmax', 'AdaptiveLogSoftmaxWithLoss',
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'BatchNorm3d', 'SyncBatchNorm', 'InstanceNorm1d', 'InstanceNorm3d', 'LocalResponseNorm',
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'RNNBase', 'RNN', 'LSTM', 'GRU', 'RNNCell', 'LSTMCell', 'GRUCell', 'Transformer', 'TransformerEncoder',
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'TransformerDecoder', 'TransformerEncoderLayer', 'TransformerDecoderLayer', 'Identity', 'Bilinear',
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'Dropout3d', 'AlphaDropout', 'EmbeddingBag', 'CosineSimilarity', 'PairwiseDistance', 'CTCLoss', 'NLLLoss', 'PoissonNLLLoss', 'KLDivLoss', 'BCEWithLogitsLoss',
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'MarginRankingLoss', 'HingeEmbeddingLoss', 'MultiLabelMarginLoss', 'SmoothL1Loss', 'SoftMarginLoss',
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'MultiLabelSoftMarginLoss', 'CosineEmbeddingLoss', 'MultiMarginLoss', 'TripletMarginLoss', # 'DataParallel', 'DistributedDataParallel',
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'clip_grad_norm_', 'clip_grad_value_',
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'parameters_to_vector', 'vector_to_parameters', 'BasePruningMethod', 'PruningContainer',
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'RandomUnstructured', 'L1Unstructured', 'RandomStructured', 'LnStructured', 'CustomFromMask',
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'random_unstructured', 'l1_unstructured', 'random_structured', 'ln_structured', 'global_unstructured',
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'custom_from_mask', 'remove', 'is_pruned', 'weight_norm', 'remove_weight_norm', 'spectral_norm',
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'remove_spectral_norm', 'PackedSequence', 'pack_padded_sequence', 'pad_packed_sequence', 'pad_sequence', 'pack_sequence'
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]
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def pjmap_append(pytorch_func_name, pytorch_args, jittor_func_module, jittor_func_name, jittor_args, extras=None, links=None, delete=None):
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''' adding map to pjmap for converting new function, example: convert AvgPool2d to Pool
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args:
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* `pytorch_func_name`: Pytorch function name
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* `pytorch_args`: Pytorch parameter list
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* `jittor_func_module`: to which module the Jittor function belongs
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* `jittor_func_name`: Jittor function name
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* `jittor_args`: Jittor parameter list
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* `extras`: parameter assignment
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* `links`: connection parameters
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* `delete`: delete parameters
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example:
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from jittor.utils.pytorch_converter import pjmap_append
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pjmap_append(pytorch_func_name='AvgPool2d',
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pytorch_args='kernel_size, stride=None, padding=0, dilation=1, return_indices=False',
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jittor_func_module='nn',
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jittor_func_name='Pool',
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jittor_args='kernel_size, stride=None, padding=0, dilation=None, return_indices=None, ceil_mode=False, op="maximum"',
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extras={"op": "'mean'"})
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'''
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if links == None: links = {}
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if extras == None: extras = {}
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if delete == None: delete = []
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assert isinstance(links, dict)
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assert isinstance(extras, dict)
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assert isinstance(delete, list)
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pjmap[pytorch_func_name] = {
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'pytorch': {
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'args': pytorch_args,
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},
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'jittor': {
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'module': jittor_func_module,
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'name': jittor_func_name,
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'args': jittor_args,
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},
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'links': links,
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'extras': extras,
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'delete': delete,
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}
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def raise_unsupport(name, ori_src):
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ret = f"raise RuntimeError('''original source: <{ori_src.strip()}>, {name} is not supported in Jittor yet. We will appreciate it if you provide an implementation of {name} and make pull request at https://github.com/Jittor/jittor.''')"
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print(ret+'\n')
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ret = ast.parse(ret).body[0]
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return ret
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class Converter:
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def __init__(self, ex_pjmap):
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import copy
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self.pjmap = copy.deepcopy(pjmap)
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if ex_pjmap:
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self.pjmap.update(ex_pjmap)
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self.unsupport_ops = set(unsupport_ops)
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support_ops = {}
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for key in self.pjmap.keys():
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module = self.pjmap[key]['jittor']['module']
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name = self.pjmap[key]['jittor']['name']
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if module == 'nn':
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support_ops[key] = name
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if key in self.unsupport_ops:
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self.unsupport_ops.remove(key)
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self.support_ops = support_ops
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self.import_flag = []
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def replace(self, a):
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if hasattr(a, "attr") and a.attr in self.unsupport_ops:
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ori_src = astunparse.unparse(a)
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return raise_unsupport(a.attr, ori_src)
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if hasattr(a, "id") and a.id in self.unsupport_ops:
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ori_src = astunparse.unparse(a)
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return raise_unsupport(a.id, ori_src)
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if hasattr(a, "attr"):
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if a.attr in self.support_ops.keys(): a.attr = self.support_ops[a.attr]
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if hasattr(a, "id"):
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if a.id in self.support_ops.keys(): a.id = self.support_ops[a.id]
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return None
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def convert_(self, prefix, func_name, ags, kws, ori_src):
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info = self.pjmap[func_name]
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p_prefix = info['pytorch']['prefix'] if 'prefix' in info['pytorch'].keys() else None
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if p_prefix is not None and prefix in p_prefix:
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p_ags = info['pytorch']['args_prefix']
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j_ags = info['jittor']['args_prefix']
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else:
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p_ags = info['pytorch']['args']
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j_ags = info['jittor']['args']
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if 'delete' in info.keys():
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delete = info['delete']
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else:
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delete = None
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j_prefix = info['jittor']['prefix'] if 'prefix' in info['jittor'].keys() else None
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j_module = info['jittor']['module']
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j_name = info['jittor']['name']
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links = info['links']
|
|
extras = info['extras']
|
|
jj_ags = []
|
|
jj_kws = {}
|
|
pp_ags = []
|
|
pp_kws = {}
|
|
if j_ags == '' and p_ags == '':
|
|
# no args in Pytorch and Jittor.
|
|
if p_prefix is None:
|
|
return f"{j_module}.{j_name}()"
|
|
else:
|
|
if prefix in p_prefix:
|
|
return f"{j_prefix}.{j_name}()"
|
|
else:
|
|
return f"{prefix}.{j_name}()"
|
|
else:
|
|
j_ags = j_ags.replace(' ','').split(',')
|
|
for j_ag in j_ags:
|
|
if '=' in j_ag:
|
|
k,v = j_ag.split('=')
|
|
jj_kws[k] = v
|
|
else:
|
|
jj_ags.append(j_ag)
|
|
p_ags = p_ags.replace(' ','').split(',')
|
|
for p_ag in p_ags:
|
|
if '=' in p_ag:
|
|
k,v = p_ag.split('=')
|
|
pp_kws[k] = v
|
|
else:
|
|
pp_ags.append(p_ag)
|
|
if len(jj_ags) == 0 and len(pp_ags) != 0:
|
|
return f"raise AttributeError('''origin source: <{ori_src.strip()}>, {func_name} in Jittor has no Attribute {pp_ags[0]}''')"
|
|
# raise AttributeError(f"{func_name} in Jittor has no Attribute {pp_ags[0]}")
|
|
if delete is not None:
|
|
for d in delete:
|
|
if d in pp_ags:
|
|
jj_ags.append(d)
|
|
if d in pp_kws.keys():
|
|
jj_kws[d] = None
|
|
if len(pp_ags) > len(ags) + len(kws):
|
|
return f"raise RuntimeError('''origin source: <{ori_src.strip()}>, There are needed {len(pp_ags) + len(list(pp_kws.keys()))} args in Pytorch {func_name} function, but you only provide {len(ags) + len(kws)}''')"
|
|
# raise RuntimeError(f'There are needed {len(pp_ags) + len(list(pp_kws.keys()))} args in Pytorch {func_name} function, but you only provide {len(ags) + len(kws)}')
|
|
ags_ = []
|
|
for i in range(len(pp_ags)):
|
|
if i < len(ags):
|
|
if '*' in pp_ags[i]:
|
|
ags_.append('(' + ', '.join(ags[i:]) + ')')
|
|
ags = ags_
|
|
break
|
|
else:
|
|
ags_.append(ags[i])
|
|
else:
|
|
break
|
|
if len(pp_ags) + len(list(pp_kws.keys())) < len(ags) + len(kws):
|
|
return f"raise RuntimeError('''origin source: <{ori_src.strip()}>,There are only {len(pp_ags) + len(list(pp_kws.keys()))} args in Pytorch {func_name} function, but you provide {len(ags) + len(kws)}''')"
|
|
# raise RuntimeError(f'There are only {len(pp_ags) + len(list(pp_kws.keys()))} args in Pytorch {func_name} function, but you provide {len(ags) + len(kws)}')
|
|
j_ags_flag = np.zeros(len(jj_ags))
|
|
j_ags_values = {}
|
|
j_kws_values = {}
|
|
for i,ag in enumerate(ags):
|
|
if len(pp_ags) == 0:
|
|
ag_name = list(pp_kws.keys())[i]
|
|
elif i < len(pp_ags):
|
|
ag_name = pp_ags[i]
|
|
elif i >= len(pp_ags) and (i-len(pp_ags)) <= len(list(pp_kws.keys())):
|
|
ag_name = list(pp_kws.keys())[i-len(pp_ags)]
|
|
else:
|
|
return f"raise RuntimeError('''origin source: <{ori_src.strip()}>,The args number is not matc{func_name} in Jittor has no Attribute {ag_name}''')"
|
|
# raise RuntimeError(f'The args number is not matc{func_name} in Jittor has no Attribute {ag_name}')
|
|
if ag_name in links.keys():
|
|
ag_name = links[ag_name]
|
|
if ag_name in jj_ags:
|
|
j_ags_flag[jj_ags.index(ag_name)] = 1
|
|
j_ags_values[str(jj_ags.index(ag_name))] = ag
|
|
elif ag_name in jj_kws.keys():
|
|
j_kws_values[ag_name] = ag
|
|
else:
|
|
return f"raise AttributeError('''origin source: <{ori_src.strip()}>, {func_name} in Jittor has no Attribute {ag_name}''')"
|
|
# raise AttributeError(f'{func_name} in Jittor has no Attribute {ag_name}')
|
|
for i,kw in enumerate(kws):
|
|
kw_name, kw_value = kw.split('=')
|
|
if kw_name in links.keys():
|
|
kw_name = links[kw_name]
|
|
if kw_name in jj_ags:
|
|
j_ags_flag[jj_ags.index(kw_name)] = 1
|
|
j_ags_values[str(jj_ags.index(kw_name))] = kw_value
|
|
elif kw_name in jj_kws.keys():
|
|
j_kws_values[kw_name] = kw_value
|
|
else:
|
|
return f"raise AttributeError('''origin source: <{ori_src.strip()}>, {func_name} in Jittor has no Attribute {kw_name}''')"
|
|
# raise AttributeError(f'{func_name} in Jittor has no Attribute {kw_name}')
|
|
len_jj_ags = len(jj_ags) if len(jj_ags) == 0 or jj_ags[0] != '' else 0
|
|
if j_ags_flag.sum() < len_jj_ags:
|
|
missing_args = []
|
|
for i in range(len(jj_ags)):
|
|
if j_ags_flag[i] == 0:
|
|
missing_args.append(jj_ags[i])
|
|
return f"raise AttributeError('''origin source: <{ori_src.strip()}>, the needed args of {func_name} in Jittor is {', '.join(jj_ags)}, so you need to give value of {', '.join(missing_args)}.''')"
|
|
# raise AttributeError(f"the needed args of {func_name} in Jittor is {', '.join(jj_ags)}, so you need to give value of {', '.join(missing_args)}.")
|
|
if extras:
|
|
for k in extras.keys():
|
|
if k in jj_ags:
|
|
j_ags_values[str(jj_ags.index(k))] = extras[k]
|
|
elif k in jj_kws.keys():
|
|
j_kws_values[k] = extras[k]
|
|
else:
|
|
return f"raise AttributeError('''origin source: <{ori_src.strip()}>, there is not attribute named {k} in Jittor {func_name}, you should delete it in {func_name} extras.''')"
|
|
# raise AttributeError(f"there is not attribute named {k} in Jittor {func_name}, you should delete it in {func_name} extras.")
|
|
if delete is not None:
|
|
for d in delete:
|
|
if d in j_ags_values:
|
|
del j_ags_values[d]
|
|
if d in j_kws_values.keys():
|
|
j_kws_values.pop(d)
|
|
j_ags_ = [j_ags_values[str(i)] for i in range(len(list(j_ags_values.keys())))]
|
|
j_kws_ = [key + "=" + j_kws_values[key] for key in j_kws_values.keys()]
|
|
j_func = f"{j_module}.{j_name}({', '.join(j_ags_+j_kws_)})"
|
|
if p_prefix is None:
|
|
return f"{j_module}.{j_name}({', '.join(j_ags_+j_kws_)})"
|
|
else:
|
|
if prefix in p_prefix:
|
|
return f"{j_prefix}.{j_name}({', '.join(j_ags_+j_kws_)})"
|
|
else:
|
|
return f"{prefix}.{j_name}({', '.join(j_ags_+j_kws_)})"
|
|
return j_func
|
|
|
|
def dfs(self, a):
|
|
if isinstance(a, ast.Import):
|
|
if 'torch' in astunparse.unparse(a) and 'init' in astunparse.unparse(a):
|
|
self.import_flag.append('init')
|
|
return ast.parse('from jittor import init').body[0]
|
|
if 'torch' in astunparse.unparse(a) and a.names[0].asname == 'nn':
|
|
self.import_flag.append('nn')
|
|
return ast.parse('from jittor import nn').body[0]
|
|
if 'torch' in a.names[0].name:
|
|
return 'delete'
|
|
elif isinstance(a, ast.ImportFrom):
|
|
if 'torch' in a.module:
|
|
return 'delete'
|
|
elif isinstance(a, ast.Call):
|
|
for idx, ag in enumerate(a.args):
|
|
ret = self.dfs(ag)
|
|
if ret is not None:
|
|
a.args[idx] = ret
|
|
for idx, kw in enumerate(a.keywords):
|
|
ret = self.dfs(kw)
|
|
if ret is not None:
|
|
a.keywords[idx] = ret
|
|
ori_src = astunparse.unparse(a)
|
|
func = astunparse.unparse(a.func).strip('\n').split('.')
|
|
prefix = '.'.join(func[0:-1])
|
|
func_name = func[-1]
|
|
if func_name in self.unsupport_ops:
|
|
ret = raise_unsupport(func_name, ori_src)
|
|
return ret
|
|
if func_name in self.pjmap:
|
|
ags = [astunparse.unparse(ag).strip('\n') for ag in a.args]
|
|
kws = [astunparse.unparse(kw).strip('\n') for kw in a.keywords]
|
|
ret = self.convert_(prefix, func_name, ags, kws, ori_src)
|
|
ret_tmp = ret
|
|
ret = ast.parse(ret).body[0]
|
|
if hasattr(ret,'value'):
|
|
return ret.value
|
|
else:
|
|
print(ret_tmp+'\n')
|
|
return ret
|
|
if ".load_state_dict" in astunparse.unparse(a.func):
|
|
a.func.attr = 'load_parameters'
|
|
if astunparse.unparse(a.func).strip('\n').endswith(".size"):
|
|
ags = [astunparse.unparse(ag).strip('\n') for ag in a.args]
|
|
if len(ags) != 0:
|
|
con = astunparse.unparse(a.func).split('.size')[0] + '.shape[' + ','.join(ags) + ']'
|
|
else:
|
|
con = astunparse.unparse(a.func).replace('size', 'shape')
|
|
return ast.parse(con).body[0].value
|
|
elif isinstance(a, ast.Expr): pass
|
|
elif isinstance(a, ast.Attribute) or isinstance(a, ast.Name):
|
|
ret = self.replace(a)
|
|
if ret is not None:
|
|
print(ret)
|
|
return ret
|
|
elif isinstance(a, ast.FunctionDef):
|
|
if a.name == 'forward': a.name = 'execute'
|
|
if hasattr(a, '__dict__'):
|
|
for k in a.__dict__.keys():
|
|
if isinstance(a.__dict__[k], list):
|
|
delete_flag = []
|
|
for i,a_ in enumerate(a.__dict__[k]):
|
|
ret = self.dfs(a_)
|
|
if ret == 'delete':
|
|
delete_flag.append(True)
|
|
continue
|
|
if ret is not None:
|
|
a.__dict__[k][i] = ret
|
|
delete_flag.append(False)
|
|
tmp = [a_ for i,a_ in enumerate(a.__dict__[k]) if delete_flag[i] == False]
|
|
a.__dict__[k] = tmp
|
|
else:
|
|
ret = self.dfs(a.__dict__[k])
|
|
if ret is not None:
|
|
a.__dict__[k] = ret
|
|
|
|
|
|
def convert(code, ex_pjmaps=None):
|
|
''' Model code converter, example:
|
|
|
|
from jittor.utils.pytorch_converter import convert
|
|
pytorch_code = """
|
|
class Model(nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.conv1 = nn.Conv2d(1, 10, 3)
|
|
self.conv2 = nn.Conv2d(10, 32, 3)
|
|
self.fc = nn.Linear(1200, 100)
|
|
|
|
def forward(self, x):
|
|
x = self.conv1(x)
|
|
x = self.conv2(x)
|
|
x = x.view(x.size(0), -1)
|
|
x = self.fc(x)
|
|
return x
|
|
"""
|
|
jittor_code = convert(pytorch_code)
|
|
print("## Generate Jittor code:", jittor_code)
|
|
exec(jittor_code)
|
|
model = Model()
|
|
print("## Jittor model:", model)
|
|
'''
|
|
|
|
a = ast.parse(code)
|
|
converter = Converter(ex_pjmaps)
|
|
converter.dfs(a)
|
|
a.body.insert(0, ast.parse('import jittor as jt').body[0])
|
|
if 'init' not in converter.import_flag:
|
|
a.body.insert(1, ast.parse('from jittor import init').body[0])
|
|
if 'nn' not in converter.import_flag:
|
|
a.body.insert(2, ast.parse('from jittor import nn').body[0])
|
|
return astunparse.unparse(a)
|